Cardiovascular Health Care Economics (Contemporary Cardiology)

CONTEMPORARY

CARDIOLOGY

Cardiovascular Health Care Economics Edited by

William S. Weintraub, MD

CARDIOVASCULAR HEALTH CARE ECONOMICS

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CONTEMPORARY CARDIOLOGY CHRISTOPHER P. CANNON, MD SERIES EDITOR Coronary Disease in Women: Evidence-Based Diagnosis and Treatment, edited by Leslee J. Shaw, PhD and Rita Redberg, MD, FACC, 2004 Cardiovascular Health Care Economics, edited by William S. Weintraub, MD, 2003 Heart Failure: A Clinician's Guide to Ambulatory Diagnosis and Treatment, edited by Mariell L. Jessup, MD, 2003 Cardiac Repolarization: Basic and Clinical Research, edited by Ihor Gussak, MD, PhD, Charles Antzelevitch, PhD, Stephen C. Hammill, MD, co-edited by Win-Kuong Shen, MD, and Preben Bjerregaard, MD, DMSc, 2003 Management of Acute Coronary Syndromes, Second Edition, edited by Christopher P. Cannon, MD 2003 Aging, Heart Disease, and Its Management: Facts and Controversies, edited by Niloo M. Edwards, MD, Mathew S. Maurer, MD, and Rachel B. Wellner, MPH, 2003 Peripheral Arterial Disease: Diagnosis and Treatment, edited by Jay D. Coffman, MD and Robert T. Eberhardt, MD, 2003 Essentials of Bedside Cardiology: With a Complete Course in Heart Sounds and Murmurs on CD, Second Edition, by Jules Constant, MD, FACC, 2003 Minimally Invasive Cardiac Surgery, Second Edition, edited by Daniel J. Goldstein, MD and Mehmet C. Oz, MD, 2003 Platelet Glycoprotein IIb/IIIa Inhibitors in Cardiovascular Disease, Second Edition, edited by A. Michael Lincoff, MD, 2003 Nuclear Cardiology Basics: How to Set Up and Maintain a Laboratory, edited by Frans J. Th. Wackers, MD, Wendy Bruni, CNMT, and Barry L. Zaret, MD, 2003

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Primary Angioplasty in Acute Myocardial Infarction, edited by James E. Tcheng, MD, 2002 Cardiogenic Shock: Diagnosis and Treatment, edited by David Hasdai, MD, Peter B. Berger, MD, Alexander Battler, MD, and David R. Holmes, Jr., MD, 2002 Management of Cardiac Arrhythmias, edited by Leonard I. Ganz, MD, 2002 Diabetes and Cardiovascular Disease, edited by Michael T. Johnstone, MD and Aristidis Veves, MD, DSC, 2001 Blood Pressure Monitoring in Cardiovascular Medicine and Therapeutics, edited by William B. White, MD, 2001 Vascular Disease and Injury: Preclinical Research, edited by Daniel I. Simon, MD, and Campbell Rogers, MD 2001 Preventive Cardiology: Strategies for the Prevention and Treatment of Coronary Artery Disease, edited by JoAnne Micale Foody, MD, 2001 Nitric Oxide and the Cardiovascular System, edited by Joseph Loscalzo, MD, PhD and Joseph A. Vita, MD, 2000 Annotated Atlas of Electrocardiography: A Guide to Confident Interpretation, by Thomas M. Blake, MD, 1999 Platelet Glycoprotein IIb/IIIa Inhibitors in Cardiovascular Disease, edited by A. Michael Lincoff, MD, and Eric J. Topol, MD, 1999 Minimally Invasive Cardiac Surgery, edited by Mehmet C. Oz, MD and Daniel J. Goldstein, MD, 1999 Management of Acute Coronary Syndromes, edited by Christopher P. Cannon, MD, 1999

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CARDIOVASCULAR HEALTH CARE ECONOMICS Edited by

WILLIAM S. WEINTRAUB, MD Emory University School of Medicine, Atlanta, GA

HUMANA PRESS TOTOWA, NEW JERSEY

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© 2003 Humana Press Inc. 999 Riverview Drive, Suite 208 Totowa, New Jersey 07512 www.humanapress.com All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, microfilming, recording, or otherwise without written permission from the Publisher. The content and opinions expressed in this book are the sole work of the authors and editors, who have warranted due diligence in the creation and issuance of their work. The publisher, editors, and authors are not responsible for errors or omissions or for any consequences arising from the information or opinions presented in this book and make no warranty, express or implied, with respect to its contents. Due diligence has been taken by the publishers, editors, and authors of this book to assure the accuracy of the information published and to describe generally accepted practices. The contributors herein have carefully checked to ensure that the drug selections and dosages set forth in this text are accurate and in accord with the standards accepted at the time of publication. Notwithstanding, as new research, changes in government regulations, and knowledge from clinical experience relating to drug therapy and drug reactions constantly occurs, the reader is advised to check the product information provided by the manufacturer of each drug for any change in dosages or for additional warnings and contraindications. This is of utmost importance when the recommended drug herein is a new or infrequently used drug. It is the responsibility of the treating physician to determine dosages and treatment strategies for individual patients. Further it is the responsibility of the health care provider to ascertain the Food and Drug Administration status of each drug or device used in their clinical practice. The publisher, editors, and authors are not responsible for errors or omissions or for any consequences from the application of the information presented in this book and make no warranty, express or implied, with respect to the contents in this publication. Production Editor: Robin B. Weisberg. Cover Illustration: From Fig. 2 in Chapter 6, “Health Status Assessment,” by John A. Spertus and Mark W. Conard. Cover design by Patricia F. Cleary. For additional copies, pricing for bulk purchases, and/or information about other Humana titles, contact Humana at the above address or at any of the following numbers: Tel.: 973-256-1699; Fax: 973-256-8341, E-mail: [email protected]; or visit our Website: www.humanapress.com This publication is printed on acid-free paper. ∞ ANSI Z39.48-1984 (American National Standards Institute) Permanence of Paper for Printed Library Materials. Photocopy Authorization Policy: Authorization to photocopy items for internal or personal use, or the internal or personal use of specific clients, is granted by Humana Press Inc., provided that the base fee of US $20.00 is paid directly to the Copyright Clearance Center at 222 Rosewood Drive, Danvers, MA 01923. For those organizations that have been granted a photocopy license from the CCC, a separate system of payment has been arranged and is acceptable to Humana Press Inc. The fee code for users of the Transactional Reporting Service is: [0-89603-874-2/03 $20.00]. Printed in the United States of America. 10 9 8 7 6 5 4 3 2 1 Library of Congress Cataloging-in-Publication Data Cardiovascular health care economics / edited by William S. Weintraub p. ; cm. -- (Contemporary cardiology) Includes bibliographical references and index. ISBN 0-89603-874-2 (alk. paper); 1-59259-398-4 (e-book) 1. Cardiology--Economic aspects--United States. [DNLM: 1. Cardiovascular Diseases--economics--United States. 2. Cost-Benefit Analysis--United States. 3. Health Expenditures--United States. 4. Hospital Costs--United States. WG 120 C26752 2003] I. Weintraub, William S. II. Contemporary cardiology (Totowa, N.J. : Unnumbered) RA645.C34C39 2003 338.4'33621961'00973--dc21 2003010184

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PREFACE Wonder rather than doubt is the root of all knowledge. —Abraham Joshua Heschel How do people choose to allocate resources when it is not possible to pay for all desired goods and services? In principle, the invisible hand of the market guides resource use; with regulators, generally governmental agencies, assuring a level playing field and preventing various forms of abuse, but otherwise trying to stay out of the way. Free markets are guided by a principle called willingness-to-pay, which economists define as that price, governed by supply and demand, which consumers are willing to pay for a service (1). Services in society that are deemed a “right,” such as education, are not governed by free markets, as society may view that all people have a right to those services, independent of their ability to pay. At the level of the individual consumer, Medicine is largely, although not entirely, in the class of a “right,” more like education than a good governed by willingness-to-pay such as automobiles. From the larger point of view of society, there is intense concern over the price of medical services since there is a perception that it is not priced by willingness-to-pay. The concern for value in medicine is a major societal issue. We can define value in health care as good care at a fair price. Whether society is achieving value in health care is a major issue all over the world. Health care expenditures in the United States have risen dramatically in the last half of the 20th century. Between 1960 and 2000, federal health care expenditures rose from $2.9 billion to $411.5 billion and total national expenditures from $28.65 billion to $1.30 trillion (2). This represents an increase in percent of gross national product over this period from 5.1 to 13.2%. This unprecedented and unparalleled increase in expense for one sector of the American economy is placing American medicine in considerable peril. The Centers for Medicare & Medicaid Services (formerly Health Care Financing Agency) expects expenditures to double in the next 10 years, reaching 17% of the gross national product (Fig. 1) (3). An understanding of the critical issues involved in health care economics can be understood by assessing the role of Medicare, the federal government health program for the aged and disabled and the largest payer for medical services in the United States (4). The Medicare program is comprised of two parts. Hospital Insurance (HI), or Medicare Part A, pays for hospital, home health, skilled nursing facility, and hospice care for the aged and disabled. The Supplementary Medical Insurance (SMI), or Medicare Part B, pays for physician, outpatient hospital, home health, and other services for the aged and disabled. The HI trust fund is financed primarily by payroll taxes paid by workers and employers. Current tax revenues are used mainly to pay benefits for current beneficiaries. The SMI trust fund is financed primarily by transfers from the general fund of the US Treasury and by monthly premiums paid by beneficiaries. Income not currently needed to pay benefits and related expenses is held in the HI and SMI trust funds, invested in US Treasury v

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Preface

Fig. 1. National health expenditures as a share of gross domestic product (GDP). Between 2001 and 2011, health spending is projected to grow 2.5% per year faster than the GDP, so that by 2011 it will constitute 17% of the GDP (Source: CMS, Office of the Actuary, National Health Statistics Group).

Fig. 2. Medicare spending in the United States. Overall Medicare spending grew from $3.3 billion in 1967 to nearly $241 billion in 2001. Overall spending includes benefit dollars, administrative costs, and program integrity costs. Represents federal spending only (Source: CMS, Office of the Actuary).

securities. The growth in expenditures in recent decades is shown in Fig. 2 (3). Although revenue and expenses are currently in balance, this is only maintained by transfer from general revenues. In approximately 13 years, expenses are projected to exceed revenues, which will ultimately exhaust the Medicare trust fund, with a current estimated date of 2030. Current policy does not address the critical issues in health care financing that our society will face over the next several decades if current projections prove correct. Cardiovascular disease consumes substantial societal resources in economically advantaged countries, and thus is responsible for a considerable part of the projected economic challenges in the future. In the United States alone, the American Heart Association estimates that the cost of cardiovascular disease in 2002 will total $329.2 billion

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Preface

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(4). Of this total, $199.5 billion will be related to direct consumption of medical resources and an additional $129.7 billion will be related to lost productivity resulting from early death and disability. Costs related to coronary artery disease lead the other categories at $111.8 billion, but this is just a little over one third of the total. Given its magnitude, there is a strong societal interest that the $199.5 billion in direct costs be spent wisely and that the $129.7 billion in lost productivity be minimized. The field of health care economics has developed as a discipline to address these enormous societal issues. It is not the purpose of this book to address policy. It is the purpose of this book to show how services may be rationally valued, that is, how outcomes may be assessed, how cost can be derived, and how choices can rationally be made. Cardiovascular Health Care Economics is divided into two sections; the first concerning methods and the second concerning various cardiovascular health care services. The information in Cardiovascular Health Care Economics is not designed on its own to be the sole text to guide health services researchers. It is designed to assist health services researchers by being the first place to look for economic studies in cardiovascular medicine and methods in health care economics and as a guide for further reading. It is also designed to be an introduction and reference to cardiovascular health care economics for health care professionals. Health care economics has grown in recent years, partly to help society make better decisions currently and partly in response to the looming crisis ahead. All people in industrial societies face the issues and decisions presented in this book. Thus, Cardiovascular Health Care Economics is a book for all those concerned about making good choices and assuring continuing access to high-quality health care in the decades to come. ACKNOWLEDGMENTS I would like to thank Nancy Murrah, Bruce Wagner and Lesley Wood, without whose help and incredible patience this book could never have been created. William S. Weintraub, MD REFERENCES 1. Allenet B, Sailly J-C. Willingness of pay as a measure of benefit in health. Journal D’Economie Medicale 1999;17:301. 2. http://www.hcfa.gov/stats/nhe-oact/tables/t1.htm 3. http://cms.hhs.gov/charts/default.asp 4. 2002 Heart and Stroke Statistical Update. American Heart Association. Dallas. 2001.

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CONTENTS Preface ............................................................................................................................ v Contributors ................................................................................................................... xi

PART I. METHODS 1 2

3 4

5 6 7 8 9

Nonfederal US Hospital Costs .......................................................................... 1 Steven D. Culler and Adam Atherly Estimating the Costs of Cardiac Care Provided by the Hospitals of the US Department of Veterans Affairs ................................................ 15 Paul G. Barnett, Patricia Lin, and Todd H. Wagner Estimating the Costs of Health Care Resources in Canada ........................... 31 Gordon Blackhouse US Physician Costs: Conceptual and Methodological Issues and Selected Applications .......................................................................... 45 Edmund R. Becker Indirect Health Care Costs: An Overview ...................................................... 63 Stephen J. Boccuzzi Health Status Assessment ............................................................................... 81 John A. Spertus and Mark W. Conard Utility Assessment ........................................................................................ 101 John A. Spertus and Robert F. Nease, Jr. Introduction to Cost-Effectiveness Analysis ................................................ 111 Robert F. Nease, Jr. Cost-Effectiveness Analysis Alongside Clinical Trials: Statistical and Methodological Issues ....................................................................... 123 Elizabeth M. Mahoney and Haitao Chu

PART II. CLINICAL APPLICATIONS 10

11 12

Costs of Care and Cost-Effectiveness Analysis: Primary Prevention of Coronary Artery Disease ..................................................................... 157 Kevin A. Schulman and Padma Kaul Economics of Therapy for Acute Coronary Syndromes .............................. 173 Daniel B. Mark Cost-Effectiveness of Percutaneous Coronary Interventions ...................... 187 David J. Cohen and Ameet Bakhai

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Economic Comparisons of Coronary Angioplasty and Coronary Bypass Surgery .................................................................. 223 Mark A. Hlatky 14 Costs of Coronary Artery Surgery and Cost-Effectiveness of CABG vs Medicine .............................................................................. 233 Sean C. Beinart and William S. Weintraub 15 Costs of Care and Cost-Effectiveness Analysis: Other Cardiac Surgery ............................................................................. 249 Vinod H. Thourani and William S. Weintraub 16 Congestive Heart Failure .............................................................................. 259 Mikhail Torosoff, Claude-Laurent Sader, and Edward F. Philbin, III 17 Current Economic Evidence Using Noninvasive Cardiac Testing .............. 285 Leslee J. Shaw, Rita Redberg, and Charles Denham 18 Cost-Effective Care in the Management of Conduction Disease and Arrhythmias ....................................................................................... 303 David J. Malenka and Edward Catherwood 19 Comparing Cost-Utility Analyses in Cardiovascular Medicine .................. 329 Wolfgang C. Winkelmayer, David J. Cohen, Marc L. Berger, and Peter J. Neumann 20 Beyond Heart Disease: Cost-Effectiveness as a Guide to Comparing Alternate Approaches to Improving the Nation’s Health ........................ 357 Tammy O. Tengs and Nicholas P. Emptage 21 Using Economic Studies for Policy Purposes .............................................. 365 Rajiv Shah and Kevin G. M. Volpp 22 Medicare, the Aging of America, and the Balanced Budget ....................... 389 Paul Heidenreich Afterword: The Future of Economics in Cardiovascular Care and Research ............................................................................................ 417 William S. Weintraub Index ........................................................................................................................... 421

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Contributors

CONTRIBUTORS ADAM ATHERLY, PhD, Department of Health Policy and Management, Rollins School of Public Health, Emory University, Atlanta, GA AMEET BAKHAI, MBBS, MRCP, Clinical Trials and Evaluation Unit, Royal Brompton and Harefield NHS Trust, London, UK and Beth Israel Deaconess Medical Center, Brookline, MA PAUL G. BARNETT, PhD, VA Health Economics Resource Center, and VA Cooperative Studies Program Coordinating Center, Menlo Park, CA EDMUND R. BECKER, PhD, Department of Health Policy and Management, Rollins School of Public Health, Emory University, Atlanta, GA SEAN C. BEINART, MD, Division of Cardiology, Emory University School of Medicine, Atlanta, GA MARC L. BERGER, MD, Outcomes Research and Management, Merck & Co. Inc., West Point, PA GORDON BLACKHOUSE, MBA, MSc, Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada STEPHEN J. BOCCUZZI, PhD, FAHA, Merck & Co. Inc., West Point, PA EDWARD CATHERWOOD, MD, MS, Section of Cardiology, Dartmouth-Hitchcock Medical Center, Lebanon, NH HAITAO CHU, MD, MS, Department of Biostatistics, Rollins School of Public Health, Emory University, Atlanta, GA DAVID J. COHEN, MSC, Beth Israel Deaconess Medical Center and Harvard University School of Public Health, Brookline, MA MARK W. CONARD, MA, Mid-America Heart Institute and the University of Missouri-Kansas City, Kansas City, MO STEVEN D. CULLER, PhD, Department of Health Policy and Management, Rollins School of Public Health, Emory University, Atlanta, GA CHARLES DENHAM, MD, HCC Corporation, Austin, TX NICHOLAS P. EMPTAGE, MA, Department of Psychology and Social Behavior, School of Social Ecology, University of California-Irvine, Irvine, CA PAUL HEIDENREICH, MD, MS, Stanford University School of Medicine and the VA Palo Alto Health Care System, Palo Alto, CA MARK A. HLATKY, MD, Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA PADMA KAUL, PhD, Division of Cardiology, Department of Medicine, University of Alberta, Edmonton, Alberta, Canada PATRICIA LIN, MPH, VA Health Economics Resource Center, and VA Cooperative Studies Program Coordinating Center, Menlo Park, CA ELIZABETH M. MAHONEY, ScD, Division of Cardiology, Emory University School of Medicine, Emory Center for Outcomes Research, Atlanta, GA DAVID J. MALENKA, MD, Section of Cardiology, Dartmouth-Hitchcock Medical Center, Lebanon, NH xi

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DANIEL B. MARK, MD, MPH, Department of Medicine, Duke University Medical Center and Outcomes Research and Assessment Group, Duke Clinical Research Institute, Durham, NC ROBERT F. NEASE, JR., PhD, Internal Medicine Department, Washington University School of Medicine and Express Scripts, St. Louis, MO PETER J. NEUMANN, ScD, Harvard Center for Risk Analysis, Harvard University, Boston, MA EDWARD F. PHILBIN, III, MD, FACC, Division of Cardiology, Department of Medicine, Albany Medical College, Albany, NY RITA REDBERG, MD, MPH, Division of Cardiology, University of California-San Francisco, San Francisco, CA CLAUDE-LAURENT SADER, MD, Division of Cardiology, Department of Medicine, Albany Medical College, Albany, NY KEVIN A. SCHULMAN, MD, Center for Clinical and Genetic Economics, Duke Clinical research Institute, Duke University Medical Center, Durham, NC RAJIV SHAH, MD, Leonard Davis Institute of Health Economics, The Wharton School, University of Pennsylvania, Philadelphia, PA; Bill and Melinda Gates Foundation, Seattle, WA LESLEE J. SHAW, PhD, Outcomes Research, American Cardiovascular Research Institute, Atlanta, GA JOHN A. SPERTUS, MD, MPH, FACC, Mid-America Heart Institute and the University of Missouri-Kansas City, Kansas City, MO TAMMY O. TENGS, ScD, Department of Planning, Policy and Design, School of Social Ecology, University of California-Irvine, Irvine, CA VINOD H. THOURANI, MD, Department of Surgery, Emory University Hospital, Atlanta, GA MIKHAIL TOROSOFF, MD, PhD, Division of Cardiology, Department of Medicine, Albany Medical College, Albany, NY KEVIN G. M. VOLPP, MD, PhD, Philadelphia Veterans Affairs Medical Center, University of Pennsylvania School of Medicine, the Wharton School, Leonard Davis Institute of Health Economics, Philadelphia, PA TODD H. WAGNER, PhD, VA Health Economics Resource Center and VA Cooperative Studies Program Coordinating Center, Menlo Park, CA WILLIAM S. WEINTRAUB, MD, Division of Cardiology, Emory University School of Medicine, Atlanta, GA WOLFGANG C. WINKELMAYER, MD, ScD, Division of Pharmacoepidemiology and Pharmaeconomics, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA

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I

METHODS

1

Nonfederal US Hospital Costs Steven D. Culler, PhD and Adam Atherly, PhD CONTENTS INTRODUCTION PROBLEMS WITH ESTIMATING NONFEDERAL US HOSPITAL COSTS THEORETICAL DISCUSSION OF ESTIMATING HOSPITAL COSTS PRACTICAL APPROACHES FOR ESTIMATING HOSPITAL COSTS ISSUES IN DATA FROM RANDOMIZED CONTROLLED TRIALS THE FUTURE OF HOSPITAL COST ESTIMATES REFERENCES

INTRODUCTION Since the early 1980s, one major focus of US health policymakers has been controlling the growth rate of expenditures in the US health care system. Despite numerous cost-containment efforts, advances in the ability to treat illness—particularly through new medical technology—have resulted in an increasing share of resources being consumed by the health care industry. During the year 2000, 13.1% of the US gross national product was consumed by health care, up from 8.8% in 1985 (1). Continuing concern about the impact of technological improvements on resource consumption has resulted in third-party payers and policymakers increasingly evaluating the cost of incremental improvements in health outcomes. For example, the Food and Drug Administration now requires that new drugs not only prove that they are efficacious, but also cost-effective. As a result, there has been a rapid growth in the number of costeffectiveness analyses (CEA), a trend particularly associated with the approval of new medications and medical devices (2). This chapter provides a practical overview of the key issues in obtaining estimates of nonfederal US hospital costs that meet CEA criteria. There are several reasons for the focus on hospital costing in this book (Chapter 3 focuses on estimating costs in federal US hospitals, whereas Chapter 4 focuses on non-US hospital costs). First, even though expenditures for hospital services as a percent of all US health care have declined since the implementation of Medicare’s prospective payment system, expenditure on hospital services still account for more than 32% of all US health care expenditures (see Table 1) (1). Second, for the vast majority of CEA, the health care resources consumed in the hospital are often the single largest component of the study’s direct medical From: Contemporary Cardiology: Cardiovascular Health Care Economics Edited by: W. S. Weintraub © Humana Press Inc., Totowa, NJ

1

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Cardiovascular Health Care Economics Table 1 Hospital Expenditures as a Share of Total US Health Care Expenditures for Selected Years Year

Dollars (in billions)

% of US health expenditures

1960 1965 1970 1975 1980 1985 1990 1995 1999

$9.2 $13.8 $27.6 $51.9 $101.5 $166.6 $253.9 $343.6 $390.9

34.4% 33.7% 37.8% 40.0% 41.3% 39.1% 36.5% 34.8% 32.3%

Source: US Department of Health and Human Services, Health Care Financing Administration, Annual Statistics.

costs. As a result, a relatively small difference (reduction) in the utilization of hospital resources can result in a significant difference in treatment costs between two treatment arms. Finally, advances in our ability to treat illness have resulted in patients with chronic diseases living longer and consuming more health care resources near the end of their lives (3). Estimating hospital resource consumption is particularly important for patients with coronary artery disease (CAD) because these patients tend to have multiple hospitalizations, especially in the advanced stages of the disease. All aspects of a CEA, from defining the relevant outcome(s) to determining which resource costs should be measured, are driven by the decision regarding the perspective of the study. As a general rule, most studies adopt the societal perspective (4). Studies taking the societal perspective attempt to measure all resources directly and indirectly consumed in an intervention, not just the costs of medical care to a particular individual, organization, payer, or sector of the economy. The overall goal of taking a societal approach is to be able to evaluate interventions from the perspective of the public interest rather than from the perspective of individual organizations. The practical advantage of the societal perspective is that it enhances the ability of researchers and policymakers to compare the results of cost-effectiveness (CE) study across interventions (lifestyle changes vs medication or surgical treatment) and diseases (treatments for CAD vs diabetes). The most important effect of the study’s perspective on estimates of hospital costs is that it defines which resources consumed during the hospitalization should be included, and it determines how to value the resources consumed (see Table 2). For example, if the study uses the societal perspective, then all hospital resources consumed should be included and the appropriate measure of hospital cost is the market (social) value of the resources consumed. Alternatively, if the study uses a governmental or a third-party payer’s perspective, then only services covered by the benefit package would be included, and the appropriate measure of hospital resources consumed would be the actual amount reimbursed to the hospital by the governmental program or the thirdparty payer. However, the interpretation of the CE of an intervention from a nonsocietal perspective has little policy value, because “cost” will depend on contractual allowances,

Chapter 1 / Nonfederal US Hospital Costs

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Table 2 Study Prospective, Hospital Resources Included, and Appropriate Price for Hospital Costs Study prospective Societal perspective Third-party payers (government and private) Patients

Hospital

Hospital resources included

Appropriate measure of resource price

All present and future hospital resources consumed by patients All covered services

Opportunity cost of resources (input prices) Actual reimbursement

All services not covered by health insurance (copayment and deductibles) All hospital resources

Bill charges for noncovered services Actual price paid for all resources (total cost)

services covered by the benefit package, and the level of copayments and deductibles. As a result, the remainder of this chapter assumes that the study prospective adopted is the societal perspective. The remainder of this chapter focuses on the following. First, we discuss several problems with estimating hospital costs. Second, we provide a theoretical review of estimating hospital cost in the framework of CEA. Third, we review several practical methods of identifying and measuring hospital resource consumption, identifying the advantages and disadvantages of each approach. Fourth, we describe several problems with estimating hospital cost in a typical clinical trial. Finally, we provide several recommendations for improving future hospital cost estimates.

PROBLEMS WITH ESTIMATING NONFEDERAL US HOSPITAL COSTS In theory, estimating costs for CEA is quite straightforward. The theoretical price for any resource consumed in CEA is its opportunity cost (i.e., the value of the foregone benefits because the resource is not available for its next best alternative use). In highly competitive markets (where firms are “price takers”), the market price of resources reflects the opportunity cost of those resources and, therefore, can be used to estimate costs. However, most health care analysts agree that the US health care market, in general, and the hospital industry, in particular, have numerous market imperfections and do not meet the economic requirements of a competitive market. This means that market prices do not reflect the opportunity cost of the resources consumed, and prices (charges) for hospital services do not prove to be accurate estimates of the opportunity costs of resources consumed in producing hospital services (5). Hospital resource costs required to complete CEA can be measured even if the hospital industry does not meet economist’s conditions for competitive markets (the auto industry is not a competitive market, however, very precise estimates of the resources consumed to build a car are known to managers). However, a number of historical and institutional factors have been identified as obstacles impeding the availability of resource cost information in the hospital industry. One reason for the lack of hospital resource cost information is that nonprofit hospitals and public hospitals (city- and

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Cardiovascular Health Care Economics

county-owned nonfederal hospitals) account for the vast majority of nonfederal US hospitals. Many of these hospitals have not historically followed strict business management procedures. Instead, the management culture in the hospital industry has focused on attempting to expand services provided to the community rather than on understanding the cost of providing services. For example, many nonprofit hospitals have made capital budgeting (and other operating) decisions based on community needs or physician desires rather than on detailed financial analysis of the proposed services. As a result, many hospitals have traditionally (and sometimes unknowingly) subsidized services and programs that they view as having important social or community needs with profits from other services. A second problem with estimating the resource cost in nonfederal US hospitals is blamed on the historical cost-based reimbursement system used by third-party payers in the United States. Under the cost-based system, measuring the total hospital cost accurately was more important for reimbursement purposes than for knowing the accurate cost of resources consumed by any individual patient. As a result, few hospitals implemented sophisticated managerial cost accounting systems that could measure the cost of hospital resources provided to individual patients. In a 1990 study of hospital cost accounting systems, 70% of hospitals surveyed indicated that the most detailed level of available cost information by their cost accounting system was at the departmental level, only 17% of the hospitals indicated that they could collect procedure-level cost data (6). Since the early 1990s, an increasing number of hospitals have adopted more advanced cost accounting methods to provide managers with superior cost information to respond effectively to their changing environment. However, because these systems are being implemented to enhance contract negotiation, it does not appear that most hospitals’ cost accounting systems can easily provide the cost information required to value hospital resources consumed by individual patients for CEA. Another argument for why hospital resource costs are hard to estimate is that hospitals are multiproduct organizations that produce thousands of individual services. With multiple services being produced using expensive capital equipment, a hospital has a large amount of costs in joint cost pools. Joint costs are resources that are used to produce two or more services. For example, a heart–lung bypass pump is a resource that can be used to treat patients undergoing coronary bypass graft surgery, value replacement, or a heart transplant. Regardless of the sophistication of the cost accounting system, all joint costs must be arbitrarily allocated among the products produced, and, as a result, there is no theoretical way of identifying the true resource costs of each service (7).

THEORETICAL DISCUSSION OF ESTIMATING HOSPITAL COSTS Cost Definitions and Cost Issue for Hospital Costs It is useful to subdivide hospital costs into variable and fixed costs. Variable costs are those costs that vary proportionately with patient volume and the intensity of treatment associated with each intervention. Typical examples of variable costs include such items as medications, medical devices, nursing time, and diagnostic testing. On the other hand, fixed costs vary only as a function of time. The annual depreciation cost of capital equipment is an example of a fixed cost. Variable costs are also often referred to as marginal costs, incremental costs, or avoidable costs. In theory, variable or marginal costs are considered avoidable; i.e., if the treatment/intervention were not

Chapter 1 / Nonfederal US Hospital Costs

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performed, the resources consumed would not be realized. Fixed costs are sometimes referred to as sunk costs. The fixed costs are sunk, in the sense that if the treatment/intervention was not provided, these resources have already been obtained by the hospitals, and the amount of money spent on these resources is not impacted by whether or not the patient uses these services (because the money has already been spent, there is no opportunity to use it to obtain other resources). Theoretically, CEA should only include marginal costs and exclude fixed costs. There are two categories of cost items in hospital costing that warrant a short discussion. First, hospital overhead costs (sometimes referred to in the managerial account literature as indirect or allocated costs), such as administrative costs, utilities, and general maintenance and housekeeping, could be classified as either variable or fixed. However, whereas most overhead cost items (heating bills, laundry expenses, and electricity) may have fixed components (such as monthly based service charges), costs also increase as patient volume increases. Therefore, although it is difficult to attribute those increases directly to individual patients or treatment arms, some allowance should be made for overhead costs in CEA. Second, many hospital services require significant investments in long-term assets (plant and medical equipment). Although the investment in the plant and equipment does not change as the result of increased patients, long-term assets do wear out (depreciate) over time. The depreciation of long-term assets should be included as a variable cost in CEA. There are a number of suggestions in the literature for how to measure capital investment costs in CEA. Capital investment costs have three components: depreciation, opportunity cost, and operating cost. Theoretically, both the depreciation and opportunity cost of each long-term depreciable asset can be measured by calculating “equivalent annual cost” of the asset (8). Once the equivalent annual cost has been calculated, it is the “per-use cost” that should be included in CEA. Although it is beyond the scope of this chapter, the calculation of the per-use cost usually requires allocating the equivalent annual cost across all services that use the asset. For example, the equivalent annual cost of an extracorporeal circulation bypass pump may need to be allocated across heart transplant patients, coronary artery bypass grafting (CABG) patients, and percutaneous coronary intervention (PCI) patients. The operating cost associated with capital equipment would be included as a variable operating cost.

Total Cost vs Relevant Cost CEA is often interested in determining the differences in cost between two treatment arms/interventions. As a practical matter, the cost differences used in CEA can be obtained by measuring the total cost in each treatment arm/intervention or by measuring only those resources that are consumed in one arm/intervention, but not in the other arm/intervention (these costs are sometime referred to as relevant incremental costs as opposed to irrelevant costs—any cost item that is identical in all study arms). For example, assume a CEA is interested in measuring the hospital cost for percutaneous transluminal coronary angioplast (PTCA) patients having a (coronary) stent-implanted vs PCTA patients not receiving a stent. In addition, assume that the stent, costing $1500, is the only difference in hospital resource consumption between patients undergoing a PCTA without a stent (total hospital cost of $12,500) and patients undergoing a PCTA with a stent implanted (total hospital cost of $14,000). In this example, the desired cost differential between the two study arms is $1500. This amount can be

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obtained by subtracting the total hospital cost of a hospitalization in each arm or by finding the cost differences between the relevant resource items (the cost of one stent in this example was $1500).

Time Effects One of the goals of CEA is to evaluate alternative procedures similarly. Time impacts hospital costs in CEA in two ways. First, the time value of money implies that a dollar spent today on hospital resources is worth more than a dollar spent in the future. As a result, hospital costs over the study period should be discounted into present value. Health outcomes should also be discounted at the same discount rate used for costs (9). The discount factor reflects the social rate of time preference (adopting the societal perspective for CEA). What the actual discount factor should be, in practice, is a matter of debate. Lipscomb et al. suggest a 3% discount factor for the United States (10). Alternatively, the British National Health Service uses a rate of 6% (11), the World Bank uses 3% (12), and the Center for Disease Control recommends 5% (12). The higher the value of the discount rate, the smaller the present value of any future health costs. A higher discount rate favors any intervention that has lower costs today and higher costs in the future years over any intervention that used the same total number of services, but consumes most of those services in the initial period. Box 1 demonstrates the impact of increasing the discount rate and the timing of events on hospital cost. As a result, standard practice is now evolving toward the use of a discount rate of 3%, with a sensitivity analysis applying discounting rates varying from 0% to 7% (13). A second added complication with multiperiod interventions is inflation. Inflation increases the current price of goods and services in the economy, but does not increase the opportunity cost (real resource cost). The goal of CEA is to measure opportunity costs: the value of alternative uses of resources. A comparison of costs requires that resource prices be transformed into real dollars. In the United States, inflation rates are published for both the general economy (the consumer price index [CPI]) and health care specifically (the medical component of the CPI). Because the medical component is more specific to health care, it is generally preferred in comparison to the overall CPI.

PRACTICAL APPROACHES FOR ESTIMATING HOSPITAL COSTS The most common problem in CEA involving hospital services is that the desired cost of most hospital services is not readily available. Recall that the objective of hospital costing is to determine the cost of providing hospital services, not the market price of these services. A review of previous CEA, in general, and those involving CAD, in particular, indicates that most studies have used one of four methods for estimating hospital costs. These approaches included (1) estimating hospital costs based on the type of hospital episode, (2) estimating hospital costs using administrative or reimbursement data, (3) estimating hospital costs from study-specific utilization data collection efforts, and (4) estimating hospital costs from hospital-specific micro-cost accounting data. The first approach is typically used when the main concern is the number and type of each hospitalization or when the follow-up period is lengthy (prevention studies interested in the impact of exercise on reducing the cost of CAD). The latter approaches are typically used when it is important to “cost out” every resource consumed in the treatment of each

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Box 1 The Impact of the Discount Rate and Timing of Events on Hospital Cost in CEA Study: Assume patients are randomized to coronary artery bypass graft (CABG) surgery or percutaneous coronary intervention (PCI). Study time frame: 5 years Hospital services consumed: For this example, assume that all patients underwent both CABG and PCI during the study period. Further assume that patients randomized to the CABG arm received the CABG in the initial period and the PCI in the last period, whereas patients randomized to the PCI arm received the PCI procedure in the initial period and the CABG in the last period. Hospital resource cost: Assume that the real dollar cost of a CABG procedure is $25,000 and the real dollar cost of a PCI is $15,000. Discount hospital cost: The table below shows the discounted total hospital cost over the 5 years for the two study arms for the three suggested discount rates. Discount rate

PCI total hospital cost CABG total hospital cost Cost differences

0%

3%

7%

$40,000 $40,000 $0

$36,565 $37,939 –$1374

$32,825 $35,695 –$2870

Conclusion: Although the two interventions consumed the same resources, once the time value of money is taken into account, the PCI alternative has a lower cost because the higher cost procedure (CABG) does not occur until the fifth year. Finally, as the discount factor increases from 3% to 7%, the cost advantage of the PCI alternative increases.

patient in either intervention. The following section provides a brief overview of each approach and discusses their advantages and disadvantages.

Estimating Hospital Costs Based on the Type of Hospital Episode CEA that are associated with Markov decision models and prevention-effectiveness studies are often interested in identifying the number and type of hospital episodes during the follow-up period (typically the remainder of a patient’s life). The typical solution in these studies is to use “gross-costing” or “macro-costing” to estimate hospital costs. In these studies, the desired measure of hospital resources consumed is usually measured by a macro-level unit, such as the number of hospital episodes or the number of days a patient is hospitalized, as opposed to measuring the individual services consumed in each hospital episode. Because it is too costly and time-consuming to follow individual patients for the desired follow-up period, hospital resources consumed

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during the follow-up period are estimated from external sources. Estimates of hospital resources are obtained from a variety of sources, including expert opinions (Delphi Panels), clinical literature (meta-analysis), or actuarial tables. The cost of the resources consumed during the hospitalization is typically estimated using an average cost for the relevant type of hospital episode (one possibility is to use Medicare’s cost by diagnosis-related groups [DRG]) or average cost-per-patient day. For example, assume that an exercise rehabilitation program for patients over 65 years of age with CAD is expected to reduce future in-patient days by 10 over the remainder of their life in comparison to the average number of in-patient days for patients not participating in the exercise program. Further assume that the average hospital cost per day for CAD patients is $2500. In this example, the hospital cost savings of the exercise program is estimated to be $25,000 (relevant change in resources consumed in 10 days multiplied by the average cost per day [$2500], ignoring discounting). One advantage of this approach is reduction of study data collection efforts and the cost of extending clinical trials over long follow-up periods. A second advantage of this approach is that the sensitivity analysis associated with resources consumed and the cost of those resources can provide an indication of the key factors that impact the CEA of the interventions. There are two major disadvantages using the gross-costing approach. First, the accuracy of these studies depends critically on the estimates of resources consumed. Often, there are few, if any, randomized clinical trial results in the literature to base estimates of future resource consumption, and the patient population in published studies may not be appropriate for the current study. Second, the grosscosting approach assumes all patients with a given type of hospital episode consume the same resources or services (i.e., all CAD patients consume $2500 worth of resources per day). As a result, the ability of gross-costing to measure differences in resource costs depends on whether the macro-resource consumption measures available reflect the true differences in resources consumed between the two interventions. Overall, gross-costing is an appropriate approach for estimating hospital cost if the goal of the intervention is to reduce the number of hospitalizations, but is not expected to reduce the intensity of any hospitalization in either treatment arm.

Estimating Hospital Cost Using Administrative or Reimbursement Data CEA associated with clinical trials are often interested in identifying the specific health care services consumed under each intervention. This approach is typically referred to as “micro-costing” in the CEA literature. One method of identifying direct health care services consumed, particularly for hospital services, is to use administrative data or billing information found in claims data sets. One advantage of this approach is that nearly all hospitals can generate an itemized bill detailing the services provided to patients during a hospital episode. An additional advantage of using hospitals’ unified bills (UB-92) is that the revenue codes have been standardized across hospitals, therefore, similar services tend to be categorized within similar codes. Overall, UB-92 billing information provides a fairly low-cost means for study investigators to identify all procedures and services consumed by patients during any hospitalization. Another important advantage of using UB-92 billing information is that most hospital financial offices can provide this information to the study’s data coordinating center in an electronic format so that the individual UB-92 forms do not need to be entered into the study database by hand.

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The major disadvantage of UB-92 billing information is that the charges identified on a UB-92 do not represent the opportunity costs of the hospital resources consumed. In general, previous researchers have converted billed charges (found on UB-92 forms) into estimates of resource costs by using some level of the hospital’s cost-to-charge ratios found in the Annual Medicare Cost Reports (it is possible to use cost-to-charge ratios for other sources). Most investigators have used either “whole hospital level” or “departmental level” cost-to-charge ratios to convert UB-92 billed charges into estimates of resource cost. When using the average hospital cost-to-charge ratio, the resource cost of a hospitalization can be estimated as the product of total billed charges during each hospital episode and the hospital’s overall or average cost-to-charge ratio available from the Medicare Cost Report. When using the departmental cost-to-charge ratio approach, estimates of hospital costs are obtained by adjusting hospital charges to costs at the departmental level using the appropriate departmental cost-to-charge ratio and departmental charges aggregated from the itemized UB-92 billed. Both of these approaches have been used numerous times, and there are advantages and disadvantages of each approach. The main advantage of the average hospital costto-charge ratio approach is that this approach reduces the amount of subjective decision making by the investigator when converting charges to costs. Total billed charges from the UB-92 form are multiplied by a single cost-to-charge ratio. All investigators using this average hospital approach should obtain similar estimates of hospital resource costs, because both total billed charges (UB-92) and the hospital’s average cost-tocharge ratio (Annual Medicare Cost Report) are obtained from sources external to the study. The major disadvantage of the whole hospital approach is that it assumes that all services provided within a hospital are marked up by the same ratio. Clearly, this is not the case among nonfederal US hospitals and is an important limitation of this approach, especially for patients that only consume services from one or two departments during their hospitalization. In general, the average hospital cost-to-charge approach is the most appropriate in costing studies when patients consume a fairly wide variety of services from a number of different hospital departments. At first glance, adjusting charges at the departmental level appears to provide a more intuitive method for estimating the cost of a hospitalization. However, there are several problems with using departmental cost-to-charge ratios. First, there is no direct mapping of UB-92 revenue codes into the departments reported on the Annual Medicare Cost Report. Investigators are left to their own discretion as to how they assign UB-92 revenue codes to departments. In addition, which UB-92 revenue codes are assigned to which cost departments may depend on whether the investigator assigned the codes based on the first two digits (30X—any laboratory) of the revenue code or the first three digits (305—hematology). To date, few investigators have described in any detail how they assigned revenue codes to departments. The second major problem with using departmental cost-to-charge codes relates to the lack of consistent cost accounting procedures in assigning hospital costs to departments. Although it is not the purpose of this chapter to provide a detailed discussion of cost allocation, it is important to note that a hospital’s finance office has significant choice in how overhead costs are allocated to individual departments on the Annual Medicare Cost Report. In addition, the Activity Based Accounting literature has demonstrated that departmental or product cost-to-charge ratios vary significantly, depending on which cost drivers are used to allocate overhead cost to departments. As

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a result, it is possible that a hospital’s departmental cost-to-charge ratios reported on the Annual Medicare Cost Report are less accurate than the overall average cost-tocharge ratio for the hospital (15). Overall, the major limitation of using departmental cost-to-charge ratios is that multiplying charges assigned to departments by departmental cost-to-charge ratios may not reflect the department’s true cost-to-charge ratio. Investigators that estimate hospital cost using administrative or reimbursement data must choose between two flawed approaches. As the role of CE studies continues to expand, future research needs to examine the variation in estimated hospital costs using the two approaches. In particular, research needs to determine if these two approaches provide cost estimates that differ by types of services provided, length of hospitalization, number and types of hospitals in the study, and characteristics of the patients treated.

Estimating Hospital Costs from Study-Specific Utilization Data Collected by Sites A third approach used to identify hospital resource consumption involves the collection of resource utilization data by the study investigation team. One advantage of this approach is that the data collection effort can be focused on hospital resources that are expected to have the greatest impact on treatment costs between the two study arms. This may be particularly useful if the study involves an intervention that results in the use of significantly different amounts of a hospital resource (e.g., basic nursing care) that is not reflected in the UB-92 bill. A second potential advantage is that this approach, by focusing on the most relevant resource consumption items, reduces the amount of data collected. This may also reduce the cost of collecting utilization data. However, there are several disadvantages to this approach. First, it is possible that the data collection form developed for the study may overlook one or more important resource consumption items that could impact the hospital cost in either study arm. Second, this approach, like any primary data collection effort, requires that individuals be trained to collect the utilization data. In addition, the accuracy and completeness of the data collected may vary significantly over the study and may be costly to verify the accuracy of the utilization data collected (especially if the utilization data is based on patient recall of services used). Finally, it should be noted that even if the utilization data is collected accurately, the study investigator still must estimate the cost of each resource used. Cost estimates of resources identify using this approach can be estimated from charges for these services (with the same problems as administrative data) or estimated with micro-cost accounting information (see the following section).

Hospital-Specific Micro-Cost Accounting Information During the 1990s, hospital administrators needed more accurate cost information to negotiate contracts with managed care organizations. This need for more detailed cost information resulted in hospitals upgrading their cost accounting systems. Today, the availability of micro-cost account information at selected hospitals provides an alternative source of cost information, especially for studies limited to a single hospital. In the future, resource cost estimates from micro-cost accounting systems may prove to be the gold standard for estimating the cost of hospital resources consumed. One limitation of micro-cost accounting information is that micro-cost accounting information systems are developed to aid managers in making business decisions. To

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the extent that these systems incorporate business rules that maximize third-party payers’ reimbursement, they may not produce accurate estimates of the resource costs needed for CEA (overhead and depreciation may be allocated by the cost accounting system in a way that overcharges selected services). In addition, the accuracy of microcost accounting systems depends on the how joint and overhead costs are allocated to individual departments and services (similarly to department specific cost-to-charge ratios) so that even with micro-cost accounting systems, the cost of hospital resources consumed during a hospitalization is only an estimate. Finally, the current use of micro-cost accounting information is limited to those few facilities that have appropriate cost accounting systems.

ISSUES IN DATA FROM RANDOMIZED CONTROLLED TRIALS Many CEA are performed in the context of randomized controlled trials (RCTs). Although the advantages of RCTs are considerable, there are some drawbacks to attaching CEA onto a trial designed around a clinical endpoint. First, clinical trials often have trial protocols that dictate the level of care to be delivered. For example, clinical trials may require more frequent monitoring and testing than would be expected outside of the trial. Care should be taken to separate the cost of resources consumed in these protocol-driven visits from the cost of resources that would be consumed in standard practice, especially if the service is only required for one intervention. For example, if a left heart catheratization is required at the end of the follow-up period for all patients to evaluate the clinical endpoint, then the cost of the left heart catheratization should not be included in the cost analysis. One could make a similar argument for the resources associated with any additional revascularizations that were identified during a follow-up interventional procedure by the study, especially if the patient had not indicated any reoccurrence of angina pain prior to the study-induced intervention. Second, many RCTs are conducted through medical schools associated with teaching hospitals. A number of health service research studies have shown that patients treated in teaching hospitals consume significantly more health care resources than similar patients treated at nonteaching hospitals (16). To date, few cost studies have discussed the possible implications of carrying out clinical trials in teaching hospitals. However, this practice raises several questions. First, because one advantage of CEA that take the societal prospective is that they can be used to compare the CE of different types of treatment, an intervention carried out in teaching hospitals will most likely appear less cost-effective than it really is, especially when compared to a communitybased preventative intervention that does not include a hospitalization. Second, it is possible that the observed cost difference in a study is a result of the excess utilization of hospital services and tests provided in teaching hospitals. A third disadvantage of completing CEA within RCTs is that they tend to have a significant number of inclusion and exclusion criteria. It is not clear that the case-mix of patients in the trial is similar to the case-mix of patients that will receive the procedure in community practice. To the extent that patients included in clinical trials are either lower risk patients or have more limited access to health care than patients that receive the treatment in the community, the hospital resource costs identified in the CEA may not reflect the true cost differential.

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Cardiovascular Health Care Economics Table 3 Major Sources of Variation or Uncertainty Associated with Each Approach for Estimating Hospital Costs Source or variation or uncertainty Resources consumed

Type of hospital episode

Administrative data approach Study-specific utilization data

Micro-cost accounting data

Macro-level hospital resource use in follow-up period is seldom based on observed utilization of the study population UB-92 billing codes many not distinguish differences in the intensity of resources used Study design issues and data collection problems could result in inaccurate resources consumption information Data on resource consumed by patients must be obtained from an alternative source

Price of resources No standard estimates of macro-level hospital resource costs are available The accuracy of cost-tocharge ratios is unknown Cost of services consumed must be estimated from an alternative source Variation in how the cost accounting system allocates indirect costs

A final disadvantage associated with randomized trials for CEA is the time horizon. Randomized trials often follow patients for a limited time (e.g., 6 month or 1 year). Because many important costs and benefits are experienced further downstream, data from such trials may be misleading.

THE FUTURE OF HOSPITAL COST ESTIMATES In a world of limited resources, the systematic economic assessment of the impact of health care treatments and health care outcomes has become an essential component in the evaluation of clinical medicine and medical decisions. CEA can scientifically evaluate the effectiveness, benefit, and costs of multiple health strategies and provide a metric for evaluating different and competing demands on society’s limited resources. Since the early 1990s, a growing consensus has been developing about many of the principles of CEA. However, a review of cost studies indicates that there appears to still be significant limitation and disagreement related to measuring hospital resource costs. Investigators attempting to estimate hospital cost using administrative or reimbursement data must often choose between partially flawed approaches. Currently, investigators have two choices to obtain cost estimates. First, investigators can engage in a detailed resource utilization collection process for each intervention, then estimate the resource price of each services provided (with or without micro-cost accounting system), thereby obtaining a hospital cost estimate that is most likely absolutely wrong. Alternatively, investigators may collect administrative/billing data and adjusting bill charges and, with a set of costto-charge ratios, create a hospital cost estimate that, at best, is relatively right. Table 3 identifies the major limitations for measuring both hospital resource consumption and resource prices for each of the approach discussed in this section. Each of these approaches has variation or uncertainty associated both with the collection of

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Table 4 Possible Activities that Would Improve the Standardization of Hospital Cost Estimates Possible standardization of approach Resources consumed Type of hospital episode

Develop longer-term estimates of resources consumed by patients with each major chronic disease

Administrative data approach

Panel of cost experts need to examine if there are hospital resources that are not being captured by UB-92 codes Develop a set of audit procedures to determine the accuracy of the studies data collection effort Use UB-92 or study data methods to collect resource consumption

Study-specific utilization data

Micro-cost accounting data

Price of resources Develop and update a set of average resource cost by DRG category for each macro-level hospital resource episodes Develop and update a national average cost per unit of resource for each UB-92 code Use estimated costs develop from administrative or micro-costing approaches Develop a hospital level cost accounting database for major hospital service

the quantity of health care resources consumed and the approaches to estimating the prices of the research. Done properly, all four approaches can yield reasonable CEA results. However, all four approaches can also produce misleading CE ratios. As the role of CE studies continue to expand, future research needs to address and standardize these limitations. In particular, researchers need to determine if these limitations result in cost estimates that differ by types of services provided, length of hospitalization, number and type of hospitals in the study, and characteristics of the patients treated. Looking forward, the demand for identifying and measuring hospital cost is expected to continue to increase. In particular, investigators need to improve hospital cost estimates by attempting to standardize measuring hospital resource costs. Table 4 provides a list of preliminary studies that may help standardize the conduct of CEA. For example, the current UB-92 billing system provides a fairly comprehensive list of services provided to patients. If a set of resource cost (similar to physician relative value units) could be developed for each UB-92 services, then estimates of hospital cost could be made without using individual hospital cost-to-charge ratios. In summary, improving the standardization of hospital cost measures will, at a minimum, allow investigators to perform sensitivity analysis associated with the cost approach utilized.

REFERENCES 1. Centers for Medicare and Medicaid Services: see http://ems.hhs.gov/statistics/nhe/historical. 2. Elixhauser A, Luce B, Taylor W, Reblando J. Health care CBA/CEA: an update on the growth and composition of the literature. Med Care 1993;31(Suppl);JS1–JS11.

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3. Wolinsky F, Culler S, Callahan C, Johnson R. Hospital consumption among older adults, a prospective analysis of episodes, length of stay, and charges over a seven-year period. J Gerontol 1994;49:S240–S252. 4. Gold M, Siegel J, Russell L, Weinstein M. (eds.) Cost Effectiveness in Health and Medicine. Oxford Press, New York, 1996. 5. Finkler S. The distinction between cost and charges. Ann Int Med 1982;96:102–109. 6. Orloff T, Little C, Clume C, et al. Hospital cost accounting: who’s doing what and why. Health Care Manag Rev 1990;15:73–78. 7. Finkler S. Cost Accounting for Health Care Organizations: Concepts and Applications. Aspen Publishers, Inc., Gaithersburg, MD 1994; pp. 40–41. 8. Richardson A, Gafni A. Treatment of capital costs in evaluating health care programmes. Cost and Management 1983;Nov–Dec:26–30. 9. Atherly A, Culler S, Becker E. The role of cost effectiveness in health care evaluation. Q J Nucl Med 2000;44:2;112–120. 10. Lipscomb J, Weinstein M, Torrance G. Time preference. In: Gold, M, Siegel J, Russell L, Weinstein M. (eds.) Cost Effectiveness in Health and Medicine. Oxford Press, New York, 1996; pp. 214–235. 11. Parsonage M, Neuburger H. Discounting and health benefits. Health Econ 1992;1:71–76. 12. World Bank. World Health Development Report. Washington, DC, 1993. 13. Centers for Disease Control and Prevention. A Practical Guide to Prevention Effectiveness: Decision and Economic Analyses. US Department of Health and Human Services Atlanta, GA, 1994. 14. Weinstein M, Siegel J, Gold M, Kamlet M, Russell L, for the panel on cost effectiveness in health and medicine. Recommendations of the panel on cost-effectiveness in health and medicine. JAMA 1996;276:1253–1258. 15. Ashby J. The accuracy of cost measures derived from Medicare Cost Report data. Hospital Cost Management and Accounting; 3:1–8. 16. Custer W, Willke R. Teaching hospital costs: the effects of medical staff characteristics. Health Serv Res 1991;25:831–857.

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Estimating the Costs of Cardiac Care Provided by the Hospitals of the US Department of Veterans Affairs Paul G. Barnett, PhD, Patricia Lin, MPH, and Todd H. Wagner, PhD CONTENTS BACKGROUND COST DETERMINATION METHODS METHODS RESULTS DISCUSSION REFERENCES

BACKGROUND Ischemic heart disease is among the leading causes of death in the United States (1), and one of the most frequently treated diseases in US Department of Veterans Affairs (VA) health care facilities. Each year, VA facilities provide more than 150,000 hospital stays for patients with this condition, including some 15,000 stays for myocardial infarction (MI) and some 23,000 stays for unstable angina (2). As part of its mission, VA conducts clinical trials to improve the quality and effectiveness of patient care, including several trials examining strategies for treating ischemic heart disease. Costeffectiveness (CE) is an increasingly important part of these studies. The VA health care system has unique features that present both opportunities and challenges for clinical trials. Patients have a uniform set of insurance benefits and few copayments, allowing trial participants equal access to health care. VA has comprehensive utilization databases, which make it possible to track the quantity of care received by an individual throughout the health care system. However, health economics studies are more challenging because of the lack of billing data, which are ordinarily used by non-VA hospitals in the United States to estimate the cost of care. Given the lack of billing data, VA health economics researchers use several alternative methods to estimate the cost of care, including direct measurement, preparation of From: Contemporary Cardiology: Cardiovascular Health Care Economics Edited by: W. S. Weintraub © Humana Press Inc., Totowa, NJ

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pseudo-bills, and applying cost functions estimated from non-VA data (3). Researchers are also beginning to use the Decision Support System (DSS), a patient care cost accounting system that has been adopted by VA. This chapter describes methods of estimating VA health care costs, with the goal of identifying methods most suitable for CE studies of treatment for ischemic heart disease. We focus on the cost of hospitalization, ordinarily the most expensive component of treatment. Using hospital stays for MI as an example, we evaluate the accuracy of cost data from DSS. We compare these data to cost-adjusted charges from Medicare claims data at non-VA hospitals. We also examine the effect of DSS data practices, as reported by site managers, on DSS cost estimates.

COST DETERMINATION METHODS Cost-effectiveness analysis (CEA) ordinarily requires comprehensive information on cost. As a result, VA analysts ordinarily rely on several alternate costing methods (4), which include direct measurement, pseudo-bill, cost functions, and DSS data.

Direct Measurement Direct measurement is a useful and potentially accurate means of determining health care cost. An activity analysis is used to determine the labor employed. Supply and space costs are also determined. Direct measurement can be used to find the cost of a specific intervention or a few diagnostic tests or procedures. Because this method is labor-intensive, it is not feasible to use it to find all the health care costs incurred by patients.

Pseudo-Bill The pseudo-bill approach combines VA utilization data and a non-VA reimbursement or charge schedule. The estimate mimics the itemized bills used by health care payers, giving the method its name. Hypothetical Medicare reimbursement for VA ambulatory care can be determined because VA uses the Current Procedures and Terminology (CPT) and Medicare Health Care Procedures Coding System (HCPCS) codes to characterize services and supplies provided to out-patients. The Medicare Resource-Based Relative Value System can be used to determine Medicare reimbursement for physician and other provider visits, laboratory tests, and medical supplies. Medicare makes separate payments to facilities, such as hospital out-patient clinics, ambulatory surgery centers, and free-standing diagnostic centers. These facility reimbursements can be estimated with the new Medicare prospective payment system, which is based on the Ambulatory Payment Category of each procedure. Because Medicare does not reimburse providers for certain types of care, such as preventive services and dental procedures, other charge schedules are needed to estimate the cost of these services. Pseudo-bills have also been created using charge schedules from non-VA providers. Regardless of the method used, analysts must adjust charges to reflect actual economic costs. One way for VA investigators to do this is to find all ambulatory charges at a medical center and compare these to the total ambulatory costs reported in the VA department level cost report, the Cost Distribution Report. Pseudo-bill methods are not a very useful way of estimating in-patient cost. The Medicare reimbursement rate may not be sensitive to the effect of an intervention on

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hospital costs because Medicare makes a fixed payment based on the diagnosis-related group (DRG) of the hospital stay. Therefore, Medicare-based cost estimates may not accurately capture the resources used that are not reflected in the DRG assignment. An in-patient pseudo-bill could be constructed to take advantage of a charge schedule of a non-VA hospital, however, it would be very expensive for a VA investigator to do this, as hospital charge schedules are based on very detailed information on each specific resource used in the stays. It is unlikely that such an extreme level of detail is needed for accuracy. Most of the variance in hospital costs can be explained by just a relatively few characteristics of the stay.

Cost Function The cost-function method requires less detailed utilization data. It relies on the characteristics of hospital stays that explain most of the variation in their cost. A regression is estimated using data from non-VA hospital stays. The dependent variable is cost-adjusted charges. The independent variables are the characteristics of the stay, such as diagnosis and length of stay. The model is then applied to VA utilization data to simulate charges. This approach has been used to estimate the cost of VA stays for acute MI. A cost function was estimated using a large sample of stays of patients hospitalized for heart attack in Seattle area hospitals (5). Independent variables included total days of stay, days of intensive care, vital status at discharge, and whether the patient had cardiac catheterization (CATH), coronary artery bypass grafting (CABG), or percutaneous transluminal coronary angioplasty (PTCA). The cost-function method requires data on non-VA patients with comparable conditions. Such data may be available from hospital discharge datasets. A suitable data source represents a relatively economical means of estimating VA hospital costs. The approach requires the assumption that patterns of resource use in the non-VA sample are the same as in the VA sample. Explanatory variables are limited to those that occur in both the VA and non-VA data set. One obstacle that needs to be addressed is physician services. Charge data from US hospitals do not ordinarily include these costs. Physicians bill payers separately. It is difficult to access physician claims and associate them with a particular hospital stay. One alternative is to create a pseudo-bill for in-patient services provided by physicians. There are two significant problems with this approach. Hospital discharge records characterize procedures using the International Classification of Diseases–9th Revision (ICD-9) codes. These codes are much less specific than the CPT codes used for physician billing. Medicare and other payers do not have schedules of the physician reimbursement associated with ICD-9 procedure codes. Another problem is that hospital discharge data include only procedures and do not characterize other services provided by physicians, such as consultations and daily visits. An alternative method is to use data on the average payment for physician services found in other studies. One study of Medicare-financed hospital stays provides the average Medicare reimbursement for physician services provided to hospitalized patients for each DRG (6).

Decision Support System VA has implemented the DSS, a software and database that includes a cost allocation system. DSS is an activity-based costing system that determines the costs of inter-

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mediate health care products and tallies them to find the total cost of hospital stays and out-patient encounters. It has the potential of providing cost estimates that are far more accurate than methods currently used in VA CE studies. Indeed, if the system is properly implemented, the cost estimates should be more sensitive to variation in resource use than the cost-adjusted charges used in non-VA CE studies. There are several concerns about the accuracy of DSS (7). DSS has been implemented relatively recently by VA. It is not known if facilities accurately distribute staff costs among departments or estimate the relative effort required to produce different health care products. Because VA physicians do not bill for their services, they do not have the same incentive that non-VA physicians have to document their work, therefore, VA databases do not reflect the same level of detail found in non-VA physician claims databases. For example, some VA sites do not record CATH procedures in a way that allows DSS to determine their cost (7). The extent of this problem and its effect on DSS cost estimates is unknown. A previous study estimated the cost of VA hospital stays for acute MI using DSS data from four sites that were reported to have exemplary data (5). The relationship between cost and characteristics of stay was used to estimate the cost of the initial VA hospital stay of patients enrolled in the VA Non-Q Wave Infarction Strategies in Hospital clinical trial. The DSS-based cost estimates yielded the same CE results as non-VA cost estimates. We sought to expand on this work to learn the quality of DSS data at other sites.

METHODS We evaluated the quality of DSS data from VA by conducting a medical center-level survey of data quality, then evaluating the effect of data quality on cost.

DSS Site Survey We sent surveys to DSS managers at 71 VA medical centers where CATH are performed. Respondents were asked if CATH was offered at their site, whether CATH workload was collected by DSS, and, if so, whether it was used to estimate cost. They were asked their opinion of the quality of their site’s data. The survey was distributed in the summer of 1999, and 63 surveys were completed and returned. Two respondents reported that there was no CATH laboratory at their site, leaving 61 complete responses from sites with CATH laboratories.

VA Cost and Utilization Data Patients hospitalized for MI were identified in the national VA hospital discharge database, the Patient Treatment File. We selected patients with a primary diagnosis of new MI (ICD-9 code of 410.0–410.9, but excluding codes with the fifth digit of 2, indicating subsequent nonacute treatment of MI). We identified 10,377 stays that ended in the year prior to September 30, 1999. We limited our analysis to the 61 sites that offered CATH and responded to the survey, leaving 6261 observations. We matched these stays to cost data in the VA DSS National Discharge Extract. Because this DSS extract does not separately identify the cost of long-term or rehabilitation care, we excluded 178 stays that involved these types of care. We dropped 18 additional observations because they did not appear in the DSS National Extract with a cost estimate, leaving data on 6065 stays. These data were combined with indicators of the quality of

Chapter 2 / Cost of VA Cardiac Care

19

DSS data at that hospital and the value of the Medicare hospital wage index for the hospital’s location. This index is used by Medicare to adjust hospital payments for geographic variation in wages. VA does not pay the cost of financing capital acquisitions, which are borne by the US Treasury Department. We added to the VA DSS costs an estimate of the average cost of capital of US hospitals. The Medicare capital reimbursement to US hospitals was an average of $727 per discharge in 1996. We assumed that capital costs are proportionate to total costs. The average Medicare hospital stay had a case-mix index of 1.42 DRG relative value units. Stays in this cohort of VA patients had an average casemix index of 1.845 (1.29 times the Medicare average). We estimated the average capital cost of these VA stays as $1003 ($727 × 1.29, adjusted to 1999 dollars). Because this was 8.3% of the average VA cost, we added 8.3% to all DSS costs estimates. DSS cost estimates do include the cost of depreciation. As we have no way of deducting depreciation cost from DSS estimates, we have double-counted this cost. The cost of financing capital acquisitions exceeds depreciation, especially in VA, as many facilities are fully depreciated.

Medicare Data To compare the DSS data to non-VA cost estimates, we identified a comparable sample of Medicare claims. Medicare predominantly serves adults over 65 years of age, but it also covers younger individuals with disabilities. We studied hospital stays of all individuals who obtained care from VA between 1992 and 1994 who appeared in the 1996 Medicare Provider Analysis and Review file. We identified 13,809 stays with primary diagnosis of new MI at non-VA hospitals in the continental United States. There were 9552 stays at hospitals that provided at least one CATH. We found the Medicare wage index for each hospital; there were 144 stays at hospitals that we could not identify the wage index, leaving 9408 observations in our data set. The cost of each stay was estimated by multiplying charges by a hospital-specific cost-to-charge ratio. We estimated the cost of physician services using the average DRG-specific Medicare physician payment (6). We adjusted this cost by $51 for every day that the stay deviated from the national mean length of stay for that DRG. This is the typical Medicare reimbursement for a daily physician visit to an in-patient.

Definition of Comorbid Conditions We used ICD-9 diagnostic codes to identify the following additional conditions reported in the hospital discharge record: cardiogenic shock (785.51), cardiac arrest (427.5), tachycardia (785.0, 427.0, 427.1, 427.2), and pulmonary edema (428.0, 428.1, 518.4). We characterized 12 comorbidities using the ICD-9 codes used to create a modified Charlson index from discharge data (8). These were previous MI, peripheral vascular disease, cerebrovascular disease, dementia, chronic pulmonary disease, rheumatologic disease, liver disease, diabetes, hemiplegia or paraplegia, renal disease, cancer, and AIDS. We characterized psychiatric and substance abuse conditions with the same ICD-9 codes used in a study of substance abuse treatment (9).

Inflation Adjustment All costs were adjusted to 1999 dollars using the consumer price index for all urban consumers.

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Cardiovascular Health Care Economics

Statistical Analysis Statistical tests were conducted with hospital-level error terms. We estimated random effects regressions to avoid the bias in the standard errors that would occur if there is correlation among patients treated at the same hospital.

RESULTS The study data include 6065 stays for MI at VA hospitals that occurred in 1999. The average DSS cost estimate for these stays was $13,238. The average length of stay was 7.1 days. Data from Medicare hospitals include 9408 stays for MI that occurred in 1996. The average cost-adjusted charges for these stays was $18,860. The average length of stay was 7.4 days. There were many significant differences in the care provided in the two systems and in the characteristics of patients (Table 1). These characteristics are associated with differences in cost. Patients treated at Medicare hospitals were more likely to undergo some procedures. CABG was performed in 15.9% of the stays at Medicare hospitals, significantly more than the 5.1% rate at VA hospitals. PTCA was performed in 24.4% of Medicare stays and in 22.1% of the VA stays (difference not statistically significant). CATH was performed during 59.7% of Medicare stays. This rate was significantly lower for VA, where it was performed in 50.6% of stays. Patients treated in a VA hospital were more likely to be treated in an intensive care unit (ICU). Of the 73.5% of VA stays that involved some time in the ICU, the average length of stay in the ICU was 4.1 days. Although a smaller percent (53.7%) of Medicare stays involved time spent in the ICU, the average length of stay in the ICU was a little longer (4.5 days). Patients treated in Medicare hospitals were, on average, 72 years old, whereas those treated in VA hospitals were significantly younger, an average of 65.5 years of age. Medicare patients were more likely to have many of the diagnoses associated with more severe heart disease, including higher rates of cardiogenic shock, cardiac arrest, tachycardia, pulmonary edema, and previous heart attack. In addition, they had higher rates of comorbid conditions, including chronic pulmonary disease and peripheral vascular disease. They were also more likely to die during their hospital stay. Patients hospitalized in VA facilities were more likely to have diabetes, renal disease, AIDS, and psychiatric and substance abuse comorbidities.

DSS Survey Results Of the 61 VA medical centers that offered CATH and completed a survey, 51 (83.6%) reported that they gathered CATH workload data in a way that could be used by DSS. There were 31 sites (50.8% of the total) that estimated the costs of this workload. We asked DSS site managers to give their opinion about the quality of this cost and workload data. Managers at 27 of these 31 sites were at least moderately confident in the quality of the workload data. There were 24 managers who were at least moderately confident in the quality of the data on CATH products, and 21 who were at least moderately confident in the quality of the units of relative value used to estimate the cost of these products.

Chapter 2 / Cost of VA Cardiac Care

21

Table 1 Mean of Variables Characterizing Stays in Medicare and VA Hospitals Medicare hospitals

Variable CAC ACUTDAYS ICUDAYS ICUSTAY WAGINDX CABG PTCA CATH AGE SEX DIED SHOCK EDEMA ARREST TACHY OLDMI PVD CVDIS CPD RHEUM HEMIPARA RENAL CANCER ANYDIAB AIDS CHARL DEMENTIA SCHIZO PTSD DEPRES NSYKD NSADX ANYSADX ANYSYKDX

Total cost Acute days of stay Intensive care days of stay Stay with intensive care days (%) Medicare wage index for hospital Bypass surgery (%) Angioplasty (%) Cardiac catheterization (%) Age in years Gender (% female) Died in hospital stay (%) Cardiogenic shock (%) Pulmonary edema (%) Cardiac arrest (%) Tachycardia (%) Previous heart attack (%) Peripheral vascular disease (%) Cerebrovascular disease (%) Chronic pulmonary disease (%) Rheumatologic disease (%) Hemiplegia-paraplegia (%) Renal disease (%) Cancer (%) Any diabetes diagnosis (%) AIDS (%) Modified Charlson comorbidity index Dementia (%) Schizophrenia (%) Post-traumatic stress disorder (%) Depression (%) Number of psychiatric diagnoses Number of substance abuse diagnoses Any substance abuse diagnosis (excluding nicotine) (%) Any psychiatric diagnosis (%)

18,860 7.4 2.4 53.7 0.977 15.9 24.4 59.7 72.0 2.4 10.2 4.8 34.8 4.0 12.1 8.4 5.9 2.5 25.8 1.1 1.6 2.3 2.9 29.4 0.0 0.819 1.2 0.4 0.1 1.6 0.058 0.088 2.4 5.5

VA hospitals 13,238a 7.1 2.8 73.5a 0.993 5.1a 22.1 50.6a 65.5a 1.5a 8.2a 1.8a 22.9a 2.2a 7.0a 4.2a 5.7 3.3b 20.0a 1.0 0.5a 4.1a 3.3 33.7a 0.3a 0.776b 0.8c 1.4a 1.3a 2.9a 0.110a 0.174a 5.6a 10.4a

a

Denotes significant difference (p < .001). Denotes p < .01. c Denotes p < .05. b

We used the survey responses to assign the 61 sites to three mutually exclusive groups. The 20 (32.8%) sites that responded to all three data-quality questions with at least moderate confidence were called “GOODDATA” sites. The 11 sites (18%) that estimated costs, but lacked confidence in data quality, were assigned to a group called “LACKCONF.” The remaining 30 sites (49.2%) that do not estimate the costs of their CATH laboratory were called the “NOESTIM” sites.

22

Cardiovascular Health Care Economics Table 2 Mean Cost of Stay by Most Complex Procedure Performed and Type of Hospital VA hospitals

CABG PTCA CATH NONE All stays

Medicare hospitals

GOODDATA

LACKCONF

NOESTIM

45,006 20,711 12,710 10,205 18,860

41,697 16,263a 15,256a 10,920 15,248

40,682 16,131a 13,410 10,181 15,520

34,789a 11,693a,b,c 10,682c,d,e, 9824e 11,484a

a

Denotes significant difference from Medicare hospitals (p < .001). Difference from VA GOODDATA hospitals (p < .001). c Difference from VA LACKCONF hospital (p < .05). d Difference from Medicare hospitals (p < .05). e Difference from VA GOODDATA hospitals (p < .05). b

Mean Cost by Type of Hospital The mean cost of stays appears in Table 2. This table distinguishes costs based on the four types of hospital: Medicare hospitals and VA hospitals’ three levels of data quality. Table 2 distinguishes stays according to the most complex procedure performed. Stays that involved CABG had an average cost of $45,006 at Medicare hospitals. Stays involving CABG were less costly at VA hospitals, but the difference was statistically significant only at the NOESTIM sites. Both CABG and PTCA were performed during some of these stays. The 100 stays at the Medicare hospitals had an average cost of $51,561. There were 14 of these stays at VA hospitals with an average of $54,347 in reported cost. The next category of stay was those in which PTCA was performed. Medicare stays of this type cost an average of $20,711. All three types of VA hospital reported a lower cost. Stays that involved neither of these procedures, but included a diagnostic CATH, cost an average of $12,710 at Medicare hospitals. Reported VA costs were significantly higher at GOODDATA sites and significantly lower at NOESTIM sites. Stays in which none of these procedures were performed had an average cost of $10,205 at Medicare hospitals. Costs reported at VA hospitals were not significantly different, except for the NOESTIM sites, which reported significantly lower costs. There were also some statistically significant differences between the types of VA sites. Stays were shorter at NOESTIM sites than they were at GOODDATA sites. The NOESTIM sites were also less likely to perform PTCA than either the LACKCONF or GOODDATA sites and less likely to perform CATH than the LACKCONF sites. Patients at the GOODDATA sites were more likely to be discharged with a diagnosis of cardiogenic shock than were the patients from the other sites. Patients at the GOODDATA sites were more likely to have a diagnosis of pulmonary edema than the LACKCONF sites. Although the means reported in Table 2 are informative, these comparisons may be misleading. Other characteristics might explain differences in cost. Medicare stays involved older patients with more severe cardiac conditions and medical comorbidities,

Chapter 2 / Cost of VA Cardiac Care

23

whereas VA patients were more likely to have psychiatric and substance abuse diagnoses. We conducted a regression analysis to compare costs while controlling for case-mix.

Cost Regression We regressed costs on characteristics of the hospital, patient, and treatment provided. This random-effects regression included a hospital-level error term to estimate unbiased standard errors. Patient level variables included procedures, length of stay, days in the ICU, and indicators of comorbid conditions. To avoid the assumption that costs were proportionate to the length of stay, we included the square and cube of both the length of stay and the number of days in the ICU. Hospital level variables included the Medicare wage index and indicators of hospital type. There were three indicators for VA sites, associated with the reported quality of DSS data (GOODDATA, LACKCONF, and NOESTIM). Medicare hospitals were the reference category. The hospital site indicators were interacted with procedure and length of stay terms. The results of this regression appear in Table 3. As expected, procedures, additional days of stay, and additional days of stay in intensive care were all associated with higher costs. Stays at hospitals in areas with higher wages (WAGINDX) cost more. Higher costs were associated with death during the stay (DIED) and the cardiac comorbidities of cardiogenic shock (SHOCK), tachycardia (TACHY), pulmonary edema (EDEMA). Lower costs were associated with diabetes (ANYDIAB), cancer, and the presence of any psychiatric diagnosis (ANYSYKDX) and greater age. Patients who were at least 70 years old (AGE70) did not incur any significantly higher cost, controlling for these other factors. Hospital scale, measured as the annual number of stays for MI, was not a statistically significant predictor of cost, nor were its log or multiplicative inverse. Interactions between site character and ICU days were not significant. These variables were not included in the model.

Effect of Hospital Type on Cost We wanted to determine if cost differed by the type of hospital, holding all other factors equal. This could not have been determined by simple inspection of the 21 parameters that involved the type of hospital. We simultaneously evaluated the parameters for each type of hospital using a Chow test. We evaluated the parameters using the characteristics of the average patient for each type of stay. We divided stays into four types based on the most complex procedure performed. Because our concern was estimating VA costs, we used the average VA patient to construct our estimates. For each type of stay, we calculated the mean of all variables observed in VA stays. Given the characteristics of the average VA patient, we calculated the fitted value for the regression for each type of hospital. We then calculated the difference in the fitted value for different types of hospital for this typical patient and calculated the confidence interval surrounding this difference using the variance–covariance matrix from the regression. This analysis is reported in Table 4. For all patients who received CABG at VA hospitals, we found the mean length of stay and days in ICU and the mean value of all other variables. Simulating cost using the regression revealed that this stay would have been reported to cost $49,518 if it

24

Cardiovascular Health Care Economics Table 3 Random-Effects Regression of Cost of Hospital Stays for MI

WAGINDX DIED CATH PTCA CABG GOODDATA LACKCONF NOESTIM GOODDATA*CABG GOODDATA*PTCA GOODDATA*CATH LACKCONF*CABG LACKCONF*PTCA LACKCONF*CATH NOESTIM*CABG NOESTIM*PTCA NOESTIM*CATH AGE AGE70 ACUTDAYS (ACUTDAYS)2 (ACUTDAYS)3 GOODDATA*ACUTDAYS GOODDATA*(ACUTDAYS)2 GOODDATA*(ACUTDAYS)3 LACKCONF*ACUTDAYS LACKCONF*(ACUTDAYS)2 LACKCONF*(ACUTDAYS)3 NOESTIM*ACUTDAYS NOESTIM*(ACUTDAYS)2 NOESTIM*(ACUTDAYS)3 ICUDAYS (ICUDAYS)2 (ICUDAYS)3 ANYDIAB CANCER SHOCK ARREST TACHY EDEMA ANYSYKDX INTERCEPT

Parameter

SE

P value

11,195.83 4168.16 2422.76 8494.20 18,746.27 –1142.77 –3268.75 –973.36 –6220.66 –5557.72 228.20 –3,489.51 –4,269.98 1078.37 –10,440.21 –8053.84 –1883.59 –36.18 –63.93 1220.85 13.09 –0.11 –77.11 1.50 –0.12 715.99 –56.01 0.70 196.32 –20.08 0.13 610.16 13.06 –0.04 –411.32 –818.21 5081.47 646.64 865.56 526.11 –543.53 –8425.07

800.24 262.99 195.44 225.49 275.89 846.19 1,113.11 673.13 915.16 546.23 471.67 1189.60 605.06 569.96 836.20 511.42 380.84 12.00 207.50 40.78 1.47 0.01 110.67 5.05 0.06 169.29 9.73 0.13 78.97 2.91 0.02 37.36 1.58 0.01 144.23 381.86 373.91 399.01 239.28 155.53 252.65 1115.80

0.001 0.001 0.001 0.001 0.001 0.177 0.003 0.148 0.001 0.001 0.629 0.003 0.001 0.058 0.001 0.001 0.001 0.003 0.758 0.001 0.001 0.001 0.486 0.766 0.032 0.001 0.001 0.001 0.013 0.001 0.001 0.001 0.001 0.001 0.004 0.032 0.001 0.105 0.001 0.001 0.031 0.001

took place at a Medicare hospital. This same stay would have been reported to cost $39,017 at a VA GOODDATA hospital, and $34,033 at a VA NOESTIM hospital, amounts that are significantly less than the Medicare cost. The cost at VA LACKCONF sites was not significantly different from Medicare hospitals. The cost of NOESTIM

Chapter 2 / Cost of VA Cardiac Care

25

Table 4 Effect of Hospital Type on Cost of Stay with Mean Characteristics, Holding all Other Factors Equal VA hospitals

CABG PTCA CATH NONE All stays

Medicare hospitals

GOODDATA

LACKCONF

NOESTIM

49,518 21,816 14,292 11,526 16,464

39,017a

47,027b

14,759a

16,967a

12,792e 9801e 13,191a

13,780 9670e 14,291e

34,033a,c,d 11,235a,b,d 11,453a,f 10,465 12,102a

a

Denotes significant difference from Medicare hospitals (p < .001). Difference from VA GOODDATA hospitals (p < .001). c Difference from VA GOODDATA hospitals (p < .05). d Difference from VA LACKCONF hospital (p < .001). e Difference from Medicare hospital (p < .05). f Difference from VA LACKCONF hospital (p < .05). b

sites was significantly lower than other VA sites. The cost of NOESTIM hospitals was 12.8% lower than GOODDATA sites and 27.6% lower than LACKCONF sites. The stay of the typical VA heart attack patient who received PTCA would have cost $21,816 at Medicare hospitals. All three types of VA hospitals would have reported significantly lower costs. Again, the cost of NOESTIM sites was significantly lower than other VA sites. The cost of NOESTIM hospitals was 23.9% lower than GOODDATA sites and 33.8% lower than LACKCONF sites. The typical VA stay that involved neither of these procedures, but included a diagnostic CATH, would have cost $14,292 at a Medicare hospital. The GOODDATA and LACKCONF sites would have reported costs that were not significantly different, but costs at NOESTIM sites would have been significantly lower. The cost of NOESTIM hospitals were 10.5% lower than GOODDATA sites and 16.9% lower than the LACKCONF sites. The stay of the typical VA patient who had none of these procedures performed would cost an average cost of $11,526 at Medicare hospitals. Cost reported at VA hospitals was not significantly different, except for the GOODDATA sites, which reported significantly lower cost. The cost at NOESTIM hospitals was 6.8% higher than GOODDATA sites and 8.2% higher than LACKCONF sites, but these differences were not statistically significant. To explore the source of the differences, we conducted additional regressions of VA hospital stays using subtotals of different types of cost as the dependent variable, including cost of laboratory, pharmacy, radiology, nursing care, surgery, and all other costs (regressions not shown). The cost of the CATH laboratory is reported in the “all other” category. For stays involving CABG, NOESTIM sites had significantly lower costs in all cost categories except pharmacy. For stays involving PTCA or CATH alone, the NOESTIM sites had significantly lower costs in the “all other” category. LACKCONF sites had significantly higher “all other” costs for CABG stays, higher laboratory costs for

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Cardiovascular Health Care Economics

CABG and PTCA stays, and higher pharmacy cost for stays in which catheterization was the most complex cardiac procedure performed.

DISCUSSION As VA hospitals do not routinely bill patients, billing data are not available to estimate VA costs. We estimated a cost function that can be used to estimate the cost of VA stays for acute MI. The function was estimated using data from Medicare and the VA implementation of the DSS. Using the parameters based on Medicare data, we estimated the cost of a typical stay to be $16,464. Using the parameters estimated with DSS data from VA sites that use good data practices, yielded a cost of $13,191. These estimates represent the cost of a typical VA stay for MI. The cost function can also be used to estimate the cost of stays that are not typical. The estimate reflects multiple factors that affect resource use, including procedures performed, length of stay, number of days spent in the ICU, patient vital status at discharge, patient age, and comorbid conditions. However, cost functions may not accurately simulate the cost of extreme cases. This regression analysis also provides two ways of validating DSS data. We compared groups of VA hospitals with groups assigned according to self-reported data quality. We also compared VA cost data with cost-adjusted charges from Medicare hospitals. We found that VA DSS captured the effect of procedures and length of stay on resource use. We also found that DSS data practices are important. About half of the VA stays took place at sites that did not incorporate data on CATH workload when calculating cost. These sites assigned the cost of the CATH laboratory to all patients who received medical care in proportion to their length of stay, regardless of whether they obtained this service. It is not surprising that these sites reported significantly lower costs for stays involving catheterization procedures than did the sites that assigned catheterization laboratory costs to the patients who were actually catheterized. We identified the magnitude of the problem. In comparison to sites with good data practices, sites that did not appropriately assign CATH costs had 12.8% lower cost for stays involving CABG, 23.9% lower cost for the remaining stays that involved PTCA, and costs that were 10.5% lower for stays with diagnostic catheterization. These sites reported 6.8% higher cost for stays in which none of these procedures was performed, but this difference was not statistically significant. It appears that cost estimates were less seriously flawed at VA sites that gathered CATH workload data, but lacked confidence in the quality of their DSS data. These sites estimated costs that were higher than the sites that were more confident in their data quality, but the difference was statistically significant only for stays that involved CABG. A number of VA sites responded to the DSS survey, indicating that they planned to gather CATH workload in the near future. The quality of DSS cost estimates will improve as additional sites measure workload and improve the quality of their data. At present, those who wish to use DSS to estimate the cost of this type of care are advised to inquire about the basis of cost estimates, especially to learn if the cost of medical procedures has been allocated to the patients who received them. We sought to further validate DSS data from VA hospitals by comparing it to data on similar stays funded by Medicare. We found the 20 VA medical centers that have

Chapter 2 / Cost of VA Cardiac Care

27

high-quality DSS data had lower costs than Medicare hospitals. The average cost was $18,660 at Medicare hospitals, $15,248 at VA hospitals that use good data practices, or 18.2% lower. When we controlled for differences in patient age, case-mix, length of stay, procedures performed, and geographic differences in wages, costs at these VA hospitals were still 19.9% lower than the cost of Medicare hospitals, a difference that remained statistically significant. Costs at these 20 VA hospitals were 21.2% lower for stays involving CABG, 32.3% lower for stays involving PTCA, and 10.5% lower for stays with CATH, but no revascularization procedure. Stays in which none of the procedure were performed cost 15% less. We cannot conclude that VA hospitals had lower costs than Medicare hospitals because the data from the two systems were not contemporaneous. The Medicare stays occurred in 1996, which are our most recent data available. The VA stays occurred in the year that ended on September 30, 1999, the first year in which a national file of DSS cost data are available. Although these time frames are less than 3 years apart, and we adjusted for the effects of inflation, there were significant changes in practice patterns that undoubtedly affected resource use. There is evidence that the cost for hospitalization for cardiac care has been declining. A study of CABG patients between 1988 and 1996 reported that costs declined by an average of $1118 per year (10). This is consistent with our finding that there was lower cost in the more contemporary data, the VA observations. One factor associated with lower cost is reduced length of stay. We found that stays for acute MI at VA medical centers are becoming shorter. Among VA hospitals that provide CATH, stays for MI were an average of 8.7 days long in 1996, significantly longer than the 7.4-day average length of stay in our Medicare cohort for 1996. This observation is consistent with the findings of other studies (11). VA stays were longer even though VA hospitals had lower rates of CABG, and CABG is associated with longer stays. Shorter stays are associated with lower costs. The trend of decreasing length of stays at both VA and Medicare hospitals makes it impossible for us to draw any conclusions about the difference in cost between Medicare and VA care. Unfortunately, we do not yet have Medicare data from 1999 that can be compared to VA costs. Through negotiation of national contracts, VA pharmaceutical costs are lower than those of the non-VA sector. Lower pharmaceutical costs may contribute to the lower cost of VA hospital stays. We found that VA patients had lower rates of the comorbidities that are associated with higher cost. We controlled for the effect of these comorbidities in making our cost comparison. Unmeasured case-mix intensity may explain some of the cost differences we observed. Other studies have found higher rates of comorbidities in VA patients than in Medicare patients (11). We found lower rates in VA patients. This difference is not surprising, however, given the restricted nature of our Medicare group. It was made up only of veterans who had previously used VA services. These Medicare-using veterans were not only sicker than other VA patients, they were also an average of 6.5 years older. Patients treated at Medicare hospitals were cared for by physicians who bill Medicare on a fee-for-service basis. Patients treated at VA hospitals were treated by salaried physicians and medical residents, perhaps at a lower cost. To make the two data sources similar, we estimated the cost of VA capital and the cost of physician services received by Medicare patients. We used simplifying assumptions to

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Cardiovascular Health Care Economics

assign these costs to individual stays. We assigned capital cost in proportion to the other costs of VA stays. We assigned physician cost in proportion to the DRG weight of the Medicare stay, with some adjustment for length of stay. Although these assumptions may have resulted in erroneous estimates of the cost of a particular stay, the average cost that was assigned represented reasonable estimates that likely do not explain the differences we observed. Finally, we used a hospital-level cost-to-charge ratio to adjust charges of Medicare hospitals. This was required because department-level charges were not available from the hospital discharge data. The conventional wisdom is that the charges for ancillary services are higher relative to costs than are the charges for routine daily services. The use of a hospital-wide cost-to-charge ratio would overstate the cost of stays in which a disproportionate amount of ancillary charges were incurred. The DSS estimate of VA costs includes physician costs, but does not include the cost of malpractice liability. VA incurs liability for malpractice expense, but this cost is paid by settlements from the US Department of Justice. This expense is relatively small, however, it represents $60 million in comparison to the $18 billion VA health care budget. It is unlikely to account for much of the differences we observed. Our primary concern was not whether VA costs were higher or lower than non-VA hospitals, but whether DSS cost estimates are plausible. We found VA costs were lower than Medicare cost-adjusted charges. This finding needs to be placed in the context of other cost studies. The literature reports a wide range of costs for cardiac hospital stays. The mean cost of stays for acute MI in 1994–1995 varied from $10,038 at small rural hospitals to $14,306 at teaching hospitals, where bypass surgery was twice as likely (12). This estimate did not include the physician component. Patients in the Emory Angioplasty vs Surgery Trial incurred $41,972 in hospital and physician costs when CABG was performed and $27,793 if angioplasty was performed (10). Although these amounts are expressed in 1997 dollars, the data reflects resource use patterns of the late 1980s. A study of DSS data of cardiac patients at the University of Colorado between 1992 and 1995 found stays in which CABG was performed cost an average of $27,091; stays involving angioplasty cost an average of $8982 (13). These data do not include the physician component, and many stays did not involve diagnosis of heart attack. The values that we observed for the Medicare sites and the VA sites using good data practices were within the range of estimates found in previous studies. Both cost estimates reflect the effect of the characteristics of hospital, treatment, and patient on resource use and should be useful for estimating CE in clinical trials. The cost of stays at VA hospitals that do not have high-quality DSS data may be simulated using the cost-function that we estimated. Simulations based on Medicare data for 1996 will have costs that are about 20% greater than simulations based on VA data from 1999.

ACKNOWLEDGMENTS The authors gratefully acknowledge data provided by Steven Wright, PhD, of the Massachusetts Veterans Epidemiology Research and Information Center, the helpful comments of Laura Peterson, MD, MPH, and the financial support of the Department of

Chapter 2 / Cost of VA Cardiac Care

29

Veterans Affairs Cooperative Studies Program and Health Services Research and Development Service.

REFERENCES 1. Centers for Disease Control. Chronic Diseases and Their Risk Factors: The Nation’s Leading Causes of Death. US Department of Health and Human Services, Atlanta, GA, 1999. 2. Every NR, Fihn SD, Sales AE, et al. Quality Enhancement Research Initiative in ischemic heart disease: a quality initiative from the Department of Veterans Affairs. QUERI IHD Executive Committee. Med Care 2000;38(6 Suppl 1):I49–I59. 3. Barnett P. Review of methods to determine VA health care costs. Med Care 1999;37:AS9–AS17. 4. Swindle R, VanDeusen-Lukas C, Alexander-Meyer D, et al. Cost analysis in the Department of Veterans Affairs: Consensus and future directions. Med Care 1999;37:AS3–AS8. 5. Barnett PG, Chen S, Boden W, et al. Cost-effectiveness of conservative management of non Q-wave myocardial infarction. Circulation 2002, 105:680–684. 6. Miller ME, Welch WP. Analysis of Hospital Medical Staff Volume Performance Standards: Technical Report. The Urban Institute, Washington DC, 1993. 7. Barnett PG, Rodgers JH. Use of the decision support system for VA cost-effectiveness research. Med Care 1999;37:AS63–AS70. 8. Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol 1992;45:613–619. 9. Peterson K, Swindle R, Phibbs C, et al. Determinants of re-admission following inpatient substance abuse treatment: a national study of VA programs. Med Care 1994;32:535–550. 10. Weintraub WS, Craver JM, Jones EL, et al. Improving cost and outcome of coronary surgery. Circulation 1998;98(19 Suppl):II23–II28. 11. Petersen LA, Normand SL, Daley J, McNeil BJ. Outcome of myocardial infarction in Veterans Health Administration patients as compared with medicare patients. N Eng J Med 2000;343:1934–1941. 12. Chen J, Radford MJ, Wang Y, et al. Performance of the “100 top hospitals”: what does the report card report? Health Aff 1999;18:53–68. 13. MaWhinney S, Brown ER, Malcolm J, et al. Identification of risk factors for increased cost, charges, and length of stay for cardiac patients. Ann Thorac Surg 2000;70:702–710.

3

Estimating the Costs of Health Care Resources in Canada Gordon Blackhouse, MBA, MSc CONTENTS INTRODUCTION ESTIMATING HOSPITAL COSTS IN CANADA ESTIMATING THE COSTS OF PHYSICIAN SERVICES IN CANADA ESTIMATING THE COSTS OF PHARMACEUTICAL PRODUCTS IN CANADA TWO CANADIAN CARDIAC-COSTING EXAMPLES REFERENCES

INTRODUCTION The costing of health care resources consists of three steps: the identification of resources, the measurement of resources, and the valuation of resources (1). The processes of resource identification and measurement generally apply across countries. However, valuing health care resources can vary widely between countries. This chapter discusses the valuation of health care resources in the Canadian setting. This discussion is limited to the valuation of direct health care costs. Specifically, the various sources and issues surrounding valuation of hospital resources, physician services, and drug utilization in Canada are outlined. Note that even though these three categories of resources are discussed separately, all three often have to be estimated for a specific in-patient episode. Throughout the chapter, a distinction is made between “gross-costing” and “microcosting” (2). Gross-costing refers to aggregate valuations, such as the cost per hospitalization by diagnosis or the cost per day of hospitalization by diagnosis. Micro-costing refers to the process of valuing individual resources utilized by patients during a given hospitalization or episode. Micro-costed resources could include such items as length of stay by type of ward, surgical procedures, diagnostic tests and procedures, along with drug utilization. The choice of micro- vs gross-costing can be viewed as more of a function of the cost identification and measurement process. Therefore, we refrain from discussing the relative merits of each and, instead, provide guidance in valuing resources based on both methods of costing. From: Contemporary Cardiology: Cardiovascular Health Care Economics Edited by: W. S. Weintraub © Humana Press Inc., Totowa, NJ

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Cardiovascular Health Care Economics

The remainder of this chapter is separated into four sections. The first section discusses various sources that can be used to estimate Canadian hospital costs. In this section, there is a description of both nonstandardized and standardized sources of hospitals costs, such as provincial case-costing initiatives and published in-patient cost lists. The second section outlines methods for estimating the cost of physician services in Canada, whereas the third section deals with various sources that can be used to estimate pharmaceutical costs. Two recent Canadian cardiac economic evaluations are used in the final section to help illustrate the application of several Canadian health care resource cost sources described in this chapter.

ESTIMATING HOSPITAL COSTS IN CANADA In Canada, acute-care hospitals are publicly funded through global-operating budgets. That is to say, they operate on a sum of money based primarily on the hospital’s size and the particular services the hospital provides. There is no direct billing to patient or third-party payers for a hospital stay other than for extra services, such as private room privileges. As a result, there has been little incentive for hospitals to track detailed resource and cost information on a patient-specific basis. Therefore, most Canadian hospitals do not have information systems with integrated resource utilization and resource cost data. This makes the valuation of hospital resource use a bit more problematic in Canada in comparison with the United States, where charge data are readily available. In recent years, a couple of provinces have initiated case-costing systems in a number of their hospitals. This has resulted in increased availability of reliable hospital costs. These estimates are, to some extent, better than those from US hospital billing systems, as they are more representative of actual costs and do not have to be adjusted using estimates of cost-to-charge ratios. Unfortunately, the number of hospitals that are involved in case-costing initiatives are relatively few in Canada. Specific criteria may be used to decide which and how many hospital site(s) will be used to represent Canadian hospital resource costs. These criteria may include the degree to which the hospital site(s) chosen for resource valuation represent (1) the sites where a particular intervention under study would be performed, (2) the sites participating in the clinical trial where the evaluation is based on, or (3) the hospital sites of the particular province(s) (3). Often, the hospital(s) chosen by such criteria will not have a case-costing system in place. In such cases, less than ideal hospital costing information will have to be used. Sources of hospital cost information are categorized into standardized and nonstandardized sources. Nonstandardized sources refer to individual hospitals that are not part of a provincial case-costing system. Standardized sources include sources for aggregate Canadian hospital expense data (Canadian Institute for Health Information), aggregate provincial case-costing data, individual hospitals participating in provincial case-costing initiatives, and standard in-patient cost lists.

Nonstandardized Sources for Hospital Resource Costs It may be necessary to base hospital resource costs on the hospital(s) without a sophisticated costing system. Hospitals may only be able to provide summary fiscal departmental expense and workload data. This allows for estimation of certain micro-unit

Chapter 3 / Health Care Costs in Canada

33

costs, such as the cost per day in a particular nursing ward, the cost for an emergency room visit, or the cost-per-workload measurement unit in the hospital’s immunology laboratory. However, such estimates would comprise of departmental direct costs only. In order to incorporate indirect overhead expenses into these unit costs, a fully allocated costing model would have to be developed. The sophistication and extent of the overhead allocation will partly depend on the types of hospital unit cost needed for a particular study. We used this approach to estimate micro-unit costs from a number of hospitals in the past. It can be a very time-consuming process and requires access to the hospital’s entire accounting data. Most hospitals would be able to provide a per-patient-day gross-cost estimate based on total operating expenses. Hospitals will often be able to provide more than summary departmental cost and workload data. Unfortunately, the cost data that they can provide is frequently less than ideal. For example, a few years ago, we collected unit cost data from a number of hospitals across Canada for a pregnancy study. All hospitals indicated that they had a costing system with indirect cost allocation in place. Although some hospitals were able to provide unit costs with overhead appropriately allocated, most simply provided direct unit costs, along with a global overhead inflation factor or a per diem overhead dollar figure. Two of the items for which we were collecting unit costs were vaginal and cesarean deliveries. Only one hospital was able to provide those costs directly. The remaining hospitals were only able to provide total labor and delivery room budgets for the previous fiscal year, along with the number of vaginal and cesarean deliveries for the same year. We had to piece together estimates of these items based on assumptions of relative nursing acuity.

Standardized Sources for Hospital Resource Costs CANADIAN INSTITUTE FOR HEALTH INFORMATION The Canadian Institute for Health Information (CIHI) is a federally charted, nonprofit organization that collects and produces a variety of databases, publications, reports, and guidelines related to Canadian health care services. Among the information CIHI collects are financial and statistical data from hospitals across the country. These data are based on the account structure contained in the Guidelines for Management Information Systems in Canadian Health Service Organizations (MIS Guidelines) (4). The MIS Guidelines, which are maintained and published by CIHI, define a framework for the compilation and comparison of financial and statistical data. This data are currently collected as part of CIHI’s Annual Hospital Survey. Prior to 1995/1996, this data were collected as part of Statistics Canadas’ Annual Return of Health Care Facilities-Hospitals. Summary information was reported in a Statistics Canada publication entitled Hospital Statistics (5). The most recent publication was based on fiscal 1994/1995 data. This publication includes such gross-cost estimates as total hospital-operating expenses per admission, and per-patient day aggregated among all reporting hospitals across Canada. It also provides this data stratified by province, size of hospital, teaching vs nonteaching hospitals, as well as the specialty of hospital. CIHI has not produced a similar publication report since it took over the database now known as the Annual Hospital Survey. CIHI is currently reviewing data from its most

34

Cardiovascular Health Care Economics

recent available fiscal year for consideration of formal publication. However, CIHI will respond to individual research requests for this and other databases it maintains. CIHI’s largest database is the Discharge Abstract Database (DAD), which contains clinical, demographic, and administrative data on patient discharges from hospitals across the country. Specific data collected for each admission includes the most responsible diagnosis, principal procedure, patient gender, date of birth, institution number, length of stay, and admission category. CIHI also maintains and publishes resource intensity weights (RIW) for in-patient admissions based on case-mix group (CMG). Every hospitalization collected in the DAD is assigned to a specific CMG according to principal diagnostic and procedure codes. CMGs are subdivided into age categories and one of four complexity groups (Plx level). The complexity groups correspond to the types of comorbid conditions. Each CMG is assigned to an RIW that represents its relative resource use. The RIW assigned to each CMG has historically been based on individual case-cost data from Maryland. CIHI has recently started to incorporate Ontario case-cost data into its database in order to assign RIWs. RIWs do not provide a cost per CMG, but provide only a measure of relative resource intensity across different diagnoses. PROVINCIAL CASE-COSTING INITIATIVES Over the last few years, case-costing initiatives have been developed and implemented in the provinces of Alberta and Ontario. British Columbia is currently developing a similar case-costing system. Canadian health economists have benefited greatly from the advent of these systems, as they have provided a very good source for reliable hospital-costing data. In the following section, two provincial case-costing systems currently established are described: Health Costing in Alberta (6) and the Ontario Case-Costing Project (OCCP) (7). More detail is provided on the OCCP because it is more established. Health Costing in Alberta. Health costing in Alberta was transferred to the provincial Ministry of Health and Wellness in January 1998. Prior to that time, the initiative was known as the Provincial Costing Project (PCP) and was cosponsored by Alberta Health and the Regional Health authorities. In Alberta, responsibility for health services is distributed to a number of health regions. Five health regions participated in the PCP with a sixth region joining since the transfer to the Health Ministry. The PCP resulted in the development and establishment of a common costing framework and process. Hospitals within the regions participating in the project are able to use this framework to produce patient-specific costs for both in-patient and ambulatory care. Inpatient episodes are grouped using refined diagnosis-related groups (RDRG). The RDRG classifies in-patient records into refinement group numbers (RGN). Seven types of variables are used to define RGNs: principal diagnosis, additional or secondary diagnosis, procedures, age, sex, discharge status, and length of stay. The OCCP. In the absence of reliable patient-specific cost data in Canadian hospitals, RIWs have been developed and used to estimate resource consumption of inpatient admissions. Historically, RIW estimates have been based on a combination of Canadian hospital resource data and charge data from US hospital databases. In order to develop a more reliable case-weight system than one based on US data, the OCCP was proposed in 1992.

Chapter 3 / Health Care Costs in Canada

35

Thirteen Ontario hospitals, often referred to as the first-generation hospitals, were selected to participate and began collecting acute in-patient case-cost data in 1993. Case-cost data for day surgeries began in 1994. In 1995, 22 second-generation casecosting hospitals were added to the project with the intention of expanding the scope of case costing to chronic care, rehabilitation, and ambulatory care. Participating hospitals are required to develop and implement the necessary systems and procedures to comply with the OCCP standards and methodology for producing case costs. Details on these standards and methods are published in the Ontario Guide to Case Costing. This manual provides very specific guidance on what it classifies as the four steps of case costing: gathering the data, allocating indirect costs, calculation of functional center unit costs, and distribution of costs to patients. OCCP recommendations for data gathering are based on the MIS Guidelines. The Functional Centre Framework of the MIS Guidelines organizes hospital activity into functional centers (e.g., diagnostic imaging, intensive care unit [ICU]), whereas the Chart of Accounts standardizes how functional center expenses and statistics are to be tracked. The MIS Guidelines also specify a number of workload measurement systems, which can be used to estimate resource use in patient care departments. For the purposes of allocating indirect costs, functional centers are designated as either a transient cost center (TCC) or absorbing cost center (ACC). TCCs are comprised of the support and administrative departments and are henceforth referred to as overhead departments. ACCs are generally patient care departments, such as nursing wards, operating room, or diagnostic laboratories. According to OCCP standards, the costs of the overhead departments are allocated to the patient care departments using the simultaneous equation allocation method (SEAM). A significant advantage to using SEAM over other allocation methods is that SEAM takes into account the interaction of the overhead department with each other. For each overhead department, a specific “allocation base” is used by SEAM to allocate overhead costs to patient care departments. Standard costallocation bases are used by OCCP hospitals to ensure comparability of results. Once the indirect costs are allocated and added to the direct costs of patient care departments direct costs, there are two more steps involved in the process of OCCP case costing. These steps include the calculation of patient care department unit costs and distributing these costs to patients. OCCP allows two approaches for these final steps. One approach is to calculate a cost-per-workload measurement unit for patient care departments, then assign costs to patients based on the number of workload units consumed by the patent in each patient care department during their admission. The other approach available is to calculate the cost for each intermediate product in the patient care department (e.g., test procedure, patient day) and assign costs to patients based on the number and type of intermediate products consumed during a given admission. Although the two methods should produce identical total-cost estimates for a patient stay, the latter produces much more useful information from the perspective of a health economist. Hospitals using the latter approach will have fully allocated unit costs for nearly all tests, surgical procedures, diagnostic procedures, and per-day nursing unit stays readily available.

Gross-Cost Estimates Using Case-Costing Sources In both provincial costing systems, cost data across hospitals/regions are amalgamated. Therefore, aggregate gross-cost estimates according to diagnostic classification

36

Cardiovascular Health Care Economics

is available from the two systems. The most recent publication regarding health costing in Alberta Health is its 1999 annual report. This document, which is downloadable from Alberta Health’s website, includes cost estimates by RGN using cost data from fiscal 1997/1998. For each RGN, the report provides the average cost per hospitalization, the average length of stay per costed hospitalization, along with number of hospitalizations that were costed. The OCCP currently has case cost data available for fiscal years 1994/1995 and 1995/1996. The fiscal 1994/1995 database contains approximately 210,200 cases from 11 hospitals, whereas the 1995/1996 database contains 99,800 records from 7 hospitals. Only hospital case-cost data that has passed OCCP audits are included in the database and used to produce data analyses. OCCP can produce case-costing information on request. They have several frequently requested analyses available on their website. These include reports summarizing average costs per hospitalization based on CMG, ICD9-CM main diagnosis, and ICD9-CM principal procedure. Each of these reports provides average direct, indirect, and total cost per hospitalization, along with the average length of stay per hospitalization. These average cost and length of stay data are supplemented with corresponding standard deviation, minimum, and maximum values. A sample of case-cost data for some cardiac-related diagnosis from Health Costing in Alberta is provided in Table 1. A similar sample from OCCP is provided in Table 2. Micro-Cost Estimates Using Case-Costing Sources. Case-costing projects are able to provide aggregated cost data by diagnoses or procedure grouping. However, it is often necessary to micro-cost hospital resources in order to properly evaluate differences in resource consumption between treatment groups. Fortunately, individual hospitals participating in the provincial case-costing initiatives have become an ideal source for hospital micro-unit costs. Not all case-costing hospitals are ideal as sources for micro-cost data. Recall that OCCP offers two options for hospitals to assign patient care department costs. One option is to base departmental costs on departmental workload measurement unit, whereas the other is to base costs on intermediate products, such as specific tests or procedures. Hospitals opting for the former approach are less likely to have microcosts readily available. Not all hospitals provide the same services. Therefore, when deciding on which case-cost hospital to use as a source for micro-costs related to a medical specialty area, such as cardiology, it would be necessary to use a hospital that provided these services. STANDARD IN-PATIENT COST LISTS Standard in-patient cost lists have been published in the provinces of Alberta (8) and Manitoba (9). Most recently, guidelines for estimating standardized in-patient costs across provinces have been published in A National List of Provincial Costs for Health Care: Canada 1997/98 (10). Costs for these lists are based on what has been termed as the cost-per-weighted-case approach. Using this approach, standardized costs for CMGs are generated by multiplying CMG-specific RIW by an average cost per weighted case (hospitalization). The average cost per weighted case is derived from aggregate hospital financial and RIW data collected by the CIHI. The National List of Provincial Costs for Health Care provides both Canada-wide and provincial-specific average costs per weighted case.

Table 1 Sample Case-Cost Data from Health Costing in Alberta RGN

37

1160 1161 1162 1163 1170 1171 1172 1173 1180 1181 1182 1183 1190 1191 1192 1193 1200 1201 1202 1203 1210 1211 1212 1240 1241 1242 1

Description Perm Card Pace Impl w/o Ami, Hrt Fail/Shk with no CC Perm Card Pace Impl w/o Ami, Hrt Fail/Shk-class C CC Perm Card Pace Impl w/o Ami, Hrt Fail/Shk-class B CC Perm Card Pace Impl w/o Ami, Hrt Fail/Shk-class A CC Card Pace Rev Exc Device Replacement with no CC Card Pace Rev Exc Device Replacement—class C CC Card Pace Rev Exc Device Replacement—class B CC Card Pace Rev Exc Device Replacement—class A CC Card Pace Device Replacement with no CC Card Pace Device Replacement with class C CC Card Pace Device Replacement with class B CC Card Pace Device Replacement with class A CC Vein Ligation and Stripping with no CC Vein Ligation and Stripping with class C CC Vein Ligation and Stripping with class B CC Vein Ligation and Stripping with class A CC Other Circulatory System OR procedures with no CC Other Circulatory System OR Procedures—Class C CC Other Circulatory System OR Procedures—Class B CC Other Circulatory System OR Procedures—Class A CC Circulatory Disorders w Ami with no CC Circulatory Disorders w Ami with class C CC Circulatory Disorders w Ami with class B CC Circulatory Disorders except Ami with no CC Circulatory Disorders except Ami with class C CC Circulatory Disorders except Ami with class B CC

Average cost

Costed cases

Average LOS of costed cases1

Average LOS of all cases2

12,378 14,528 17,303 19,026 2757 4003 7,981 10,798 6427 11,189 4723 10,030 1440 1284 3366 25,516 3852 3671 3826 16,621 3677 5357 9791 2943 4205 10,345

200 201 81 35 27 25 11 5 45 11 5 6 149 7 6 4 7 5 111 28 518 600 208 211 554 50

2.8 4.7 8.1 12.7 1.6 2.3 6.0 6.0 1.6 1.4 3.2 12.5 1.0 1.0 1.5 22.0 5.1 6.0 4.7 19.4 6.3 8.9 12.1 3.7 5.7 11.8

3.3 4.9 8.2 14.1 2.0 2.5 6.4 6.0 2.0 4.1 7.9 24.4 1.2 1.0 1.4 22.0 6.4 16.1 7.9 29.9 6.0 9.0 12.5 3.9 7.3 17.4

Represents cases used in Costing process (i.e., net of exclusions and outliers). Represents all provincially reported cases (i.e., inclusive of outliers). Reprinted with permission from Health Costing in Alberta, 1999 Annual Report. Schedule 5—1997/98 Inpatient Case Costing Results. 2

Table 2 Sample Case-Cost Data from the OCCP Direct cost per case ($) CMG (96) 215 CARD CATH w CHF 216 CARD CATH w VENTR TACHYCARDIA 217 CARD CATH w UNSTABLE ANGINA 218 CARD CATH w/o Spec CARD COND 219 ENDOCARDITIS 220 PULMONARY EMBOLISM w CC 221 PULMONARY EMBOLISM NOCC 222 HEART FAILURE,>70CC 223 HEART FAILURE,>70NOCC/<70CC 224 HEART FAILURE,<70NOCC 225 HYPERTENSIVE HEART DIS 226 OTH CIRC DX,>70CC 227 OTH CIRC DX,>70NOCC/<70CC 228 OTH CIRC DX,<70NOCC 229 ATHEROSCLEROSIS,>70CC 230 ATHEROSCLEROSIS, >70NOCC/<70CC 231 ATHEROSCLEROSIS, <70NOCC (MHRH) 232 ACQUIRED VASC DIS(MNRH) 233 HYPERTENSION(MNRH) 234 CONGENITAL CARD DIS(MNRH) 235 ANGINA PECTORIS,>70CC 236 ANGINA PECTORIA,>70NOCC/<70

No. of Cases

Ave

Std Dev Min

Max

Indirect cost per case ($) Ave

Total cost per case ($)

Std Dev Min

Max

Ave

Std Dev Min

Max

26 17

4,753 3336 1053 17,825 2300 1448 1071 5974

2131 1,145 478 987 656 404

4515 3047

6885 4365 1531 22,340 3287 2069 1475 9021

20

2627 1273 1258

1114

2557

3742 1713 1855

5539

744 262 129 1818 3004 3,407 186 13,569 1360 781 239 3615 958 598 115 3999 1176 803 74 6246 891 585 95 4268 672 427 106 3150 759 676 75 4032 1030 986 242 4988 773 559 74 2777 459 303 82 1509 899 672 78 3163 697 374 162 1744

2163 8871 4522 3036 3923 2979 2275 2442 3566 2732 1639 2927 2379

8096

10.1 4.5

5.9 4.7

2 1

20 18

5.5

3.7

1

14

2.0 .5 20.1 16.0 9.8 4.5 6.6 3.1 7.7 4.9 5.7 3.6 4.4 2.7 5.7 4.0 7.0 4.7 5.0 3.9 2.8 1.9 6.6 4.3 4.4 3.0

1 3 2 1 1 1 1 1 1 1 1 1 1

3 61 24 14 25 18 14 19 18 18 9 19 14

1419 5867 3162 2079 2747 2088 1603 1683 2536 1959 1180 2027 1682

523 5424 1578 1076 1811 1300 947 1169 2258 1297 675 1405 945

23

1520

756

585

3176

681

358 162

1455

2201 1085

748

4479

3.0

1.8

1

7

2037 1764 1211 956 851 589 1693 943 1369 939

373 151 371 462 202

6903 4769 1685 4501 6883

944 490 415 677 512

777 154 430 63 226 201 392 151 314 87

2766 2899 718 1794 1590

2982 1701 1266 2370 1881

544 329 572 656 327

9669 7197 2403 6295 8334

6.3 3.8 3.3 4.3 3.0

5.3 3.1 3.2 2.4 2.0

1 1 1 1 1

20 13 8 11 10

FY 1995/96—Typical Cases Reprinted with permission from OCCP website (www.OCCP.com) Data Analyses/Average Costs per CMG/FY 1995–96/CMG1996.

771 479 5441 8754 1089 33,409 2306 841 10,359 1632 389 9455 2572 378 18,687 1842 447 11,243 1338 313 7924 1808 168 10,441 3217 708 16,395 1818 308 8128 950 236 4363 2046 365 9847 1282 520 5662

Std Ave Dev Min Max

232 15 79 68 668 476 99 42 40 88 52 54 50

22 85 4 56 250

327 3622 903 19,840 591 6893 271 5457 304 12,441 274 7198 206 4774 93 6,408 422 11,407 234 6146 149 2981 287 6685 304 4216

492 475

Length of stay (days)

2511 1350 813 1320 1211

Chapter 3 / Health Care Costs in Canada

39

ESTIMATING THE COSTS OF PHYSICIAN SERVICES IN CANADA The previous section discussed issues regarding and various sources for hospital resource costs in Canada. Generally, these estimates do not include the costs of physician services that must be added in order to fully value the health care resources consumed during a particular hospitalization. Physician fees can be better attributed to a micro-costed hospitalization than a gross-costed hospitalization, because the former has more information on the individual services provided. Of course, fees for physician services not only apply to in-patient care, but also to ambulatory care.

Sources for the Cost of Physician Services in Canada In Canada, physicians are reimbursed through publicly funded provincial medical care plans. These plans insure provincial residents for medically necessary physician services. Although the definition of “medically necessary” physician services does vary slightly between provinces, it generally includes hospital and office visits, diagnosis and treatment of illness and injuries, along with care and treatment surrounding operations. Physicians are primarily reimbursed on a fee-per-service basis, although a few provinces do allow reimbursement on a per-patient or contract basis. The fee received for physician services vary by province and are specified in negotiated provincial fee schedules. These schedules provide ideal sources for estimating the cost of physician services, as they list the amount physicians are paid for their various services. An alternative source for physician fees is an occasional publication from CIHI that includes fees across provinces for broad categories of services. This document, that is produced from CIHI’s National Physician Database, is entitled the National Grouping System Categories Report (11).

Provincial Fee Schedules The content of physician fee schedules vary by province, however, there are common elements to be found in most of them. These include listings of physician fees for physician consultations and assessments, diagnostic tests and procedures, and therapeutic and surgical procedure. Methods of applying these fees differ between types of health care resources. Provincial fee schedules usually list fees for assessments and consultations for both general and specialty physician practitioners. There are different levels of assessments and consultations for which physicians can bill for. Explanations of when different types of assessments and consultations are to be applied in the fee schedule are often included. However, these explanations are often unclear, and it may be necessary to consult with the provincial medical care plan or a physician practicing in the province. These fees are useful for assigning physician fees for both in-patient and out-patient physician visits. The Ontario Schedule of Benefits (12) specifies that daily physician visits can be billed at a specific fee ($17.10), where a premium ($8) can be added for ICU or CCU visits. Assessment fees for family practice can be used to estimate the fee of an ambulatory care visit to a family doctor. Similarly, assessment fees specified for specialty physician visits can be used to estimate fees for visits to specialty clinics. Diagnostic and therapeutic tests and procedures include diagnostic radiology, diagnostic ultrasound, pulmonary function studies, and both noninvasive and invasive diagnostic and therapeutic procedures. It is often straightforward to apply physician fees to these types of

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Cardiovascular Health Care Economics

resources particularly diagnostic radiology and ultrasound. In most cases, it is simply a matter of finding the procedural fee that corresponds to the resource of interest. There will be instances when descriptions will be unclear, requiring additional consultation with someone familiar with billing practices. Some of the invasive procedures may require the use of an anesthetist. In these cases, the fee schedules will provide an associated anesthetist fee. Applying physician fees to surgical procedures is similar to applying fees to the tests and procedures described previously. The provincial fee schedules provide the surgeon and anesthetist fee associated with specific surgical procedures. The assignment of appropriate fees for surgical fees is more complicated for certain provincial fee schedules. For example, the Ontario Schedule of Benefits values anesthetist fees on a costper-unit basis. The total number of units is made up of procedure-specific basic units and time-based units. Therefore, in order to accurately apply anesthetist fees to a procedure using this schedule, an estimate of the duration of this procedure is needed.

CIHI’s National Grouping Categories Report This publication produced by the CIHI provides the costs of various categories of physician services that are standardized across provinces. Costs by province are provided for 14 broad categories of services for each of 18 specialties, including internal medicine, psychiatry, general practice, and specific surgical specialties. The physician categories used in the report provided are as follow: • • • • • • • • • • • • • •

Consultations Major assessments Other assessments Hospital care days Special calls Physiotherapy/counseling Major surgery Minor surgery Surgical assistance Anesthesia Obstetrical services Diagnostic/therapeutic services Special services Miscellaneous services

This document can be very useful if fees for multiple provinces are required in an analysis. However, it does not provide costs for detailed services that are found in individual provincial fee schedules.

ESTIMATING THE COSTS OF PHARMACEUTICAL PRODUCTS IN CANADA Often, pharmaceutical products represent one or more of the interventions that are compared in a health economic evaluation. In such cases, it is obviously very important to accurately estimate the costs of such products. There are a number of sources that can be used to value utilization of drug products in Canada. These include hospital pharmacies, provincial drug reimbursement schedules, and data from the large pharmaceutical information firm, IMS HEALTH Canada.

Chapter 3 / Health Care Costs in Canada

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Hospital Pharmacies Case-costing hospitals are required to track the costs of patient-specific drug use. The OCCP requires its hospitals to assign these costs based on the average-unit cost of groups of similar drugs or on unit costs for individual drugs. Hospitals choosing the latter approach will be able to provide precise estimates for specific drugs. These hospitals also must also assign pharmacy labor costs to patients. Therefore, unit drug costs sourced from these hospitals may also incorporate pharmacy labor costs. It is worth noting that any gross case-cost estimates will include drug utilization costs.

Provincial Drug Benefit Formularies Provincial health care plans insure all residents for pharmaceutical agents prescribed while in the hospital. In addition, there are provincial drug plans that insure certain populations for out-patient medication prescriptions. The population covered by these drug plans differ by province, but are generally based on certain demographic characteristics, such as age and income level. The pharmaceutical products, which are insured by these drug plans, are specified in provincial drug benefit formularies. These formularies also specify the prices that the drug plans pay for these medications. The drug prices listed in the formularies represent the price paid by the province to pharmacies. Therefore, using this source to estimate drug costs is particularly relevant in economic evaluations taken from a provincial Ministry of Health perspective.

IMS HEALTH Canada Database IMS HEALTH Canada, a large pharmaceutical information firm, collects data on all drugs sold in a sample of pharmacies. The sample includes sales in all regions of the country. The drug prices found in the IMS database represent retail drug prices that may be different than the prices set in provincial drug benefit formularies. This database provides an alternative source for costs of drugs that are not listed in provincial formularies. For each medication, the database can provide the Canadian and provincial average retail price per individual unit as dispensed.

Pharmacy Dispensing Fees and Markups Consideration must be made to dispensing fees and markups when estimating the costs of medications in Canada. Each province has a different scheme for the dispensing fee charged for a prescription and the markup that retail pharmacists are allowed to apply. Several provinces (Alberta, British Columbia, Quebec, New Brunswick, and Nova Scotia) do not allow any type of pharmacy markup on medication costs. The provincial standard dispensing fees and markups apply only to out-patient pharmacy prescriptions. Of course, there are administrative and dispensing costs involved in inpatient pharmacies as well. However, these costs are hospital-specific and do not conform to specific provincial standards.

TWO CANADIAN CARDIAC-COSTING EXAMPLES In order to illustrate the application of several of the sources of Canadian health care resource costs, costing examples of two recently completed cardiac economic evaluations are provided. Specifically, costing details for the economic analysis of the Canadian Implantable Defibrillator Study (CIDS) (13) and the economic evaluation of

42

Cardiovascular Health Care Economics Table 3 Unit Costs for Health Care Resources Collected in the CIDS Economic Evaluation

Resource item ICD (device cost) ICD implantation procedure (excluding device) ICD generator replacement (including generator) Day in ICU ward Day in non-ICU ward Coronary artery bypass grafting Valve replacement Swan-Ganz catheter Emergency room visit Amiodarone (200 mg): unit cost (200 mg):Pharmacy markup Pharmacy dispensing fee

Hospital cost

Physician fee

Total unit costs

22,000 17,093 27,938 1116 419 3806 2078 9187 1080 174

– 1074 1074 25 17 1093 673 1415 237 –

22,000 18,093 29,012 1141 436 4899 2751 10,602 1317 174 2.06 0.21 4.11

Canadian patients participating in the Efficacy and Safety of Subcutaneous Enoxaparin Non-Q Wave Coronary Events (ESSENCE) clinical trial (14) are provided.

CIDS The CIDS economic evaluation compared costs and outcomes of patients at high risk of ventricular arrhythmia randomized to either initial therapy with implantable cardioverter defribrillator (ICD) or amiodarone. The analysis was taken from a provincial (Ontario) Ministry of Health perspective with a time horizon of 6 years. The effectiveness measure used in the analysis was life expectancy. Utilization of selected health care resources were collected prospectively for a subset of patients during the clinical trial. Unit costs were applied to patient utilization data to estimate the total costs attributable to patients during the trial. Table 3 provides a summary of the key resource utilization items collected in the study. The total unit cost applied to each item broken down by hospital cost and physician fees are provided. A hospital participating in the OCCP was used as the source for the hospital micro-unit costs. This hospital was able to provide the cost of an implantable defibrillator implantation net of the actual device cost. This was necessary because a different source for the cost of the ICD device was used in the analysis. Specifically, the Ontario Ministry of Health reimbursement cap for ICD devices was used in the analysis. The Ontario Schedule of Benefits was used to identify appropriate physician fees for the various resource items. The cost of amiodarone (200 mg) was based on the hospital’s drug acquisition cost. The allowable pharmacy markup in Ontario at the time of the study was 10%, whereas the standard pharmacy dispensing fee was $4.11. The pharmacy markup and dispensing fees were applied to the out-patient use of amiodarone only.

Economic Evaluation of Canadian Patients in ESSENSE Trial The ESSENCE clinical trial compared outcomes of patients hospitalized with unstable angina or non-Q wave myocardial infarction (MI) randomized to receive either

Chapter 3 / Health Care Costs in Canada

43

Table 4 Coefficients of Regression Model Used to Estimate Total Hospitalization Costs in Canadian ESSENCE Economic Evaluation* Independent variable Constant ICU/CCU length of stay Non-ICU/CCU length of stay CABG PTCA with stent PTCA without stent Cardiac catheterization Age (per year) Death during hospitalization

Coefficient 370 1662 570 7575 2840 5226 936 –9 4303

Lower 95% CI Upper 95% CI –428 1607 541 7135 2395 4833 657 –21 3133

1169 1717 600 8015 3284 5619 1215 2.5 5473

* Coefficients for dependent variable (total hospital cost) included both hospital cost and physician cost. CI, confidence intervals; ICU, intensive care unit; CCU, critical care unit; CABG, coronary artery bypass grafting; PTCA, percutaneous transluminal coronary angioplasty. Adj. R-square = .93

Table 5 Drug Costs Used in Canadian ESSENCE Economic Evaluation Drug Exoxaparin Unfractionized heparin

Dose

Unit cost

1 mg 1000 U

$0.20 $0.11

low-molecular-weight heparin (Enoxaparin) or regular heparin. The outcome was a 1year composite rate of death, MI, or recurrent angina. An economic evaluation of Canadian trial subjects was recently completed using 1year follow-up data. A provincial (Ontario) Ministry of Health perspective was taken for the analysis. Utilization of selected health care resources was collected prospectively during the clinical trial. Specifically, the following resources were collected for hospitalizations, length of stay by type of ward (ICU, non-ICU), coronary artery bypass grafting (CABG), percutaneous transluminal coronary angioplasty (PTCA) with stent, PTCA without stent, cardiac catheterization (CATH), and drug consumption. A hospital participating in the OCCP provided case-cost records for 1044 hospitalizations with an admitting diagnosis of either unstable angina or non-Q wave angina. For each hospital record the OCCP provided, there was a record of the total costs, utilization of each of the individual resources collected in the clinical trial (i.e., length of stay by ward, CABG, PTCA [with/without stent], CATH), along with demographic information. Appropriate physician fees as identified in the Ontario Schedule of Benefits were added to the total cost of each hospitalization record. A multivariable regression model was estimated using total hospitalization cost as the dependent variable with the individual resource items and demographic data used as independent variables. The coefficients of the resource variables were used as the unit microcosts in the analysis. The results of the model are shown in Table 4. As can be seen, the variables in the model explained 93% of the variation in total costs. The unit costs for the study drugs that are shown in Table 5 were based on the hospitals’ acquisition cost.

44

Cardiovascular Health Care Economics

REFERENCES 1. Drummond MF, O’Brien BJ, Stoddart GL, Torrance GW. Methods for Economic Evaluation of Health Care Programmes, 2nd edition. Oxford University Press, Oxford, 1997. 2. Gold MR, Siegel JE, Russell LB, Weinstein MC. Cost-effectiveness in Health and Medicine. Oxford University Press, New York, 1996. 3. Goeree R, Hannah M, Myhr T, Blackhouse G. Hospital Selection for unit cost estimates in multicentre economic evaluations. Pharmacoeco 1999;15:561–572. 4. Canadian Institute for Health Information. Introduction to the MIS guidelines. Canadian Institute for Health Information, Ottawa, Canada, 1999. 5. Statistics Canada. Hospital Statistics: Preliminary Annual Report, 1994–95. Catalog number 83–241, 1996. 6. Health Resourcing Branch, Alberta Health and Wellness. Health Costing in Alberta 1999 Annual Report. Health Resourcing Branch, Alberta Health and Wellness, Alberta, Canada, 1999. 7. Ontario Case Cost Project. Ontario Guide to Case Costing. version 2, 1999. 8. Jacobs P, Roos N. A Manual of Standard Costs for Alberta. Institute of Pharmaco-Economics, Edmonton, Alberta, 1997. 9. Jacobs P, Shanahan M, Roos M, Farnworth M. Cost list for Manitoba Health Services. Manitoba Centre for Health Policy and Evaluation, Department of Community Health Sciences, Faculty of Medicine, University of Manitoba, Winnipeg, Manitoba, 1999. 10. Jacobs P, Assiff L, Bachynsky J, et al. A National List of Provincial Costs for Health Care: Canada 1997/98. Institute of Health Economics, Edmonton, Alberta, Canada, 2000. 11. Canadian Institute for Health Information. National Physician Database: National Grouping System Categories Report. Ottawa, Canada, 1998. 12. Ontario Ministry of Health. Schedule of Benefits: Physician Services Under the Health Insurance Act, February 1, 1998. Queen’s Printer, Toronto, Ontario, Canada, 1999. 13. O’Brien BJ, Connolly SJ, Goeree R, et al. Cost-effectiveness of the implantable cardioverter defibrillator: results from the Canadian Implantable Defibrillator Study (CIDS). Circulation 2001; 103:1416–1421. 14. O’Brien B, Willan A, Blackhouse G, et al. Will the use of low-molecular-weight heparin (Enoxaparin) in patients with acute coronary syndrome save costs in Canada? Am Heart J 2000;139:423–429.

4

US Physician Costs Conceptual and Methodological Issues and Selected Applications

Edmund R. Becker, PhD CONTENTS INTRODUCTION CONCEPTUAL OVERVIEW OF PHYSICIAN COSTS MAJOR APPROACHES FOR ESTIMATING PHYSICIAN COSTS PHYSICIAN BILLING DATA DEVELOPING PHYSICIAN COSTS USING HOSPITAL BILLING DATA DIRECT OBSERVATION CONCLUDING COMMENTS REFERENCES

INTRODUCTION Health care costs are an essential component of any economic evaluation and, ultimately, any thorough analysis of health services requires an economic evaluation. Because approximately 70% of all health care costs are directly or indirectly controlled or influenced by physicians’ decisions (1), it is important that physician influence and cost activities in health outcomes research be taken into account. Yet, many research studies fail to take physician services into account because they are difficult to capture, or when physician services are compared to hospital services, they represent such a minor portion of the overall cost, they are not included. In recent years, several conceptual and methodological approaches have evolved to include physician costs in research studies. However, there has been little systematic review or comparison of these methods, or the consequences of using these methods, on estimating physician costs. In this chapter, we review three major approaches and methods for measuring physician costs. These cost approaches include the following: 1. Physician costs based on the Medicare Fee Schedule (MFS) and the resource-based relative value scale (RBRVS) using Current Procedural Terminology (CPT). 2. Physician costs derived from hospital data. 3. Physician costs based on methods of direct observation (i.e., direct observation, random observation, time-motion studies, patient flow analyses). From: Contemporary Cardiology: Cardiovascular Health Care Economics Edited by: W. S. Weintraub © Humana Press Inc., Totowa, NJ

45

46

Cardiovascular Health Care Economics

In addition, using empirical data from the Emory University Hospital (EUH), we focus on the RBRVS method and discuss the overall impact of physician costs on potential differences among the selected approaches.

CONCEPTUAL OVERVIEW OF PHYSICIAN COSTS What are physician costs? Much of the discussion about hospital costs in the methodological chapters of this book also applies to physician costs. At times, there is considerable confusion in the health care literature among terms like costs, charges, prices, expenditure, reimbursement, financial costs, and economic costs. To an economist, cost refers to the sacrifice (of benefits) made when a given resource is consumed in a treatment or program (2). Thus, costs can refer to more than expenditures and include other resources that are not included in market prices. Conceptually, financial costs for the economist represent real money outlays for the resources required to produce a program or to provide a patient with a particular treatment. Economic costs refer to the opportunity costs of a program or treatment; that is, the value of the resources if the resources have been used for another purpose. Economic costs include not only financial costs, but also the value of resources for which no financial outlays were made. Thus, resources (e.g., forgone wages, travel time, spousal support after illness, and so on) are all conceptually relevant for economic costs (3). Different perspectives can also be used to evaluate costs (i.e., individual, provider, payer, or societal). There has been considerable discussion among the various perspectives. As a general rule, most studies adopt the societal perspective (3a). Studies taking the societal perspective attempt to measure all outcomes of an intervention, both positive and negative. Similarly, the societal perspective demands that all resources consumed in an intervention are considered, not just the costs of medical care to a particular individual, organization, payer, or sector of the economy. For the purposes of this chapter, I focus on US physician costs based primarily on the payer perspective using elements of financial costs. It should be noted that although the societal perspective is considered the most desirable perspective, virtually all physician cost estimates are from the payer perspective. Physician cost calculations are driven almost entirely by the types of data available based on the level of hospital or physician activities. Moreover, in most cases, the data are driven by the billing forms used by various third-party payers. These data can be generated from either the hospital or physicians office (i.e., in-patient or out-patient data). Using the differences in data, Table 1 reports the major approaches for calculating physician costs based on the types and levels of data available. We differentiate between three major approaches—physician billing data, hospital data, and direct observation. It is important to note that the lack of specific physician cost data does not necessarily prevent the inclusion of physician cost estimates. Depending on the availability of the other types of data, these sources of data can be used to generate physician cost estimates. Because some of the other approaches are typically based on one approach or a combination of hospital and physician data approaches, the physician data is discussed first. In addition, because physician cost estimates based on the RBRVS in the MFS are relatively new and offer substantially more detail than the other methods, this approach is examined in more detail using data from EUH.

Table 1 Major Approaches for Calculating Physician Costs Based on the Types of Data Available Level of physician data

Types of data

Sources of data

Key aspects of calculating physician costs

Research examples

CPT codes

Physician billing data from hospital or physician office RBRVS values in the MFS

Establishing a conversion factor Missing physician data No RVUs for CPT codes

Adams et al., 2001 (4); Becker et al., 2000, 2001 (5,6); Weintraub et al., 1995, 1999, 2000 (7–9)

Hospital data

DRG Hospital charges Specific procedures or services Focused study Time motion study Random observation Patient flow analysis

Hospital billing records

Crosswalking hospital procedures to physician services Developing dollar amounts

Litwin et al., 1998 (10) Klein et al., 2002 (11)

Studies designed to capture physician data

Converting time into monetary amounts Mapping observed physician services to a framework

Phillips et al., 2000 (12)

47

Physician data

Direct observation

48

Cardiovascular Health Care Economics

MAJOR APPROACHES FOR ESTIMATING PHYSICIAN COSTS Based on the type of data available or the types of data generated in the study, three general approaches can be used to estimate physician costs. These approaches are each discussed in this section, along with the major pros and cons of each approach.

PHYSICIAN BILLING DATA Physician billing data is typically captured by hospitals (in-patient and out-patient) and physician practices with CPT coding. CPT was developed and is maintained by the American Medical Association (AMA). Because the MFS reports the relative value units (RVUs) by physician costs and practice costs by CPT code, physician billing data has the potential for offering a wealth of costing data. Yet, based on CPT, our ability to reasonably describe and compare what different, or even similar, physicians do is extremely limited. Although rates can be established for providers on selected characteristics of medical practice (i.e., X-rays per 1000 visits, patient visits per day, and so on), it is virtually impossible to evaluate or compare physician care across different types of services, such as consultations, visits, surgery, pathology, and laboratory testing. Moreover, within a particular clinical department or practice setting, it is difficult to accurately summarize and differentiate what physicians do. One approach for profiling major hospital services is to use the resource-based physician work weights developed in the MFS. With the implementation of the MFS to pay for physicians’ services in 1992, the MFS legislation implemented the RBRVS approach (13). The RBRVS approach has been widely hailed as making payments among and within specialties more fair, equitable, and objective (14). Although most activities related to the RBRVS have focused on physician payment levels, the RVUs for physician work as described in the MFS have important clinical and managerial implications for evaluating the costs associated with physician clinical activities.

Physician RVU Work Units in the MFS The RBRVS is an index that assigns relative weights to each medical and surgical service based on the resource inputs involved in the service. Although the relative weights are being used by payers to determine payments for each service, a portion of the weights reflect physician work (time, physical effort and skill, mental effort and judgment, and stress) that are involved in providing each clinical service. As a result, the RBRVS weights represent a unique tool for analyzing and managing physician costs and resource use. The development of the RBRVS has taken more than 15 years (15), with the final effort that began in 1985 involving a broad national effort by virtually all major health care providers and payers. The major participants in the project include the AMA, Centers for Medicare and Medicaid Service (CMS), formerly the Health Care Financing Administration, Physician Payment Review Commission (PPRC), National Institutes of Health (NIH), and virtually all major medical and surgical specialty societies (16).

Application of RBRVS to Physician Costing The RBRVS is rapidly gaining wide acceptance among major payers. By 1996, 22 states had implemented the RBRVS system for their Medicaid programs, with three more states actively exploring their use. In its 1994 survey, Blue Cross and Blue Shield

Chapter 4 / US Physician Costs

49

(BCBS) Association found that about half its members were using RBRVS to pay some of their physicians. Among major insurers, the RBRVS is being used predominately in managed care plans. In a managed care survey done for PPRC, 30 plans in a sample of 108 reported the use of an RBRVS, with about half of these plans indicating they had used the MFS mostly intact (17). As a reflection of their support for the RBRVS, the AMA House of Delegates, the governing board of the AMA, approved a measure that encourages all insurers to adopt an RBRVS (18). The extent to which RBRVS has saturated the physician payment arena can be seen from the transcript of the public meeting held by the Medicare Payment Advisory Commission (MEDPAC) in December 2002. At its quarterly meeting, MEDPAC reported that all the plans in its 2002 survey of large health plans, which represented all the Blue Cross-Blue Shield plans plus six of the largest managed care companies in the nation, had physician fee schedules that were influenced by RBRVS. About 60% of the fee schedules, MEDPAC reported, were RBRVS-based or RBRVS-type fee schedules. The remaining 40% of the physician fee schedules were characterized as loosely inspired by or influenced over time by RBRVS fees (18a). As reflected by its rapid adoption, the RBRVS is more than a basis for the MFS. In developing RBRVS values, surveyed physicians in all the major medical and surgical specialties responded to clinical situations and patients that were most typical of their practice (19). The age and clinical depiction of the patient were not just for Medicare patients; these depictions represented medicine as it is currently practiced. As a result of the wide spectrum of input from medical specialty societies and associations, health care payers and providers, and the refinements by the CMS, AMA, and PPRC, the RBRVS has wide application beyond Medicare (20). There are several basic aspects of the payment structure that give the Medicare relative value scale its appeal. As noted previously, to identify services performed by physicians, most major payers and providers use the Physician’s Current Procedural Terminology Fourth edition (CPT-4) (21). This coding system, designed by physicians, identifies over 9000 distinct services, visits, and procedures (generally referred to as procedures or services in this chapter). The Medicare relative value scale contains relative weights for each of the more than 9000 CPT-4 services and calibrates them all on a common scale. For each of the CPT-4 services, the Medicare relative value identifies two main resource inputs required to produce physician services: (1) the physician work RVUs and (2) practice cost RVUs. These factors are summed up to produce the total RVU for a given service. The physician work component is the sum of three time periods: pre-, intra-, and postservice. The intraservice period is when the physician sees the patient or performs a procedure; preservice and postservices are those activities performed for the patient before and after the intraservice period. The measurement and impact of pre- and postservice work are extremely important. Pre- and postservice work accounts for nearly 50% of the total work of a typical surgical service and 30% of a typical visit or consultation (22). In summary, physician work RVUs represent unique and comprehensive measures of the physician resource input for more than 9000 medical and surgical services. The relative values in the Medicare relative value scale were developed by scientific surveys of physicians and extensively refined by expert medical panels (23). The values are easy to use, readily available, and rapidly gaining acceptance beyond Medicare among both payers and providers. Consequently, it is feasible using physician billing codes (CPT-4 codes) to construct physician cost profiles and apply the RVU weights to

50

Cardiovascular Health Care Economics

each physician service as part of the surgical episode and develop physician resource profiles of the surgical episode. How have these RBRVS units been used?

Applications of RBRVS to Physician Costs From hospitals with centralized physician billing, all physician services or some subset (e.g., AMI or CABG services or by department) as defined by CPT codes and modifiers, and any other physician characteristics of interest (e.g., specialty, experience, and so on) over a designated period of time, can be collected. Then, using the relative value weights from the MFS, RVUs can be assigned to each CPT code, and the total RVUs summarized and compared for each area of investigation of interest. To convert the service RVUs into cost estimates, a conversion factor is needed. That is, a dollar value is needed to multiply against the RVUs to convert them into physician costs. We typically use the Medicare national conversion factor for the period of time under investigation. One could also use the conversion factor for other time periods if the intent is to make the physician costs reflect practice costs at a certain point in time. If physician costs represent a major portion of the clinical investigation, it may be advisable to use a second conversion factor to evaluate the sensitivity of the cost estimates to the conversion factor. In these cases, one could use the national conversion factor based on BCBS or the Health Insurance Association of America (HIAA) data, for example. Two conversion factors provide physician cost estimates for both Medicare and a major private payer. The difference between these two cost estimates can also provide an indication of the additional physician costs for private payers in comparison to Medicare. In addition, the difference between the physician costs in each are in using the two conversion factors; this should indicate the sensitivity of the cost estimates to the value of the conversion factor, and how critical the physician component is to the total cost estimate.

Cardiovascular Services at EUH To illustrate this approach and the usefulness of the physician work RVUs, we analyzed all physician services associated with the delivery of cardiovascular services at EUH. The database used in our analysis represents all of the cardiovascular-related physician services provided to patients for the last 6 months of 1997. The database contains 31,567 individual services. By service and patient, we collected for all services CPT codes, CPT modifier (if any), physician charges, department, name of physician providing the service, and payer. In addition, for each patient, the major diagnosisrelated group (DRG) at the time of discharge was identified. After constructing the database, 256 services were dropped because they were alpha codes (codes beginning with a nonnumeric value) and did not represent physician services according to the MFS. As a result, there are no RBRVS values for these codes in the fee schedule. These services represent just $27,333 in physician charges (0.001% of total charges). There were 582 services for which we had no RBRVS physician work values but, following the review of the codes by physician panels, it was determined that these codes described physician services. RBRVS values for these codes were calculated by two methods. For CPT codes where there were more than 10 services, in consultation with medical experts, we matched these services with comparable services in the MFS and assigned the physician work RVUs. For CPT codes where there were 10 or less services, surrogate RBRVS physician work units were calculated from the charges for

Chapter 4 / US Physician Costs

51

the service by using the conversion factor of $42. We divided the average physician charge for a particular service by $42 and multiplied the resulting quotient by 54.2%. The $42 conversion factor was selected because it represents a typical level of reimbursement for these services. The 54.2% factor was used because this is the mean share physician work representative of total work. The final database used in the analysis contained 31,311 records and represented $15,716,588 in physician charges. CPT coding conventions use modifiers to reflect changes in the physician effort for certain services. For example, if a physician needed to provide a significant separately identifiable evaluation and management service on the day of a surgical procedure, the physician would identify the service with a CPT code plus a CPT modifier. Modifiers cover a wide range of physician activities, ranging from services that are reduced in the scope of work to those that have assistants at surgery or multiple surgeries. Physician work RVUs for services with CPT modifiers need to be adjusted to reflect the appropriate level of work associated with these services. As a general guide, we used CMS’ payment policies for modifiers to adjust the work values. For the major modifiers, we made the following adjustments to the physician work RVUs. For assistants-at-surgery (CPT modifier codes 80, 81, or 82), physician work RVUs were adjusted to reflect CMS’ payment at 16% of the total. For bilateral surgeries and multiple surgeries (CPT modifiers 50 and 51), the physician work RVUs, these service modifiers were adjusted to reflect 50% of the work. Ten medical service classification categories were developed from CPT coding using the AMA’s classification and definitions in the CPT-4 (24). These major service categories were (1) anesthesia, (2) visits and consultations, (3) mental health services, (4) tests and procedures, (5) gastroenterology services, (6) ophthalmologic services, (7) cardiovascular services (8) radiology services, (9) surgery, and (10) other services. Because there were few services in the mental health, gastroenterology, and ophthalmology categories, these services were classified into the other services category. Based on department designations at EUH, six departments were identified: anesthesiology, internal medicine, pathology, radiology, surgery, and all other departments. Linking all associated physician services with each patient over a 3-month period defines the episodes of care for these cardiovascular patients. For each patient, all CPT codes, CPT modifiers (if any), patient charges, and date of the service were compiled. CPT codes and modifiers were used to assign total RVUs and work RVUs using the 1995 MFS (25). For CPT codes that were redefined by CMS in 1992, CMS crosswalks were used and adjusted for annual changes in the RBRVS to maintain all RVUs on a common scale (26). For services that did not have a 1996 RVU or RVU from an earlier MFS—283 services—we divided the physician charges by EUH’s overall conversion factors to approximate RVUs. Because anesthesia RVUs are not developed separately for the MFS, we used the anesthesia physician work RVUs imputed by CMS in the 1995 MFS (27). All anesthesia CPT codes were cross-checked and validated with the American Society of Anesthesia’s (ASA) Crosswalk between anesthesia and surgical codes (28). To calculate total RVUs for anesthesia services, anesthesia physician work RVUs were increased by 30.5%, consistent with the national practice cost adjustment percentages in the 1992 MFS (29). CPT coding uses CPT modifiers to reflect changes in the physician effort for certain services. Modifiers cover a wide range of physician activities, ranging from services that are reduced in the scope of work because the surgeon provided only the surgical

52

Cardiovascular Health Care Economics

service and not the pre- and postservice follow-up to those patients that have assistantsat-surgery or the performance of multiple surgeries. Consequently, RVU values for these services need to be adjusted to reflect the appropriate level of work and practice costs that are associated with each service. As a guide to adjust RVUs for modifiers, we followed CMS’ payment policies for modifiers. For example, for an assistant-atsurgery (CPT modifiers 80, 81, or 82), RVUs were adjusted to reflect CMS’ payment level of 16% of the RVUs for the primary surgeon. Other CMS rules for modifiers were similarly followed (30). Because the initial measurement of practice costs was not resource-based in the MFS, but charge-based, there were problems with the RVUs in the MFS (31–34). Legislation mandated that the problems with practice costs be corrected in 1998, and CMS engaged contractors to address this issue and incorporate them into the MFS (35). Because of the concerns with practice costs, we report separate results for both total RVUs and physician work RVUs.

Cardiovascular Analysis Results As reported in Table 2, over the last half of 1997, a total of 31,311 physician services were identified, representing 2052 separate episodes of patient care. The number of episodes of care ranged from a low of 67 cardiac valve procedures with cardiac catheterization (CATH) (DRG 104) to 1035 percutaneous cardiovascular procedures. On average, there were 15.3 services per episode of care. For individual DRGs, the mean number of services ranged from a low of 10.9 services per episode of care for permanent cardiac pacemaker implant without acute myocardial infarction (AMI), heart failure or shock (DRG 116), to 24.9 for the cardiac valve procedure with CATH (DRG 104), a range of 14 physician services per episode of care. The physician services represented $15,716,588 in physician charges, 98,451 physician RBRVS work units, and 224,467 physician total RBRVS units. Dividing total physician charges by the total RBRVS units results in a mean conversion factor of $70 per RBRVS unit, which is typical of national physician charge per RBRVS unit findings (36). Table 3 compares the differences of the physician charges and physician work RBRVs percentages by major clinical departments and DRG. Overall, in comparison to the results based on physician charges, using RBRVS physician work resulted in three departments experiencing a gain and two departments experiencing a decline in the share of the service performed. The departments of internal medicine, pathology, and radiology experienced gains of 5.5%, 0.1%, and 1.5%, respectively. In contrast, anesthesiology experienced a decline of 1.4%, and cardiothoracic surgery had a decline of 5.7%. In general, these results are consistent with the national RBRVS trend of placing higher relative value weights on evaluation and management services and lower relative value weights on surgical services than physician charges. However, viewing individual DRGs for a specific medical center show some interesting results that would not be predicted from the national trends. When comparing the differences based on RBRVS work vs physician charges, all departments experienced an increase in at least one DRG. Surprisingly, the only department to experience an increase across all seven cardiovascular DRGs was radiology. In addition, whereas internal medicine experienced the greater overall increase (5.5%), and cardiothoracic surgery experienced the greatest overall decline (5.7%) among these departments, just three DRGs (106, 107, and 110) are responsible for the

Table 2 Physician Charges, Work RBRVs, Total RBRVs, DRG Counts, Total Number of Physician Services, and Mean Number of Services by DRGa

DRG

53

DRG 104–Cardiac Valve w CC DRG 105–Cardiac Valve without CC DRG 106–Coronary Bypass with CC DRG 107–Coronary Bypass without CC DRG 110–Major Cardiovascular with CC DRG 112–Percutaneous Cardiovascular DRG 116–Permanent Cardiac Pacemaker without AM Total for all DRGs a

Percentage of column total in parenthesis.

Physician charges (%)

Physician work RBRVs (%)

Physician total RBRVs (%)

Total no. of DRGs (%)

Total no. of physician services

Mean no. of services per DRG

$908,137 (5.8) $1,516,784 (9.7) $3,431,615 (21.8) $4,870,574 (31.0) $756,595 (4.8) $3,803,222 (24.2) $429,661 (2.7)

6161 (6.3) 9206 (9.4) 17,591 (17.9) 23,598 (24.0) 4534 (4.6) 33,411 (33.9) 3950 (4.0)

13,511 (6.0) 21,011 (9.4) 41,952 (18.7) 57,807 (25.8) 10,406 (4.6) 72,026 (32.1) 7754 (3.5)

67 (3.3) 104 (5.1) 261 (12.7) 370 (18.0) 114 (5.6) 1035 (50.4) 101 (4.9)

1670 1939 5332 6137 1922 13,212 1099

24.9 18.6 20.4 16.6 16.9 12.8 10.9

2052

31,311

15.3

$15,716,588

98,451

224,467

Table 3 Physician Charge and RBRVS Work Shares and Percent Difference by Clinical Department and DRG Anesthesia

DRG DRG 104– Cardiac Valve with CC DRG 105– Cardiac Valve without CC DRG 106– Coronary Bypass with CC DRG 107– Coronary Bypass without CC DRG 110–Major Cardiovascular with CC DRG 112– Percutaneous Cardiovascular DRG 116– Permanent Cardiac Pacemaker without AMI Average over DRGs

Internal medicine

Pathology

Radiology

Cardiothoracic surgery

% % % % % % % % % % Based on Based on Based on Based on Based on Based on Based on Based on Based on Based on physician RBRVS % physician RBRVS % physician RBRVS % physician RBRVS % physician RBRVS % charges work Difference charges work Difference charges work Difference charges work Difference charges work Difference 19.4

17.6

1.8

26.2

27.0

+0.8

1.0

1.1

+0.1

1.3

1.9

+0.6

52.1

52.3

+0.2

20.8

19.1

–1.9

8.1

10.1

+2.1

1.1

1.2

+0.1

1.1

1.6

+0.5

68.9

67.9

–1.0

23.4

23.3

–0.1

8.2

13.2

+5.0

0.2

0.2

0.0

1.8

3.1

+1.3

66.4

60.2

–6.2

24.9

25.8

+0.9

2.7

5.0

+2.3

0.1

0.1

0.0

1.5

2.5

+1.0

70.9

66.6

–4.3

25.5

21.0

–4.5

20.9

27.8

+6.9

1.3

1.1

–0.2

5.8

8.1

+2.3

46.6

42.0

–4.6

2.2

2.5

+0.3

87.3

86.8

–0.5

0.4

0.4

0.0

2.8

4.6

+1.8

7.3

5.9

–1.4

4.4

4.4

0.0

89.6

89.5

–0.1

0.4

0.4

0.0

2.5

2.9

+0.4

3.2

2.8

–0.4

17.1

15.7

–1.4

33.3

38.8

+5.5

0.6

0.7

+0.1

2.4

3.9

+1.5

46.6

40.9

–5.7

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major portion of this change. For the internal medicine department, the increase for DRGs 106, 107, and 110 were 5%, 2.3%, and 6.9%, respectively. In contrast, for these same three DRGs in the cardiothoracic surgery department, the declines were 6.2%, 4.3%, and 4.6%, respectively. Among these major cardiovascular DRGs with more than 50 patients in each DRG for the five departments, the implantation of a permanent cardiac pacemaker without AMI (DRG 116) showed the smallest differences between the percentage share based on physician charges and the percentage share based on RBRVS physician work. For DRG 116, no department experienced more than a 0.4% change. In contrast, among the departments, DRG 110—major cardiovascular procedures with CATH—showed the greatest changes between the estimates based on physician charges and RBRVS physician work. Four of the five departments had changes between the share based on physician charges and RBRVS physician work that were 2.3% or higher. Table 4 reports physician work RVUs by type of service and department. Considering all the cardiovascular services in the last six months of 1997, using the physician work portion of the MFS, the results show that the surgery department and internal medicine department had nearly even shares of physician work—39.7% and 37.6%, respectively. If the services being examined were specific services like coronary artery bypass surgery or coronary stents, these work shares might be used to allocate global payments from a particular payer or dividing up shares among subsets of specialists. This illustration shows the applicability and flexibility of using physician billing data based on CPT codes combined with other data characteristics like the DRG and physician specialty or department. Other examples of the RBRVS approach and methods are presented in Becker et al. (5,6), Lincoff et al. (37), and Weintraub (9).

Issues with RBRVS Approach for Physician Billing Data There are numerous issues with using physician billing data. As noted previously, some of these issues have to do with the completeness of the data. Often, there are services included in hospital billing records that might not represent physician services. Services, like some of the alpha-numeric services at EUH, are not physician services. In this example, these services were dropped. However, it might be possible to include some of them where they are coded using CPT codes. In the following discussion, Phillips et al. demonstrates how the RVUs can be used to estimate resource use for nurse practitioners (NPs), physician assistants (PAs), and ancillary providers (12). In accordance with Medicare rules, all visits by NPs and PAs are awarded 85% of the physician’s RVU, whereas ancillary providers are awarded 75% of the physician’s RVUs. This can assist researchers in broadening their cost calculations for other types of providers. Where there are physician services, but no RVUs exist, the previous example demonstrates a crude, but effective, approach for filling in the missing RVUs. By dividing the existing charge or cost by the conversion factor used, RVUs can be generated for these types of services. If these services represent a large share of the services being evaluated, or they are unevenly distributed in one arm and not another, these services need to be carefully evaluated to be certain they do not bias results. We would anticipate that, as Medicare’s experience with RBRVS in the MFS grows, there will be a growing amount of information that is available at the CPT code level that could be linked by CPT codes. In their final report, Per Case Prospective Payment for Episodes of Hospital Care, Mitchell et al. reported Part A and Part B payments by

Table 4 Physician Work RVUs by Type of Physician Service and Clinical Departmenta Type of service Evaluation and management

Anesthesia Department

Work Col Row RVUs % %

Surgery 0 0.0 2 0.0 Internal medicine 4514 100 Anesthesia Pathology 0 0.0 Radiology 0 0.0 Other 0 0.0 departments Total 4516 100 a

Work RVUs

Pathology and laboratory

Col Row Work Col Row Work Col Row % % RVUs % % RVUs % %

0.0 474 0.0 5719

5.8 1.5 69.4 18.8

0.0 0.0 0 203 25.2 0.7

36.6 885 0.0 1 0.0 0 0.0 1159

10.7 7.2 0.0 0.1 0.0 0.0 14.1 48.7

4 531 0 66

10.3

Radiology

8.238 100

Columns may not sum to total due to rounding.

17.1

804

0.0 66.1 0.0 8.2 100

0 0.0 152 10.2

0.0 0.5

0.0 0 0.0 0.0 98.0 0 0.0 0.0 0.0 1244 84.0 40.7 2.8 85 5.7 3.6 1.0 1481 100

1.8

Surgical

Cardiovascular

Work RVUs

Col Row % %

29,896 1947

78.7 93.3 5.1 6.4

4956 13.1 40.2 0.0 0.0 0.0 478 1.3 15.6 692 1.8 29.1 37,968

100

47.0

Work RVUs 81 20,982 1801 0.0 26 6

Other services

Total

Col Row Work Col Row % % RVUs % %

Work Col Row RVUs % %

0.4 0.3 91.6 68.8

5.0 4.8

32,059 39.7 100 30,363 37.6 100

166 3.4 1.3 11 0.2 1.9 1312 26.7 42.9 371 7.6 15.6

12,326 15.3 100 542 0.7 100 3060 3.8 100 2379 2.9 100

7.9 0.0 0.1 0.0

22.805 100

14.6 0.0 0.8 0.2 28.2

1609 32.7 1450 29.5

349 100

6.1

80,729 100

100

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DRG for all Medicare DRGs (38). In their report, they show mean physician RVUs by preadmission days, admission, and postdischarge days by DRG. These could easily be linked to major cardiovascular services and conversion factors used to convert to cost amounts. Physician cost data from hospital billing records is discussed in more detail in the next section. A final issue with using physician billing data and the MFS relative weights has to do with the conversion factor. As we noted previously, there is considerable range among conversion factors. Consequently, depending on the relationship between physician costs and other costs, there use could be considerable variation in the share attributable to physician costs because of the conversion factor. As pointed out earlier, there may need to be some sensitivity analysis around the conversion factor.

DEVELOPING PHYSICIAN COSTS USING HOSPITAL BILLING DATA The second major data source is hospital billing data. Because many studies do not have physician-specific data, but some form of hospital billing data, which contains or can be linked to the DRG, a popular approach for estimating physician costs is to base physician costs on hospital data related to the DRG of the hospitalized patients. DRGs are required for Medicare payments for hospital patients. To receive payment for in-patient services, hospitals are required to fill out a UB-92 (1992 uniform billing form developed by Medicare), which assigns standardized diagnoses and procedures codes to each patient based on the International Classification of Diseases–9th Revision (ICD-9) (39). Medicare billing intermediaries group these diagnoses to one of nearly 500 DRG classifications and report this information, along with other patient characteristics to Medicare, and payment is based on the prospective reimbursement payment methodology first established in 1983. Most US hospitals are now paid a fixed amount, determined prospectively, for the operating cost of each patient according to one of the DRG classifications. Each patient is assigned a classification based on diagnosis, surgery, age, discharge destination, and gender. Each DRG has a cost associated with it, based primarily on Medicare billing and cost data. Medicare summarizes and reports this information and data by a variety of difference classifications and conceptual frameworks. For example, summary descriptions of various types of Medicare data can be viewed and downloaded at the Medicare website (www.cms.gov/data/download/default.asp). The files are available in various formats and, if ordered, are typically available free of charge or for a nominal charge. By using the relationship between hospital costs (Part A) and physician costs (Part B) based on national Medicare data for a specific year, physician costs can be estimated and assigned to selected services, where no specific physician data previously existed based on the ratio between mean hospital DRG costs and mean physician DRG costs. Litwin et al., for example, used the overall relationship between Medicare Part A and Part B to estimate gross physician costs for physician services in his analysis of radical prostatectomy among Medicare beneficiaries (10). Based on the Medicare data on the relationship between physician and hospitals services (Part A and B), Litwin et al. calculated that physician services represented approximately 20% of the total hospital and physician payments for Medicare services for radical prostatectomy for the 1991–1993 period. Consequently, one might use the 20% share as a mark-up of hospital costs to account for the costs of physician services. That is, if an investigator had hospital costs

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through hospital UB-92 forms or other sources, using a 1.2 multiplicative factor to mark up hospital costs would provide a crude estimate of physician costs. Klein et al. used this approach to estimate physician costs in the comparison of transesophageal echocardiography-guided therapy with cardioversion for patients with atrial fibrillation, the ACUTE trial (11). This approach can be refined to be more precise by using the specific DRGs pertaining to the patient’s diagnosis. Because Medicare publishes DRG reimbursement levels by Part A (hospital) and Part B (physician), using the Medicare DRG-specific reimbursement levels for a DRG can be applied. Thus, for example, considering DRG 112—percutaneous cardiovascular procedures. In fiscal year 1999, Medicare reports a reimbursement of $582 million for 61,460 hospital discharges, an average reimbursement of $9467. Mitchell et al., in their final report, Per Case Prospective Payment for Episodes of Hospital Care, reported Part A and Part B payments by DRG for all Medicare DRGs (38). Consequently, for in-patient services with a variety of DRGs and hospitals costs, the ratio between hospital and physician services can be calculated and applied for each specific DRG. Thus, in the example we used previously (DRG 112), Mitchell et al. report a mean of 83.1 RVUs for all physician services (this includes the physician services performed on the percutaneous transluminal coronary angioplasty [PTCA] during the preadmission, admission, and postadmission portion of the service) and 62.1 RVUs for admission portion (only) for the typical Medicare PTCA using 1992 Medicare data. Using the 1997 Medicare national conversion factor of $34.73 for physician services, we could calculate the total typical Medicare physician cost for this service at $2886 (83.1 × $34.73), and for physician costs for the admission portion, $2157 (62.1 × $34.73) of the PTCA. Thus, using the total physician portion of the PTCA ($2886/$9467), the average physician share of the total service is 30.5%, whereas using the admission only portion of the PTCA services, the share is 22.8% ($2157/$9467). This discussion reveals the sensitivity of the physician shares to the specific DRG being investigated, the availability of data, and the choice of conversion factor. However, it does allow an investigator with little physician data to estimate physician costs.

Issues with Hospital Billing Data There are several issues with using hospital billing data and the Medicare approach. First, there are issues of identifying the appropriate populations. The Medicare data typically contains data on total charges, covered charges, and Medicare reimbursement. Although it is most appropriate to use the Medicare reimbursement to reflect physician costs, it is not always easy to find Medicare reimbursement by the year of interest or DRG of interest broken out by both Part A and Part B. Because Medicare reports Part A and B data in many different forms (Part A or B only or Part A and B), it is essential to be certain what Medicare data is being used. In addition, the totals can include inpatient and/or out-patient reimbursement. Second, if conversion factors are used to convert the RVUs into dollar amounts, the magnitude of the conversion factors can vary dramatically. In using EHU data in the earlier example, we found the conversion factor was more than double that 1997 Medicare conversion factor. As noted earlier, this is typical of conversion factors by private payers or other organizations. Consequently, the conversion factor needs to be oriented to the population under study.

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Physician cost estimates using a conversion factor are likely more sensitive to the choice of dollar amount of the conversion factor than applying a ratio of Part B to Part A and B costs, such as the Litwin et al. example (10) referred to previously. This assumes that the relationship between hospital and physician expenditures for private payers and organizations is approximately the same as it is between Medicare hospital and physician payments. Our investigations using limited data from various sources suggest there is wide variability among physician shares using the DRG approach. Given the wide range of potential conversion factors noted among various payers, it is difficult to conclude such variability should not be reasonably expected. Third, if the application is by DRG, has the definition of the DRG changed? With coronary stents, for example, Medicare designated to new DRG reflect the change in technology and the growing use of stents in angioplasty. For example, DRG 514–518 are new DRGs as of October 2001. These DRGs represent a range of International Classification of Diseases services and percutaneous coronary intervention (PCI) services with and without catherizations, and with and without stents that have been developed to reflect physician current practices. These changes, or any such changes, need to be incorporated into any research efforts and crosswalked to the appropriate physician services to capture the physician costs associated with these services. Of course, it almost goes without saying that it might be several years before an aggregate data is available from Medicare or other sources to reflect the physician shares of these new DRGs.

DIRECT OBSERVATION The final approach for collecting physician costs is direct observation. A variety of methods can be used to collect physician data through direct observation. Some of these techniques use provider time as a major determinant of physician activities and through (1) direct observation of services, (2) time-motion studies like time diaries of activities or activity duration, (3) random observation of provider activities that represent snapshots in time of physician activities, and (4) patient flow analysis using time forms that a client carries from provider to provider. Haddix et al. provide a detailed discussion of these various approaches and methods for incorporating them in economic analyses (40). Direct observation of services requires the counting and summary of physician activities. This method can employ diaries or other techniques for capturing physician activities. Phillips et al. used the direct observation to establish levels of physician resource use for generalist and specialist activities outside the hospital setting by using physicians’ billing office logs. Billing logs were used to identify the number of services and procedures used. Based on discussion with expert physicians, known features of the population and descriptions in the Physicians’ Current Procedure Terminology: CPT 96 were then assigned CPT codes. The RVU associated with the codes was identified using the RBRVS in the MFS. The RVUs were then translated into dollars per RVUs for visits and surgery using the Medicare reimbursement level from the year corresponding to the data. In addition, this approach can be used to estimate the resource costs of other practitioners as well. Phillips et al., for example, used the RVUs to estimate resource use for NPs, PAs, and ancillary providers. In accordance with Medicare rules, all visits by NPs and PAs are awarded 85% of the physician’s RVU, whereas ancillary providers are awarded 75% of the physicians RVUs.

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Issues with Direct Observation Phillips et al. also demonstrates some of the critical assumptions and mapping applications that need to be considered. Because the MFS identifies resource use over a broad range of visits and consultations, differentiating between a brief visit and a comprehensive visit is important. In these cases, expert panels or expertise is helpful. Also, the conversion factor selected can have an important bearing on the final cost estimates, especially if the study is focusing on economic costs. That is, although the Medicare conversion factor may be appropriate for some investigations, studies have indicated there can be a threefold difference among the conversion factors of various payers. Consequently, selecting and applying a conversion factor using these techniques can be critical, particularly if the absolute values are important. The latter two approaches in which there is limited physician data rely on a combination of the other methods to generate some estimates. Weinstein, for example, had limited physician data, but used the Medicare relationship between hospital and physician data to generate broad estimates for physician costs. Phillips et al. had only direct observations of general categories of physician services and translated them into CPT physician services. Then, using MFS values and the Medicare conversion factor, the physician services were translated into physician costs. Thus, the availability of limited physician data does not or should not necessarily prevent the generation of physician cost estimates. Although there may appear to be many obvious limitations with the limited or no physician data approach, given the lack on any definitive benchmarks in physician costing, and the potential that hospital costs or direct observation may be as valid as any of the methods, there is no reason, absent of some comparative research, to refrain from using representative methods.

CONCLUDING COMMENTS Any effort to use a limited set of data to extrapolate physician costs to a large database generates a considerable number of assumptions. The greater the number of assumptions in a research study, the potentially greater number of problems. However, given the limited set of physician cost approaches and the general inability to generate a “gold standard,” it is difficult to judge the applicability or bias of any of the approaches described in this chapter. Although we have emphasized the RBRVS approach in the MFS, realistically, the collection of CPT for both in-patient and out-patient services is not always a reasonable or cost-effective method. In fact, in light of some of the national discussions around RBRVS, and the estimates of the anticipated and actual financial impacts on specialties, we expected substantial differences among DRGs between the estimates based on physician charges and those based on RBRVS physician work. However, the differences between the estimates based on physician charges and physician RBRVS work were much smaller than anticipated. Because our initial efforts were narrowly focused in specific areas of cardiovascular services, this may be a premature observation. However, overall, for most of the major cardiovascular services, the physician share has appeared to average around 20%. Of course, there are many confounding factors (e.g., surgical vs nonsurgical services, in-patient vs out-patient, length of follow-up, specialty mix, and so on).

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Because there has been such limited physician costs research and comparability across services or settings using any of these approaches, this is one area where the call for additional research is especially important.

REFERENCES 1. Volpp KG, Schwartz JS. Myths and realities surrounding health reform. JAMA 1994;271:1370–1372. 2. Drummond MF, Stoddart GL, Torrnace GW. Methods for the Economic Evaluation of Health Care Programmes. Oxford University Press, Oxford, 1994. 3. Gorsky RD, Haddix AC, Shaffer PS. Cost of an Intervention. In: AC Haddix, et al. (eds.), Prevention Effectiveness. Oxford University Press, Oxford, 1996, pp. 57–75. 3a. Gold MR, Siegel JE, Russell LB, Weinstein MC. Cost-effectiveness in health and medicine. Oxford University Press, Oxford, 1996. 4. Adams EK, Bronstein JM, Becker ER, Hood CR. Payment levels, resource use, and insurance risk of Medicaid versus private insured in three states. J Health Care Fin 2001;28:72–91. 5. Becker ER, Mauldin PD, Culler SD, et al. Applying the resource-based relative value scale to the Emory Angioplasty vs. Surgery Trial (EAST). Am J Cardiol 2000;85:685–691. 6. Becker ER, Hall K. Physician services in an academic Neurology Department: using the resourcebased relative-value scale to examine physician activities. J Health Care Finance 2001;27:79–91. 7. Weintraub WS, Mauldin PD, Becker ER, Kosinski AS, King SB, III. A comparison of the costs of and quality of life after coronary angioplasty or coronary surgery for multivessel coronary artery disease: results from the Emory Angioplasty versus Surgery Trial (EAST). Circulation 1995;92:2832–2940. 8. Weintraub WS, Culler SD, Kosinski A, et al. Economics, health related quality of life and cost effectiveness methods for the TACTICS (Treat Angina with Aggrastat and Determine Cost of Therapy with Invasive or Conservative Strategy)—TIMI 18 Trial. Am J Card 1999;83:317–322. 9. Weintraub WS, Thompson TD, Culler SD, et al. Targeting patients undergoing angioplasty for thrombus inhibition: a cost-effectiveness and decision support model. Circulation 2000;102:392–398. 10. Litwin MS, Pasta DJ, Stoddard ML, et al. Epidemiological trends and financial outcomes in radical prostatectomy among Medicare beneficiaries, 1991 to 1993. J Urol 1998;160:445–448. 11. Klein AL, Murray RD, Becker ER, et al. Economic analysis of transesophageal echocardiography guided approach to cardioversion of patients with atrial fibrillation: the ACUTE economic data at eight weeks. In press. 12. Phillips VL, Paul W, Becker ER, et al. Health care utilization by old-old long-term care facility residents: how do Medicare fee-for-service and capitation rates compare? J Am Geriatr Soc 2000;48:1330–1336. 13. Hsiao WC, Braun P, Yntema D, Becker ER. Estimating physician work for a resource-based relative value scale. N Engl J Med 1988;319:835–841. 14. Iglehart J. The new law of Medicare’s payments to physicians. N Engl J Med 1990;322:1247–1252. 15. Hsiao WC, Stason WB. Toward developing a relative value scale for medical and surgical services. Health Care Financ Rev 1979;1:23–28. 16. AMA. AMA Policy on the Harvard Resource-Based Relative Value Scale and Related Issues: A Summary of American Medical Association Board of Trustees. Report AA (I-88). AMA, Chicago, IL, December 6, 1988. 17. Physician Payment Review Commission. Annual Report to Congress, 1995. Washington, DC, 1995, pp. 399–408. 18. Johnson J. Saving fee for service under reform. Am Med News 1993;36:1,26–27. 18a. Medicare Payment Advisory Commission Transcript of Public Meeting held December 12, 2002. Washington DC. MEDPAC. (www.medpac.gov) 19. Hsiao WC, Braun P, Yntema D, Becker ER. Estimating physicians’ work for a resource-based relative value scale. N Engl J Med 1988;319:835–841. 20. McCormick LA, Burge RT. Diffusion of Medicare’s RBRVS and related physician payment polices. Health Care Financ Rev 1994;16:159–173. 21. American Medical Association. CPT 1993: Physicians’ Current Procedural Terminology, Fourth Edition. AMA, Chicago, IL, 1993. 22. Hsiao W, Braun P, Kelly N, Becker ER. Results, potential effects, and implementation issues of the resource-based relative value scale. JAMA 260:2429–2436.

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23. Hsiao WC, Braun P, Yntema D, Becker ER. Estimating physicians’ work for a resource-based relative value scale. N Engl J Med 1988;319:835–841. 24. American Medical Association. CPT 1993: Physicians’ Current Procedural Terminology, Fourth Edition. AMA, Chicago, IL, 1996. 25. Medicare Program; Physicians Medicare Fee Schedule for Calendar Year 1996; Physician Policies and Relative Value Unit Adjustments; Final Rule With Comment Period. Federal Register 1995;60:63,123–63,357. 26. Medicare Program; Physicians Medicare Fee Schedule for Calendar Year 1992; Physician Policies and Relative Value Unit Adjustments; Final Rule With Comment Period. Federal Register 1991;56:59,511–59,512. 27. Medicare Program; Physicians Medicare Fee Schedule for Calendar Year 1995; Physician Policies and Relative Value Unit Adjustments; Final Rule With Comment Period. Federal Register 1994;59:63,456–63,459. 28. A Soc Anesthesiologists. 1994 Crosswalk: A Guide for Surgery/Anesthesia CPT Codes. ASA, Park Ridge, IL, 1994. 29. Medicare Program; Physicians Medicare Fee Schedule for Calendar Year 1992; Physician Policies and Relative Value Unit Adjustments; Final Rule With Comment Period. Federal Register 1991;56:59,568. 30. Medicare Program; Physicians Medicare Fee Schedule for Calendar Year 1992; Physician Policies and Relative Value Unit Adjustments; Final Rule With Comment Period. Federal Register 1991;56:59,514–59,516. 31. Becker ER, Dunn D, Hsiao WC. Relative cost differences among physicians’ specialty practices. JAMA 1988;260:2397. 32. Latimer EA, Becker ER. Incorporating practice costs into the RBRVS. Med Care 1992;30:NS50–NS59. 33. Becker ER, Adams EK. Physician practice cost payments in the medicare fee schedule: the implications for primary care specialties of not being resource based? J Gen Int Med 1995;10:33–39. 34. Blumenthal D, Epstein AM. Physician-payment reform—unfinished business. N Engl J Med 1992;326:1330–1334. 35. Mcllrath S. Low response kills survey. Am Med News 1996;39:1,37. 36. Physician Payment Review Commission. Annual Report to Congress, 1997, Appendix A. Washington, DC, 1997. 37. Lincoff AM, Mark DB, Tcheng JE, et al. Economic assessment of platelet glycoprotein IIb/IIIa receptor blockade with abciximab and low-dose heparin durgin precutaneous Coronary Revascularization: Results from the EPILOG randomized trial. Circulation 2000;102:2923–2929. 38. Mitchell JB, McCall NT, Burge RT, Boyce S. Per Case Prospective Payment for Episodes of Hospital Care. U.S. Department of Commerce: National Technical Information Services, Springfield, VA (#PB95-226023), Contract no. 500-92-0020, April 25, 1995. 39. Practice Management Information Corporation (PMIC), ICD-9-CM, International Classification of Disease—9th Revision, 1998. PMIC, Los Angeles, CA, 1997. 40. Gorsky RD, Haddix AC, Shaffer PA. Costs of an intervention in Prevention Effectiveness (Haddix AC, Teutsch SM, Shaffer PA, Dunet DO, eds.). New York: Oxford University Press, 1996, pp. 57–75.

5

Indirect Health Care Costs An Overview

Stephen J. Boccuzzi, PhD, FAHA CONTENTS INTRODUCTION COMPONENTS OF COST WORK LOSS WORKER LOSS AND REPLACEMENT CONTRIBUTORS TO INDIRECT COSTS INDIRECT COSTS: A MATTER OF PERSPECTIVE MEASURING INDIRECT COSTS INDIRECT COSTS AND CARDIOVASCULAR DISEASES DATA AND MEASUREMENT GUIDELINES FOR INDIRECT COST STUDIES THE PROCESS OF ESTIMATING INDIRECT COSTS SUMMARY AND CONCLUSIONS REFERENCES

INTRODUCTION The quantifiable costs associated with human disease and illness are typically categorized into two unique components, including direct and indirect costs. Direct costs usually represent the costs associated with medical resource utilization, which include the consumption of in-patient, out-patient, and pharmaceutical services within the health care delivery system. The term indirect costs has come to be defined as the expenses incurred from the cessation or reduction of work productivity as a result of the morbidity and mortality associated with a given disease. Indirect costs typically consist of work loss, worker replacement, and reduced productivity from illness and disease. These losses are typically valued from either societal, individual, or employer perspectives (1,2). The indirect costs associated with disease morbidity include wages or income lost by people who lose work time because of their illness or disability. Mortality costs represent the present value of future earnings lost by those individuals who die prematurely, as well as worker replacement costs for the employer. This lost output is categorized and reported in a number of ways, including lost wages or income, shortFrom: Contemporary Cardiology: Cardiovascular Health Care Economics Edited by: W. S. Weintraub © Humana Press Inc., Totowa, NJ

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or long-term work disability, home health care costs, lost leisure time, production loss to employers, and the lost taxes cost to society (1,3,4). Although, at times, indirect costs may be difficult to quantify because of a lack of quality data, they nonetheless, often represent a significant percentage of the total cost associated with many diseases. For example, indirect costs have been estimated to be as high as one-half of the total economic burden associated with the cardiovascular disease process (5). Indirect costs are not commonly estimated as a result of the lack of the robust primary data required to accurately estimate and value them. To compensate for this data deficiency, most calculations are modeled or estimated utilizing data from external sources that provide relevant normative or benchmark data. Despite these challenges, indirect costs are an important component for measuring the additional impact of a disease beyond the traditional direct in-patient and out-patient medical costs. Furthermore, as concerns about health care spending continue to grow, governments, payers, and employers alike will struggle and seek ways to rationalize the total economic burden associated with increasing health care expenditures. They will look beyond the direct costs associated with disease morbidity and mortality and include appropriate estimates or measurements of indirect costs. The aim of this chapter is to review the issues and challenges related to the measurement and analysis of indirect costs.

COMPONENTS OF COST Indirect costs include the costs generated from three potential sources, including mortality, absenteeism, and the reduced productivity of employees. Recently, the evaluation of indirect costs has evolved to include the concept of presenteeism, which includes not only the degree of productivity associated with an individual’s time at work, but also the quality of work, satisfaction with work, as well as the potential adverse impact on other workers (6). Each of these sources are subsequently examined in greater detail throughout this chapter. A framework for the distinct and unique indirect cost components is presented in Table 1. This illustration not only presents the multidimensional nature of indirect costs (i.e., morbidity, mortality, and productivity/presenteeisn), but also provides a sense of the complexity of both defining and measuring the associated costs within each one of these dimensions.

WORK LOSS Indirect costs are incurred and most easily measured when a person misses work as a result of a medical condition. The amount of time lost from illness may also vary because of differences in the condition (e.g., acute vs chronic, level of severity). Individuals with poorer health will obviously miss more work and suffer from a reduced ability to contribute while at work result in less employment efficiency in comparison to healthier staff (7). It is also important to appreciate that some treatments for illness (e.g., percutaneous transluminal coronary angioplasty [PTCA], coronary artery bypass grafting [CABG], and other surgeries) may involve serious but temporary alterations in health, though mortality is a significant risk in all these situations. This shorter duration of work absence may only affect short-term current earnings and not result in significant decreases in annual or longer-term worker income. Furthermore, this absense will not require significant employer commitments for replacement costs in comparison to

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Table 1 Components of Indirect Costs Work loss

Morbidity

Worker replacement Mortality or early retirement

Absenteeism, short-term Lost economic or long-term disability contributions to as compensated society (forgone (employer) and future income uncompensated at present value) (individual) lost time Change in job status: On the job training full- to part-time status and retraining or change in occupation to employers Idle capital (employer) Hiring/relocation costs Lost output

Caregiver time and costs (individual)

Temporary replacement costs

Productivity Work and product quality

Adverse effects on worker and colleagues

Poor service or Job stress product quality

Reduced productivity Reduced output

Need to rework Reduced job Lost completed satisfaction business products Warranty and Poor Incremental replacement concentration costs (e.g., costs overtime) Health Status Lost wages (e.g., vitality, and benefits dexterity) Teamwork and collegiality Attrition

illnesses that involve chronic impairments, longer-term disability, and larger losses in income (e.g., congestive heart failure [CHF], symptomatic cardiac arrhythmias, and cardiomyopathies). In addition, many diseases associated with productivity loss are acute and, therefore, curable and will not incur longer-term losses, whereas other chronic diseases, in addition to consuming significant direct health care resources, may require a change in occupation, work responsibilities, or require significant costs for retraining (1,6,7). At approximately age 50, research has shown that various diseases can have a significant impact on earnings (20–30% reductions). These losses may be related to a significant untoward event (e.g., myocardial infarction [MI]), but more often increases as the disease chronically progresses over a period of 10+ years. Examples of prevalent diseases with significant indirect costs include coronary heart disease, hypertension/CHF, arthritis, bronchitis, emphysema, and various psychiatric diseases. These diseases were found to reduce both the individuals’ wage rate and the labor supply. Most of the debilitating effects of these diseases were also found to subside to some degree over time, with the exception of various psychiatric conditions, which have been known to be associated with chronic debilitating effects (7). Estimations of work loss can and usually do include both work and leisure activities. It is evident that an individual cannot enjoy social or recreational activities, nor perform household chores, if they are limited in their capacity to perform their job respon-

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sibilities. This has a significant and meaningful impact, though estimating a dollar cost for such loss may be difficult. Indirect costs can also be viewed as including the time a patient and family member spends traveling to and receiving care from health care professionals, time spent in hospitals and rehabilitation facilities, as well as time lost from work by family members supporting other members of the family. Although some argue that these examples of individual losses do not add to any lost income, economists would debate the fact that it is still appropriate to estimate these losses at the marginal value of an individual’s income because people trade off income for leisure. These are also other issues beyond lost time; for many individuals and employers, chronic disease conditions can also produce unwanted profession changes, necessitate staff replacement, increase retraining requirements, as well as contribute to lost opportunities for staff promotions and the need for early retirement. These are all reasonable factors to consider when defining indirect cost outcomes and measurement methods to assess work loss (4,8,9).

WORKER LOSS AND REPLACEMENT Indirect costs related to premature death directly ends the income contributions of the deceased individual. This death not only impacts the members of the deceased person’s family, but also affects the extended family, friends, employers, customers, and all of society. Independent of this loss to the individual and their family (i.e., household income and quality of life), death will also have economic implications to others. For example, an employer may suffer loss from reduced profitability, costs in job fulfillment, and hiring costs. There is also a loss to society from the lost economic contributions, e.g., a loss from reduced spending in the economy, potential temporary reduction in products being brought to the market, and reduced tax contributions from the individual’s wages. In this regard, the total income or job level of the individual can be a significant factor in approximating this loss. Individuals with lower income will have a lower calculated value for their lives in comparison to individuals with a higher earning potential. These dollars are also extremely sensitive to the rate of dollars this individual would earn in the future (1,10,11).

CONTRIBUTORS TO INDIRECT COSTS Lost-work output that results from lost productivity (cessation or reduction), as a consequence of disease morbidity or mortality, could impact various components of both income to the individual and profits to the employer. These costs are valued based on lost-wage earnings, as well as the market value of some home or workplace activity that is now unavailable. Even when an individual is at work, they may not be as productive as they were prior to the onset of an acute or chronic medical condition. Consider a person suffering from angina pectoris who stays at work after an episode of ischemic pain, requiring sublingual nitroglycerin. This individual will not only be less productive during the time associated with the acute episode of pain, but will also likely accomplish far less for the remainder of that day had they not experienced the ischemic attack. The measurement of reduced productivity is difficult to conduct in most workplace environments. Current research in this area has, for the most part, focused on productionoriented tasks, where decrements in productivity can be easily measured by the reduction in output of the worker, such as assembly-line workers and customer service

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Fig. 1. Relative contributions of work loss and reduced productivity to indirect costs. Adapted from ref. 13 with permission.

representatives from call centers (12,13). Research work by Burton et al. has demonstrated that for many common medical conditions, the impact of reduced productivity far exceeds the cost of work loss from absenteeism and short-term disability (Fig. 1) (13). Although it is easy and useful to assess the impact of disease on productivity in these clear cases, there is a great need to extend this measurement activity to the more difficult and critical areas of employment associated with our more knowledge-based and technically oriented workforce. In addition to decrements in employee productivity and output, several other factors should be considered, including absenteeism, employee turnover, short-term and long-term medical disability, workers’ compensation, overtime and replacement workers, total health plan payments, Employee Assistance Program costs, nonoccupational disability days, and health promotion/accident prevention (OSHA) costs. In a study of 43 employers based on the previously mentioned metrics, median costs for the 1998 calendar year were estimated to be $9992 per employee per year (14). Another important factor to include when assessing indirect costs is short- and longterm disability. Disability status has been found to be a significant factor in predicting labor force participation and productivity. However, it is difficult to accurately measure aspects of worker disability accurately when using self-report tools. The tools typically used in the collection of disability information will have limitations as a result of biases introduced with varying levels of employee comprehension, health status, age or factors related to psychological issues, or economic incentives (15).

INDIRECT COSTS: A MATTER OF PERSPECTIVE An evaluation of indirect costs should first consider the perspective from which the analysis is performed (Table 2). Indirect costs are typically evaluated from one of three perspectives, including the society, individual, or employer. Each of these viewpoints provides a different perspective and requires a different research methodology from which to value the impact of disease on work loss and productivity (11,16). The societal perspective views the consequences of premature morbidity and mortality as the cost incurred by society. This view further represents the perspective most commonly employed in health economics research to assess indirect costs. Measure-

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Cardiovascular Health Care Economics Table 2 Indirect Cost Perspectives Individual

Employer

Societal Costs to society from illness and/or death related to disease Present value of forgone future income, lost workforce impact Loss of contributions to the economy from decreased personal income and employer revenues

Cost focus

Personal income and loss of leisure time

Additional costs adversely affecting profits

Mortality impact

Loss of personal or household income and support to family Loss of income, sick-leave days, reduced income due to disability and loss of leisure time

Cost of replacing workers including hiring and training Workloss, idle assets, nonwage costs, benefits, fixed payroll costs and lost profits

Morbidity impact

Adapted from ref. 11 with permission.

ment of indirect costs from this perspective is predicated on the value of the individual’s labor contribution to society in terms of the potential for the person to generate income. This includes lost contributions to the economy from decreased spending, lost taxes, and an increased burden on the social and health care systems. These losses can be further delineated along the morbidity/mortality continuum, with mortality measured in terms of the present value of lost future economic contributions to society and morbidity defined as lost income as a consequence of work loss. The individual’s viewpoint typically represents lost wages, lost leisure time, and decreased quality of life. This loss in work capacity translates to a loss in household or individual income for the individual and his or her dependents, as well as the value of lost leisure time or quality of life. Leisure time is valued at the marginal cost of lost wages. This is because it is assumed that an individual forgoes income for such leisure time and also avoids the purchase of support services (e.g., household chores) by performing such services themselves. The employer perspective is a more recent and evolving perspective that is gaining momentum as employers try to assess the return on investment for the dollars invested in the health and well-being of its workers. From the employer perspective, lost productivity includes a valuation of many factors related to the investment in human capital, including work productivity, hiring costs, training costs, replacement/temporary worker costs, the cost of idle assets, and nonwage costs from absenteeism (e.g., lost profits). In addition, investments related to health insurance costs and wellness programs can be considered key components of an employer’s investment in their workforce. It is also important to appreciate that the cost of morbidity and mortality to the employer will be different from the traditionally employed societal perspective, because it will exclude the present value of future earnings lost from premature mortality. In addition, the cost of morbidity will also differ because it includes idle assets and nonwage cost factors, for which the specific amounts are conditional on the nature of the industry. In considering mortality, the employer perspective will value loss of life as the costs because of decreased production and worker replacement. There is also a

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third factor related to the employer perspective, which is the potential second-order effect of morbidity and mortality on the other workers in the workforce. For example, the effects of mental illness may not be restricted solely to the individual with the disease, as other workers may have their productivity affected from tensions and disruptions created in the workplace. In a tight labor market, it may also be difficult to recruit qualified employees as replacements for those who have died unexpectedly. Such gaps in the workforce can adversely affect others and will force employers to concern themselves with other intangible issues related to employee productivity, such as worker morale, retention, and work-life balance. These issues will be critical in order to maintain a healthier and more productive workforce. This will include decreasing employee turnover, lowering the costs associated with medical and pharmacy utilization, short- and long-term disability, as well as employee replacement and retraining (10,11,16–18). Clearly, each of these perspectives represents different measurement and evaluation challenges. Each also focuses on a different component related to financial risk or value that will drive policy and/or coverage decisions relative to managing the health status of the workforce. As our workforce ages, and we live and remain productive longer, these perspectives will individually and together form an interesting interrelationship regarding the costs, utility, and willingness to pay to avoid the untoward consequences of disease.

MEASURING INDIRECT COSTS Estimation of indirect cost dollars has traditionally been performed utilizing one of two principal methods: the human capital or the friction cost method. Each of these methods has advantages and disadvantages, which are outlined in the following sections.

Human Capital Method The human capital or lost wages method is perhaps the most commonly and widely utilized methodology for estimating indirect costs (1,19–21). This approach provides a measure of the cost of disease based on the assumption that wage earnings reflect the cost of work loss and decreased productivity. In other words, wage rates are essentially equivalent to the value of the marginal revenue generated by an additional worker under full-time employment. This method calculates the value of potential lost production or income resulting from disease, beginning at the age where these limitations from disease arise until the age of retirement. Therefore, lost productivity is simply a function of the individual’s total compensation (i.e., wages, benefits, bonuses, and other incentives) and their time lost from work. Indirect costs using this method are estimated by multiplying the projected or measured number of workdays missed as a consequence of disease by the estimated or measured average daily income of the individual. For estimates of reduced productivity while on the job, a percent reduction in worker productivity is estimated by the equivalent percent reduction in the days worked, with suboptimal performance as a consequence of disease symptoms. For individuals who experience permanent disability or premature death, the total productive value or income from that age until the age of retirement is included in the societal perspective of the indirect cost estimate. This methodology will typically utilize fixed time periods or variable time periods, such as those representing life expectancy. Approaches can include only calculations for paid labor or inclusion of all related factors to decreased productivity and lost wages. Valuing lost wages can also vary from utilization of industry-wide wage bench-

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marks to minimum wage or the wage rate for specific groups, as the labor market does not enjoy perfect interchangeability between its workers. CONSIDERATIONS AND LIMITATIONS Although this methodology has face validity, is simple and straightforward, it is not without limitations and criticisms. This approach has been criticized as biased, especially as it relates to various types of individuals working in the economic marketplace (1). This bias appears to be relevant for males or professionals who typically have higher salaries in comparison to other groups within the workforce, such as women, youth, minorities, elderly, or the unemployed. Additionally, these other work groups may not be in good health. If so, they may have higher rates of unemployment or gaps in employment and, therefore, lower incomes, which will again cause this method to overestimate the value of potential production losses, where the actual loss to society may be significantly smaller (1,4,12). Another bias may be related to the fact that shortterm disability or intermittent work loss may actually be absorbed by other existing staff or eventually made up by the absent person on return to work or by unemployed individuals, which would again cause an overestimate of the loss. However, from an employer’s perspective, this method has been criticized as biased for a different reason. From the employers perspective, it is thought to underestimate the employees’ value to the profitability of the company. From a societal perspective, it can be argued that for long-term absences, the individuals work can be covered by someone who is unemployed, having little overall effect on the completion of labor activities. A final and important consideration that is related to this method is the lack of valuation of quality of life, leisure time, as well as the value of other types of health and leisure activities (12,21,22).

The Friction Cost Method The second approach that will be reviewed is called the friction cost method (23). This methodology attempts to consider existing economic circumstances within the organization, as well as the labor market based on the assumption that the amount of production lost as a result of disease will depend on the time required by the organization to return to the initial production level. It is assumed that production losses will be related to the time (friction period) needed to replace a sick worker. To accurately estimate indirect costs using the friction cost method, it is necessary to consider the frequency of friction periods, the length of the friction period, the valuation of lost productivity, and the macroeconomic consequences. The friction period represents the costs associated with transitions between healthy and disabled and/or deceased, therefore reflecting the time an organization needs to restore worker productivity to the preillness level. The friction period is also typically longer than the worker vacancy period because time may elapse between the productivity loss and the decision to hire and train the replacement workers to return to the initial state of productivity. It is assumed that the production level will usually be regained after part of the friction period has elapsed. Therefore, the indirect costs calculated will consist of the value of the production lost and/or the extra costs to maintain production, or if permanent replacement is necessary, the costs of recruitment and training. This is typically assumed to be 80% of the average value of production per employee. Costs of absences longer than the friction periods are equal to the costs of the friction period because it is assumed that

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replacement (from a societal perspective) will come after completion of the friction period. Even though the employer pays sick-leave benefits to the absent worker, in addition to the salary for a replacement worker, the opportunity cost of the replacement is virtually zero because the new worker would have been otherwise unemployed. Assuming this, the indirect costs would include the value of the lost productivity plus the extra costs of maintaining productivity, as well as for filling the vacancy and training new staff. The medium-term economic consequences of illness that extend beyond the friction period will also need to be considered. These should be estimated if the change in absence, disability, and mortality for the study population represents a substantial part of the total amount of the absence, disability, or mortality. In this mediumterm scenario, further changes in labor costs per unit of output, labor supply changes, and so on, should be included, especially as they influence the labor market or other economic indicators. Typically, medium-term production losses will span a period of about five years (23–27). CONSIDERATION AND LIMITATIONS: FRICTION METHOD One criticism of the frictional cost method is that it is not thought to represent the full costs of productivity loss, that is, the inclusion of other losses to an individual, such as leisure time and changes in quality of life. The lack of inclusion of key societal cost components, especially as it relates to the loss of leisure by the sick individuals, as well as the individuals who are recruited out of the unemployed labor pool, will potentially lead to an underestimation of costs using this method. Additionally, this method requires significant amounts of data to assess the cost of lost production, including the length of the friction period and the elasticity of the annual labor time vs labor productivity. This focus on the system (national economy), rather than a traditional societal perspective, also will lead to the omission of certain expenditures and costs from the indirect cost calculations. In the same vein, it would not be applicable to either an employer or individual perspective because of its macroeconomic focus and application. Another argument against the frictional cost model is that it does not rely on neoclassical (the marginal value of an employee equals the labor cost) or other economic theories and, therefore, is believed by some to lack the necessary theoretical framework to support its approach (10,12,21,28,29). It is obvious that labor costs can overestimate the indirect costs or perhaps underestimate costs, as in the case where a firm has difficulties replacing key critical personnel. A critical issue to consider is that, most often, firms have surplus labor or the capacity to compensate for the loss of an individual without incurring major losses in productivity. In addition, depending on the job, some productivity may be reclaimed on return to work, however, this may come at a cost to the employee (e.g., loss of leisure time) (27,28). Additionally, most studies using the friction cost method must rely on national data, because it is hard to collect the detailed data at an individual institutional level necessary to support this approach.

INDIRECT COSTS AND CARDIOVASCULAR DISEASES The American Heart Association (AHA) has developed and published estimates for direct and indirect costs associated with all cardiovascular diseases and stroke ($299.2 billion). The indirect cost estimates from a societal perspective were estimated to be approximately $119.3 billion for 2001 or about 66% of the estimated direct costs

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($179.9 billion) for these diseases. These US-based indirect costs were approximately $28.5 billion (24%) for lost productivity from morbidity and approximately $90.8 billion (76%) for lost productivity related to disease mortality (5). An evaluation of the household income losses associated with ischemic heart disease (IHD) was recently conducted using annual household income as a proxy for lost productivity. This research assumed that a lower level of job productivity should equate to reduced income, less overtime, fewer promotions, and reduced output as measured by a decreased total annual household income. Also, the impact of illness/disability on other family members was also incorporated into this estimate, as it included income from the entire household. These results indicated that IHD was associated with a reduction of $3013 in annual income, independent of occupational class, age, comorbidities, size of household, and educational level. This equated to an annual loss of an estimated $6.5 billion in 1996 US dollar loss (3). In contrast to the AHA estimates, these results differ because of the perspective from which the calculation was determined (employer vs employee). In the United States, the employee does not typically bear the full burden of the financial loss related to their disease. Another study evaluating the indirect cost of coronary heart disease (CHD) from an employer perspective was estimated to be approximately $4298 per diagnosed employee per year. This estimate, from the employer perspective, was primarily from work loss as a result of morbidity (i.e., $4092 or 95.2% of the total), rather than from a loss to the workforce from mortality, which was only $206 per employee with CHD or 4.8% of the total indirect cost. This analysis, utilizing an employer perspective, included the cost of mortality, along with hiring costs, training and replacement worker costs, and morbidity costs valued as lost productivity, idle assets, and nonwage costs from absenteeism (30). An economic analysis of IHD in Switzerland using the human capital approach from a societal perspective demonstrated indirect costs of approximately $11.3 million per 100,000 population in comparison to $9.9 million for direct costs in 1993 (31). Another similar analysis evaluating the direct and indirect costs of cardiovascular disease in South Africa in 1991 estimated indirect costs based on the discounted value of earning loss as a result of premature morbidity and mortality. Indirect costs in this study were found to present approximately 57–59% of the total economic burden associated with cardiovascular disease in this country (32). Another analysis of CHD and stroke in Sweden revealed indirect costs to be just as significant to the cardiovascular disease burden as direct costs, especially in the year after an acute event (33).

DATA AND MEASUREMENT Indirect cost analyses are typically evaluated from data obtained from one of four sources, including (1) employer data on reduced production and work loss as a result of random illness, chronic illness, or disability (i.e., short- or long-term), (2) retrospective administrative data sources, (3) primary data collected in observational studies or randomized clinical trials, or (4) self-report data from the experience of individuals with a specific disease (e.g., National Health Interview Survey). Typically, primary data sources are limited because of the difficulty and costs associated with its collection, as well as having the information necessary for accurately evaluating the various cost components of productivity loss.

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Indirect studies can be conducted utilizing various study designs. Studies evaluating changes in productivity or the association of disability to disease can be evaluated in various types of longitudinal cohort studies. These studies can involve pre- vs postdesigns with historical or parallel controls, but typically, are uncontrolled and utilize survey data or existing productivity data from administrative databases or employer/ government records. The observational cross-sectional survey is a very common method for capturing this information through the use of self-report tools to assess work loss and lost work capacity associated with various diseases or the disability associated with the condition. In many respects, these naturalistic studies reflect the real world, but do not provide the rigorous controls required for dealing with typical confounding factors associated with clinical or demographic differences associated with the study cohort. In these cases, regression models are often employed to control for known biases and other confounding factors. Controlled clinical trials can also provide a paradigm for the assessment of indirect costs. This more rigorous study design controls for many aspects of selection bias and usually involves more detailed collection of information during the study period, but typically, these more rigid designs do not always reflect real-world patient or physician behavior. Depending on the duration of the study, alternative ways to estimate the long-term consequences of an outcome may require modeling activity or the use of other administrative databases to extend the impact beyond the study follow-up period. An important consideration of trials that are placebo-controlled is that they may not approximate real-world effectiveness and may have little relevance to guide policy and decision making, especially in managed care. These various data collection methodologies, however, will bring with them challenges as they relate to obtaining accurate generalizable data consistent with universally recognized standards that provide both consistency between studies, as well as good internal vs external validity to support both regulatory submissions in addition to policy and health care coverage decisions (34,35).

GUIDELINES FOR INDIRECT COST STUDIES Many countries have published guidelines to support economic evaluations of pharmaceutical products, but there is no consistency in their recommendations regarding indirect costs (36–40). Most of these pharmacoeconomic study guidelines provide economic evaluation and reimbursement standards for various health authorities and typically utilize single-payer societal (governmental system) perspectives. Despite the existence of these standards, they provide little guidance to support standards for indirect cost studies. The Canadian guidelines accept indirect costs as long as they are separated from direct costs, but there are no methodological recommendations on the methods for quantifying them (33,34). Australia discourages the inclusion of indirect costs in their guidelines (35), and the Dutch guidelines (36) suggest that indirect costs can be incorporated in economic analyses, but should be again separated from direct cost analyses. They also recommend the friction cost methodology. The US Public Health Service panel on cost-effectiveness in health and medicine has authored recommendations arguing that most of the indirect costs are already included in the patients’ reported quality-adjusted life years weight and would result in double counting if these costs were to also be included as indirect costs (37). As was outlined earlier, there are only a small number of studies that incorporate indirect costs in the area of cardiovascular disease, even though evidence suggests it represents a considerable economic

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burden. Despite the lack of guidelines, standards, and consensus regarding methodological approaches, especially regarding valuing lost productivity, ongoing research is necessary to develop this discipline and insure that it contributes to a better appreciation of the economic burden of disease.

THE PROCESS OF ESTIMATING INDIRECT COSTS The following sections summarize a sequential approach toward evaluating indirect costs in health care research.

Step 1 Determine the economic perspective that will drive the indirect cost analysis. As discussed there are three potential perspectives: societal, individual, or employer. A societal perspective will focus on not only the individual’s current and future contribution as a productive member to society to include a significant loss of future economic contributions dependent on the age of the person. In addition, other perspectives can be utilized, including those related to the individual, which include a focus on lost income, change in vocation, and early retirement. Calculation of indirect costs from the perspective of the employer will focus on understanding the burden of indirect costs as it relates to worker productivity and company profitability. It will not include the same magnitude of forgone lost income from mortality as in the societal or individual perspectives. In addition, it can support the development, implementation, and evaluation of preventive and therapeutic services needed to optimize the contribution of the labor force to the company’s mission. Finally, governmental and regulatory agencies may also have the need to evaluate the impact of indirect costs as it relates to health care policy and coverage decisions.

Step 2 Identify the data sources available to support the analysis. General requirements necessary to support indirect cost studies should include disease prevalence (mortality and morbidity rates), along with the estimation and valuation of work loss, worker loss or replacement, and reduced productivity associated with a disease. Once the work loss has been quantified, it should be valued with appropriate cost estimates. There are a number of sources to assess lost work days and the cost of these workdays for purposes of indirect cost calculations. Acquiring the data on work loss and the associated costs typically employ one of three methods. 1. Direct observation: Primary capture of data by the researcher. This method tends to be the most robust and objective if standardized in its capture and reporting. It may incur significant costs related to data capture and development of a clean robust database to support analysis, unless the data is collected and managed for other purposes (i.e., payroll). 2. Patient self-report: A good reliable source of information, but will suffer from the bias associated with patient interpretation of the questions and their response. This also require costs for data capture and cleaning to support the analysis. 3. Secondary data sources: There are a number of retrospective databases available that report work loss. A common source for these is the US government.

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Secondary data sources can provide a lower cost alternative to support indirect cost analyses that utilize existing labor statistics or benchmark data derived from organizations, which typically measure or estimate these metrics. These aggregated benchmarks, although useful in many ways to support analyses, may not be precise enough, relevant, or contemporary to capture the subtleties of the population studies. In addition, these data may not contain all the relevant variables desired to be included in such analysis. Therefore, although less expensive and more readily available, the data may be limited to support the research question. If retrospective analysis will be utilized, there are many governmental and private data sources available to support the evaluation. Data for work loss, if not available or collected, can be estimated from US government national databases, such as the National Health Interview Survey (NHIS) (41). This is a nationwide household survey comprised of a probability sample of the civilian noninstitutionalized population of the United States. The average days of work lost was derived from survey data, which measured work-loss days from various diseases (e.g., CHD) for currently employed persons, where a work-loss day is one on which a currently employed person 18 years of age or over missed more than half a day from a job or business. Data for required worker replacement (i.e., mortality from CHD), if not based on actual mortality data, could utilize death rates by age group from the National Center for Health Statistics (NCHS) analysis of death certificates (42). Information for valuing work loss can utilize data for wages and benefits from the Bureau of Labor Statistics (BLS) (43). BLS data are collected in ongoing, monthly state surveys of the payroll records of all nonfarm businesses with at least 250 employees and a representative sample of smaller establishments. Data are available for private industry, state, and local government, civilian sectors, and also by private industry category. A source for the relative contributions of wage and nonwage costs to private industry is the Bureau of Economic Analysis, US Department of Commerce (44). The Bureau estimates the wage and nonwage components of the gross product for each industry group. The costs of hiring replacement workers were from the Employment Management Association’s annual Cost-Per-Hire survey. If not available or collected, data from this employer survey are provided for exempt and nonexempt employees.

Step 3 Identify the disease cohort and outcome metrics to be included in the evaluation. It will be critical to determine how the population of interest will be identified. This can include the use of administrative claims data, including combinations of diagnostic (ICD-9-CM codes), procedure information (CPT-4), and pharmacy (NDC) codes mapped to various conditions. Alternative self report or primary health care data from electronic medical records or chart abstractions can be used to support this requirement.

Step 4 Establish the actual model and method used to value the work loss incurred from disease. This analytic activity can be determined from a simple algebraic formulation of the problem, as well as defining the relevant parameters:

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Study population (n). The number of individuals continuously enrolled during the study period from the administrative database or individuals with complete follow-up or who were randomized (intention to treat) in the study. Prevalence of disease (p). The prevalence of the disease of interest in the study population calculated from administrative or normative data (e.g., individuals with disease conditions as defined by ICD-9 CM codes or other cohort groupings). Days of work lost (d). The average number of days of work lost per patient related to the disease of interest (e.g., NHIS data for the study period). Average daily wages (w). Employer costs related to employee compensation for timeframe of interest from private industry (e.g., calculated from the BLS). Average daily benefits (b). The average cost to the employer per employee per day for benefits (e.g., value derived from the BLS). Death rate (dr). Death rates for a particular disease as determined in the study, normative data or the administrative dataset (e.g., ICD-9-CM codes or analysis using NCHS analysis of death certificates). Average cost per hire (ch). If not known, this data can be derived from statistics for the average dollar cost per hire (e.g., data provided by the Employment Management Association and the US Department of Commerce). Hours of training for new hires (t). The number of hours a new worker spends related to on-the-job training during the first three months of employment. There are various components that can use used to develop these costs. These include hours spent by specially trained personnel in providing the recently hired worker with formal training; line supervisors and management personnel in providing informal individualized training and extra supervision; coworkers away from other tasks in providing informal individualized training and extra supervision; the new employee watching other workers do the job rather than doing it himself; and company personnel in providing orientation.

Calculations and Formulas Days Lost (DL) for a particular disease are calculated as: DL = n × p × d

The number of deaths (ND) owing to a particular disease is determined by the following formula: ND = n × dr

Loss of productivity (LP) owing to absenteeism because of IHD (i.e., the morbidity cost) is calculated as follows: LP = DL(w + b)

Loss to the workforce (LW) from death owing to disease (i.e., mortality cost) is: LW = ND(ch + t[w + b])

The indirect cost (IC) is: IC = LP + LW

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Step 5 Consider the necessity of additional analysis. There are two additional analyses that may need to be included: Calculation of uncertainty or robustness of the estimates: This is done by sensitivity analyses. The essence of sensitivity analysis is to acknowledge that there may be certain parameters that are not known with certainty. If so, then define a range of possible values for the uncertain parameter(s) in the model and execute calculations across this range of value. The output will be a range for potential values for the indirect costs. Future vs present values: Typically, discount rates should be applied to control for the value of past or future dollars. Alternatives include controls for inflation using the consumer price index or other economic or inflationary benchmarks.

SUMMARY AND CONCLUSIONS In future decades, the health care industry will reshape itself, influenced by an aging population, rapid advances in medical technology, and increased globalization. The cost of these changes will most likely be borne by the government and private payers (employers). To that end, workplace productivity will continue to evolve as a critical factor, justifying the investment made in health care as our workforce ages and changes with regard to the skill type required to support the labor market. When an individual is unable to work because of health-related problems, that individual, in addition to experiencing a loss of income and leisure time, will reduce their contribution to both their employer and society. It is well understood that productivity will be a function of human health, which will ultimately influence the capacity and motivation of an individual to contribute in the workplace. Given the importance and relevance of indirect costs, it is critical that its calculations and estimates are robust and applicable to the perspective from which it will be used. Indirect costs should become an integral component of economic evaluation, especially in the United States, as employers continue to bear the economic burden of providing health care to its employees. A now more transient workforce coupled with an everchanging corporate environment filled with mergers, acquisitions, and consolidations will bring requirements for greater insights into the costs associated with lost productivity. The current “do more with less” business paradigm has also placed increasing stress on the workforce, bringing challenges related to work-life balance, as well as modifying the associated poor health habits, such as alcohol abuse, smoking, and obesity, which are all capable of contributing to the premature onset of certain diseases. Indirect costs are not currently supported by rigorous standards, nor is there good data to support their measurement. More research will be needed to analyze the costs of health care benefits and other programs relative to their capacity to improve the health of the workforce. In addition, the competitive nature of attracting and retaining quality workers will necessitate providing more than a minimum of health care benefits. In summary, this chapter has provided an overview related to the issues, perspectives, and measurement challenges associated with evaluating indirect costs. Although this young discipline requires further exploration and development, it represents an emerging body of knowledge that will be integral to our evaluation of the economic impact of disease and our approaches to rationalizing its payment.

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ACKNOWLEDGMENTS James F. Murray, PhD, for his input and careful review of the chapter and to John R. Cook, PhD, for his editorial comments.

REFERENCES 1. Hodgson TA, Meiners MR. Cost-of-illness methodology: a guide to current practices and procedures. Milbank Mem Fund Q Health Soc 1982;60:429–462. 2. Eisenberg JM. Clinical Economics- A guide to the economic analysis of clinical practices. JAMA 1989;262:2879–2886. 3. Herrin J, Cangialose CB, Boccuzzi SJ, et al. Household income losses associated with ischaemic heart disease for US employees. Pharmacoeconomics 2000;3:305–314. 4. Koopmanschap MA, Rutten FF. Indirect costs in economic studies: confronting the confusion. Pharmacroeconomics 1993;4:446–454. 5. American Heart Association. 2001 Heart and Stroke Statistical Update. American Heart Association, Dallas, TX, 2000. 6. Burton WN, Conti DJ, Chen CY, et al. The economic burden of lost productivity due to migraine headache: a specific worksite analysis. J Occup Environ Med 2002;44:523–529. 7. Bartell A, Taubman P. Health and labor market success: the role of various diseases. Rev Econ Stat 1979;61:1–8. 8. Jacobs P, Fassbender K. The measurement of indirect costs in the health economics evaluation literature. Int J Technol Assess Health Care 1998;14:799–808. 9. Drummond M. Cost-of-illness, a major headache? Pharmacoeconomics 2992;2:1–4. 10. Weinstein MC, Siegel JE, Garber AM, et al. Productivity costs, time costs and health related quality of life: a response to the Erasmus Group. Health Econ 1997;6:505–510. 11. Berger ML, Murray JF, Xu J, Pauly M. Alternative valuations of work loss and productivity. J Occup Environ Med 2001;43:18–24. 12. Berdt ER, Finklestein SN, Greenberg PE, et al. Workplace performance effects from chronic depression and its treatment. J Health Econ 1998;17:511–535. 13. Burton WN, Conti DJ, Chen CY, et al. The role of health risk factors and disease on worker productivity. J Occup Environ Med 1999;41:863–877. 14. Goetzel RZ, Guindon AM, Turshen IJ, Ozminkowski RJ. Health and productivity management: establishing key performance measures, benchmarks and best practices. J Occup Environ Med 2001;43:10–17. 15. Stern S. Measuring the effect of disability on labor force participation. J Human Resources 1989;24:360–394. 16. Hodgson TA. Costs of illness in cost-effectiveness analysis: a review of the methodology. Pharmacoeconomics 1994;6:536–552. 17. Jacobs P, Fassbender K. The measurement of indirect costs in the health economics evaluation literature. Int J Technol Assess Health Care 1998;14:799–808. 18. Rizzo JA, Abbott TA, Pashko S. Labour productivity effects of prescribed medicines for chronically ill workers. Health Econ 1996;5:249–265. 19. Rice DP. Estimating the cost of illness. Am J Public Health 1967;57:424–440. 20. Cooper B, Rice DP. The economic cost of illness revisited. Soc Sec Bull 1976;39:21–36. 21. Liljas B. How to calculate indirect costs in economic evaluations. Pharmacoenoomics 1998;13:1–7. 22. Brouwer WBF, Koopmanschap MA, Rutten FFH. Productivity cost measurement through quality of life? A response to the recommendation of the Washington Panel. Health Econ 1997;6:253–259. 23. Koopmanschap MA, Rutten FF, van Ineveld BM, van Roijen L. The friction cost method for measuring indirect costs of disease. J Health Econ 1995;14:171–189. 24. Koopmanschap MA, van Ineveld BM. Toward a new approach for estimating indirect costs of disease. Soc Sci Med 1992;34:1005–1010. 25. Koopmanschap MA, Rutten FFH. The impact of indirect costs on outcomes of health care programs. Health Econ 1994;3:385–393. 26. Koopmanschap MA, Rutten FFH. The consequence of production loss or increased costs of production. Med Care 1996;34:12.

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27. Koopmanschap MA, Rutten FFH. A practical guide for calculating indirect costs of disease. Pharmacoeconomics 1996;10:460–466. 28. Correspondance. How to calculate indirect costs in economic evaluations. Pharmacoeconomics 1998;13:563–569. 29. Johannesson M, Karlsson G. The friction cost method: a comment. J Health Econ 1997;16:249–255. 30. Guico-Pabia CJ, Murray JF, Teutsch SM, et al. Indirect costs of ischemic heart disease to employers. Am J Manag Care 2001;7:27–34. 31. Sagmeister M, Gessner U, Horisberger B, Gutzwiller F. An economic analysis of ischaemic heart disease in Switzerland. Eur Heart J 1997;18:1102–1109. 32. Pestana JAX, Steyn K, Leiman A, Hartzenberg GM. The direct and indirect costs of cardiovascular disease in South Africa in 1991. S A Med J 1996;86:679–684. 33. Zethraeus N, Molin T, Henriksson P, Jonsson B. Costs of coronary heart disease and stoke: the case of Sweden. J Intern Med 1999;246:151–159. 34. Evans CJ, Crawford B. Data collection methods in prospective economic evaluation: how accurate are the results. Value in Health 2000;3:277–286. 35. Greenberg PE, Birnbaum HG, Kessler RC, et al. Impact of illness and its treatment on workplace costs: regulatory and measurement issues. J Occup Environ Med 2001;43:56–63. 36. Canadian Coordinating Office for Health Technology assessment. A Guidance Document for the Costing Process. Canadian Coordinating Office for Technology Assessment, Ottawa, Canada, 1996. 37. Canadian Coordinating Office for Health Technology assessment. Guidelines for Economic Evaluation of Pharmaceuticals: Canada, 2nd ed. Canadian Coordinating Office for Technology Assessment, Ottawa, Canada, 1997. 38. Commonwealth Department of Human Services and Health, Australia. Australian Guidelines. Australian Government Publishing Service, Canberra, Australia, 1995. 39. Riteco JA, Heij LJ, Luijn JC, et al. Richtlijnen Voor farmaco-economisch onderzoek. Amstelveen: College voor zorgverzekeringen, 1999. 40. Siegel JE, Torrance GW, Russel LB, et al. and the Members on the Panel of Cost Effectiveness in Health and Medicine. Pharamcoeconomics 1997;11:159–168. 41. Botman SL, Jack SS. Combining National Health Interview survey datasets: issues and approaches. Statistics in Med 1995;14:669–677. 42. National Death Index, Division of Vital Statistics, National Center for Health Statistics. (www.cdc.gov) accessed 2002, Hyattsville, MD. 43. United States Bureau of Labor Statistics. (www.bls.gov) accessed 2002, Washington, DC. 44. United States Department of Commerce. (www.commerce.gov) accessed 2002, Washington, DC.

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Health Status Assessment John A. Spertus, MD, MPH, FACC and Mark W. Conard, MA CONTENTS INTRODUCTION DEFINING HEALTH STATUS QUANTIFYING HEALTH STATUS—TYPES OF INSTRUMENTS AND REQUIRED ATTRIBUTES AVAILABLE DISEASE-SPECIFIC MEASURES IN CARDIOVASCULAR DISEASE APPLICATIONS OF HEALTH STATUS MEASURES CONCLUSIONS REFERENCES

INTRODUCTION Physicians treat patients either to extend their survival or to make them feel better. Quantifying this latter objective is the purpose of health status assessment; the evaluation of patients’ perceptions of their symptoms, functioning, and quality of life. This chapter defines health status, reviews the types of health status instruments available and their required attributes, and discusses current applications of health status assessment. It concludes with a summary of currently available measures in cardiovascular disease and reviews challenges in the implementation and analysis of health status instruments.

DEFINING HEALTH STATUS The nomenclature surrounding health status is confusing. For the purposes of this chapter, health status refers to patients’ symptoms, functional status, and quality of life. Symptoms can refer to generic concepts, such as pain, or may focus, in particular instruments or those symptoms directly referable to the disease being studied (e.g., angina in patients with coronary artery disease [CAD]). Functional status refers to the physical, emotional, and social consequences of the disease process. It often includes an assessment of routine activities and functions, such as walking to the kitchen or interacting with family (1). A characteristic of functional status that distinguishes itself from the concept of quality of life is that it can often be estimated or measured by constructs that are external to patients themselves. For example, physical function can be assessed with an exercise test, and social function can be evaluated with a structured From: Contemporary Cardiology: Cardiovascular Health Care Economics Edited by: W. S. Weintraub © Humana Press Inc., Totowa, NJ

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Fig. 1. A simplified diagram of the domains of health status on coronary artery disease.

interview with family and friends. Quality of life, on the other hand, can only be assessed by patients themselves. Quality of life is a complex concept that can mean different things to different people, and it has been used to distinguish individuals or groups of individuals to predict outcomes and to determine the effectiveness of therapeutic interventions (2,3). It is inversely related to the discrepancy between patients’ assessments of their current functioning and their desired functioning, such that the larger the gap between current function and how patients would like or expect to function, the worse their quality of life. For example, an elderly retired man with occasional angina that limits his ability to walk more than two blocks may not be particularly bothered by this limitation if his routine daily activities do not exceed his angina threshold. Consequently, he may have a well-preserved quality of life. A 38year-old manual laborer, on the other hand, might find similar limitations devastating and have a very poor quality of life, despite similar symptoms and functioning in comparison with her senior counterpart. Thus, only through the direct solicitation of patients can the quality of their lives be assessed. In their review of the literature, Gill and Feinstein found that the term quality of life was substituted for other terms, such as health status or functional status, which tends to confuse the meaning of both health status and quality of life (3). Although the terms, quality of life and health status, are used interchangeably, we consider quality of life to be a component of health status, which will also encompass the concepts of symptoms and functional status. Figure 1 provides a simplified diagram of the relationship between the different components of health status (4). It is important to note that, although a linear relationship is suggested among the different components of health status, the true association is more complex. For example, whereas functional status is depicted as being determined primarily by symptoms, depression—a manifestation of poor mental functioning—is associated with worse symptoms (5) and can even serve as a risk factor for the initial development of CAD (6,7). Some of the importance in quantifying health status is its ability to capture some of the variability in patient and provider preferences that drive medical decision making. For example, understanding the health status of patients may explain why one patient

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might be more or less willing to undergo a risky procedure, such as bypass surgery, whereas another may choose a more conservative strategy, even if both patients have similar clinical assessments of the severity of their underlying disease (e.g., similar coronary stenoses or similar ejection fractions). Therefore, the formal assessment of patients’ health status allows clinicians to evaluate how patients perceive their illness to affect their lives and how effective they perceive their treatments to be. Clinicians, scientists, and managed care organizations are increasingly recognizing the value of incorporating formal health status assessments into their practice and research (8).

QUANTIFYING HEALTH STATUS—TYPES OF INSTRUMENTS AND REQUIRED ATTRIBUTES A variety of techniques and measures are used to quantify patients’ health status (9). The selection of the appropriate type requires an understanding of the expected benefits of an intervention, an understanding of the study population, and a clear formulation for what kind of data, potential analyses, and interpretations are desired from the investigation. Three broad classes of patient-centered instruments can be used to assess health status in patients: generic health status measures, disease-specific health status measures, and utilities. This latter concept is addressed in Chapter 7.

Types of Health Status Measures GENERIC HEALTH STATUS MEASURES Generic health status instruments capture patients’ perceptions about how their overall health causes symptoms, impacts their function, and limits their quality of life. Measured symptoms often include global concepts, such as pain and fatigue, that can be the manifestation of multiple disease processes, and no attempt is made to attribute which of a patient’s comorbidities may be responsible for particular symptoms or functional limitations. Consequently, generic measures of health status broadly assess symptoms (e.g., pain), function, and perceptions of health across various diseases and patient populations. The universal applicability of the items asked by a generic health status measure allows the effects of different treatments or health interventions to be quantified and compared (1,10,11). An additional advantage of generic health status measures is that they can detect the impact of medication side effects that occur outside of the cardiovascular system. Such side effects may be missed by a measure that focuses exclusively on the disease being studied. Finally, because generic measures provide a common metric with which to compare the therapeutic benefits of one disease’s treatment with those of another, they are frequently used in population-based health assessments. This also allows the health status of a study population to be benchmarked against national norms. Examples of generic health status measures are described in Table 1 and in several recent reviews (2,11–13). A consequence of these instruments measuring overall health status, including the effect of patients’ comorbid diseases as well as their CAD, is that they tend to be less sensitive than disease-specific measures in quantifying changes in health status realized by interventions directed at only one of a patient’s comorbid conditions (11,13,14). For example, a patient with extensive, multivessel coronary disease and severe rheumatoid arthritis may have physical function limitations resulting from both angina and joint pain. If successful coronary revascularization is performed, and

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Cardiovascular Health Care Economics Table 1 Sample of Generic Health Status Measures Self-administered

Number of items

Nottingham health profile (58)

Yes

38

1. Sleep 2. Pain 3. Emotional reactions 4. Social isolation 5. Physical mobility 6. Energy level

SF-36 (59)

Yes

36

SF-12 (59a)

Yes

12

1. Physical functioning 2. Role-physical 3. Bodily pain 4. General health 5. Vitality 6. Social functioning 7. Role-emotional 8. Mental health Physical and Mental Component Scores

Sickness impact profile (19)

Yes

136

Activities of daily living, including physical and psychosocial interactions

EuroQol (60)

Yes

6

1. Mobility 2. Self-care 3. Usual activities 4. Pain/discomfort 5. Anxiety/depression

Quality of well-being scale (61)

No

38

1. Mobility 2. Physical activity 3. Symptoms/problems

Questionnaire

Domains

the patient’s angina is completely eliminated, the patient may still be limited by his or her arthritic pain. Despite the intervention’s success, a generic health status measure may not detect an improvement after revascularization. Consequently, generic measures may fail to capture changes in the status of a particular disease that patients and their physicians might consider to be important. DISEASE-SPECIFIC HEALTH STATUS MEASURES Disease-specific measures are designed to assess specific groups or patient populations, often with the goal of focusing on clinically meaningful aspects of the disease under study (11). Such measures can be more responsive to changes in patients’ health, in part, because they highlight more relevant manifestations of the illness and, in part, because they can tailor their response categories to a more relevant range of function than generic measures of health status (1). This allows disease-specific instruments to tap the areas of life that are most affected by a specific illness or condition (e.g., CAD), certain patient populations (e.g., elderly), areas of function (e.g., sleep), or symptoms

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(e.g., fatigue) (13). Because these instruments typically address those domains of health that are focused on by clinicians, the domains of disease-specific instruments also tend to be more interpretable than the domains captured by generic measures (11). For example, the clinical interpretations of anginal frequency and the physical limitations due to angina are more tangible than more broad concepts of emotional role functioning and vitality. Hence, disease-specific data are often more “actionable” (i.e., it “makes sense” and/or suggests a course of therapeutic intervention) than more abstract generic health status domains. In patients with cardiovascular disease, disease-specific instruments are likely to be more responsive than generic measures to changes in patients’ cardiovascular symptoms (15,16). This is particularly important when a therapy is tested in patients with comorbid conditions. In the example of the patient with both severe arthritis and coronary disease mentioned previously, a disease-specific measure should be able to quantify the improvements in physical functioning gained from the coronary revascularization procedure, whereas a generic measure may not. Table 2 provides an overview of several disease-specific health status measures for cardiovascular disease.

Required Attributes of Health Status Measures It is a common feeling of researchers to want to design a new instrument for quantifying health status so that a more refined assessment of patient outcomes can be garnered from their studies. Whenever possible, however, existing measures should be used when good quality instruments are available. The rationale for this approach is that before an instrument can be used, it should have been explicitly demonstrated to be valid (measure what it is supposed to), reliable (provide reproducible assessments over time in stable patients), responsive (be sensitive to clinical change), and interpretable (an understanding of the clinical significance of changes in score). Furthermore, the use of established metrics enables comparisons of different studies and different treatments on the same scale. When selecting a measure for use, a range of design and performance characteristics should be considered. When evaluating potential measures, it is valuable to consider both features of their design and performance. Health status measures need certain specific attributes in their design to be effective. Recent literature points toward two integral elements: multidimensionality and subjectivity (17). Multidimensionality, occasionally described as content validity, refers to the completeness with which the instrument covers the range of relevant aspects or dimensions of health status (i.e., patient symptoms, patient functional status, and patient quality of life). These dimensions must include those levels of function most relevant to the user’s purpose of administering the instrument. Subjectivity refers to the flexibility of the instrument in being tailored to the individual experiences and values of the patient completing the measure; a challenge given that the specific factors determining the quality of a person’s life are both highly personal and virtually limitless. Gill and Feinstein suggest that researchers should include a single global rating of overall quality of life and one of health-related quality of life, patients should rate both the severity and importance of the problems, and instruments should allow for supplemental items that patients can add to indicate factors that were not originally included (3). Furthermore, the responses to individual items should cover a broad range of function so as to avoid a ceiling (many patients scoring at the highest range of function) or floor (many patients being at the lowest range of function) effect (18). Finally, the num-

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Cardiovascular Health Care Economics Table 2 Sample of Disease-Specific Measures Designed for Cardiovascular Disesase

Questionnaire

Self-administered

Number of items

Domains

Coronary Artery Disease CCSC (28) SAQ (16)

No Yes

Variable 19

Physical limitation and symptoms 1. Physical limitation 2. Anginal stability 3. Anginal frequency 4. treatment satisfaction 5. Disease perception/quality of life

APQLQ (37)

Yes

22

1. Physical Activities 2. Somatic Symptoms 3. Emotional Distress 4. Life Satisfaction

SAQ (30)

Yes

13

Functional capacity

MacNew QLMI (36)

Yes

27

1. Physical 2. Emotional 3. Social

CHP (38)

Yes

19

1. CCS Scale 2. Quality of life 3. Mental health

Heart Failure CHQ (45)

No

Variable

1. Dyspnea during daily activities 2. Fatigue 3. Emotional function

LiHFE (39)

Yes

21

1. Physical 2. Emotional 3. Total

KCCQ (15)

Yes

23

1. Physical limitation 2. Symptoms 3. Quality of life 4. Social function 5. Self-efficacy 6. Summary score

Abbreviations: CCSQ, Canadian Cardiovascular Society Classification; SAQ, Seattle Angina Questionnaire; APQLQ, Angina Pectoris Quality of Life Questionnaire; QLMI, Quality of Life After Myocardial Infarction; CHP, Cardiac Health Profile; CHQ, Chronic Heart Failure Questionnaire; LiHFE, Minnesota Living with Heart Failure questionnaire; KCCQ, Kansas City Cardiomyopathy Questionnaire.

ber of items to be completed must be as small as possible so as to minimize the response burden for the patient. Thus, the design of an instrument must carefully balance the completeness of data capture (breadth of topics assessed, relevance of items, and range of responses) with the burden and complexity of completing the instrument. Beyond the careful and complete construct of an instrument, certain quantifiable measurement properties must also be explicitly demonstrated. These include validity,

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reliability, responsiveness, and interpretability (3,11). The extent to which a health status measure must meet these priorities may differ according to whether the instrument is measuring change over time (an evaluative instrument) or comparing patients at a specific point in time (a discriminative instrument). VALIDITY Validity refers to the ability of the instrument’s domains to measure what they are supposed to measure. Types of validity include content validity, face validity, criterion validity, and construct validity (13). Content validity refers to the extent to which the domains of interest are comprehensively sampled by the items or questions in the measure. Content validity may vary by purpose. For example, to assess a population’s general health, a generic health status measure, such as the Sickness Impact Profile, might have good content validity (19). However, it might not have good content validity for a study of patients with coronary disease where an assessment of angina frequency would be very important. Face validity reflects whether an instrument appears to be measuring what it is supposed to be assessing. The items should be clinically reasonable and “common sense,” often from a clinician’s perspective, to determine the face validity of a health status instrument. Criterion validity refers to determining if an instrument is measuring what it is intended to by comparing the results of the instrument with those of a “gold” standard. Because of the highly personal nature of patients’ experiences of their illness, identifying criterion standards for comparison can seldom be accomplished. In the absence of a criterion standard, the validity of health status measures is often established using the concept of construct validity. Construct validity consists of comparisons between different measures that quantify a similar dimension of health status. It consists of predicting logical relationships between measures or characteristics of the population and comparing questionnaire responses with these measures. RELIABILITY Reliability can refer to either the internal consistency of a measure or its reproducibility. Internal reliability describes whether or not the items of the instrument are homogeneous (i.e., that all the items are measuring the same construct). It is usually quantified by examining Chronbach’s alpha (range 0–1), where an internally consistent domain is considered to have a value of 0.8 (20). In contrast, reproducibility refers to the consistency of answers over time in stable patients. Guyatt et al. refers to a discriminative health status measure as being reliable if it has a high “signal-to-noise” ratio (13). That is, the variability in scores between patients (signal) is much greater than the variability within patients (“noise”). Often, the noise is assessed by repeatedly administering the same instrument on different occasions to the same patients during a period in which their underlying condition has been stable. The test–retest reliability of the instrument is supported by evidence of a high correlation between scores, using either the Pearson’s correlation coefficient, a paired t test, or the intraclass correlation coefficient (21). RESPONSIVENESS In contrast to reliability, responsiveness refers to the sensitivity of changes in instrument scores, reflecting changes in clinical status. Responsiveness is the “signal” in the signal-to-noise ratio and reflects the magnitude of the difference in score in patients who have improved or deteriorated (13). Responsiveness of health status instruments can be measured using the relative efficiency statistic (ratio of paired t statistics), corre-

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lation of scales’ changes with other measures, receiver operating characteristic curves, and responsiveness statistics (ratio of minimal clinically important difference to the variability in stable subjects) (11). Some argue that if an instrument is valid and reliable, then it must also be responsive. Yet, because of the difficulty in establishing validity, we feel that it is critically important to independently demonstrate the responsiveness of an instrument to change. After all, it is the responsiveness of the instrument that is most important in having it serve as a sensitive endpoint in clinical applications. INTERPRETABILITY Interpretability informs the meaning of a change in score. In the case of a discriminative evaluation, interpretability facilitates knowing whether a certain score indicates that a patient is functioning normally or has some degree of impairment in health status. Interpreting discriminative measures is greatly aided by knowing the normal reference ranges for clinically significant populations of patients. Toward this end, general health status measures, such as the EQ-5D or the SF-36, for which population norms are readily available, can be useful. To facilitate its interpretation, the developers of the SF-36 have begun to propose transformations of scale scores, such that a score of 50 represents the population norm of the United States, and differences of 10 points represent a standard deviation from that norm. For evaluative instruments, interpretability ascertains whether the change in scores represent a trivial, small but important, moderate, or large improvement or deterioration (13). This framework focuses on the need to understand whether changes in scores are clinically meaningful. How many points must a given instrument change for patients or their physicians to be able to appreciate that their condition has changed? For an evaluative instrument, several methods exist to make health status scores interpretable (22). The most common approach is to anchor changes in scores over time against other mechanisms of quantifying patients’ changes in health. Other mechanisms of quantifying change may entail capturing patients at different time-points in the severity and course of their illness, or assessing patients’ or their physicians’ assessment of change using a technique other than the instrument being tested. Another method of facilitating the interpretation of changes in scores over time is to link such changes to subsequent outcomes, thus affording an interpretation of the prognostic significance of changes in scores over time. Other approaches have focused on distribution-based methods, including examining individual effect sizes, utilizing the standard error of measurement (SEM), focusing on mean square error, and hierarchical linear modeling. When trying to relate changes in scores, it is important to understand the perspective of the analysis. Group changes refer to the mean differences in scores for groups of patients. The statistical significance of such group changes is highly dependent on the numbers of patients analyzed. For example, in the Global Utilization Streptokinase and Tissue Plasminogen Activator for Coronary Arteries trial, where 2165 American and 311 Canadian patients were serially administered the Duke Activity Score Index (DASI) (range 0–58), a 5-point difference in 1-year scores was reported (p < 0.001). Although highly statistically significant, this magnitude of difference may not represent a meaningful difference in scores for an individual, where changing one response on the questionnaire from yes to no may result in a reduction of 8 points. Clarification of this distinction is particularly important when investigators seek to summarize changes in health status by extrapolating mean differences in groups to individual patient scores.

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Facilitating the interpretation of health status scores is an area of active research, and improved techniques are being developed. For example, Guyatt and colleagues are defining an important intraindividual (as opposed to a group) change in score and, noting in the context of a clinical trial, the number of patients needed to be treated to confer a clinically meaningful benefit in one patient (23). Wyrwich and colleagues have been examining the effectiveness of utilizing the SEM as a way to interpret changes in health status scores (24). They have found that one SEM change in a group of cardiac patients corresponded well to minimal clinically important differences on the Chronic Heart Failure Questionnaire (CHQ). Similar findings were found among a group of Chronic Obstructive Pulmonary Disease (COPD) patients and the Chronic Respiratory Disease Questionnaire (25). Another recent innovation for interpreting disease-specific health status scores over time is the benefit statistic (26). This approach examines the ratio of observed changes in score against the best possible scores that could have been obtained. It allows the percent attainment of perfect disease-specific health status (i.e., alleviation of the manifestations of a particular disease) to be quantified. Ongoing work will likely further illuminate the best methods for interpreting changes in health status over time.

AVAILABLE DISEASE-SPECIFIC MEASURES IN CARDIOVASCULAR DISEASE Given the previous considerations, it is useful to review the evolution of health status measures in cardiovascular disease. Much activity is currently underway to develop health status measures for a variety of specific diseases, ranging from atrial fibrillation to congenital heart disease. The field is most advanced, however, in coronary disease and congestive heart failure (CHF). A brief history of measures development and a description of available metrics are provided in these diseases as an example of how quantifying patients’ health status has developed. As new measures are introduced, investigators should consider the required attributes and consult various repositories of health status measures to see whether newly proposed instruments meet the requisites for accurately capturing patients’ perspectives of their disease. Potential sources for identifying instruments include internet-based repositories (e.g., www.qlmed.org). When querying these registries, however, extreme diligence on the users’ part is required to ensure that the provided instruments have the requisite psychometric properties to be performed as desired. As noted previously, researchers should select previously developed measures of health status to insure that the measures are sufficiently valid, reliable, responsive, and interpretable and to enhance the standardization of health status assessment. We recommend that if existing measures do not completely capture all of the domains that are desired, then additional questions or domains, ideally drawn from other established instruments, be used to supplement more established questionnaires. When using a domain from an established questionnaire, all questions of that domain should be used so that interpretable scores can be generated.

Disease-Specific Measures in CAD Traditionally, disease-specific health status measures in CAD have been based on physicians summarizing a patient’s functional status after obtaining a history. The original clinical standard for quantifying health status was the New York Heart Association (NYHA) Functional Classification system (27), which assesses cardiac status among

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four categories based on symptoms during varying degrees of physical activity. Although developed in 1947 and widely used since, this measure is imprecise and inconsistent, depending on the physician or patient interpretations (1). A revision of the NYHA criteria for patients with coronary disease that incorporated more precise terms and definitions of function is the Canadian Cardiovascular Society Classification system (CCSC) (28). The CCSC is a four-level, physician-assigned scale that synthesizes physical functioning and symptoms to describe the amount of exertion required to precipitate angina. Since its creation, the CCSC has been used in numerous investigations and has become a routine part of clinical care (29). In 1981, Goldman and colleagues improved the accuracy of the CCSC by constructing a formal interview, the Specific Activity Scale, that more clearly delineates activities patients can or cannot perform (30). More reliable and valid data than that of the NYHA can be obtained when this interview is translated into a four-point classification system. More importantly, they noted that patients modified their activity levels to minimize the frequency of their angina, and a system that did not explicitly define patients’ current activity levels could underestimate disease progression (31). In 1989, self-administered measures of physical function in cardiovascular disease were introduced with the DASI (32). This instrument quantifies, in a generic fashion, physical capacity by asking patients to define which of twelve activities they can perform. Responses are weighted and can be added to generate a score between 0 and 58 that correlates with peak oxygen consumption on an exercise bicycle. All of these measures examine functional status as measured by physical activity, but they do not fully incorporate all aspects of health status. In 1992, the Seattle Angina Questionnaire (SAQ) was first introduced. It consists of 19 items grouped into five domains: physical limitations, angina stability, angina frequency, treatment satisfaction, and disease perception/quality of life (14). It was the first self-administered measure to extend measurement beyond the physical limitations of coronary disease. Furthermore, it benefited from advances in the methodology of health status assessment and explicitly demonstrated the validity, reliability, and responsiveness of each domain at the time of its initial publication (16). It is the only cardiac-specific health status measure endorsed by the Medical Outcomes Trust (33). It is also the only disease-specific health status instrument to have demonstrated prognostic ability for both mortality and hospital admission (34). Other disease-specific measures for coronary disease have also been developed. One that attempts to encompass several aspects of health status is the Quality of Life After Myocardial Infarction (QLMI) (35). The QLMI has recently been revised (MacNew QLMI) and includes 27-items grouped into three domains: emotional, physical, and social (36). It is self-administered and assesses the health status of individuals that are recovering from myocardial infarctions (MI). The MacNew QLMI appears to have strong reliability and responsiveness indices, however, its evaluative properties require further research (10). Another self-administered, disease-specific measure is the Angina Pectoris Quality of Life Questionnaire (APQLQ) (37). It consists of 22 items that are divided into four scales: physical activities, somatic symptoms, emotional distress, and life satisfaction. It has been reported to have good psychometric properties for classifying purposes, but the APQLQ requires further research to determine its level of responsiveness and test-retest reliability (10). A final measure, the Cardiac Health Profile (CHP) consists of three parts: the degree of angina pectoris, the quality of life, and a subjective scoring of psychosocial cost benefit (38). The latter two sections of the

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CHP are based on visual analogous scales, covering nine areas and consisting of 16 and 2 questions, respectively. It appears to have good reliability and validity, as well as being responsive.

Disease-Specific Measures in CHF In addition to the NYHA, several disease-specific instruments for CHF have been developed. One of the most widely used instruments is the Minnesota Living with Heart Failure Questionnaire (LiHFE) (39). This self-administered questionnaire consists of 21 questions concerning the physical and emotional consequences of CHF. The undefined term of heart failure is used to qualify limitations in “preventing you from living as you wanted to over the last month.” Beyond its summary score, it can be divided into two domains: physical and emotional. Although it has demonstrated responsiveness to treatment effects in some multicenter clinical trials (40–43), other investigations of similar agents have not demonstrated consistent effects (44). These discrepant results not only highlight the difficulty inherent in quantifying health status, but also demonstrate the need for continued research into the development of diseasespecific instruments for CHF. Gordon Guyatt, a leader in the methodology of functional status assessment, developed a second disease-specific measure for CHF. This measure, the Chronic Heart Failure Questionnaire (CHQ), is a 16-item, interview-administered instrument that defines three domains of limitation: dyspnea during daily activities, fatigue, and emotional function (45). It has been shown to be both reliable, and, in a randomized crossover clinical trial of digoxin therapy in heart failure, it has also been demonstrated to be responsive to clinical change (46). Its usefulness, however, is limited by the complexity of administration and on the need for an interviewer to appropriately implement the instrument. Consequently, it has fallen from favor in both the clinical trial and health care quality assessment realms. Recently, a new standardized, comprehensive, disease-specific measure has been introduced for CHF (15). The Kansas City Cardiomyopathy Questionnaire (KCCQ) is a 23-item, disease-specific instrument that quantifies physical limitations, symptoms (frequency, severity, and recent changes over time), quality of life, social interference, and self-efficacy. It is self-administered and takes about 5 to 8 minutes to complete. Each domain has been explicitly demonstrated to valid, reliable, and responsive in quantifying the health status of patients with CHF.

APPLICATIONS OF HEALTH STATUS MEASURES Outcomes research is a multidisciplinary approach for defining what works in medicine, in whom, and in what setting treatments are most effective. Toward that end, health status represents one of the critical outcomes to be evaluated. Figure 2 provides a pictorial overview of relevant outcomes in cardiovascular disease. Potential settings in which quantification of patients’ health status may be useful, including clinical trials, disease management, quality measurement, and, eventually, patient care itself.

Clinical Trials The gold standard for the acquisition of evidence to guide clinical practice is the randomized controlled clinical trial. Curiously, clinical trials in cardiology have been

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Fig. 2. Pictoral overview of relevant outcomes in cardiovascular disease.

reluctant to emphasize patient-reported health status as primary or secondary outcome. Rather, cardiovascular research has traditionally focused on “hard outcomes,” such as death, MI, and the need for revascularization procedures. This presumably arose from a desire to be more scientific and objective in characterizing the effects of treatment on patients. Yet, even some of these apparently objective outcomes are not necessarily more reproducible or consistent than patient-derived health status instruments. For example, the definition of MI has recently changed to include patients with elevated troponin enzymes even in the absence of elevated CPK-MB levels, the traditional marker of myocardial damage. Does the long-term implication of a rise in troponin necessarily mean the same as the historical definition of a MI? This change in definition emphasized the shifting nature of what has traditionally been considered a “hard” endpoint. Similarly, repeat procedures are a common endpoint in clinical trials, and yet, the use of subsequent revascularization may by applied quite variably to different patients and by different practitioners. For example, in the setting of worsening symptoms, some physicians may recommend revascularization, whereas others may choose to alter medications. The variability of practice patterns throughout the country further emphasize the absence of objectivity in using revascularization as an outcome (47). Further compounding the challenge of endpoint selection in clinical trials is the frequent reliance on “surrogate” outcomes. Surrogate outcomes are those anatomic and physiologic markers of disease severity that are often occult to patients themselves. Examples include angiographic narrowing of the coronary arteries, estimates of ischemic burden, ejection fraction, and physiologic parameters, such as lipid levels, blood pressure, and duration of ST segment deviation on a Holter monitor (29). Although these data can be useful in understanding the mechanisms of a disease or its progression, they are not meaningful in understanding the impact of therapy on patients. As eloquently described by Fleming and DeMets, surrogate endpoints may misrepresent the true effect of a therapy on patients and should not be relied on as sole evidence of therapeutic efficacy (48). There is a pressing need to more consistently quantify the impact of therapy on patients’ perspectives of their health. Consequently, an important trend is developing to

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apply health status assessments as endpoints in clinical trials. Assessing patients’ health status facilitates the quantification of patients’ experience of their disease and the potential benefits of treatment. It is through the understanding provided by such assessments that we can learn the way in which a treatment affects patients, and by which clinicians will be able to counsel patients on the relative risks and benefits of a treatment option. Without incorporating assessments of health status into clinical trials, there is no opportunity for clinicians to understand, from patients’ perspectives, the consequences of treatment or to tailor therapy to meet the individual needs and values of individual patients. Furthermore, the reproducibility of health status measures, such as the SAQ, is quite comparable to other objective measures, such as exercise treadmill testing, echocardiographic assessments of valvular stenoses, and the angiographic evaluation of coronary arteries. For example, the intraclass correlation coefficient (where a value of 1.0 represents perfect reproducibility, and 0 represents complete erratic test performance) of SAQ domains range from 0.76 to 0.83 (16). The intraclass correlation coefficient of treadmill duration on exercise tests is 0.70 (49), and the correlation between angiographic readings of coronary stenoses with calipers vs videodensitometry is 0.76 (50,51). If the “hardness” and objectivity of data is quantified by its reproducibility, as some have argued, then health status measures should not to be excluded as endpoints in clinical trials merely because the source of these data are patients, rather than a technological machine (52). Selecting measures for inclusion in clinical trials requires consideration of several factors. First, it is important to understand the nature of the tested intervention and its potential influence on health status. Both intended benefits and potential side effects should be considered. For example, is a therapy intended to alleviate the symptoms of angina, or is its sole purpose to prevent future episodes of atherosclerotic plaque disruption and subsequent acute coronary syndromes? In the former scenario, a diseasespecific measure would be critical, whereas in the latter situation, a generic health status measure intended to capture the consequences of subsequent infarcts and the influence of possible side effects may be more appropriate. Furthermore, if economic analyses are an important component of the clinical trial, then direct utility assessments should be considered (see Chapter 7). For most trials, we recommend the use of both a disease-specific and a generic health status measure. This allows a reasonable assessment of disease-specific changes in patient’s health status while also placing these changes in the larger context of overall health status. Once a study hypothesis and the types of instruments (disease-specific vs generic vs utilities) to be used are defined, then several considerations can guide the selection of the most appropriate measures from among those that are available. First and foremost, it is important that the instruments are psychometrically sound and meet the validity, reliability, responsiveness, and interpretability requirements described previously. Additional considerations include the functional range of the measure, sociodemographic relevance of the measure, logistical requirements of administering the measure, and availability of instrument translations. Functional range refers to the floor and ceiling of the measure. The floor phenomenon occurs when the activities or symptoms of a measure are at such a high level of functioning that many patients will score at the lowest level of potential responses. This results in a negatively skewed distribution that has little room to detect deterioration in function over time. Conversely, a ceiling effect

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results in many respondents scoring at the top of the scale, resulting in limited ability to detect improvement. A second consideration in evaluating potential instruments is reviewing their relevance to particular sociodemographic variables. Gender, education, and age neutrality are critical considerations in an instrument. For example, an instrument that inquires about the ability to play contact sports may have little relevance to older patients or to those individuals that have never participated in contact sports, and this may violate the validity and sensitivity of the instrument in these subject groups. Examining the logistics of administering a particular instrument is another consideration in selecting instruments. This involves carefully examining the response burden of selected measures and determining the required mechanism of administration. Response burden refers to the number of questions that the instrument contains. If the response burden is too great, patients will likely begin to randomly answer questions without their responses accurately reflecting their health state or begin to skip questions resulting in missing data. Appropriate instruments should be chosen that balance the breadth of desired domains to be measured with the response burden and logistic complexity of administering lengthy questionnaires. Careful consideration to the feasibility of an interview as opposed to a self-administered questionnaire administration should also be entertained. Finally, given the growth in international trials, the availability of appropriate cultural and linguistic translations of a questionnaire is an increasingly important requisite in the selection of health status measures. The current standard for translation involves a minimum of two initial translations, from the original language to the new one. The newly translated instruments are then backtranslated after which international harmonization is performed. Finally, limited pilot testing among patients is required to ensure comprehensibility and relevance.

Disease Management Disease management represents the desire to manage populations of patients as opposed to individuals. Often viewed from the perspective of a health care system, disease management programs necessitate evaluating the health of a population of patients and creating interventions or services that can optimize the health of these individuals. To successfully implement such a program, however, requires measuring the health of the population first, developing a program to augment their health, implementing the program, then reassessing the populations’ health both to assess the impact of the program and further refine the intervention. Health status assessments can be extremely useful in disease management programs. Surveys of patients can define their symptom burden and functional limitations. In diseases, such as coronary disease and CHF, patients with poor symptoms and marked limitations in their function and/or quality of life can be readily identified. Improved access to specialty care can then be offered through disease management programs so as to optimize the health of those individuals most severely impacted by the disease. Improving these patients’ health is an important opportunity to elevate the overall health of the population and the use of health status measures can facilitate this process.

Quality of Care Quality-of-care assessments are most often conceptualized in terms of structure, process and outcomes (53). Structure refers to the infrastructure available for patient care. It often includes the availability of services, the training of physicians and their

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staff, and the incorporation of policies and protocols for patient treatment. Examples of structural aspects of care might be the availability of on-site bypass surgery or the percentage of staff trained in advanced cardiac life support within an institution. Process refers to how individual patients are treated. Often derived from guidelines, performance measures are efforts to quantify that the “right” care is delivered to the “right” patients at the “right” time. The use of aspirin within the first 24 hours of a MI is an example of a process measure that is often used to judge the quality of care. Measuring the mortality rate of surgeons performing bypass surgery as is done in New York or Northern New England is an example of an outcome measure used to judge quality of care (54,55). Theoretically, the use of health status as an outcome for quality-of-care assessments is a natural extension of current quality assessment and improvement efforts. Measuring “how patients are doing” would seem to be one of the best barometers for quantifying the quality with which they are being treated. Yet, several barriers prevent the use of health status assessments in quality-of-care assessments. These include the complexity of introducing health status assessments into the process of patient care at the time of initial treatment and in follow-up. However, careful planning and improvements in computer technology and the distribution of Internet access are likely to eventually overcome these obstacles (5). A remaining and important barrier is the development of risk-adjustment models that account for patient characteristics associated with subsequent patient health status outcomes. As future research defines those variables most influential in determining subsequent outcomes, health status will likely become an increasingly important component of quality assessment and improvement.

Individual Patient Care Tailoring patient therapy to individual patients is a critical component of delivering medical care. Although risk stratification in the application of coronary revascularization is a cornerstone of current cardiovascular care, these models primarily use mortality as the outcome of interest. Yet, in defining the relative risks and benefits of revascularization, the projected benefits in terms of alleviating the symptoms and suffering of coronary disease are rarely incorporated into risk-stratification models. As future research better defines the health status outcomes of alternative treatment strategies, the use of risk-adjusted models incorporating symptom relief, physical function, and quality of life are sure to become important components of recommending treatments to individual patients.

Considerations in the Implementation and Analysis of Health Status IMPLEMENTING HEALTH STATUS ASSESSMENTS IN A CLINICAL TRIAL The most important issue in the implementation of health status measures in clinical trials is proper planning. The relevant health status assessments, the measures and the timing of their collection, need to be established from the initial design of the trial. This is essential in order to alert all participating sites of the import and necessity of collecting health status data with the same rigor and vigilance as any other data in the trial. All too often, health status is considered after the trial has been designed (and in some cases, after the trial has begun), thus precluding the collection of complete data and often introducing potential selection biases. Furthermore, the data collectors need to be trained in the proper administration of instruments so as to avoid “leading” patients

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toward particular answers. Although not critical in double-blinded trials, open-label investigations are best performed when someone unfamiliar with the actual treatment received provides questionnaires to patients. ISSUES IN HANDLING MISSING DATA With the proper design and implementation of a clinical trial, missing data should be minimized. There is no analytic approach capable of overcoming incomplete or sloppy data collection. Although the issue of missing data is not unique to health status assessment, it does lead to analytic difficulties because of the potential introduction of unintended biases. The handling of missing health status data is an area of active investigation, and no definitive solution is available. There are several potential sources of missing data. Health status data may be missing either because the instrument could not be obtained from the subject, or the subject was not able to complete enough items within the instrument to generate domain scores. Sources of the former circumstance include death of the patient, loss to followup, and refusal to participate in the health status assessment. Ways of handling deaths can include imputing a score of 0 for health status domains, carrying the last available data forward, and ignoring the missing data (56). These methods, as well as newly developed approaches, attempt to incorporate death into the analyses of health status data (57). Although the incorporation of death and health status information is one of the great strengths of utilities (see Chapter 7), some feel that integrating descriptive health status measures and death cannot be done, and that they should be described independently so that the end-user of the data can integrate both death and health status separately as they interpret the study’s results. For patients known to be alive, and for whom additional health status data is unavailable, additional approaches to handling their missing data might include Markov chain imputations, “hot deck,” and multiple imputations (2). Regardless of how missing data are handled, when patients are completely lost to follow-up or refuse to complete follow-up instruments, the baseline differences between those with and without follow-up must be described so that readers can understand the potential selection biases and use this information to interpret the study’s results. Another source of missing data is when items within an instrument are skipped or missed. If the design of the instrument cannot handle the missing data, then several options exist (2). A frequently used approach in dealing with incomplete data is to impute the missing data. This involves estimating the missing data using available data along with other information obtained about the patient. These imputed data replace the missing data to complete the analysis. As with the previously mentioned methods, imputation of data has disadvantages that include either over-or underestimating the true nature of the population’s health status as the “new” data are not the actual results from the population, and incorrect conclusions may be drawn from the imputations. However, a potential advantage to imputing data is that after the missing data are imputed standard statistical methods can be used, as long as caveats are made concerning the imputations that were involved (2). One potential imputation strategy for recreating domain scores from incomplete data is to use unweighted sum scores from the mean of those items that were completed. This method is best used when patients have completed at least half of the items in a specific scale. A disadvantage of this method is that it can produce odd scores that are between the scale scores com-

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puted for those patients with complete data. A second approach is to use regression imputation, where the missing data are replaced by predicted values obtained from a regression of the missing item variable on the remaining items from the scale or other available patient data. Regardless of the method(s) selected, sensitivity analyses in which missing data are handled using several different methods should be performed to examine the robustness of the study’s findings to different approaches of handling data.

CONCLUSIONS Health status assessment is a rapidly evolving discipline of quantifying patients’ experiences of their illness. The requisite attributes of a health status measure include its validity, reliability, responsiveness, and interpretability. The application of accepted health status instruments as endpoints in clinical trials or observational research, tools in disease management, markers of health care quality, and aids in patient care is certain to grow. Understanding the principles behind the design and use of health status measures should accelerate their use and acceptance.

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17. Henrich G, Herschbach P. Questions on life satisfaction—A short questionnaire for assessing subjective quality of life. Eur J Psychol Assess 1974;16:150–159. 18. Streiner DL, Norman GR. Health measurement scales: A Practical guide to their development and use. Oxford University Press, Oxford, NY, 1995, pp. 231. 19. Bergner M, Bobbitt RA, Carter WB, Gilson BS. The Sickness Impact Profile: development and final revision of a health status measure. Med Care 1981;19:787–805. 20. Nunnally JC, Bernstein IH. Psychometric Theory. McGraw-Hill, NY, 1994, pp. 752. 21. Deyo RA, Diehr P, Patrick DL. Reproducibility and responsiveness of health status measures. Statistics and strategies for evaluation. Control Clin Trials 1991;12:142S–158S. 22. Wyrwich KW, Wolinsky FD. Identifying meaningful intra-individual change standards for healthrelated quality of life measures. J Eval Clin Pract 2000;6:39–49. 23. Guyatt GH, Juniper EF, Walter SD, et al. Interpreting treatment effects in randomised trials. BMJ 1998;316:690–693. 24. Wyrwich KW, Nienaber NA, Tierney WM, Wolinsky FD. Linking clinical relevance and statistical significance in evaluating intra-individual changes in health-related quality of life. Med Care 1999;37:469–478. 25. Wyrwich KW, Tierney WM, Wolinsky FD. Further evidence supporting an SEM-based criterion for identifying meaningful intra-individual changes in health-related quality of life. J Clin Epidemiol 1999;52:861–873. 26. Spertus JA, Jones PG, Coen M, et al. Transmyocardial CO(2) laser revascularization improves symptoms, function, and quality of life: 12-month results from a randomized controlled trial. Am J Med 2001;111:341–348. 27. Harvey RM, Ferrer MI, Turino GM. Philosophy of the New York Heart Association regarding assessment of cardiovascular function. Circulation 1982;66:249. 28. Compeau L. Grading of angina pectoris. Circulation 1975;54:522–523. 29. Braunwald E, Jones RH, Mark DB, et al. Diagnosing and managing unstable angina. Agency for Health Care Policy and Research. Circulation 1994;90:613–622. 30. Goldman L, Hashimoto B, Cook EF, Loscalzo A. Comparative reproducibility and validity of systems for assessing cardiovascular functional class: advantages of a new specific activity scale. Circulation 1981;64:1227–1234. 31. Goldman L, Cook EF, Mitchell N, et al. Pitfalls in the serial assessment of cardiac functional status. How a reduction in “ordinary” activity may reduce the apparent degree of cardiac compromise and give a misleading impression of improvement. J Chronic Dis 1982;35:763–771. 32. Hlatky MA, Boineau RE, Higginbotham MB, et al. A brief self-administered questionnaire to determine functional capacity (the Duke Activity Status Index). Am J Cardiol 1989;64:651–654. 33. Paget L, Tarlov AR. The Medical Outcomes Trust: Improving medical outcomes from the patient’s point of view. J Outcomes Manage 1996;3:18–23. 34. Jones P, Spertus J, McDonell M, et al. Health status predicts long-term outcomes in coronary artery disease. Circulation 2001;37:491A. 35. Hillers TK, Guyatt GH, Oldridge N, et al. Quality of life after myocardial infarction. J Clin Epidemiol 1994;47:1287–1296. 36. Valenti L, Lim L, Heller RF, Knapp J. An improved questionnaire for assessing quality of life after acute myocardial infarction. Qual Life Res 1996;5:151–161. 37. Wiklund I, Comerford M, Dimenas E. The relationship between exercise tolerance and quality of life in angina pectoris. Clin Card 1991;14:204–208. 38. Wahrborg P, Emanuelsson H. The cardiac health profile: content, reliability and validity of a new disease-specific quality of life questionnaire. Coron Artery Dis 1996;7:823–829. 39. Rector T, Kubo S, Cohn J. Patient’s self-assessment of their congestive heart failure; part 2: content, reliability and validity of a new measure, the Minnesota Living with Heart Failure Questionnaire. Heart Failure 1987;3:198–209. 40. Massie BM, Berk MR, Brozena SC, et al. Can further benefit be achieved by adding flosequinan to patients with congestive heart failure who remain symptomatic on diuretic, digoxin, and an angiotensin converting enzyme inhibitor? Results of the flosequinan-ACE inhibitor trial (FACET). Circulation 1993;88:492–501. 41. Rector TS. Effect of ACE inhibitors on the quality of life of patients with heart failure. Coron Artery Dis 1995;6:310–314.

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42. Rector TS, Johnson G, Dunkman WB, et al. Evaluation by patients with heart failure of the effects of enalapril compared with hydralazine plus isosorbide dinitrate on quality of life. V-HeFT II. The VHeFT VA Cooperative Studies Group. Circulation 1993;87:VI71–VI77. 43. Rogers WJ, Johnstone DE, Yusuf S, et al. Quality of life among 5025 patients with left ventricular dysfunction randomized between placebo and enalapril: the Studies of Left Ventricular Dysfunction. The SOLVD Investigators. J Am Coll Cardiol 1994;23:393–400. 44. Gundersen T, Wiklund I, Swedberg K, et al. Effects of 12 weeks of ramipril treatment on the quality of life in patients with moderate congestive heart failure: results of a placebo-controlled trial. Ramipril Study Group. Cardiovasc Drugs Ther 1995;9:589–594. 45. Guyatt GH, Nogradi S, Halcrow S, et al. Development and testing of a new measure of health status for clinical trials in heart failure. J Gen Intern Med 1989;4:101–107. 46. Guyatt GH, Sullivan MJ, Fallen EL, et al. A controlled trial of digoxin in congestive heart failure. Am J Cardiol 1988;61:371–375. 47. Dartmouth Medical School. Center for the Evaluative Clinical Sciences. American Hospital Association. Center for Health Care Leadership. The Dartmouth Atlas of Health Care. 1999 American Hospital Publishing, Chicago, IL, pp. 72–82. 48. Fleming TR, DeMets DL. Surrogate end points in clinical trials: are we being misled? Ann Intern Med 1996;125:605–613. 49. Sullivan M, Genter F, Savvides M, et al. The reproducibility of hemodynamic, electrocardiographic, and gas exchange data during treadmill exercise in patients with stable angina pectoris. Chest 1984;86:375–382. 50. Balkin J, Rosenmann D, Ilan M, Zion MM. Reproducibility of measurements of coronary narrowings by videodensitometry: unreliability of single view measurements. Int J Card Imaging 1990;5:119–124. 51. Balkin J, Rosenmann D, Ilan M, Zion MM. Reproducibility of measurements of coronary narrowings by videodensitometry and by digital calipers. Cardiology 1992;81:238–244. 52. Feinstein AR. An additional basic science for clinical medicine: IV. The development of clinimetrics. Ann Intern Med 1983;99:843–848. 53. Measuring and improving quality of care: a report from the American Heart Association/American College of Cardiology First Scientific Forum on Assessment of Healthcare Quality in Cardiovascular Disease and Stroke. Circulation 2000;101:1483–1493. 54. Hannan EL, Kilburn H, Jr., Racz M, et al. Improving the outcomes of coronary artery bypass surgery in New York State. JAMA 1994;271:761–766. 55. O’Connor GT, Plume SK, Olmstead EM, et al. A regional prospective study of in-hospital mortality associated with coronary artery bypass grafting. The Northern New England Cardiovascular Disease Study Group. JAMA 1991;266:803–809. 56. Fairclough D, Gelber R. Quality of life: statistical issues and analysis. In: Spilker B (ed.) Quality of Life and Pharmacoeconomics in Clinical Trials, vol. 1. Lippincott—Raven, Philadelphia, PA, 1996, pp. 427–437. 57. Diehr P, Patrick DL, Spertus J, et al. Transforming self-rated health and the SF-36 scales to include death and improve interpretability. Med Care 2001;39:670–680. 58. Hunt SM, McKenna SP, McEwen J, et al. The Nottingham Health Profile: subjective health status and medical consultations. Soc Sci Med 1981;15:221–229. 59. Ware JE, Jr., Sherbourne CD. The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Med Care 1992;30:473–483. 59a. Ware J, Jr., Kosinski M, Keller SD. A 12-item short-form health survey: construction of scales and preliminary tests of reliability and validity. Med Care 1996; 34(3):220–233. 60. Brooks R. EuroQol: the current state of play. Health Policy 1996;37:53–72. 61. Kaplan RM, Bush JW, Berry CC. Health status index: category rating versus magnitude estimation for measuring levels of well-being. Med Care 1979;17:501–525.

7

Utility Assessment John A. Spertus, MD, MPH, FACC and Robert F. Nease, Jr., PhD CONTENTS OVERVIEW OF MEDICAL DECISION MAKING UTILITIES: A METHOD OF WEIGHTING TREATMENT OUTCOMES REQUIREMENTS FOR ASSIGNING UTILITIES TO HEALTH STATES METHODS OF ASCERTAINING PATIENTS’ UTILITIES SOCIETAL VS PATIENT PERSPECTIVES IN ASSIGNING UTILITIES CONCLUSION REFERENCES

OVERVIEW OF MEDICAL DECISION MAKING Medical decision making is complicated. Choosing to apply a specific treatment in the care of a given patient requires a careful assessment of the potential risks and benefits of the intervention. Consequently, a major objective of clinical research is to define the natural history of a disease and to study the impact of various interventions on the disease course, where “course” includes an assessment of both patients’ survival and their quality of life (see Chapter 6). Medical interventions can influence disease outcomes in multiple ways. Consider that a new treatment is developed. If this new treatment strategy is labeled as A, and the current standard of care strategy is labeled as B, then Table 1 identifies nine potential patterns in which treatment A can affect survival and quality of life in comparison with strategy B. If cost were irrelevant, then identifying the outcomes of strategies A and B would facilitate a rational treatment preference in several of the scenarios outlined in the third column of Table 1. However, when the influence of a treatment on survival and quality of life are not in the same direction (i.e., scenarios 3 and 7), then the appropriate course of action is not straightforward. Furthermore, when costs need to be considered, a more detailed quantification of outcomes is necessary to define the value gained for the resources expended. The disciplines that address these evaluations are decision analysis and economic evaluations of health care.

Decision Analysis: Coronary Artery Disease (CAD) as an Example Decision analysis is a systematic approach to making decisions under conditions of uncertainty (1). By partitioning the decision process into its individual components and From: Contemporary Cardiology: Cardiovascular Health Care Economics Edited by: W. S. Weintraub © Humana Press Inc., Totowa, NJ

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Cardiovascular Health Care Economics Table 1 Scenario 1 2 3 4 5 6 7 8 9

Survival

Quality of life

Preferred Rx

A>B A>B A>B A=B A=B A=B A
A>B A=B AB A=B AB A=B A
A A ? A Equal B ? B B

Fig. 1. A prototypical decision model.

assigning probabilities to the potential consequences of each choice, one can select the most rational course based on the available information. Figure 1 diagrams a simple model. In this example, the goal is to maximize the chance of a good outcome. The probability of a good outcome from choosing Action A is p. The probability of a good outcome with Action B is q. If p > q, then the best outcome would be obtained from Action A. Alternatively, if q > p, then the best outcome would be obtained by Action B. Well-designed clinical trials can often provide information about the probabilities, p and q, of certain outcomes associated with a studied action. When the outcome is mortality vs perfect health, then the decision analysis is as straightforward as that diagrammed in Fig. 1. For example, if a patient with asymptomatic, but critical left main CAD, were faced with the decision of bypass surgery (Action A) vs medical therapy, and 3-year mortality was the outcome of interest, then the probability p = 0.92 and q = 0.6 (2). In this case, the logical course would be to proceed with surgery. Often, there is no clear mortality advantage between the alternative therapies for CAD. For example, percutaneous revascularization vs medical therapy in a patient with moderate single-vessel CAD is a much more difficult decision process to model. The difficulty lies primarily in that a good outcome may be less angina at a given level of activity than the bad outcome. In the absence of an “all-good” outcome vs an “all-bad”

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Fig. 2. A hypothetical decision tree for single-vessel angioplasty.

outcome, a method for ranking the relative value of potential outcomes to a patient becomes critical. This is the purpose of utility assessment.

UTILITIES: A METHOD OF WEIGHTING TREATMENT OUTCOMES A utility measure is a rating of preference, under conditions of uncertainty, for a potential health state. By standard convention, a utility value of 1 is assigned to perfect health, and a value of 0 is assigned to death. Values can then be assigned to clinical states below perfect health, according to the degree of limitation experienced by afflicted individuals. Once assigned, these values can be substituted into decision analysis models to aid in “weighting” the potential outcomes of a decision process. Defining utilities for patients can be both theoretically and practically challenging. In essence, a utility represents the distillation of a patient’s health status (see Chapter 6 for a more detailed discussion of the components of health status and the steps and challenges of quantifying health status) into a single numerical value between 0 and 1. Several techniques have been developed to assist in the acquisition of patients’ utilities. Currently, the most widely accepted approach is the Standard Gamble because it conforms to the Von Neumann and Morgenstern model for making decisions under conditions of uncertainty (3). However, recent work has demonstrated that human behavior may not conform to the theoretical models used for the Standard Gamble, and, thus, other approaches to describe health state values have been developed (4–9). Further discussion of the assessment of utilities is described in the next section. If, in the hypothetical case of single-vessel angioplasty, a clinical trial demonstrated a 3% morality rate and an average utility of 0.96 for survivors at one year in comparison with 2% mortality and an average utility of 0.93 for medical therapy, then the decision model would look like Fig. 2. The overall value for angioplasty would equal 0.97(0.96) + 0.03(0) = 0.93. The value associated with medical therapy would equal 0.98(0.93) + 0.02(0) = 0.91. In this hypothetical example, greater value would be conferred by the choice of angioplasty over medical therapy for the treatment of singlevessel CAD and for patients with utilities for treatment outcomes that were similar to the averages described, angioplasty would be the preferred treatment choice. As is evident in the example of single-vessel angioplasty, decision analysis is an attempt to integrate survival, and the quality of that survival (i.e., patient’s health status— their symptoms, functioning, and quality of life), into a single analysis. Whereas health

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Fig. 3. Integrating multiple disease outcomes.

status is often quantified by measures, such as the Short Form-36 (10) or the Seattle Angina Questionnaire (11), utilities represent the distillation of multiple health status domains into a single number so that the quality of life can be combined with the quantity of life to generate quality-adjusted life years (QALYs). Once utilities are assigned, the paradigms of decision analysis can then be applied. It is also possible to integrate economic costs into the formal evaluation of treatment decisions. Figure 3 diagrams the range of outcomes associated with coronary disease and how they are integrated into a cost-utility analysis (CUA). There are several appealing attributes of utilities. First, by condensing the range of a patient’s health status into a single number, decision analyses and economic assessments can be more readily performed. In addition, the ability to use a common metric for multiple diseases allows comparisons across diseases to be performed, as is possible with generic health status measures. Disadvantages of utilities, in comparison with health status measures, include a potential loss of responsiveness and the inherent loss of descriptive data that is often clinically interpretable, actionable, and useful in describing outcomes to patients. Therefore, depending on the purpose of an analysis, the inclusion of a utility assessment may be an invaluable tool in understanding the value of alternative treatment options. For example, health care systems strive to maximize the health of the population that they serve. Although it would be desirable to administer all services capable of conferring any potential health benefit to all patients, limited resources make such a policy untenable. The goal of economic analyses is to weigh the potential benefits of an intervention or program against the costs associated with the delivery of that service. Formal approaches, such as cost-effectiveness (CE), cost-benefit, and CUA, seek to explicitly define programmatic benefits and costs so that decisions regarding program maintenance and development can maximize aggregate health benefits to a population of concern within the constraints of limited resources (1,12,13).

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An initial step in the formal economic evaluation of a program or intervention is to quantify benefits in terms of some unit of output. QALYs are a metric that is being used with increasing frequency (see Fig. 3). This measure modifies the number of years of life gained by the quality of that additional life. As shown in Eq. 1, QALYs are derived by multiplying the mean survival advantage of a program by the utility of life in the attained state. Quality-adjusted life years = Years of life × Utility of that life

(1)

The use of utility measures can allow the incorporation of both morbidity and mortality into a single number that reflects the potential trade-off between these two endpoints. Dividing the incremental benefit by the associated incremental cost permits an estimate of the benefits derived per dollar spent. A health care program, such as the government, a health maintenance organization, or a private insurer, can then rank all programs by their benefit–cost ratio. Proceeding from the top to the bottom of the list, programs can be selected that maximize benefit until all resources are consumed (14,15).

REQUIREMENTS FOR ASSIGNING UTILITIES TO HEALTH STATES Medical decision making requires choosing a treatment or diagnostic path at a point in time when the outcome of that decision is unknown. Because of the uncertainty inherent in the decision process, utilities ascribed to the ultimate outcomes should be determined under similar conditions of uncertainty. The most common approach to capture this information is by using the Standard Gamble. The Standard Gamble presents patients with a choice: either accept a given health state or risk a chance of death to have perfect health. By continually altering the risk of dying in order to achieve a perfect state of health, a point of equivalence should be reached at which a patient cannot decide which is a better choice, perpetuation of a given health state or an immediate risk of death to achieve perfect health. The point of indifference between a health state and a risk of death is the patient’s utility for that health state. Obviously, explaining the concept of the Gamble and asking patients to choose between a risk of death and the perpetuation of a given health state is a time-consuming and cognitively challenging process. Furthermore, the validity of acquiring such data can be questioned, in part, because human behavior does not assign a linear relationship to probabilities. For example, the perceived difference in the minds of patients between a risk of 95% and 90% is far greater then the difference between 65% and 60%. The disproportionate difference between similar absolute differences in risks results from the value that is applied by patients and society, such that more weight might be applied to certain ranges of utility improvement than others. Such considerations challenge the theoretical foundation on which CUA are conducted. An alternative approach to the Standard Gamble is the Time Trade-Off method for assigning health values. A health value differs from a health utility in that the value is defined under conditions of certainty rather than the uncertainty inherent in the Gamble method. For example, in the Time Trade-Off, patients are instructed that they will live a specified number of years in a given health state. Then, they are asked how many years less they would live if they could live those years in perfect health. For example, a patient may be instructed that they would live 10 years in their current health state and are asked whether they would be willing to live only 9 years in perfect health. The time in perfect health is progressively shortened until the patient reaches a point of

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Fig. 4. The relationship between utilities and values as a function of patient’s willingness to accept risk.

indifference between longevity and quality of life. The ratio of time in perfect health to the guaranteed time in the diseased health state at the point of indifference is the value assigned to that health state (e.g., if the patient was indifferent to the trade-off of 9 years of perfect health in comparison with 10 years in their current health state, then their time trade-off assigned value would be 9/10 or 0.90). Utilities and values are not equivalent. The dark diagonal line in Fig. 4 demonstrates a relationship of identity between values, which are determined under conditions of certainty and utilities, which are determined under conditions of uncertainty. The degree to which patients are either risk-averse or risk-seeking influences this relationship. If patients are not willing to assume risk, then they would report a utility that was higher than the value that they would assign to the same condition (the finely dotted curve). Conversely, if patients were risk-seeking in their behavior, then they would assign a lower utility than would be expected for a given value (the dashed curve). Because of the complexities in eliciting health state utilities, patients’ difficulties in understanding probabilities, and patients’ unique degree of risk-seeking behavior, great debate exists in the literature about which measure is the most appropriate in decision analyses and economic modeling.

METHODS OF ASCERTAINING PATIENTS’ UTILITIES Utilities can be assessed in any number of ways, ranging from face-to-face interviews to paper-based instruments. The creation of versatile visual decision aids, coupled with face-to-face interviews by experienced research associates, are probably the most accurate and expensive methods for acquiring patients’ utilities. In general, the fidelity of the assessments rises with the cost, posing a design decision for researchers wishing to perform utility assessment work. Computer-based utility assessment approaches offer a good balance between fidelity and cost. In addition, the use of computerized tools offers a level of standardization, both within and across studies. Within a study, the use of a computerized utility assessment tool ensures that all subjects receive consistent stimuli, an important consideration given the emerging focus on patient-to-patient variation in preferences for health

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outcomes (16–18). Furthermore, to the degree that computerized tools promote the use of identical or similar elicitation methods, they improve comparability across studies. U-Titer II (19) and IMPACT (20,21) are authoring environments that allow researchers to rapidly build and deploy automated utility assessment interviews. These applications provide substantial flexibility in interview flow, presentation of descriptions of the health outcomes being assessed, selection of the metrics used (e.g., Standard Gamble, Time Trade-Off), and the process used to determine indifference (i.e., the specific utility value). They also offer the ability to provide colorful and compelling visual aids, including video clips of people with various health problems and animations to support different utility assessment metrics (e.g., wall of faces for Standard Gamble). Finally, they automatically record results into electronic form and can capture the structure of subjects’ responses (e.g., time required to complete an assessment), as well as provide automatic feedback in real time to subjects whose responses are not internally consistent. Automated utility assessment interviews can be performed over the internet (20) or in standalone mode. Deploying the assessments over the Internet offer some advantages; interviews that are revised or updated are instantly available to all users, and data collection can be centralized across multiple sites. Stand-alone interviews, on the other hand, do not require a connection to the internet, typically run faster, can be tailored to the hardware used, and are less prone to centralized failure (e.g., breakdown of an Internet server or corruption of the website would make the interview unavailable to all users). Although computerized programs are the optimal method for acquiring utilities, there are times when cost effectivenss analyses (CEA) are desired, but limited resources preclude the proper collection of utilities. Several approaches can be attempted in this circumstance. One method is to use a simple rating scale or “feeling thermometer” that asks patients to place their current health along an axis from death to perfect health. Although this can be conveniently obtained, the validity of this method has never been adequately demonstrated, and, given the complexity of the decision process needed to solicit utilities, it does not seem appropriate to presume that the data are similar to what would have been obtained had a more rigorous assessment of utilities been undertaken. Recent work suggests that single-item utility measures tend to be less precise and upwardly biased toward higher estimates of utility (22). Given the import of the intended applications of CEA (i.e., policy implications for allocating scarce resources), we believe that the conclusions need to be predicated on the highest quality data, and this should be attained from a formal Standard Gamble. A second approach for circumventing the complexity of direct utility assessments is to use a health status questionnaire that has had weights developed that convert the responses to a utility. The utility assigned can be either based on models used to generate the preferences from the perspective of the individual or from the perspective of society. This distinction is an important one and is addressed in the next section. An example of a questionnaire that can be converted into a utility from the individual’s perspective is the Quality of Well Being Scale (23). Other authors are developing methods for converting other health status measures, such as the SF-36, into utilities (24), but these methods have not yet been adequately validated. The challenge in mapping health status measures to utilities is difficult because the relationship between functional status assessment and utility measures is complex. In fact, some have even argued that functional status and quality of life cannot be assessed

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without soliciting patients’ unique and individual values (25). Supporting this assertion is the poor correlation between the Canadian Cardiovascular Society Classification, a physicians’ assessment of patients’ functional status, and the Standard Gamble and Time Trade-Off methods of utility assessment (17). Furthermore, a recent attempt to convert the Duke Activity Status Index was also unsuccessful (26). As demonstrated by these prior attempts, the correlation between functional status measures and utilities is poor. Circumventing this challenge, several investigative teams have sought to design general health status questionnaires that have societal-based weights from which utilities can be assigned. Two of the more prominent examples include the Health Utility Index and the EuroQOL or EQ-5D. The Health Utility Index (27,28) was developed using multiattribute utility theory. The Mark III version contains eight attributes: vision, hearing, speech, ambulation, dexterity, emotion, cognition, and pain. Each attribute has a societal-based assessment of the diminution in utility associated with that particular level of functioning. A combination of levels across attributes constitutes a health state. These weights are then summed across attributes to generate the individual patient’s utility as assessed from society’s perspective. The EuroQOL, or EQ-5D (29), is an international effort to create a simple instrument with which societal-based utilities can be generated. It consists of five questions inquiring about mobility, self-care, usual activities, pain/discomfort, and depression/anxiety. There are three responses corresponding to no, moderate, or severe limitations/problems. Weights have been assigned to each response and are summed to create a societal-based utility score. In addition, the EQ-5D contains a feeling thermometer as its sixth item. This 0–100 scale is anchored from the worst imaginable health state to the best imaginable health state. When patients indicate their current health state along this axis, that value is used to represent their utility from their perspectives.

SOCIETAL VS PATIENT PERSPECTIVES IN ASSIGNING UTILITIES Throughout the previous section, we alluded to either the patients’ or society’s perspective in assigning utilities. The importance of this distinction becomes clear when analyzing the acquired utilities. Analyzing the outcomes of an intervention, especially in relationship to costs requires an explicit articulation of the decision maker’s perspective (14,30). For example, if an individual patient is selecting between treatment options and has such good medical insurance that they incur none of the treatment costs, then a simple decision model, such as that described in Fig. 2, would be appropriate, and this patient would want the utilities from similar patients’ perspectives incorporated into their decision model. On the other hand, a state-based insurance program, such as that attempted in Oregon (15), would have a different perspective. In this situation, policy planners would want a societal-based assessment of the utility/disutility of alternative health states so that a ratio of the incremental benefit of using society’s resources to purchase health care services could be attained. In designing clinical trials and observational studies intended to impact public policy, and for which utility assessments are needed, it is critical that the investigator consider the intended applications of their analyses so that the optimal mode of utility solicitation can be selected. The reader is referred to the publications from the US Public Health Service’s Panel on Cost-Effectiveness in Health and Medicine for more detailed discussions of these issues (31).

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CONCLUSION Utility assessments are important tools for synthesizing patients’ health into a single number that can, in turn, facilitate decision and economic analyses. Defining the proper perspective for the analysis greatly assists in the selection of the best mechanism of utility assessment. For societal-based perspectives, the use of health status surveys designed to measure societal-based utilities may be a viable alternative. To assist in clinical decision making, however, a patient-based perspective may be most appropriate. For these circumstances, a direct patient assessment with a Standard Gamble or Time Trade-Off is desired. Visual aids and computer-based techniques have made the valid and reproducible acquisition of such utilities a viable option in current studies, and these should be considered if relevant to the study’s purpose.

REFERENCES 1. Weinstein MW, Fineberg HV. Clinical Decision Analysis. WB Saunders Company, Philadelphia, PA, 1980. 2. Califf RM, Harrell FE, Lee KL, et al. The evolution of medical and surgical therapy for coronary artery disease. JAMA 1989;261:2077–2086. 3. Von Neumann J, Morgenstern O. Theory of Games and Economic Behaviour. Princeton University Press, Princeton, NJ, 1944. 4. Cohen BJ Is expected utility theory mornative for medical decision makng? Med Decis Making 1996;16:1–6. 5. Cohen BJ. Reply: Utilitarianism, risk aversion, and expected utility. Med Decis Making 1996;16:14. 6. Baron J. Why expected utility theory is normative, but not prescriptive. Med Decis Making 1996;16:7–9. 7. Eeckhoudt L. Expected utility theory—Is it normative or simply “practical”? Med Decis Making 1996;16:12–13. 8. Douard J. Is risk neutrality rational? Med Decis Making 1996;16:10–11. 9. Wu G. The strengths and limitations of expected utility theory. Med Decis Making 1996;16:9–10. 10. Ware JE, Snow KK, Kosinski M, Gandek B. SF-36 Health Survey Manual & Interpretation Guide. The Health Institute, New England Medical Center, Boston, MA, 1993. 11. Spertus JA, Winder JA, Dewhurst TA, et al. Development and evaluation of the Seattle Angina Questionnaire: a new functional status measure for coronary artery disease. J Am Coll Cardiol 1995;25:333–341. 12. Torrance GW. Measurement of health state utilities for economic appraisal: a review. J Health Econ 1986;5:1–30. 13. Detsky AS, Naglie IG. A clinician’s guide to cost-effectiveness analysis. Ann Intern Med 1990;113:147–154. 14. Russell L, Siegel J, Daniels N, et al. Cost-effectiveness analysis as a guide to resource allocation in health: Roles and limitations. In: Gold M, Siegel J, Russell L, Weinstein M (eds.), Cost-Effectiveness in Health and Medicine. Oxford University Press, New York, 1996, pp. 3–24. 15. Klevit H, Bates A, Castanares T, et al. Prioritzation of health care services: a progress report by the Oregon Health Services Commission. Arch Intern Med 1991;151:912–916. 16. Nease RF, Jr., Owens DK. A method for estimating the cost-effectiveness of incorporating patient preferences into practice guidelines. Med Decis Making 1994;14:382–392. 17. Nease RF, Jr., Kneeland T, O’Connor GT, et al. Variation in patient utilities for outcomes of the management of chronic stable angina. Implications for clinical practice guidelines. Ischemic Heart Disease Patient Outcomes Research Team. JAMA 1995;273:1185–1190. 18. Owens DK, Shachter RD, Nease RF, Jr. Representation and analysis of medical decision problems with influence diagrams. Med Decis Making 1997;17:241–262. 19. Sumner W, Nease R, Littenberg B. U-titer: a utility assessment tool. Proc Annu Symp Comput Appl Med Care 1991:701–705. 20. Lenert LA. iMPACT3: online tools for development of web sites for the study of Patients’ preferences and utilities. Proc AMIA Symp 2000:1172.

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21. Lenert LA, Michelson D, Flowers C, Bergen MR. IMPACT: an object-oriented graphical environment for construction of multimedia patient interviewing software. Proc Annu Symp Comput Appl Med Care 1995:319–323. 22. Lenert LA, Sherbourne CD, Reyna V. Utility elicitation using single-item questions compared with a computerized interview. Med Decis Making 2001;21:97–104. 23. Kaplan RM, Bush JW, Berry CC. Health status: types of validity and the index of well-being. Health Serv Res 1976;11:478–507. 24. Brazier J, Usherwood T, Harper R, Thoams K. Deriving a preference-based single index from teh UK SF-36 Health Survey. J Clin Epidemiol 1998;51:1115–1128. 25. Gill TM, Feinstein AR. A critical appraisal of the quality of quality-of-life measurements. JAMA 1994;272:619–626. 26. Nichol G, Lleyellyn-Thomas HA, Thiel EC, Naylor CD. The relationship between cardiac functional capacity and patients’ symptom-specific utilities for angina: Some findings and methodologic lessons. Med Decis Making 1996;16:78–85. 27. Torrance GW, Furlong W, Feeny D, Boyle M. Multi-attribute preference functions. Health Utilities Index. Pharmacoeconomics 1995;7:503–520. 28. Feeny D, Furlong W, Boyle M, Torrance G. Multi-attribute health status classification systems: Health utilities index. Pharmacoeconomics 1995;7:490–502. 29. Kind P. The EuroQoL instrument: An index of health-related quality of life. In: Spilker B (ed.), Quality of Life and Pharmacoeconomis in Clinical Trials, Lippincott-Raven, 2nd ed. Philadelphia, PA, 1996, pp. 191–201. 30. Torrance GW, Siegel J, Luce B. Framing and designing the cost-effectiveness analysis. In: Gold M, Siegel J, Russell L, Weinstein M (eds.), Cost-Effectiveness in Health and Medicine. Oxford University Press, NY, 1996, pp. 54–81. 31. Gold M, Siegel J, Russell L, Weinstein M. (eds.) Cost-Effectiveness in Health and Medicine. Oxford University Press, NY, 1996.

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Introduction to Cost-Effectiveness Analysis Robert F. Nease, Jr., PhD CONTENTS THE GOAL OF COST-EFFECTIVENESS ANALYSIS THE INCREMENTAL COST-EFFECTIVENESS RATIO THE IMPORTANCE OF INCREMENTAL COST-EFFECTIVENESS THE CHALLENGE OF MEASURING EFFECTIVENESS TIME HORIZON AND DISCOUNTING PERSPECTIVE IN CEA SUMMARY REFERENCES

THE GOAL OF COST-EFFECTIVENESS ANALYSIS Cost-effectiveness analysis (CEA) is a discipline that seeks to generate insight about the economic efficiency with which various interventions (e.g., coronary artery bypass graft surgery [CABG]) generate health benefit. It is related to other analytic tools, such as decision analysis and cost–benefit analysis. One unique aspect of CEA is the explicit separation of the impact of an intervention on cost from its impact on health benefit. Although other approaches combine these effects into a single measure (cost-benefit analysis into monetary units, such as dollars, and decision analysis into a unit-less measure, utility), CEA divides the impact of an intervention into two parts: cost and health benefit (1,2). Why keep costs and health benefit separate during an analysis? The answer is straightforward: collapsing these two types of effects into a single measure cannot be accomplished without making an assertion about the trade-off between money and health. Imagine, for example, a surgery that costs $100,000 and extends life expectancy by 1 year. Declaring such an intervention to be “good” in an overall sense depends on how one feels about $100,000 relative to increasing life expectancy by 1 year. If years of life are worth no more than $50,000, then the intervention isn’t a good deal. On the other hand, if extra years of life are worth $500,000 each, then the intervention is a steal. And although any individual might be able to make that trade-off for him or herself (or even trade-offs that seem less Dorian Gray-ish … how much would you have paid to avoid that last head cold?), asserting that a specific trade-off is right for a group From: Contemporary Cardiology: Cardiovascular Health Care Economics Edited by: W. S. Weintraub © Humana Press Inc., Totowa, NJ

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Cardiovascular Health Care Economics Table 1 Hypothetical Costs and Remaining Life Expectancy for Three Options Option Do nothing Medical therapy Surgery

Cost

Remaining life expectancy

$0 $50,000 $150,000

10 years 14 years 15 years

of patients—or the entire US population—is laden with challenges, even for the most cool-headed policymaker. Thus, by separating the impact of interventions on cost and health effects, analysts created a mechanism that allowed them to perform their analyses without addressing the delicate question of how much health is worth. This approach led to the cost-effectiveness ratio from that same intervention (CER).

THE INCREMENTAL COST-EFFECTIVENESS RATIO The CER is a “bang-for-buck” (or more accurately a “buck-for-bang”) measure. It represents the increase in cost associated with an intervention divided by the gain in health benefit (1): Increase in cost CER = ————————– Gain in health benefit

(1)

In the example given previously, the increase in cost is $50,000, and the gain in health benefit is 1 year of life expectancy. The CER for that (hypothetical) intervention is therefore $50,000 per year of life gained ($50,000/1 year).

THE IMPORTANCE OF INCREMENTAL COST-EFFECTIVENESS Note that Eq. 1 uses “increase in cost” and “gain in health benefit” rather than the total cost and the total health benefit. This ratio is referred to as the incremental or marginal CER. All state-of-the-art CEA use incremental CER. To understand why it’s important to use incremental CER, consider the following simple example. Suppose two interventions are available, in addition to the option of doing nothing all. Table 1 shows the costs and life expectancy for the “do nothing” alternative as well as the two interventions. Also imagine that you are not willing to pay any more than $75,000 per additional year of life (perhaps because at that price, you can no longer sleep at night thinking about how many infants in developing countries you could vaccinate for that kind of money). What would you do? One way to approach this problem is to divide the remaining life expectancy for each of the alternatives by their cost: $0 per year for doing nothing, about $3600 per year for medical therapy ($50,000/14 years), and $10,000 per year for surgery. (These figures represent average CER.) Because your top price for years of life is $75,000, you might be tempted to accept surgery as a good deal at $10,000 per year … but you’d be mistaken (3). A better way to look at the problem is to work your way down the options (in order of increasing cost) until the incremental investment required to move from one intervention to another no longer generates health benefit at an acceptable rate.

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Table 2 Data for Incremental CER Option Do nothing Medical therapy Surgery

Cost

Remaining life expectancy

$0 $50,000 $150,000

10 years 14 years 15 years

Incremental Incremental life cost expectancy – $50,000 $100,000

– 4 years 1 year

To see how this approach works, Table 2 presents costs, remaining life expectancies, incremental costs, and incremental life expectancies for the options. Each incremental cost and life expectancy reflects the change in cost and life expectancy for that option relative to the option above it. (Because the “do nothing” option is the cheapest, it doesn’t have an incremental cost or incremental life expectancy.) For example, the incremental cost for medical therapy is $50,000 ($50,000–$0), and the incremental life expectancy is four years (14 years–10 years). Let’s work through the options, with the least expensive intervention (“do nothing”) as the starting point. Our first question is whether medical therapy is a good buy. With it, we can pick up 4 additional years of life expectancy (the incremental benefit) for a cost of $50,000 (the incremental cost). Thus, the incremental CER of medical therapy relative to doing nothing is $12,500 per year of life gained ($50,000/4 years). That’s much less than our top price of $75,000 per year of life gained, so it’s a good deal … we’ll take it. Now we’re faced with a new decision: is moving from medical therapy to surgery worth it? Surgery costs $100,000 more than surgery (the incremental cost), but produces just one extra year of life (the incremental benefit). In this case, the incremental CER of surgery relative to medical therapy is $100,000 per year of life ($100,000/1 year) … not a good enough deal, based on our $75,000 per year of life cut-off. So we’d be better off spending the extra $100,000 (i.e., what we would have spent to get from medical therapy to surgery) on something else … like vaccinating those aforementioned infants. Note that incremental CE can be extremely sensitive to the choice of comparators. If, for example, a nefarious analyst wished to promote surgery, he might delete medical therapy as an option in his analysis. In that case, the incremental CE of surgery would be calculated relative to the “do nothing” option. The incremental cost would be $150,000, and the incremental benefit would be 5 years, for an incremental CER of $30,000 per year of life gained … far less than our $75,000 per year of life cut-off. If we fell prey to this approach, we’d incorrectly decide to go for the surgery (which would, in fact, be reasonable if medical therapy wasn’t an option). Rule: The incremental CER for an intervention depends as much on the costs and effects of the comparator as it does on the intervention being considered. Corollary: Always examine which interventions were used in a CEA to ensure that all viable options were included, excluding a relevant alternative can distort the incremental CER.

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THE CHALLENGE OF MEASURING EFFECTIVENESS Accurately measuring both costs and effectiveness is a challenge when performing a CEA. And although there are several thorny issues in measuring costs, we will focus here only on the difficulties of measuring effectiveness. (In a nutshell, societal CEA requires that costs reflect the opportunity costs of all affected resources, regardless of their “price” [1,4]. In practice, analysts often use shortcuts to get at costs, including cost-to-charge ratios that attempt to convert prices—or charges—to costs.) In the simple example presented in Tables 1 and 2, we characterized the health benefit of interventions in terms of gains in life expectancy. Life expectancy is only one part of the story, however; interventions may affect the quality of life as much or more (or even to the detriment to) length of life. Suppose that in our example, the condition being treated affects both quality of life (e.g., severe chest pain), that medical therapy increases life expectancy but does nothing to improve quality of life, and that surgery both increases life expectancy and quality of life. Is medical therapy still the preferred option?

Quality-Adjusted Life Years As with the broader issue of health benefit and money, answering that question demands that we come up with a trade-off between length of life and quality of life. (If cost weren’t a consideration, analysts could keep length-of-life effects and quality-of-life effects separate and let the policymakers deal with the trade-off, but we’ve already gone to that well once.) The most widely accepted measure of health benefit that captures both length-of-life and quality-of-life effects is the quality-adjusted life year (QALY) (5–7). QALYs acknowledge that not all years of life are equally desirable. Specifically, QALYs assume that living some length of life in a reduced state of health is equivalent to living a shorter length of time in good health. Suppose, for example, that living 5 years with severe chest pain is equivalent to living 2 years in good health. The quality adjustment for severe chest pain is therefore 0.4 (2 years in good health/5 years with pain); each year with severe chest pain is 0.4 QALYs. (This quality adjustment is quite low; most studies, in fact, show that the quality adjustment for severe angina is much higher than 0.4 [8].) Despite evidence to the contrary, analysts almost always assume that the quality adjustment for a health state applies regardless of the length one spends in that state (1). This assumption makes it much more feasible to model the effects of an intervention on length and quality of life. Using this assumption, then, 10 years with symptoms would equate to 4 QALYs (10 years × 0.4), 14 years with symptoms would equate to 5.6 QALYs (14 years with symptoms × 0.4), and 15 years in good health would equate to 15 QALYs (15 years without symptoms × 10). (A quality adjustment of 1.0 is an assertion that living without symptoms is equivalent to living in good health.)

QALYs for Multiple Health States Over Time The assumption that quality adjustments apply, regardless of duration, also allows us to easily determine QALYs for a “trajectory” through a number of health states. Imagine, for example, that a patient experiences good vision (and health) for 5 years, monocular blindness for 4 years, then binocular blindness for 4 years. If the quality

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Table 3 Using QALYs in CEA Option Do nothing Medical therapy Surgery

Cost

Remaining LE

Remaining QALYs

Incremental cost

Incremental QALYs

$0 $50,000 $150,000

10 years 14 years 15 years

4 (10 × 0.4) 5.6 (14 × 0.4) 15 (15 × 1.0)

– $50,000 $100,000

– 1.6 9.4

adjustment for good health is 1.0, monocular blindness is 0.8, and binocular blindness is 0.2, then the overall QALY associated with that scenario (assuming quality of life is affected only by vision) is: (5 yr × 1.0) + (4 yr × 0.8) + (2 yr × 0.2) = 8.6 QALYs

Using QALYs in the Incremental CER Let’s return to our example of Tables 1 and 2. Table 3 presents the options, costs, remaining life expectancies, and QALYs, as well as incremental costs and incremental QALYs. Now let’s work though the incremental CER. For simplicity’s sake, we’ll assume that the most you’d be willing to pay for gaining 1 QALY of life remains $75,000. Doing nothing costs us nothing … and gets us 4 QALYs. Let’s decide whether medical therapy is a good deal. The cost of going from the “do nothing” option to medical therapy remains $50,000, but the gain in health benefit is only 1.6 QALYs. Thus, the incremental CER for medical therapy relative to doing nothing is about $31,000 per QALY ($50,000/1.6 QALYs). That’s below our top price of $75,000 per QALY, but substantially less attractive than when we ignored quality of life. What about moving from medical therapy to surgery? The incremental cost is $100,000, and the incremental benefit is 9.4 QALYs, for an incremental CER (for surgery relative to medical therapy) of about $10,700 per QALY … a much better deal than the $100,000 per year of life when quality-of-life effects were ignored. When we include quality-of-life effects in this example, surgery becomes attractive relative to medical therapy. Rule: The incremental CER for an intervention can depend as much on the effects of quality of life as it can on length of life. Corollary: Always check to ensure that all quality-of-life effects are addressed in a CEA … unless you are certain that such effects are irrelevant. If including quality-of-life effects can alter the results of a CEA then it must be true that the numerical values of those quality adjustments might also alter the results. (After all, there’s no difference between excluding the quality-of-life effects and setting all the quality adjustments to 1.) In short, the results may be sensitive to the assumptions about the quality adjustments.

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Where should we get the quality adjustments? Most analysts agree that utility assessments provide the strongest basis for estimating the quality adjustments (1,5,8). For more information about utility assessment, see Chapter 7.

Other Measures of Effectiveness Sometimes analysts stop short of estimating the effects of interventions on QALYs and, instead, present the cost per health outcome. If, for example, we were considering an intervention that reduced the risk of coronary events (e.g., myocardial infarction), we could calculate the incremental cost per coronary event averted. This approach has a couple of advantages. First, the results almost certainly require fewer assumptions; direct evidence from well-designed trials about the effects of the interventions on coronary events may be available, but such evidence on the effect on length of life may be missing. Second, quality adjustments can be avoided altogether, eliminating the subjective assessments associated with those adjustments. However, there are important limitations to this approach. Determining whether preventing an event is worth the cost (e.g., $100,000 per coronary event averted) requires an assessment of the effects of that event on length and quality of life, and making that assessment implicit, rather than explicit, does not remove the need to make that assessment. In addition, from a policy perspective, use of events as the measure of effectiveness makes it very difficult to compare the CE of interventions across different conditions. For example, is an intervention that costs $100,000 per coronary event averted a better use of resources than one that costs $150,000 per birth defect averted? For these reasons, most state-of-the-art CEA use QALYs as the measure of effectiveness.

TIME HORIZON AND DISCOUNTING Time plays an important role in CEA in two ways. First, the analyst must decide the time horizon of the analysis (i.e., over what timeframe to consider the effects of the intervention on costs and health). Second, the analyst needs to address the possibility that the timing of events (both costs and health effects) may affect the importance of those events.

Time Horizon for the Analysis In general, there are two (potentially) competing factors in choosing a time horizon for a CEA. Ideally, the time horizon should cover the entire period over which the interventions may have an effect. For situations in which the interventions may affect mortality—either by changing survival rates over time or imposing a risk of death (e.g., major surgery)—this period is for the patient’s lifetime. On the other hand, analysts are (understandably) hesitant to extend the time horizon beyond that reflected in the data on which the analysis is based because doing so increases the number of assumptions employed. Choosing between a lifetime horizon, and the horizon supported by direct evidence, can make a difference in an analysis. To understand why that’s true, imagine that surgery reduces the annual chance of death from 3% to 1% for a specific patient population, but imposes a perioperative mortality of 3%. Now suppose that medical therapy reduces the chance of mortality from 3% to 2%, with no upfront mortality. Figure 1 shows the resulting survival curves (i.e., the fraction of patients surviving over time with each of the two treatment options). Notice that the survival curves cross; by about year 3, the extra mortality of surgery is more than made up for by the greater reduction in annual mortality relative to medical therapy.

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Fig. 1. Hypothetical survival curves for surgery and medical therapy.

Table 4 Life Expectancy as a Function of Time Horizon for the Analysis Life expectancy (yrs) Time horizon 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Surgery

Medical therapy

1.0 1.9 2.9 3.8 4.8 5.7 6.6 7.5 8.4 9.3 10.2 11.0 11.9 12.7 13.6 14.4

1.0 2.0 2.9 3.9 4.8 5.7 6.6 7.5 8.3 9.1 10.0 10.8 11.5 12.3 13.1 13.8

The life expectancy over a fixed time horizon is equal to the area under the survival curve. (Proving this fact requires calculus and is, therefore, an exercise left for the enthusiast.) Table 4 shows the life expectancy for each of the two options as a function of the time horizon selected. Notice that the life expectancy for medical therapy is greater than that for surgical therapy, as long as the time horizon for the analysis is less than about 7

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years. If, for example, the trial on which the analysis was based offered only 5 years of follow-up data, and we limited the analysis accordingly, medical therapy would offer a slightly greater life expectancy. However, if we extended the analysis to 10 years (either by lengthening the study or by forecasting), surgery would offer a greater life expectancy. In short, there is a tension between performing a lifetime analysis (which is correct if any of the interventions have effects on long-term outcomes, such as mortality), and keeping the analysis as close as possible to the data on which the analysis is based. Many analysts perform the analysis both ways to investigate whether the results differ depending on the timeframe of the analysis. Unfortunately, this approach is helpful only in situations for which the time horizon doesn’t affect the results of the analysis. For other situations, the analyst (or reader) must decide whether to use an analysis that is almost certainly wrong (a time horizon constrained to the follow-up period) or one that is not clearly correct (a lifetime time horizon that extends beyond the known data). Rule: Selection of a time horizon for the analysis can affect the results, and there are good reasons to use either a lifetime horizon or the time horizon suggested by the length of follow-up data … but no overwhelming reason to use one rather than the other.

Discounting in CEA When is a dollar not worth a dollar? When you have to wait to use it. Separate from inflation, economists (and young children who want their allowance as soon as possible) attribute a “time value” to money (1,9). This value relates to the opportunity cost associated with not having the money available to invest or spend in the interim. For example, $100 in 1 year from now is generally considered to be worth less than $100 today, even if prices remained fixed (i.e., absent inflation), because the $100 could be used in the interim. The idea behind discounting is to convert a stream of costs over time to an equivalent upfront cost, called the net present value (NPV). This conversion is usually accomplished by applying a discount rate. Most analysts use a discount rate in the range of 3% to 5% (roughly equivalent to the risk- and inflation-free value of money implied by behavior in the marketplace). The NPV of a cost C incurred T years into the future is: NPV = C/(1 + i)T

For example, with a discount rate of 5%, the NPV of a cost of $1000 that occurs one year into the future is $952 (= $1000/1.05). The higher the discount rate, the more valuable money is today than it is in the future. Discounting rewards interventions that accumulate savings early, as well as those that delay costs. It’s relatively easy to understand discounting money; borrowing or saving money with an interest rate is familiar to most people. But health effects are discounted in CEA as well. Although it’s hard to comprehend what the NPV of years of life (or QALYs) means, they can be discounted just as dollars can. In fact, there is a general consensus that health effects should be discounted at exactly the same rate as costs. The basic rationale for this approach is that because CEA assumes a fixed trade-off

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between health effects (e.g., QALYs) and dollars, both must be treated equally in terms of time value (and therefore discounting) (1,9). Rule: Both costs and health effects should be discounted to reflect time value. Most analysts use a discount rate of 3–5% (9).

PERSPECTIVE IN CEA Why an Analysis Requires a Perspective The single most important design decision an analyst can make in performing a CE study is the selection of the perspective. Although many perspectives are possible, the three most frequently examined are that of the patient, the payer, and society. Choosing a perspective is crucial when performing a CEA. In practical terms, the perspective chosen affects which costs and health effects are included in the analysis. For example, a patient facing a treatment decision might be shielded from much of the cost of care because of insurance coverage; from the patient’s perspective, the main issues might be the health effects of the treatment. The payer, on the other hand, may bear the bulk of the costs and may value the effects of the treatment differently than the patient does. These perspectives can differ substantially; consider an expensive treatment that improves quality of life, but has no effect on length of life or productivity. From the patient’s perspective, the treatment may be cost effective, because it offers a benefit at a low cost; from the payer’s perspective, the treatment may not be cost effective, because it is expensive and offers little benefit. Note that although these perspectives are potentially in conflict, neither is wrong: the analysis for the patient reflects the decision he or she faces and his or her resources at stake. Similarly, the analysis for the payer reflects the decision it faces, as well as the resources it has at stake. In fact, on careful examination, the two perspectives address somewhat different decisions. The payer, for example, may be considering whether to cover a treatment in an insurance policy it offers employers. In contrast, the patient is deciding whether to undergo the treatment given the specifics of his or her insurance coverage, among other things. Lack of clarity about the decision perspective impairs the ability to rationally analyze most decisions (10).

The Societal Perspective The most common perspective used currently in CEA is that of society. Taking the societal perspective casts a very broad net, attempting to account for costs and health effects at the level of society. Because the scope is large, societal CEA are often more “comprehensive” than those performed from the patient or payer perspective (11). But taking the societal perspective introduces some unique challenges and limitations. With the societal perspective, transfers within society are typically ignored (unless the transfer affects other resources). For example, a CEA of a safety program might include the reduction in litigation costs from the payer perspective; such costs would be ignored in most societal CEA because a settlement simply involves transferring resources between members of society (12). In addition, from the societal perspective, costs are not necessarily the same as prices; a cost is the value of the next best use

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of the resource (1). Because prices reflect what a market may bear rather than the next best use of the resources, there can be a difference between prices (or “charges” in most health applications) and costs. From the perspective of a patient or payer, however, the price paid is the cost. Valuing health benefits can be tricky with the societal perspective as well. For example, should the quality adjustments used in estimating QALYs be obtained from patients or from members of the community at large? A recent panel of experts advocates using community-based measures of preference for QALYs, rather than those of patients (6). But this recommendation means that, for example, men sampled from the community may provide assessments of the quality adjustments for outcomes associated with breast cancer, and women may provide assessments of the quality adjustments for outcomes associated with prostate cancer. Unlike the perspective of the patient and the payer, however, the biggest limitation of taking the societal perspective is the absence of a decision maker that shares that perspective (7). The United States does not have socialized medicine, and, thus, society makes few medical care decisions. (Medicare coverage and reimbursement decisions likely come the closest in terms of specific treatments; other society-level decisions— such as mandatory seat-belt laws might also apply.) It is important to note that the use of CEA using the societal perspective by professional organizations or health care providers to make individual care decisions may be based on considerations at odds with the individual patients. In short, the societal perspective risks using quality adjustments from people who do not have the disease of interest and costs that do not reflect what anyone is paying to analyze a decision for no which there is no specific decision maker (7). Rule: Selecting a perspective is crucial when performing a CEA. Although comprehensive, the societal perspective may be in conflict with that of any actual decision maker.

SUMMARY • CEA attempts to generate insight about the economic efficiency of various interventions. • All state-of-the-art CEA use incremental CER. The incremental CER summarizes the increase in cost, owing to an intervention divided by the gain in health benefit offered by that intervention. • The incremental CER for an intervention depends as much on the costs and effects of the comparator as it does on the intervention being considered, excluding a relevant alternative can distort the incremental CER. • QALYs capture both length-of-life and quality-of-life effects. Almost all state-of-the-art CEA use QALYs. • Selection of a time horizon for the analysis can affect the results, and there are good reasons to use either a lifetime horizon or the time horizon suggested by the length of followup data. • Both costs and health effects should be discounted to reflect time value. Most analysts use a discount rate of 3–5%. • Choosing a perspective is crucial when performing a CEA. Although comprehensive, the societal perspective may be in conflict with that of any actual decision maker.

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REFERENCES 1. Weinstein M, Fineberg H, Elstein AS, et al. (eds.) Clinical Decision Analysis. Sauders, Philadelphia, PA, 1980. 2. Eisenberg JM. Clinical economics. A guide to the economic analysis of clinical practices. JAMA 1989;262:2789–2886. 3. Harris RA, Nease RF. The importance of patient preferences for comorbidities in cost-effectiveness analyses. J Health Econ 1997;16:113–119. 4. Luce BR, Manning GE, Siegel JE, Lipscomb J. Estimating costs in cost-effectiveness analysis. In: Gold MR, Siegel JE, Russell LB, Weinstein MC. (eds.), Cost Effectiveness in Health and Medicine. Oxford University Press, NY, 1996, pp. 176–213. 5. Torrance GW. Social preferences for health states. An empirical evaluation of three measurement techniques. Socio-EconPlanning 1976;10:129–136. 6. Gold MR, Patrick DL, Torrance GW, et al. Identifying and valuing outcomes. In: Gold MR, Siegel JE, Russell LB, Weinstein MC (eds.), Cost Effectiveness in Health and Medicine. Oxford University Press, NY, 1996, pp. 82–134. 7. Nease RF Jr. Challenges in the validation of preference-based measures of health-related quality of life. Med Care 2000;(9 Suppl):II155–II159. 8. Nease RF Jr, Kneeland T, O’Connor G, et al. Variation in patient utilities for outcomes associated with the management of chronic stable angina. Implications for clinical practice guidelines. JAMA 1995;273:1185–1190. 9. Lipscomb J, Weinstein MC, Torrance GW. Time preference. In: Gold MR, Siegel JE, Russell LB, Weinstein MC (eds.), Cost Effectiveness in Health and Medicine. Oxford University Press, NY, 1996, pp. 214–247. 10. Nease RF Jr. Increasing the transparency of medical decision-making. PhD dissertation, Stanford University, 1988, p. 2602, MCU89-19452. 11. Russell LB, Siegel JE, Daniels N, et al. Cost-effectiveness analysis as a guide to resource allocation in health. Roles and limitations. In: Gold MR, Siegel JE, Russell LB, Weinstein MC (eds.), Cost Effectiveness in Health and Medicine. Oxford University Press, NY, 1996, pp. 3–24. 12. AuBuchon JP, Littenberg B. A cost-effectiveness analysis of the use of a mechanical barrier system to reduce the risk of mistransfusion. Transfusion 1996;36:222–226.

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Cost-Effectiveness Analysis Alongside Clinical Trials Statistical and Methodological Issues

Elizabeth M. Mahoney, ScD and Haitao Chu, MD, MS CONTENTS INTRODUCTION STATISTICAL CONSIDERATIONS IN THE ANALYSIS OF COST DATA COMBINING COSTS AND EFFECTS CE ACCEPTABILITY CURVES NET BENEFIT APPROACH TO HANDLING UNCERTAINTY IN CEA POWER AND SAMPLE SIZE CALCULATIONS FOR TRIAL-BASED CE STUDIES A BAYESIAN FRAMEWORK FOR CEA AND DECISION MAKING ADDITIONAL CONSIDERATIONS SUMMARY AND FUTURE DIRECTIONS REFERENCES

INTRODUCTION Over the past quarter century, as costs have become an important factor in medical decision making, indices of cost-effectiveness (CE) have increasingly been used in the evaluation of medical therapies. The traditional approach to cost-effectiveness analysis (CEA) utilizes decision-analytic models based on estimates of cost and effectiveness outcomes obtained from nonsampled secondary data (from the literature, insurance claims databases, and expert opinion) and employs sensitivity analysis to examine the variability in results as uncertain model inputs are varied over reasonable ranges. Throughout the past decade, there has been an increasing trend for economic studies to be incorporated into large clinical trials. This has allowed for cost-effectiveness to be evaluated directly, using primary patient-level data on both clinical outcomes and costs. The stochastic, or random, nature of this data, resulting from patient-to-patient (sampling) variability, allows for uncertainty associated with estimates of cost-effectiveness to be estimated using methods of statistical analysis. This chapter presents an overview of statistical and other methodological considerations in the evaluation of cost-effectiveness using experimentally obtained data from randomized controlled trials. From: Contemporary Cardiology: Cardiovascular Health Care Economics Edited by: W. S. Weintraub © Humana Press Inc., Totowa, NJ

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STATISTICAL CONSIDERATIONS IN THE ANALYSIS OF COST DATA Parametric Approaches to Comparing Costs Before introducing the complexities that arise when combining estimates of cost differences between two treatments with estimates of clinical effect differences, it seems appropriate to start by discussing characteristics of cost data that can pose challenges for the economic data analyst. Health economic decisions regarding the efficient allocation of scarce resources involve consideration of the total cost of treating all patients with a specific disease with the particular treatment in question. In such settings, it is the arithmetic mean, which is the per-person cost of implementing the treatment or intervention, that is the most relevant measure for summarizing and comparing costs (1). Characteristics of cost distributions can complicate the task of the data analyst needing to carry out a formal comparison of mean costs. The distribution of cost data tends to be skewed, with a large proportion of costs at the lower end of the distribution and a long right tail. This is because of the fact that costs are usually not less than zero, whereas in essence, there is no upper limit to how high they can be. As the appropriateness of many statistical tests and models relies on an approximately normal underlying distribution of the data, many common tests, such as the two-sample t test for the comparison of means, may not be appropriate. When working with non-normally distributed data, one option is to apply a transformation that normalizes the underlying distribution, then apply normal theory tests, such as the t test, to the transformed data. A frequently used transformation in this setting is the logarithmic (base e) transformation; other possibilities include the square root and reciprocal transformations. When analyzing cost data, a shifted log transformation is often used in which a constant (usually 1) is added before taking the log, include observations in order that are actually zero in the analysis. However, the log transformation results in a comparison of geometric, rather than arithmetic, means; only if the variances of the two groups on the log scale are equivalent, is a test of geometric means equivalent to a test of arithmetic means (2). In general, results from such tests carried out on transformed cost data do not yield results that are directly relevant to health economic decision making. An example of cost distributions from Treat Angina with Aggrastat and Determine Cost of Therapy with an Invasive or Conservative Strategy (TACTICS) Thrombolysis in Myocardial Infarction (TIMI) 18, a clinical trial comparing an early invasive vs conservative approach to the treatment of patients with unstable angina or non-ST segment elevation myocardial infarction (MI), are presented in Fig. 1. The distribution of cumulative 6-month costs for each of the treatment arms are presented, as well as corresponding distributions after applying the log transform. As is the case with most cost distributions, both treatment arms have skewed distributions, a heavy right tail and the log transformation, indeed, brings both of the distributions closer to the normal distribution in shape. Mean costs for the early invasive and conservative arms are $21,813 and $21,227, respectively. Neither the t test on the original scale nor the t test on logtransformed costs is statistically significant (p = 0.52 and 0.60, respectively).

Nonparametric Approaches to Comparing Costs Because the skewness of most cost distributions renders the conventional parametric approaches to comparing groups generally inappropriate for the comparison of costs, an alternative approach is to use a nonparametric test, such as the Wilcoxon rank sum

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Fig. 1. Distribution of 6-month costs from TACTICS-TIMI 18.

test. which is based on the ranks of the observations and is therefore not influenced by the outlying values, or the Kolgomorov-Smirnov test, which is a test of the equivalence of the cost distributions. However, neither of these approaches yield results that reflect a comparison of the total costs of implementing one treatment vs the other, and, therefore, they are not particularly useful with respect to health economic evaluation. INTRODUCTION TO THE NONPARAMETRIC BOOTSTRAP An alternative method for comparing the average costs of two alternative treatments is through application of the nonparametric bootstrap approach to statistical inference, which was developed in the late 1970s by Efron (3–5). This approach, which has been greatly facilitated over the past few years with the availability of powerful desktop computing, avoids making any parametric assumptions regarding the sampling distribution of a statistic by deriving an empirical estimate of the sampling distribution by drawing a large number of samples with replacement from the original data. The statistic of interest is then calculated for each of the samples, and the bootstrap replicates of the original statistic yield an empirical estimate of the sampling distribution. Using x to denote the cost variable, the central limit theorem states that asymptotically (i.e., as n, the sample size—the size of the original sample as well as all bootstrap samples, tends toward infinity) and under suitable conditions, the sampling distribution of the sample mean, denoted ¯x (note: the bar above a variable is used to denote the mean of

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that variable), of n-independent observations, is normally distributed around the true population mean, denoted as µ, and the population variance, σ2, can be estimated using the sample variance, s2. The bootstrap approach has been advocated as the most appropriate general method for comparing arithmetic mean costs, as it can be used to draw inferences about the difference in mean costs between treatment groups without making any distributional assumptions. Because of the large degree of variability that characterizes most cost distributions, combined with the fact that most clinical trials are not adequately powered to detect a significant difference in costs (a point taken up later in the Power and Sample Size Calculations for Trial-Based CE Studies section), many have advocated the use of confidence intervals rather than hypothesis tests for the comparison of costs (and the evaluation of cost-effectiveness). The deficiencies of hypothesis testing are summarized by the expression: absence of evidence is not evidence of absence (6). The failure to find a statistically significant difference does not mean that a difference does not exist; it may be the result of low power to detect such a difference given the sample size, the distributional characteristics of the data, and the particular test performed. BOOTSTRAP CONFIDENCE LIMITS FOR THE COST DIFFERENCE The bootstrap method offers a nonparametric approach for the derivation of confidence limits for the incremental difference in costs. Using nT and nC to denote the number of patients in the new treatment and control groups, respectively, the bootstrap process can be summarized by the following three steps: 1. Sample with replacement nT cost observations from the sample of patients who received the new treatment and nC cost observations from the sample of patients who received the control treatment, and calculate the average cost for each treatment group from the two bootstrap samples. 2. Calculate the difference between the two average costs calculated in step 1. 3. Repeat steps 1 and 2 many times (at least 1000 times for the calculation of confidence intervals) yielding a set of bootstrap estimates of the difference between treatment groups in average costs, which forms the empirical estimate of the sampling distribution of the difference in costs.

From the bootstrap distribution of the cost difference, confidence intervals can be derived using a variety of methods (1). The most straightforward approach is the percentile method in which the approximate 100(1–α) percent confidence interval is given by the 100(α/2) and 100(1–α/2) percentiles of the empirical distribution. Other approaches to the derivation of bootstrap confidence intervals are discussed in Nonparametric Bootstrap Methods section in the context of bootstrap estimates of the confidence interval for cost-effectiveness ratios. Figure 2 presents a histogram of the bootstrap distribution of the difference in mean costs for the TACTICS-TIMI 18 data presented in Fig. 1. The estimated confidence interval for the difference in mean costs ($586), obtained using the percentile method, is (–$1087, $2486).

Adjusting for Covariates Often a researcher is interested in comparing mean costs between treatment groups after adjusting for the effect of a set of covariates. A common approach under these

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Fig. 2. Histogram of 2000 bootstrap replicates of the difference in 6-month costs (invasive–conservative) from TACTICS-TIMI 18.

circumstances is to fit a linear (least-squares) regression model using log-transformed costs as the dependent variable (in order to meet the normality assumptions of the model). A problem arises, however, in the comparison of adjusted mean costs, as a result of the fact that although such a regression model yields unbiased estimates of adjusted (or conditional) mean cost estimate on the log scale, retransformation of those results back to the original scale does not yield unbiased estimates of mean costs for each treatment group on the original scale. One frequently applied solution to this retransformation problem is to apply the smearing estimate proposed by Duan (7). Alternative estimators have also been proposed; an overview of the performance of different approaches, under varying characteristics of the underlying sampling distribution, is presented by Manning and Mullahy (8).

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COMBINING COSTS AND EFFECTS The Incremental Cost-Effectiveness Ratio When a new intervention yields improved health outcomes at increased overall costs, the appropriate summary measure of CE is the incremental cost-effective ratio (ICER), defined as the ratio of the difference in mean costs between two treatments divided by the difference in mean effectiveness, as shown below, CT – CC ∆C ICER = ———– = —– ET – EC ∆E

where CT and CC, and ET and EC denote costs and effects for the treatment and control groups, respectively. The ICER measures the additional cost associated with the new intervention per 1 unit increase in effectiveness; for the time being, we do not specify the units of the effectiveness outcome. Note that the incremental (also referred to as marginal) aspect of the ICER cannot be overemphasized. The new treatment must always be compared to the next best (referred to as the standard or control) treatment, as economic decisions regarding the allocation of limited resources are always made at the margin (see Chapter 9 for a more complete discussion of the importance of incremental analyses). When data on both costs and effects are collected prospectively in a clinical trial, conventional statistical methods of hypothesis testing and (perhaps more appropriately) estimation are possible for drawing inferences regarding both the cost and effectiveness outcomes. In this setting, an estimate of the ICER can be obtained as follows, ¯ ^ CT –¯ CC = —– ∆¯ C ICER = ———– ¯ ¯ ¯ ET – EC ∆ E ^

where the hat in ICER indicates that it is an empirical estimate of the ICER from a ¯ C, and so on, denote estimated means for each of those sample, and the bars in ¯ CT, C ^ variables. Statistical inference (e.g., confidence interval generation) relating to ICER, however, poses analytic challenges because of the fact that both numerator and denominator are estimated with uncertainty, and the estimation of confidence intervals for ratio statistics, such as the ICER, is not straightforward. The distribution of a ratio statistic is usually unknown and frequently unstable, particularly when the confidence interval around the denominator includes zero, because as the denominator approaches zero, the ICER approaches infinity. Only when both numerator and denominator follow a standard normal distribution is the distribution of a ratio statistic actually known; in those circumstances, the ratio statistic would have a Cauchy distribution, a distribution for which the mean does not exist, and the variance is infinite (9,10)! This results from the fact that variables with this distribution can take very extreme values when the denominator approaches zero. Because clinical trials are usually designed to detect the smallest meaningful difference in effectiveness between treatments, many trials yield clinical results in which treatment effects are close to zero, which render trial-based estimates of CE ratios particularly unstable.

The Cost-Effectiveness Plane The cost-effectiveness plane, first introduced by Black (11), is a useful means for conceptualizing decision rules for CEA, as well as presenting results from health eco-

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Fig. 3. The cost-effectiveness plane.

nomic studies. As illustrated in Fig. 3, the cost-effectiveness plane is a two-dimensional space in which the x axis represents the average difference (new treatment minus control) in effectiveness (∆¯ E ) per patient, and the y axis represents the average difference in cost (∆¯ C) per patient. The four quadrants of the cost-effectiveness plane correspond to four qualitatively different relationships between clinical effect and cost differences between the new treatment and the control. In quadrant I, there is a clinical benefit with the new treatment, though this benefit comes with additional cost. In this situation, the decision as to whether to adopt the new treatment must be based on a judgment of whether the additional cost is justified by degree of additional benefit. Proceeding in a clockwise direction, in quadrant II, the new treatment has greater effectiveness and costs less and, thus, is considered to dominate the control; the logical decision would be to adopt the new treatment. In quadrant III, the new treatment is less effective, but less expensive, and the decision as to which treatment to adopt, as with quadrant I, would have to be based on some judgment related to whether the additional cost of the standard therapy is justified given the level of additional benefit. Quadrant IV corresponds to the situation in which the difference in clinical effect between treatment groups favors the control, which is also found to be less expensive. In this situation, the new treatment is considered to be dominated by the control, and the rational decision would be not to adopt the new treatment. It is often, if not usually, the case that when new health care interventions are found to confer benefit relative to standard therapy, the additional benefit comes at increased cost. In such situations, the clinical effectiveness and cost impacts of the new intervention relative to the standard can be summarized by a point in quadrant I of the cost-

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effectiveness plane. The joint cost and effectiveness impacts of four hypothetical new therapies for four different diseases, each compared to standard treatment for the corresponding disease, are represented in quadrant I by lower case letters a, b, c, and d. The optimal point within quadrant I would be in the lower right-hand corner, with the greatest possible gains in effectiveness at the least possible increase in cost. Accordingly, intervention c is dominant to interventions a and b because it yields a greater increase in effectiveness over the standard, at lower (relative to a) or equivalent (relative to b) cost. The choice between interventions d and c, however, would depend on the willingness-to-pay threshold, in terms of cost per unit of health gain. If such a threshold were to lie between the ICER associated with interventions d and c (such a threshold is represented by the dotted line extending from the origin in Fig. 3), then a logical decision would be to adopt intervention d, and not c. (Technical note: in Fig. 3, if the points a, b, c, and d, corresponded to four new therapies for the same disease, all compared to the same standard treatment, and if treatment d was considered to be cost-effective (i.e., below the willingness to pay threshold), the evaluation of the cost-effectiveness of treatment c should be carried out using treatment d as the standard or control treatment. In other words, the incremental cost comparing treatments d and c should be compared to the incremental benefits comparing treatments d and c, in the evaluation of the cost-effectiveness of treatment c. For further discussion of this point, see ref. 12).

Confidence Intervals for CE Ratios In the above example, we assumed that the incremental costs and effects of treatments a, b, c, and d were known exactly, and, thus, evaluation of relative cost-effectiveness did not have to take into consideration variability around estimates of the ICER. When estimates of cost and effect differences obtained from clinical trials are used to estimate cost-effectiveness ratios, the sampling variability associated with both costs and effects should be taken into consideration through the derivation of confidence intervals for the ICER estimate. THE CONFIDENCE BOX The CE plane is especially useful in considering approaches to the estimation of confidence limits for an estimated CE ratio. Methodological research concerning the derivation of confidence intervals for CE ratio was stimulated by the publication of a paper by O’Brien et al. in 1994 (13). In this paper, examination of this issue began with the presentation of the confidence box approach to arriving at bounds for the CE ratio. As a first step in this approach, the point on the plane that corresponds to the incremental cost and effectiveness differences between a new treatment and a control treatment is identified. The line connecting this point to the origin has a slope equivalent to the ICER. The confidence box approach involves calculating the confidence limits for the incremental cost and effectiveness differences using standard methods and plotting them on the CE plane, centered around the estimated ICER. A box can then be generated by the intersecting lines drawn vertically from the boundaries of the effect difference confidence limits and horizontally from the cost difference confidence limits. This is illustrated in Fig. 4 using data from a hypothetical clinical trial, for which the mean (sd) lifetime costs associated with a new treatment and a standard treatment are $19,000 ($5000) and $17,000 ($5000), respectively. With effectiveness measured in

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Fig. 4. The confidence box in the cost-effectiveness plane.

terms of quality-adjusted life years (QALYs) the mean (sd) effectiveness for the new and standard treatments are 13 (1), and 12.75 (1), respectively. Therefore, the estimated ICER for these data is $8000 [($19,000–17,000)/(13–12.75)] per QALY gained. Natural best- and worst-case limits for this estimated ICER might be defined by the upper left and lower right corners of the confidence box. The slope of the line passing through the point defined by the upper 95% confidence limit for cost and lower 95% confidence for effectiveness might be considered an upper confidence limit for the ICER, and the lower limit defined by the slope of the line passing through the point, representing the lower 95% confidence limit for costs, and the upper 95% confidence limit for effectiveness might be considered a lower confidence limit for the ICER. However, these boundaries do not represent 95% confidence limits for the ICER. The probability that the true cost difference lies within the estimated 95% confidence interval for the cost difference is 0.95, and the same applies to the effectiveness difference. Under the assumption of independence between differences in cost and differences in effectiveness, the probability that both cost and effectiveness differences lie within the corresponding confidence intervals is 0.952 or 0.9025. Therefore, the confidence region defined by the box is closer to a 90% confidence region, in the setting in which cost and effectiveness differences are independent. O’Brien et al. (13), recognized that the more likely shape for the bivariate distribution of cost and effect differences was that of an ellipse, with lines representing equal probability around the central point-estimate, similar to a geographic contour map, and that the assumption of independence between

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cost and effect differences yielded further limitations of this approach. Briggs and Fenn (14) pointed out that the area between the two lines, representing the upper and lower confidence bounds for the ICER in Fig. 4, will always be larger than the area represented by the box itself, and, therefore, the box generates a conservative region for the ratio. TAYLOR SERIES METHOD An alternative method for deriving confidence limits for the CER, proposed by O’Brien et al. (13), applies the Taylor Series approximation (also referred to as the Delta method) to the ratio. The Taylor approximation can be applied to any function, y, of two random variables, x1 and x2 (i.e., y = f (x1, x2)), in order to derive the variance of y. An advantage of this approach is that it accounts explicitly for potential correlation between x1 and x2, which in the context of the ICER, represent the difference in costs (x1) and the difference in effects (x2); in many settings, this correlation is nonzero. The general formulation of the Taylor series approximation, which involves some calculus, is shown below, var(y) = (∂y/∂x1)2 var(x1) + (∂y/∂x2)2 var(x2) + 2(∂y/∂x1)(∂y/∂x2) cov(x1, x2)

where var stands for variance, cov stands for covariance, and ∂ denotes the partial ^ derivative. By reformulating this equation in terms of—–— ICE——— R, the coefficient of variation for both x1 and x2 (defined for x1 as cov(x1) = √var(x1)/(¯x1)2), and the correlation —–————— coefficient between x1 and x2(ρ = cov(x1, x2)/√var(x1)var(x2)), and setting x1 equal to ∆¯ C and x2 equal to ∆ ¯ E, this equation can be rewritten as follows, ^ ^ ¯ 2 + cv(∆E) ¯ 2 – 2ρ cv(∆C)cv(∆ ¯ ¯ var(ICER) = (ICER)2 [cv(∆C) E)] Once an estimate of the variance of the ICER is obtained, standard parametric assumptions (i.e., that the estimated ICER is statistically well-behaved, such that with a large enough sample, it has an approximately normal distribution) can be used to derive a confidence interval as shown below, —–——— —–——— ^ ^ ^ ^ (ICER – zα/2 var(I CER), ICER + zα/2 var(I CER))

√

√

O’Brien et al. (13) recognized that the assumption of normality for the distribution of the CER, specifically when there is non-negligible probability that the denominator of the ratio, the difference in effectiveness between treatments, is zero, is a drawback to this approach. FIELLER’S THEOREM In the first half of the 20th century, a general approach to calculating confidence intervals for ratio estimates, which is based on the assumption of joint normality of the numerator and denominator, was proposed by Fieller (15,16). (Joint normality means that the numerator and denominator are both normally distributed, though they may not be independent, i.e., there may be some degree of correlation between them.) This approach was later advocated as a means for calculating confidence intervals around ICERs by Chaudhary and Stearns (17) and Willan and O’Brien (18). The approach has an advantage over the Taylor series approach as it avoids the assumption of normality for the ratio estimate itself, and thus accommodates skewness in the ICER distribution. Under the assumption of joint normality of the difference in costs (∆C) and difference in effectiveness (∆E), and using R to denote the ICER, the distribution of the quantity

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defined as ∆¯ C – R∆ ¯ E also has a normal distribution (because the distribution of the sum or difference of two normally distributed variables has a normal distribution) with a mean of zero, and thus dividing ∆¯ C – R∆ ¯ E by its standard deviation, as shown below, ¯ + √var(∆C)

R2

¯ – R∆ E¯ ∆C ¯ – 2 R cov(∆C, ¯ ∆ E) ¯ var(∆ E)

yields a variable with a standard normal distribution. Setting this expression equal to the critical point from the standard normal distribution, zα/2 for a (1–α) 100% confidence interval, yields a quadratic equation in R, from which the roots of the equation, which represent the Fieller confidence limits for the ICER, can be derived. In the interest of space, expressions for these qualities are not presented here, and the reader is instead referred to any of the following sources (14,17–20). If the assumption of joint normality is met, confidence limits derived using Fieller’s method are exact. In contrast to confidence intervals derived using the Taylor series approach, confidence intervals ^ derived using Fieller’s theorem are not symmetric around ICER, if the true distribution of the ICER is skewed. NONPARAMETRIC BOOTSTRAP METHODS Each of the approaches to the calculation of confidence limits for the ICER described previously have the limitation that they require making some parametric assumptions regarding the underlying distribution of data from which the estimates are derived. Bootstrap methods offer an alternative nonparametric approach to the derivation of confidence limits around estimated ICERs (14,17,19,21). When data on costs and health outcomes are available for two samples of patients, nT patients who received the new treatment and nC patients who received the control, the bootstrap approach to the estimation of confidence limits around the estimated ICER involves the following four steps: 1. Sample with replacement nT cost/effect pairs from the sample of patients who received CT and ¯ ET for the bootstrap sample. the new treatment, and calculate ¯ 2. Sample with replacement nC cost/effect pairs from the sample of patients who received ¯ C and E ¯ C for the bootstrap sample. the control, and calculate C 3. Use the bootstrap means from steps 1 and 2 above to calculate the difference in mean costs, the difference in mean effectiveness, and the estimated ICER for the bootstrap replicate sample. 4. Repeat steps 1–3 many times (at least 1000 times for the calculation of confidence intervals), yielding a set of bootstrap estimates of the ICER, which forms the empirical estimate of the sampling distribution (of the ICER). ^

Several approaches to the derivation of confidence intervals for ICER have been proposed (21). A familiar approach applies the normal approximation and involves esti^ mating the standard error of ICER from the bootstrap distribution and constructing a ^ confidence interval under the assumption that ICER is distributed normally, i.e., a 95% confidence interval could be derived as follows: —–——— ^ ^ ^ CI(ICER) = ICER ± 1.96 var(I CER)

√

However, because the assumption of normality is often not reasonable, this approach can yield misleading results. A straightforward approach that avoids this assumption of normality is the percentile method, which uses the 100(α/2) and 100(1–α/2) percentiles

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of the empirical bootstrap sampling distribution as the estimated confidence limits (in the case of a sample of 1000 bootstrap replicates, this would involve selecting the 26th and 975th of the 1000 replicates as the lower and upper 95% confidence limits). This percentile approach was presented in the Bootstrap Confidence Limits for the Cost Difference section in the context of deriving confidence limits for an estimate of the cost difference. However, some commentators have pointed out two characteristics of the bootstrap distribution that can contribute to inaccurate confidence using the percentile method. The first characteristic is the fact that the bootstrap estimates of ratios, such as the ICER are characterized by a degree of bias, and the second relates to the fact that the sampling distribution is often skewed. As a result, a modification of the percentile method, the bias-corrected and accelerated approach was proposed by Efron (5), for which algebraic adjustments are made to the percentiles selected to serve as the confidence interval endpoints in order to adjust for potential bias. In the interest of space, corresponding formulas are not presented here, and the reader is instead referred to Briggs, Mooney, and Wonderling (20) and Briggs, Wonderling, and Mooney (21) for details. COMPARISON OF METHODS Results from simulation studies (20,22) show that though the performance of different approaches to confidence interval estimation for the ICER vary according to the underlying distribution of the data and the correlation between numerator and denominator, Fieller’s method tends to consistently perform better than the Taylor series method, and of the bootstrap methods, the bias-corrected and adjusted approach and the percentile methods are preferable to the approach based on the normal approximation. If the bootstrap estimate of the sampling distribution of the ICER is asymmetric, the bias-corrected and accelerated approach will yield a more accurate confidence interval than the percentile approach (17). If Fieller’s method and the bootstrap approaches yield similar results, the joint normality assumption regarding cost and effectiveness differences, which underlies Fieller’s method, is likely reasonable and, Fieller’s method should be considered the method of choice for two reasons: (1) parametric methods are usually more powerful than their nonparametric counterparts when the parametric assumptions hold; (2) Fieller’s approach is more replicable; two analysts applying the bootstrap approach will arrive at (slightly) different results because of chance alone (19). Fieller’s method, however, is not useful when the power for the effectiveness difference is low. In such circumstances, the distribution of the effectiveness difference is not normal and the estimated variance of the CE ratio can extend to positive and/or negative infinity. EXAMPLES Figure 5 presents the joint distribution of cost and effect differences resulting from bootstrap resampling of costs and QALYs for data simulated according to the distributions used for Fig. 4, and Fig. 6 presents the bootstrap distribution of the associated ICER statistic (note that the distribution has been truncated on the right). The slope of the solid line in Fig. 5 represents ICER ($7856 per unit of effectiveness for this simulated data), and 95% confidence limits for ICER, derived using four of the methods described previously, are presented as well. Note that because both costs and QALYs were simulated using normal distributions, confidence limits from application of Fieller’s method and the percentile bootstrap approach are very similar; in some instances, the lines representing those confidence limits are virtually superimposed.

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Fig. 5. Bootstrap distribution of cost and effect differences from simulated data.

Fig. 6. Bootstrap estimate of the sampling distribution of the ICER from simulated data presented in Fig. 5.

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Fig. 7. Illustration of positive (A) and negative (B) correlation between cost and effect differences.

Table 1 Influence of Different Degrees of Correlation Between Cost and Effects on Confidence Limits for the ICER: Results from Four Methods

Methods Box method Taylor’s Theorem Fieller’s Theorem Percentile bootstrap

Correlation (p) = 0.5

Correlation (p) = 0

Correlation (p) = –0.5

Lower 95% Upper 95% confidence confidence limit limit

Lower 95% Upper 95% confidence confidence limit limit

Lower 95% Upper 95% confidence confidence limit limit

3589 4478 5335 5437

20,852 11,356 14,034 14,054

3577 3243 4499 4549

20,414 12,469 16,242 16,294

3558 2256 3939 3895

20,338 13,378 18,379 17,758

The simulated data, from which the bootstrap distribution presented in Figs. 5 and 6 are derived, were generated assuming no correlation between cost and QALY variables. Figure 7 illustrates the impact of correlation between costs and effect differences on the associated joint distribution for simulated data, with the same mean and variances as those presented in Fig. 5. In Fig. 7A, there is a moderate degree of positive correlation (p = 0.5) between incremental costs and incremental effects and in Figure 7B costs there is a moderate degree of negative correlation (p = –0.5). The esti^ mated confidence limits for ICER, presented in Table 1, illustrate how positive correlation tends to decrease the variability of the estimated ICER, yielding tighter confidence limits, whereas negative correlation increases the variability. This is seen for all methods of deriving confidence limits other than the box method, which does not take into account potential correlation between cost and effects. An example of a setting in which there is positive correlation between costs and effects is an intervention for the acute treatment of stroke. In this setting, an intervention that reduces mor-

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tality is likely associated with an overall increase in health care costs. Alternatively, interventions that avoid the morbidity associated with chronic diseases, such as asthma, tend to be associated with lower health care costs; in such settings, there is negative correlation between costs and effects (23). Analyses of the simulated data presented in the examples in Table 1 was facilitated by the fact that the distribution of cost and effect differences fell in only one quadrant of the CE plane. When working with real trial data, however, the analysis is often not so straightforward. A problem associated with the interpretation of confidence intervals for ICER is that the interpretation of a ICER is ambiguous without information regarding the quadrant of the CE plane to which the estimate corresponds; this ambiguity is especially problematic when dealing with estimates corresponding to points in quadrants II and IV of the CE plane. A negative ICER estimate corresponding to a point in quadrant II favors the new treatment, whereas a negative ICER in quadrant IV favors the standard treatment. Moreover, it is not possible to unambiguously rank points in quadrants II and IV on the basis of the corresponding ICER estimate (24,25). As a result, if the distribution of a ICER extends into the negative region, statistical inference, including the interpretation of confidence intervals, is problematic. This is illustrated by borrowing an example from Briggs, O’Brien, and Blackhouse (19). Consider three treatments that yield different points in quadrant II: point A corresponds to a treatment yielding a 1 life-year clinical benefit at a $2000 savings; point B corresponds to a treatment yielding a 2 life-year benefit at a $2000 savings; point C corresponds to a treatment with a 2 life-year benefit at a savings of $1000. These points correspond to ICERs of –$2000/life year, –$1000/life year and –$500/life year, respectively. Whereas treatment A has the lowest ICER, and treatment C has the highest, it is treatment B that is actually most preferable, as it has the highest number of life years saved at the greatest savings. A modified percentile bootstrap interval was proposed as a solution of the first problem described previously (26,27). This approach involves assigning the lowest (i.e., most favorable) value, zero, to points in quadrant II, and highest possible values, ∞, to points in quadrant IV. This modification can significantly improve the coverage probability of confidence limits derived using the percentile bootstrap method, however, it can also yield confidence limits that extend from 0 to positive infinity when a relatively large proportion of points in both of quadrants II and IV are remapped in this manner. Figure 8 presents bootstrap estimates of the joint distribution of cost and effectiveness differences from the TACTICS-TIMI 18 trial, and Fig. 9 presents the bootstrap distribution of the associated ICER estimates. Effectiveness in this example is measured in terms of the difference in 6-month death/MI rates (conservative–invasive). In circumstances such as this, in which the joint distribution of the cost and effectiveness differences falls within all four quadrants of the cost-effectiveness plane, the calculation and interpretation of confidence intervals around the estimated ICER can be especially complex, because of the issues associated with negative confidence limits raised previously. In such circumstances, one option is to report the estimated ICER, along with the proportion of the distribution falling within the dominant and dominated quadrants of the cost-effectiveness plane. In this example from TACTICS-TIMI 18, the estimated ICER is $17,758 per death or MI prevented, with 26% of the joint distribution of cost and effect differences falling within the dominant quadrant, suggesting a 26% probability of the invasive strategy, providing clinical benefit without additional cost (28).

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Fig. 8. Bootstrap distribution of cost and effect differences from TACTICS-TIMI 18.

Fig. 9. Bootstrap estimate of the sampling distribution of the ICER for the TACTICS-TIMI 18 data presented in Fig. 8.

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Fig. 10. Confidence interval for ICER using the angular transformation approach, simulated data.

THE ANGULAR TRANSFORMATION APPROACH An alternative method for the generation of confidence limits for the ICER that is especially useful when a significant portion of the distribution of cost and effect differences falls in quadrants II and IV of the CE plane (in which the effect difference is numerically small, and, therefore, the ratio is unstable) was proposed by Cook and Heyse (29) and involves applying an angular transformation (through the use of polar coordinates) to the points in the CE plane in order to derive the confidence region. We discuss here two approaches, denoted A and B, applying an angular transformation in the context of the bootstrap estimate of the empirical joint distribution of cost and effect differences. These approaches are illustrated in Fig. 10 using simulated data. Approach A involves identifying boundaries of the confidence region in the CE plane such, that 47.5% of the points lie in a counter-clockwise rotation from the line, representing ICER, and 47.5% of the points lie in a clockwise rotation. Approach B involves extending the line that passes through ICER into the diagonally opposite quadrant so that it passes through the point (–∆C, –∆E), and denoting the portion of the plane external to the confidence region as the region that falls between two lines identified, such that 2.5% of the distribution lies between each of the lines and the extension of the ICER line. Note that approach A is based on the assumption that the distribution of ^ ICER is centered around the line represented by the ICER estimate, whereas approach B is based on a related, but not equivalent, assumption that the region least likely to contain the ICER is the region farthest in distance on the CE plane from the ICER estimate itself. Under some conditions, the two approaches can yield considerably differ-

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ent confidence intervals. When exactly 50% of the joint distribution lies to either side of the line represented by the ICER, approach A and approach B will yield identical results. (Also, note that when the entire distribution of cost and effectiveness differences lies within quadrant I, as in Fig. 5, approach A yields results that are equivalent to those derived using the bootstrap percentile method, a one-dimensional approach.) In Fig. 10, whereas the point estimate of the ICER, $14,591 per unit of health gain, falls within quadrant I, approach A yields 95% confidence limits, which range from –$986 in quadrant IV to –$1849 in quadrant IV, and exclude only a narrow slice of the quadrant IV (the confidence region includes all of quadrants I, II, and III). Such confidence limits would not be unambiguously interpretable without being plotted on the CE plane. Approach B yields a confidence region that is somewhat narrower, ranging from –$2173 in quadrant II to $489 in quadrant III, though this region still includes all of quadrants I and IV and portions of quadrants II and III. The confidence region derived using approach B might be considered more optimal for this data than that derived using approach A, resulting from the fact that it is smaller. In general, the optimal angular transformation approach in this bootstrap setting will depend on the degree of correlation between cost and effectiveness differences; if there is positive correlation, approach A will tend to be optimal, whereas approach B is optimal in settings in which that correlation is negative (as it is for the simulated data used in this example). If there is no correlation between cost and effect differences, confidence regions derived using the two approaches will largely overlap. The angular transformation is intuitively appealing, and the previous example illustrates how the interpretation of an ICER is ambiguous without information regarding the quadrant of the CE plane in which the estimate falls. Application of any of the methods for deriving confidence intervals presented in previous sections to the setting in which the joint distribution of cost and effect differences falls within quadrants II and IV (i.e., the denominator of the ICER is relatively small) can yield estimates that suffer from instability as well as a lack of unambiguous interpretability. The angular transformation yields both stable and interpretable estimates, though it requires the results to be presented in the two-dimensional plane as opposed to being presented as a one-dimensional confidence interval. An example from the clinical literature of the application of this approach to the derivation and presentation of CEA results is the CEA of Jönsson et al. (30) of lipid lowering in patients with diabetes from the 4S study.

CE ACCEPTABILITY CURVES Although statistical issues relating to the derivation of confidence intervals for the ICER have been discussed extensively in the recent health economics literature, a pragmatic alternative approach to representing uncertainty around an ICER estimate, via the generation of a cost-effectiveness acceptability curve, was proposed by Van Hout et al. in 1994 (31). This approach has been advocated as a means of avoiding some of the practical problems associated with confidence interval estimation and as being more useful in the decision-making setting (14). A CE acceptability curve represents uncertainty around an estimated ICER by the proportion of the joint distribution of the cost and effect differences for which the associated ICER is less than the threshold CE ratio, denoted by λ, which is the maximum price society is willing to pay for an incremental gain in health (also referred to as the ceiling ratio). In terms of bootstrap replications

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plotted on the CE plane, such as those presented in Figs. 5, 7, and 8, uncertainty can be presented as the proportion of bootstrap replications, which fall below and to the right of the line with a slope of λ. Because the actual value of λ is usually not known, the CE acceptability curve is generated by varying λ over a range of possible values. A CE acceptability curve generated from bootstrap replications of the simulated data presented in Fig. 5 is shown in Fig. 11A, and an acceptability curve corresponding to the TACTICS-TIMI 18 data plotted in Fig. 8, is shown in Fig. 11B. The 50% point on the acceptability curve corresponds to a point estimate of the ICER. Ninety-five percent confidence limits for ICER are represented by the 2.5% and 97.5% points on the acceptability curve. The curve intersects the vertical axis at the one-sided p-value for the cost difference which, in terms of the bootstrap distribution in the cost-effectiveness plane, is represented by the proportion of cost and effect pairs lying below the horizontal line (incremental costs < 0) and tends toward one minus the p value for the effect difference, which, in terms of the bootstrap distribution, is represented by the proportion of points lying to the left of the vertical line (incremental effects < 0). The representation of uncertainty around estimated ICERs through the use of CE acceptability curves avoids the problems described previously, while actually presenting more information on uncertainty than confidence intervals. Moreover, by summarizing for every value of λ, the evidence in support of the treatment in question being cost-effective, CE acceptability curves are especially relevant to the decision-making process in which the correct value of the ceiling ratio, as well as the level of confidence (i.e., 1-α), required for the decision to be made, is not usually known and may vary from setting to setting. For any value of the ceiling ratio, it is directly apparent from a CE acceptability curve what the probability of the treatment being cost-effective (and not cost-effective) is.

NET BENEFIT APPROACH TO HANDLING UNCERTAINTY IN CEA Up to this point, the cost-effectiveness decision rule for implementation of a new treatment has been based on the probability that the estimated CE ratio is less than some threshold ratio, λ, as stated below, ∆C/∆E < λ

Recently, two alternative formulations of this decision rule, derived by algebraic manipulation of the above inequality, have been proposed. Together, these formulations have generated what has come to be referred to as the net benefit approach to CEA and associated decision making. In one formulation, proposed by Stinnett and Mullahy (25), the net health benefit (NHB) of a new treatment are expressed as shown below, NHB = ∆E – (∆C/λ) > 0

Alternatively, Tambour et al. (32) proposed a net monetary benefit (NMB) approach, defined below, NMB = λ∆E – ∆C > 0

The two above expressions for the net benefit have equivalent implications, as they are essentially only a rescaling of the effect and cost differences, yielding a net benefit statistic on the effect and cost scales. Accordingly, the NHB is the monetary value of the health effect less the difference in costs. Positive net benefits indicate that an intervention

142 Fig. 11. CE acceptability curves corresponding to simulated data (A) and data from TACTICS-TIMI 18 (B).

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represents good value for the money. The primary advantage of the net benefit approach is that, by using the ceiling ratio, λ, to turn the cost-effectiveness decision rule into a linear expression rather than a ratio, statistical evaluation of the null hypothesis, H0: ∆C/∆E > λ proceeds in a more straightforward fashion owing to the fact that the corresponding sampling distributions are more stable. The expression for the variance of both of the net benefit statistics is easily derived as the linear combination of two variables that are, with sufficient sample size, normally distributed. ^ ¯ – 1/λ2 var(∆ C) ¯ – 2/λ cov(∆ E, ¯ ∆ C) ¯ var(NHB) = var(∆ E) ^ ¯ – var(∆ C ¯ ) – 2λcov(∆ E, ¯ ∆C ¯) var(NMB) = λ2 var(∆ E)

Confidence intervals for both net benefit measures can, therefore, be obtained in the usual fashion, i.e., the 95% confidence limits for NHB are calculated as shown below. —–——– ^ ^ NHB ± 1.96 var(NHB)

√

Use of the net benefit approach also has an advantage in that a positive NHB (or NMB) unambiguously favors the new intervention and values of NHB (or NMB) become continuously more favorable as they increase from negative infinity. A disadvantage of the net benefit approach is that it depends on an estimate of the ceiling ratio in order to be applied, and, therefore, uncertainty around the appropriate or relevant value of λ limits its usefulness. Stinnett and Mullahy (25) address this limitation by recommending that the analysis be carried out over a range of possible values for λ and present the NHB and associated confidence limits as a function of λ graphically. (They also point out that the problem of uncertainty with respect to λ is not limited to the NHB approach, and they maintain that the explicit consideration of λ required by a NHB analysis should be considered an advantage rather than a drawback of the approach.) Such a curve crosses the horizontal axis at the point estimate of the ICER, and where the upper and lower confidence limit curves cross the x axis corresponds to the upper and lower confidence intervals for the ICER. Heitjan (33,34) demonstrated that confidence limits for the ICER obtained using this approach are exactly equivalent to those obtained using Fieller’s method. From this curve, a CE acceptability curve can also be generated as the plot of the probability that NHB > 0 vs λ. Such a plot could be generated using bootstrap resampling or by deriving the distribution of the NHB statistic by assuming joint normality of cost and effect differences, as is done when applying Fieller’s theorem to derive the distribution of the ICER. Figure 12A presents the NHB curve corresponding to the simulated data initially presented in Fig. 5, and Fig. 12B presents the NHB curve corresponding to the TACTICS-TIMI 18 data presented in Fig. 8.

POWER AND SAMPLE SIZE CALCULATIONS FOR TRIAL-BASED CE STUDIES Any of the parametric (i.e., nonbootstrap) approaches described previously for deriving confidence intervals for CE ratios can be manipulated in order to carry out power and sample size calculations to aid in the design of cost-effectiveness studies. However, in addition to the usual acceptable Type I and Type II (α and β) error rates, estimates of the mean, variance, and (for all methods other than the box method) covariance of cost and effect differences, these calculations require specification of the ceiling ratio, which

144 Fig. 12. NMB curves corresponding to simulated data (A) and data from TACTICS-TIMI 18 (B).

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serves as the critical value of the CER, such that the trial should have 100β% power to demonstrate that cost-effectiveness is less than λ with 100 (1-α)% certainty. The null hypothesis, the hypothesis that the CER is unacceptable, can be stated as follows H0: ∆C/∆E > λ, if ∆E > 0 ∆C/∆E < λ, if ∆E < 0

This one-sided hypothesis can be tested by calculating a one-sided confidence interval for the CE ratio, and determining whether the upper confidence limit lies below the line designated by λ, in which case, the null hypothesis would be rejected in favor of the alternative hypothesis, supporting of the cost-effectiveness of the new treatment. (Technical note: a one-sided 95% confidence interval can be obtained by calculating a two-sided 90% confidence interval and, in this case, disregarding the lower, left-hand limit, which has no purpose in this decision-making setting). Briggs and Gray (35) apply the box method to carry out power and sample-size calculations, and Willan and O’Brien (36) and Gardner et al. (37) apply variations of Fieller’s method, assuming joint normality of cost and effect differences. In a simulation study, Al et al. (38) demonstrate how the required sample size increases if negative correlation between cost and effect differences is assumed (in comparison to no correlation) and decreases if a positive correlation is assumed. This is because of the increased variability of the ICER estimate when costs and effects are negatively vs positively correlated, as was shown in Fig. 7 and Table 1. Briggs, O’Brien and Blackhouse (19) advocate use of the net benefit approach for power and sample-size calculations, owing to the more simplified analyses involved, and Laska, Meisner, and Siegel (39) apply a similar approach with and without assuming normality of the cost and effect differences. In general, because costs tend to be more variable than measures of clinical effectiveness, the sample size required to demonstrate cost-effectiveness is usually larger than that required for the clinical effectiveness outcomes. In practice, however, the sample size of clinical trials is determined, such that there is adequate power for the clinical outcome(s). The ethical dilemma associated with needing to continue a trial beyond the point at which clinical efficacy has been demonstrated was raised by O’Brien et al. (13), who suggested that opinions regarding this dilemma would likely depend on the nature of the disease and the importance of the cost-effectiveness question. For a nonlife-threatening disease, continuation of a trial to the point at which there is adequate power to test both efficacy and cost-effectiveness endpoints may be possible (i.e., patients may agree to participate for personal, as well as more altruistic, reasons), if third-party reimbursement status of a new therapy depends on the demonstration of cost-effectiveness. Taking the perspective of the United Kingdom National Health Service (NHS), Briggs (40) posits that failure to recruit enough patients to give unequivocal treatment and policy recommendations could be seen as unethical, leading to delay in providing cost-effective treatments, delay in curtailing cost-ineffective treatments, and a consequent underachievement off potential health gain from available resources from within the NHS. It is likely to continue to be the case, however, that cost-effectiveness evaluations alongside clinical trials will need to be carried out within the sample-size constraints determined for the clinical evaluation, with the resultant greater degree of uncertainty in terms of wider confidence intervals. As a result, the weight of evidence supporting the cost-effectiveness of the intervention in question should be considered rather than a reliance on conventional significance

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levels. This is perhaps most naturally evaluated and summarized through the use of CE acceptability curves, as advocated by Briggs and Fenn (14). This approach informs the policymaker, for any given value of λ, the probability that a treatment is cost-effective.

A BAYESIAN FRAMEWORK FOR CEA AND DECISION MAKING This chapter would not be complete without at least a brief mention of the role that a Bayesian interpretation of probabilities from CEA has in facilitating the interpretation of results. To begin with, the distinction between classical, or frequentist, inference and Bayesian inference, must be made. Although frequentist statistics have traditionally been the dominant mode, Bayesian methodology has become an increasingly more active area of statistical development over the past couple of decades. The frequentist bases probability statements on the distribution of the observables (i.e., the data) given an underlying model. For example, in the hypothesis testing setting, the null hypothesis serves as the underlying model, and the distribution of the data is used to make a probability statement regarding how likely it was to observe the data that was observed (or more extreme data) if the null hypothesis was true. Traditionally, the null hypothesis is rejected if that probability is less than 0.05. In contrast, the Bayesian conditions on what is known (i.e., the data) and uses probability distributions to describe uncertainty about the unknowns, the model parameters. Many maintain, quite convincingly, that the Bayesian approach is the most natural approach in the decision-making setting. The essence of the Bayesian approach is a learning process, whereby beliefs concerning the distribution of parameters (in Bayesian parlance, prior distributions) are updated (to posterior distributions), on the basis of available data, through the use of Bayes Theorem. Recalling that the odds favoring an event with a probability of p equals p/1 – p, and using diagnostic testing as an illustration, Bayes Theorem states that the posterior odds of a disease (i.e., given the test results) is derived by multiplying the prior odds of the disease (i.e., before the test) by the likelihood ratio, defined as the ratio of the probability of the test result given the disease is present to the probability of the result if the disease is absent. Though a frequentist interpretation of CE acceptability curves, that they represent one minus the probability of obtaining the result that was obtained (or a more extreme result) given that the net benefit, conditional on the ceiling ratio, λ, is nonpositive, is possible (41), it has been argued that the most natural interpretation of CE acceptability curves is a Bayesian one in which the curves are taken to represent the probability that the intervention is cost-effective (14,31). Indeed, labels used for the vertical axis of CE acceptability curves presented in this chapter reflect this interpretation. Bayesian methods can be classified into three main approaches, which differ according to the nature of the prior information (or prior distribution). One approach is to assume no prior information (i.e., an uninformative prior), in which case the posterior distribution is dominated by the observed data. A Bayesian analysis based on an uninformative prior is very similar to a frequentist analysis based on the observed data (while allowing a more natural Bayesian interpretation) (42). Empirical Bayesian methods involve the estimation of prior distributions using available information from prior studies; such methods are very similar to a frequentist approach based on the pooling of all available data. Subjective Bayesian methods involve soliciting prior information from experts. Historical contention between frequentist and Bayesian camps was the result of the perception that all Bayesian methods were highly subjective and sensitive to the prior beliefs employed, while frequentist methods were objective and

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robust (43). However, even to those at odds with the subjective Bayes approach, the advantages of the Bayesian approach when an uninformative prior method is used in the setting of cost-effectiveness related decision making is difficult to dispute, as illustrated previously in the context of the interpretation of CE acceptability curves, which is far more intuitive using the Bayesian, rather than the frequentist, framework.

ADDITIONAL CONSIDERATIONS Heterogeneity and Stratified Analyses To this point, this chapter has been largely concerned with the representation of uncertainty (or conversely, precision) around estimates of cost-effectiveness from clinical trials data. It is important to recognize that underlying the overall cost-effectiveness results from a trial may be a degree of variation in results because of heterogeneity in the patient population. The treatment effect may be larger or smaller for different subsets of the population, and this, in turn, may have considerable impact on estimates of incremental cost-effectiveness. Given appropriate consideration to the avoidance of mining the data for subgroup effects, subgroup analyses that represent both heterogeneity and precision can be valuable in the decision-making process regarding the cost-effectiveness of new interventions. Briggs et al. (19) advocate the use of costeffectiveness acceptability curves for the presentation of cost-effectiveness results for subgroups. In consideration of the use of cost-effectiveness information, Briggs and Gray (44) uses the example of coronary artery bypass grafting (CABG) vs medical management to illustrate the extent to which marginal changes in the ICER occur at the clinical margin—i.e., as the intervention is extended to individuals with less severe clinical disease, ICER increases. Weintraub et al. (45) and Mahoney et al. (46) present other examples of cost-effectiveness analyses carried out explicitly at the clinical margin in the evaluation of glycoprotein IIb/IIIa inhibitors for the prevention of adverse events following angioplasty, and intravenous amiodarone for the prevention atrial fibrillation following CABG. In the economic analysis from TACTICS-TIMI 18, costeffectiveness results were presented for high-risk subgroups defined according to the presence of high levels of troponin T and ST-segment changes at baseline (46). A recent paper by Hoch, O’Brien, and Blackhouse (47) demonstrates that in addition to avoiding the statistical issues encountered in the analysis of CE ratios, the net benefit framework also facilitates the use of a regression approach in the evaluation of cost-effectiveness. They demonstrate how a simple regression model with net monetary benefit as the outcome, and a treatment group indicator as the only predictor (or covariate), yields results exactly equivalent to those obtained using the standard approach to CEA. They also show how the regression results can be used to obtain a cost-effectiveness acceptability curve by plotting (1-p/2) against λ, where p is the p value corresponding to the coefficient for the treatment effect. Perhaps the greatest appeal to their methods, however, is the ability with this regression approach to adjust cost-effectiveness estimates for potential imbalance between groups, as well as the ability to examine the effect of a covariate on an intervention’s incremental net benefit (i.e., to identify potential sources of heterogeneity in the cost-effectiveness results) through the use of (treatment by covariate) interaction terms in the regression model. It seems likely that this paper will have significant impact on the future direction of both methodological and applied cost-effectiveness research.

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How Benefits are Measured SHORT-TERM VS LONG-TERM As discussed in the 1996 report from the Panel on Cost-Effectiveness in Health and Medicine, the optimal effectiveness measure for CE ratios is considered to be QALYs (48). This measure incorporates both survival and health-related quality-of-life benefits and, therefore, allows for the benchmarking of CE estimates across different types of interventions. Historically, $50,000 per QALY has been considered an appropriate societal willingness-to-pay threshold. However, this cut-off was proposed decades ago, and many have proposed that it should be adjusted considerably upward (see the specification of the threshold cost-effectiveness ratio section). The duration of many clinical trials is not sufficiently long to provide an accurate estimate of the long-term course of disease or recovery from which life years, QALYs, and thus the incremental gain in life years or QALYs can accurately be derived. The shortterm time horizon for many trials therefore renders a CEA based on in-trial data, in terms of cost per life year or QALY gained, limited in relevance for policy setting. In TACTICS-TIMI 18, for example, patients with unstable angina/non-ST segment elevation MI were randomized to an early invasive vs conservative strategy. The primary clinical endpoint was the composite of death, nonfatal MI and rehospitalization for an acute coronary syndrome (ACS) at 6 months (49). As presented in the example in the Statistical Considerations in the Analysis of Cost Data section, costs over 6 months were higher on average for the invasive strategy by $586, though this difference was not statistically significant, and the confidence interval around this estimate was quite large (–$1087, $2486) (28). Whereas the rate of the primary endpoint was significantly lower for the invasive strategy (15.9% invasive vs 19.4% conservative) (49), 6-month death rates were similar (3.3% vs 3.5%), yielding very small differences between treatment groups in both life years and QALYs (the difference between treatments in QALYs over the 6month time horizon translates into less than 9 hours!). Because this measure of effectiveness of the invasive strategy likely has little relevance to the actual long-term impact of the strategy, an estimate of cost-effectiveness based on it is virtually meaningless. Although life years and QALYs over the 6-month trial period in TACTICS-TIMI 18 were similar, there was a significant difference between groups in both the primary endpoint and the combined endpoint of death or MI (49). A more meaningful shortterm economic analysis is one in which benefits are measured in terms of endpoints such as these, which have more relevance to the longer-term impact of the treatment strategies being compared. For several reasons, however, the appropriateness of including the endpoint of rehospitalization for ACS, a component of the primary clinical endpoint, in the effectiveness measure (in addition to death and MI) in a CEA may not be appropriate. One reason is that this endpoint has unproven prognostic significance over the long term, relative to the two irreversible endpoints of death and MI. Willingness to pay to avoid a hospitalization that does not involve dying or having an MI may be considerably different from the willingness to pay to prevent a major cardiac event (involving an MI or death). Also, including rehospitalizations for ACS in the effectiveness measure for the CEA raises the issue of double counting addressed by the panel (48). Because these rehospitalizations clearly impact the numerator of the ICER, inclusion of them in the effectiveness measure (the denominator) as well is inconsistent with the objectives of the analysis. Therefore, a cost per death/MI-prevented analysis was per-

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formed for TACTICS. As described previously (see the Examples section), this analysis yielded an ICER estimate of $17,758 per death/MI prevented, with 26% of the bootstrap distribution falling in the dominant quadrant of the cost-effectiveness plane (28). PROJECTING BEYOND THE TRIAL Short-term CEA based on in-trial data, such as the TACTICS-TIMI 18 analysis described previously, tend to have limited utility for health economic decision making because of the lack of threshold standards for ICER expressed in terms short-term endpoints, such as cost per death/MI prevented. As a result, long-term projections of shortterm results are often carried out in order to arrive at cost per life year or cost per QALY estimates, as such results can be benchmarked against other interventions competing for funds from the same pool of resources and, therefore, have greater potential utility for policy setting. This is often done by developing a model for projecting average life expectancy (and possibly average costs, if the in-trial cost differences are not believed to accurately represent the lifetime incremental cost differences for the two treatments) for each of the treatment groups on the basis of short-term event rates, and, in the case of a cost per QALY (also referred to as cost-utility) analysis, making assumptions regarding the utility of different disease states for the derivation of QALYs. Such models often take the form of Markov models, which are particularly well-suited to disease processes characterized by risk that is continuous over time, when the timing of events is important, and when important events can occur repeatedly over time (50). A Markov model is defined by a set of mutually exclusive and exhaustive health states; at any point in time, each person in the model must reside in one, and only one, of the health states and at fixed increments of time (referred to as the Markov cycle length), persons are assumed to transition between health states according to a set of transition probabilities. Values are assigned to each of the health states, representing the cost and utility of spending one cycle in that state; together, with the length of stay in each of the states, these values allow for the estimation of average costs and effects/utility associated with different treatment strategies, from which estimates of long-term cost-effectiveness of different treatments can be calculated. A complete overview of the different approaches to the evaluation of Markov models is beyond the scope of this chapter, and the reader is instead referred to any of the following references (50–52). Uncertainty around estimates of long-term CEA based on projections of in-trial results is typically examined through sensitivity analyses rather than statistical methods, because it is not possible to account for sampling error in the results from projection models. A one-way sensitivity analysis involves systematically varying each variable (i.e., unknown parameter) in a model over a plausible range of values while holding other variables at their most plausible level. This approach is easily generalized to twoway and multiway sensitivity analyses, though the presentation and interpretation of results from such analyses are not as straightforward. Alternatively, best (and worst) case scenarios can be generated by setting all variables equal to their most optimistic (and pessimistic) values. In the context of a Markov model, sensitivity analyses would include examining the effect of varying the values of the transition probabilities. Probabilistic sensitivity analysis using Monte Carlo simulation (53), which involves assigning distributions to parameters in the projection model (i.e., for example transition probabilities in a Markov model), and repeatedly sampling from those distributions in order to obtain a distribution of projected outcomes, offers advantages to the other approaches, as it

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Fig. 13. Difference in cumulative 6-month (invasive–conservative), TACTICS-TIMI 18, reproduced from ref. 28.

acknowledges that though the exact values of each of the transition probabilities are unknown, some values are more likely than others (in this sense, it is inherently Bayesian). As pointed out by Briggs (54), although one-way sensitivity analyses tend to underestimate, and extreme scenario analyses will tend to overestimate, the uncertainty associated with results of an economic evaluation, probabilistic analyses may be expected to produce more realistic results and also allows for a probabilistic analysis of the CE results that involve any of the methods presented in this chapter. Of course, any CEA that involves estimates of costs and/or life years or QALYs over a period of time greater than 1 year requires discounting of both cost and effectiveness outcomes to arrive at the net present value (NPV) of those quantities, which are used in the analysis. In the interest of space, the rationale and formula for calculating the NPV of costs and effects are not presented here, and the reader is instead referred to Chapter 9. Model-based long-term CEA should always include sensitivity analyses examining the impact of varying the discount over a reasonable range (usually between 3% and 5%). For TACTICS-TIMI 18, a fairly simple deterministic (i.e., nonstochastic) long-term CEA was carried out by applying age and sex-specific estimates of life expectancy for patients with coronary heart disease with and without an acute MI from the Framingham Heart Study (55) to patients who did and did not experience a nonfatal MI during the course of the trial. The incremental cost associated with the invasive strategy was assumed to be well represented by the in-trial estimated cost difference at 6 months. Figure 13 graphically presents the difference in cumulative 6-month costs (invasive–conservative) for the TACTICS-TIMI 18 trial, with associated confidence limits for the cost difference obtained from bootstrap resampling. The relative flatness of the curve beyond 90 days was used to support the assumption that the 6-month difference in cumulative costs of $586 provides a reasonable estimate of the long-term incremental costs of the invasive strategy.

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For the long-term CEA for the Platlet glyco-protein IIb/IIIa in Unstable Angina: Receptor Suppression Using Integrilin Therapy (PURSUIT) trial, the impact of a nonfatal MI on life expectancy for patients with ACS was estimated using data from the Duke Cardiovascular Database and used in the estimation of life expectancy for patients in the trial who did and did not experience a nonfatal MI during the 6-month trial period (56). Life expectancy for patients in PURSUIT who survived 6 months without a nonfatal MI was estimated to be 16 years, and the prevention of a nonfatal MI was estimated to yield on average 1/8 of the savings in life years achieved by preventing early death (57). (In other words, patients in the PURSUIT trial who experienced a nonfatal MI during the 6-month follow-up period were estimated to have a 14-year life expectancy.) A second long-term analysis was carried out for TACTICSTIMI 18, based on those life expectancy estimates for ACS patients with and without an acute MI obtained for the PURSUIT trial/Duke database analysis. When trial-based economic studies are based on data from a subset of the overall trial population (i.e., for TACTICS-TIMI 18, the economic study was based on cost data from all US non-Veterans Affairs (VA) patients for whom UB-92 formulations of the hospital bills were obtained; 1722 of the overall 2220 patient trial population), the possibility exists for the long-term analysis to utilize effectiveness data from the overall trial, rather than the economic subset. The choice between these two approaches depends on the opinion of the investigator, regarding which is the most valid estimate of the clinical effect, as well as the population to which the results are to be considered generalizable. An additional consideration is the extent to which the incremental cost estimate is generalizable to the overall trial population. The long-term cost-effectiveness projections for TACTICSTIMI 18 were carried out using estimates of effectiveness from both the overall trial and the US/non-VA patient subset. This analysis yielded estimates of cost per life year gained, ranging from $8371 to $25,769, depending on the assumptions used in the analysis (28).

Multinational Studies Issues related to the generalizability of cost and effect differences are especially relevant in the evaluation of cost-effectiveness from multinational trials when the results of CEA are to be generalized to specific countries. (This is also a concern in multicenter studies for which there is interest in obtaining results generalizable to specific health care delivery systems.) Cross-country differences in, and interactions between, clinical and economic factors can threaten the direct generalizability of the results of a trial from one country to another (58). Practice patterns within one country may be influenced by cost constraints and may subsequently influence both clinical and economic outcomes. In a country where intensive treatments for a particular condition are already available, a new clinical intervention may have less clinical impact than in a country where alternative treatments are less available. Alternatively, it is possible that a particular intervention being evaluated is especially effective when used in combination with other higher levels of care, which may not be available in some countries (58). As a result of these interrelationships, the interpretation of pooled economic results from multinational trials can be exceptionally difficult. Indeed, pooled results from a trial may not accurately represent the results observed in any single center or country. Drummond et al. (59,60) give exhaustive consideration to the possible factors affecting the generalizability of results from health economic studies. Different approaches to the evaluation of cost-effectiveness from multinational trials all suffer from their own limitations (58). A common approach is to use trial-wide clini-

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cal results with costs based on trial-wide resource utilization, using unit prices of the country in question. Whereas this approach adjusts for price differences between countries, it does not adjust for differences in treatment patterns that may be related to those price differences. Alternatively, trial-wide clinical results could be used in conjunction with cost results, based only on the patients from the country of interest. This approach does not account for the influence that different practice patterns can have on outcomes and is also limited by sample size. It also does not allow for a true stochastic CEA, as cost data is only available for a subset of patients. A third approach is to evaluate costeffectiveness using both clinical results and costs, based only on patients from the country of interest. This approach suffers from the obvious limitation of low sample size. Jonsson and Weinstein (61) proposed methods for estimating country-specific CE ratio for the economic evaluation of Globalization Utilization of Streptokinase and tPA for Occluded Arteries (GUSTO) IIb, a trial that compared the use of heparin vs recombinant hirudin in patients with ACS. Generally speaking, their approach parallels the approach taken to the analysis and interpretation of clinical results, whereby the relative risk reduction from a given treatment is assumed to be reasonably constant across study populations, however, variability in absolute baseline risk across populations is taken into consideration. With respect to country-specific incremental cost estimates, their proposed method was to calculate a pooled proportional difference in resource utilization (from which costs would be derived) and apply this proportional reduction to the country-specific baseline costs. Unfortunately, however, the economic study for which these methods were proposed was never conducted, as the experimental treatment, recombinant hirudin, did not complete clinical development. Wilke et al. (62) utilize a regression approach to gain an understanding of how clinical and economic outcomes interact when evaluating cost-effectiveness and propose methods for incorporating interactions when making country-specific estimates. Their approach involves testing for homogeneity of the clinical outcomes, costs and CE ratios across the different countries contributing data to the analysis, in order to determine whether reporting a pooled result is appropriate or whether it is more appropriate to report separate ratios for each country. They also propose methods for using estimated differences between countries to customize CE ratios for specific countries. Although rigorous proof of the generalizability of overall multinational trial results to particular countries of interest is likely beyond the scope of any trial or analysis, the issues raised at the beginning of this section deserve consideration when attempting to obtain country-specific cost-effectiveness estimates from overall trial results.

Specification of the Threshold Cost-Effectiveness Ratio An ongoing issue that plagues CE researchers is that there is no universally agreed on value for the threshold CER. Laupacis et al. (63) suggested that interventions with cost per QALY less than $20,000 (Canadian) were good value for the money, whereas interventions costing over $100,000 were likely poor value. In the United States, a ratio of $50,000 has often been used a threshold for evaluating cost-effectiveness. In the past, some have justified this threshold on the basis of it being roughly equivalent to the cost of caring for a dialysis patient, reasoning that the federal entitlement to Medicare insurance coverage for patients with chronic renal failure implies societal willingness to pay (64), though many maintain that continued justification for this convenient round number criterion is largely lacking (65). A recent paper by Hirth et al. (64), pre-

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sented results from a study that used the value-of-life literature to obtain estimates of societal willingness to pay for a QALY. Results from this study varied greatly, both within and between four different methods used to estimate the value of a QALY. The lowest median value per QALY, $24,777 (1997 dollars), was obtained from studies that used the human capital approach based on earning power. Revealed preference occupational studies, which infer the value of life from actual behaviors that reflect a willingness to pay to reduce risk (e.g., the extent to which riskier jobs command higher wages than less risky jobs), yielded the highest median value ($428,286). Revealed preference nonoccupational safety studies and contingent valuation (willingness to pay) studies yielded intermediate median values per QALY ($93,402 and $161,305, respectively). The great degree of variation in these estimates does not foster progress toward the establishment of a consensus regarding the appropriate threshold value, though it does support the current opinion held by many cost-effectiveness researchers that a threshold of $50,000 for the United States is too low.

SUMMARY AND FUTURE DIRECTIONS In this chapter, we have presented an overview of the different approaches that have been proposed over the past decade for dealing with uncertainty in the evaluation of costeffectiveness, using data collected in the context of clinical trials. Throughout the chapter, we demonstrated the utility of the cost-effectiveness plane as an aid in the evaluation of uncertainty around estimates of CE ratios. We also demonstrated how the nonparametric bootstrap approach to the derivation of empirical estimates of the sampling distribution of incremental costs and effects can be used to derive estimates of confidence intervals around estimated ICER and to generate cost-effectiveness acceptability curves. Though historically, most of the approaches to evaluating uncertainty around estimates of CE have been concerned with the derivation of confidence limits for estimates of the incremental CE ratio, limitations of most of those methods because of the instability of the ratio statistic when the incremental effect difference is relatively small, as well as the lack of meaningful and unambiguously interpretable results, have, to a considerable extent, redirected the field toward the adoption of the net benefit framework and the use of cost-effectiveness acceptability curves for inference and the presentation of results. The net benefit approach yields stable results, and cost-effectiveness acceptability curves are intuitively appealing, for they concisely summarize the weight of evidence in support of the cost-effectiveness of a new intervention across a range of critical thresholds. Although such phraseology reflects a Bayesian interpretation of probability, the similarity of results from a Bayesian approach that uses an uninformative prior and results from a frequentist analysis illustrates how these two schools of thought are not necessarily as divergent as history might suggest. Increased adoption of the Bayesian interpretation of probability in cost-effectiveness analysis, increased use of probabilistic sensitivity analysis in models used to project clinical and economic outcomes from clinical trials over the long term, and continued development of the net benefit approach, including further refinement and application of the regression approach to net benefit analysis, are anticipated future directions for the field of health economic evaluation.

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33. Heitjan DF, Moskowitz AJ, Whang W. Bayesian estimation of cost-effectiveness ratios from clinical trials. Health Econ 1999;8:191–201. 34. Heitjan DF. Fieller’s method and net health benefits. Health Econ 2000;9:327–335. 35. Briggs AH, Gray AM. Power and sample size calculations for stochastic cost-effectiveness analysis. Med Decis Making 1998;18(2 Suppl):S81–S92. 36. Willan AR, O’Brien BJ. Sample size and power issues in estimating incremental cost-effectiveness ratios from clinical trials data. Health Econ 1999;8:203–211. 37. Gardiner JC, Huebner M, Jetton J, Bradley CJ. Power and sample assessments for tests of hypotheses on cost-effectiveness ratios. Health Econ 2000;9:227–234. 38. Al MJ, van Hout BA, Michel BC, Rutten FF. Sample size calculation in economic evaluations. Health Econ 1998;7:327–335. 39. Laska EM, Meisner M, Siegel C. Power and sample size in cost-effectiveness analysis. Med Decis Making 1999;19:339–343. 40. Briggs A. Economics notes: handling uncertainty in economic evaluation. Br Med J 1999;319:120. 41. Lothgren M, Zethraeus N. Definition, interpretation and calculation of cost-effectiveness acceptability curves. Health Econ 2000;9:623–630. 42. Briggs AH. A Bayesian approach to stochastic cost-effectiveness analysis. Health Econ 1999;8:257–261. 43. Briggs AH. A Bayesian approach to stochastic cost-effectiveness analysis. An illustration and application to blood pressure control in type 2 diabetes. Int J Technol Assess Health Care 2001;17:69–82. 44. Briggs A, Gray A. Using cost effectiveness information. Br Med J 2000;320:246. 45. Weintraub WS, Thompson TD, Culler S, et al. Targeting patients undergoing angioplasty for thrombus inhibition: a cost-effectiveness and decision support model. Circulation 2000;102:392–398. 46. Mahoney EM, Thompson TD, Veledar E, et al. Cost-effectiveness of targeting patients undergoing cardiac surgery for therapy with intravenous amiodarone to prevent atrial fibrillation. J Am Coll Cardiol 2002;40:737–745. 47. Hoch JS, Briggs AH, Willan AR. Something old, something new, something borrowed, something blue: a framework for the marriage of health econometrics and cost-effectiveness analysis. Health Econ 2002;11:415–430. 48. Weinstein MC, Siegel JE, Gold MR, et al. Recommendations of the Panel on Cost-effectiveness in Health and Medicine. JAMA 1996;276:1253–1258. 49. Cannon CP, Weintraub WS, Demopoulos LA, et al. Comparison of early invasive and conservative strategies in patients with unstable coronary syndromes treated with the glycoprotein IIb/IIIa inhibitor tirofiban. N Engl J Med 2001;344:1879–1887. 50. Sonnenberg FA, Beck JR. Markov models in medical decision making: a practical guide. Med Decis Making 1993;13:322–338. 51. Beck JR, Pauker SG. The Markov process in medical prognosis. Med Decis Making 1983;3:419–458. 52. Kuntz KM, Weinstein MC. Modeling in economic evaluation. In: Drummond M, McGuire A (eds.), Economic Evaluation in Health Care: Merging Theory with Practice. Oxford University Press, Inc., NY, 2001, pp. 141–171. 53. Doubilet P, Begg CB, Weinstein MC, et al. Probabilistic sensitivity analysis using Monte Carlo simulation. A practical approach. Med Decis Making 1985;5:157–177. 54. Briggs AH, Gray AM. Handling uncertainty in economic evaluations of healthcare interventions. Br Med J 1999;319:635–638. 55. Peeters A, Mamun AA, Willekens F, Bonneux L. A cardiovascular life history. A life course analysis of the original Framingham Heart Study cohort. Eur Heart J 2002;23:458–466. 56. Mark DB, Harrington RA, Lincoff AM, et al. Cost-effectiveness of platelet glycoprotein IIb/IIIa inhibition with eptifibatide in patients with non-ST-elevation acute coronary syndromes. Circulation 2000;101:366–371. 57. Mark DB, Lee TH. Conservative management of acute coronary syndrome: cheaper and better for you? Circulation 2002;105:666–668. 58. Willke RJ, Glick HA, Polsky D, Schulman K. Estimating country-specific cost-effectiveness from multinational clinical trials. Health Econ 1998;7:481–493. 59. Drummond MF, Bloom BS, Carrin G, et al. Issues in the cross-national assessment of health technology. Int J Technol Assess Health Care 1992;8:671–682. 60. Drummond M, Pang F. Transferability of economic evaluation results. In: Drummond A (ed.), Economic Evaluation in Health Care. Oxford University Press Inc., NY, 2001, pp. 256–276.

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II

CLINICAL APPLICATIONS

10

Costs of Care and Cost-Effectiveness Analysis Primary Prevention of Coronary Artery Disease

Kevin A. Schulman, MD and Padma Kaul, PhD CONTENTS INTRODUCTION ECONOMIC ANALYSIS OF PRIMARY PREVENTION STUDIES ON CE OF PRIMARY PREVENTION OF CAD HYPERTENSION DIABETES SMOKING CONCLUSIONS REFERENCES

INTRODUCTION Primary prevention entails the identification of a population of patients with a high probability of progression to a disease within a time period of interest. Once the “at-risk” population has been identified based on these criteria, an intervention must be available to reduce the risk of progression to the disease state. These interventions can act directly on the mechanism of the disease (i.e., treatment of high blood cholesterol) or indirectly to reduce morbidity or mortality related to the disease (mammography for breast cancer). Coronary artery disease (CAD), by virtue of its high prevalence, and its impact on mortality and morbidity, can be considered a principal candidate for primary prevention (1). According to the American Heart Association (AHA), approximately 60 million people in the United States suffer from cardiovascular disease. Its prevalence ranges from 5% among 20- to 24-year-olds to 75% among people aged 75 and over (2). Other than in the very elderly (age 75 years or older), the prevalence of CAD is consistently higher among men than women within each age category. Based on epidemiological data from the Framingham Heart Study, the lifetime risk of developing CAD at age 40 is one in two for men and one in three for women (3). In addition to age and sex, it is well established that the presence of other risk factors, such as elevated cholesterol, hypertension, smoking, and diabetes, is associated From: Contemporary Cardiology: Cardiovascular Health Care Economics Edited by: W. S. Weintraub © Humana Press Inc., Totowa, NJ

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with a higher risk of developing CAD (4–7). From the economic perspective proposed earlier, these risk factors (alone or in combination) identify cohorts of patients at increasing risk for developing CAD over a fixed time period. These risk factors are not equivalent, as the absolute risk of progression to CAD varies across these risk factors or based on the combination of the factors. In a prospective cohort study of 5345 patients, aged between 30 and 74 years with a 12-year follow-up, Wilson et al. found that among men, smokers, patients with stage II–IV hypertension, and patients with total cholesterol of 240 mg/dL or higher were twice as likely to develop CAD, whereas diabetics and patients with high-density lipoprotein (HDL) levels below 35 mg/dL had a relative risk of 1.5 of developing CAD. The relative risk associated with diabetes and lower HDL cholesterol levels was higher among women (8). The incremental risk associated with these factors also translates into higher costs of medical care in the long term. In a prospective cohort study with an average follow-up of 23 years, patients with a favorable risk profile in middle age had significantly lower Medicare costs in older age in comparison to patients with a cardiovascular risk factor (9). A favorable risk profile was characterized by blood pressure 120/80 mm Hg or lower, serum cholesterol level lower than 5.2 mmol/L, no smoking, no electrocardiographic abnormalities, and no history of diabetes or myocardial infarction (MI). Risk identification has two direct consequences: (1) it affects the proportion of the population eligible for the intervention, and (2) it impacts the probability of the identified population progressing to CAD. The level of risk also guides the intensity of the intervention. For example, the National Cholesterol Education Program (NCEP) Adult Treatment Panel III (ATP III) guidelines recommend that the first line of therapy among low risk patients be dietary modification, and that cholesterol reduction using drug therapy be reserved for patients who have total cholesterol levels 190 mg/dL or higher (10). One of the main reasons for advocating drug therapy only among higher risk patients is the considerable cost associated with intervention and the relatively low risk of progression. In the current era of cost-conscious medicine, economic justification of therapies is becoming an integral component of clinical practice guidelines, drug marketing plans, and health insurance plans. In this chapter, we provide a brief review of the principles of economic analysis applied to primary prevention and summarize the recent literature examining the cost-effectiveness (CE) of primary prevention of CAD. This chapter is then divided into sections, with each section focusing on studies related to a specific risk factor.

ECONOMIC ANALYSIS OF PRIMARY PREVENTION Economic analysis of primary prevention entails an assessment of the benefits and costs of both the screening and intervention strategies. Benefits of primary prevention include the clinical and economic benefits for patients related to avoidance of disease. Costs include the costs of screening, the costs of intervention for the at-risk population, and the costs of side effects related to screening or treatment. Clinical benefits of primary prevention are those that result from the avoidance of disease. These benefits include effects on morbidity or mortality. The timing of these benefits needs to be determined—they can occur immediately or after a time lag related to the effect of the intervention on the natural history of the disease. These benefits can

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be measured in clinical terms (years of life saved, MI avoided) or in patient terms (quality-adjusted life years [QALY]). Economic benefits follow from the clinical benefits and can include direct medical and nonmedical costs, productivity costs, and intangible costs related to disease avoidance. Costs of primary prevention include the costs of screening. These are the costs of determining the risk of disease for individuals within a population. Screening can be low cost on an individual patient basis (blood pressure) or high cost (coronary angiography, colonoscopy). Screening costs can also include marketing or communication costs to ensure that the population receives the screening test (the costs of recruiting the population to a screening test are low for the initial segment of the population and approach infinity for recalcitrant participants in a voluntary screening effort). The costs of the intervention include the cost of the treatment for each person identified at risk for the disease. The costs of side effects can include any costs that occur as an adverse result of the intervention (anaphylaxis, fatal, or nonfatal events). Identification of an at-risk population usually involves the ascertainment of the presence of risk factors for the disease within a population. Risk factors are clinical or epidemiologic characteristics of individuals related to disease progression. These risk factors may be causally or noncausally related to the disease. The critical parameters of characterizing a risk factor are our ability to detect the risk factor, and the association between the presence of the risk factor and progression to the disease state over time. Risk factors can be considered dichotomous—either present or absent (cigarette smoking)—or continuous (number of pack-years of smoking). Continuous risk factors can be treated as a continuous categorical or dichotomous variable by understanding the relationship between the level of the risk factor and the disease. For example, the age-adjusted 10-year CAD rate among patients with total cholesterol levels ≥ 240 mg/dL is 18.6 in comparison to 8.2 among patients with total cholesterol levels < 200 mg/dL (8). Receiver-operating characteristic (ROC) curves or logistic regression analyses are common methods of exploring these relationships. Readers interested in a comprehensive overview of these methods can consult several excellent sources (11,12).

STUDIES ON CE OF PRIMARY PREVENTION OF CAD The articles summarized in the following sections were identified by searching MEDLINE. We focused the discussion on current publications (post-1995), although when appropriate, we referenced landmark papers from earlier years. The quality of a cost-effectiveness analysis (CEA) is directly attributable to the source of its estimates of costs and effectiveness. In order to provide a quick guide to the quality of the analyses, we offer a grading system that is adapted from earlier guidelines for evaluating clinical evidence (13). Individual grades are assigned to the quality of the data on costs and effectiveness; therefore, each study has a grade in the form of a ratio. For example, Grade B/A would indicate that evidence on costs had a Grade B, whereas the evidence on effectiveness had a Grade A. The grades associated with the levels of evidence are presented in Table 1.

Cholesterol A majority of the recent studies examining the CE of primary prevention of CAD have focused on cholesterol reduction therapies. This is primarily because of evidence

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Cardiovascular Health Care Economics Table 1 Grading of Levels of Evidence Used in CEA Cost

Grade

Description

Effectiveness Grade

Description Evidence from a large randomized clinical trial or a meta-analysis of multiple randomized trials Evidence from large cohort studies

A

Evidence from a large randomized clinical trial

A

B

Evidence based on claims data from national or private insurers Evidence based on hospital accounting systems

B

C

D

Evidence based on opinions from experts without reference; independent surveys or recollections of participating patients

C

D

Evidence based on extrapolations from randomized clinical trial focused on secondary prevention to the primary prevention setting Evidence based on opinions from experts without reference

from two landmark clinical trials, showing that cholesterol treatment was associated with lower incidence of MI and mortality among patients without established CAD. The first study, the West of Scotland Coronary Prevention Study (WOSCOPS) found that treating men with moderate hypercholesterolemia with the 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) reductase inhibitor (statin) pravastatin significantly reduced the incidence of MI and death from cardiovascular causes (14). A more recent study, AFCAPS/TexCAPS, examined the effect of extending the use of statin therapy (lovastatin) to men and women with average cholesterol levels (180–264 mg/dL) and low-density lipoprotein cholesterol (LDL-C) levels (130–190 mg/dL) and below average HDL cholesterol (HDL-C) levels (≤45 mg/dL) (15). As in WOSCOPS, the AFCAPS/TexCAPS study reported significantly lower MI and death rates among the treatment arm in comparison to the placebo arm. The first NCEP ATP guidelines were published in 1988, and in the subsequent two iterations, they have continuously incorporated evidence from randomized controlled trials, as well as from long-term epidemiologic studies (16,17,10). The most recent NCEP guidelines (ATP III) focused on the primary prevention of heart disease among patients with multiple risk factors. The guidelines recommend a fasting lipoprotein profile, including total LDL, and HDL cholesterol and triglyceride levels, once every 5 years among all adults over the age of 20 years. The report lists LDL levels lower than 100 mg/dL as optimal; total cholesterol levels less than 200 mg/dL as desirable; and HDL levels less than 40 mg/dL as low. It is suggested that cholesterol goals be modified depending on the presence of major risk factors, such as cigarette smoking, hypertension, low HDL cholesterol, family history of CAD, and increasing age. Patients can be classified into three groups based on the their level of risk of developing CAD. Among low-risk patients with none or one risk factor, the first line of therapy is to initiate therapeutic lifestyle changes (TLC); however, if the LDL levels rise above 190 mg/dL, the option of drug therapy may be considered. High-risk patients with two or more risk fac-

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tors are segmented further into two groups: those with a 10–20% 10-year risk of developing CAD and those with a less than 10% 10-year risk of developing CAD. Among the former, drug therapy is called for when LDL, levels are more than 130 mg/dL, and in the latter group, drug therapy should be initiated when the levels are greater than 160 mg/dL. Table 2 provides a summary of several studies that have evaluated the CE of cholesterol-lowering therapies. In order to facilitate comparisons across studies, we have updated all cost data to 2000 US dollars using the consumer price index (CPI) inflation estimates. In the case of studies conducted outside the United States, cost data were first converted to US dollars using the purchasing power parity (PPP) index for the specific year, then updated to 2000 dollars using the appropriate inflation rate. One of the most recent studies by Prosser et al. (18) evaluated the CE of the NCEP ATP II guidelines. The authors examined the impact of risk factors on the CE ratios (CERs) of cholesterol-lowering therapy. A total of 240 risk subgroups according to age, sex, smoking, blood pressure, LDL levels, and HDL levels were identified. Interventions included the Step I diet therapy and statin therapy (defined as a dose of 40 mg/d of pravastatin). The analysis was conducted from a societal perspective over a 30year timeframe. CERs were calculated in a stepwise fashion: diet therapy in comparison to no primary prevention and statin therapy in comparison to diet therapy. Overall costs included costs of intervention, coronary heart disease care, and noncoronary heart disease care in 1997 US dollars. Effectiveness of interventions was measured in terms of QALYs, which were calculated using previously published data. Progressively lower costs per QALY were associated with increasing age among both men and women. Ratios for diet therapy in comparison to no primary prevention among patients with LDL levels between 4.2–4.9 mmol/L ranged from $1900 per QALY for men aged 75–84 years to $500,000 per QALY for women aged 35–44 years. Among these two groups of patients, the presence of three risk factors reduced the CER to $58,000 per QALY among young women; however, it had no impact on the ratio for older men. Statin therapy in comparison to diet therapy had significantly higher CER, ranging from $1,400,000 per QALY among women aged 35–44 years with no risk factors to $95,000 per QALY among men aged 75–84 years with three risk factors. As noted by the authors, the results of the study support the NCEP guidelines by showing that diet therapy is probably the most cost-effective option for low-risk patients. In contrast, the NCEP recommendation that all patients with LDL 4.9 mmol/L or greater, and patients with LDL levels of 4.2–4.9 mmol/L and with two or more risk factors, be treated with a statin may not be as cost-effective. This analysis provides quantitative evidence to support the hypothesis that primary prevention is most economically attractive when targeted toward high-risk patients. CERs associated with cholesterol-lowering therapies among the elderly should be interpreted with caution. The Framingham data show that although absolute risk of CAD increases with age, there is a decrease in the relative risk associated with a particular risk factor, such as high cholesterol (7). This dichotomy is likely to result in the inappropriate selection of patients for aggressive therapy. Taking these findings into consideration, the current NCEP ATP III guidelines recommend that TLC be the first line of therapy among the elderly, and that drug therapy be considered only in the presence of multiple risk factors (10). Pickin et al. (19) examined the CE of simvastatin therapy among subgroups of the population with different levels of CAD risk. Three groups of patients were considered

Table 2 Summary of Studies Examining CE of Cholesterol-Lowering Therapies CE ratios by risk groups (2000 US dollars) Study (ref.) Prosser et al. (18)

Drug Pravastatin

Effect

Currency

QALY 1997 US $

Cholesterol levels 4.2–4.9 mmol/L >4.9 mmol/L

162

Pickin et al. (19) Simvastatin Perreault et al. (22) Lovastatin

LE LE

1997 £ 1992 C $

6.67 mmol/L 7.84 mmol/L 9.90 mmol/L

Hamilton (27)

Lovastatin

LE

1992–93 C $

Pharoah (28)

Simvastatin Pravastatin Pravastatin

LE

£

LE

1996 £

Caro et al. (29)

≥6.6 mmol/L 7.0 mmol/L

QALY, quality-adjusted life year; LE, life expectancy; CE, cost effectiveness.

Sex Male Female Male Female All Male Female Male Female Male Female Male Female Male Male

Low

Medium

High

$2568–171,200 $2033–107,000 $2033–23,540 $40,660–535,000 $8774–139,100 $8774–62,060 $2033–107,000 $2033–63,130 $2033–7169 $20,330–192,600 $87,740–139,100 $8774–49,220 $9889–43,448 $7295–35,829 $5188–13,294 $50,414–97,600 $27,968–54,179 $49,434–96,791 $34,173–68,473 $43,057–71,050 $23,438–38,494 $39,167–66,064 $28,074–47,003 $20,623–46,433 $10,609–23,685 $26,573–58,960 $17,937–40,779 $38,856–73,751 $20,066–48,123 $49,289–149,802 $35,196–101,579 $6727–407,438 $7804–19,576

$5382–13,448

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for primary prevention: those at 3%, 2%, and 1.5% risk of developing CAD per year. Costs of statin therapy (at a dose of 27.4 mg/d) and savings, as a result of preventing the use of health services, such as hospital admissions, percutaneous coronary interventions (PCI) or coronary artery bypass grafting surgeries (CABG), were included. At each level of CAD risk, estimates of effectiveness were based on data from clinical trials (14,20). Cost per year of life gained was approximately $13,612 among patients with a 3% risk of CAD, $17,762 among patients with a 2% risk of CAD, and $207,500 among those with a 1.5% risk of CAD. The higher costs among the lower risk groups were driven primarily by the number of patients needed to be treated in order to prevent one case of CAD (30 in the 2% risk cohort and 40 in the 1.5% risk cohort in comparison to 20 among the 3% risk cohort). The price of the drug was also a major determinant of the cost-per-life year gained. The study relies on the Sheffield risk and treatment table to categorize patients according to their level of risk and, therefore, is limited by the assumptions used to identify the levels of risk (21). For example, in the logistic regression model on which the Sheffield table is based, hypertension is represented as a dichotomous variable, and HDL cholesterol levels have been excluded, calling into question its accuracy and flexibility. In addition, some of the authors’ CE estimates have been based on interpolation from a secondary prevention trial and a primary prevention trial, which evaluate different statin therapies and, therefore, may be subject to interpretation (14,20). In a Canadian study, Perreault and colleagues calculated the average and marginal CE ratios of increasing doses of lovastatin for primary prevention of CAD (22). The analyses were conducted from a societal perspective, and costs were reported in 1992 Canadian dollars. Incremental CERs corresponding to three dosage levels, 20 mg/d, 40 mg/d, and 80 mg/d were calculated for three baseline total cholesterol (TC) levels: 6.67 mmol/L, 7.85 mmol/L, and 9.90 mmol/L. Within each cholesterol category, smokers and patients with diastolic blood pressure of 100 mm Hg or more were considered as high risk. Separate CER were calculated for men and women. The average CER for patients with baseline TC level of 6.67 mmol/L ranged from $29 (105 per life year saved among high-risk men treated with 20 mg/d) to $101 (567 among low-risk men treated with 80 mg/d). At the TC level of 9.90 mmol/L, the CER ranged from $11,040 for high-risk men treated with 20 mg/d to $61,357 for low-risk women treated with 80 mg/d. As a result of the nonlinearity of the dose–response relationship between lovastatin and TC reduction (23–25), average CER for each dosage level were considerably lower than the marginal CER comparing incremental doses. Each twofold increase in dosage was associated with a 75% increase in cost, but a significantly smaller percentage change in lipid levels. For example, 20 mg/d was associated with a 17% reduction in TC, whereas 40 mg/d was associated with only a 22% reduction in TC (20). Therefore, the marginal CER of increasing dosage from 20 mg/d to 40 mg/d was $277,400 among low-risk men and $229,125 among low-risk women. Given that the cost of the drug is one of the most important drivers of costs of treatment of high blood cholesterol, this study provides important information on the CE of incremental dosage levels. Their results are consistent with those reported earlier (26). The authors show that a dose of 80 mg/d of lovastatin is not cost-effective even among high-risk patients. This is useful information from both a policy, as well as a physician’s perspective.

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In another Canadian study, Hamilton et al. evaluated the lifetime CE of 20 mg of lovastatin per day for treatment of high blood cholesterol (27). The patient population consisted of men and women aged 30–70 years with TC levels equal to the 90th percentile of the US distribution in their age and sex group and had mean age- and sexadjusted HDL cholesterol levels. High-risk patients were defined as smokers with diastolic blood pressure of over 100 mm Hg, whereas patients who did not smoke and had blood pressure of 80 mm Hg were considered low risk. As in the Perreault study, estimates of effectiveness were derived from the Expanded Clinical Evaluation of Lovastatin (EXCEL) study (23). Among low-risk men, CE per year of life saved in 1993 Canadian dollars ranged from $35,526 among 50-year-olds to $73,121 among 30-year-olds. Intervention among lowrisk women was consistently less cost-effective, with ratios ranging from $44,445 among 60-year-olds to $151,132 among 30-year-olds. Among high-risk men and women, the ratios ranged from $17,231 among 50-year-olds to $42,458 among 70-year-olds, and from $30,540 among 60-year-olds to 101,868 among 30-year-olds, respectively. The Hamilton et al. study is one of the few studies that have incorporated the benefits from an increase in HDL cholesterol levels. Increase in HDL cholesterol lowered CER by approximately 40%. This finding suggests that earlier CE studies of lovastatin, which did not incorporate HDL cholesterol levels, may have overestimated CER. Another unusual aspect of this study is the inclusion of non-CAD costs resulting from longer life expectancies. These additional costs added between 3% (for patients at age 30) and 23% (for patients at age 70) to the CER. Several studies from the United Kingdom have examined the cost implications of applying the results of clinical trials to the health care system. Pharoah and Hollingworth (28) explored the implications of changing the criteria for intervention based on CER at a health authority level in the United Kingdom. In Cambridge and Huntingdon, health authorities, 18,100 men had a cholesterol concentration greater than 6.4 mmol/L. Taking into account the cost of drugs and the cost savings associated with preventing CAD events, the authors estimate a CER of $227,850 per life year saved. In a similarly designed study, Caro and colleagues (29) used data from the West of Scotland Coronary Prevention Study (WOSCOPS) study (14) to examine the economic efficiency of using Pravastatin in preventing cardiovascular disease among Scottish men. Using data from the clinical trial, the authors estimate that in a cohort of 10,000 men, a 40 mg/d dose would prevent 318 cerebrovascular events over a 5-year period. The costs considered, therefore, included the cost of the drug and the cost savings resulting from the events prevented. Gain in life years as a result of pravastatin therapy in comparison to no primary intervention was estimated using the life tables method. The resulting CER was $12,588 per year of life gained when the costs were not discounted, and $31,581 per year of life gained when a 6% discount rate was applied. If only those patients whose 10-year risk of developing CAD was above 20% were treated, the CER were $8682 (undiscounted) and $21,692 per life year gained. The results from the recent studies are consistent with those conducted earlier in the decade (26,30–32). As in the earlier studies, there continues to be some common themes across all the studies: (1) primary prevention of CAD increases costs, but provides clinical benefits; (2) the efficiency of cholesterol intervention depends on the absolute risk of CAD, and as risk increases, interventions become more economically attractive; and (3) CER are lower for men than women. However, as is evidenced from

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Table 1, there are also discrepancies among study results. There is considerable variation in CER of cholesterol-lowering therapies even after accounting for temporal and currency differences. These differences persist both within and across risk categories. To some extent, the differences in the results may be explained by the differences in the assumptions related to cost data. For example, in contrast to Perreault et al. and Prosser et al., assumption of four and five lipid profiles, respectively, for patients receiving drug therapy followed by two profiles every subsequent year, Pickin et al. included no costs related to lipid measurements. The other major source of variation in CERs is the type of drug. Statins have been established as the primary therapy for cholesterol level reduction, however, the relative CE of the individual drugs that comprise this class of drugs continues to be debated. Koren et al. examined the mean total cost of care to reach NCEP cholesterol level goals using four alternative statins and found atorvastatin to be the most cost-saving strategy in comparison to simvastatin, lovastatin, and fluvastatin (33). As the prices of these drugs yield to market pressures, we may see aggressive cholesterol-lowering therapy become attractive across a larger segment of the population.

HYPERTENSION The association between hypertension and the increased risk of cardiovascular events, such as stroke, MI, and congestive heart failure (CHF), has been shown both in the context of clinical trials and in population-based observational studies (34,35). Hypertension is one of most prevalent risk factors of CAD in the United States, with more than 40% of the population over 55 years of age having elevated blood pressure (36). The Joint National Committee on Prevention, Detection, Evaluation and Treatment of High Blood Pressure (JNC VI) defines high blood pressure as systolic pressure of over 140 mm Hg or diastolic pressure of over 90 mm Hg (37). Diet modification and physical exercise are the lowest cost alternatives to managing hypertension. However, it is recommended that pharmacologic interventions be employed if the lifestyle modifications are not effective within 3 to 6 months (38). Several pharmacological interventions have been shown to be effective in reducing CAD events among hypertensive patients. In a systematic review and meta-analysis of randomized clinical trials, Psaty et al. showed that treatment with low-dose diuretics was associated with a lower relative risk of coronary disease (0.72, 95% confidence interval [CI] 0.61–0.85) (39). The evidence on the effectiveness of β-blockers and angiotensinconverting enzyme (ACE) inhibitors is the strongest among subgroups of patients with established coronary disease or CHF. CE of hypertension treatment is sensitive to several factors. Jonsson and Johannesson have shown that the higher risk of CAD among the elderly and among men in comparison to women translates into lower CER among these subgroups of patients (40,41). Another source of variation in CER is the large differential in the cost of hypertension drugs, with the average wholesale price in 1997 US dollars for a 30-day supply at the lowest recommended dose for diuretics ranging from $4–28, for β-blockers $15–37, for ACE inhibitors $15–28, and for calcium antagonists $26–50 (38). Pearce et al. compared the CE of first-line antihypertensive drug classes for the prevention of stroke, MI, or premature death among patients with uncomplicated mild-tomoderate hypertension (42). The analysis included five classes of drugs: diuretics,

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β-blockers, ACE inhibitors, α-blockers, and calcium blockers. The most representative drug for each class was chosen using two criteria: most prescribed in the United States and the least expensive based on the 1996 average wholesale price. Assuming all classes of drugs were equally effective, the use of ACE inhibitors, calcium channel blockers (CCB) or α-blockers to prevent one nonfatal MI, nonfatal stroke, or death was associated with an increase in cost of $30,160 to $115,159 among the elderly and between $89,440 and $341,506 among middle-aged patients. In a sensitivity analysis, assuming ACE inhibitors, CCB, and α-blockers 50% increase in efficacy, only the least expensive ACE inhibitors and CCB were as cost-effective as the diuretic HCTZ. The authors provide a simple “back of the envelope” calculation for comparing the use of diuretics and β-blockers to ACE inhibitors, CCB, and α-blockers in preventing major adverse events. Although the analysis is effective in providing rough estimates of the CE of these drugs, it suffers from the simplicity of its design and assumptions, such as that of equal effectiveness across all classes of drugs (effectiveness of these agents can be considered across two measures: intermediate measures, such as changes in blood pressure and final outcomes, such as prevention of mortality and morbidity; often, effectiveness of therapy for final outcomes has not been assessed). Costs are restricted to the direct costs of drugs. An attempt was made to include direct costs of routine out-patient physician visits and laboratory tests, however, this was assumed to be constant over all categories of drugs. The cost of side effects, the impact of patient compliance, and the change in costs of drugs over the time period were not considered. In summary, there is no debate regarding the economic attractiveness of treating hypertension, with a view to preventing CAD: several earlier studies have established this fact and the more topical question is the relative CE of the treatment options. To date, few investigators have assessed this question from the perspective of final outcomes of therapy (morbidity and mortality). This is especially important given the controversies surrounding the benefits of two classes of agents—CCBs and α-blockers (43,44). It is also essential to understand the benefits associated with higher cost treatment options in comparison to the least expensive generic medications for patients without other cormorbid conditions.

DIABETES The results of the Diabetes Mellitus Insulin Glucose Infusion in Acute Myocardial Infarction (DIGAMI) study showed that the cost-per-life year gained by intense insulin treatment after acute MI in patients with diabetes mellitus was $17,407 and the cost per QALY was $24,823 (45). However, little data exist on the CE of diabetes treatment in the primary prevention setting. In one of the first studies of its kind, Gray et al. conducted an economic analysis alongside the United Kingdom Prospective Diabetes Study Group (UKPDS 41) randomized controlled trial of an intensive blood glucose control policy in patients with type 2 diabetes. As part of the trial, 3867 patients were randomized to either conventional management, consisting of diet therapy or to intensive management with insulin (46). Median follow-up was 10 years, and main clinical endpoints included death or the development of diabetic complications, including coronary heart disease, cerebrovascular disease, amputation, laser treatment for retinopathy, cataract extraction, and renal failure. The economic analysis was conducted from a

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health care purchaser perspective. Direct costs included costs of conventional and intensive treatments, visits to diabetic clinics and tests, and treatment of complications, including in-patient stays and out-patient health care. In 1997, the total cost of treatment in the intensive arm was $7221 in comparison to $6067 among the conventional therapy arm for a difference of $1154. This increase in treatment costs was offset by a substantial decrease in the costs of complications. In terms of effectiveness, patients who were treated with intensive blood glucose control gained 0.60 years in event-free survival time during the course of the trial, and the lifetime gain was estimated to be 1.14 years. The incremental cost of intensive blood glucose therapy per event-free year gained was £1166. Data from the landmark Diabetes Control and Complications Trial (DCCT) were used to model lifetime estimates of benefits and costs of intensive insulin therapy in comparison to conventional therapy in patients with type I diabetes (47). Patients on intensive therapy were estimated to experience fewer complications, such as blindness, end-staged renal disease (ESRD), and lower extremity amputation. The longer length of life among intensive therapy patients translated into $28,661 per year of life gained. The disabling nature of the complications of diabetes is also likely to impact the quality of life. When length-of-life estimates were adjusted using health utilities associated with these complications, the incremental cost per QALY was $19,987. Both the Hypertension Optimal Treatment (HOT) Study and the United Kingdom Prospective Diabetes Study (UKPDS) 38 have shown that the lower blood pressure goal for diabetic patients (130/85 mm Hg) recommended by the JNC VI is associated with better outcomes (48,49). However, more complex and, therefore, more expensive drug regimens are necessary to effect this lower blood pressure goal. The UKPDS group conducted a CEA of lowering blood pressure among hypertensive patients with type 2 diabetes. They compared health care resource use, time free from diabetesrelated endpoints, and life years gained between 758 patients with tight blood pressure control and 390 patients with less tight control (50). Median follow-up of 8.4 years showed that the higher costs of antihypertensive treatment in the tight control arm were more than offset by the reduced hospitalizations and complications costs. The incremental cost per extra year free from endpoints was $1626, and the incremental cost per life year gained was $1116. Elliot et al. reported a similar economically attractive result more recently (51). They found that treating 60-year-old diabetic patients with hypertension to lower their blood pressure to the 130/85 mm Hg increased life expectancy from 16.5 to 17.4 years. The total lifetime medical costs (in 1996 US dollars) decreased from $59,495 to $58,045 (difference $1450), which were mainly driven by the prevention of adverse events, such as heart failure, stroke, MI, and ESRD. Over the last decade, diabetes has emerged as one of the primary risk factors for CAD. This has been driven, in part, by the increasing incidence of diabetes in an aging population. It is estimated that CAD is the cause of death among 80% of diabetic patients (52). Also, diabetes is unlikely to mainifest itself in isolation: it is associated with the presence of other factors, such as low HDL, hypertension, and obesity. This clustering of risk factors automatically categorizes diabetic patients into the group that is at highest risk for CAD. Based on the fact that primary prevention among the highest risk group is the most cost-effective, we can extrapolate that diabetic patients comprise the most economically attractive group for primary prevention interventions.

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SMOKING The results of CE studies of smoking cessation conducted more than a decade ago continue to hold. In 1989, Cummings et al. found that physician counseling resulted in a 2.7% decrease in smoking rates at 1 year. Assuming that the cost of physician advice was $12, the authors calculated CER of $1000–1400 per life year saved for men and $1700–3000 per life year saved for women (53). Oster et al. examined the CE of a combination strategy of nicotine gum and physician counseling (54). The costs of per year of life saved ranged from $8120 among men aged between 45 and 49 years to $12,757 among men aged between 64 and 69 years, and between $13,580 among women aged between 50 and 54 years and $18,690 among women aged 64 to 69 years. These results were primarily driven by the authors’ assumption that only 1% increase in smoking cessation as a result of using nicotine gum in comparison to counseling alone. In a more recent analysis, Fiscella and Franks examined the CE of the transdermal nicotine patch as an adjunct to physician’s smoking cessation counseling (55). In 1995 dollars, the authors estimated the monthly cost of the patch to be $111.9, and the cost of physician time to be $6.67. The addition of nicotine patch therapy to physician counseling produced one additional quitter at a cost of $7332. The incremental CE of the patch for 45-year-old patients was $7118 per life year saved for men and $5163 per life year saved for women. Accounting for the increase in quality of life as a result of smoking cessation, CER ranged from $4390 per QALY among 35–39-year-old men to $10,943 per QALY among 65–69-year-old men. Cromwell et al. examined the CE of the Clinical Practice Recommendations outlined in the AHCPR Guideline for Smoking Cessation (56). The model assumed that primary care physicians would screen all adult patients for smoking status and provide counseling sessions to motivate smokers to quit. Three counseling sessions with the primary care physician and two counseling interventions with smoking cessation specialists were included. These interventions were modeled with and without the use of the nicotine patch and nicotine gum. The total cost of the program was estimated at $6.3 billion in the first year of implementation, resulting in 1.7 million new quitters for an average price of $3779 per quitter. From a societal perspective, the cost-per-life year saved was calculated to be $2587 and $1915 for every QALY saved making prevention of smoking an extremely cost-effective intervention. Table 3 provides a summary of CER of interventions targeted toward smoking cessation. Physician counseling remains by far the most attractive strategy, although the use of nicotine patches or gum also result in CER that can be considered favorable in comparison to other interventions. The established association between cigarette smoking and heart disease, as well the increased risk of lung cancer among smokers, has resulted in most health plans adopting interventions to encourage patients to quit smoking.

CONCLUSIONS In this chapter, we have reviewed some of the recent literature examining the CE of primary prevention of CAD. Despite established guidelines on the conduct and reporting of economic analyses, there continues to be high variability in the methodology used to develop economic models and their assumptions. The current literature is also limited by the scarcity of clinical and economic data related to certain interventions,

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Table 3 Summary of Smoking Studies Study

Intervention

Alternative intervention

Population (age in years)

Incremental CE in 2000 US dollars

Men aged 35–39 Men aged 50–54 Women aged 35–39 Women aged 65–69 Men aged 45–49 Men aged 65–69 Women aged 50–54 Women aged 65–69 Men aged 35–39

1634/YLS 1167/YLS 3405/YLS 2335/YLS 6569/YLS 10,325/YLS 10,988/YLS 15,130/YLS 4961/QALY

None None None

Men aged 50–54 Women aged 35–39 Women aged 65–69 All adults (18+) All adults (18+) All adults (18+)

6048/QALY 5918/QALY 7891/QALY 4537/QALY 1712/QALY 2718/QALY

None

All adults (18+)

1555/QALY

None

All adults (18+)

5132/QALY

None

All adults (18+)

2426/QALY

Cummings (53)

Physician counseling

None

Oster (54)

Nicotine gum and physician counseling

None

Fiscella (55)

Nicotine patch

Physician counseling

Cromwell (56)

Minimal counseling Full counseling Nicotine patch + min counseling Nicotine patch + full counseling Nicotine gum + min counseling Nicotine gum + full counseling

YLS, year of life saved; QALY, quality adjusted life years.

such as reducing obesity, increasing exercise, and understanding newer markers of high risk, such as homocystine and C-reactive protein. Based on the review of the literature, we can conclude that economic attractiveness of primary prevention increases for higher risk groups. However, there is a paucity of clinical data on effectiveness of interventions for older patients (>70 years), as well as comparative effectiveness of newer agents in the treatment of hypertension based on final measures of morbidity and mortality. Advancements in genetics also have the potential for major impacts on primary prevention strategies. Although costs of screening patients may continue to be high, the population most likely to benefit from aggressive intervention may become easier to identify. As our ability to discern high-risk populations improves, the economic attractiveness of intervention in this group will increase.

REFERENCES 1. McGovern PG, Pankow JS, Shahar E, et al. Recent trends in acute coronary heart disease: mortality, morbidity, medical care and risk factors. N Eng J Med 1996;334:884–890. 2. Data from the American Heart Association. 2000 Heart and Stroke Statistical Update. American Heart Association, Dallas, TX, 2000. 3. Lloyd-Jones DM, Larson MG, Beiser A, Levy D. Lifetime risk of developing coronary heart disease. Lancet 1999;353:89–92.

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4. Grundy SM, Balady GJ, Criqui MG, et al. Primary prevention of coronary heart disease: guidance from Framingham: a statement for healthcare professionals from the AHA Task Force on risk reduction. Circulation 1998;97:1876–1887. 5. Wallis EJ, Ramsay LE, Haq IU, et al. Coronary and cardiovascular risk estimation for primary prevention: validation of a new Sheffield table in the 1995 Scottish Health Survey population. BMJ 2000;320:671–676. 6. Smith SC, Greenland P, Grundy SM. Prevention conference V: beyond secondary prevention: identifying the high-risk patient for primary prevention. Circulation 2000;101:111–116. 7. Grundy SM, Pasternak R, Greenland P, et al. Assessment of cardiovascular risk by use of multiplerisk-factor assessment equations: a statement for healthcare professionals from the American Heart Association and the American College of Cardiology. Circulation 1999;100:1481–1492. 8. Wilson PWF, D’Agostino RB, Levy D, et al. Prediction of coronary heart disease using risk factor categories. Circulation 1998;97:1837–1847. 9. Daviglus ML, Liu K, Greenland P, et al. Benefit of a favorable cardiovascular risk-factor profile in middle age with respect to Medicare costs. N Eng J Med 1998;339:1122–1129. 10. Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive Summary of the Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). JAMA 2001;285:2486–2497. 11. Hosmer DW, Lemeshow S. Applied Logistic Regression Analysis. John Wiley and Sons, Inc., New York, NY, 1989. 12. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982;143:29–36. 13. Evidence Based Cardiology. Yusuf S, Cairns JA, Camm AJ, Fallen EL, Gersh BJ. (eds.), 1998, London BMJ Books. 14. Shepard J, Cobbe SM, Ford I, et al. for the West of Scotland Coronary Prevention Study Group. Prevention of coronary heart disease with pravastatin in men with hypercholesterolemia. N Eng J Med 1995;333:1301–1307. 15. Downs JR, Clearfield M, Weis S, et al. Primary prevention of acute coronary events with lovastatin in men and women with average cholesterol levels: results of AFCAPS/TexCAPS. JAMA 1998;279:1615–1622. 16. Adult Treatment Panel. Report of the National Cholesterol Education Program Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Arch Intern Med 1988;148:36–69. 17. Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Summary of the Second Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel II). JAMA 1993;269:3015–3023. 18. Prosser LA, Stinnett AA, Williams LW, et al. Cost-effectiveness of cholesterol-lowering therapies according to selected patient characteristics. Ann Intern Med 2000;132:769–779. 19. Pickin DM, McCabe CJ, Ramsay LE, et al. Cost-effectiveness of HMG-CoA reductase inhibitor (statin) treatment related to the risk of coronary heart disease and cost of drug treatment. Heart 1999;82:325–332. 20. Scandinavian Simvastatin Survival Study Group. Randomized trial of cholesterol lowering in 4444 patients with coronary heart disease: the Scandinavian Simvastatin Survival Study (4S). Lancet 1994;344:1383–1389. 21. Haq IU, Jackson PR, Yeo WW, et al. Sheffield risk and treatment table for cholesterol lowering for primary prevention of coronary heart disease. Lancet 1995;346:1467–1471. 22. Perreault S, Hamilton VH, Lavoie F, Grover SA. Treating hyperlipidemia for the primary prevention of coronary disease: are higher dosages of lovastatin cost-effective? Arc Intern Med 1998;158:375–381. 23. Bradford RH, Shear CL, Chremos AN, et al. Expanded Clinical Evaluation of Lovastatin (EXCEL) study results: efficacy in modifying plasma lipoproteins and adverse events profile in 8245 patients with moderate hypercholesterolemia. Arch Intern Med 1991;151:43–49. 24. Bradford RH, Shear CL, Chremos AN, et al. Expanded Clinical Evaluation of Lovastatin (EXCEL) study: design and patient characteristics of a double-blind, placebo-controlled study in patients with moderate hypercholesterolemia. Am J Cardiol 1990;66:44B–55B. 25. Havel RJ, Hunninghake DB, Illingworth DR, et al. Lovastatin (Mevinolin) in nonfamilial hypercholesterolemia: a multicenter study. JAMA 1986;107:609–615.

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26. Schulman KA, Kinosian B, Jacobson TA, et al. Reducing high blood cholesterol level with drugs. JAMA 1990;264:3025–3033. 27. Hamilton VH, Racicot F-E, Zowall H, et al. The cost-effectiveness of HMG-CoA reductase inhibitors to prevent coronary heart disease: estimating the benefits of increasing HDL-C. JAMA 1995;273:1032–1038. 28. Pharoah PDP, Hollingworth W. Cost-effectiveness of lowering cholesterol concentration in patients without pre-existing coronary heart disease: life table method applied to health authority population. BMJ 1996;312:1443–1448. 29. Caro J, Klittich W, McGuire A, et al. The West of Scotland coronary prevention study: economic benefit analysis of primary prevention with pravastatin. BMJ 1997;315:1577–1582. 30. Goldman L, Weinstein MC, Goldman PA, Williams LW. Cost-effectiveness of HMG-CoA reductase inhibition for primary and secondary prevention of coronary heart disease. JAMA 1991;265:1145–1151. 31. Weinstein M, Stason W. Cost-effectiveness of interventions to prevent or treat coronary heart disease. Ann Rev Public Health 1985;6:41–43. 32. Glick H, Heyse JF, Thompson D, et al. A model for evaluating the cost-effectiveness of cholesterollowering treatment. Int J Technol Assess Health Care 1992;8:719–734. 33. Koren MJ, Smith DG, Hunninghake DB, et al. The cost of reaching National Cholesterol Education Program (NCEP) goals in hypercholesterolemic patients: a comparison of atorvastatin, simvastatin, lovastatin and fluvastatin. Pharmacoecomics 1998;14:59–70. 34. MacMahon SW, Cutler JA, Neaton JD, et al. Relationship of blood pressure to coronary and stroke morbidity and mortality in clinical trials and epidemiological studies. J Hypertens 1986;4(Suppl 6):S14–S17. 35. Kannel WB. Implications of the Primary prevention trials against coronary heart disease. J Hypertens 1990;8(Suppl 7):S245–S250. 36. US Department of Health and Human Services. Health, United States, 1999: With Health and Aging Chartbook, 1999, National Center for Health Statistics, Hyattsville, MD. 37. The Sixth Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC VI). Arch Intern Med 1997;157:2413–2446. 38. Moser M. The cost of treating hypertension: can we keep it under control without compromising the level of care? Am J Hypertens 1998;11:120S–127S. 39. Psaty BM, Smith NL, Siscovick DS, et al. Health outcomes associated with antihypertensive therapies used as first-line agents: a systematic review and meta-analysis. JAMA 1997;277:739–745. 40. Jonsson B, Johannesson M. Cost benefit of treating hypertension. Clin Exp Hypertens 1999;21:987–997. 41. Whitworth J. Cost-effectiveness analysis in the treatment of hypertension: a medical view. Clin Exp Hypertens 1999;21:999–1008. 42. Pearce KA, Furberg CD, Psaty BM, Kirk J. Cost-minimization and the number needed to treat in uncomplicated hypertension. Am J Hypertens 1998;11:618–629. 43. Pahor M, Psaty BM, Alderman MH, et al. Health outcomes associated with calcium antagonists compared with other first-line antihypertensive therapies: a meta-analysis of randomized controlled trials. The Lancet 2000;356:1949–1954. 44. The ALLHAT Collaborative Research Group. Major cardiovascular events in hypertensive patients randomized to doxazosin versus chlorthalidone in antihypertensive and lipid lowering treatment to prevent heart attack trial (ALLHAT): preliminary results. JAMA 2000;283:1967–1975. 45. Almbrand B, Johannesson M, Sjostrand B, et al. Cost-effectiveness of intense insulin treatment after acute myocardial infarction in patients with diabetes mellitus: results from the DIGAMI study. Eur Heart J 2000;21:733–739. 46. Gray A, Raikou M, McGuire A, et al. Cost-effectiveness of and intensive blood glucose control policy in patients with type 2 diabetes: economic analysis alongside randomized controlled trial (UKPDS 41). BMJ 2000;320:1373–1378. 47. The Diabetes Control and Complications Trial Research Group. Lifetime benefits and costs of intensive therapy as practiced in the diabetes Control and Complications trial. JAMA 1996;276:1409–1415. 48. Hannson L, Zanchetti A, Julius S, et al. On behalf of the HOT Study Group. Effects of intensive blood pressure lowering and low-dose aspirin in patients with hypertension: principal results of the Hypertension Optimal Treatment (HOT) randomized trial. Lancet 1998;351:1755–1762. 49. Turner R, Holman R, Stratton I, et al. For the United Kingdom Prospective Diabetes Study Group. Tight blood pressure control and risk of macrovascular and microvascular complications in type 2 diabetes: UKPDS 38. BMJ 1998;317:707–713.

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50. UK Prospective Diabetes Study Group. Cost effectiveness analysis of improved blood pressure control in hypertensive patients with type 2 diabetes: UKPDS 40. BMJ 1998;317:720–726. 51. Elliot WJ, Weir DR, Black HR. Cost-effectiveness of the lower treatment goal (of JNC VI) for diabetic hypertensive patients. Arch Intern Med 2000;160:1277–1283. 52. Peters AL. Diabetes: A model for universal secondary cardiovascular disease prevention practices. Prev Cardiol 1999;2(Suppl):51–54. 53. Cummings SR, Rubin SM, Oster G. The cost-effectiveness of counseling smokers to quit. JAMA 1989;256:1315–1318. 54. Oster G, Huse PM, Delea TE, et al. Cost-effectiveness of nicotine gum as an adjunct to physician’s advice against cigarette smoking. JAMA 1988;256:1315–1318. 55. Fiscella K, Franks P. Cost-effectiveness of the transdermal nicotine patch as an adjunct to physicians’ smoking cessation counseling. JAMA 1996;275:1247–1251. 56. Cromwell J, Bartosch WJ, Fiore MC, et al. Cost-effectiveness of the clinical practice recommendations in the AHCPR guideline for smoking cessation. JAMA 1997;278:1759–1766.

11

Economics of Therapy for Acute Coronary Syndromes Daniel B. Mark, MD, MPH CONTENTS INTRODUCTION REPERFUSION THERAPY ANTIPLATELET THERAPY ANTITHROMBIN THERAPY SECONDARY PREVENTION CONCLUSION REFERENCES

INTRODUCTION Acute coronary syndromes (ACS), defined as acute myocardial infarction (MI) and unstable angina, share a number of common features. These include an underlying ruptured or eroded atherosclerotic coronary plaque as the most frequent initiating pathophysiologic event, similar clinical manifestations, and a clinical course that lasts for usually no more than 30 days (1). The management of ACS is hospital-based and often resource-intensive. Thus, economic analyses of these syndromes and their therapies are typically focused on the initial hospitalization. Conceptually, therefore, it is useful to divide the costs of care for ACS into five major categories (Fig. 1). In ST-segment elevation acute MI patients who are eligible for reperfusion therapy, the choice of therapy can have a significant effect on the cost of care. For example, outside of the United States, streptokinase (SK) remains the preferred thrombolytic regimen, and it is also the least expensive (at approximately $300 per dose) (2,3). In the United States, less than 10% of thrombolytic administration is SK. The other three agents (tissue plasminogen activator [t-PA], reteplase, and tenecteplase) all cost about $2200 per typical dose. A second major cost component is the typical hospital stay for an uncomplicated patient. For an acute MI patient, the typical US stay is 1 day in the intensive care unit (ICU) and 3 or 4 days in a monitored floor setting. For unstable angina, the length of stay is shorter, and ICU-based care may not be required. The “hotel” consists of hospital care plus associated nursing costs, which have averaged $1400–2800 for an ICU day and $500 for a non-ICU day in some recent analyses (4). From: Contemporary Cardiology: Cardiovascular Health Care Economics Edited by: W. S. Weintraub © Humana Press Inc., Totowa, NJ

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Fig. 1. Cost components of ACS.

Another key cost component in the management of each ACS patient is risk stratification. The two most common options are an “early invasive” strategy using diagnostic cardiac catheterization (CATH) for all patients without contraindications and an “early conservative” strategy that employs catheterization for high-risk subjects and noninvasive stress testing for most of the others (5–7). According to the most recent data from the TACTICS-TIMI 18 trial, the early invasive strategy costs about $1800 more than the early conservative strategy for the initial hospitalization phase. This figure reflects the fact that 51% of the conservative arm patients were referred for catheterization, and about 67% of those who had catheterization in both arms were referred for subsequent revascularization. Complications are one of the most significant sources of higher costs in the ACS cohort. Complications may arise either from the atherosclerotic disease process itself or from the therapy provided for the disease. For example, about 7% of acute MI patients will develop cardiogenic shock, the care of which may require many expensive ICU days (8). Thrombolytic therapy may lead to major bleeding in about 5% of patients, which requires ICU care, multiple transfusions, and other resuscitative therapies (9). Strokes complicate acute MI in about 1.7% of cases and increase hospital costs by 44% in those patients (10). The total cost of an index hospitalization for ACS, therefore, will result from an admixture of the components outlined previously, some operating to increase costs, others to decrease them. In reviewing the material in this chapter, two important principles of modern medical economic analysis must be kept in mind. First, costs should always be considered in conjunction with clinical outcomes. Money flows into the health care system to achieve certain objectives. Value is the balance between extra money spent and extra benefits produced. Cost-effectiveness analysis (CEA) is simply a quantitative statement of this balance. Second, costs and health benefits should be viewed from a long-term (societal) perspective. For example, as is discussed later

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in this chapter, initiation of secondary prevention with statin therapy can reduce the future need for revascularization procedures and long-term costs. However, the economic benefits from statin therapy are not evident for the first 10 months of therapy, and they amplify with the duration of therapy. Clearly, in this case, the short-term balance sheets would not absolutely reflect the long-term net costs or return on investment. A full economic analysis must consider not only the early therapies, but also the long-term consequences (so-called induced costs and induced-cost savings) of those therapies. In this chapter, we review what is known about the economics of the major therapies for ACS: reperfusion therapy (both thrombolytic and percutaneous coronary intervention [PCI]), antiplatelet therapy, antithrombin therapy, and secondary prevention. Risk stratification strategies and the use of revascularization procedures are reviewed in Chapter 14.

REPERFUSION THERAPY Reperfusion therapy has been demonstrated to improve survival and reduce complications in acute ST elevation MI (11). The first regimen to achieve widespread use, intravenous SK, was a “low-tech” product and fairly inexpensive. More recent bioengineered agents, such as recombinant t-PA, are “high tech” and much more expensive. Percutaneous reperfusion strategies are similarly technologically sophisticated and expensive. For any of these strategies to be judged a good value, they must produce either significant (if not complete) offsets of their price through improved efficiency of care and/or reductions in expensive complications, or they must produce benefits large enough to “justify” their costs (i.e., they must have cost-effectiveness ratios [CER] ideally $50,000 or less per quality-adjusted life-year [QALY] added) (12).

Thrombolytic Therapy Although multiple large-scale clinical trials have demonstrated the mortality benefits of SK therapy, none of them included economic data. Although it is possible that SK therapy is cost saving in the long run because of reduced complications, there are no empirical data with which to test this proposition. Several groups have modeled the cost-effectiveness (CE) of SK therapy using the drug cost (not adjusted for cost offsets) and the published clinical trial survival benefits as input data. Of these efforts, the analysis by Naylor and colleagues is particularly useful (13). They estimated a CER of $2000 to $4000 per life year saved for SK, assuming each survivor would live about 10 years. Results were more favorable for anterior MIs than inferior MIs because of the larger survival benefit produced, but, for both groups, SK therapy was firmly in the “best buy” category of cardiovascular therapies. Krumholz and coworkers used the two largest clinical trials of SK therapy (GISSI-1 and ISIS-2) to model its CE in patients aged 75 and over (14). For an 80-year-old, they projected an undiscounted life expectancy of 2.7 years and a CER of $21,200 per life year saved (1990 dollars). For a 70-year-old, the CER was $21,600 per life year saved. However, some doubts have been raised about the benefits of thrombolytic therapy in the very elderly (15,16). The relevance of any model-based analysis, such as that of Krumholz et al. (14), is very much dependent of the validity of the incremental health benefits incorporated into the analysis.

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The GUSTO I trial established that recombinant t-PA was clinically superior to SK (17). Genentech’s decision to put a premium price on its new biotech drug brought into question the value for money prominently into the public debate on the role of this new agent. In a careful prospective economic analysis, the GUSTO investigators found that the cumulative 1-year medical cost for the SK arms (combined) was $24,575 in comparison with $24,990 for the t-PA arm (exclusive of thrombolytic therapy cost) (18). The average wholesale cost of 100 mg of t-PA was $2750, whereas that of SK was $320. Adding these costs in, the net incremental cost of therapy in the t-PA arm was $2845. Life expectancy, projected out from the empirical GUSTO I 1-year survival data, was 15.27 years for the SK arms (combined) and 15.41 for the t-PA arm, yielding an incremental (undiscounted) life expectancy owing to t-PA of 0.14 years per patient. Therefore, the incremental CER for t-PA was $32,678 per year of life saved. When typical discounted prices ($270 for SK, $2200 for t-PA) were substituted for the average wholesale price of the two thrombolytic agents, the CER improved to $27,115 per year of life saved. These results compare favorably with other therapies considered as good value for money (19). Since the GUSTO I trial, two other recombinant thrombolytics that are mutants of the t-PA molecule have been approved for use by the Food and Drug Administration. The GUSTO III trial compared t-PA with reteplase in 15,059 patients (20). At one year, major cardiovascular events (including death, stroke, and major bleeding) were the same. Because the price of the two agents was the same as well, the two drugs appear economically indistinguishable. Although, theoretically, the double-bolus regimen of reteplase would require less monitoring (and thus, less nurse time) than the bolus-infusion regimen of t-PA, it is very unlikely that any clinical care environment is efficient enough to actually realize these small savings. The ASSENT II trial compared tenecteplase with t-PA (TNK-t-PA) in 16,944 patients and found an identical effect on mortality (9). Bleeding complications and the need for transfusions were modestly reduced by tenecteplase. The cost of tenecteplase is the same as t-PA. As with reteplase, the bolus administration (single bolus in this case) reduces nursing time costs, but likely by an amount too small to recover. Convenience to the Emergency Department staff, rather than significant economic advantage, has fueled the growing popularity of this agent. Two large trials have compared standard thrombolytic therapy with a combination regimen consisting of half-dose thrombolytic plus full-dose abciximab. The objective was to determine if either safety or efficacy could be improved through a synergy of antithrombotic/antiplatelet mechanisms. In GUSTO V, combination half-dose reteplase plus abciximab reduced nonfatal MI, but did not alter mortality (21). In ASSENT 3, both combination half-dose tenecteplase plus abciximab and full-dose tenecteplase plus enoxaparin reduced nonfatal MI relative to full-dose tenecteplase plus unfractionated heparin (22). Economic analyses for both of these trials are underway.

Primary Percutaneous Coronary Reperfusion Primary PCI has been used as an alternative reperfusion strategy to thrombolytic therapy for more than 10 years. The consensus for most of those years was that the PCI strategy was clinically equivalent to thrombolytic-based strategies, but a niche therapy, feasible only for the small proportion of patients presenting to hospitals that had the facilities, expertise, and desire to do the procedure on an emergency basis.

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Several of the earlier trials comparing primary PCI and thrombolytic therapy included economic data. Of these, the largest was GUSTO IIb (1994–1995), which compared primary percutaneous transluminal coronary angioplasty (PTCA) and t-PA in 1138 patients enrolled in 57 hospitals throughout nine countries (23). The trial showed a significant 33% reduction in the composite of death, nonfatal MI, and stroke, but by 6 months, this therapeutic benefit had attenuated by about half and was no longer significant. In the economic analysis, direct PTCA had a $900 cost savings in hospital costs, whereas t-PA had a $600 cost savings in physician costs, leaving a net cost advantage of $300 for direct PTCA at the end of the initial hospitalization (24). In interpreting these results, it is noteworthy that the rate of diagnostic CATH in the US tPA patients was 70%. At the end of 6 months, the net cost advantage of primary PTCA was approximately $100. The trial was viewed as a clinical and economic “toss-up.” In contrast to the GUSTO IIb results, an observational analysis from the Myocardial Infarction Triage Intervention (MITI) registry found no mortality benefit for primary PTCA and a 13% lower cost for thrombolytic therapy at 3 years (25). Although the reason for the different cost results in GUSTO IIb and the MITI registry cannot be completely discerned from published data, the MITI thrombolytic cohort represented a combination of t-PA (68%) and SK (32%). Similar to GUSTO IIb, 74% of the thrombolytic therapy group underwent diagnostic catheterization. In addition, the thrombolytic therapy group had a 1.1-day longer hospital stay. More recent data from the National Registry of Myocardial Infarction (NRMI) registry showed a mortality benefit for primary PCI over thrombolysis for high- and intermediate-volume centers (≥17 primary PCIs per year) (26). The 138-patient AIR-PAMI study has suggested that the benefit of primary PCI persists for high-risk patients even when they present to community hospitals and have to be transported to an intervention center, a process that added approximately 100 minutes to the time to reperfusion (27). Further support for the interventional strategy comes from the DANAMI-2 trial, which showed a significant reduction in the composite of death, recurrent MI, or stroke at 30 days for patients transferred for emergency PCI vs onsite thrombolysis (28). Unfortunately, these more contemporary tests of PCI vs thrombolysis have not included prospective cost data. Given the earlier finding from GUSTO IIb that the costs of the initial hospitalization are close to a “toss-up,” adding the cost of emergency transport may make the primary PCI strategy significantly more expensive. In AIR-PAMI, the onsite thrombolysis group had a 55% rate of diagnostic catheterization and a 52% rate of revascularization (27). Thus, the decision not to transfer a patient early is associated with less invasive resource use during that hospitalization. However, in this study, the primary PCI group had a 1.4-day shorter hospital stay (p = 0.02). Recent estimates show that fewer than 20% of US hospitals and 10% of European hospitals are equipped to perform primary PCI (29). Also, only a fraction of these hospitals are prepared to offer this service emergently on nights and weekends. Lieu and colleagues examined the impact of different service scenarios on the initial cost of primary PTCA, using data from Kaiser Permanente (30). In a hospital with existing catheterization facilities and night call, primary PTCA cost would be about $1600 per procedure (1993 dollars). If night call for technical personnel had to be added to an existing facility without it, the cost per procedure would double to $3200. If a new catheterization laboratory had to be built, costs would range from about $4000 to $7000 per procedure. These figures reflect only the procedure itself, not the costs of hospitalization for MI care.

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Much of the recent trial work in the area of primary PCI has focused on newer strategies for percutaneous revascularization. In particular, routine stenting and adjunctive use of glycoprotein IIb/IIIa inhibitors have been tested in several trials. PAMISTENT randomized 900 patients with ST elevation MI to primary stent vs balloon PTCA (31). Use of glycoprotein (GP) IIb/IIIa was very low (5%). At 6 months, the stent arm had fewer target-vessel repeat revascularizations, but a nonsignificant increase in mortality and a nonsignificant decrease in reinfarction. Economic analysis of this trial showed that index hospital costs for the stent arm were about $2000 higher, owing primarily to the extra costs of the stents (32). Over the subsequent year, decreased need for follow-up procedures reduced that excess cost by about half. Assuming that current technology was in use, including longer stents unavailable during the PAMI-STENT trial, the cost per QALY of moving from primary PTCA to primary stenting was < $30,000. The RAPPORT trial tested the benefits of adding abciximab to primary PTCA (33). Abciximab reduced target-vessel repeat PTCA, but not total PTCA, and had no effect on death or reinfarction. Furthermore, abciximab increased major bleeding. The CADILLAC trial tested balloon vs stent and abciximab vs no abciximab in 2665 acute MI patients (34). Stenting produced a nonsignificant mortality decrease over balloon PTCA (in contrast with PAMI-STENT results), whereas abciximab decreased recurrent ischemia and related target-vessel revascularization for both PCI arms. The ADMIRAL trial compared abciximab with placebo in 300 acute MI patients undergoing primary stenting (35). At 30 days, abciximab lowered mortality by 49%, reinfarction by 49%, and target-vessel revascularization by 58%. Major bleeding was not increased by abciximab. Neither the CADILLAC nor ADMIRAL trial has yet reported economic data. The extra costs of abciximab average about $1400, whereas stenting adds about $2000 per patient. To the extent that these upfront costs are offset by later cost savings as a result of reduced follow-up procedures, the net 1-year cost of these newer strategies may be $1000 or less. Thus, in 2003, the pendulum has swung toward greater enthusiasm for primary PCI. Building a new network of primary PCI centers across the country to ensure that most citizens have ready access would be extremely expensive and likely not cost-effective. Transporting patients from community hospitals to experienced PCI centers adds cost and prolongs time to reperfusion. Nonetheless, outcomes still appear improved, at least for higher-risk patients. Even if the net cost of the PCI strategy relative to thrombolysis is increased by $2000 to $3000, if primary PCI saves significantly more lives than the thrombolytic alternatives, it would likely have a favorable CER.

ANTIPLATELET THERAPY The antiplatelet agents currently used in ACS are aspirin, clopidogrel, and the intravenous GP IIb/IIIa inhibitors. Aspirin is one of the most economically attractive therapies in cardiovascular medicine. For the cost of pennies a day, it produces a reduction in mortality in acute ST elevation MI, similar in magnitude to SK (36). In non-ST elevation MI, aspirin significantly reduces both mortality and nonfatal MI rates (1). Unfortunately, none of the large randomized aspirin trials ever included an economic component. Nonetheless, the cost of generic aspirin therapy is so low that by preventing MIs, it is

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almost certainly cost saving in the long run. Aspirin-related major complications, such as gastrointestinal bleeding or intracranial hemorrhage, partially reduce the cost saving associated with aspirin, but are likely too infrequent to cancel it completely. Gaspoz and colleagues recently estimated that an intervention to increase the use of aspirin from current levels (85% of eligible patients) to all eligible patients would cost about $11,000 per QALY gained (37). Two large-scale trials have established the benefits of clopidogrel therapy for secondary prevention. The Clopidogrel vs Aspirin in Patients at Risk of Ischemic Events (CAPRIE) trial showed an 8.7% decrease in a composite outcome event from clopidogrel vs aspirin (38). In the more recent Clopidogrel in Unstable Angina to Prevent Recurrent Events (CURE) trial, aspirin plus clopidogrel decreased cardiovascular death, MI, or stroke by 20%, but increased major bleeding (39). Gaspoz and colleagues used the Coronary Heart Disease Policy Model, a computer simulation of the US population and the results of these two trials, to examine the CE of lifetime clopidogrel therapy (37). Use of clopidogrel for aspirin-intolerant patients (about 5% of coronary artery disease [CAD] patients) was economically attractive, with a CER of $11,000 per QALY. Routine clopidogrel therapy (with or without aspirin) had a CER of $130,000 per QALY, an economically unattractive result. Only among the highest-risk subset of patients did the CER of clopidogrel therapy begin to approach $50,000 per QALY. However, clopidogrel therapy applied for a shorter period, following the presentation for ACS, might be much more cost-effective. An analysis of this issue is currently ongoing by the CURE investigators. Three intravenous GP IIb/IIIa inhibitors are in current use in the United States: abciximab, eptifibatide, and tirofiban. Each has been tested in both ACS cohorts and PCI cohorts. The PCI data is reviewed by Cohen in Chapter 14. Although expectations were high for abciximab based on the earlier PCI data, the GUSTO IV trial failed to demonstrate a significant reduction in death or MI in non-ST elevation ACS patients (40). Partly as a consequence of these results, some have argued that the benefit of GP IIb/IIIa in ACS patients is restricted to those undergoing PCI. However, careful metaanalysis of six major trials involving 31,402 ACS patients suggests a benefit for these agents even without a planned PCI procedure (41). PURSUIT was the largest trial of GP IIb/IIIa in ACS (42). At 30 days, eptifibatide produced a 1.5% absolute decrease in death or MI in comparison with placebo (p = 0.04). An economic substudy was conducted prospectively in PURSUIT using the 3522 enrolled US patients (43). In the US cohort, which had a base rate of CATH of 85%, addition of eptifibatide therapy did not reduce major procedure use or shorten length of stay. In contrast, in Western Europe, where a more moderate use of the early invasive strategy was employed, there was evidence that eptifibatide produced a partial cost offset (44). With a cost of approximately $1000 and an incremental life expectancy of 0.11 life years, adding an extra life year with eptifibatide cost about $14,000. Neither of the two tirofiban ACS trials, PRISM and PRISM-PLUS, has published an economic analysis. PRISM-PLUS had a diagnostic catheterization rate of 90%, similar to the US PURSUIT cohort. In addition, the cost of a 71-hour infusion of tirofiban is around $1000 (similar to eptifibatide), and the death plus MI reduction in PRISMPLUS was similar to that in the US PURSUIT cohort. Thus, it is likely that a formal CEA of PRISM-PLUS would find tirofiban therapy to be economically attractive, with a CER similar to that in PURSUIT.

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ANTITHROMBIN THERAPY First-generation antithrombin therapy consists of unfractionated intravenous heparin. The available small clinical trials show that this agent reduces death and nonfatal MI during the acute phase of non-ST elevation ACS (1). Several different low-molecular-weight heparins have been compared with unfractionated heparin in ACS. In FRISC II, dalteparin plus aspirin significantly reduced death or MI at 6 days compared with aspirin alone, but the therapeutic effect did not persist at 6 months (45). Enoxaparin plus aspirin has been compared with unfractionated heparin plus aspirin in two trials, ESSENCE (3171 patients) and TIMI IIB (3910 patients). Taken together, these two trials showed that enoxaparin reduced death or MI by 23% at 8 days (46). This clinical benefit persisted to 1 year (47). Economic analysis of ESSENCE was performed using cost data from the US patients in the trial (48). The cost of the enoxaparin therapy for 2.5 days (the mean duration of therapy in the United States) was $155 per patient vs $80 for unfractionated heparin. The enoxaparin strategy reduced invasive procedure use and length of stay, resulting in a net cost savings of $760 to $1200 per patient. Thus, from an economic standpoint, enoxaparin is a dominant therapy: improved clinical outcomes and lower net cost. Several direct antithrombins have been tested in ACS patients to determine if they provided an advantage over unfractionated heparin (an indirect antithrombin). Hirudin has been the most carefully studied. In 6054 ST elevation acute MI patients from the TIMI 9 and GUSTO IIb trials, hirudin had no effect on mortality and had a borderline reduction of nonfatal MI. In 8011 non-ST elevation ACS patients in GUSTO IIb, hirudin did not significantly reduce the composite of death or MI (49). However, in a pooled analysis of 35,970 patients with ACS in 11 trials, employing five different agents, direct thrombin inhibitors reduced nonfatal MI by 20%, relative to unfractionated heparin, but had no effect on mortality (50). Hirudin and bivalrudin had the strongest evidence for benefit.

SECONDARY PREVENTION Whether ACS patients are managed aggressively with early invasive evaluation, or more conservatively with watchful waiting and noninvasive stress testing, all eligible patients need to be considered for long-term preventive therapy with aspirin, a β blocker, an angiotensin-converting enzyme (ACE) inhibitor, and a statin. In addition, smoking cessation and cardiac rehabilitation are key parts of management in the transition from the acute phase to the chronic phase of the atherosclerotic illness. Aspirin has already been discussed as part of the antiplatelet therapy, as has the recent economic analysis of chronic clopidogrel use. The effectiveness and low cost of aspirin make it a “best buy” among secondary prevention therapies. Routine lifetime clopidogrel therapy, because of its high cost and small incremental benefit over aspirin, does not appear economically attractive (37). Several trials have shown that β blockers reduce death and nonfatal MI after acute MI. Goldman and colleagues found that the post-MI use of propranolol (at a cost of $200/year) was quite economically attractive, with CER ranging from $2300 per life year added (high-risk subjects) to $13,600 per life year added (low-risk subjects). The effectiveness data in which this model was based all date from the prethrombolytic era.

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A more recent analysis from the same group examined increasing use of β blockers from current levels (44% of eligible patients in 2000) to target levels (92% of eligibles) (51). The incremental cost per QALY from full-use β blocker therapy was $4500. A phased strategy of achieving target levels of β blocker use, starting with all first-MI survivors over the next 20 years, was found to be dominant: saving 72,000 lives and reducing costs. The benefits of ACE inhibitor therapy in secondary prevention were first demonstrated in the Survival and Ventricular Enlargement (SAVE) trial (52). In 2231 MI survivors with ejection fraction (EF) of 40% or less, captopril reduced mortality by 19%. Based on data from SAVE, Tsevat and colleagues estimated the CE of captopril therapy as $10,400 per QALY or better (53). The strategy tested in GISSI 3, 6 weeks of lisinopril therapy in acute MI patients, was recently reported to have a CER of $2080 per extra 6-week death avoided (54). The most influential secondary prevention trial of ACE-inhibitor therapy to date is the Heart Outcomes Prevention Evaluation (HOPE) trial (55). This study randomized 9297 patients with vascular disease or multiple risk factors to ramipril or placebo. Over a mean follow-up of 4.5 years, ramipril reduced mortality by 16% (p = 0.005) and nonfatal MI by 20% (p < 0.001). Furthermore, revascularization was reduced by 15% in the ramipril arm (p = 0.002). Lamy has recently reported an economic analysis of the trial (56). The cost of ramipril therapy is approximately $440 per year or $1480 over the period of the study follow-up. Hospitalization costs were decreased in the ramipril arm by $614 and revascularization costs by $750. Thus, over the 4.5 years of HOPE follow-up, ramipril therapy appeared to pay for itself. As used in HOPE, ramipril appears to be an economically dominant therapy: better clinical outcomes at an equivalent cost. Reduction of low-density lipoprotein (LDL)-cholesterol levels to less than 100 mg/dL is a goal of secondary prevention established by the National Cholesterol Education Program (57). The prognostic benefit of statin therapy for secondary prevention has now been well established. In addition, several studies have examined the economics of this type of preventive therapy. The Scandinavian Simvastatin Survival Study (4S) demonstrated a 30% decrease in all-cause mortality over 5.4 years with 20–40 mg of simvastatin per day (58). Patients in this trial had pretreatment total cholesterol levels of 210–310 mg/dL, despite dietary therapy. Economic analysis showed that simvastatin reduced the total hospital days over 5.4 years by 5138 days (p < 0.001) (59). Reduction in the need for hospitalization took 10 months to become evident and 22 months to become statistically significant. Intriguingly, the magnitude of this benefit appears to increase with the duration of therapy. Using these data, Pederson and coworkers (59) calculated a $3872 per-patient cost saving from simvastatin therapy resulting from reduced hospitalization. The cost of the drug was $4400 over 5 years (discounted), and an additional $250 was added for laboratory monitoring. Combining these incremental costs and cost savings yielded a net cost of therapy of $148 per patient per year, or $778 per patient over the follow-up of the 4S trial. In a separate analysis, the 4S investigators examined the CE of simvastatin using a Markov model based on the 4S data (60). Costs were derived from four Swedish hospitals and converted to the equivalent amount of US dollars. CER were $5400 per life year added for a 59-year-old man and $10,500 per life year added for a 59-year-old woman. The ratio was more favorable for a 70-year-old man with a total cholesterol of

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309 mg/dL ($3800 per life year added) and less favorable for a 35-year-old woman with a total cholesterol of 213 mg/dL ($27,400 per life year added). The Cholesterol and Recurrent Events (CARE) trial demonstrated that 40 mg/day of pravastatin produced a 24% reduction, with a mean total cholesterol of 209 mg/dL (61). Economic analysis found a mean cost per year of pravastatin therapy of $925 per year (62). Over the 6 years of trial follow-up, the pravastatin arm had fewer hospitalizations, resulting in a $1700 cost savings. Extrapolating to the life expectancy of the study cohort, the incremental cost of the pravastatin strategy was about $11,000 (discounted at 3%). The (nonsignificant) mortality difference in CARE extrapolated to a lifetime incremental gain of 0.35 QALYs per patient in the pravastatin arm. The resulting CER was $31,000/QALY saved. For patients with an LDL-cholesterol lower than 125 mg/dL, however, pravastatin therapy was projected to be both more costly and less effective than placebo. Thus, there are good data supporting the economic attractiveness of secondary prevention with aspirin, β blockers, ACE inhibitors, and statins. Two meta-analyses have examined the benefits of cardiac rehabilitation in post-MI patients (63,64). The pooled data suggest a 20–25% reduction in death and MI, but no individual trial was large enough to demonstrate this persuasively. Given the uncertainty in clinical benefit, economic analysis of cardiac rehabilitation is problematic. Oldridge analyzed the CE of an 8-week rehabilitation program in post-MI patients with depression and/or anxiety (65). No difference in cardiac events was observed, but the rehabilitation program was associated with an improved quality of life and a net increment of 0.052 QALYs gained per patient over 1 year of follow-up. The CE of cardiac rehabilitation in this study was about $10,000/QALY added. A more recent CEA of cardiac rehabilitation post-MI estimated a cost per year of life added of about $5000 (66).

CONCLUSION ACS are a very prevalent and highly dramatic phase in atherosclerotic CAD. The acute phase of this illness accounts for about 50% of total 10-year costs: $23,510 for the acute phase and $21,819 for the postacute phase (67). Thus, treatment decisions made during ACS generate a stream of clinical and economic consequences that stretch far into the future.

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49. The GUSTO IIb Investigators. A comparison of recombinant hirudin with heparin for the treatment of acute coronary syndromes. N Engl J Med 1996;335:775–782. 50. Direct thrombin inhibitors in acute coronary syndromes: principal results of a meta-analysis based on individual patients’ data. Lancet 2002;359:294–302. 51. Phillips KA, Shlipak MG, Coxson P, et al. Health and economic benefits of increased beta-blocker use following myocardial infarction. JAMA 2000;284:2748–2754. 52. Pfeffer MA, Braunwald E, Moye LA, et al. Effect of captopril on mortality and morbidity in patients with left ventricular dysfunction after myocardial infarction. Results of the survival and ventricular enlargement trial. N Engl J Med 1992;327:669–677. 53. Tsevat J, Duke D, Goldman L, et al. Cost-effectiveness of captopril therapy after myocardial infarction. J Am Coll Cardiol 1995;26:914–919. 54. Franzosi MG, Maggioni AP, Santoro E, et al. Cost-effectiveness analysis of early lisinopril use in patients with acute myocardial infarction. Results from GISSI-3 trial. Pharmacoeconomics 1998;13:337–346. 55. The Heart Outcomes Prevention Evaluation Study Investigators. Effects of an angiotensin-convertingenzyme inhibitor, ramipril, on cardiovascular events in high-risk patients. N Engl J Med 2000;342:145–153. 56. Lamy A, Gafni A, Pogue J, Yusuf S. Cost-effectiveness of ramipril in high risk patients: analysis of the HOPE study. (Abstr). Can J Cardiol 2000;16(Suppl F):233F. 57. National Cholesterol Education Program (Adult Treatment Panel III). Detection, evaluation, and treatment of high blood cholesterol in adults. NIH Publication, No. 01-3670, 2001, Bethesda, MD. 58. Scandinavian Simvastatin Survival Study Group. Randomised trial of cholesterol lowering in 4444 patients with coronary heart disease: the Scandinavian Simvastatin Survival Study (4S). Lancet 1994;344:1383–1389. 59. Pedersen TR, Kjekshus J, Berg K, et al. Cholesterol lowering and the use of healthcare resources: results of the Scandinavian Simvastatin Survival Group. Circulation 1996;93:1796–1802. 60. Johannesson M, Jonsson B, Kjekshus J, et al. Cost effectiveness of simvastatin treatment to lower cholesterol levels in patients with coronary heart disease. N Engl J Med 1997;336:332–336. 61. Grines CL, Marsalese DL, Brodie BR, et al. Safety and cost-effectiveness of early discharge after primary angioplasty in low risk patients with acute myocardial infarction. J Am Coll Cardiol 1998;31:967–972. 62. Tsevat J, Kuntz KM, Orav EJ, et al. Cost-effectiveness of pravastatin therapy for survivors of myocardial infarction with average cholesterol levels. Am Heart J 2001;141:727–734. 63. Oldridge NB, Guyatt GH, Fischer ME, Rimm AA. Cardiac rehabilitation after myocardial infarction: combined experience of randomized clinical trials. JAMA 1988;260:945–950. 64. O’Connor GT, Buring JE, Yusuf S, et al. An overview of randomized trials of rehabilitation with exercise after myocardial infarction. Circulation 1989;80:234–244. 65. Oldridge N, Furlong W, Feeny D, et al. Economic evaluation of cardiac rehabilitation soon after acute myocardial infarction. Am J Cardiol 1993;72:154–161. 66. Ades PA, Pashkow FJ, Nestor JR. Cost-effectiveness of cardiac rehabilitation after myocardial infarction. J Cardiopulm Rehabil 1997;17:222–231. 67. Eisenstein EL, Shaw LK, Anstrom KJ, et al. Assessing the clinical and economic burden of coronary artery disease: 1986–1998. Med Care 2001;39:824–835.

12

Cost-Effectiveness of Percutaneous Coronary Interventions David J. Cohen, MD, MSc and Ameet Bakhai, MBBS, MRCP CONTENTS INTRODUCTION CORONARY REVASCULARIZATION CORONARY ANGIOPLASTY FOR SINGLE-VESSEL DISEASE CABG FOR MULTIVESSEL DISEASE PERCUTANEOUS VS SURGICAL REVASCULARIZATION FOR MULTIVESSEL DISEASE NEWER PERCUTANEOUS INTERVENTIONAL DEVICES RHEOLYTIC THROMBECTOMY DISTAL PROTECTION DEVICES BRACHYTHERAPY FOR THE TREATMENT OF IN-STENT RESTENOSIS ADJUNCTIVE PHARMACOTHERAPY ADJUNCTIVE GLYCOPROTEIN IIB/IIIA INHIBITION FOR PCI PRIMARY ANGIOPLASTY VS REPERFUSION IN AMI STENTING VS PTCA FOR AMI INVASIVE EARLY MANAGEMENT OF PATIENTS WITH ACUTE CORONARY SYNDROMES CONCLUSIONS REFERENCES

INTRODUCTION Over the last decade, discussions and concerns about medical costs have moved from the peripheral domain of the economist and health service researcher to the center stage of public attention. Medical costs now receive more attention in the national lay press than they do in major journals. Unfortunately, much of this coverage is negative and repeats a single troublesome question: Is the United States spending too much for health care and getting too little in return? One area that has received particular scrutiny in recent years, and is likely to remain under close watch in the future, is coronary revascularization. From: Contemporary Cardiology: Cardiovascular Health Care Economics Edited by: W. S. Weintraub © Humana Press Inc., Totowa, NJ

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During the 20 years since its original description by Gruentzig (1) percutaneous transluminal coronary angioplasty (PTCA) has undergone tremendous growth and development. Currently, more than 1 million PTCA procedures are performed each year in the United States alone, and coronary angioplasty is now performed twice as often as coronary artery bypass grafting (CABG) (2). The technical aspects of percutaneous coronary intervention (PCI) have evolved rapidly in recent years. Devices available to the interventional cardiologist now include stents, lasers, atherectomy catheters, intracoronary brachytherapy, and several other new devices that are presently under clinical investigation. Moreover, coronary interventionalists are beginning to reap the benefits of the biotechnology revolution, with the development of important medical adjuncts to angioplasty, such as the platelet glycoprotein IIb/IIIa receptor antagonists and drug-eluting stents. As these procedures continue to develop and proliferate, their contribution to medical costs are growing as well. The cost of PCI is currently estimated at $10 billion per year, and the cost of all forms of coronary revascularization is estimated at between $20 and $25 billion in the United States alone. As a rapidly evolving field, there is a need to understand the impact of each component of the interventional armamentarium on clinical outcomes and costs relative to other available therapies, including medical therapy and CABG. This chapter provides an overview of the current knowledge regarding the economic aspects of percutaneous coronary revascularization.

CORONARY REVASCULARIZATION A coronary angioplasty in its simplest form involves the inflation of a balloon within a coronary artery at the site of an atherosclerotic lesion. This balloon inflation will compress the atherosclerotic matter and stretch the vessel to accommodate the compressed plaque material. On deflation, the vessel has a wider lumen to allow blood flow through. Prior to 1987, angioplasty predominantly consisted of balloon inflations (also known as balloon angioplasty). Since 1987, stent technology as an adjunct to balloon inflation has enabled the interventional cardiologist to implant a small metal prosthesis within the artery to scaffold the vessel open (3). Stent technology is discussed further, but much of the following existing clinical data referenced reflects PTCA performed without stent technology. There are two important issues to be addressed in the evaluation of the costs and other economic implications of coronary angioplasty: the appropriate reference strategy (e.g., medicine and CABG) and the major determinants of cost outcomes. Coronary angioplasty was initially proposed as a less invasive low-cost alternative to coronary bypass surgery. Over the last decade, however, the cumulative experience with this technology would suggest that, more often, it represents a more invasive highcost alternative to medical therapy. Approximately half of all patients who undergo coronary angioplasty in the United States have single-vessel coronary disease, and the vast majority of patients (even those with multivessel disease) receive only single-vessel revascularization (4).

CORONARY ANGIOPLASTY FOR SINGLE-VESSEL DISEASE Any economic evaluation of coronary angioplasty for single-vessel disease must begin with an understanding of what an angioplasty procedure “costs.” With angioplasty,

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as with most medical technologies, it is not possible to define a single representative cost for all patients. Medical care costs vary in complex ways regarding patient-specific, provider-specific, treatment-specific, and geographic factors, and these costs do not always relate to medical outcomes in easily predictable ways. Thus, there is no dollar figure that can represent the “cost” of coronary angioplasty, any more than one mortality rate can represent the true mortality rate for all myocardial infarction (MI) patients. With these caveats in mind, it is possible to cite some representative figures for PTCA costs from the literature. In the early 1990s, Topol and coworkers studied a private insurance claims database of 2100 PTCA patients and found an average hospital charge of around $10,000 for the baseline hospitalization, with an additional $4000 for physician fees and $4000–5000 more in charges during the first year following the procedure (5). These costs were similar to hospital charges found in a series of 119 elective PTCA patients treated at Duke University during 1986 (6). Both of these charge figures would be expected to substantially overestimate the true marginal cost of providing an additional PTCA. More recent estimates of the cost of coronary angioplasty may be derived from the “usual care” arms of several multicenter clinical trials. In the Coronary Angioplasty Versus Excisional Atherectomy Trial (CAVEAT), mean hospitals costs (calculated from charges) for the PTCA arm were $8300 (7,8). More recently, the average initial hospital cost for patients treated with conventional balloon angioplasty in the Balloon vs Optimal Atherectomy Trial (BOAT) was $9950, including procedural costs and physician fees of approximately $3600 and $1700, respectively (9). Finally, in the Evaluation of PTCA to Improve Long-term Outcome by cF7E3 Glycoprotein receptor blockade (EPILOG) and Randomized Efficacy Study of Tirofiban for Outcomes and Restenosis (RESTORE) trials of glycoprotein IIb/IIIa blockers in patients undergoing conventional balloon angioplasty, initial hospital costs for patients in the control arm ranged from $9600 to $12,100 (10,11). Thus, a representative hospitalization for coronary angioplasty (without a stent) would cost between $8000 and $12,000 (excluding the cost of the diagnostic catheterization), depending on the specific center, treatment pattern, and patient characteristics. Understanding whether angioplasty is economically attractive requires more than a basic grasp of the procedural and hospital costs. This determination requires the comparison of both the costs and clinical benefits of PTCA with alternative management strategies. For most patients currently undergoing PTCA, the appropriate strategy for comparison is medical therapy. To date, there are no US-based published empirical cost comparisons between PTCA and medical therapy alone. Initial results in a large consecutive cohort of coronary artery disease (CAD) patients from the Duke cardiovascular database have shown that the initial costs for patients undergoing PTCA are twice as high as those for initial medical therapy (Mark DB, personal communication). In the only US-based randomized trial to compare PTCA with medical therapy, the Angioplasty Compared to Medicine study (ACME) investigators found that medical resource utilization was considerably higher for patients assigned to initial PTCA in comparison with those assigned to initial medical therapy (12). Specifically, PTCA patients were hospitalized for a mean of 3.8 days during the 6-month study period in comparison with 2.4 days for medically treated patients. About 7% of the patients assigned to PTCA underwent subsequent bypass surgery (emergent or elective) during follow-up in comparison with none of the medical therapy patients (p < 0.01). As the ACME trial

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was performed in the Veterans Affairs (VA) system, no direct medical care cost comparison was performed. The ACME trial did, however, demonstrate that when compared with medical therapy, balloon angioplasty increases short- and intermediate-term medical care costs. More recently, the second Randomised Intervention Treatment of Angina trial (RITA-2) compared an initial PTCA vs medical therapy strategy in the United Kingdom (13). Investigators found that PTCA resulted in greater symptom relief initially, but with a higher combined rate of death or MI (6.3% vs 3.3%, p = 0.02). At 3 years, there was an overall mean additional cost per patient randomized to PTCA of £2685 (1998: $1 ~ – £0.60) (74% higher) than patients given an initial medical management strategy (13a). Given the higher cost of interventional therapy for most patients, the cost-effectiveness (CE) of PTCA for patients with single-vessel disease depends on whether the benefits of such therapy are “worth the cost.” In the case of PTCA for single-vessel disease, no study to date has demonstrated that percutaneous coronary revascularization prolongs life expectancy. Given the generally excellent long-term prognosis of such patients with medical therapy (14), it would be difficult for any form of revascularization therapy to offer a significant survival advantage. Indeed, in RITA-2, there was a small excess of deaths or heart attacks. The CE of single-vessel PTCA, therefore, depends on the value assigned to reduction of anginal symptoms. One study has explicitly examined the CE of balloon angioplasty for patients with single-vessel coronary disease (15). In 1989, Wong and colleagues developed a computer simulation model to estimate the relative CE of angioplasty, bypass surgery, and conservative (i.e., medical) therapy of patients with chronic stable angina. For the purposes of analysis, patients were grouped by age, gender, coronary anatomy, ventricular function, and the severity of angina. For each group, the model estimated lifetime medical care costs, quality-adjusted life expectancy and cost-effectiveness ratios (CER). They found that in comparison with medical therapy, angioplasty increased qualityadjusted life expectancy in all patient subgroups, regardless of the severity of angina, ventricular function, or number of diseased vessels. In general, angioplasty appeared to be cost-effective when compared with medical therapy for all patients with single-vessel disease, except those with very mild angina. For example, in patients with severe angina, normal ventricular function, and single-vessel (left anterior descending [LAD] coronary artery) disease, the quality-adjusted life expectancy with angioplasty (as initial therapy) was 18.3 quality-adjusted life years (QALY) in comparison with 17.4 QALY with initial conservative therapy, with an estimated CER of $6000 per QALY gained. Their model predicted that PTCA would be highly cost-effective in comparison with medical therapy for all subgroups of patients with single-vessel disease and severe angina (incremental CER < $10,000 per QALY). For patients with only mild angina, however, initial PTCA was projected to be significantly less attractive, with incremental CER in the order of $80,000–100,000 per QALY. Although the previous analyses are based on data from the late 1980s, it is unlikely that incorporation of more recent data would change their findings appreciably. If anything, one would suspect that the CE of PTCA for single-vessel CAD has improved since the late 1980s. Since that time, the development of new devices, such as coronary stents and adjunctive antiplatelet therapy, have led to significant improvements in the clinical outcomes of percutaneous coronary revascularization (16–18). Concurrently, the hospital costs of balloon angioplasty have decreased because of reductions in

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resource costs, as well as from improved efficiency of practice (e.g., combined diagnostic angiography, percutaneous revascularization, and routine same-day sheath removal) (19,20). At Boston’s Beth Israel Deaconess Medical Center, the median length of stay for uncomplicated PTCA has fallen from 3 days to 1 day since the early 1990s. Thus, although ideal data are not available, the best available data suggest that balloon angioplasty is reasonably cost-effective for patients with single-vessel coronary disease and severe angina, but only marginally cost-effective for patients with mild angina or no symptoms. If studies suggesting that PTCA may improve survival in patients with chronic stable angina and silent myocardial ischemia can be confirmed (21), the CE of PTCA when compared with medical therapy would improve even further. These data are reflected in current PTCA guidelines, which recommend PTCA as an alternative to medical therapy in patients with an appropriate anatomy of coronary disease and symptoms impairing quality of life or extensive myocardial ischemia on noninvasive provocation testing (22).

CABG FOR MULTIVESSEL DISEASE CABG has been shown to improve survival in subsets of patients with severe CAD who have a high mortality rate with medical therapy. These subsets include patients with left main disease, three-vessel disease with reduced left ventricular function, and two-vessel disease, including stenosis of the proximal LAD (23–26). In addition, CABG provides more complete symptom relief than does medical therapy in patients with severe angina (27,28). Based on data from the early 1980s, Weinstein and Stason examined the CE of bypass surgery relative to medical therapy for various patient risk groups (29). They found that the CE of bypass surgery was highly favorable for patients with left main or severe three-vessel disease (CER $4000–7000 per QALY gained). For patients with less severe anatomic disease, the CE of CABG primarily reflected quality-of-life benefits and ranged from $20,000 per QALY gained for severely symptomatic patients to more than $400,000 per QALY gained for patients with only mild angina. It is difficult to know whether these CE estimates still apply to the current practice of bypass surgery. Since the early 1980s (when these analyses were performed), bypass surgery has become safer and more durable, with the introduction of improved myocardial protection and the routine use of internal mammary artery (IMA) grafting (30,31). In addition, the cost of bypass surgery has decreased substantially, with increasing attention to early extubation, streamlined care plans, early discharge, and most recently, the use of off-pump procedures and minimally invasive techniques (32). Simultaneously, medical therapy has also improved with the widespread use of antiplatelet therapy βblockade, angiotensin-converting enzyme (ACE) inhibitors and aggressive lipid lowering for both primary and secondary prevention. Because the benefits of medical therapy are also likely to accrue to some extent, in patients managed surgically, it is likely that the net effect of these changes on the CE of CABG has been modest.

PERCUTANEOUS VS SURGICAL REVASCULARIZATION FOR MULTIVESSEL DISEASE As an alternative means of mechanical revascularization, considerable attention has been focused on the relative CE of balloon angioplasty and coronary bypass surgery for

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patients with multivessel coronary disease. To date, at least nine studies have compared PTCA costs with those of CABG. The main studies are summarized in Table 1. In contrast to single-vessel disease, where observational data and simulation models provide the only insight into the CE of PTCA, in the case of multivessel disease at least five randomized clinical trials (RCT) have been performed comparing conventional PTCA with bypass surgery. Although each of these studies have specific inclusion and exclusion criteria, using different timeframes and cost-measurement techniques, several general observations can still be made. First, the initial hospital cost for PTCA is approximately 30–50% lower than that of bypass surgery, and these cost savings persist for the first year of follow-up. The absolute magnitude of this cost difference is highly dependent on the cost-accounting methodology used. In a 1986 study, Hlatky and colleagues at Duke University found that hospital charges for bypass surgery were more than $10,000 higher than those for PTCA ($19,644 vs $9556) (6). The estimated difference in hospital cost narrowed considerably when charges were converted to costs. For example, when only the costs of supplies were considered to be variable, the cost difference between balloon angioplasty and bypass surgery was estimated to be only $1900. When it was assumed that the costs of both supplies and personnel were variable, the difference increased to $4600. Finally, when all costs were assumed to be variable (as they would be in the long run), the cost difference was approximately $7800. Regardless of the accounting methodology, however, the initial cost of PTCA remained about half that of bypass surgery in this study. Second, despite the substantial initial cost savings with multivessel PTCA, over a 3–5year follow-up period, much of these initial cost savings are lost because of the need for repeat PTCA or bypass surgery in approximately 50% of patients. Weintraub and colleagues have reported 3- and 8-year economic data for the 386 patients randomized to balloon angioplasty or bypass surgery in the Emory Angioplasty vs Surgery Trial (EAST) (35,36). Initial hospital costs and professional charges for the PTCA group were an average of $19,824 compared with $27,793 for the CABG group. By the end of 3 and 8 years of follow-up, however, mean PTCA costs had increased to 91% and 95% of those for bypass surgery, and the difference was no longer statistically significant. In patients with focal two-vessel disease, however, the 3-year cost of PTCA remained significantly lower than for bypass surgery ($20,875 vs $23,639, p < 0.001). In the RITA study, mean initial hospital costs in the PTCA arm were 52% lower than that of the CABG group at £3592 and £6192, respectively (37). This difference narrowed considerably during follow-up, and by 5 years after initial treatment, aggregate costs in the PTCA group were 95% of those initially treated with coronary bypass surgery at £8842 and £9268 (1997: $1 ~ – £0.67), respectively, as a result of sixfold higher follow-up procedural costs in the PTCA arm (38). Results of a 5-year economic substudy of the Bypass Angioplasty Revascularization Investigation (BARI) have recently been reported as well (39,40). To date, this study represents the largest and most comprehensive US economic evaluation of alternative revascularization strategies for patients with multivessel coronary disease. Among 934 patients randomized to PTCA or bypass surgery, initial cost of care was 35% lower with PTCA ($21,113 vs $32,347). Over the first 3 years of follow-up, this cost difference narrowed progressively, such that by the end of 5 years of follow-up, aggregate costs with PTCA remained slightly (5%) but significantly lower than with bypass surgery ($56,225

Table 1 Cost Studies Comparing Percutaneous Coronary Revascularization with Bypass Surgery Date

Method

N

# Diseased vessels

Reeder et al. (33)

1979–1981

OBS

168

1,2,3

Medical charges

Kelly et al. (34) EAST (35)

1987–1990

OBS RCT

163 384

1,2,3 2,3

Hospital and MD charges Hospital costs and MD charges

RITA (37)

1993–1994

RCT

999

2,3

Hospital costs

Study

Cost measure

193 Hospital, procedural, and medication costs BARI (39)

1988–1995

RCT

952

2,3

Hospital and out-patient costs, and MD fees

ARTS (41)

1997–1998

RCT

1200

2,3

Hospital costs and MD fees

SOS (44)

1997–1999

RCT

967

2,3

Hospital and out-patient costs

OBS, observational study; RCT, randomized controlled trial.

Time period

PTCA cost

CABG cost

Initial hospitalization 1 year 1 year Initial hospitalization 3-year total Initial hospitalization London center Non-London center 2-year total London center Non-London center Initial revascularization

$7571 $11,384 $7689 $16,223 $23,734

$12,154 $13,387 $13,559 $24,005 $25,310

£3753 £3024

£7319 £5722

£6916 £5448 $21,113

£8739 £6498 $32,347

5-year total Initial revascularization 1-year total Initial revascularization 1-year total

$56,225 7366 EU 10,665 EU £4205 £6419

$58,889 11,295 EU 13,638 EU £7396 £8914

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vs $58,889, p = 0.047). Subgroup analysis demonstrated that PTCA remained approximately $6000 less expensive than CABG for patients with two-vessel disease, but that 5year costs were no different for patients with three-vessel disease. Because bypass surgery was associated with a trend toward improved survival in BARI, formal costeffectiveness analysis (CEA) was performed to determine whether routine CABG would be economically attractive for such patients. The BARI investigators found the overall CER for bypass surgery in comparison with angioplasty to be $26,000 per year of life gained. Thus, although this analysis suggests that CABG may be an economically attractive initial revascularization strategy for patients with multivessel disease, the confidence limits around this CER were wide and included a 13% probability that the CER was > $100,000 per life year gained. Further analyses will be required to identify patient- and treatment-specific determinants of long-term cost and CE in these populations. It is important to note that the studies discussed previously have largely compared conventional balloon angioplasty to coronary bypass surgery. Although stents and their impact on clinical and economic outcomes will be discussed further, it is appropriate to examine the role of multivessel coronary interventions (with stents) in comparison to surgery in the setting of randomized trials. Two large RCTs comparing multivessel stenting with bypass surgery are the Arterial Revascularization Therapy Study (ARTS) and Stent or Surgery study (SoS), both of which included prospective evaluations of both health care costs and quality of life. At 1-year follow-up of the ARTS, there were no significant differences in mortality between multivessel stenting (2.5%) and CABG (2.8%) groups, with overall 1- and 2-year event-free survival rates of 88% and 85% with CABG vs 74% and 69% with stenting (41,42). This difference in event rates was mostly driven by repeat revascularization rates of 16.8% in the stent group vs in the CABG group. Nonetheless, repeat revascularization rates with the stenting group were approximately half those seen in earlier multivessel PTCA trials, representing a considerable clinical improvement of stenting with respect to balloon angioplasty. The ARTS economic analysis calculated total procedural costs of $6441 for the stent and $10,653 for the CABG groups and 1-year total direct medical costs of $10,665 and $13,638 (p < 0.001), respectively. The incremental CER of CABG over stenting was $21,000 for each patient that remained event-free at 1 year. Long-term follow-up is planned to determine whether there is further erosion of the cost differences over 3–5 years. Whether similar findings would be seen in the US healthcare system (where stents are substantially more expensive, and lengths of stay after bypass surgery are typically shorter) remains an open question also. The SoS study randomized a total of 988 patients with two- or three-vessel disease: 500 to the surgery group and 488 to the stent group (43). Incomplete revascularization was allowed. During the median available follow-up period of 2 years (range 1–4 years), 20.7% of the patients randomized to the stent arm required one or more additional revascularization procedures (PCI or CABG) compared to 6.0% in the surgery group (hazard ratio 3.85, 95% confidence interval [CI] 2.56 to 5.79, p < 0.001). The majority of these additional procedures occurred in the first year: 17.2% in the stent group in comparison to 4.2% in the surgery group. Similarly, over the available follow-up period, the mortality rates for stent and surgery arms were 4.5% and 1.6%, respectively. The respective 1-year mortality rates (used for the economic analyses) were 2.5% for the stent and 0.8% for the surgery groups. In SoS, the surgical mortality rates were felt to be much lower than in daily clinical practice, and there were disproportionate numbers of cancer deaths in the two arms (eight

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in the stent arm in comparison to one in the surgery arm). With regard to costs (2000: $1 ~ – £0.66) the initial hospitalization costs were: £4205 for the stent group vs £7396 for the surgery group, p < 0.001 (44). Additional follow-up costs up to 1 year after randomization were stent £2214 vs surgery £1515, giving total respective 1-year costs of £6419 (stent) and £8914 (surgery). In summary, both observational studies and recent randomized trials have consistently demonstrated that multivessel PCI is considerably less resource-intensive and less costly than bypass surgery during the initial hospitalization. However, because of the need for more frequent repeat revascularization procedures, the initial economic advantage of multivessel PTCA diminishes over time. Because bypass surgery does not appear to confer a survival benefit in comparison with multivessel PTCA in patients suitable for both procedures (except possibly for treated diabetics) (45–47), quality-oflife outcomes should play an important role in determining the relative CEs of these procedures. In general, the randomized trials have shown that initial recovery is quicker after PTCA, but that at 1–3-year follow-up, patients treated with initial PTCA have more frequent angina and require more anti-anginal medications (36,39,48). Beyond 3-year follow-up, however, these modest advantages of bypass surgery are largely attenuated. In BARI, for example, there were no major differences in quality of life between the PTCA and CABG groups at 5-year follow-up (39). As the current evidence suggests that most of the clinical and economic differences between PTCA and CABG for the treatment of multivessel coronary disease are minor and transient, neither procedure is clearly superior on the grounds of CE.

NEWER PERCUTANEOUS INTERVENTIONAL DEVICES Although coronary angioplasty represents a significant addition to the therapeutic armamentarium of the cardiologist, it has limitations. Despite considerable technical advances from its earliest days, balloon angioplasty remains limited by short-term complications, including abrupt vessel closure (often resulting in acute MI or emergent bypass surgery) in 4–8% of patients and restenosis, requiring additional revascularization procedures in 25–40% of patients with initially successful procedures. In addition, a substantial proportion of patients with significant obstructive coronary disease are technically unsuitable for coronary angioplasty. These limitations of conventional PTCA have prompted the development of a variety of new devices, including atherectomy catheters (directional, rotational, and extraction), excimer laser angioplasty, and coronary stents. Although economic analyses of these devices have generally lagged behind their proliferation in clinical practice, a number of single-center observational studies and controlled clinical trials have been performed for these devices. Because most of the clinical studies that have been conducted to date have addressed the issue of whether these new techniques are truly superior to balloon angioplasty, the available economic studies have the incremental cost difference between the new technique and conventional PTCA as their primary focus.

Coronary Stents Of the new interventional techniques, coronary stenting has undergone the closest economic scrutiny. Several factors have contributed to this intense interest. First, coronary stenting is expensive. In 1997, the price of a single Palmaz-Schatz coronary stent was

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$1600—more than four times the cost of a typical angioplasty balloon. Even with increasing competition in recent years, the cost of the average coronary stent used at Beth Israel Deaconess Medical Center in 1999 was $1350 and in 2000 was $1100. Moreover, coronary stents are the only nonreusable interventional devices, and about 30–40% of lesions will require an additional stent for long lesions or exit or entry dissections. Finally, intracoronary stenting is the first new coronary intervention to demonstrate improved angiographic (49–51) and clinical (49,50) outcomes in comparison with conventional balloon angioplasty. As a result, the use of stents has grown rapidly in practice, ranging from 37% in a large coronary angioplasty series in 1997 (52), to 70% in series from the year 2000 (53,54). This has raised concerns that their overuse might have undesirable economic consequences (55–57). Since the mid-1990s, several important studies have examined the relative costs of stenting and balloon angioplasty in a variety of patient populations and clinical settings (Table 2). The STress REStonosis Study (STRESS) trial randomized 410 patients undergoing elective revascularization of a single discrete coronary stenosis to balloon angioplasty or Palmaz-Schatz coronary stent implantation. At 6-month follow-up, patients assigned to initial stenting had less angiographic restenosis (31% vs 42%, p < 0.05) and required less frequent clinically driven target vessel revascularization (10% vs 15%, p = 0.06) in comparison with patients assigned to initial PTCA (49). The STRESS economic sub-study included 207 consecutive patients randomized to stenting or PTCA at 8 of 13 US clinical sites (58). Stent patients required more contrast volume, angioplasty balloons, and stents per procedure than patients who underwent conventional PTCA. As a result, catheterization laboratory costs were $1200 higher for stenting than for balloon angioplasty. In addition, the use of high-dose oral anticoagulation after stenting in the STRESS trial led to significant increases in major vascular complications with stenting (10% vs 4%) and a 2-day longer hospital stay. Thus, mean initial hospital costs were approximately $2200 higher for stenting than for PTCA ($9738 vs $7505). Over the first year of follow-up, patients treated with initial stenting required fewer subsequent hospital admissions and fewer repeat revascularization procedures. As a result, follow-up medical care costs (not including out-patient or indirect costs) were, on average, $1400 lower after stenting. However, these “downstream” cost savings were insufficient to fully offset the higher initial cost of stenting. Thus, over the full 1-year study period, cumulative medical care costs were approximately $800 higher with stenting when compared with PTCA ($11,656 vs $10,865, p < 0.001). Although advances in stent deployment techniques (e.g., routine high-pressure postdilation and aspirin + theinopyridine antiplatelet agents) have both improved the safety of stenting significantly and reduced length of stay, these benefits appear to have been offset by increasing resource intensity of the stent procedure itself (59). In the Benestent 2 trial, which used the heparin-coated Palmaz-Schatz stent and the current dual antiplatelet/antithrombotic regimen (60), initial hospital costs remained more than $2000 higher with stenting than with balloon angioplasty ($10,376 vs $8198, p < 0.001) (61). Although 1-year cardiac event rates were substantially lower with stenting (21% vs 11%), aggregate 1-year costs remained approximately $1200 per patient higher with stenting when compared with PTCA ($12,489 vs $11.364, p = 0.04). Thus, the CER for stenting in the Benestent 2 population was approximately approximately $12,000 per additional 1-year event-free survivor.

Table 2 Selected Cost Studies Comparing Coronary Stenting with Balloon Angioplasty Study STRESS (58)

Date

Method

N

Cost measure

1991–1993

RCT

207

Hospital costs and MD fees

Timeframe Initial hospitalization 1-year total

Benestent 2 (61)

1995–1996

RCT

823

Hospital costs and MD fees

Initial hospitalization 1-year total

EPISTENT (64)

1996–1997

RCT 1438

Hospital costs and MD fees

Initial hospitalization

197

1-year total

Duke University (65)

1995–1996

OBS

496

Hospital costs and MD fees

Initial hospitalization 1-year total

Stent-PAMI (120)

1996–1997

RCT

900

Hospital costs (RCC) and out-patient costs MD fees

Initial hospitalization 1-year total

Device PTCA Stent/Warf PTCA Stent/Warf PTCA Stent/Ticlid PTCA Stent/Ticlid PTCA/abciximab Stent/Ticlid Stent/abciximab/ Ticlid PTCA/abciximab Stent/Ticlid Stent/abciximab/ Ticlid PTCA Stent/Ticlid PTCA Stent/Ticlid PTCA Stent/Ticlid PTCA Stent/Ticlid

Mace

21%* 15%*

21% 11%

25.30% 24.00% 20.10%

30%* 14%*

22% 13%

Cost $7505 $9738 $10,865 $11,656 $8198 $10,376 $10,726 $11,618 $11,357 $11,923 $13,228 $17,370 $17,109 $17,951 $10,076 $13,294 $22,571 $22,140 $15,004 $16,959 $19,595 $20,571

OBS, observational study; RCT, randomized controlled trial; Stent/Warf, stenting with oral anticoagulation; Stent/Ticlid, stenting with combined antiplatelet therapy (aspirin + ticlopidine or aspirin + clopidogrel). MACE, major adverse cardiac events (death, MI, or repeat revascularization); RCC method, hospital charges converted to costs based on hospital-specific cost-to-charge ratios. * Event rate indicates only repeat revascularization.

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An economic evaluation of coronary stenting was also performed in conjunction with the Evaluation of Platelet IIb/IIIa Inhibitor for STENting (EPISTENT) trial, which compared three strategies of percutaneous coronary revascularization: PTCA + abciximab, stent + abciximab, and stent + placebo (62,63). In patients who received abciximab, at 1-year follow-up, stenting reduced the rate of composite adverse outcomes (death, MI, or any target-vessel revascularization) relative to PTCA: 20.1% vs 25.3% (p = 0.01). As was seen in the previous randomized trials, stenting increased initial hospital costs by $1900 per patient and did not fully “pay for itself” by 1-year follow-up (64). Thus, aggregate 1-year costs were approximately $600 per patient higher with stenting in comparison with PTCA alone (both on a background of abciximab therapy). One study that suggests that stents may save money (or at least be cost-neutral) when compared with conventional PTCA is a single-center registry from Duke University Medical Center (65). Peterson and colleagues examined in-hospital and 1-year costs for a consecutive group of stent patients (n = 384) and “stent-eligible” PTCA patients (n = 159). Although initial hospital costs were more than $3200 higher for the stent group, stent patients were much less likely to be rehospitalized (22% vs 34%) or undergo repeat revascularization (9% vs 26%) during follow-up. As a result, 1-year costs were actually slightly lower in the stent group ($22,140 vs $22,571 p = 0.26). Potential explanations for the differences between the Duke registry experience and the randomized trials include the higher risk nature of the Duke population (as suggested by higher rates of follow-up CABG), higher single-center treatment costs, and possible unmeasured confounding. Given the consistent results of the randomized trials, it seems reasonable to conclude that based on current device costs, coronary stenting improves outcomes, but increases costs for most patients. Thus, the CE of elective coronary stenting depends on whether its proven clinical benefits—namely, a reduction in recurrent angina and the need for repeat revascularization procedures—are sufficient to justify the additional long-term costs of the procedure. To formally address the issue, we developed a decision analytic model to study the long-term costs and clinical effectiveness of alternative strategies for treating patients with symptomatic single-vessel coronary disease (66). Detailed description of the model is available elsewhere (67). Originally based largely on observational data, the model has been updated to incorporate the pooled clinical results of the STRESS, Benestent and the Benestent 2 studies, as well as 1996 cost data from the Beth Israel Hospital experience. Based on this model, we estimated that stenting for single-vessel coronary disease had an incremental CER of $33,700 per QALY gained— similar to the CE of treating mild diastolic hypertension (68). Thus, although coronary stenting remains more expensive than conventional PTCA—even in the long run—its CE appears to compare favorably with other medical practices. Given recent reductions in the price of stents, as well as technical modifications, such as the availability of longer stents, high-pressure stent delivery balloons, and the increasing feasibility of direct stenting (i.e., stenting without predilation), it is likely that the CE of stenting is even more favorable than our model (which is based on 1997 data) would suggest. An alternative view of the CE of stenting is provided by the EPISTENT data (63). As noted previously, among patients who received abciximab, stenting was associated with a trend toward reduced 1-year mortality (1.0% vs 2.1%) and an incremental cost of $600 per patient. By combining these results with long-term survival projections

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based on the Duke Cardiovascular Databank, the EPISTENT investigators estimated the CER for stenting to be $5300 per year of life gained. However, several important points should be noted about this study. First, this CER may represent an overestimate because the authors did not consider any quality-of-life benefits related to the reduced rates of recurrent angina or repeat procedures associated with stenting. Second, the results of EPISTENT only apply to coronary stenting superimposed on a background of abciximab therapy. Finally, this CER is driven entirely by the 1-year survival advantage seen with stenting in this trial. As previous studies of stenting have failed to demonstrate a similar survival benefit, one may question whether this observation represents a true synergistic effect of stents + abciximab or random “play of chance.”

Direct Stenting in Comparison to Conventional Stenting with Predilatation One of the many strategies employed to reduce the costs of stenting includes the implantation of a stent without the traditional predilation of the lesion by balloon angioplasty (i.e., direct stenting). Although this method may provide some clinical advantages of reducing platelet activation and ischemic time, the major advantage of direct stenting is economic. Preliminary observations suggest that the strategy of direct stenting is applicable with modern stents in up to about 40–60% of all coronary interventions. Most trials have reported similar clinical outcomes in selected lesion types (avoiding calcified lesions in markedly tortuous vessels). Several studies have examined the economic outcomes of direct stenting in comparison with conventional stent techniques. Briguori et al. performed a retrospective comparison of patients undergoing direct and conventional stenting (69). Direct stenting was successful in 94% of cases in this single-center analysis, with no in-hospital deaths, MI, or emergency bypass surgery. In the direct stenting group, there were significant reductions in procedure time (by 30%), radiation exposure time (by 25%), contrast dye, balloon use, and cost (1305 vs 2210 EU). In a prospective randomized study of 122 patients with single nonoccluded lesions, Danzi et al. reported that procedural costs were significantly lower with direct stenting ($2398 against $3176, p < 0.001), with similar 6-month event-free survival rates and incidence of angiographic restenosis (70). Carrie et al. reported similar findings in the multicenter, randomized Benefit Evaluation of Direct Coronary Stenting (BET) study with mean procedural costs of $956 and $1164 with and without direct stenting (p < 0.0001) (71).

Provisional Stenting Although stenting improves angiographic and clinical outcomes when compared with balloon angioplasty, a strategy of universal stent implantation may not be optimal for all patients. First, it is clear from many series that the results of stenting in the “real world” are substantially worse than those achieved in the select patient subsets enrolled in RCT (72,73). Second, widespread adoption of stenting has led to the development of a new and challenging disease entity—in-stent restenosis. Although vascular brachytherapy has recently emerged as a potential solution to this frustrating entity, brachytherapy is costly and may be associated with important clinical complications (74). Finally, coronary stenting remains expensive. Although numerous studies indicate that the CE of routine stenting is acceptable from a health policy perspective, the incremental cost to the health care system of coronary stenting currently approaches $5 billion per year in the United States alone.

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These limitations have led some cardiologists to propose the concept of provisional stenting—i.e., an initial strategy of aggressive balloon angioplasty with stenting if necessary to treat PTCA-induced complications or if the results of PTCA are suboptimal (75). Several lines of evidence suggest this might be a reasonable approach for many patients. Examination of the results of balloon angioplasty in the mid-1990s has suggested that PTCA results have improved substantially in comparison with historical data, presumably as a result of the ability of stenting to reverse abrupt closure and severe dissections. In addition, it appears that the availability of a stent “safety net” has allowed the PTCA operator to choose a more aggressive balloon strategy than was possible in the prestent era. For example, with rates of bailout stenting between 13% and 15%, conventional PTCA in the BOAT, EPILOG, and EPISTENT trials achieved rates of target vessel revascularization of only 18% (62,76,77). Finally, a post-hoc analysis of several trials suggests that restenosis after PTCA can be predicted based on the initial PTCA results. For example, in the Benestent 2 trial, 53% of patients in the PTCA arm achieved optimal angiographic results (residual stenosis <30% by quantitative angiography) by balloon angioplasty alone. In this group, 6-month event rates were only 14%—similar to the 11% event rate achieved by stenting. In contrast, for the 47% of patients who were left with suboptimal angiographic results, the 6-month incidence of death, MI, or target lesion revascularization was 27%. Similarly, in the DEBATE I trial, the combination of an optimal angiographic result (residual stenosis < 35%) and a normal Doppler-derived coronary flow reserve (CFR > 2.5) identified a group of patients with a 16% angiographic restenosis rate—similar to the best results of stenting (78). Recently, several clinical trials have attempted to formally test whether a strategy of provisional stenting can produce clinical outcomes comparable to routine stent implantation (Table 3). The Optimal Coronary Balloon Angioplasty with provisional Stenting vs primary stenting (OCBAS) trial randomized 116 patients after successful balloon angioplasty to a strategy of routine stenting or provisional stenting (79). In the provisional stent strategy, the decision to place a stent was based on a 30-minute follow-up angiogram, demonstrating a loss of MLD greater than 0.3 mm or a more than 10% increase in residual stenosis at the PTCA site. Only 14% of patients in the PTCA group required crossover to stent implantation. At 6-month follow-up, there was no difference in angiographic restenosis, and 1-year event-free survival was 81% in the routine stent group and 83% in the provisional stenting group. Cumulative hospital costs (based on South American resource costs) were substantially lower for the provisional stenting strategy ($6745 vs $10,368 per patient, p = 0.02). The large cost difference achieved in this study reflects the high acquisition cost of stents ($3000 per stent) in South America at the time of the study. In the Optimal PTCA vs Routine Primary Stent strategy trial (OPUS-1), routine stenting was compared with a provisional stent strategy guided only by the immediate postprocedure angiogram (80). “Optimal PTCA” was defined as a residual stenosis less than 30% by QCA or less than 20% by visual estimate. If these criteria could not be met, then patients in the balloon angioplasty group would go on to stenting also. Of note, OPUS-1 was restricted to patients who had a discrete coronary lesion less than 20 mm in length in a vessel with reference diameter larger than 3.0 mm—a relatively ideal population for stenting. Although planned enrollment was 2184 patients, the trial was terminated at 479 patients as a result of slow recruitment and limited funding. Overall, 37% of patients in the PTCA arm crossed over to provisional stenting. At 6-month follow-up, the rate of target vessel revascularization was significantly lower with routine

Table 3 RCT Comparing Provisional with Universal Stenting Study OCBAS (79)

Date

N

1995–1996

116

Provisional stent technique

Cost measure

30-minute repeat angiogram Hospital costs

Timeframe Initial hospitalization 1-year total

201

OPUS (80)

1996–1998

DESTINI (81) 1996–1998

479

301

Visual estimate or QCA

QCA + CFR

Itemized procedure costs, hospital costs, and MD fees

Initial hospitalization

Itemized procedure costs, hospital costs, and MD fees

Initial hospitalization

6-month total

6-month total

RCT, randomized controlled trial; QCA, quantitative coronary angiography; CFR, coronary flow reserve.

Stent strategy MACE Provisional Universal Provisional Universal Provisional Universal Provisional Universal Provisional Universal Provisional Universal

16.9% 19.2%

14.9% 6.1%

14.8% 15.0%

Cost $5618 $8820 $6745 $10,368 $8434 $9234 $10,490 $10,206 $10,439 $11,044 $12,303 $13,218

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Cardiovascular Health Care Economics

stenting than with provisional stenting (3.9% vs 10.1%, p < 0.05). Although initial hospital costs were significantly higher with routine stenting, there was no difference in mean aggregate costs at 6-month follow-up ($10,206 vs $10,490). A further provisional stent trial was the Doppler Endpoint STenting INternational Investigation Coronary Flow Reserve (DESTINI) (81). This international trial tested whether provisional stenting, guided by the combination of Quantitative Coronary Angiography (QCA) and Coronary Flow Reserve (CFR) could produce clinical outcomes comparable to routine stent implantation. Compared with OCBAS and OPUS, inclusion criteria for DESTINI were broad and included long lesions, multivessel disease, and smaller vessels. In the guided PTCA arm, criteria for provisional stenting included residual stenosis greater than 35% by QCA, grade C or greater dissection, or inability to achieve doppler-derived CFR greater than 2.0. Overall, a doppler flow wire was required in 66% of provisional stenting patients (at a cost of $424 per device), and crossover to stenting was required in 57% of these patients. On an intention-to-treat analysis, the probability of 1 or more major adverse cardiac event at 12 months was 17.8% in the elective stenting group and 18.9% in the guided PTCA group (p = NS). The incidence of repeat target lesion revascularization at 1 year was 14.9% in the elective stent group and 15.6% in the guided PTCA group (p = NS). Prospective economic analysis was performed in the subset of US patients (n = 301) and found that the provisional stent strategy reduced initial hospital costs by $600 per patient and 6-month aggregate costs by $900 per patient (82). Sensitivity analysis demonstrated that, despite the additional cost of the doppler flow wire, the provisional stent strategy remained cost saving unless the cost of a stent was less than $700. Taken together, the available evidence suggests that provisional stenting can achieve results comparable to routine stenting for many patients. However, coronary angiography alone is likely insufficient to guide such a strategy. Additional diagnostic tools (e.g., early repeat angiography, physiologic lesion assessment, or possibly intravascular ultrasound) appear to be necessary in order to guide selection of appropriate patients for stent implantation, which may be required in 50–60% of patients, depending on lesion complexity. At present, it does appear that a strategy of aggressive balloon angioplasty with aggressive provisional stenting can result in modest long-term cost savings when compared with universal stenting. Unfortunately, the prospective payment system currently in place in the United States (in which stent procedures are reimbursed at a substantially higher level than balloon angioplasty) does not encourage physicians or hospitals to provide such cost-effective care. Moreover, as stent prices continue to decline in the next several years, and the cost of stenting is further reduced by techniques, such as direct stent implantation, the added time and resources required to perform optimal balloon angioplasty will ultimately limit the attractiveness of a provisional stent strategy, with the exception of very select lesion subsets. Certainly, in Europe and Canada, where stent prices are substantially lower than in the United States, provisional stenting is not an economically attractive strategy (83).

Directional Atherectomy Directional coronary atherectomy (DCA) was the first new device to receive Food and Drug Administration (FDA) approval for percutaneous treatment of coronary stenoses. Several single-center and small multicenter series have demonstrated that DCA can be performed safely, with residual stenoses of 10–15% and angiographic restenosis rates of 28–31% (84–86). Until more recently, however, controlled clinical trials had failed to

Table 4 Cost Studies Comparing Atherectomy Devices with Conventional PTCA Study

203

Method

Population

N

Guzman et al. (88) 1991

OBS

Elective native and vein graft intervention

252

Nino et al. (89)

1989–1992

OBS

Ellis et al. (90)

1992–1993

CAVEAT (8) BOAT (9)

DART (92)

VEGAS 2 (95)

Date

Timeframe

Device

Cost

Adjusted hospital charges

Initial hospitalization

384

Equipment costs (hospital purchase price)

Initial procedure

OBS

Consecutive 1258 attempted coronary interventions

Hospital accounting system costs and MD fees

Initial hospitalization

1991–1992

RCT

605

Hospital costs

Initial hospitalization

$7059 $7420 $8855 $15,168 $1337 $2145 $2924 $3053 $8520 $9360 $9243 $10,343 $18,891 $10,637 $11,904

1994–1996

RCT

Single-vessel treatment, de novo lesion, discrete De novo lesion, discrete

PTCA-native DCA-native Rota-native TEC-SVG PTCA Rota TEC Laser PTCA DCA Laser Rota TEC PTCA DCA

714

Itemized procedure costs, hospital costs, MD fees

Initial hospitalization

Itemized procedure costs, hospital costs, out-patient costs, and MD fees Itemized procedure costs, hospital costs, out-patient costs, and MD fees

Initial hospitalization

PTCA DCA PTCA DCA PTCA Rota PTCA Rota Angiojet UK Angiojet UK

$10,080 $11,895 $13,524 $14,539 $11,587 $14,416 $15,521 $19,053 $16,942 $22,210 $24,389 $29,109

1995–1997

1995–1997

RCT

RCT

De novo lesion, type A 444 or B, vessel diameter <2.7 mm Native coronary or SVG with extensive thrombus

349

Cost measure

1-year total

6-month total Initial hospitalization 1-year total

OBS, observational study; RCT, randomized controlled trial; DCA, directional atherectomy; SVG, saphenous vein graft; Rota, rotational atherectomy (Rotablator); TEC, transluminal extraction catheter. * 30-day event rates (death, MI, repeat revascularization).

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Cardiovascular Health Care Economics

replicate the results of these selected series or to demonstrate sustained angiographic or clinical benefits when compared with PTCA (7,87). However, in the randomized BOAT study, directional atherectomy was associated with a lower 6-month angiographic restenosis rate and a trend toward less repeat revascularization of the target vessel in comparison with conventional balloon angioplasty (76). In the early 1990s, several observational studies compared initial hospital costs for these two procedures (Table 4). In general, these studies found that directional atherectomy increased hospital costs by $500 to $1000 in comparison with conventional PTCA—primarily because of additional catheterization laboratory supplies (88–90). Two randomized trials compared the costs of DCA to PTCA. The Coronary Angioplasty vs Excisional Atherectomy Trial (CAVEAT) was the first randomized trial to compare directional coronary atherectomy with conventional PTCA (7). In an economic substudy that included 605 patients enrolled at 19 of 32 clinical sites, the CAVEAT investigators found that DCA increased cardiac catheterization laboratory costs by $700 (8). Although the mean length of stay was identical for DCA and PTCA in this trial, DCA also increased nonprocedural hospital costs by an additional $600, so that the overall cost difference in initial hospital costs was $1300. Given the lack of additional clinical benefit seen with DCA in CAVEAT, this cost difference persisted at the 6-month follow-up. In the BOAT trial, detailed health economic data were collected for 714 of 989 randomized patients (9). Similar to CAVEAT, the BOAT investigators found that DCA increased procedural costs by approximately $1300 and other hospital costs by an additional $500, such that initial hospital costs were $1800 higher with DCA than with conventional PTCA ($11,895 vs $10,080). Although follow-up resource utilization tended to be somewhat less in patients randomized to DCA, the resulting downstream cost savings were insufficient to fully offset the higher initial costs of DCA. Thus, aggregate 1-year medical care costs remained $1000 higher with DCA than with PTCA ($14,539 vs $13,524). Although DCA did improve clinical outcomes when compared with PTCA, the resulting CER of $25,000 per repeat revascularization avoided is substantially higher than the CER for stenting based on Benestent 2 and EPISTENT ($10,000–12,000 per repeat revascularization avoided). Thus, economic analysis suggests directional atherectomy is an economically inefficient treatment for lesions that are also amenable to stenting. Whether directional atherectomy is a cost-effective treatment for specific lesion subsets that are unfavorable for stenting (e.g., ostial LAD lesions and bifurcation stenoses), or for debulking prior to stenting, will be addressed by the ongoing Atherectomy before Multilink Improves lumen Gain Outcome (AMIGO) trial (91).

Rotational Atherectomy A further technique used as an adjunct to coronary angioplasty is rotational atherectomy, which involves the abrasion of a coronary lesion by the rotational action of a fast spinning (>140,000 revolutions per minute) burr. The randomized Dilation vs Rotational Ablation Trial (DART) study examined the economic impact of rotational atherectomy in comparison with balloon angioplasty (92). In this study, 444 patients with type A and B coronary stenoses in relatively small native coronary arteries (mean reference diameter = 2.5 mm) were randomly assigned to undergo conventional PTCA or rotational atherectomy. Although rotablator treatment was associated with a reduction in the incidence of

Chapter 12 / Cost-Effectiveness of PCI

205

residual dissections and less need for bailout stenting, it increased initial hospital costs by more than $2800 per patient (Table 4). Over the 1-year follow-up period, there was no difference in angiographic restenosis or the need for repeat revascularization. As a result, overall 1-year costs were nearly $4000 higher with rotablator than with conventional PTCA. Guzman and colleagues found similar results (88). Given the substantially higher costs and lack of improved outcomes, rotational atherectomy does not appear cost-effective as a stand-alone treatment for lesions that are amenable to standard PTCA.

RHEOLYTIC THROMBECTOMY Despite advances in mechanical and pharmacological therapies, thrombus-containing lesions are at high risk for adverse events, remaining a challenging subset for percutaneous coronary revascularization. Recently, the Angiojet—a rheolytic thrombectomy catheter based on the Bernoulli/Venturi effect—was approved by the FDA for management of intracoronary thrombus (93). In the VEin Graft AngioJet Study (VEGAS) 2 randomized trial (94), rheolytic thrombectomy was compared with standard therapy using sustained intracoronary urokinase for patients with extensive intracoronary thrombus. Although both treatments resulted in substantial resolution of angiographic thrombus, rheolytic thrombectomy was associated with substantial reductions in length of stay (4.2 vs 4.9 days) and procedural complications, including MI (12.8% vs 30.2%, p < 0.001) and vascular complications (2.8% vs 11.2%, p = 0.002). As a result, rheolytic thrombectomy reduced initial hospital costs (excluding physician fees) by more than $3500 in comparison with intracoronary urokinase ($15,311 vs $18,841, p < 0.001), and these cost savings were largely maintained at 1-year follow-up (95). One limitation of the VEGAS 2 trial is that patients in the control arm all received a prolonged infusion of intracoronary urokinase (by protocol). Although much of the cost savings seen in this study were related to the high cost of urokinase (approximately $2000 per patient) and the need for staged procedures in most control patients, regression analysis demonstrated that nearly $1400 of the cost savings were attributable to reduced ischemic and bleeding complications. Given the limited efficacy of glycoprotein IIb/IIIa inhibitors for patients with extensive intracoronary thrombus (96), it is likely that much of the cost savings related to improved safety and efficacy of treatment with the Angiojet would have been preserved. Thus, for this highly challenging group of patients with extensive intracoronary thrombus, rheolytic thrombectomy appears to be an economically dominant therapy that both improves clinical outcomes and reduces cost.

DISTAL PROTECTION DEVICES Recently, a number of different distal protection devices have been developed, including both balloon occlusion devices and distal filter entrapment devices to capture and retrieve debris during coronary and carotid interventions. The Guardwire balloon occlusion catheter (Percusurge, Inc.) is the first of these to be approved by the FDA, following results of European registries and the SVG Angioplasty Free of Emboli Randomized (SAFER) randomized trial (97). In the SAFER trial, 801 patients with stenoses of at least 50% severity in bypass vein grafts with 3–6 mm diameter reference lumens were randomized to SVG stenting using the PercuSurge GuideWire system or, in the control group, a conventional guidewire. All vein grafts had at least Thrombosis

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Cardiovascular Health Care Economics

In Myocardial Ischemia (TIMI) grade 1 flow at baseline. The rate of MI at 30 days was about 40% lower in the device arm at 8.6% vs 14.7% (p = 0.008). Cohen and colleagues have recently reported results from a prospective economic analysis conducted alongside the SAFER study (98). Procedural costs were higher by approximately $1536 for the distal protection group ($6490 vs $4954, p < 0.001), and this difference reduced to $582 when all hospital costs were compared, mainly owing to the lower rate of acute complications of acute MI (AMI), death, unplanned repeat revascularizations, and shorter length of stay with distal protection. At 30 days, the difference was $603. Using a survival model extrapolated from the 30-day results, they estimated that the life expectancy between these two groups would be increased by about 0.22 years (0.17 discounted at 3%) from 11.19 to 11.41 years. This translated to a CER of $3718 per year of life saved. Thus, these findings demonstrate that embolus protection is a highly effective and cost-effective adjunct to PCI of vein grafts lesions, and, consequently, embolus protection should be considered the standard of care for the vast majority of vein graft interventions.

BRACHYTHERAPY FOR THE TREATMENT OF IN-STENT RESTENOSIS One of the major challenges during the current era of “stent-mania” has been the treatment of in-stent restenosis (ISR). Recently, brachytherapy has been shown to reduce both angiographic and clinical restenosis in patients undergoing repeat coronary intervention for ISR by about 35–70%, based on results of the INHIBIT, WRIST, GAMMA-1, START, and SCRIPPS studies (99–103). However, brachytherapy involves a considerable initial and on-going expenditure for each procedure. Therefore, Seto et al. constructed a 2-year model to determine its CE according to the underlying risk of recurrent restenosis (103). In the base case analysis, they assumed that brachytherapy results in a 45% reduction in the risk of target-vessel revascularization at a cost of $3900 per procedure (including equipment, overhead, and professional fees). For patients with focal ISR, they estimated the CE of brachytherapy to be $23,991 per adverse event avoided, but for diffuse ISR, with its higher projected recurrent restenosis rate (35%), brachytherapy was projected to be cost-saving (provided relative risk reduction was greater than 33%). In support of these modeled observations, a prospective economic evaluation of the brachytherapy technique, alongside the GAMMA-1 placebo, controlled randomized trial was performed (105). In the GAMMA-1 trial, 252 patients had optimal treatment of their ISR lesion by balloon angioplasty, with or without adjunct rotational atherectomy, excimer laser, or further stenting and were then randomized to either iridium-192 or nonradioactive ribbon (otherwise identical) exposure (101). Overall, initial procedure costs were approximately $3300 higher for patients in the brachytherapy arm when compared with standard care, and initial hospital costs (including professional fees and the amortized start-up cost of the brachytherapy program) were approximately $4100 higher for the brachytherapy group when compared with standard care (p < 0.001). Mean 1-year follow-up medical care costs were $2200 per patient lower in the brachytherapy group in comparison with conventional treatment (p = 0.32), but these savings were insufficient to fully offset the higher initial cost of brachytherapy. Thus, the overall 1-year costs remained $1800 per patient higher for the brachytherapy group ($28,543 ± 18,847 vs $26,737 ± 19,432, p = 0.46). The incre-

Chapter 12 / Cost-Effectiveness of PCI

207

mental CER for brachytherapy was $17,690 per repeat revascularization procedure avoided, which is somewhat higher than the CER for stenting when compared with balloon angioplasty in both the Benestent 2 and Stent-Primary Angioplasty in Myocardial Infarction (stent-PAMI) trials. Since the completion of this trial, both the costs and clinical effectiveness of brachytherapy have improved. Extended antiplatelet therapy has reduced the incidence of late vessel occlusion/thrombosis from 5–6% (as was seen in GAMMA-1) to <1% (106), and registry studies have demonstrated no loss of clinical efficacy with elimination of the use of routine intra-vascular ultrasound (IVUS) (107). When we accounted for these advances in an updated reanalysis of GAMMA-1, we found that follow-up cost savings with brachytherapy had increased to $3600 per patient with slightly lower aggregate 1-year treatment costs with brachytherapy ($26,352 vs $26,729, p = 0.87), making brachytherapy more appealing. With risk stratification and continuing technical improvements, the cost offsets of brachytherapy over 1 year, as a result of its clinical benefits, are likely to match, if not exceed, the initial increased costs.

ADJUNCTIVE PHARMACOTHERAPY In addition to new mechanical devices, the 1990s have witnessed the development of new pharmacological agents that can be used as adjuncts to percutaneous coronary revascularization. Although several of these agents are still gaining acceptance (e.g., low-molecular-weight heparin and bivalirudin), intravenous glycoprotein IIb/IIIa inhibitors have been shown to significantly reduce ischemic complications in patients undergoing PCI. In the Evaluation of cTE3 for the Prevention of Ischemic Complications (EPIC) trial, patients treated with abciximab at the time of “high risk” PTCA were found to have a 33% reduction in the combined endpoint of death, nonfatal MI, emergency repeat PTCA, emergency bypass surgery, stent placement, and balloon pump insertion (17). More recently, the EPILOG trial reported similar benefits in a less selected patient population undergoing balloon angioplasty or directional atherectomy (18), and the EPISTENT trial confirmed that these benefits also apply to patients undergoing coronary stenting (62). Consequently, despite concerns about the high cost of abciximab, its use has become commonplace at many institutions.

ADJUNCTIVE GLYCOPROTEIN IIB/IIIA INHIBITION FOR PCI Mark and colleagues performed a prospective economic analysis in conjunction with the EPIC trial (108). Excluding drug costs, they found that total in-hospital costs were similar between the three treatment groups. However, during the 6-month period after hospital discharge, medical care costs were significantly lower for patients treated with abciximab bolus and infusion than for patients treated with placebo ($2881 vs $3998, p < 0.05). This difference was mainly because of less frequent rehospitalization and fewer repeat revascularization procedures in the abciximab treatment group. Thus, total 6-month medical care costs (excluding drug costs) were $1270 lower for the abciximab bolus and infusion group than for placebo. When the average drug cost ($1407 per patient) was included in the analysis, total 6-month costs were $300 higher for the bolus and infusion group when compared with placebo ($18,269 vs $17,999, p = NS). Thus, the EPIC results suggest that despite its higher initial cost, use of abciximab has a relatively minor impact on long-term medical care costs, at least in high risk

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PTCA patients. Moreover, multivariable analysis of the EPIC results suggests that abciximab could be cost-saving if the excess bleeding complications associated with its use could be substantially reduced or eliminated (108). In the EPILOG trial, use of low-dose heparin in conjunction with abciximab effectively eliminated any excess bleeding complications while preserving the initial clinical benefits seen in EPIC. As a result, abciximab treatment reduced initial hospital costs by $874 per patient, with the exception of drug costs. When drug costs were included, the net incremental cost of abciximab was $583 (10). Thus, from the hospital’s perspective, much of the cost of abciximab is offset by reduced complications. From a societal perspective, however, the EPILOG results were somewhat less favorable. In contrast to EPIC, in EPILOG, there was no additional benefit with abciximab beyond the initial hospitalization—thus, eliminating much of the economic benefit seen in the earlier trial. Follow-up costs in EPILOG were actually $653 higher in the abciximab group in comparison with placebo, such that overall 6-month costs were approximately $1200 per patient higher with abciximab. Whether this difference in the two trials reflects the different heparin doses, the higher frequency of bailout stenting in EPILOG, or simple random variation, is unclear. Tirofiban, a small molecule glycoprotein IIbIIIa inhibitor, has also undergone formal economic evaluation in conjunction with the RESTORE trial (11). Among 1920 US patients undergoing high-risk PTCA or DCA, tirofiban reduced ischemic complications (including death, nonfatal MI, or repeat revascularization) from 17.1% to 14.4% (p = 0.10). Initial hospital costs were $625 less for patients who received tirofiban in comparison with placebo. When the cost of tirofiban (approximately $700 per patient for a 36-hour infusion) was included, the net incremental cost of therapy was less than $100 both during the initial hospitalization and over 1-month follow-up. Thus, similar to abciximab, tirofiban also appears to be highly cost-effective (if not cost-saving) for patients undergoing high-risk balloon angioplasty. Whether these clinical and economic benefits extend to lower risk PTCA patients, as well as patients undergoing coronary stenting, are currently unknown. A prospective economic evaluation of eptifibatide in the setting of stenting was performed with the Enhanced Suppression of the Platelet IIb/IIIa Receptor with Integrilin Trial (ESPRIT) using, costs for initial hospitalization and 1-year follow-up (109,110). ESPRIT was a randomized, double-blind, crossover permitted trial of eptifibatide vs placebo for patients undergoing PCI with anticipated stent implantation. Eptifibatide or placebo were started during the intervention and continued for 18–24 hours thereafter. All 2064 patients were included in the retrospective economic analysis. Eptifibatide reduced the risk of MI and death at 1 year by 37% vs placebo. In resource terms, the net in-hospital cost per patient was calculated as the cost of eptifibatide ($495) minus the cost offset, resulting from eptifibatide treatment (cost savings of $169 from lower procedure duration and $61 from reduced ischemic complications; cost increases of $15 for increased length of stay and $18 for increased vascular access site complications) for a net cost increment of $291 per patient. One year after the index procedure, a cumulative cost offset of 71% was seen with eptifibatide, such that the mean net cost per patient was reduced from $495 to $146. Based on a survival model from the Duke Cardiovascular Databank and the 1-year outcomes of the trial, the estimated CER for eptifibatide was $1407 per year of life saved. The clinical and economic impact of abciximab was also assessed in the EPISTENT study. In this study, 2399 patients undergoing planned PTCA were randomized to stent

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and placebo, stent and abciximab, or balloon angioplasty + abciximab. Among the subgroups that underwent stenting, treatment with abciximab was associated with a 52% reduction in the primary composite endpoint of death, MI or urgent repeat revascularization (62). At 1-year follow-up, treatment with abciximab was associated with a significant reduction in mortality among stented patients (1.0% vs 2.4%, p = 0.04) (63). A prospective economic study performed in parallel with EPISTENT found that initial costs were $1305 higher with abciximab (comparing the two stent groups), and followup costs were approximately $373 lower (owing to a slight reduction in the need for repeat revascularization procedures), such that overall 1-year costs remained $932 per patient higher with abciximab than with placebo in the two stent groups (64). Despite this higher cost, formal CEA based on these results suggests that adjunctive abciximab therapy may be highly cost-effective for patients undergoing coronary stenting. Based on projected long-term survival data from the Duke Cardiovascular Databank, Topol and colleagues estimated that abciximab would increase life expectancy for stent patients by 0.15 years per patient, yielding an incremental CER for treatment of $6213 per year of life gained. One limitation of this study is that the absolute mortality benefit is based on a small number of deaths (8 vs 19). As a result, it is not possible to determine whether other strategies of abciximab administration (e.g., targeting high-risk patients or provisional abciximab for treatment of complications) could further improve the CE of this therapy.

PRIMARY ANGIOPLASTY VS REPERFUSION IN AMI Since the early 1990s, PTCA has been considered as both an alternative and an adjunct to thrombolysis in the setting of AMI, particularly when reperfusion has not been achieved. The economic implications of these alternative reperfusion strategies have been evaluated in several randomized trials (Table 5). In the Mayo clinic trial, Gibbons and colleagues used crude cost-to-charge ratios, to approximate hospital costs for 108 patients randomized to tissue-plasminogen activator (t-PA) thrombolysis or primary angioplasty (111). Both initial hospitalization costs and total (6-month medical care costs) were reduced in the PTCA arm when compared to the t-PA arm: $16,811 vs $21,400 (p = 0.01) and $17,292 vs $24,129 (p = 0.09), respectively. These cost differences were driven by a reduction in mean length of stay (8 vs 11 days) and a reduction in re-admission rates of 4% vs 18%. A retrospective economic evaluation was also performed in conjunction with the PAMI trial (112). In this study, in-hospital mortality was significantly lower in patients randomized to PTCA in comparison with thrombolytic therapy using t-PA (2.3% vs 7.2%, p = 0.03). Similarly to the Mayo trial, initial costs (including physicians fees) tended to be lower in the PTCA group ($27,653 vs $30,227), although this difference was not statistically significant. Finally, an economic substudy was performed in conjunction with the angiographic substudy from the GUSTO-IIb trial (113). In this study, the composite 30-day endpoints of death, MI, or disabling stroke were significantly reduced from 13.7% to 9.6% with the PTCA strategy when compared with thrombolysis (p = 0.03). In contrast to the PAMI and the Mayo studies, the total in-hospital costs in GUSTO-IIb of the PTCA group marginally exceeded those for the thrombolysis group ($17,753 vs $17,398, p = 0.15). At 6 months, the costs in the PTCA and thrombolysis arms were $18,641 and $19,395 respectively (p = 0.19).

Table 5 Summary of Costs and Effects of Direct PTCA Against Thrombolysis for Patients with AMI

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Trial

N

Cost measure

Timeframe

Net cost PTCA— thrombolysis (lower)

Mayo* (111)

108

Charges

Initial and 6 months

($6837)

PAMI-1 (112)

358

Charges

Initial

($2574)

GUSTO IIB (113) 1138

Costs

Initial and 1 year

$302

MITI* (117)

Costs

Initial and 3 years

$2122

3145

Clinical outcomes/overall 6-week death/MI Same In-hospital death/MI Better 1 month death/MI/CVA Slightly better In-hospital death Same

Treatment strategy

Rates

t-PA 4% PCI 2% t-PA 12.0% PCI 5.1% t-PA 13.7% PCI 9.6% Any thrombolytic 5.6% PCI 5.5%

* Initial and follow-up hospitalizations. CVA, cerebrovascular accidents; t-PA, tissue-plasminogen activator; MI, myocardial infarction.

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Thus, analyses from randomized trials have found the cost of PTCA and thrombolysis to either favor PTCA or to differ only slightly. In view of the improved clinical outcomes that have been demonstrated with PTCA (114), it would thus appear that primary PTCA for AMI is an economically attractive strategy (if not an economically dominant one). Whether the clinical and economic results from these randomized trials can be replicated in the “real world,” particularly in older patients or patients attending centers without catheterization procedures, remains the subject of ongoing debate (115,116). In an analysis of observational data from the Myocardial Infarction Triage and Intervention registry (MITI), Every and colleagues examined initial admission and 3-year costs for patients who presented with AMI and were managed with either primary angioplasty (n = 1050) or thrombolysis (n = 2095) (117). In contrast to the randomized trials, there was no significant difference in the mortality rate during hospitalization between the thrombolysis and PTCA cohorts (5.6% vs 5.5%, respectively, p = 0.93) or at 3 years (12.0% vs 13.6% respectively, p = 0.79). Moreover, mean medical care costs at 3 years were significantly higher with PTCA patients than with thrombolytic therapy (mean values: $25,459 vs $22,163; median values: $19,600 vs $16,500; p < 0.001). Whether the divergent findings between the randomized trials and the registry outcomes reflect differences in experience and skill levels between the highly selected hospitals and operators who were selected for the clinical trials in comparison with the “real world,” or reflect analytic challenges, such as unmeasured confounding or differences in cost-accounting methodology across the MITI hospitals, is currently unknown. Furthermore, one should recognize that although PTCA may appear to be the more favorable strategy for specific patients at selected centers, considerable capital expense would be necessary to make primary angioplasty a viable treatment strategy for the vast majority of AMI patients both in the United States and worldwide. One simulation study addressing this issue has suggested that the marginal cost of providing a primary PTCA would vary substantially according to the level of infrastructure already in place at an institution (118). If an angioplasty program were already present (as is the case in the randomized trials cited earlier), the marginal cost of providing a primary angioplasty was estimated to be approximately $1597. On the other hand, if the hospital currently offered only diagnostic angiography, the marginal cost would increase to $3206. Finally, if the costs of developing a full cardiac catheterization program were also included in the analysis, the cost of primary PTCA would increase to $7387 per procedure. Clearly, these costs would have a profound effect on the CE of providing primary angioplasty, suggesting that even within the US health care system, creation of further catheterization laboratories to support universal primary angioplasty is probably not an economically attractive option at present.

STENTING VS PTCA FOR AMI In the last 5 years, various improvements in antithrombotic regimes have occurred to reduce the risk of subacute thrombosis with intracoronary stenting in the setting of an AMI. Therefore, stenting in the context of an AMI became a viable option. The StentPAMI trial (119) was the first randomized trial to prospectively address the economic impact of a primary angioplasty strategy for AMI with and without routine stenting. The combined primary endpoint at 6 months of death, reinfarction, disabling stroke, or target-vessel revascularization occurred in fewer patients in the stent strategy than the

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balloon arm (12.6% vs 20.1%, p < 0.01), although the mortality endpoint alone was higher in the stent arm (4.2% vs 2.7%, p = 0.27). For the economic analysis, StentPAMI (120) examined initial hospital resource utilization and costs, also including 1year aggregate costs for further events and re-admissions using a bottom-up costing methodology. Compared with conventional PTCA, stenting increased procedural costs by approximately $2000 per patient ($6538 vs $4561, p < 0.001). During the 1-year follow-up period, stenting was associated with significant reductions in the need for repeat revascularization (13% vs 22%, p < 0.001) and rehospitalization (24% vs 31%, p = 0.03). Therefore, follow-up costs for Stent-PAMI over the year were significantly lower with stenting ($3613 vs $4592, p = 0.03). Nonetheless, mean 1-year total costs remained approximately $1000 per patient higher with stenting than with PTCA ($20,571 vs $19,595, p = 0.02). The CER for stenting when compared with PTCA was $10,550 per repeat revascularization avoided. In a sensitivity analysis incorporating year 2000 stent prices and technological advances, the 1-year total cost differential fell to less than $350 per patient, and the CER improved to $3753 per repeat revascularization avoided. A parallel quality-of-life substudy documented that stenting improved multiple dimensions of health-related quality of life over the first year of follow-up, thus increasing quality-adjusted life expectancy by 0.015 QALY (120). If one assumed that stenting and balloon angioplasty resulted in similar rates of 1-year survival, the cost-utility ratio (CUR) for primary stenting was $65,066 per QALY in the original analysis or $22,067 using updated costs. However, these results were highly sensitive to even small differences in 1-year mortality. Indeed, in the Stent-PAMI trial, there was a trend toward increased 1-year mortality with stenting (5.5% vs 3.1%, p = 0.10), despite reduction of other events, such as repeat revascularizations and unplanned admissions. The CUR for primary stenting was less than $50,000 per QALY gained only if stenting did not increase 1year mortality by more than 0.2% vs PTCA (Fig. 1). Under these conditions, PTCA was projected to improve both quality-adjusted life expectancy (by 0.14 QALY) and reduced overall health care costs. However, these results are inconsistent with other similar trials (122–125), serving to demonstrate that the difference between angioplasty and stenting is marginal and highly sensitive to small differences in critical outcomes (e.g., mortality). An example of a study where stenting proved to be the dominant clinical and economical strategy for AMI is the evaluation by authors from the Netherlands, who were involved in the Zwolle trial (125). In this study, patients with AMI were randomly allocated to primary stenting (n = 112) or balloon angioplasty (n = 115). After 24 months, mortality rates were identical in both arms (3%) with rates of reinfarction at 1% in the stent arm and 9% in the balloon angioplasty group (p = 0.04 for the combined endpoint of death/MI). The average stent use per patient in the balloon angioplasty group (crossover) was 0.24 compared to 1.17 in the stent group (p < 0.0001). Subsequent target-vessel revascularisation was necessary in 15 patients (13%) after stenting and in 39 (34%) after balloon angioplasty (p < 0.001). Despite the higher initial costs of stenting of Dutch guilders (Dfl) 21,484 against Dfl 18,625 for angioplasty (p < 0.001), the respective cumulative costs at 24 months were comparable at Dfl 31,423 and Dfl 32,933 (p = 0.83). The incremental CER was estimated at Dfl 6297. Taken in aggregate, current data would suggest that for patients with AMI, primary stenting increases long-term costs only slightly when compared with standard balloon

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Fig. 1. Impact of excess mortality on cost effectiveness in the stent-PAMI trial comparing stenting vs PTCA in patients with acute myocardial infarction.

angioplasty, with incremental CER that are reasonably compared with other common health care investments. Further studies are ongoing to clarify the role of adjunctive glycoprotein IIb/IIIa blockers in conjunction with primary PTCA or stenting (Controlled Abciximab and Device Investigation to Lower Late Angioplasty Complications [CADILLAC] trial [126]) as well as that of “facilitated PCI.”

INVASIVE EARLY MANAGEMENT OF PATIENTS WITH ACUTE CORONARY SYNDROMES Four large randomized trials (TIMI IIIB; n = 1473, VANQWISH; n = 920, FRISC-2; n = 2457, TACTICS-TIMI 18; n = 2220) have addressed the benefits and risks of strategies of routine vs ischemia-driven coronary angioplasty in patients with unstable angina and non-Q wave MI (127–131). Whereas TIMI IIIB and VANQWISH showed no clinical benefits of the routine invasive strategy, the latter two larger studies have shown significant clinical benefits of the invasive strategy over the conservative approach. To date, the health economic analyses from FRISC-2 and VANQWISH have been published, and preliminary economic data from the TACTICS trial have been presented publicly as well. In the VANQWISH study, Barnett and colleagues estimated costs for 876 patients, randomized to either the conservative strategy (angiography rate of 23%) or the invasive strategy (angiography rate 94%) between 1993 and 1995 (132). The patients in the invasive arm had a longer hospital stay (by 0.6 hospital days) and a $4523 higher cost for the index hospitalization. After the initial hospitalization, the conservative arm patients spent an average of 1.3 additional days in the hospital, had more frequent CABG (11.7% vs 6.8%), but fewer PCIs (5.3% vs 6.3%), than the invasive arm

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over a mean follow-up duration of 23 months. After the follow-up period, differences in cost of care narrowed from the initial $4523 to $2338 final cost, recouping about 50% of the initial difference. Of note, the conservative arm not only had lower aggregate costs in this trial, but also had improved survival during follow-up (life expectancy 1.86 vs 1.79 life years). Thus, the VANQWISH results suggest that a conservative strategy of ischemia-driven angiography and revascularization is preferable on both clinical and economic grounds. The results of VANQWISH are at odds with the clinical and economic results from both the TACTICS trial and FRISC-2. In TACTICS, the rates of angiography in the invasive and conservative strategies were 51% and 97%, respectively. In contrast to VANQWISH, all TACTICS patients received tirofiban, and coronary stents were used in over 80% of percutaneous revascularization procedures (131). Over a 6-month follow-up period, the clinical benefits of the invasive in comparison to the conservative strategy were an absolute reduction in death of 0.2%, in MI of 2.1%, and in rehospitalization for ischemia of 2.7%. In the preliminary economic analysis, in-hospital costs for the early invasive arm were about $1994 higher and, by 6 month follow-up, were only $629 higher (133). Survival rates and quality of life were similar in the two arms at 6 months, making formal CE difficult to perform, but the cost per event (death, MI, or rehospitalization) avoided was about $14,000. Whether this “disease-specific” outcome is economically attractive is currently unknown. However, in their economic analysis of the PURSUIT trial, Mark and colleagues suggested that short-term reduction in MI would translate into modest long-term survival benefits. Comparison of the costs and clinical benefits of the TACTICS trial suggests that the incremental CE of the early invasive strategy was acceptable when viewed in the context of a patient’s overall life expectancy. To date, the results of the FRISC-2 study have likely been the most favorable toward the early invasive strategy. In this European trial, patients with non-Q wave MI and unstable angina were treated with dalteparin (or placebo after 5–7 days) and randomized to an invasive or a conservative strategy. The primary endpoint of the study, a composite of death or MI at 12 months was lower in the invasive group (10.4% vs 14.1%, p = 0.005). Recently, Janzon and colleagues performed an economic evaluation of FRISC-2 and reported that costs at 1 year were 6757 Swedish Kronnes (SEK) (approximately $650) with an associated incremental CER of 1,404,000 SEK (approximately $136,000) per life saved with the invasive strategy when compared with the conservative strategy (134). Although it is difficult to make definite statements about the different results of these three trials, the balance of evidence suggests that with contemporary treatments (i.e., GpIIbIIIa inhibitors, stents, low-molecular-weight heparin), there are substantial clinical benefits with the early invasive strategy with only a modest increase in medical care costs. Furthermore, subgroup analyses of both the FRISC-2 and TACTICS would suggest that the clinical (and economic) benefits of the aggressive strategy are even larger when confined to the highest risk patients (131,135).

CONCLUSIONS In today’s health care climate, decisions about medical interventions need to reflect measures of cost as well as clinical benefit. With more than a million procedures performed in the United States each year at a direct cost of more than $10 billion,

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percutaneous coronary revascularization is clearly a “big ticket” item and prime target for cost-reduction measures. Although the field of economic technology assessment is still in its infancy, many high-quality economic studies have been performed to evaluate new techniques or treatment strategies in interventional cardiology. As in most medical studies, economic evaluations of percutaneous coronary revascularization techniques have generally found that newer and more aggressive treatments tend to increase costs compared with the established alternatives. Nonetheless, CEA incorporating the results of such studies suggest that most treatments that have been shown to improve clinical outcomes are likely cost-effective. For example, despite increasing medical care costs, balloon angioplasty appears to be reasonably cost-effective in comparison with medical therapy for patients with moderate-to-severe angina and one- or two-vessel coronary disease. Similarly, coronary stenting increases long-term costs for most patients, but is associated with improved outcomes when compared with conventional PTCA. Formal CEA suggests that these benefits are “worth the cost,” at least for patients with single discrete lesions treatable by a single stent. A few advances—such as glycoprotein IIb/IIIa inhibition in high-risk patients undergoing balloon angioplasty and primary PTCA for acute AMI—may even improve outcomes without increasing overall health care costs. On the other hand, many new coronary interventions have yet to be shown to improve clinical outcomes in comparison with conventional PTCA. Given the higher procedural and hospital costs associated with these devices, it is difficult to justify their use at present, except for very specific situations where angioplasty is unlikely to be successful or in ongoing clinical investigations.

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12. Parisi AF, Folland ED, Hartigan P. A comparison of angioplasty with medical therapy in the treatment of single-vessel coronary artery disease. N Engl J Med 1992;326:10–16. 13. RITA-2 Investigators. Coronary angioplasty versus medical therapy for angina: the second Randomised Intervention Treatment of Angina (RITA-2) trial. RITA-2 trial participants. Lancet 1997;350:461–468. 13a. Sculpher M, Smith D, Clayton T, et al. Coronary angioplasty versus medical therapy for angina. Health service costs based on the second Randomized Intervention Treatment of Angina (RITA-2) trial. Eur Heart J 2002;23(16):1291–1300. 14. Hlatky MA, Califf RM, Kong Y, et al. Natural history of patients with single-vessel disease suitable for percutaneous transluminal coronary angioplasty. Am J Cardiol 1983;52:225–229. 15. Wong JB, Sonnenberg FA, Salem DN, et al. Myocardial revascularization for chronic stable angina: analysis of the role of percutaneous transluminal coronary angioplasty based on data available in 1989 Ann Int Med 1990;113:852–871. 16. Rankin JM, Spinelli JJ, Carere RG, et al. Improved clinical outcome after widespread use of coronary-artery stenting in Canada. N Engl J Med 1999;341:1957–1965. 17. The EPIC investigators. Use of a monoclonal antibody directed against the platelet glycoprotein IIb/IIIa receptor in high-risk coronary angioplasty. N Engl J Med 1994;330:956–961. 18. The EPILOG investigators. Platelet glycoprotein IIb/IIIa receptor blockade and low-dose heparin during percutaneous coronary revascularization. N Engl J Med 1997;336:1689–1696. 19. O’Keefe JH, Gernon C, McCallister BD, et al. Safety and cost effectiveness of combined coronary angiography and angioplasty. Am Heart J 1991;122:50–54. 20. Friedman HZ, Cragg DR, Glazier SM. Randomized prospective evaluation of prolonged versus abbreviated intravenous heparin therapy after coronary angioplasty. J Am Coll Cardiol 1994;24:1214–1219. 21. Davies RF, Goldberg AD, Forman S, et al. Asymptomatic Cardiac Ischemia Pilot (ACIP) study twoyear follow-up: outcomes of patients randomized to initial strategies of medical therapy versus revascularization. Circulation 1997;95:2037–2043. 22. Gibbons et al. ACC/AHA/ACP-ASIM guidelines for the management of patients with chronic stable angina. J Am Coll Cardiol 1999;33:2092–2197. 23. Veterans Administration Coronary Artery Bypass Surgery Cooperative Study Group. Eleven-year survival in the Veterans Administration randomized trial of coronary bypass surgery for stable angina. N Engl J Med 1984;311:1333–1339. 24. Varnauskas E. for the European Coronary Surgery Study Group. Twelve-year follow-up of survival in the randomized European Coronary Surgery Study. N Engl J Med 1988;319:332–337. 25. Passamani E, Davis KB, Gillespie MJ. for the CASS principal investigators and their associates. A randomized trial of coronary artery bypass surgery. Survival of patients with a low ejection fraction. N Engl J Med 1985;312:1665–1671. 26. Yusuf S, Zucker D, Peduzzi P, et al. Effect of coronary artery bypass graft surgery on survival. Overview of 10-year results from randomized trials by the Coronary Artery bypass Graft Surgery Trialists Collaboration. Lancet 1994;344:563–570. 27. CASS Principal Investigators. A randomized trial of coronary artery bypass surgery. Quality of life in patients randomly assigned to treatment groups. Circulation 1983;68:951–960. 28. European Coronary Surgery Study Group. Prospective randomized study of coronary artery bypass surgery in stable angina pectoris. Lancet 1980;2:491–495. 29. Weinstein MC, Stason WB. Cost-effectiveness of coronary artery bypass surgery. Circulation 1982;66(Suppl III):III-56–III-66. 30. Loop FD, Lytle BW, Cosgrove DM, et al. Influence of the internal mammary artery graft on 10-year survival and other cardiac events. N Engl J Med 1986;314:1–6. 31. O’Connor GT, Plume SK, Olmstead EM, et al. A regional intervention to improve the hospital mortality associated with coronary artery bypass graft surgery. The Northern New England Cardiovascular Disease Study Group. JAMA 1996;275:841–846. 32. Arom KV, Emery RW, Petersen RJ, et al. Cost-effectiveness and predictors of early extubation. Ann Throrac Surg 1995;60:127–132. 33. Reeder GS, Krishan I, Norbrega FT, et al. Is percutaneous coronary revascularization less expensive than bypass surgery? N Engl J Med 1984;311:1157–1162. 34. Kelly ME, Taylor GJ, Moses HW, et al. Comparative cost of myocardial revascularization: percutaneous transluminal coronary angioplasty and coronary bypass surgery. J Am Coll Cardiol 1985;5:16–20.

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35. Weintraub WS, Mauldin PD, Becker E, et al. A comparison of the costs and quality of life after coronary angioplasty or coronary surgery for multi-vessel coronary artery disease. Results from the Emory Angioplasty Versus Surgery Trial (EAST). Circulation 1995;92:2831–2840. 36. Weintraub WS, Becker ER, Mauldin PD, et al. Costs of revascularization over eight years in the randomized and eligible patients in the Emory Angioplasty versus Surgery Trial (EAST). Am J Cardiol. 2000;86(7):747–752. 37. Sculpher MJ, Seed P, Henderson RA, et al. Health service costs of coronary angioplasty and coronary artery bypass surgery: the Randomised Intervention Treatment of Angina. Lancet 1994;344:927–930. 38. Henderson RA, Pocock SJ, Sharp SJ, et al. Long-term results of RITA-1 trial: clinical and cost comparisons of coronary angioplasty and coronary-artery bypass grafting. Lancet 1998;352:1419–1442. 39. Hlatky MA, Rogers WJ, Johnstone I, et al. Medical care costs and quality of life after randomization to coronary angioplasty or coronary bypass surgery. Bypass Angioplasty Revascularization Investigation (BARI) Investigators. N Engl J Med 1997;336:92–99. 40. Hassan SA, Hlatky MA, Boothroyd DB, et al. Outcomes of non-cardiac surgery after coronary bypass surgery or coronary angioplasty in the Bypass Angioplasty Revascularization Investigation (BARI). Am J Med 2001;110:260–266. 41. Serruys PW, Unger F, Sousa JE, et al. The Arterial Revascularization Therapies Study Group. Comparison of Coronary-Artery Bypass Surgery and Stenting for the Treatment of Multivessel Disease. N Engl J Med 2001;344:1117–1124. 42. Serruys PW, Unger F, Crean PA, et al. Arterial revascularization therapy study (ARTS): a randomized trial of stenting in multivessel coronary disease versus bypass surgery. Two year results. (Abstract) Eur Heart J 2001;22:(Abstr), p. 232. 43. Stables RH. Design of the ‘Stent or Surgery’ trial (SoS): a randomized controlled trial to compare coronary artery bypass grafting with percutaneous transluminal coronary angioplasty and primary stent implantation in patients with multi-vessel coronary artery disease. Semin Interv Cardiol 1999;4:201–207. 44. Booth J, Zhang Z, Mahoney E, et al. Cost and cost effectiveness at 1 year for patients with multi-vessel disease randomized to coronary artery bypass grafting or percutaneous coronary intervention with stent implantation: Results from the Stent or Surgery Trial. Heart 2002;87(Suppl II):3. 45. Pocock SJ, Hendrson RA, Rickards AF, et al. Meta-analysis of randomized trials comparing coronary angioplasty with bypass surgery. Lancet 1995;346:1184–1189. 46. The Bypass Angioplasty Revascularization Investigation (BARI) Investigators. Comparison of coronary bypass surgery with angioplasty in patients with multivessel disease. N Engl J Med 1996;335:217–225. 47. Bakhai A, Stables RH, Prasad S, Sigwart U. Trials comparing coronary artery bypass grafting with percutaneous transluminal coronary angioplasty and primary stent implantation in patients with multivessel coronary artery disease. Curr Opin Cardiol 2000;15:388–394. 48. RITA Trial Participants. Coronary angioplasty versus coronary artery bypass surgery: the Randomised Intervention Treatment of Angina (RITA) trial. Lancet 1993;341:573–580. 49. Fischman DL, Leon MB, Baim DS, et al. A randomized comparison of coronary stent placement and balloon angioplasty in the treatment of coronary artery disease. N Engl J Med 1994;331:496–501. 50. Serruys PW, de Jaegere P, Kiemeneij F, et al. A comparison of balloon expandable stent implantation with balloon angioplasty in patients with coronary artery disease. N Engl J Med 1994;331:489–495. 51. Versaci F, Gaspardone A, Tomai F, et al. A comparison of coronary artery stenting with angioplasty for isolated stenosis of the proximal left anterior descending coronary artery. N Engl J Med 1997;336:817–822. 52. Ritchie JL, Maynard C, Every NR, Chapko MK. Coronary artery stent outcomes in a Medicare population: less emergency bypass surgery and lower mortality rates in patients with stents. Am Heart J 1999;138:437–440. 53. Cutlip DE. Stent thrombosis: historical perspectives and current trends. J Thromb Thrombolysis 2000;10:89–101. 54. Meads C, Cummins C, Jolly K, et al. Coronary artery stents in the treatment of ischemic heart disease: a rapid and systematic review. Health Technol Assess 2000;4:1–153. 55. Serruys PW, Strauss BH, van Beusekom HM, et al. Stenting of coronary arteries: has a modern Pandora’s box been opened? J Am Coll Cardiol 1991;17:143B–154B. 56. Topol EJ. Caveats about elective coronary stenting. New Engl J Med 1994;331:539–541. 57. Topol EJ. Coronary-artery stents-guaguing, gorging, and gouging. N Engl J Med 1998;339:1702–1703.

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58. Cohen DJ, Krumholz HM, Sukin CA, et al. In-hospital and one-year economic outcomes after coronary stenting or balloon angioplasty: results from a randomized clinical trial. Circulation 1995;92:2480–2487. 59. Sukin CA, Baim DS, Caputo RP, et al. The impact of optimal stenting techniques on cardiac catheterization laboratory resource utilization and costs. Am J Cardiol 1997;79:275–280. 60. Serruys PW, van Hout B, Bonnier H, et al. Randomised comparison of implantation of heparincoated stents with balloon angioplasty in selected patients with coronary artery disease (Benestent II). Lancet 1998;352:673–681. 61. Cohen DJ, van Hout B, Juliard JMJ, et al. Economic outcomes after coronary stenting or balloon angioplasty in the Benestent II Trial. The US Perspective (abstract). Circulation 1997;96:I-455. 62. EPISTENT Investigators. Randomized placebo-controlled and balloon-angioplasty-controlled trial to assess safety of coronary stenting with use of platelet glycoprotein-IIb/IIIa blockade. Evaluation of Platelet IIb/IIIa Inhibitor for Stenting. (EPISTENT) Lancet 1998;352:87–92. 63. Topol EJ, Mark DB, Lincoff AM, et al. Outcomes at 1 year and economic implications of platelet glycoprotein IIb/IIIa blockade in patients undergoing coronary stenting: results from a multi-center randomized trial. EPISTENT Investigators. Evaluation of Platelet IIb/IIIa Inhibitor for Stenting. Lancet 1999;354:2019–2024. 64. Zwart-van Rijkom JE, van Hout BA. Cost-efficacy in interventional cardiology; results from the EPISTENT study. Evaluation of Platelet IIb/IIIa Inhibitor For Stenting Trial. Eur Heart J 2001 22:1476–1484. 65. Peterson ED, Cowper PA, DeLong ER, et al. Acute and long-term cost implications of coronary stenting. J Am Coll Cardiol 1999;33:1610–1618. 66. Cohen DJ, Taira DA, Berezin R, et al. Cost-effectiveness of coronary stenting in acute myocardial infarction: results from the stent primary angioplasty in myocardial infarction (Stent-PAMI) trial. Circulation 2001;104:3039–3045. 67. Cohen DJ, Breall JA, Ho KKL, et al. Evaluating the potential cost-effectiveness of stenting as a treatment for symptomatic single-vessel coronary disease: use of a decision-analytic model. Circulation 1994;89:1859–1874. 68. Stason WB, Weinstein, MC. Allocation of resources to manage hypertension. N Engl J Med 1977;296:732–739. 69. Briguori C, Sheiban I, De Gregorio J, et al. Direct coronary stenting without pre-dilation. J Am Coll Cardiol 1999;34:1910–1915. 70. Danzi GB, Capuano C, Fiocca L, et al. Stent implantation without pre-dilation in patients with a single, noncalcified coronary artery lesion. Am J Cardiol 1999;84:1250–1253. 71. Carrie D, Khalife K, Citron B, et al. Comparison of direct coronary stenting with and without balloon predilatation in patients with stable angina pectoris. BET (Benefit Evaluation of Direct Coronary Stenting) Study Group. Am J Cardiol 2001;87:693–698. 72. Sawada Y, Nokasa H, Kimura T, Nobuyoshi M. Initial and six month outcome of Palmaz-Schatz stent implantation. STRESS/Benestent equivalent vs. non-equivalent lesions (abstract). J Am Coll Cardiol 1996;27:252A. 73. George C, Kennard E, Holubkov R, Detre K. Are STRESS results generalizable? The NACI-PSS experience (abstract). J Am Coll Cardiol 1997;29:495A. 74. Costa MA, Sabate M, van der Giessen WJ, et al. Late coronary occlusion after intracoronary brachytherapy. Circulation 1999;100:789–792. 75. Narins CR, Holmes DR Jr, Topol EJ. A call for provisional stenting. The balloon is back. Circulation 1998;97:1298–1305. 76. Baim DS, Cutlip DE, Sharma SK, et al. Final results of the Balloon vs. Optimal Atherectomy Trial (BOAT). Circulation 1998;97:322–331. 77. Lincoff AM, Tcheng JE, Califf RM, et al. Sustained suppression of ischemic complications of coronary intervention by platelet GP IIb/IIa blockade with abciximab. One-year outcome in the EPILOG trial. Circulation 1999;99:1951–1958. 78. Serruys PW, di Mario C, Piek J, et al. Prognostic value of intracoronary flow velocity and diameter stenosis in assessing the short- and long-term outcomes of coronary balloon angioplasty. The DEBATE Study (Doppler Endpoints Balloon Angioplasty Trial Europe). Circulation 1997;96:3369–3377. 79. Rodriguez A, Ayala F, Bernardi V, et al. Optimal coronary balloon angioplasty with provisional stenting versus primary stent (OCBAS). Immediate and long-term results. J Am Coll Cardiol 1998;32:1351–1357.

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80. Weaver WD, Reisman MA, Griffin JJ, et al. Optimum percutaneous transluminal coronary angioplasty compared with routine stent strategy trial (OPUS-1): a randomized trial. Lancet 2000;355:2199–2203. 81. Di Mario C, Moses JW, Anderson TJ, et al. Randomized comparison of elective stent implantation and coronary balloon angioplasty guided by online quantitative angiography and intracoronary Doppler. DESTINI Study Group (Doppler Endpoint Stenting International Investigation). Circulation 2000;102:2938–2944. 82. Cohen DJ, Taira D, Di Mario C, et al. In-hospital and 6-month follow-up costs of universal vs. provisional stenting. Results from the DESTINI trial (abstract). Circulation 1998;98:I-499. 83. Serruys PW, de Bruyne B, Carlier S, et al. Randomized comparison of primary stenting and provisional balloon angioplasty guided by flow velocity measurement. Doppler Endpoints Balloon Angioplasty Trial Europe (DEBATE) II Study Group. Circulation 2000;102:2930–2937. 84. Hinohara T, Robertson GC, Selmon MR, et al. Restenosis after directional coronary atherectomy. J Am Coll Cardiol 1992;20:623–632. 85. Fishman RF, Kuntz RE, Carrozza JP Jr., et al. Long-term results of coronary atherectomy: predictors of restenosis. J Am Coll Cardiol 1992;20:1101–1110. 86. Simonton CA, Leon MB, Baim DS, et al. ‘Optimal’ directional coronary atherectomy: final results of the Optimal Atherectomy Restenosis Study (OARS). Circulation 1998;97:332–339. 87. Adelman AG, Cohen EA, Kimball BP, et al. A comparison of directional atherectomy with balloon angioplasty for lesions of the left anterior descending coronary artery. N Engl J Med 1993;329:228–233. 88. Guzman LA, Simpfendorfer C, Fix J, et al. Comparison of costs of new atherectomy devices and balloon angioplasty for coronary artery disease. Am J Cardiol 1994;74:22–25. 89. Nino CL, Freed M, Blankenship L, et al. Procedural cost of new interventional devices. Am J Cardiol 1994;74:1165–1166. 90. Ellis SG, Miller DP, Brown, et al. In-hospital cost of percutaneous coronary revascularization. Critical determinants and implications. Circulation 1995;92:741–747. 91. Hyperlink for the AMIGO trial. http://www.tctmd.com/clinical-trials/393/573/03_Amigo.pdf Accessed April 1st, 2002. 92. Seto TB, Taira DA, Berezin RH, et al. Economic outcomes after rotational ablation or balloon angioplasty. Results from the randomized DART trial (abstract). Circulation 1998;98:I-412. 93. Silva JA, Ramee SR, Cohen DJ, et al. Rheolytic thrombectomy during percutaneous revascularization for acute myocardial infarction: experience with the AngioJet catheter. Am Heart J 2001;141:353–359. 94. Kuntz RE, Baim DS, Cohen DJ, et al. A trial comparing rheolytic thrombectomy with intracoronary urokinase for coronary and vein graft thrombus (the Vein Graft AngioJet Study [VeGAS 2]). Am J Cardiol 2002;89:326–330. 95. Cohen D, Cosgrove R, Berezin R, et al. Cost-effectiveness of rheolytic thrombectomy for thrombuscontaining coronary lesions. Results from the VEGAS 2 trial (abstract). J Am Coll Cardiol 1999;33:234A. 96. Ellis SG, Lincoff AM, Miller D, et al. Reduction in complications of angioplasty with abciximab occurs largely independently of baseline lesion morphology. EPIC (Evaluation of 7E3 for the prevention of ischemic complications) and EPILOG (Evaluation of PTCA to improve long-term outcome with abciximab GP IIb/IIIa receptor blockade) Investigators. J Am Coll Cardiol 1998;32:1619–1623. 97. Baim DS, Wahr D, George B, et al. Randomized Trial of a Distal Embolic Protection Device During Percutaneous Intervention of Saphenous Vein Aorto-Coronary Bypass Grafts. Circulation 2002;105:1285–1290. 98. Cohen DJ, Murphy SA, Lavelle T, et al. Cost-effectiveness of distal protection for patients undergoing vein graft intervention: Results from the SAFER trial. Circulation 2001;104(Suppl.):3661. 99. Waksman R, Raizner AE, Yeung AC, et al. Use of localised intracoronary beta radiation in treatment of in-stent restenosis: the INHIBIT randomised controlled trial. Lancet 2002;359:551–557. 100. Waksman R, Bhargava B, White L, et al. Intracoronary beta-radiation therapy inhibits recurrence of in-stent restenosis. Circulation 2000;101:1895–1898. 101. Leon MB, Teirstein PS, Moses JW, et al. Localized intracoronary gamma-radiation therapy to inhibit the recurrence of restenosis after stenting. N Engl J Med 2001;344:250–256. 102. Waksman R, White RL, Chan RC, et al. Intracoronary gamma-radiation therapy after angioplasty inhibits recurrence in patients with in-stent restenosis. Circulation 2000;101:2165–2171. 103. Teirstein PS, Massullo V, Jani S, et al. Catheter-based radiotherapy to inhibit restenosis after coronary stenting. N Engl J Med 1997;336:1697–1703.

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104. Seto TB, Cohen DJ. Cost-effectiveness of adjunctive inta-coronary brachytherapy for the treatment of in-stent restenosis. Vasc Rad Monitor 2001;4:9–15. 105. Cohen DJ, Leon MB, Teirstein P, et al. Economic assessment of gamma-brachytherapy for treatment of in-stent restenosis: Results from the randomized GAMMA-1 trial. J Am Coll Cardiol 2000;35(Suppl.):82A–83A. 106. Waksman R, Ajani AE, White RL, et al. Prolonged antiplatelet therapy to prevent late thrombosis after intracoronary gamma-radiation in patients with in-stent restenosis: Washington Radiation for InStent Restenosis Trial plus 6 months of clopidogrel (WRIST PLUS). Circulation 2001;103:2332–2335. 107. Teirstein PS, Moses JW, Tripuraneni P, et al. Prolonged anti-platelet therapy and reduced stenting improve the safety of coronary radiation therapy: The SCRIPPS III Registry (Abstract). Circulation 2000;102:II-568. 108. Mark DB, Talley JD, Topol EJ, et al. Economic assessment of platelet glycoprotein IIb/IIIa inhibition for prevention of ischemic complications of high risk coronary angioplasty. Circulation 1996;94:629–635. 109. Cohen DJ, O’Shea JC, Pacchiana CM, et al. ESPRIT investigators. In-hospital costs of coronary stent implantation with and without eptifibatide (the ESPRIT Trial). Enhanced Suppression of the Platelet IIb/IIIa Receptor with Integrilin. Am J Cardiol 2002;89:61–64. 110. Rao SV, et al. Highlights from the American Heart Association annual scientific sessions 2001: November 11 to 14, 2001: Am Heart J 2002;143:217–228. 111. Gibbons RJ, Holmes DR, Reeder GS, et al. Immediate angioplasty compared with the administration of a thrombolytic agent followed by conservative treatment for myocardial infarction. The Mayo Coronary Care Unit and Catheterization Laboratory Groups. N Engl J Med 1993;328:685–691. 112. Stone GW, Grines CL, Rothbaum D, et al. Analysis of the relative costs and effectiveness of primary angioplasty versus tissue-type plasminogen activator: the Primary Angioplasty in Myocardial Infarction (PAMI) trial. The PAMI Trial Investigators. J Am Coll Cardiol 1997;29:901–907. 113. Mark DB, Granger CB, Ellis SG, et al. Costs of direct angioplasty versus thrombolysis for acute myocardial infarction: Results from the GUSTO II randomized trial. Circulation 1996;94(Suppl.):973. 114. Weaver WD, Simes RJ, Betriu A, et al. Comparison of primary coronary angioplasty and intravenous thrombolytic therapy for acute myocardial infarction: a quantitative review. JAMA 1997;278:2093–2098. 115. Weaver WD, Litwin PE, Martin JS, et al. Effect of age on use of thrombolytic therapy and mortality in acute myocardial infarction. The MITI Project Group. J Am Coll Cardiol 1991;18:657–662. 116. Rathore SS, Berger AK, Weinfurt KP, et al. Race, sex, poverty, and the medical treatment of acute myocardial infarction in the elderly. Circulation 2000;102:642–648. 117. Every NR, Parsons LS, Hlatky M, et al. A comparison of thrombolytic therapy with primary coronary angioplasty for acute myocardial infarction Myocardial Infarction Triage and Intervention Investigators. N Engl J Med 1996;335:1253–1260. 118. Lieu TA, Lundstrom RJ, Ray GT, et al. Initial cost of primary angioplasty for acute myocardial infarction. J Am Coll Cardiol 1996;28:882–889. 119. Grines CL, Cox DA, Stone GW, et al. Coronary angioplasty with or without stent implantation for acute myocardial infarction. N Engl J Med 1999;341:1949–1956. 120. Cohen DJ, Taira DA, Berezin R, et al. Cost-effectiveness of coronary stenting in acute myocardial infarction: results from the stent primary angioplasty in myocardial infarction (Stent-PAMI) trial. Circulation 2001;104:3039–3045. 121. Rinfret S, Grines CL, Cosgrove RS, et al. Quality of life after balloon angioplasty or stenting for acute myocardial infarction: one-year results from the Stent-PAMI trial. J Am Coll Cardiol 2001;38:1614–1621. 122. Saito S, Hosokawa G, Tanaka S, Nakamura S. Primary stent implantation is superior to balloon angioplasty in acute myocardial infarction: final results of the primary angioplasty versus stent implantation in acute myocardial infarction (PASTA) trial. PASTA Trial Investigators. Catheter Cardiovasc Interv 1999;48:262–268. 123. Rodriguez A, Bernardi V, Fernandez M, et al. In-hospital and late results of coronary stents versus conventional balloon angioplasty in acute myocardial infarction (GRAMI trial). Gianturco-Roubin in Acute Myocardial Infarction. Am J Cardiol 1998;81:1286–1291. 124. Scheller B, Hennen B, Severin-Kneib S, et al. Long-term follow-up of a randomized study of primary stenting versus angioplasty in acute myocardial infarction. Am J Med 2001;110:1–6. 125. Suryapranata H, Ottervanger JP, Nibbering E, et al. Long term outcome and cost-effectiveness of stenting versus balloon angioplasty for acute myocardial infarction. Heart 2001;85:667–671.

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126. Stone GW. Stenting and IIb/IIIa Receptor Blockade in Acute Myocardial Infarction: An Introduction to the CADILLAC Trial. J Invasive Cardiol 1998;10(Suppl. B):36B–47B. 127. The TIMI IIIB Investigators. Effects of tissue plasminogen activator and a comparison of early invasive and conservative strategies in unstable angina and non-Q-wave myocardial infarction: results of the TIMI IIIB Trial Thrombolysis in Myocardial Ischemia. Circulation 1994;89:1545–1556. 128. Boden WE, O’Rourke RA, Crawford MH, et al. Outcomes in patients with acute non-Q-wave myocardial infarction randomly assigned to an invasive as compared with a conservative management strategy: Veterans Affiars Non-Q-Wave Infarction Strategies in Hospital (VANQWISH). N Engl J Med 1998;338:1785–1792. 129. FRagmin and Fast Revascularisation during InStability in Coronary artery disease Investigators. Invasive compared with non-invasive treatment in unstable coronary artery disease: FRISC II prospective randomized multicentre study. Lancet 1999;354:708–715. 130. Wallentin L, Lagerqvist B, Husted S, et al. Outcome at 1 year after an invasive compared with a noninvasive strategy in unstable coronary-artery disease: the FRISC II invasive randomized trial. FRISC II Investigators. Fast Revascularisation during Instability in Coronary artery disease. Lancet 2000;356:9–16. 131. Cannon CP, Weintraub WS, Demopoulos LA, et al. for the TACTICS-TIMI18 Investigators. Comparison of Early Invasive and Conservative Strategies in Patients with Unstable Coronary Syndromes Treated with the Glycoprotein IIb/IIIa Inhibitor Tirofiban. (TACTICS-TIMI 18) N Engl J Med 2001;344:1879–1887. 132. Barnett PG, Chen S, Boden WE, et al. Cost-Effectiveness of a Conservative, Ischemia-Guided Management Strategy After Non-Q-Wave Myocardial Infarction: Results of a Randomized Trial. Circulation 2002;105:680–684. 133. Weintraub WS. Economics of the TACTICS-TIMI 18 trial. Paper presented at: American College of Cariology 50th Annual Scientific Session; March 18–21, 2001. Orlando, Fla. 134. Janzon M, Levin LA, Swahn, E. The Fast revascularisation during InStability in Coronary artery disease (FRISCII) Investigators E. Cost-effectiveness of an invasive strategy in unstable coronary artery disease. Results from the FRISC II invasive trial. Eur Heart J 2002;23:31–40. 135. Diderholm E, Andren B, Frostfeldt G, et al. ST depression in ECG at entry indicates severe coronary lesions and large benefits of an early invasive treatment strategy in unstable coronary artery disease; the FRISC II ECG substudy. The Fast Revascularisation during Instability in Coronary artery disease. Eur Heart J 2002;23:41–49.

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Economic Comparisons of Coronary Angioplasty and Coronary Bypass Surgery Mark A. Hlatky, MD CONTENTS INTRODUCTION CORONARY ANGIOPLASTY VS BYPASS SURGERY RECENT RANDOMIZED TRIALS IMPLICATIONS OF ANGIOPLASTY SURGERY TRIALS CONCLUSIONS REFERENCES

INTRODUCTION Coronary angioplasty was pioneered by Andreas Gruentzig, who envisioned that a balloon placed on the tip of a catheter could be used to dilate areas narrowed by atherosclerosis without the need for bypass surgery (1). Since his initial report of the feasibility of this revolutionary method, angioplasty has been refined and improved to the point where it is has surpassed bypass surgery as the most common method of coronary revascularization. The more than 900,000 angioplasty procedures performed in the United States alone in 1998 (2) means that this procedure has an enormous economic impact, well over $10 billion a year. With perhaps more money spent each year on angioplasty than any other single therapy in cardiovascular medicine, one might ask whether the results of the procedure justify its expense. The answer to this important question depends on the other alternatives available. Angioplasty was developed as a method to avoid or postpone coronary bypass surgery, so it is appropriate to compare angioplasty with surgery as alternative approaches to coronary revascularization. But because angioplasty was so much easier and safer than bypass surgery, it very quickly was applied to patients who had never before been considered as candidates for coronary artery bypass grafting. In this group of patients with minimal coronary disease, angioplasty is more appropriately compared with medical therapy (see Chapter 15). In this chapter, I use the general term angioplasty to encompass all the catheter-based techniques of coronary revascularization, but there are different technologies available under this broad umbrella, most notably, coronary stents. The remainder of From: Contemporary Cardiology: Cardiovascular Health Care Economics Edited by: W. S. Weintraub © Humana Press Inc., Totowa, NJ

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this chapter I compares angioplasty and coronary bypass surgery. This discussion relies heavily on the results of randomized controlled clinical trials because they provide the most reliable evidence from any comparative evaluation of therapies.

CORONARY ANGIOPLASTY VS BYPASS SURGERY Gruentzig (1) developed coronary angioplasty as an alternative to bypass surgery, yet there are relatively few patients for whom there is a realistic choice between these two procedures. Patients with single-vessel coronary disease are ideally suited for angioplasty, because only one or two lesions need to be treated. The only patients with single-vessel disease now sent for bypass surgery are those who cannot be treated with an angioplasty for technical reasons, and a few patients with a proximal lesion of the left-anterior descending artery, in whom a single-internal mammary graft would be an attractive alternative. Although nearly all patients with limited coronary disease in need of coronary revascularization will receive an angioplasty, almost all patients with extensive coronary disease will receive bypass surgery. In the latter group, angioplasty is more technically challenging and less likely than bypass surgery to provide complete revascularization of all lesions. Furthermore, bypass surgery has been shown to reduce mortality significantly in patients with extensive disease (3), and, thus, it is difficult to recommend any other procedure in this group of patients. Therefore, the only real controversy regarding the choice of coronary revascularization method lies in those patients with a moderate extent of disease in whom either angioplasty or surgery is feasible. Although only 10–15% of patients with coronary disease fall into this segment of the spectrum of coronary disease (4), they are utilized as the testing ground for comparing angioplasty and surgery as methods of coronary revascularization. The first generation of randomized trials was performed in the late 1980s and early 1990s, comparing angioplasty and surgery primarily in patients with multivessel coronary disease (Table 1). In the late 1990s, a second generation of trials began after coronary stents and glycoprotein IIb/IIIa inhibitors became available, but the long-term results of these trials are not yet known. The clinical outcomes of the randomized trials of bypass surgery and angioplasty are fairly consistent. Two published quantitative overviews (5,6) show no significant difference in the rates of mortality or myocardial infarction (MI) between the two procedures through 5 years of follow-up. Angina was more reliably relieved by surgery, despite the greater use of repeated revascularization procedures among patients assigned to angioplasty. The difference in symptom relief between procedures narrowed over time, reflecting the effects of crossovers between treatment groups and progression of the underlying coronary disease. Economic endpoints were assessed prospectively in several trials of angioplasty and bypass surgery (Table 2). Economic assessment was considered essential because of the anticipated similarity of clinical outcomes: cost would be a key consideration if rates of death and MI were not significantly different between the two coronary revascularization methods. The trials reporting economic endpoints were performed in different health care systems (United States, United Kingdom, and Argentina) and used different techniques to assess costs. In view of these substantial methodologic differences, it is easiest to examine the ratio of costs of the two procedures (i.e., angioplasty costs as a percentage of bypass surgery costs). This approach normalizes for differences in monetary units between trials, which differ between countries and change over time because of inflation.

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Table 1 Randomized Trials Comparing Coronary Angioplasty and Bypass Surgery Trial GABI EAST ERACI-I RITA-I BARI-I CABRI MASS-I Lausanne Toulouse AWESOME ERACI-II SOS ARTS

Recruitment

Patients randomized

Multivessel disease

Follow-up (years)

Major references

1986–1991 1987–1990 1988–1990 1988–1991 1988–1991 1988–1992 1988–1991 1989–1993 1989–1993 1995–2000 1996–1998 1996–1999 1997–1998

359 392 127 1011 1829 1054 142* 134 152 454 450 988 1205

100% 100% 100% 55% 100% 98% 0% 0% 76% 82% 100% 100% 99%

1 8 3 6.5 7 1 5 5 5 3 1 2 1

(25) (10,11) (17,18) (14,16) (7) (26) (27,28) (29,30) (31) (32) (23) (33) (24)

* Randomized to angioplasty or surgery. Seventy-two patients were further randomized to medical therapy. Abbreviations: ARTS, Arterial Revascularization Therapies Study; AWESOME, Angina With Extremely Serious Operative Mortality Investigation; BARI, Bypass Angioplasty Revascularization Investigation; CABRI, Coronary Angioplasty vs Bypass Revascularization Investigation; EAST, Emory Angioplasty vs Surgery Trial; ERACI, Argentine Randomized Trial of Percutaneous Transluminal Coronary Angioplasty vs Coronary Artery Bypass Surgery; GABI, German Angioplasty Bypass Surgery Investigation; MASS, Medicine, Angioplasty or Surgery Study; RITA, Randomized Intervention Treatment of Angina; SOS, Stent Or Surgery.

Table 2 Randomized Trials Reporting Economic Outcomes Trial BARI-I EAST RITA-I ERACI-I ARTS ERACI-II

Country US US UK Argentina 19 countries Argentina

Stents No No No No Yes Yes

Follow-Up 8.0 8.0 6.0 3.0 1.0 1.5

Reference (8) (12,13) (15,16) (17,18) (24) (23)

The largest trial of coronary angioplasty and surgery was the Bypass Angioplasty Revascularization Investigation (BARI), which was conducted in 18 clinical sites and enrolled 1829 patients (7). Seven BARI sites participated in the prospective economic evaluation, termed SEQOL (Study of Economics and Quality of Life). Patients with multivessel disease, technically suitable for either angioplasty or bypass surgery, were randomized. Medical costs were assessed using hospital bills (charges converted to costs), physician reimbursements (Medicare fees), and medications (average wholesale costs). The cost of the initial angioplasty procedure was 65% that of the initial bypass surgery procedure ($21,113 vs $32,347, p < 0.001) (8). Over the subsequent 2 years,

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Fig. 1. Cumulative cost of angioplasty and bypass surgery patients (vertical axis) vs follow-up time (horizontal axis) in BARI (9).

however, the angioplasty patients had higher follow-up costs, largely because of a higher rate of repeat revascularization procedures. By 5 years of follow-up, the cost of angioplasty was 95% that of bypass surgery ($56,225 vs $58,889, p = 0.045). Subsequent follow-up to 8 years (Fig. 1) shows that angioplasty remains slightly less costly than surgery (9). The Emory Angioplasty vs Surgery Trial (EAST) was the other major US trial of angioplasty and surgery (10,11). The major difference between EAST and BARI was that EAST was a single-center trial, whereas BARI was a multicenter trial, and, consequently, EAST enrolled fewer patients than BARI. The EAST economic analysis was based on all 392 participants, however, the BARI economic analysis was conducted in a subset of seven centers, including just over half the randomized patients (934 of 1829, 51%). The methods used to measure cost in the EAST trial included conversion of hospital charges to costs using the Medicare ratio of costs to charges and professional fees (the costs of out-patient physician visits, drugs, and procedures were not measured). EAST found the mean initial hospital costs plus physician fees for angioplasty were 68% of those of bypass surgery ($16,223 vs $24,005, p < 0.0001) (12). Over extended follow-up (Fig. 2), the cost advantage of angioplasty was substantially eroded, with angioplasty costs rising to 94% of surgery costs at 3 years ($23,734 vs $25,310, p < 0.0001) (12) and to 95% at 8 years (13). The higher follow-up costs in the angioplasty group were apparently the result of the use of repeat revascularization procedures in 45% of angioplasty patients vs only 13% of surgery patients by 3 years of follow-up (10). The Randomized Intervention Treatment of Angina (RITA-1) was another large trial of angioplasty and bypass surgery in the prestent era (14). This study also included an economic assessment, and as it was conducted in the United Kingdom, the methods were

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Fig. 2. Cumulative cost of angioplasty and bypass surgery patients (vertical axis) vs follow-up time (horizontal axis) in EAST. (Reproduced with permission from ref. 13.)

somewhat different from those of the two US trials, BARI and EAST. The National Health Service does not bill patients directly for services, so data were collected regarding the use of major resources, and cost weights were assigned to the use of each resource (15). The investigators counted coronary revascularization procedures, coronary angiograms, and hospital admissions (including length of stay in intensive care and in the general ward). Cost weights were assigned based on unit costs in two hospitals, one in London and one outside of London. Despite the substantial differences in methodology, the findings of the RITA-I trial were quite similar to those of BARI and EAST. The initial procedure cost of angioplasty was between 51 and 53% of the cost of bypass surgery, based on hospital costs inside and outside London, respectively (15). After 2 years of follow-up, angioplasty costs had risen to between 79 and 84% of surgery costs, depending on whether cost weights from inside or outside London were used (15). By 5 years, angioplasty costs had risen to 95% of surgery costs (Fig. 3) (16). The final trial of angioplasty and bypass surgery from the late 1980s and early 1990s that measured economic outcomes was the Argentine Randomized Trial of Percutaneous Transluminal Coronary Angioplasty vs Coronary Artery Bypass Surgery in Multivessel Disease (ERACI-I) trial (17). This study only measured use of coronary revascularization and assigned cost weights to complicated and uncomplicated procedures. The costs of other hospitalizations, out-patient visits, and medication were not measured in ERACI-I. The pattern of costs observed in ERACI-I was similar to those of the other trials: initial procedure costs for angioplasty patients were 33% those of surgery patients and rose to 53% at 1 year of follow-up (17) and to 57% at 3 years of follow-up (18). The same pattern of cost was shown in all the randomized trials of bypass surgery and angioplasty. The initial cost advantage of angioplasty was substantial in every study, but in every trial, the cost advantage was reduced by half or more in the first year

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Fig. 3. Cumulative cost of angioplasty and bypass surgery patients (vertical axis) vs follow-up time (horizontal axis) in RITA-I. (Reproduced with permission from ref. 16.)

of follow-up, when the incidence of restenosis is highest. In longer term follow-up, the late medical costs for the angioplasty and surgery patients tended to equalize, yielding fairly similar total costs over the medium-term follow-up (3–7 years). A plot of the relative costs of angioplasty and surgery as a function of follow-up time (Fig. 4) shows the consistency of the economic results. The natural history of coronary disease may affect the longer term (more than 7 years) comparison of angioplasty and surgery. The saphenous veins used in bypass surgery develop accelerated atherosclerosis, which becomes increasingly evident in very long-term follow-up. None of the randomized trials has yet reported follow-up to 10 years or more, the minimum time needed to detect late vein graft problems. If these problems develop in the patients enrolled in these trials, it is possible that angioplasty will gain a late cost advantage over bypass surgery as repeat procedures become necessary. BARI is actively following its patients to more than 10 years, so data on this question should be available by the year 2003.

RECENT RANDOMIZED TRIALS The first generation of randomized trials (BARI, EAST, RITA, and ERACI) were conducted before coronary stents came into general use. Because the higher follow-up costs of angioplasty are a result of repeat revascularization procedures, and coronary stents reduce the incidence of restenosis and the number of repeat procedures (19,20), it was reasonable to hypothesize that stent use might alter the economic comparisons of angioplasty and bypass surgery. In single-vessel disease, the use of coronary stents

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Fig. 4. Angioplasty cumulative cost as a percentage of surgery cumulative cost (vertical axis) vs follow-up time (horizontal axis) from reported randomized trials.

actually increased 1-year costs in comparison with balloon angioplasty (21) because the higher cost of implanting the stent was not recouped by the lower costs of repeat revascularization procedures. Thus, it is not clear whether a strategy of stenting patients with multivessel disease would increase or decrease total costs. Data on this question are available from a decision model and from the Arterial Revascularization Therapy Study (ARTS) and ERACI-II trials. Yock and coworkers used a decision model to synthesize data from stent angioplasty trials, angioplasty surgery trials, and observational studies to simulate the results of BARI if had stents been used in the trial instead of balloon angioplasty (22). The study compared the strategies of provisional stenting (i.e., use of a stent only after a suboptimal balloon angioplasty result), routine stenting, and contemporary bypass surgery. The model projected that using stents would increase the cost of angioplasty if applied routinely (+0.7%), but would lower angioplasty costs if applied provisionally (–3.4%). Because efficiency programs reduced the cost of bypass surgery between the late 1980s and the late 1990s, the projected cost of the provisional stenting over 4 years was only 1.7% lower than bypass surgery, whereas the projected cost of routine stenting was 2.5% higher than for bypass surgery among patients with multivessel disease. One key insight from this analysis was that the efficacy of stenting in reducing repeat procedures is lower among patients with multivessel disease than among patients with single-vessel disease, because the likelihood of restenosis is independent in the treated vessels. Thus, for example, a 15% restenosis rate in one treated vessel translates to 28% in two treated vessels.

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When two or three stents are used, the cost increases, but the need for repeat procedures is still substantial. Therefore, this model suggests that stenting may not be cost-saving when applied to patients with multivessel coronary disease. Recent trials of angioplasty and bypass surgery have included stents as part of the angioplasty strategy (Table 1). Rodriguez and coworkers performed a second trial (ERACI-II), which compared bypass surgery and angioplasty after coronary stenting became available (23). They randomized 450 patients with multivessel coronary disease and followed them for a mean of 18.5 months. As in ERACI-I, they counted major resource use and assigned costs: $4500 for an uncomplicated angioplasty, $3000 for the first stent and $2800 for each additional stent, $2974 for a bolus and 12-hour infusion of abciximab, and $11,000 for an uncomplicated coronary bypass. These cost weights were based on the reimbursements from the Argentine National Social Security System. They found the cost of the initial angioplasty was 5.5% higher than bypass surgery ($11,372 vs $10,736, p = ns), and at the end of 1.5 years of follow-up, the cost of angioplasty was 10% higher than surgery ($12,320 vs $11,160, p = ns). The ARTS randomized patients with multivessel coronary disease to angioplasty with stenting vs bypass surgery. This trial included an economic evaluation based on resource-use profiles and cost weights because it was conducted in 19 different countries. The resources measured included “big ticket” items (hospital days and revascularization procedures), but also assessed out-patient physician visits (24). Cost weights were assigned based on those found in the Netherlands. Initial procedure costs in the stent group were 60% those of surgery ($6441 vs $10,653, p < 0.001), rising to 78% after 1 year of follow-up ($10,665 vs $13,638, p < 0.001) (24). This pattern of costs is quite consistent with the data from the earlier studies of angioplasty and surgery studies. Thus, it appears that the results of earlier trials of angioplasty and bypass surgery remain relevant in the area of coronary stents.

IMPLICATIONS OF ANGIOPLASTY SURGERY TRIALS The economic assessments performed in the randomized trials of coronary angioplasty and bypass surgery provide important lessons for the evaluation of therapies. The most important insight is that long-term follow-up is essential to provide a fair comparison of treatments. Initially, angioplasty is much less costly than surgery but not as economically attractive from the perspective of several years. Initial cost savings may be reduced or even eliminated completely by later complications and procedures. Many clinical trials in cardiovascular medicine are performed with short-term endpoints, such as 30 days. Short-term follow-up may not provide a complete, accurate, and, therefore, fair comparison of therapies. The challenges of economic assessment in clinical trials are also highlighted by the experience in angioplasty and surgery. BARI and EAST used hospital billing records as the basis of economic data collection and converted charges to costs using a set of correction factors. This method was well suited to US hospitals in the late 1980s, potentially yielding more accurate data regarding individuals. However, it is unsuitable for hospitals that do not generate individual hospital bills, such as Veterans Affairs medical centers in the United States and most countries with national health insurance (e.g., Canada and United Kingdom). Trials in these settings require a different set of economic methods, typically resource consumption profiles with cost weights. In principle, a sufficiently

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detailed list of resources consumed by a patient would be sufficient to completely reproduce a typical US hospital bill, so these approaches are more similar than they appear on the surface. The real issue is the level of detail about resource use needed for accurate assessment of costs at the individual patient level. Detailed assessments of this issue have not been performed, but it is reasonable to expect that a parsimonious set of key resources would be sufficient to establish costs. This method would then be applicable in different health care systems.

CONCLUSIONS Angioplasty is less costly than bypass surgery among patients with multivessel coronary disease, but a long-term perspective shows the initial cost savings (>30%) is far less than the savings over several years (≤5%).

REFERENCES 1. Gruentzig AR, Senning A, Siegenthaler WE. Nonoperative dilatation of coronary-artery stenosis. Percutaneous transluminal coronary angioplasty. N Engl J Med 1979;301:61–68. 2. Hall MJ, Popovic JR. 1998 Summary: National Hospital Discharge Survey. Advance data from vital and health statistics; no. 316. National Center for Health Statistics, Hyattsville, MD, 2000. 3. Yusuf S, Zucker D, Peduzzi P, et al. Effect of coronary artery bypass graft surgery on survival: overview of 10-year results from randomised trials by the Coronary Artery Bypass Graft Surgery Trialists Collaboration. Lancet 1994;344:563–570. 4. Detre KM, Rosen AD, Bost JE, et al. Contemporary practice of coronary revascularization in U.S. hospitals and hospitals participating in the Bypass Angioplasty Revascularization Investigation (BARI). J Am Coll Cardiol 1996;28:609–615. 5. Sim I, Gupta M, McDonald K, et al. A meta-analysis of randomized trials comparing coronary artery bypass grafting with percutaneous transluminal coronary angioplasty in multivessel coronary artery disease. Am J Cardiol 1995;76:1025–1029. 6. Pocock SJ, Henderson RA, Rickards AF, et al. Meta-analysis of randomised trials comparing coronary angioplasty with bypass surgery. Lancet 1995;346:1184–1189. 7. Bypass Angioplasty Revascularization Investigation (BARI) Investigators. Comparison of coronary bypass surgery with angioplasty in patients with multivessel disease. N Engl J Med 1996;335:217–225. 8. Hlatky MA, Rogers WJ, Johnstone I, et al. Medical care costs and quality of life after randomization to coronary angioplasty or coronary bypass surgery. N Engl J Med 1997;336:92–99. 9. Hlatky MA, Boothroyd DB, Johnstone IM. Economic evaluation in long-term clinical trials. Stat Med 2002;21:2879–2888. 10. King SB, Lembo NJ, Weintraub WS, et al. A randomized trial comparing coronary angioplasty with coronary bypass surgery. N Engl J Med 1994;331:1044–1050. 11. King SB, Kosinski AS, Guyton RA, et al. Eight-year mortality in the Emory Angioplasty Versus Surgery Trial (EAST). J Am Coll Cardiol 2000;35:1116–1121. 12. Weintraub WS, Mauldin PD, Becker E, et al. A comparison of the costs of and quality of life after coronary angioplasty or coronary surgery for multivessel coronary artery disease. Results from the Emory Angioplasty Versus Surgery Trial (EAST). Circulation 1995;92:2831–2840. 13. Weintraub WS, Becker ER, Mauldin PD, et al. Costs of revascularization over eight years in the randomized and eligible patients in the Emory Angioplasty Versus Surgery Trial (EAST). Am J Cardiol 2000;86:747–752. 14. RITA Trial Participants. Coronary angioplasty versus coronary artery bypass surgery: the Randomised Intervention Treatment of Angina (RITA) trial. Lancet 1993;341:573–580. 15. Sculpher MJ, Seed P, Henderson RA, et al. Health service costs of coronary angioplasty and coronary artery bypass surgery: the Randomized Intervention Treatment of Angina (RITA) trial. Lancet 1994;344:927–930. 16. Henderson RA, Pocock SJ, Sharp SJ, et al. Long-term results of RITA-1 trial: clinical and cost comparisons of coronary angioplasty and coronary-artery bypass grafting. Lancet 1998;352:1419–1425.

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17. Rodriguez A, Boullon F, Perez-Balino N, et al. Argentine randomized trial of percutaneous transluminal coronary angioplasty versus coronary artery bypass surgery in multivessel disease (ERACI): Inhospital results and 1-year follow-up. J Am Coll Cardiol 1993;22:1060–1067. 18. Rodriguez A, Mele E, Peyregne E, et al. Three-year follow-up of the Argentine randomized trial of percutaneous transluminal coronary angioplasty versus coronary artery bypass surgery in multivessel disease (ERACI). J Am Coll Cardiol 1996;27:1178–1184. 19. Fischman DL, Leon MB, Baim DS, et al. A randomized comparison of coronary-stent placement and balloon angioplasty in the treatment of coronary artery disease. N Engl J Med 1994;331:496–501. 20. Serruys PW, de Jaegere P, Kiemeneij F, et al. A comparison of balloon-expandable-stent implantation with balloon angioplasty in patients with coronary artery disease. N Engl J Med 1994;331:489–495. 21. Cohen DJ, Krumholz HM, Sukin CA, et al. In-hospital and one-year economic outcomes after coronary stenting or balloon angioplasty. Circulation 1995;92:2480–2487. 22. Yock CA, Boothroyd DB, Owens DK, et al. Projected long-term costs of coronary stenting in multivessel coronary disease based on the experience of the Bypass Angioplasty Revascularization Investigation (BARI). Am Heart J 2000;140:556–564. 23. Rodriguez A, Bernardi V, Navia J, et al. Argentine randomized study: Coronary angioplasty with stenting versus coronary bypss surgery in patients with multiple-vessel disease (ERACI II): 30-day and one-year follow-up results. J Am Coll Cardiol 2001;37:51–58. 24. Serruys PW, Unger F, Sousa JE, et al. Comparison of coronary-artery bypass surgery and stenting for the treatment of multivessel disease. N Engl J Med 2001;344:1117–1124. 25. Hamm CW, Reimers J, Ischinger T, et al. A randomized study of coronary angioplasty compared with bypass surgery in patients with symptomatic multivessel coronary disease. N Engl J Med 1994;331:1037–1043. 26. CABRI Trial Participants. First-year results of CABRI (Coronary Angioplasty versus Bypass Revascularisation Investigation). Lancet 1995;346:1179–1184. 27. Hueb WA, Bellotti G, Almeida de Oliveira S, et al. The Medicine, Angioplasty or Surgery Study (MASS): A prospective, randomized trial of medical therapy, balloon angioplasty or bypass surgery for single proximal left anterior descending artery stenoses. J Am Coll Cardiol 1995;26:1600–1605. 28. Hueb WA, Soares PR, de Oliveira SA, et al. Five-year follow-up of the Medicine, Angioplasty, or Surgery Study (MASS). Circulation 1999;100(Suppl II):II-107–II-113. 29. Goy JJ, Eeckhout E, Burnand B, et al. Coronary angioplasty versus left internal mammary artery grafting for isolated proximal left anterior descending artery stenosis. Lancet 1994;343:1449–1453. 30. Goy JJ, Eeckhout E, Moret C, et al. Five-year outcome in patients with isolated proximal left anterior descending coronary artery stenosis treated by angioplasty or left internal mammary artery grafting. A prospective trial. Circulation 1999;99:3255–3259. 31. Carrié D, Elbaz M, Puel J, et al. Five-year outcome after coronary angioplasty versus bypass surgery in multivessel coronary artery disease. Results from the French Monocentric Study. Circulation 1997;96(Suppl II):II-1–II-6. 32. Morrison DA, Sethi G, Sacks J, et al. Percutaneous coronary intervention versus coronary artery bypass graft surgery for patients with medically refractory myocardial ischemia and risk factors for adverse outcomes with bypass: A multicenter, randomized trial. J Am Coll Cardiol 2001;38:143–149. 33. The SoS Investigators. Coronary artery bypass surgery versus percutaneous coronary intervention with stent implantation in patients with multivessel coronary artery disease (the Stent or Surgery trial): a randomized controlled trial. Lancet 2002;360:965–970.

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Costs of Coronary Artery Surgery and Cost-Effectiveness of CABG vs Medicine Sean C. Beinart, MD and William S. Weintraub, MD CONTENTS INTRODUCTION DIRECT COSTS OF CABG PREDICTORS OF PROCEDURE COST DEMOGRAPHIC AND CLINICAL PREDICTORS OF CABG COST PROCEDURE-RELATED PREDICTORS OF COST PROVIDER AND HOSPITAL FACTORS INFLUENCING COST GEOGRAPHIC VARIATION IN CABG COST POSTOPERATIVE COMPLICATIONS AS A FACTOR FOR COST LONG-TERM COSTS AFTER CABG COST-EFFECTIVENESS OF CABG VS MEDICAL THERAPY STRATEGIES TO REDUCE COST FOR CABG SUMMARY REFERENCES

INTRODUCTION It has been nearly 40 years since the first coronary artery bypass graft surgery (CABG) was performed. Since then, several randomized trials have confirmed that CABG prolongs survival and improves quality of life in patients with severe coronary disease (1). As a result, there has been more than a threefold increase in the number of CABG surgeries performed in the United States since 1979. In 1998, 553,000 CABG procedures were done (2), contributing approximately 10–20% to the estimated direct cost of $53.4 billion spent that year for the treatment of coronary artery disease (CAD). Given the continually changing medical environment toward emphasizing cost and cost savings, the medical literature has been more focused on determining predictors of the cost of certain procedures and evaluating methods to reduce costs. This chapter provides an economic overview of CABG surgery, including estimates of cost From: Contemporary Cardiology: Cardiovascular Health Care Economics Edited by: W. S. Weintraub © Humana Press Inc., Totowa, NJ

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and the significant determinants of these costs. In addition, an overview of indirect costs is included, followed by a comparison of costs and cost-effectiveness (CE) between CABG and medical therapy of CAD. Finally, strategies to reduce costs associated with CABG are discussed, including a review of recent trends in CABG technique and perioperative care.

DIRECT COSTS OF CABG Determination of Costs The true cost of any procedure is difficult to ascertain and, therefore, several methods have been developed to determine accurate estimates of cost. Cost can be derived from several perspectives, such as the payer (insurance carrier), provider (hospital or physician), patient, or a more comprehensive societal viewpoint. Direct costs associated with the procedure include all resources used relating to the procedure, whereas indirect costs refer to the unmeasured value applied to lost productivity from morbidity and mortality. Measured costs include a component of the overhead or fixed cost to the interested party. Because of the inherent difficulty in estimating true costs, charges have been used as a proxy. It is important to realize that a charge is not a cost. The UB-92, a uniform billing statement used by all third-party payers, is often the pathway employed to determine the charge of a certain procedure, such as CABG, in nonfederal hospitals. In order to apply methods utilizing available procedure charge data, these data have to be converted to cost. This can be done using American Hospital Association guidelines to determine global cost-to-charge ratios, referred to as a top-down approach (3). Professional medical costs may be determined by using the resource-based relative value scale (RBRVS), a system designed to assess relative time and effort associated with physician services. A standard conversion factor is used to convert the number of relative value units (RVU) for each procedure, or Current Procedural Terminology (CPT) code, to a dollar amount. Over time, inflation is accounted for by multiplying the costs with a constant, reflecting either the medical inflation rate or the consumer price index (CPI). Estimates of cost for CABG procedures have been derived from multiple resources. The only available national estimate is based on a Medicare population study involving 92,449 patients (4). In this study, Cowper and colleagues applied cost-to-charge ratios to Medicare claims in order to determine the cost of CABG (excluding professional fees) to be $30,704 (2000 dollars) as shown in Table 1. The mean length of stay was 16 days. On average, intensive care unit (ICU) costs contributed to 25% of the overall cost, followed by operating room (21%), laboratory (15%), supplies (13%), routine room and board (13%), and pharmacy (7%). Many smaller studies have determined estimates for cost of CABG (5–9). The methods of cost calculation differed among all of them, yet they revealed similar results. Hlatky et al. performed an analysis determining cost based on patients enrolled in the Bypass Angioplasty Revascularization Investigation (BARI) trial. In this study, cost-to-charge ratios were employed to determine the mean hospital cost estimate of CABG to be $24,964 (2000 dollars) (8). Another study at Emory performed by Weintraub and colleagues also used cost-to-charge ratios to estimate the costs for patients undergoing CABG who were enrolled in the Emory Angioplasty vs Surgery Trial (EAST) (10). Their data showed the hospital cost of CABG, excluding physician fees, to be $21,410 (2000 dollars). Physician fees and other professional services were esti-

Table 1 Estimates of Direct In-Patient Costs of CABG, Excluding Professional Fees (2000 dollars)*

Study

Medicare (11) (St. Joseph Mercy Hospital) (n = 757)

Cedars Sina (12) (n = 882)

Medicare (4) (n = 92,449)

BARI (8) (n = 469)

Emory (10) (n = 188)

Beth Israel (6) (n = 89)

Selection criteria

>64 years; isolated bypass

Routine CABG without >64 years; catheterization at isolated same hospitalization bypass

Severe disease; eligible for PTCA and CABG

Multivessel disease; Elective procedure; eligible for PTCA isolated bypass and CABG

Study design

Retrospective, observational study Direct cost

Prospective nonrandomized

Cost method Age, y, mean Gender, % male Mean cost, $ (SD) Median cost, $ Mean length of stay (SD)

Duke (7) (n = 1487) Significant stenosis (>75%); eligible for CABG, PTCA, or medical therapy Retrospective, observational study

Retrospective, observational study Top-down

Randomized trial

Randomized trial

Charge-to-cost ratio

Charge-to-cost ratio

Retrospective observational study Charge-to-cost ratio

72 ± 5 67

61.4 71

61 ± 10 73

63 ± 9 84

Charge-to-cost ratio and direct cost method N/A N/A

72.7 68

Direct cost, bottom-up approach 66.7 84

21,156 (14,286)

21,156 (14,286)

30,704 (22,351)

24,964

21,410 (7619)

27,318 (7891)

29,733

18,314 6

N/A 9.6 (5.7)

25,243 16 (13)

N/A 13.3

20,546 N/A

24,990 9.3 (3.6)

N/A N/A

*All dollar amounts were discounted (or inflated) using a medical inflation rate of 3%. PTCA, percutaneous coronary angioplasty; SD, standard deviation.

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Cardiovascular Health Care Economics Table 2 Predictors of CABG Costs

Patient or disease-related factors

Procedure-related variation Provider/hospital-specific factors Geographic location Postoperative complications

Race Gender History of CAD History of prior CABG History of other medical comorbidities (stroke, diabetes mellitus, chronic renal failure, peripheral vascular disease, neoplasm) Timing of cardiac catheterization Concomitant procedures (e.g., valve replacement) Off-pump CABG Professional fees Performing surgeon individual variation Hospital-to-hospital variation Regional variation State-to-state variation Adult respiratory distress syndrome Septicemia Pneumonia Intra-aortic balloon pump Re-exploration for bleeding Deep-chest infection Life-threatening arrhythmia Postoperative atrial fibrillation Neurologic injury

CABG, coronary artery bypass graft; CAD, coronary artery disease.

mated to be approximately $13,500 (2000 dollars) (6,8,10). If professional fees were included, then mean cost estimates of CABG ranged between $34,000 and $45,000 (2000 dollars).

PREDICTORS OF PROCEDURE COST Although average costs for CABG are useful for resource allocation in large populations, individual costs vary greatly. For those patients in the Medicare study alive 30 days after discharge, the median cost for CABG in 1990 dollars was approximately $18,000, with 25th and 75th percentile $15,000 to $25,000, respectively. However, for those patients who died 7 or more days after CABG, the median cost in 1990 dollars was $40,000 with a 25th and 75th percentile of $28,000 and $68,000 (4). Several clinical variables and characteristics contribute to the wide range of costs for CABG. Several studies have attempted to discover predictors of higher cost in order to develop strategies devised at ultimately decreasing the cost associated with this procedure. Preoperative factors that contribute to medical cost include patient demographic and clinical factors, procedurerelated factors, physician and hospital services, and geographic location (Table 2).

DEMOGRAPHIC AND CLINICAL PREDICTORS OF CABG COST The studies conducted to determine demographic and patient-specific clinical predictors of CABG cost vary regarding size and type of population, methods of enrolling

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patients, clinical variables considered, and estimation methods and scope (multicenter vs site-specific). Most of these studies suggest that bypass costs are higher for patients who are older (4,5,9,11–14), female (4,5,9,13,15), or black (4,5,9,13). CABG costs are 26% higher in octogenarians than they are in younger patients. This increase has been attributed to a higher disease severity index and longer ICU stays (14). The reason that females and blacks incur higher cost has not been determined. Cardiac disease severity in all populations also contributes to increased procedural cost. Bypass surgery costs more in patients with prior CABG (5,11–13), history of myocardial infarction (MI) (9,13,15), more extensive CAD (5,9,13,15), left ventricular dysfunction (5,9,11–13,15,16), and those who undergo emergency procedures (11,13,15). Several morbidities, such as history of stroke (11,13,15), diabetes mellitus (4,5,9,13), chronic renal failure (11,12), peripheral vascular disease (12,15), and a history of neoplasm (15) also contribute to increases in the cost of CABG surgery. Although preoperative characteristics are significant, studies have determined that they explain only 16–25% of the variance in hospital costs (4,9,11).

PROCEDURE-RELATED PREDICTORS OF COST Variations of the procedure itself contribute to the cost variability of bypass surgery. Patients undergoing a cardiac catheterization during the same hospitalization as their CABG will incur higher costs than those patients who have surgery and catheterization performed at separate hospitalizations. Patients receiving valvular surgery, in addition to their bypass, will also incur greater costs. In recent years, new developments in bypass surgery, such as minimally invasive and off-pump techniques, have provided possible cost-saving alternatives to the traditional on-pump method. Studies evaluating potential cost savings and comparisons of the new procedures have been varied, showing inconsistent results (17–23). Off-pump CABG is usually approached from either a full sternotomy or lateral thoracotomy and is thought to be protective by avoiding the deleterious effects of the heart-lung bypass machine. By limiting these untoward effects, off-pump surgery could reduce costs. This type of surgery is made possible by coronary artery stabilizers, which enable the surgeon to operate on the beating heart. A randomized controlled trial (RCT) of 200 patients undergoing first-time bypass revealed that the off-pump procedure was significantly less costly than the conventional alternative, with respect to operating materials, bed occupancy, and transfusion requirements (18). In a prospective series of 200 patients, Puskas and colleagues also demonstrated that off-pump CABG decreased hospital costs by 15% and shortened hospital stay by nearly 5.7 days when compared to a matched control group of 1000 conventional CABG patients (23). In contrast, Bull et al. reported no difference in cost between the two procedures; however, their study was nonrandomized with only 80 patients (19). In addition to cost savings, off-pump CABG is reported to have excellent graft patency, better myocardial protection, and lower perioperative morbidity than conventional bypass (24). Fewer neurological deficits are reported perhaps as a result of less underperfusion and embolic events associated with cardiopulmonary bypass and aorta cross clamping (24). Off-pump surgery also decreased the need for mechanical ventilation and ICU stays (22). However, it is important to note that these conclusions are not based on randomized trials. Nonetheless, as a result of these findings, off-pump CABG

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is becoming a reasonable cost-saving alternative to on-pump bypass and may become the standard of care for certain patients, if these findings are supported by larger RCT. Minimally invasive surgical techniques are also being touted as an effective alternative to conventional bypass. In these approaches, the surgeon uses a thoracotomy with the aid of thoroscopy and other specialized devices, not a sternotomy, to gain access to the coronary artery. These techniques provide limited exposure, enabling treatment of single-vessel disease only. Studies to date have not confirmed the efficacy of minimally invasive surgery regarding graft patency, quality of life, or mortality in comparison with conventional CABG. Additionally, cost evaluations have shown uncertain benefit over other methods of bypass (17,20,21). Further analyses should be done on a larger scale before any substantive conclusions can be drawn in regards to the efficacy and cost of unproven cardiac bypass methods.

PROVIDER AND HOSPITAL FACTORS INFLUENCING COST Providers of care may have an important influence on cost. Smith and colleagues determined that the surgeon was the most important predictor of cost in a retrospective single-institution study of 604 patients undergoing CABG (5). Although there were no differences in mortality rates among the operators, the performing surgeon was the strongest predictor of length of hospital stay and days in the ICU after controlling for patient risk factors. Despite these findings, a study of 2740 patients by Goodwin et al. demonstrated that the level of training was not predictive of cost for coronary surgery (25). Although senior consultants were faster than more junior-level physicians and trainees, there was no difference in hospital cost or mortality when trainee operators were compared with attending-level physicians. The operating surgeon was not predictive of increased cost in a study by Longo et al. of 757 Medicare patients at a single institution (11). In summary, whether or not the surgeon is a predictor of increased cost varies among studies and remains undetermined. Cost of CABG also varies widely among hospitals. A study done in 1992 evaluating cost of CABG in 20 New York hospitals revealed that individual hospitals accounted for 63% of the residual variation of cost after adjusting for patient factors (26). This finding is supported on a national level by a study of more than 90,000 Medicare patients, which determined that hospitals contribute to one-fifth of the cost variation for the procedure (4). A higher cost for CABG surgeries at teaching hospitals has also been noted (4). A possible explanation for the wide variation of cost among hospitals is the use of markedly different levels of resource allocation. The factors that influence cost at individual hospitals have yet to be determined.

GEOGRAPHIC VARIATION IN CABG COST In addition to significant hospital variation, CABG resource allocation varies greatly among regions across the country. Metropolitan Insurance reported a wide range of total charges, not costs, from a high of $59,870 in California to $32,500 in Wisconsin (27). There was an equally wide range of physician charges and number of days in the hospital. Length of stay has varied as much as 116% between states (28). Among Medicare patients with similar length of stay, costs in certain states still vary significantly (Fig. 1). For example, California and Oregon have similar adjusted length of stay, yet their costs vary by 18%. State-to-state variability contributed to approximately

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Fig. 1. Geographic variation of CABG cost and length of stay by state. (Reprinted with permission from ref. 4.)

3% of the total cost variation of CABG after controlling for patient-related variables. At present, no consistent geographic cost patterns have emerged (4).

POSTOPERATIVE COMPLICATIONS AS A FACTOR FOR COST Postoperative complications also contribute to the cost of CABG. Mauldin and colleagues demonstrated that among patients at a large academic medical center, postoperative complications increased the explanatory power of their cost model from 19 to 26% (9). Postoperative complications associated with increased cost included adult respiratory distress syndrome, septicemia, pneumonia, intra-aortic balloon pump, reexploration for bleeding, fluid overload, neurologic event, wound infection, major arrhythmia, and death (9,29,30). Table 3 demonstrates the incremental cost associated with the number of postoperative complications. Neurologic injury following CABG can be devastating and occurs in two general forms. Type 1 injuries occur in 3.1% of patients and include focal stroke, transient ischemic attack, and fatal cerebral injuries. In comparison to patients without neurologic injury, type 1 injury is responsible for a 10-fold increase in CABG postoperative mortality, an additional 8 more days in the ICU, 7 more days on the ward, and an additional $10,266 of in-hospital costs. Type 2 injuries, or global decline in neurocognitive

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Cardiovascular Health Care Economics Table 3 Increase in Cost of CABG Associated with Number of Complications* Number of complications 0 1 2 3 4 5

Number of patients

Mean costs (1987 dollars)

P value

382 322 121 48 20 14

16,776 ± 5597 17,794 ± 5664 21,499 ± 11,660 23,624 ± 11,719 32,812 ± 18,757 50,609 ± 29,656

0.0001 0.0001 0.0001 0.0001 0.0001 0.0001

* Reprinted with permission from ref. 9.

function, occur in 3% of patients, and are associated with a fivefold increase in mortality rate, 4 more days in the ICU, 7 more days in the hospital ward, and an incremental in-hospital cost of $6150 more than patients without neurologic sequelae post-CABG (31,32). Puskas and colleagues evaluated over 10,000 patients registered in the Emory Cardiovascular Database to determine that postoperative stroke increased hospital costs by almost $16,000 (33). Indirect costs of cerebrovascular sequelae through lost productivity range from $90,000 to $228,000 per patient (32,34,35). Because of the significant clinical and economic impact of CABG associated with stroke and neurologic sequelae, increased efforts are being undertaken to identify further risks that can be attenuated. Several risk factors have been associated with neurologic injury after CABG, serving as a tool for identifying models for patient selection and reduction of morbidity, mortality, and cost (31). Postoperative atrial fibrillation is associated with increased morbidity and cost. Atrial fibrillation increased length of stay by 5 days and charges by $10,055, while also being associated with a two- to threefold increase in postoperative stroke (36,37). βblockers have been shown to consistently decrease the incidence of postoperative atrial fibrillation, especially when started preoperatively (29). Additionally, amiodorone administered 1 week before CABG decreases the incidence of postcardiotomy atrial fibrillation from 53 to 25%, hospital costs from $26,000 to $18,000, and length of stay from 8 to 6.5 days. These measures have become standard methods for decreasing postbypass atrial fibrillation. Deep-chest infection is another significant complication after CABG that contributes to increased cost. A recent single-institution study estimated costs associated with surgical site infection to be almost $19,000 after adjusting for other clinical variables (38). Several methods have evolved to decrease the incidence of infection during the postoperative period. Preoperative administration of antibiotics decreases the risk of postoperative infection fivefold. Using filtered or leukopoor blood may also be associated with a lower rate of infection as a result of decreased immunosuppression (29). Although most complications are difficult to predict, they are nonetheless responsible for a significant proportion of overall CABG cost. Strategies to reduce complications, thereby decreasing morbidity and mortality, are continually being investigated to provide better care and cost savings.

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LONG-TERM COSTS AFTER CABG Because of the large proportion of capitated care service contracting in today’s health care climate, long-term costs for all medical procedures have become more important. The comparison of immediate cost vs indirect cost is especially significant when applied to bypass surgery. Although the procedural cost and operative risk of CABG are high relative to alternative nonsurgical therapies, such as medicine or PTCA, patients receiving a successful CABG have fewer revascularizations and admissions. Therefore, short-term studies are insufficient in determining the long-term and indirect cost of CABG. The economic and clinical outcomes of CABG need to be evaluated with a longterm perspective. Long-term studies of CABG patients have determined that costs after discharge are consistently low relative to the other alternatives (7,8,10,39). In a cohort of 1487 CABG patients, 1-year cost was $700 (1990 dollars) when compared to $1819 for medical therapy and $3413 for PTCA. Between the second and third year, the incremental cost for CABG was $530 in comparison to $1408 and $1731 for medicine and PTCA respectively (7). The EAST trial revealed that the 3-year incremental cost for PTCA was more than threefold higher than that for CABG (10). Although long-term costs for CABG are relatively low, the need for determining the economic value of CABG in comparison with alternative medical therapies remains.

COST-EFFECTIVENESS OF CABG VS MEDICAL THERAPY Cost-effectiveness analysis (CEA) is a commonly used technique to determine the value of a health intervention by considering the effectiveness of an intervention and its cost. The cost-effectiveness ratio (CER) derived from this analysis is the dollar costper-unit improvement in health gained by a specific health intervention in comparison with another intervention (40,41). It is specifically defined as the “difference in costs between two interventions, divided by the difference in effectiveness, defined as years of life or quality-adjusted life years (QALYs)” (40). Cost2 – Cost1 CE2–1 = ———————– QALY2 – QALY1

The validity of the assumptions from which calculations are based is of paramount importance. Therefore, “sensitivity analysis” is usually performed using different assumptions to determine CE estimates. If these valuations are not significantly changed by reasonable variation of the parameters, the reader can be more confident in the validity of the analysis. Although determining an appropriate CER can be challenging, this ratio can and has been used to influence societal choice regarding the use of scarce resources (42). A CER of $20,000–40,000 per QALY is consistent with other medical programs funded by society, such as hemodialysis and treatment of hypertension. A ratio less than $20,000 per QALY would be considered extremely cost-effective, whereas a ratio greater than $60,000 per QALY would be considered expensive (40,41). In 1982, Weinstein and Stason published the only widely quoted CEA comparing CABG to medical therapy in patients with chronic stable CAD (43). The analysis was based on a 55-year-old male with no congestive heart failure and 50% obstruction in one or more arteries. The authors gathered data from several randomized and nonrandomized

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Fig. 2. Estimated differences in quality-adjusted life expectancy between surgery and medical management, by number of diseased vessels and severity of symptoms. Q (quality adjustment factor) = 1 for no angina, no concern over pain; Q = 0.9 for mild angina, sedentary lifestyle; Q = 0.7 for severe angina and active lifestyle; Q = 0.5 for very severe angina, serious psychological effects. VD = number of corresponding of diseased vessels. (Reprinted with permission from ref. 43.)

trials evaluating symptom improvement and survival after operation. Statistically significant and insignificant results were included in the study. Survival gains were assumed from these data to be –0.2, +0.6, +3.2, and +6.9 years for one-, two-, and three-vessel and left main disease, respectively. A number was assigned for severity of angina-adjusted quality of life. The quality adjustment factor (Q) was 0.5 for very severe angina or serious psychological effect, 0.7 for severe angina with an active life style, 0.9 for mild angina or a sedentary lifestyle, and 1 for no angina or concern over pain. Figure 2 demonstrates the effect of symptom level on change in QALY associated with CABG when compared with medical therapy for each degree of CAD (one-, two-, and three-vessel disease). Survival for patients with no angina and single-vessel disease is negative, yet in single-vessel disease patients with very severe angina, a 1-year survival benefit is demonstrated. Not surprisingly, patients with triple-vessel disease have a marked survival benefit over medical therapy regardless of symptom level. CE of CABG follows similar logic and is excellent when applied to subgroups of patients who benefit the most, such as a patients with severe angina and triple-vessel disease. CE of CABG is poor when survival benefit is marginal, and few symptoms are present in preoperative patients. The cost utility of CABG is presented in Fig. 3 with examples shown in Table 4 (44). In patients with severe angina, cost utility is less than $45,000/QALY and remains less than $25,000/QALY in all patients with left main and triple-vessel disease. This demonstrates that the effect of CABG on survival predominates. For single-vessel disease, symptom relief is significant, with CE ranging from $41,300/QALY for severe symptoms to $1,142,000 per QALY for mild angina. The ratio was undefined for patients with no angina. Presence of left anterior descending (LAD) disease influenced CE in patients with one- and two-vessel disease. In patients with two-vessel disease involving LAD and severe angina, CABG had a cost utility of $21,600/QALY, whereas without LAD involvement, CABG cost utility was $61,000/QALY. This difference increases with a

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Fig. 3. Cost utility for CABG in comparison to medical therapy. (Reprinted with permission from ref. 44.)

Table 4 Cost Per QALY ($/QALY) of Revascularization in Comparison with Medical Therapy* CABG for left main stenosis, with or without angina CABG for 3VD, with or without angina CABG for 2VD, with severe angina and LAD Stenosis CABG for 2VD, with severe angina, no LAD disease CABG for 2VD, no angina, with LAD stenosis CABG for 2VD, no angina, no LAD disease CABG for 1VD, severe angina PTCA for 1VD, severe angina PTCA for LAD, stenosis, mild angina

9000 18,000 22,000 61,000 27,000 680,000 73,000 9000 92,000

CABG, coronary artery bypass grafting; 1, 2, or 3VD, one-, two-, or three-vessel disease; LAD, left anterior descending; PTCA, percutaneous transluminal coronary angioplasty. * Adjusted to 1993 dollars from multiple sources in a review by Kupersmith et al. (44). (Reprinted with permission from ref. 44.)

range of $26,700 (no LAD) to $680,000 (with LAD) per QALY in patients with twovessel disease and no angina. This study demonstrates that CABG is highly cost-effective in certain clinical subgroups. This result depended heavily on effectiveness of the procedure, because CABG was cost-effective when it prolonged life or reduced symptoms substantially.

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These differences in CE were also dependent on the degree of baseline symptoms and severity of CAD. CABG surgery was very cost-effective in patients with left main disease and severe angina and very cost-ineffective for patients with single-vessel disease and no symptoms. Although an elegant analysis, this study is distinctly outdated and has several limitations. The model analyzed a nondiverse patient population (only 55-year-old, mainly white men) in addition to statistically insignificant data and nonrandomized trials. Long-term vein graft patency was also not evaluated. Since 1982, the treatment of CAD has undergone a technological and medical revolution. Most importantly, the advent and growth of PTCA since the early 1980s has had a profound impact on the management of heart disease. Many medicines available today, such as hydroxymethyl glutaryl-coenzyme A reductase inhibitors, ace inhibitors, and antiplatelet agents, were unavailable at the time of the study. Additionally, more patients today are on β-blocker and aspirin therapy than in 1982. Improvements in surgical and perfusion technique, use of arterial grafts, off-pump bypass, and improved quality oversight are commonplace, and have influenced the effectiveness of CABG. As a result, this study does not supply sufficient information to make conclusions about the CE of CABG when compared with medical therapy. However, despite its age and shortcomings, this study remains the only one of two large analyses comparing the cost utility of CABG and medicine for the treatment of chronic CAD. Another study by Wong et al. supports these conclusions. An analytic model was developed comparing CABG surgery with PTCA and conservative medical therapy. They found that revascularization was cost-effective when compared with medical therapy if severe ischemic symptoms, multivessel disease, or cardiomyopathy were present. In these situations, CABG surgery would be preferred to PTCA except in patients with one- or two-vessel disease (45). A single-institutional study of 224 patients in New York evaluated CE of CABG in octogenarians vs medical therapy (46). The CE of CABG was $10,424/QALY when compared with medicine. Although intriguing, this study was limited because it was a single-institution retrospective analysis. Several studies have been conducted comparing CE of CABG to PTCA (7,8,10,47), which are discussed in Chapter 13.

STRATEGIES TO REDUCE COST FOR CABG Over the last two decades, hospitals have developed several methods to reduce the cost of CABG. Initiatives have been devised to limit admissions to sicker patients, allowing stable patients to go home after catheterization and be admitted on the same day of the surgery (48). Objectives to decrease length of stay have been implemented successfully, demonstrating that early discharge of elderly patients does not affect 60day mortality and rate of re-admission (13,49). Other organizational processes have been used to lower cost. In 1991, the Healthcare Financing Administration began to use a global, or single, price for all in-patient care received by CABG patients. After 2 years, $17 million had been saved at four institutions, and cost was reduced in 75% of participating hospitals (50). Standardized care pathways have been utilized for perioperative ICU care, suggesting a cost benefit (51). Care maps and critical pathways are being introduced to optimize the care process and reduce practice variability, thus, enabling further cost savings while maintaining the quality of care (52).

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Long-term costs of CABG have remained low, and procedure costs may have decreased (53). Weintraub and colleagues determined that the cost of CABG, excluding professional fees, decreased from $22,689 to $15,987 (1996 dollars) over an 8-year period for Emory patients undergoing CABG. This occurred despite an increase in disease severity, increase in population age, and a decrease in mortality rate (53,54). Decreased hospital stay and CABG cost have been possible in part because of improved clinical methods. Early extubation (<6 hours) postoperatively is effective at reducing length of stay and hospital costs without compromising safety in CABG patients (52,55–58). A prospective randomized trial (n = 100) revealed that the number of days in the ICU and hospital were decreased, whereas the rate of complications remained the same in patients undergoing early extubation in comparison with patients who did not. Improvement in surgical techniques has also contributed to improved quality of care. Minimally invasive and off-pump CABG may provide further cost savings as a result of fewer perioperative complications, especially in patients who benefit the most (isolated LAD disease). The applicability of these techniques to all bypass patients, however, remains unclear. Methods to decrease the deleterious effects of cardiopulmonary bypass using anti-inflammatory agents are also being investigated with inconclusive results (59). Improved medical care may contribute to the decrease in resource use and overall CABG cost, despite a sicker and older population. Achieving lower costs for the treatment of CAD creates a conundrum. Lowering cost by treating more patients medically without surgery would result in decreased survival and functional status. Deciding patient care based solely on costs would be inappropriate. Mark concludes that avoiding revascularization in high-risk patients and operating on only the low-risk population would be one method to achieve lower costs (7). Low-risk patients, however, would gain fewer benefits than the sicker patients, and there is uncertainty as to whether this approach would lower costs in the long run. Thus, selection of healthier patients, with the sicker patients, would reduce the CE of CABG (7,39). Patient selection for CABG is a challenging decision for both the patient and physician. As surgical techniques and nonsurgical options improve, these decisions will become more individualized and complex. Cost and CE of CABG will provide one dimension of the influencing factors in the decision process. The weight of its contribution remains unclear.

SUMMARY CABG is a common and expensive treatment option for CAD, ranging between $30,000 and $45,000 per case (2000 dollars). Costs are determined by certain demographic and preoperative clinical characteristics of patients. Variation of the cost is largely influenced by postoperative complications. Costs have a wide range among physicians, hospitals, and geographic regions. Cost savings in individual hospitals are possible by improving quality and efficiency of care and implementing technological advances when determined safe and effective. Although the initial cost of CABG is high when compared to medical alternatives, the long-term costs are constant and lower than medical options. Because of the consistent improvement in quality of life in patients after CABG, as well as prolongation of life in sicker patients, it should be considered a cost-effective treatment option in patients with severe symptoms or multivessel coronary disease.

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REFERENCES 1. Yusuf S, Zucker D, Peduzzi P, et al. Effect of coronary artery bypass graft surgery on survival: overview of 10-year results from randomised trials by the Coronary Artery Bypass Graft Surgery Trialists Collaboration. Lancet 1994;344:563–570. 2. AHA, 2001 Heart and Stroke Statistical Update. American Heart Association, Dallas, TX, 2000. 3. Weintraub WS, Mauldin PD, Talley J, et al. Determinants of hospital costs in acute myocardial infarction. Am J Managed Care 1996;2:977–986. 4. Cowper PA, DeLong ZR, Peterson ED, et al. Geographic variation in resource use for coronary artery bypass surgery. IHD Port Investigators. Med Care 1997;35:320–333. 5. Smith LR, Milano CA, Molter BS, et al. Preoperative determinants of postoperative costs associated with coronary artery bypass graft surgery. Circulation 1994;90:II124–II128. 6. Cohen DJ, Breall JA, HO KK, et al. Economics of elective coronary revascularization. Comparison of costs and charges for conventional angioplasty, directional atherectomy, stenting and bypass surgery. J Am Coll Cardiol 1993;22:1052–1059. 7. Mark DB. Implications of cost in treatment selection for patients with coronary heart disease. Ann Thorac Surg 1996;61(2 Suppl):S12–S15, S33–S34. 8. Hlatky MA, Rogers WJ, Johnstone I, et al. Medical care costs and quality of life after randomization to coronary angioplasty or coronary bypass surgery. Bypass Angioplasty Revascularization Investigation (BARI) Investigators. N Engl J Med 1997;336:92–99. 9. Mauldin PD, Weintraub WS, Becker ER. Predicting hospital costs for first-time coronary artery bypass grafting from preoperative and postoperative variables. Am J Cardiol 1994;74:772–775. 10. Weintraub WS, Mauldin PD, Becker E, et al. A comparison of the costs of and quality of life after coronary angioplasty or coronary surgery for multivessel coronary artery disease. Results from the Emory Angioplasty Versus Surgery Trial (EAST). Circulation 1995;92:2831–2840. 11. Longo KM, Cowen ME, Flaum MA, et al. Preoperative predictors of cost in Medicare-age patients undergoing coronary artery bypass grafting. Ann Thorac Surg 1998;66:740–746. 12. Denton TA, Luevanos J, Matloff JM. Clinical and nonclinical predictors of the cost of coronary bypass surgery: potential effects on health care delivery and reimbursement. Arch Intern Med 1998;158:886–891. 13. Cowper PA, Peterson ED, DeLore ER, et al. Impact of early discharge after coronary artery bypass graft surgery on rates of hospital re-admission and death. The Ischemic Heart Disease (IHD) Patient Outcomes Research Team (PORT) Investigators. J Am Coll Cardiol 1997;30:908–913. 14. Avery GJ, 2nd, Ley SJ, Hill JD, Hershm JJ, Dick SE. Cardiac surgery in the octogenarian: evaluation of risk, cost, and outcome. Ann Thorac Surg 2001;71:591–596. 15. Ghali WA, Hall RE, Ash AS, Moskawitz MA. Identifying pre- and postoperative predictors of cost and length of stay for coronary artery bypass surgery. Am J Med Qual 1999;14:248–254. 16. Kay GL, Sun GW, Aoki A, Presean CA Jr. Influence of ejection fraction on hospital mortality, morbidity, and costs for CABG patients. Ann Thorac Surg 1995;60:1640–50, 1651. 17. Arom KV, Emery RW, Flavin TF, Peterson RJ. Cost-effectiveness of minimally invasive coronary artery bypass surgery. Ann Thorac Surg 1999;68:1562–1566. 18. Ascione R, Lloud CT, Underwood MJ, et al. Economic outcome of off-pump coronary artery bypass surgery: a prospective randomized study. Ann Thorac Surg 1999;68:2237–2242. 19. Bull DA, Neuwayer LA, Stringham JC, et al. Coronary artery bypass grafting with cardiopulmonary bypass versus off-pump cardiopulmonary bypass grafting: does eliminating the pump reduce morbidity and cost? Ann Thorac Surg 2001;71:170–175. 20. Ferraris VA, Ferraris SP. Cost-effectiveness of minimally invasive cardiac operations. Heart Surg Forum 2001;4(Suppl 1):S30–S34. 21. King RC, Reece TB, Hurst JL, et al. Minimally invasive coronary artery bypass grafting decreases hospital stay and cost. Ann Surg 1997;225:805–811. 22. Lancey RA, Soller BR, Vander Salm TJ. Off-pump versus on-pump coronary artery bypass surgery: a case-matched comparison of clinical outcomes and costs. Heart Surg Forum 2000;3:277–281. 23. Puskas JD, Thourani VH, Marshall JJ, et al. Clinical outcomes, angiographic patency, and resource utilization in 200 consecutive off-pump coronary bypass patients. Ann Thorac Surg 2001;71:1477–1484. 24. Jansen EW, Borst C, Lahpor JR, et al. Coronary artery bypass grafting without cardiopulmonary bypass using the octopus method: results in the first one hundred patients. J Thorac Cardiovasc Surg 1998;116:60–67.

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25. Goodwin AT, Birdi I, Ramesh TP, et al. Effect of surgical training on outcome and hospital costs in coronary surgery. Heart 2001:85:454–457. 26. Cowper PA, DeLong ER, Peterson ED, et al. Potential for cost savings in high cost coronary bypass patients: a New York analysis. J Am Coll Cardiol 1996:27(Suppl):317A. 27. Coronary artery bypass grafts: 1990 charges update. Stat Bull Metrop Insur Co 1992:73:17–24. 28. Huge geographic variation in PTCA and CABG charges, LOS. Data Strateg Benchmarks 1997:1:11–13. 29. Eagle KA, Guyton RA, Davidoff R, et al. ACC/AHA Guidelines for Coronary Artery Bypass Graft Surgery: A Report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Committee to Revise the 1991 Guidelines for Coronary Artery Bypass Graft Surgery). American College of Cardiology/American Heart Association. J Am Coll Cardiol 1999;34:1262–1347. 30. Hall RE, Ash AS, Ghali WA, Moskowitz MA. Hospital cost of complications associated with coronary artery bypass graft surgery. Am J Cardiol 1997;79:1680–1682. 31. Roach GW, Kanchuger M, Mangano CM, et al. Adverse cerebral outcomes after coronary bypass surgery. N Engl J Med 1996;335:1857–1864. 32. Mangano DT. Cardiovascular morbidity and CABG surgery—a perspective: epidemiology, costs, and potential therapeutic solutions. J Card Surg 1995:10(4 Suppl):366–368. 33. Puskas JD, Winston AD, Wright CE, et al. Stroke after coronary artery operation: incidence, correlates, outcome, and cost. Ann Thorac Surg 2000;69:1053–1056. 34. Taylor TN. The medical economics of stroke. Drugs 1997;54(Suppl 3):51–58. 35. Kaste M, Fogelholm R, and Rissanen A. Economic burden of stroke and the evaluation of new therapies. Public Health 1998;112:103–112. 36. Aranki SF, Shaw DP, Adams DH, et al. Predictors of atrial fibrillation after coronary artery surgery. Current trends and impact on hospital resources. Circulation 1996;94:390–397. 37. Mathew JP, Parks R, Savino JS, et al. Atrial fibrillation following coronary artery bypass graft surgery: predictors, outcomes, and resource utilization. MultiCenter Study of Perioperative Ischemia Research Group. JAMA 1996;276:300–306. 38. Hollenbeak CS, Murphy DM, Koeneg S, et al. The clinical and economic impact of deep chest surgical site infections following coronary artery bypass graft surgery. Chest 2000;118:397–402. 39. Talley J, Mauldin P, Becker ER. Cost effective diagnosis and treatment of coronary artery disease, 1st ed. In: Hurst JW (ed.) Topics in Clinical Cardiology. Williams and Wilkins, Baltimore, MD; 2000, p. 227. 40. Goldman L, Garber Am, Grover SA, Hlatky MA. 27th Bethesda Conference: matching the intensity of risk factor management with the hazard for coronary disease events. Task Force 6. Cost effectiveness of assessment and management of risk factors. J Am Coll Cardiol 1996;27:1020–1030. 41. Kupersmith J, Holmes-Rovner M, Hogan A, Rovner D, Gardiner J. Cost-effectiveness analysis in heart disease, Part I: General principles. Prog Cardiovasc Dis 1994;37:161–184. 42. Weintraub WS, and Krumholz H. Cost-effective strategies in cardiology. In: Fuster V, Alexander R, O’Rourke R (eds.), Hurst’s the Heart. McGraw-Hill, NY, 2001, pp. 2487–2512. 43. Weinstein MC, Stason WB. Cost-effectiveness of coronary artery bypass surgery. Circulation 1982;66:III56–III66. 44. Kupersmith J, Holmes-Rovner M, Hogan A, Rovner D, Gardiner J. Cost-effectiveness analysis in heart disease, Part III: Ischemia, congestive heart failure, and arrhythmias. Prog Cardiovasc Dis 1995;37:307–346. 45. Wong JB, Sonnenberg FA, Salem DN, Paukers SG. Myocardial revascularization for chronic stable angina. Analysis of the role of percutaneous transluminal coronary angioplasty based on data available in 1989. Ann Intern Med 1990;113:852–871. 46. Sollano JA, Rose EA, Williams DL, et al. Cost-effectiveness of coronary artery bypass surgery in octogenarians. Ann Surg 1998;228:297–306. 47. Sculpher MJ, Seed P, Henderson RA, et al. Health service costs of coronary angioplasty and coronary artery bypass surgery: the Randomised Intervention Treatment of Angina (RITA) trial. Lancet 1994;344:927–930. 48. Anderson RP, Guyton SW, Paull DL, Tidwell SL. Selection of patients for same-day coronary bypass operations. J Thorac Cardiovasc Surg 1993;105:444–452. 49. Loop FD, Christiansen EK, Lester JL, et al. A strategy for cost containment in coronary surgery. Jama 1983;250:63–66. 50. Cromwell J, Dayhoff DA, Thoumaian AH. Cost savings and physician responses to global bundled payments for Medicare heart bypass surgery. Health Care Financ Rev 1997;19:41–57.

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51. Kern H, Kox WJ. Impact of standard procedures and clinical standards on cost-effectiveness and intensive care unit performance in adult patients after cardiac surgery. Intensive Care Med 1999;25:1367–1373. 52. Ireson CL. Critical pathways: effectiveness in achieving patient outcomes. J Nurs Adm 1997;27:16–23. 53. Weintraub WS, Gaver JM, Jones EL, et al. Improving cost and outcome of coronary surgery. Circulation 1998;98(19 Suppl):II23–II28. 54. Krueger H, Goncalves JL, Caruth FM, Hayden RI. Coronary artery bypass grafting: how much does it cost? Cmaj 1992;146:163–168. 55. Lee JH, Graber R, Popple CG, et al. Safety and efficacy of early extubation of elderly coronary artery bypass surgery patients. J Cardiothorac Vasc Anesth 1998;12:381–384. 56. Lee JH, Kim KH, vanHeeckeren DW, et al. Cost analysis of early extubation after coronary bypass surgery. Surgery 1996;120:611–619. 57. Cheng DC, Karski J, Penistor C, et al. Early tracheal extubation after coronary artery bypass graft surgery reduces costs and improves resource use. A prospective, randomized, controlled trial. Anesthesiology 1996;85:1300–1310. 58. Arom KV, Emery RW, Petersen RJ, Schwartz M. Cost-effectiveness and predictors of early extubation. Ann Thorac Surg 1995;60:127–132. 59. Gott JP, Cooper WA, Schmidt FE, Jr, et al. Modifying risk for extracorporeal circulation: trial of four antiinflammatory strategies. Ann Thorac Surg 1998;66:747–754.

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Costs of Care and Cost-Effectiveness Analysis Other Cardiac Surgery

Vinod H. Thourani, MD and William S. Weintraub, MD CONTENTS INTRODUCTION TRENDS IN VALVULAR SURGERY MITRAL VALVE REPAIR VS REPLACEMENT MITRAL VALVE REPAIR VS REPLACEMENT: CEA CONCLUSIONS REFERENCES

INTRODUCTION Since the 1990s, advances in intraoperative myocardial protection and postoperative care, as well as other surgical and anesthetic techniques, have extended the range of patients who may be offered valvular surgery. With these improvements in the medical and surgical care of the cardiac patient, the benefits of surgery have been extended to patients who were previously deemed inoperable because of their high-risk status. The increasing interest in cost-effectiveness analysis (CEA) of heart valve surgical procedures is not surprising, as the constraints on health care spending becomes tighter, and competition among providers and managed care organizations becomes more intense. These analyses are further complicated by the costly market-driven technological advances in heart surgery. Currently, there is no large randomized trial assessing the cost-effectiveness (CE) of heart valve surgery in terms of cost per event prevented, cost per life year saved, and cost of quality-adjusted life year (QALY) saved. Therefore, the majority of data published thus far includes inferred in-hospital valve surgery cost, without delineation of CE. Although indications, including symptoms related to heart failure (e.g., fatigue and shortness of breath), severity of regurgitation, left ventricular and/or left atrial enlargement, decline in systolic function, and an attempt to prevent the development of permanent atrial fibrillation are well known, the severity of these findings to indicate that surgery has become appropriate and cost-effective in any given patient remains quite From: Contemporary Cardiology: Cardiovascular Health Care Economic Edited by: W. S. Weintraub © Humana Press Inc., Totowa, NJ

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difficult. Nevertheless, the indications for heart valve surgery are well documented, and valvular replacement has been shown not only to prolong life, but also to improve the quality of life (1–3). For patients undergoing coronary artery bypass grafting (CABG), research (4–8) has shown that despite progressive trend toward older and sicker patients with more complex coronary disease and associated medical illnesses, there has been a decrease in inhospital mortality rates for patients undergoing coronary artery bypass since the late 1990s (4,6). Similarly, trends for patients undergoing noncoronary cardiac surgery (e.g., heart valve surgery) have reflected an increase in mean age and associated comorbid risk factors (9). Despite this dynamic change in the characteristics of patients undergoing heart valve surgery, in comparison to studies incorporating CABG patients, a limited number of reports that document the changing patterns or the CE for large cohorts of patients undergoing valve operations are obtainable. Because the American Heart Association estimates that 78,000 valvular surgical procedures are performed annually in the United States, concerns by physicians, hospital administrators, insurance companies, and patients regarding the allocation of economic resources for patients undergoing these expensive heart valve operations are warranted. The purpose of this chapter is to examine the trends in the profile of patients undergoing heart valvular surgery, specifically mitral and aortic valvular surgery, and to evaluate the trends in outcome, resource utilization, and CE following surgical intervention.

TRENDS IN VALVULAR SURGERY Among others, investigators from Emory University (10) and the University of Toronto (9) have shown an increasing number of high-risk patients over the last decade, especially those with concomitant coronary artery disease (CAD), are presenting for heart valve surgery. The number of overall valve replacement surgeries since the early 1990s has remained relatively constant. Thourani and his colleagues at Emory University (10) showed that in patients undergoing aortic valve replacement (AVR) in 1997 in comparison to 1988, there was a statistically significant increase in older patients, and more patients had a history of hypertension, diabetes, congestive heart failure, class III–IV angina, prior myocardial infarction (MI), and prior coronary artery bypass operations. Furthermore, in 1997, more patients undergoing AVR had catheterization proven CAD (54% vs 35% in 1988), necessitating a larger percentage of patients requiring CABG in conjunction with aortic valve surgery (43% in 1997 and 32% in 1988). Similar to patients undergoing AVR, patients with mitral valve replacement (MVR) had increasing age and a history of hypertension, class III–IV angina, and prior MI. Patients undergoing MVR in 1997 were also more likely to have catheterization proven CAD and concomitant CABG with their MVR. Although the number of patients are considerably smaller, trends in demographics for patients undergoing concomitant AVR and MVR suggest that sicker patients are also presenting for double valve surgery. Despite the sicker patients, outcomes in regard to postoperative Q-wave MI, stroke, and in-hospital mortality were unchanged throughout the study period for patients undergoing AVR, MVR, or combined AVR and MVR surgery. Length of stay and hospital cost gradually increased from 1988 to 1992, after which the length of stay in 1997 for all patients was significantly reduced as compared to the length of stay in 1988; while total hospital cost in 1997 for AVR and MVR

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Fig. 1. The hospital cost in US dollars of patients undergoing heart valve replacement. AVR, aortic valve replacement; MVR, mitral valve replacement; AVR + MVR, combined aortic and mitral valve replacement.

patients was significantly reduced in comparison to 1988, being the same in concomitant AVR and MVR patients in comparison to 1988. These authors (10) have revealed that the outcome of AVR, MVR, or concomitant AVR and MVR surgery over the period from 1988 to 1997 at one institution remained constant, despite changes in multiple factors during the same period. Although currently unsubstantiated by a randomized trial, we feel that the increase in technology and intensive care of these complicated surgical patients has led to an overall appreciation of the cost required for such sophisticated procedures.

Trends in Valvular Surgery: CEA One would also expect that in the modern era of technologic advances, costs for performing heart valve surgery, especially in these higher risk patients, would have continued to increase throughout the 1990s. Prior to 1992, at Emory University, the cardiac surgery services did not utilize patient care maps, and there was minimal regulation of superfluous tests or cost containment. Therefore, from 1988 to 1992, costs for all patients undergoing heart valve surgery did, indeed, increase (10). However, in 1992, patient care maps and clinical pathways were introduced to the Emory Hospitals for all patients undergoing heart valve surgery. These pathways targeted intense preoperative teaching, aggressive early extubation, reduction of superfluous tests, and early transfer from the intensive care unit. We believe that a combination of these factors, along with a careful scrutiny of hospital costs and a conscientious effort by the cardiac surgery health care employees to curtail cost without compromising quality health care, has decreased the overall cost of patients undergoing heart valve replacement. As shown in Fig. 1, in 1997, the costs associated with individual aortic and mitral valve surgery were equal to 1988 levels, despite a sicker operative patient population (10).

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Cardiovascular Health Care Economics Table 1 Clinical and Procedural Correlates of Cost (R 2 = 0.11) Added cost

Year Age Diabetes CHF Emer/urgent Concurrent CABG MVR AVR/MVR

–$785 $112 $2591 $3940 $16,410 $7691 $1910 $9261

95% CI

T value

p Value

–$1073 to –$497 $50 to $174 $152 to $5030 $2197 to $5683 $11,295 to $21,525 $5820 to $9562 $6 to $3814 $5816 to $12,706

5.353 3.559 2.082 4.431 6.288 8.058 1.966 5.268

0.0001 0.0004 0.0375 0.0001 0.0001 0.0001 0.0494 0.0001

AVR, aortic valve replacement; MVR, mitral valve replacement; CHF, congestive heart failure; CI, confidence intervals.

Table 2 Clinical, Procedural, and Outcome Correlates of Cost (R 2 = 0.15)

Year Age Diabetes CHF Emer/urgent Concurrent CABG MVR AVR/MVR Stroke Alive

Added cost

95% CI

T value

p Value

–$758 $86 $2908 $3472 $13,263 $6766 $1748 $8652 $12,830 $11,402

–$1040 to –$476 $25 to $147 $512 to $5304 $1762 to $5181 $8215 to $18,311 $4919 to $8613 –$118 to $3614 $8314 to $8990 $8818 to $6046 $5972 to $12,016

5.270 2.763 2.379 3.980 5.149 7.180 1.835 5.023 6.103 6.422

0.0001 0.0058 0.0174 0.0001 0.0001 0.0001 0.0666 0.0001 0.0001 0.0001

AVR, aortic valve replacement; MVR, mitral valve replacement; CHF, congestive heart failure; CI, confidence intervals.

In a series of 2972 patients in one institution undergoing AVR, MVR, or combined AVR and MVR, linear regression models using the natural logarithm of hospital costs, using the clinical and procedural correlates of cost, are shown in Table 1 (10). Increasing age, male gender, diabetes, heart failure, emergent or urgent procedures, and concurrent CABG were all associated with significant increases in hospital cost. Types of valve surgery were modeled using AVR as the reference category, and the model results describe the significant increase in cost associated with both MVR and MVR plus AVR relative to AVR alone. Whereas costs increased initially from 1988 to 1992, then declined from 1992 to 1997, the net result over the time period from 1988 to 1997 was a significant average decrease in the cost of the hospital stay of $785 per year, adjusted for the other factors. When the in-hospital outcomes are included in the models, in addition to the clinical and procedural covariates (Table 2) (10), all of the clinical and procedural variables remained significant, and in-hospital death and stroke were associated with significant increases in cost. Because the ability to predict cost with preoperative variables was

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limited, the authors included the outcome variables as a cost correlate. This allowed the variabilities of the preoperative, intraoperative, and outcomes of patients to be to analyzed as correlates of cost. Adjusted for all factors the average decrease in the cost of the hospital stay was $758 per year. Linear regression models examining factors associated with the length of stay found the same factors to be significant as the models examining factors associated with the hospital costs. These same significant correlates were found whether length of stay was modeled in its original scale or using the natural logarithm, although the adjusted R2 was higher using the natural logarithm (0.23 vs 0.13). The estimated effects of the significant covariates were in the same direction as what was found when hospital cost was the dependent variable, with the exception of in-hospital death, which was associated with a decreased length of stay (yet, an increase in hospital cost). The costs associated with the significant independent predictor of survival in mitral valve surgery of concomitant CABG surgery and urgent/emergent status have been evaluated by Thourani et al. (11) In a series of 1844 consecutive patients undergoing primary MVR at Emory University Hospitals (EUH) from 1980 to 1997, 1692 patients (92%) underwent elective MVR. Of this elective population, MVR without concomitant CABG was performed in 1332 patients (79%), and MVR with concomitant CABG was performed in 360 patients (21%). During the same time period, urgent or emergent isolated primary MVR was performed in 152 patients (8%). Eighty-six patients (57%) underwent urgent or emergent MVR without CABG, whereas 66 patients (43%) had concomitant MVR and CABG. In patients undergoing MVR without CABG, the performance of the operation as an urgent/emergent status significantly increases the hospital cost and overall resource utilization in comparison to patients undergoing elective MVR without CABG (urgent/emergent status, $31,981 ± $14,170 vs elective status, $23,890 ± $12,339; p < 0.05). Interestingly, in MVR with CABG patients, urgent/emergent status vs elective status was not statistically different (urgent/emergent status, $40,535 ± $32,465 vs elective status, $33,216 ± $24,132; p = NS). For patients undergoing elective MVR surgery, the addition of CABG does significantly increase the resource utilization, leading to a significant increase in hospital cost (elective MVR without CABG, $23,890 ± $12,339 vs elective MVR with CABG, $33,216 ± $24,132; p < 0.05). The addition of CABG to patients undergoing urgent/emergent MVR did not significantly increase the in-hospital cost (urgent/emergent MVR without CABG, $31,981 ± $14,170 vs urgent/emergent MVR with CABG, $40,535 ± $32,465; p = NS).

MITRAL VALVE REPAIR VS REPLACEMENT The Society of Thoracic Surgeons Database (www.sts.org) reports that in 1998, approximately 27,000 MVR and 17,000 mitral valve repairs were performed. Although the numbers are not as overwhelming as those related to surgery for CAD, this is still a substantial societal burden, consuming more than $2 billion annually in the United States alone. Despite the staggering cost of this one procedure to the overall health care expenditures, there are no direct cost-effective studies comparing mitral valve repair with replacement. MVR has been the standard of care for mitral regurgitation for more than 30 years. In 13,936 isolated mitral replacements in the Society of Thoracic Surgeons database

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(www.sts.org), the in-hospital mortality was 6.4%, and for 8788 patients with combined mitral replacement plus coronary surgery, the mortality was 15.3% (12). More recently, the use of mitral valve repair to correct mitral regurgitation, using techniques developed by Carpentier, has been shown to be effective (13–16). Patients undergoing mitral valve repair may have potentially reduced the incidence of thromboembolism and reduced the necessity for anticoagulation when compared to those patients undergoing MVR (17). Furthermore, it is possible that the repair of a valve may lead to a greater improvement in functional durability than may be achieved with a bioprosthetic or mechanical valve, ultimately improving long-term results and reducing the costs associated with valve reoperation (16–19). For the same reasons, repair may also lead to better postoperative health status and quality of life. Although there is a vast amount of literature on both MVR and mitral valve repair, a limited number of studies in large patient populations have directly compared these two common techniques (16–19). In what is probably the first comparative study, Craver et al. from EUH (16) evaluated a consecutive case-matched series of 65 pairs of patients with mitral valve repair vs replacement. Hospital mortality was 1.5% in repair and 4.6% in replacements (p = NS). Survival at 4 years was 84% for repair and 82% for replacement (p = NS). Freedom from reoperation to replace the mitral valve at 4 years was 62 of 65 patients (95%) in the repair group and 64 of 65 patients (98%, p = NS) in the replacement group. In another study comparing mitral valve repair to replacement, Cohn and his colleagues from the Brigham and Women’s Hospital (20) noted an operative mortality of 3 of 75 patients (4%) in the repair group vs 2 of 65 patients (3%) in the replacement group. Survival at 30 months was 85 ± 6% for the replacement group and 94 ± 4% for the repair group. Akins et al. from the Massachusetts General Hospital (18) also evaluated the outcome of mitral valve repair (n = 133) and replacement (n = 130 patients). Hospital mortality was 3% with mitral valve repair vs 12% in the replacement group (p < 0.01). Median postoperative stay was shorter in repair (10 vs 12 days; p = 0.02). Late valve-related death occurred in 2% of repair patients vs 6% in replacement patients (p = 0.08). Enriquez-Sarano from the Mayo Clinic (21) evaluated outcomes in 195 patients with valve repair and 214 with replacement using multivariate analysis. Operative mortality was 2.6% with repair vs 10.3% with replacement (p = 0.002). Survival at 10 years was 68 ± 6% with repair vs 52 ± 4% with replacement (p = 0.0004). Multivariate analysis indicated an independent beneficial effect of valve repair on overall survival (hazard ratio, 0.39; p = 0.00001), operative mortality (odds ratio [OR], 0.27; p = 0.026), and late survival (hazard ratio, 0.44; p = 0.001). Overall, the studies to date, though favoring repair, are mostly small in size, with limited outcomes measures and no cost-effective or cost-descriptive analyses. More recently, Thourani et al. (22) have performed an age, sex, concomitant coronary artery bypass and concomitant AVR matched case-control study comparing 682 patients undergoing mitral valve repair vs 682 patients undergoing MVR from one institution. Preoperative demographics were similar between groups: female sex, hypertension, diabetes mellitus, previous MI, and class II–IV angina. In both groups, 97% of the cases were elective and the preoperative ejection fraction (EF) was similar: repair group, 57 ± 11% vs replacement group, 58 ± 13%. Concomitant CABG was performed in 25% of patients of each group. The postoperative stroke and MI rates were similar between groups. Length of stay was significantly less in the repair group

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(9.8 ± 9.4 days) in comparison to the replacement group (12.4 ± 13.1 days, p < 0.0001). In-hospital mortality was significantly less in the repair group (4.4% vs 7.2%, p = 0.018), and the overall 10-year survival was significantly higher in the repair group (62% vs 45%, p < 0.0001). Ten-year survival of patients less than 60 years of age was significantly higher in repair patients (81%) when compared to replacement patients (56%, p < 0.0001). Interestingly, this disparity in survival was not appreciated of patients older than 60 years of age (repair, 33% vs replacement, 35%; p = 0.50). Tenyear survival of patients with mitral valve repair alone (no concomitant CABG) was significantly higher (73%) when compared to replacement-only patients (50%, p < 0.0001). This disparity in survival was not appreciated of patients undergoing repair and concomitant CABG (30%) in comparison with replacement and concomitant CABG (33%, p = 0.62).

Quality of Life Following Mitral Valve Surgery Mitral valve disease and subsequent mitral valve repair or replacement are complex health situations. Clinical data suggest that successful recovery is based on patients meeting self-management demands common to many cardiovascular illnesses: dealing with cardiac symptoms and side effects of treatment, adhering to the recommended drug regimen, managing medications in the context of changing health status or laboratory values, and modifying other cardiac risk factors (23). For example, patients with mitral valve disease who undergo surgery may have to monitor symptoms of fatigue, shortness of breath, atrial fibrillation, and postoperative pain, self-administer anticoagulant medication and assess for abnormal bleeding, and obtain laboratory draws and adjust dosages of anticoagulants in response to International Normalized Ratio results. Several small studies have demonstrated that selected patients can successfully self-manage anticoagulation and, indeed, achieve better results than patients managed by clinicians (24–27). There are few data on health-related quality of life in patients who have had mitral valve surgery. In a very early paper, Jenkins et al. (28) studied quality of life in 89 patients undergoing valve surgery or valve plus coronary artery bypass surgery both at baseline and 6 months later. Dyspnea was largely relieved by the surgery, and most people returned to levels of function similar to that prior to surgery. Le Tourneau et al. (29) studied 24 patients who underwent mitral valve repair and 16 who underwent valve replacement. Despite an improvement in New York Heart Association functional class, exercise performance did not improve. Left ventricular EF (LVEF) did not change after replacement (64.3 ± 11.5% to 61.5 ± 12.2%), but right ventricular EF (RVEF) improved (40.4 ± 9.2% to 46.0 ± 10.0%; p = 0.02). In contrast, repair was associated with a decrease in LVEF (64.1 ± 8.5% to 57.4 ± 10.0%; p = 0.01), whereas RVEF did not change (42.9 ± 10.3% to 42.8 ± 8.6%). More recently, Goldsmith et al. (1) prospectively studied the 3-month quality of life in 40 patients undergoing mitral valve repair and 21 patients undergoing MVR. Following mitral valve repair, there was significant improvement in seven of eight quality-of-life parameters, whereas following MVR, there was significant improvement in only three of eight quality-oflife parameters. Myken et al. (30) studied quality of life, largely related to emotional functioning, in 140 patients undergoing bioprosthetic and 140 matched patients undergoing mechanical valve replacement. The authors asked a series of 35 questions, 13 related to coping

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capacity, 12 to social support, four on emotional status, and six related to emotions resulting from their surgery. Quality of life was assessed a single time at a mean of 74 months after surgery, with a range of 29–120 months. No difference in coping capacity, social support, and emotional status was noted for bioprosthetic and mechanical valves. Women had lower levels of coping capacity and emotional support. Shapira et al. (31) assessed the clinical outcome of 147 patients over age 75 undergoing valve surgery, of whom 20% had mitral valve surgery. At a mean follow-up of 30 months (range 2–55 months), 96% lived at home, 78% described their health as good to excellent, and 81% felt that the operation had improved their health.

MITRAL VALVE REPAIR VS REPLACEMENT: CEA Very little is known about the comparative cost and associated resource utilization of treating patients with mitral valve repair vs replacement. In one of the few studies on cost, Barlow et al. (32) reported a mean cost of $14,469 in 84 patients having MVR, $13,151 in 42 patients MVR with chordal preservation, and $11,606 in 127 patients having mitral repair. In 30 patients, Pagani et al. (33) noted hospital charges ($44,697 ± $4903 for replacement vs $31,337 ± $4484 for repair; p < 0.0001). In a recent study by Thourani and colleagues at Emory University (22), evaluating 1364 patients undergoing mitral valve repair (n = 682) vs replacement (n = 682), there was a trend toward reduced hospital cost in the repair group ($23,117 ± $18,764 vs $25,672 ± $16,706; p = 0.079). Currently, no studies with follow-up costs in this patient population are available. Therefore, no CEA with induced (i.e., follow-up), as well as in-hospital and professional, costs comparing replacement to repair, is available.

CONCLUSIONS Despite a trend over the past decade for patients undergoing valvular heart operations to be older and sicker by many criteria, procedural outcome, including Q-wave MI and stroke, as well as in-hospital mortality, changed little, while maintaining considerable decreases in cost and length of stay. We should continue to expect changes in the profile of patients undergoing heart valve surgery. Although the indications for heart valve surgery are well documented, and valvular replacement has been shown not only to prolong life, but also to improve the quality of life, these operations remain expensive. By continuing to critically analyze the dynamic changes not only in the patient population undergoing AVR and MVR surgery, but also in the new technological advances in surgical valve conduits, we should be more capable of predicting future needs and trends of our health care resources. Given the conflicting forces of limited health care finances with the arduous task of providing state-of-the-art quality health care services, multi-institutional analyses of in-hospital and long-term home CE of valvular surgery should be performed to assist the health care provider on proper resource utilization.

REFERENCES 1. Goldsmith IR, Lip GY, Patel RL. A prospective study of changes in the quality of life of patients following mitral valve repair and replacement. Eur J Cardiothorac Surg 2001;20:949–955. 2. Goldsmith IR, Lip GY, Patel RL. A prospective study of changes in patients’ quality of life after aortic valve replacement. J Heart Valve Dis 2001;10:346–353.

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3. Goldsmith I, Lip GYH, Kaukuntla H, Patel RL. Hospital morbidity and mortality and changes in quality of life following mitral valve surgery in the elderly. J Heart Valve Dis 1999;8:702–707. 4. Warner CD, Weintraub WS, Craver JM, et al. Effect of cardiac surgery patient characteristics on patient outcomes from 1981 through 1995. Circulation 1997;96:1575–1579. 5. Jones EL, Weintraub WS, Craver JM, et al. Coronary bypass surgery: Is the operation different today? J Thorac Cardiovasc Surg 1991;101:108–115. 6. Weintraub WS, Craver JM, Jones EL, et al. Improving cost and outcome of coronary surgery. Circulation 1998;98:II-23–II-28. 7. Christakis GT, Ivanov J, Weisel RD, et al. Cardiovascular Surgeons of the University of Toronto. The changing pattern of coronary artery bypass surgery. Circulation 1989;80(Suppl I):I-151–I-161. 8. McGrath LB, Laub GW, Graf D, Gonzalez-Lavin L. Hospital death on a cardiac surgical service: negative influence of changing practice patterns. Ann Thorac Surg 1990;49:410–412. 9. Rao V, Christakis GT, Weisel RD, et al. Changing pattern of valve surgery. Circulation 1996;94(Suppl II):II-113–II-120. 10. Thourani VH, Weintraub WS, Craver JM, et al. Ten-year trends in heart valve replacement operations. Ann Thorac Surg 2000;70:448–455. 11. Thourani VH, Weintraub WS, Craver JM, et al. Influence of concomitant CABG and urgent/emergent status on mitral valve replacement surgery. Ann Thorac Surg 2000;70:778–784. 12. Jamieson WRE, Edwards FH, Schwartz M, et al. Risk stratification for cardiac valve replacement. National cardiac surgery database. Ann Thorac Surg 1999;67:943–951. 13. Carpentier A. Plastic and reconstructive mitral valve surgery. In: Jackson JW (ed.) Operative Surgery. Butterworths, Boston, MA, 1988, p. 527. 14. Carpentier A, Chauvaud S, Fabiani-J-N, et al. Reconstructive surgery of mitral valve incompetence: ten-year appraisal. J Thorac Cardiovasc Surg 1980;79:338–348. 15. Deloche A, Jebara VA, Relland JYM, et al. Valve repair with Carpentier techniques: The second decade. J Thorac Cardiovasc Surg 1990;99:990–1002. 16. Craver JM, Cohen C, Weintraub WS. Case-matched comparison of mitral valve replacement and repair. Ann Thorac Surg 1990;49:964–969. 17. Perrier P, DeLoache A, Chauvaud S, et al. Comparative evaluation of mitral valve repair and replacement with Starr, Bjork, and porcine valve prostheses. Circulation 1984;70(Suppl 1):187. 18. Akins CW, Hilgenberg AD, Buckley MJ, et al. Mitral valve reconstruction versus replacement for degenerative or ischemic mitral regurgitation. Ann Thorac Surg 1994;58:668–676. 19. Galloway AC, Colvin SB, Baumann FG, et al. A comparison of mitral valve reconstruction with mitral valve replacement: intermediate-term results. Ann Thorac Surg 1989;47:655–662. 20. Cohn LH. Comparative morbidity of mitral valve repair versus replacement for mitral regurgitation with and without coronary artery disease: Updated in 1995. Ann Thoracic Surg 1995;60:1452–1453. 21. Enriquez-Sarano M, Schaff HV, Orszulak TA, et al. Valve repair improves the outcome of surgery for mitral regurgitation: A multivariate analysis. Circulation 1995;91:1022–1028. 22. Thourani VH, Weintraub WS, Guyton RA, et al. Outcomes and long-term survival for patients undergoing mitral valve repair versus replacement: effect of age and concomitant CABG. Unpublished data. 23. Conn VS, Taylor SG, Wiman P. Anxiety, depression, quality of life and self-care among survivors of myocardial infarction. Iss Mental Health Nurs 1991;12:321–331. 24. Ansell JE, Patel N, Ostrovosky D, et al. Long-term patient self-management of oral anticoagulation. Ach Intern Med 1995;155:2185–2189. 25. Sawicki PT for the Working Group for the Study of Patient Self-Management of Oral Anticoagulation. A structured teaching and self-management program for patients receiving oral anticoagulation: A randomized controlled trial. JAMA 1999;281:145–150. 26. Sunderji R, Campbell L, Shalansky K, et al. Outpatient self-management of warfarin therapy: A pilot study. Pharmacotherapy 1999;19:787–793. 27. Cromheecke ME, Levi M, Colly LP, et al. Oral anticoagulation self-management and management by a specialist anticoagulation clinic: a randomized cross-over comparison. Lancet 2000;356:97–102. 28. Jenkins CD, Stanton BA, Savageau JA, et al. Physical, psychological, social and economic outcomes after cardiac valve surgery. Arch Intern Med 1983;143:2107–2113. 29. Le Tourneau T, De Groote P, Millaire A, et al. Effect of mitral valve surgery on exercise capacity, ventricular ejection fraction and neurohormonal activation in patients with severe mitral regurgitation. J Am Coll Cardiol 2000;36:2263–2269.

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30. Myken P, Larsson P, Larsson S, et al. Similar quality of life after heart valve replacement with mechanical or bioprosthetic valves. J Heart Valve Dis 1995;4:339–345. 31. Shapira OM, Kelleher RM, Zelingher J, et al. Prognosis and quality oflife after valve surgery in patients older than 75 years. Chest 1997;112:885–894. 32. Barlow CW, Imber CJ, Sharples LD, et al. Cost implications of mitral valve replacement versus repair in mitral regurgitation. Circulation 1997;96(9 Suppl.):II90–II95. 33. Pagani FD, Benedict MB, Marshall BL, Bolling SE. The economics of uncomplicated mitral valve surgery. J Heart Valve Dis 1997;6:466–469.

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Congestive Heart Failure Mikhail Torosoff, MD, PhD, Claude-Laurent Sader, MD, and Edward F. Philbin, III, MD, FACC CONTENTS INTRODUCTION DEFINITION OF HEART FAILURE EPIDEMIOLOGY OF HEART FAILURE ECONOMIC BURDEN OF HEART FAILURE TREATMENT OF HEART FAILURE COST-EFFECTIVENESS ANALYSES IN HEART FAILURE ECONOMICS OF ACE INHIBITORS, ARB, AND THE COMBINATION OF ISOSORBIDE AND HYDRALAZINE IN HEART FAILURE ECONOMICS OF DIGOXIN IN HEART FAILURE ECONOMICS OF DIGOXIN IN HEART FAILURE ECONOMICS OF DIURETICS IN HEART FAILURE ECONOMICS OF ANTI-ARRHYTHMIC AGENTS, PACEMAKERS, AND IMPLANTABLE DEFIBRILLATORS IN HEART FAILURE ECONOMICS OF VENTRICULAR ASSIST DEVICES AND TRANSPLANTATION IN HEART FAILURE ECONOMICS OF VENTRICULAR ASSIST DEVICES AND TRANSPLANTATION IN HEART FAILURE ECONOMICS OF DISEASE MANAGEMENT PROGRAMS FOR HEART FAILURE CONCLUSION REFERENCES

INTRODUCTION The aging of the population and better survival among patients with chronic cardiovascular disease have set the stage for a heart failure epidemic. Being a “final common pathway” for many serious cardiac conditions (1), heart failure is characterized by substantial morbidity, high case-fatality rates, and excessive utilization of health care resources. A variety of pharmacological and nonpharmacological treatments improve symptoms and survival among patients with heart failure. Many do so From: Contemporary Cardiology: Cardiovascular Health Care Economics Edited by: W. S. Weintraub © Humana Press Inc., Totowa, NJ

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in a cost-effective manner. In this chapter we define heart failure, review its epidemiology and economic burden, and discuss heart failure management from the perspective of economic issues, with a focus on cost-effectiveness.

DEFINITION OF HEART FAILURE Heart failure is classically defined as the inability of the heart to maintain an appropriate cardiac output (at rest or under conditions of increased demand) while maintaining physiologically appropriate filling pressures within the cardiac chambers. Heart failure can be characterized by abnormalities of diastolic or systolic function (2). In diastolic heart failure, impaired cardiac relaxation is the cardinal feature (3), whereas systolic failure is attributed to the loss or dysfunction of force-generating units within the heart (4). Filling pressures progressively rise, and the ventricle(s) dilate(s) as a response to cardiac injury early in the development of the heart failure syndrome. However, these adaptive mechanisms are eventually exhausted, and clinically overt heart failure ensues. Peripheral hypoperfusion and elevated central vascular pressures, characteristic of heart failure, trigger a cascade of neurohormonal responses (5). Renin, angiotensin, aldosterone, norepinephrine, vasopressin, and endothelin, alone and in concert, contribute to the expansion of circulating blood volume, stimulate cardiomyocytes, and cause peripheral vasoconstriction (6). These responses serve to preserve the central circulation but also underlie the characteristic symptoms of breathlessness and diminished exercise tolerance (4). Moreover, these fundamentally adaptive mechanisms paradoxically promote relentlessly progressive cardiac remodeling, cause symptoms to worsen over time, and increase the patient’s risk of death. In the industrialized world, heart failure most commonly results from atherosclerotic heart disease and hypertension (5). As many as 70% of adults with heart failure have coincident coronary artery disease (CAD), whereas 50% of all cases of heart failure are attributed to coronary disease (7). Inherited forms of cardiomyopathy also exist, including mutations of various genes encoding the protein contractile elements of the cardiomyocyte (8). Acquired abnormalities of the myocardium include infiltrative diseases (e.g., hemochromatosis and amyloidosis). Chronic exposure to cardiotoxic agents, such as ethanol or cancer chemotherapeutic drugs, can cause heart failure. Even a single exposure to a noxious agent can cause irreversible heart damage, such as that which can occur with illicit drug use. Other well-recognized causes of heart failure include chamber overload secondary to cardiac valve dysfunction, infectious causes (bacterial, parasitic, and viral), nutritional deficiencies (protein, thiamin, and selenium), and endocrinopathies (hypo- and hyperthyroidism, diabetes, and acromegaly) (5).

EPIDEMIOLOGY OF HEART FAILURE According to the American Heart Association’s (AHA) 2001 Heart and Stroke Statistical Update (9), approximately 4.7 million Americans are affected by heart failure, and 550,000 new cases are diagnosed each year. The prevalence of heart failure increases with age and approaches 10% among people older than 75 years (See Fig. 1). For many years, heart failure has been the most common cause for hospitalization among patients older than 65 in the United States. In 1998, heart failure was listed as

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Fig. 1. Prevalence of CHF by age and sex.

the primary discharge diagnosis for 978,000 hospitalizations, an increase of 159.4% in comparison with 1979 (10). Additionally, heart failure is listed among the secondary diagnoses for approximately 1.5 million hospital discharges annually. Self-reported quality of life is worse among patients with heart failure than among patients with many other chronic medical conditions. In the Medical Outcomes Study, patients with heart failure demonstrated more than an 80% decline in quality of life, whereas angina was associated with a 61% decline, chronic lung disease with a 56% decline, arthritis with a 50% decline, and diabetes with a 36% decline in this measure (11). In the United States, cardiovascular disease accounted for 946,619 deaths in 1998, 40.6% of all deaths (9). Deaths among patients with acute or chronic heart failure account for a sizeable proportion of all cardiovascular deaths. Deaths directly attributable to heart failure totaled 285,000 in 1998, an increase of 135% from 1979. More than 90% of all heart failure deaths occurred among patients older than 65 years. Moreover, among patients with chronic heart failure or left ventricular dysfunction, sudden cardiac death occurs at six to nine times the rate that it does among the general population. Six-year mortality among patients with heart failure may be as high as 84% in men and 77% in women (12). Among patients hospitalized for new, recurrent, or worsening heart failure, 6-month mortality is as high as 23% (13). CAD and hypertension are the two major causes of heart failure in Western cultures. Of 7.3 million survivors of acute myocardial infarction (MI), 22% of males and 46% of females will develop heart failure within 6 years (9). Elevated blood pressure is present in 50 million Americans; the lifetime risk of hypertension is 75% or more among patients with heart failure (14). As atherosclerotic heart disease and hypertension rise in prevalence with the aging of the population, we are likely to witness a worsening of the current epidemic of heart failure in the near future. Age, gender, race, and ethnicity affect both mortality and morbidity for many cardiovascular diseases. The annual incidence of the first major cardiovascular event is

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similar among women and men, although these events tend to occur later in life among women with a lag of approximately 10 years. Before 75 years of age, the prevalence of CAD is higher in men, whereas heart failure is more common among women. At any given age, and in both genders, AfricanAmericans have higher morbidity and mortality from cardiovascular disease than whites. In 1998, death rates from cardiovascular disease were 419.3 per 100,000 among white males, 532.9 among African-American males, 294.9 among white females, and 400.7 among African-American females (9). Considering the physiology and epidemiology of this syndrome, rationale treatment goals in heart failure should include the following: 1. Preventing heart failure, when possible, through prevention or early treatment of conditions known to cause heart failure, such as coronary atherosclerosis, hypertension, alcoholism, infection, vitamin deficiencies, thyroid disease, and so forth. 2. Reversing or containing the progression of any underlying condition that has caused symptomatic heart failure. 3. Alleviating the symptoms of heart failure. 4. Preventing progression of and, in some cases, reversing, the heart failure syndrome through mechanisms, such as amelioration or reversal of those neurohormonal responses that promote worsening of heart failure over time. 5. Avoiding preventable deaths among patients with heart failure, particularly sudden cardiac death (in most patients) and terminal pump failure (especially in the subgroup of patients eligible for mechanical circulatory support and heart transplantation.)

ECONOMIC BURDEN OF HEART FAILURE National health care expenditures are expected to rise to a projected total of $2.2 trillion by the year 2008. Growth in health care spending is anticipated to be 1.8% greater than the growth rate of the gross domestic product (GDP) for the period of 1998–2008. In 2001, cardiovascular disease and stroke cost an estimated $298.2 billion in the United States. Of these expenditures, costs attributable to CAD, hypertension, cardiomyopathies, arrhythmias, and congestive heart failure (CHF) accounted for $105 billion. The total costs attributed to heart failure in 1998 were estimated to be $21 billion. Of these expenditures, $14.3 billion was directed to hospital and nursing home care. Additionally, $1.5 billion was spent on physician and professional services. Medications and durable equipment cost $1.6 billion whereas $2 billion was spent on home health care. Finally, lost productivity and lost future earnings resulting from death and disability accounted for $1.9 billion. The high resource utilization of heart failure, together with the high prevalence of this disorder, have created circumstances by which it has become one of the most economically and socially burdensome illnesses in the United States (9). As the population has aged, and people have lived longer with serious cardiovascular diseases, the prevalence of heart failure and its attendant resource use have grown and can reasonably be expected to continue to do so in the near future (see Fig. 2). Accordingly, costs directed to hospital care, physician and professional services, medications and durable equipment, and home health care will continue to increase (see Table 1) (15). Recent trends in heart failure hospital admissions, clinical outcomes, and resource utilization were assessed in a study of patients admitted to the Massachusetts General Hospital (16). The authors reported 6676 discharges with the primary diagnosis of

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Fig. 2. Rates of hospital discharge for CHF by age.

Table 1 Projected Age of the Population and Health Care Costs Population or Costs

1980

1990

1993 1995

1997 2000

2002

People over 26.1 32 33.5 34.3 34.8 35.4 35.8 age 65, millions Expenses, billions of dollars Hospital care 102.7 256.4 323 347.2 371.1 424 473.9 Physician services 45.2 146.3 185.9 201.9 217.6 258.7 293.3 Home health care 2.4 13.1 23 29.1 32.3 36 41 Prescription drugs 12 37.7 56 61.1 78.9 112.1 137.7 Nursing home care 17.6 50.9 66.4 75.5 82.8 94.1 104.9

2004 2006

2008

36.4

37.2

38.4

537 328.7 47.4 168 117.6

591.7 369 55.4 203.6 132.9

659.5 416.1 65.4 243.4 150.7

Data from ref. 15.

CHF occurring between 1986 and 1996. Over the span of the study, hospital admissions for heart failure increased in a stepwise fashion from 942 during 1986–1987 to 1302 during 1990–1991 and to 1896 during 1994–1996. Diagnostic cardiac catheterization (CATH) was performed during 15% of hospitalizations. Patients appeared to be increasingly sicker over time, as evidenced by more medical comorbidities and greater use of critical care procedures. For example, admissions to intensive care units for mechanical ventilation and invasive hemodynamic monitoring doubled from 4% during 1986–1987 to 8% during 1994–1996. There were significant increases in the use of coronary revascularization procedures with 0.5% undergoing such procedures during 1986–1987 as compared with 2.6% in 1994–1996 (p < 0.01). Additionally, more patients underwent permanent pacemaker or defibrillator implantation (0.2% during 1986–1987 as compared with 1.8% during 1994–1996; p < 0.01). Despite evidence that patients were sicker and utilized more invasive procedures, there was a significant

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decline in hospital mortality and length of stay. Hospital mortality decreased from 8.4% during 1986–1987 to 6.1% during 1994–1996 (p < 0.01). Mean length of hospital stay decreased from 6.8 days during 1986–1987 to 5.8 days during 1994–1996 (p < 0.01). At the same time, overall costs rose from the beginning of the study, peaked during the 1992–1993 period, and then declined slightly during 1994–1996. Measured in 1996 dollars, mean adjusted total costs were $11,269 in 1986–1987, $12,310 in 1990–1991, $15,011 in 1992–1993, and $14,654 in 1994–1996 (16). Similar trends in decreasing length of hospital stay and improving hospital survival were noted in a sample of 35,560 patients with the diagnosis of heart failure hospitalized in Oregon from 1991 to 1995 (17). However, cautious optimism is warranted as more studies assessing trends in other academic referral centers and community hospitals in different geographic regions are needed before extrapolating these results to the general population of patients with heart failure. Moreover, as hospital mortality poorly represents long-term survivorship in chronic conditions such as heart failure (13), changes in hospital mortality may be misleading regarding changes in the true natural history of this syndrome. A variety of factors affect resource utilization (18-24) and outcomes (18-21,25) among patients with heart failure. A study of 45,894 patients with the primary discharge diagnosis of heart failure treated at 236 hospitals in New York State during 1995 revealed longer length of hospital stay, higher hospital mortality, higher hospital charges, and higher readmission rates among AfricanAmerican patients than among whites (18). Average length of stay was 10.4 days among AfricanAmericans when compared with 9.3 days among whites (p < 0.001). Similarly, females tended to stay in the hospital longer than males (9.8 vs 9.2 days, p < 0.001). However, mortality was higher among whites than AfricanAmericans (7 vs 6.4%, p < 0.05) and among males than females (7.4 vs 6.5%, p < 0.05). Hospital charges were higher among AfricanAmericans than whites ($13,711 vs. $11,704, p < 0.001), and among females than males ($11,690 vs $11,348, p < 0.05) (18). In general, studies have shown that in-hospital clinical outcomes (hospital length of stay and hospital mortality) are comparable among family practitioners, internists, and cardiologists (19,21,25). However, process of care varies as a function of the specialty of the treating physician. For example, in one study, patients treated by cardiologists were more likely to undergo diagnostic CATH (9% vs 2% for family practitioners) (19). Moreover, the clinical practices of cardiologists and heart failure specialists are more likely to conform with published treatment guidelines. For example, these physicians are more likely to prescribe recommended medications at the appropriate doses (26). Of interest, despite differing utilization of costly procedures such as CATH, hospital charges were similar among patients treated by family practitioners, internists, and cardiologists (19,25). Although these studies lack formal economic analyses, in aggregate, they suggest that more aggressive and perhaps better quality care results from specialists without attendant increments in health care costs as assessed by hospital charges. However, these studies as retrospective analyses, not prospective randomized trials, fail to fully account for the fundamental characteristic differences between patients treated by specialists and nonspecialists. Moreover, they are limited by a lack of long-term data regarding health care costs following the period of hospitalization. Finally, payer status is related to hospital outcomes in heart failure. In a study of 43,157 patients covered by health maintenance organizations (HMO), indemnity, Med-

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icaid, and Medicare insurance, Medicaid patients were found to have the longest length of hospital stay, highest hospital charges, and highest readmission rates (20). However, considering the appreciable fundamental differences in clinical and demographic characteristics among patients in the four insurance classes, it is difficult to know how much of the observed differences in outcomes can be attributed to the insurance itself vs other factors. Considering the economic issues surrounding heart failure, potential strategies for managing the economic burden of this syndrome include the following: 1. Restricting growth in total direct expenditures through legislative or budgetary practices (i.e., rationing). 2. Restricting growth in total direct expenditures by encouraging or requiring clinicians to use the least costly treatments when alternatives exist (i.e., a restriction of choice based on direct expense, regardless of differences in efficacy). 3. By acknowledging that our society values better clinical outcomes and is willing to spend more money to attain them (but within certain economic limits), we accept that total expenditures will grow over time, but allocate discretionary resources in a deliberate and planned fashion by choosing treatment alternatives that both return proportionately more value per dollar spent and remain within those limits defined by society.

TREATMENT OF HEART FAILURE Given the high prevalence and substantial economic burden of heart disease, the varying methods of diagnostic evaluation, and a wide variety of treatment strategies, the American College of Cardiology (ACC) and the AHA formed an ACC/AHA Task Force on Assessment of Diagnostic and Therapeutic Cardiovascular Procedures in 1980. This Task Force was charged to “develop guidelines relative to the role of new therapeutic approaches and of specific noninvasive and invasive procedures in the diagnosis and management of cardiovascular disease” (5). Since then, specific guidelines have been issued with respect to CAD, hypertension, valvular conditions, use of echocardiography, use of electrophysiology testing, and other cardiovascular entities. The Guidelines for the Evaluation and Management of Heart Failure were published in 1995 (5). Similar authoritative guidelines have been set forth by the Heart Failure Society of America (HFSA) (1) and the Advisory Committee to Improve Outcomes Nationwide in Heart Failure (ACTION-HF) (27) in 1999. Although all three sets of guidelines have as their foundations evidence emanating from randomized clinical trials (RCT), the ACC/AHA guidelines are more exhaustive in reviewing both diagnostic procedures as well as treatment of both acute exacerbations and chronic stable heart failure. As most of the compelling data on the use of β-adrenergic blockers came from RCT published after 1995 (28–30), the HFSA and ACTION-HF guidelines incorporate more information on the use of these agents. Also, the HFSA guidelines place stronger emphasis on the use of cost-effective strategies in managing heart failure. As exemplified in the published treatment guidelines, high quality of care for heart failure that is evidence-based requires the use of multiple drugs in the treatment plan. Therapies to be considered include (1) angiotensin-converting enzyme (ACE) inhibitors, angiotensin II receptor blockers (ARB), or the vasodilator combination of hydralazine and isosorbide; (2) β-adrenergic receptor blockers; (3) digoxin; (4) diuretics; (5) aldosterone antagonists; and (6) anti-arrhythmic medications in selected cases.

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Because the use of intravenous inotropic therapy, anticoagulants, and anti-platelet agents in heart failure remain controversial, they are not discussed here (31–33). Nonpharmacological, device-related treatments include (1) implantable cardioverter-defibrillators, (2) intra-aortic balloon counterpulsation pumps, and (3) ventricular assist devices. Other important nonpharmacological interventions include patient education, alcohol abstention, smoking cessation, dietary modification (including salt restriction), exercise training, and multidisciplinary heart failure management programs. Finally, when heart failure remains refractory to other treatments, heart transplantation is available for selected patients.

COST-EFFECTIVENESS ANALYSES IN HEART FAILURE In the current era, ever-expanding health care costs have spawned movements to reduce resource consumption. These initiatives have been crafted from varying philosophical and economic perspectives and, in their final form, have had varying degrees of success. Cost-containment strategies that are designed to be effective by emphasizing the selective use of those medical interventions that add value to patients’ health and well-being (even with increases in the absolute cost of treatment, as is often the case) have arguably had the highest levels of acceptance among patients, physicians, and other patient advocates. In keeping with this philosophy, good clinical decision making on both a population and an individual basis requires awareness of the cost (or incremental cost) of each therapeutic intervention in relation to its expected benefit (or incremental benefit) (e.g., see Table 2). Methods of comparing costs and/or benefits include cost-identification analysis, cost-benefit analysis, cost-effectiveness analysis (CEA), and sensitivity analysis (34,35). These methods are described in greater detail elsewhere in this text.

ECONOMICS OF ACE INHIBITORS, ARB, AND THE COMBINATION OF ISOSORBIDE AND HYDRALAZINE IN HEART FAILURE Activation of the renin-angiotensin-aldosterone system is a hallmark of heart failure. ACE inhibitors block production of angiotensin II, a potent vasoconstrictor, and prevent degradation of bradykinin, which has vasodilatory and growth-modulating properties. The effects of ACE inhibition include reduced arterial and pulmonary resistance, with resulting decreases in cardiac preload and afterload, improved cardiac output, and inhibition of remodeling of vascular structures and the cardiac chambers. ARB, available only more recently, counteract the deleterious effects of angiotensin II by blocking the action of this peptide at its receptor (AT1 receptor). Although less well tested in heart failure, ARB probably have efficacy equivalent to ACE inhibitors and appear to be better tolerated (1,5,36). A meta-analysis of 32 RCT examined ACE inhibitor use in 7105 patients with heart failure (37). Treatment with ACE inhibitors reduced overall mortality from 21.9 to 15.8% with a corresponding odds ratio (OR) of 0.78 (95% confidence intervals = 0.67–0.91, p < 0.01). The composite endpoint of death or hospital admission resulting from heart failure was reduced from 32.6 to 22.4% (OR = 0.65, 0.57–0.74, p < 0.001). Patients with more advanced heart failure or more severe left ventricular dysfunction appeared to benefit most from treatment with ACE inhibitors (37). Patients with heart failure and preserved left ventricular systolic function (so-called “diastolic” heart fail-

Table 2 CE Data for Selected Treatments in CHF Cost (dollars) Study (reference)

N

Treatment

267

V-HeFT (44) 804 Isosorbide-hydralazine SAVE (43) 2231 Captopril SOLVD (45) 1917 Enalapril ELITE (51) 722 Losartan US Carvedilol (57) 1094 Carvedilol MADIT (70) 196 Defibrillator

Control Standard therapy Placebo Placebo Captopril Placebo Standard therapy

QALY, quality-adjusted life years saved; LYS, life years saved.

QALY

Treatment Control Difference 5548 20,822 24,090 4411 78,864 97,560

5429 19,099 25,546 4,464 69,328 75,980

119 1723 –1456 –53 9536 21,580

Treatment Control Gain 4.364 5.07 7.06 5.5 6.98 3.46

4.386 4.72 5.98 5.3 6.67 2.66

CER

0.02 5600/LYS* 0.35 4900/QALY* 1.08 1820/LYS 0.2 4047/QALY 0.31 4500/QALY 0.8 27000/LYS

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ure) may also benefit from ACE inhibitor treatment (2,38). One study has evaluated the clinical benefits of ACE inhibitors among patients with diastolic heart failure, and compared these benefits to those observed among patients who had reduced left ventricular systolic function (38). In this study, ACE inhibitor use among patients with diastolic heart failure nearly doubled the time to the first hospital admission in comparison with similar patients not receiving ACE inhibitors (p < 0.05). Numerous studies have examined the economic issues surrounding ACE inhibitor use in heart failure. These studies were based on the use of various ACE inhibitors and differing assumptions regarding the magnitude and duration of benefit. Moreover, they used different methods to identify or project costs. Accordingly, it is difficult to compare these studies or their conclusions with one another. This having been said, the published studies have established fairly consistently that ACE inhibitor use is a favorable or dominant strategy in heart failure. Representative studies for the various drugs are shown in Table 3 (39–44). One noteworthy observation emerged from the economic analysis of the use of captopril in the Survival and Ventricular Dysfunction (SAVE) trial (40). In the SAVE experience, the cost-effectiveness of captopril varied as a function of decade of life. A cost-effectiveness ratio of $60,800/quality-adjusted life year (QALY) was observed among 50-year-olds, $9000/QALY among 60-year-olds, $4300/QALY among 70-year-olds and $3600/QALY among 80-year-olds. (40). In light of the aggregate data, one can conclude that all ACE inhibitors that have been subjected to formal testing improve survival in heart failure, decrease morbidity, and are cost-effective. As some experts believe that all ACE inhibitors have benefit in heart failure (45), it is reasonable to postulate that the remaining (untested) agents in this drug class may be cost-effective as well. When ACE inhibitors are contraindicated or poorly tolerated, ARB or the combination of isosorbide and hydralazine can be used as acceptable alternatives. Both of these alternatives confer survival benefit when compared with placebo (36,45–52). Data from the second Veterans’ Heart Failure Trial (V-HeFT-II) (47). has been used to examine the cost-effectiveness of the isosorbide–hydralazine combination (41). In terms of clinical efficacy, both regimens (isosorbide–hydralazine combination and enalapril) were found to be superior to placebo in treating patients with left ventricular dysfunction. Treating patients with isosorbide–hydralazine was less costly than treating patients with enalapril ($5548 for isosorbide–hydralazine as compared with $8117 for enalapril) but provided less benefit in terms of life years (LY) saved (0.021 years for isosorbide–hydralazine as compared with 0.265 years for enalapril). The corresponding incremental cost-effectiveness ratios that were projected were $5600/LY saved for isosorbide–hydralazine combination and $9700/LY saved for enalapril (41). The Evaluation of Losartan in the Elderly (ELITE) trial found no difference between the ARB losartan and the ACE inhibitor captopril with respect to its primary endpoint, renal dysfunction, but did note lower mortality in the losartan group (48,49). An economic analysis based on the ELITE trial estimated the within-trial costs of the two treatment options to be equivalent (50). The authors also projected the lifetime costs of these two treatment options. Relative to captopril treatment, they estimated that losartan would increase survival by 0.2 years per patient at an incremental cost of $769, yielding a cost-effectiveness ratio of $4047/LY saved for losartan relative to captopril. However, the subsequent Evaluation of Losartan in the Elderly-2 (ELITE-2) trial failed to confirm the survival benefit of losartan seen in the earlier study (51). In aggregate,

Table 3 CE Data for Treatment with ACE Inhibitors in CHF Efficacy

Treatment

Study

Author (reference)

Captopril

MHFT

Kleber (42)

170

Captopril

SAVE

Tsevat (43)

2231

269

Enalapril

N

Enalapril

SOLVD Paul (44) V-HeFT I V-HeFT II SOLVD Cook (45)

2569 642 804 2569

Ramipril

AIRE

1014

Ramipril

AIRE

Schadlich (46)

Erhardt (47)

1014

Measure Hospitalizations for CHF per 100 patient years Limited to 4 year-survival benefit in 50-year-olds Limited to 4 year-survival benefit in 80-year-olds Lifetime projected survival, years Lifetime projected survival, years Survival benefit after 1 year of treatment Survival benefit after 3.8 years of treatment Survival benefit after 1 year of treatment Survival benefit after 3.8 years of treatment

* QALY, quality-adjusted life years saved; LYS, life-years saved.

Cost

Treatment Control

Treatment

Control

CE

7.9

12.5

946

955

8.13

8.1

32,098

30,369

96,876 DM per 100 patients (Germany) 60,800 $/QALY*

3.96

3.44

16,699

14,844

3600 $/QALY

4.65

4.364

8117

5429

9,700 $/LYS*

12.25

9.53

24,090

25,546

1

0.973

653

1082

3.8

3.511

1682

2654

1

0.97

3524

4952

3.8

3.58

13,121

16,553

2660 $/QALY 1820 $/LYS 8271 DM/LYS (Germany) 2456 DM/LYS (Germany) 30,033 SEK/LYS (Sweden) 14,148 SEK/LYS (Sweden)

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Cardiovascular Health Care Economics

these data suggest that ARB and ACE inhibitors are probably both dominant strategies relative to no angiotensin-II antagonism, and that the cost-effectiveness ratios for these two treatments are probably grossly equivalent to one another. However, it is not rational to propose that ARB have incremental clinical or economic benefit over ACE inhibitors absent confirmation of the results of the original ELITE trial.

ECONOMICS OF β-BLOCKERS IN HEART FAILURE Chronic excessive sympathetic stimulation of the failing heart results in persistent tachycardia, vasoconstriction, a lower fibrillation threshold, and loss of contractile elements from the heart through ischemic and apoptotic mechanisms. β-Adrenergic receptor blockers inhibit these adverse effects, causing amelioration of symptoms and improvement in cardiac function, as well as reduction in the incidence of sudden cardiac death, pump failure-related death, and overall mortality. In fact, multiple RCT have demonstrated pronounced beneficial effects of β-blockers on survival and functional class among patients with heart failure (1,5,28–30). A recent meta-analysis of 18 trials involving 3023 patients with heart failure confirmed a reduction in hospitalizations and improved survival with β-adrenergic blockade (53). The combined incidence of hospitalization or death was reduced by 37% (p < 0.001), whereas ejection fraction increased by an average of 29% (p < 0.001). Improvement in survival appeared to be more pronounced with the use of nonselective β-blockers (49 vs 18%, p < 0.05), whereas no significant difference was observed with respect to other endpoints. Another meta-analysis explored changes in quality of life observed with β-blocker treatment (54). Consistent improvement in heart failure class, exercise capacity, and self-assessed quality of life was confirmed across all patients groups when β-blockers were administered after initial treatment with ACE inhibitors, diuretics, and digoxin. Limited data are available regarding the long-term benefits (> 1 year) of β-blocker use in heart failure and the cost-effectiveness of these agents. Delea and co-authors analyzed economic data for carvedilol, a newer nonselective vasodilating β-blocker shown to have efficacy in heart failure (55). They performed two analyses based on differing assumptions about the duration of benefit of carvedilol. In the first model, the benefit of carvedilol was assumed to persist for only 6 months, the length of follow-up in the major clinical trial of this drug. This model, the “limited benefit” model, assumed that the clinical efficacy of the drug ended abruptly after 6 months. Their second model, an “extended benefit” model, assumed that the efficacy tapered slowly over a 3-year period after initiation. Life expectancy was estimated at 6.67 years in conventional treatment arm, 6.98 years in the limited benefit model, and 7.62 years in the extended benefit model. Projected life-time costs of heart failure-related care were estimated to be $28,756, and $36,420 and $38,867 for the three groups, respectively, reflecting the ongoing accrual of costs associated with prolonged survival in a heart failure cohort. With these assumptions, carvedilol use was associated with cost-effectiveness ratios of $29,477/QALY in the limited benefit model and $12,799/QALY in the extended benefit model (55). Although acute MI and chronic heart failure share some clinical and pathophysiological characteristics, they must be considered distinct and different entities by clinicians, epidemiologists, and economists. However, more data exist regarding the financial considerations surrounding β-blocker use after MI. The population benefits

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and cost-effectiveness of β-blocker use after MI were examined in a model of US residents aged 35 to 84 years (56). The model accounted for event rates, case-fatality rates, and costs, which were adjusted to reflect changes associated with β-blocker use. If all patients were given β-blockers for 20 years, 4300 fewer coronary heart disease deaths would occur, 3500 MIs would be prevented, and 45,000 LYs would be saved. The additional cost attendant to β-blocker use over 20 years was estimated at $570 million. At the same time, $412 million would be saved from decreased hospital admissions and other costs of care, yielding a net cost of $158 million. The cost-effectiveness ratio of this treatment strategy was estimated to be $4,500/QALY saved, a highly cost-effective and dominant strategy (56).

ECONOMICS OF DIGOXIN IN HEART FAILURE Prior to the 1990s digoxin was a mainstay of heart failure therapy. With the advent of ACE inhibitors and β-blockers, digoxin should be considered an important adjunctive treatment rather than the cornerstone of therapy. Digoxin is a steroid analog with mild inotropic and negative chronotropic effects. The inotropic action of the drug is attributed to increased concentrations of intracellular calcium. Digoxin may also modulate sympathetic tone in heart failure, perhaps by alleviating arterial baroreceptor dysfunction. Digoxin has a narrow therapeutic margin with therapeutic and toxic concentrations that are very close. Although digoxin use varies substantially across geographic regions, most experts would agree that it is indicated when atrial fibrillation accompanies heart failure or when symptoms of heart failure persist, despite treatment with ACE inhibitors, β-blockers, and diuretics (1,5). An RCT of 6802 patients with heart failure and left ventricular ejection fraction of 0.45 or less found no survival benefit when digoxin was added to background therapy of ACE inhibitors and diuretics (57). There was a trend toward a decrease in the risk of death because of worsening heart failure, but this difference failed to reach statistical significance (risk ratio = 0.88, 0.77–1.01, p = 0.06). However, in comparison with patients administered placebo, digoxin-treated patients experienced a significant reduction in the risk of hospital admission (risk ratio = 0.92, 0.87–0.98, p = 0.006) and the risk of hospital admission for heart failure (risk ratio = 0.72, 0.66–0.79, p < 0.001) (57). A formal assessment of cost-effectiveness was not conducted. A decision-analytic model was used to estimate the economic outcomes associated with digoxin continuation vs digoxin withdrawal among patients with chronic heart failure (58). These analyses were based on data derived from two digoxin withdrawal studies, the Prospective Randomized Study of Ventricular Failure and Efficacy of Digoxin (PROVED) (59) and the Randomized Assessment of Digoxin and Inhibitors of Angiotensin Converting Enzyme (RADIANCE) (60). The economic analyses assumed that digoxin discontinuation would result in a 23–50% increased risk of heart failure exacerbation within 12 weeks after withdrawal. The expenses associated with the drug, clinical monitoring, and toxic episodes were considered. Cost estimates were based on Health Care Finance Administration data and actual experience at the institution of the primary author. Based on the assumption that 50% of 2.5 million adult patients with heart failure in the United States would be candidates for treatment, it was estimated that digoxin use would be associated with savings of $406 million annually (90% range of uncertainty = $106–$822 million) (58).

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ECONOMICS OF DIURETICS IN HEART FAILURE Like digoxin, diuretics were a mainstay of heart failure therapy prior to the 1990s. Diuretics reduce circulating volume, thus decreasing cardiac preload and improving pulmonary hemodynamics. Intravenous diuretics play an enormous role in the management of acute exacerbations of heart failure because of their rapid onset of action, resulting in near immediate improvement in symptoms. However, diuretics also promote deleterious neurohormonal responses in heart failure, including activation of the renin-angiotensin-aldosterone system, and can cause electrolyte abnormalities. There are no data to suggest a survival benefit of diuretics in heart failure, in fact, the opposite may be true (61,62). Therefore, in the current era, diuretics should be reserved for patients who have fluid congestion or intravascular volume overload despite dietary salt restriction and use of other pharmacological agents (5). To our knowledge, there are few good studies of the cost-effectiveness of commonly used diuretics (such as furosemide and hydrochlorothiazide) in heart failure. A retrospective study of torsemide, a loop diuretic similar to furosemide but with a more favorable side-effect profile and a longer half-life, showed potential cost savings when that drug was employed (63). In the absence of larger studies that provide good data regarding the more commonly used diuretics, one should consider that diuretics are highly effective as symptomatic treatment and consequently prevent hospitalizations in the way that digoxin does. Diuretics provide these benefits at a relatively low cost of treatment. From this perspective, their use in acute, moderately severe, or severe chronic heart failure is probably economically favorable, even in the absence of a survival benefit. Spironolactone is an aldosterone antagonist and a nonloop diuretic, traditionally used as a second line agent in loop diuretic-resistant hypervolemia. Spironolactone has been a favored agent to treat ascites caused by hepatic cirrhosis and other conditions characterized by aldosterone excess. Aldosterone excess, characteristic of advanced heart failure, results in sympathetic activation, myocardial and vascular fibrosis and electrolyte abnormalities. A recent RCT investigated the effects of spironolactone among 1663 patients with heart failure already receiving standard therapy (64). Treatment with spironolactone over 24 months reduced mortality from 46 to 35% (p < 0.001) and reduced the risk of hospitalization for heart failure by 35% (p < 0.001). Patients treated with spironolactone were more likely to report improved symptoms (41 vs 33%) and less likely to report worsening symptoms during treatment (38 vs 48%, overall p < 0.001). No formal cost-effectiveness analysis was performed. However, as this drug appears to safe, effective, and inexpensive, it is likely that its use is economically favorable.

ECONOMICS OF ANTI-ARRHYTHMIC AGENTS, PACEMAKERS, AND IMPLANTABLE DEFIBRILLATORS IN HEART FAILURE Although serious ventricular arrhythmias and sudden cardiac death are common among patients with heart failure, no single study of “prophylactic” anti-arrhythmia therapy has found it to be effective in primary prevention of sudden cardiac death (65). However, treatment with amiodarone or an implantable cardioverter-defibrillator is highly effective in secondary prevention of sudden cardiac death and in prevention of death among patients with reduced systolic function and inducible ventricular tachycardia (1,5). Moreover, a meta-analysis of 13 RCT involving 6553 patients with recent MI or heart failure found that prophylactic amiodarone treatment reduced total mortal-

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ity by 13% (p = 0.03) and arrhythmic death by 30% (p = 0.003) (66). Symptomatic heart failure was the single best predictor of sudden/arrhythmic death. Some authors have suggested that the magnitude of benefit of a defibrillator in patients with ischemic left ventricular dysfunction is similar or superior to the impact of β-blockers for heart failure, cholesterol treatment for reducing cardiac events, or thrombolytic therapy for preventing death in acute MI (67). For example, in the Multicenter Automatic Defibrillator Implantation Trial (MADIT) (68), 196 patients with left ventricular dysfunction and nonsustained ventricular tachycardia after MI were randomized to receive either conventional anti-arrhythmia treatment or have a defibrillator implanted. After follow-up of 27 months, a 54% reduction in mortality was observed in the defibrillator group (p < 0.001). The average initial costs of $44,600 in the defibrillator group were substantially higher than the $18,900 observed in the conventionally treated arm. These differences were primarily attributed to the cost of the device and the implantation procedure. However, by 48 months, the average continuing cost of conventional treatment became more expensive ($226 per month, in comparison with $182 per month in the defibrillator group). The overall cost of conventional therapy was projected at $75,980, whereas for the defibrillator group it was $97,560. At the same time, defibrillators were assumed to result in a net gain of 0.8 LYs (3.66 years average survival in the defibrillator group vs 2.80 years in the conventional arm). The corresponding incremental cost-effectiveness ratio was $27,000/LY saved, although the authors appropriately note that the ratio would likely have been less had more modern transvenous, not thoracotomy, devices been used in MADIT (68). Accordingly, by current standards, defibrillator implantation (using either the transvenous or thoracotomy approach) in this class of patients can be considered cost-effective. Another analysis of economic outcomes found cost-effectiveness ratios ranging from cost savings of $13,975/LY saved to incremental cost of $114,917/LY saved for defibrillator therapy (69). Break-even times were calculated for defibrillator use. The break-even time is the expected number of months or years before the initial cost disadvantage of a treatment (in this case, defibrillator implantation) will be offset by its lower continuing costs. It was found that break-even times for defibrillator therapy ranged from 1 to 3 years in the various studies (69). With longer battery lives and less expensive devices, the costeffectiveness of defibrillator therapy could improve even further.

ECONOMICS OF VENTRICULAR ASSIST DEVICES AND TRANSPLANTATION IN HEART FAILURE Medical therapy has finite benefit among patients with the most advanced forms of heart failure where 1-year survival rates of 40 to 50% are observed even among welltreated patients (70). Until recently, treatment options for these terminally ill patients were limited. Among the variety of procedures and interventions that have been tested, including left ventricular volume reduction surgery, cardiomyoplasty, ventricular assist devices, and total artificial heart (71), only ventricular assist devices (72) and heart transplantation (73) have been shown to consistently improve survival in end-stage heart failure. Surgically implanted left ventricular assist devices support the failing heart by collecting circulating blood from the left ventricle and ejecting it into the aorta, thus providing life- and organ-sustaining cardiac output. Left ventricular assist devices have been in use in humans since the 1980s and have been proven to be effective in reducing

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mortality from end-stage heart failure. Occasional patients survive and thrive after removal of assist devices (74), particularly when reversible forms of heart failure or shock exist prior to device implantation. However, when implanted in the context of worsening and irreversible heart failure, ventricular assist devices are most often a bridge to cardiac transplantation. In the current era, up to 70% of patients with chronic refractory heart failure who receive ventricular assist devices as a “bridging” strategy under elective or semi-urgent circumstances will ultimately survive to leave the hospital after successful transplantation. In nonrandomized studies, this compares with 36% survival among otherwise similar, nonbridged, transplant-eligible patients (72). Two recent studies examined the evolving costs of left ventricular assist device support (72). In the first, the study population consisted of 12 patients who received a left ventricular assist device that is wearable and electrically powered and therefore available for ambulatory (out-patient) use. The length of implant-related hospital stay was 17.2 days with an average cost of $161,627. The continuing cost of support during outpatient treatment was $352 per week. Of all left ventricular assist device-supported postdischarge days, relatively few were spent in the hospital as a result of subsequent readmission (8.5% of the total postdischarge period). Using these data, the authors projected the annual cost of left ventricular assist device support to be $219,139. Of this amount, approximately 35% would occur during the initial hospitalization and 30% would be the actual cost of the device. The authors expressed the hope that with improved technology and more experience, the cost-effectiveness of this treatment would improve (72). Later, the same group reported on their 6 year experience with 90 patients who received ambulatory left ventricular assist devices (75). Of these 90 patients, 44 were discharged from the hospital. Of these 44 patients, 42 went on to receive heart transplantation, 2 underwent planned explantation, and none died prior to transplantation or explantation. The average length of out-patient support was 103 days. Of patients gainfully employed prior to implantation, 30% were able to return to work during left ventricular assist device support. In-patient costs were not discussed, but the monthly continuing health care cost for a “healthy” out-patient with a left ventricular assist device was $754, exclusive of the costs of hospital readmissions. The comparative cost of 1 day in a nonacute hospital bed was $1604 (75). Therefore, out-patient management of patients with left ventricular assist devices awaiting transplantation is economically favorable, if the comparison is in-patient management during the waiting period. Ongoing studies are examining the feasibility of this technology for patients with advanced heart failure who are not candidates for heart transplantation. Heart transplantation is performed by the surgical removal of a healthy heart from a brain-dead donor and its placement in the body of a patient with advanced heart disease who has no other reasonable treatment options. In most cases, the diseased heart of the donor is explanted and the new heart is grafted in its place (so-called, orthotopic transplantation.) Rarely used in the current era, heterotopic transplantation involves leaving the donor’s heart in place but grafting the new organ to the systemic venous and arterial circulation in another location, usually in the abdominal cavity. The recipient and donor are matched based on body size and blood type. Matching based on other factors including race, gender, and major histocompatibility complex antigens is not possible due to the limited supply of donor organs and the short travel time that current organ preservation techniques will allow. Recipients are prioritized on a regional and national

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waiting list based on clinical instability and severity of illness. Within each severity class, recipients are prioritized based on their waiting time. Of all heart transplants, 90% are performed on recipients who have advanced and refractory heart failure. The remainder are performed on recipients with failing previous heart allografts, refractory angina resulting from inoperable CAD, complex cardiomyopathy, untreatable lethal ventricular arrhythmias, or select forms of congenital heart disease. In properly selected candidates, heart transplantation prolongs survival and provides long-term relief of the symptoms attributable to their primary heart disease. In the past decade, 1year survival after transplantation was 85%, 5-year survival has been 70%, and 10-year survival was 50%. However, it has been estimated that less than 50,000 of the 4 to 5 million patients with heart failure in the Unites States would be candidates for cardiac transplantation as it is currently performed. Specific indications for heart failure continue to evolve, however, the following general guidelines exist: 1. Recipients should have no other less-invasive treatment options for their underlying disease. 2. Recipients should have serious, advanced, and refractory heart disease with predicted survival being shorter than that which typically occurs after transplantation, thus justifying the risk associated with transplantation and post-transplant care. 3. Recipients should have no other life-threatening disorder that markedly jeopardizes their survival even after successful and uncomplicated transplantation. Examples include incurable cancer and human immunodeficiency virus syndrome. 4. Recipients should have the cognitive capabilities, emotional structure and social supports necessary to comply with rigorous post-transplant care.

The economic issues surrounding heart transplantation lack some degree of clarity. Properly performed, cost-identification methods for transplantation would include the following domains: the costs of pretransplant evaluation and care, the expense of transplantation itself, and the costs of post-transplantation care. One study provided such information with costs expressed in 1987 US dollars (73). Pretransplant costs varied from $3700 to $5200. The costs of the transplant procedure and perioperative care ranged from $70,946 to $111,906. Post-transplant costs were higher during the first postoperative year, ranging from $14,016 to $17,684, whereas costs during the second year and beyond ranged from $7975 to $12,750 annually (73). Indirect and intangible costs should be considered as well. Pretransplant patients report limitation of their activities in 73% of cases, whereas 1 year after transplant, the proportion decreases to 66%. One-fourth of post-transplant patients return to work fulltime, and another 6% are working part time (73). Regrettably, we are unaware of more recent well-done studies to cite for the purpose of this review. Accordingly, debate on the cost-effectiveness of heart transplantation is ongoing. However, as transplantation is offered to so few patients with heart failure, the costs and benefits of this procedure impact only slightly on the global clinical and economic issues surrounding the entire population of patients with heart failure (76).

ECONOMICS OF DISEASE MANAGEMENT PROGRAMS FOR HEART FAILURE During the 1990s, mounting evidence suggested that the prevalence of heart failure was increasing and that the syndrome remained synonymous with poor clinical outcomes and high risk of death. Moreover, it was recognized that medical care for this

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entity was often fragmented and of poor quality, characterized by suboptimal implementation of newer, life-saving medical interventions. These factors, together with a desire to contain health care costs, spirited the growth of disease management programs for heart failure. Although disease management itself lacks a widely accepted definition, the general purpose and intent of such programs is to organize and deliver care in an effective and efficient manner through a multidisciplinary team approach. The principal goal of these programs is to address the multiple medical and nonmedical needs of the heart failure patient. Other goals include alleviating symptoms, maximizing functional capacity, and improving quality of life, thereby reducing the risk for clinical worsening, hospital admission, and death. Most disease management programs also seek to reduce the use of excessive or ineffective health care resources, thereby containing health care costs while delivering high-quality treatment. Hospital pathways for heart failure management, akin to disease management programs, also exist to facilitate the delivery of good, comprehensive, evidence-based care in the in-patient environment while maintaining a cost-conscious perspective (77). Important and effective nonpharmacological, nonsurgical treatment maneuvers for heart failure that are the focus of disease management programs include patient education, alcohol abstention, dietary modification including salt restriction, smoking cessation, and exercise training. Patient education includes instruction in the causes and physiology of heart failure, techniques of self-management, the proper use and side effects of cardiac medications, and the rationale behind recommendations for lifestyle modifications. Better understanding of these issues routinely improves patient compliance with drug treatment, sodium restriction, smoking cessation, and exercise training (71). Moreover, disease management programs use various strategies, including treatment algorithms achieved by consensus to improve health care providers’ adherence to authoritative treatment guidelines. In this way, disease management leverages the power inherent in modern medical advances toward the goal of delivering efficacious and cost-effective care for patients with heart failure. Available are two published reviews of observational and randomized trials of disease management for heart failure that summarize the convincing clinical and economical benefits of such programs (78,79). Disease management programs have resulted in a 50–85% decline in hospital admissions when historical patients or concurrent randomized controls are used as the reference (Table 4) (80–86). The only study that showed an increase in hospital admission rate was the one conducted in the Veterans’ Affairs medical system (87) and was probably not a disease management approach as other investigators have defined it. Other observed clinical benefits include a reduction in physician and emergency room visits, improved symptoms and functional status, and better compliance with treatment recommendations. Although most of these studies lack good formal economic analyses, the general experience has been that the cost savings attendant to the reduced risk of hospital admission offsets most or all of the direct and indirect costs of the programs (Table 5) (80–86). Accordingly, based on these limited data, most experts would agree that disease management programs are likely cost-effective. Arguably the most often quoted paper is that of Rich and colleagues (82). This single-center RCT enrolled 282 patients with heart failure at high risk of hospital readmission. Patients in the active treatment group received intensive education about heart failure by an experienced nurse, dietary assessment by a registered dietitian, and

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Table 4 Impact of Heart Failure Disease Management Programs on Clinical Outcomes and Resource Utilization Author (reference) Fonarow (82)

Clinical efficacy

Resource utilization

Kornowski (83)

Improved functional status and aerobic capacity Improved functional status

Rich (84)

Improved quality-of-life measures

Shah (85)

Not reported in reference

Tilney (86)

20% reduction in daily dietary salt intake Improved functional status No change in quality-of-life measures

Weinberger (87)

Improved patient satisfaction scores

West (88)

Improved symptoms and functional status 38% reduction in daily dietary salt intake

85% reduction in hospital admission rate 62% reduction in hospital admission rate 56% reduction in hospital admission rate 50% reduction in hospital admission rate 60% reduction in hospital admission rate 68% Increase in general medical visit rate 5% reduction in subspecialty clinic visit rate 36% Increase in hospital re-admission rate 23% reduction in general medical visit rate 31% reduction in cardiology visit rate 53% reduction in emergency room visit rate 74% reduction in hospital admission rate

Table 5 Economic Utility of Heart Failure Disease Management Programs

Author (reference) Fonarow (82) Kornowski (83) Rich (84) Shah (85) Tilney (86) Weinberger (87) West (88) a

Difference in health care charges or costs between treatment and control patients (dollars per patient)

Costs of disease management program (dollars per patient)

Net economic impact (dollars per patient per month)

–$15,894a NRd –$1058a NR –55%c NR NR

$6350b NR $552 NR NR NR NR

–$1591a NR –$153a NR NR NR NR

These values essentially reflect changes in hospital charges, as total health care costs were not reported. Includes charge for initial hospitalization, averaging $6050 per patient, plus $300 cost of postdischarge nursing care. c Reported as percentage only, actual value not given. d NR indicates not reported in reference. b

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discharge planning by a social worker. The drug regimen of active treatment patients was reviewed by a geriatric cardiologist to eliminate unnecessary medications, simplify treatment, and avoid potential drug–drug interactions. Intensive home follow-up was established consisting of phone calls, nurse home visits, and home care services. Patients in the control group received usual care. When outcomes were compared at 90 days postdischarge, survival in the intervention group was 54.3% in comparison with 66.9% in the control group (p = 0.04). The number of hospital readmissions was also significantly reduced (37 vs 67%, p = 0.02). Moreover, quality of life was significantly better in the intervention group (p = 0.001). The average cost of home care was higher in the intervention group with an incremental cost difference of $336, whereas the average cost of hospital readmissions was higher in control group ($3236 vs $2178, p = 0.03). After accounting for these differences and the cost of the program itself, the net result was a cost savings of $153 per patient per month in the intervention group (82). Another large study examined the effects of introducing a quality improvement program for heart failure management in the acute care community hospital setting (77). After a baseline data collection period, hospitals were randomly assigned to either a quality improvement intervention group that emphasized implementation of a critical care pathway and extensive teaching efforts about heart failure management or a control group that received “usual care.” Data for 2906 patients were collected and analyzed. Average length of hospital stay was reduced in both groups in comparison with the baseline period, but fell significantly greater in the intervention group (8 to 6.2 days, p = 0.03) than in the control group (7.7 to 7 days, p = 0.24). Despite intensive efforts, the intervention was not associated with a significant change in physician drugprescribing patterns, nor patient quality of life, readmission rate, or survival (77). This latter study emphasizes the point that ongoing, longitudinal disease management programs may have a different impact (clinically and economically) than short-term inpatient programs, even if the two are fundamentally directed at the same important clinical and social issues.

CONCLUSION The 21st century will witness an aging of the population and a coincident rise in the prevalence of CAD and hypertension, as well as better survival of patients with previously fatal heart conditions that medical science can now palliate but not cure. As heart failure is more prevalent among older people and the “final common pathway” for serious cardiovascular disorders, it is likely that the current heart failure epidemic will continue. The morbidity and mortality attendant to the heart failure syndrome are extraordinary. Health care costs account for a disproportionately high and growing proportion of the US GDP. The care of patients with cardiovascular disease in general and heart failure, in specific, account for a sizeable portion of these expenditures. In current terms, the direct and indirect costs of heart failure exceed $20 billion annually. Accordingly, heart failure has become one of the most economically and socially burdensome illnesses in the United States, and will likely remain so for years into the future. Cost-containment strategies that emphasize the selective use of those medical interventions that add value to patients’ health and well-being hold the promise of both

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wide-based acceptance and strategic success. Accordingly, good clinical decisionmaking on both a population and an individual basis requires awareness of the cost (or incremental cost) of each therapeutic intervention in relation to its expected benefit (or incremental benefit). In this chapter, we attempted to highlight those data that speak to the economic issues and cost-effectiveness surrounding various components of heart failure management. From the (regrettably, scant) available evidence, we have concluded that ACE inhibitors, β-blockers, digoxin, and anti-arrhythmia drugs and devices are widely available treatments that are clinically efficacious strategies with favorable cost-effectiveness data. It is likely that further study of ARB and spironolactone will lead to similar conclusions. Diuretics rapidly improve symptoms in acute and chronic heart failure but have no identifiable influence on mortality and are without good economic data. For the minority of patients with heart failure who qualify for heart transplantation, older data suggest that this procedure is cost-effective within the range defined by societal norms. By the same logic, the use of ventricular assist devices as a bridge to transplantation is also cost-effective. However, as most patients with heart failure do not qualify for either, it is unlikely that these technologies in their current form will impact the much larger public health dilemmas posed by heart failure. Finally, the evolving technique of disease management appears to improve patient well-being and reduce resource consumption with favorable economic outcomes. Given the clinical, social, and economic burden of heart failure, and the wealth of treatment options for this disorder, the lack of economic data surrounding heart failure management is both noteworthy and concerning. It is rational that future clinical trials incorporate economic analyses in their design. As recent years have seen new heart failure therapies tested as “add-ons” to existent background multidrug treatment strategies followed in time by addition of these successful therapies to the “background” against which yet newer agents are later tested, it is particularly important that clinicians and policymakers know the incremental benefits and costs attendant to each novel heart failure intervention.

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Current Economic Evidence Using Noninvasive Cardiac Testing Leslee J. Shaw, PhD, Rita Redberg, MD, MPH, and Charles Denham, MD CONTENTS DIAGNOSTIC COSTS FOR CARDIOVASCULAR DISEASE METHDOLOGIC APPROACHES TO ASSESS DIAGNOSTIC TESTS CURRENT EVIDENCE CONCLUSIONS REFERENCES

DIAGNOSTIC COSTS FOR CARDIOVASCULAR DISEASE Over the past few decades, encumbered health care resources have created an everincreasing societal burden. The continual rise in health care costs often exceeds that of inflation, accounting for approximately 13–16% of the US gross domestic product (GDP) (1). In the United States, recent estimates of the total expenditures for cardiovascular disease approach $300 billion annually (2), 14% of which is the costs for private payers and approximately 33% of which is Medicare costs. Furthermore, annual rates of exercise testing approach 12 million patients, half of which are performed with cardiac imaging (including ultrasound, nuclear, magnetic resonance, and positron emission tomographic imaging). Figure 1 depicts recent data from the American College of Cardiology (ACC) on reimbursement for varying subspecialties within cardiology (e.g., cardiac imaging procedures) (2). Since 1998, nuclear cardiology and echocardiographic procedures encumber approximately 10 and 18%, respectively, of allowable Medicare reimbursements. Current data suggest that cardiac imaging procedures are growing at a rate of approximately 10% annually, with the largest growth sector being hospital outpatient setting as a result of recent changes in reimbursement that focus on cost containment in that area (e.g., Hospital Out-patient Prospective Payment System [HOPPS]). As the demand and economic burden of cardiac testing continue to grow, an increasing body of clinical and cost evidence is required to justify further resource utilization. From: Contemporary Cardiology: Cardiovascular Health Care Economics Edited by: W. S. Weintraub © Humana Press Inc., Totowa, NJ

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Fig. 1. Medicare reimbursement changes for cardiology subspecialty procedures. The percent of total is listed for each cardiology subspecialty.

This developing body of evidence must include an examination of the unique methodologic approaches to the evaluation of cardiac testing, as well as the synthesis of cost efficiency and effectiveness data.

METHDOLOGIC APPROACHES TO ASSESS DIAGNOSTIC TESTS Costs of Various Diagnostic Tests COST COMPONENTS Two components are considered when determining patient costs for episodic health care: hospital and physician costs. Diagnostic tests may be added to an existing hospitalization where reimbursement is based on the patient’s diagnosis-related group (DRG) as a line item on a hospital bill (e.g., UB-92). In addition, physician charges are accrued using resource-based relative values (RBRVS). Relative value units (RVU), used to estimate physician costs for common cardiac noninvasive tests, are listed in Table 1. By using a conversion factor, determined by Medicare or the private sector, a cost estimate may be derived. For the year 2001, the Medicare national conversion factor was $38.26 (2). Recently, the Premier Innovation’s Institute, a quality assessment group of the Premier group purchasing organization, completed an evaluation of costs for diagnostic testing (3). The procedural cost database contains data that are updated on a quarterly basis. These facilities utilize internal cost accounting systems to provide procedural financial information. We evaluated the unit-operating cost of each noninvasive test modality, including the downstream cost of false-negative or false-positive test results. The cost of a noninvasive test included fixed and variable labor costs, supply and equipment costs, and other direct costs for a total direct cost estimate. Additionally, allocated overhead was included for an estimation of total cost. Utilizing the 1996–1998 database, procedural cost data were pooled for the following tests: echocar-

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Table 1 Medicare Allowable (Professional Component) Charges (National Average Data) for Selected Procedures

2001

2002 (est.)*

Approximate number of services by cardiologists nationally in 1998

Description

1998

1999

2000

Chest X-ray Heart image (3D) single or multiple Gated SPECT, planar single Heart wall motion add-on Heart function add-on Heart first pass single or multiple Electrocardiogram Cardiovascular stress test Electro-cardiogram monitor/review, 24 hours Echocardiogram Transesophageal echocardiogram Doppler echocardiogram Doppler color flow add-on Stress echocardiogram Cardiac rehabilitation/ monitor Extremity study Office/out-patient visit, new or established

$30 $261

$28 $249

$29 $257

$30 $265

$30 $262

275,603 727,610

$181

$172

$178

$183

$182

20,510

$58

$54

$55

$57

$57

408,123

$58 $325

$54 $310

$55 $321

$57 $330

$57 $329

305,915 23,903

$18 $51 $79

$17 $46 $73

$17 $46 $72

$17 $46 $72

$16 $43 $68

1,2357,423 2,273,617 1,447,084

$115 $104

$106 $96

$105 $98

$104 $104

$98 $95

3,020,581 84,177

$47 $119

$42 $113

$42 $117

$41 $120

$39 $119

2,830,533 875,969

$89 $22

$79 $19

$75 $21

$73 $23

$67 $22

278,850 307,335

$127 $56

$119 $55

$122 $62

$125 $64

$122 $66

39,589 11,460,462

* Year 2002 is estimated. Source: http://www.acc.org/advocacy/advoc_issues/impactchart.htm.

diography with and without intravenous contrast agent use, Tl-201 or Tc-99m stress single photon emission computed tomography (SPECT), treadmill exercise testing, and cardiac catheterization (CATH). Median (95% confidence intervals) cost from the database was calculated. All costs were weighted by the number of procedures per hospital. Cost estimates (range and 95% confidence intervals) are depicted in Fig. 2. Supply cost data for pharmacological stress tests (e.g., dobutamine) are also obtained from various contractual agreements, varying by volume or special pricing arrangements. For most cost analyses, the Red Book price is used as the average wholesale price for each drug, or each manufacturer provides radiopharmaceutical costs (4).

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Fig. 2. Estimated average costs for diagnostic procedures including cardiac catheterization (Cath), stress electrocardiogram (ECG), stress echocardiogram (Echo), and SPECT imaging.

Other costs include indirect costs of care (i.e., patient-related costs that are not directly billable, including out-of-pocket expenses, travel time, out-of-work costs, and lost productivity) and lost corporate performance data associated with noninvasive testing that is unavailable. Although early identification and diagnosis of coronary artery disease (CAD), through more accurate testing, may dramatically improve clinical outcomes and lower the cost of CAD throughout the entire health care system, these data have not been currently accrued in published literature. It is estimated that the lost productivity through morbidity or fatality is as high as $46 billion for CAD alone (5). This amount nearly equals the $47 billion cost of providing the care for hospital treatment of CAD. Most employers would favor a meaningful change in the process of care, allowing for more rapid diagnosis and quicker return of the patient to the working world. In the case of cardiac testing, greater accuracy and faster diagnosis will have a tangible impact on corporate performance, allowing patients to be treated more effectively and return to work more quickly. In the area of patient satisfaction, published studies demonstrate a positive correlation among accurate and fast diagnosis, patient satisfaction, and effectiveness of health care delivery (6). In the out-patient radiology setting, studies show a direct increase in patient satisfaction correlated with appropriate management of the patient’s work-up, including a triage approach to assure that the imaging modality is best suited to achieve the best diagnosis. Once patients are appropriately triaged for cardiac testing, the probability of redundant, repeated, or inconclusive tests can be greatly reduced based on current evidence. The most common downstream test following an inconclusive stress test is the more invasive and costly CATH. For this reason, tracking of inappropriate downstream catheterization (or false-positive rates) may soon be used as a measure of

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stress test laboratory performance. Other measures of patient satisfaction may include the backlog or wait for scheduling a test and patient wait time in the laboratory. A major consideration in accounting cost is the perspective used in the analysis. For most analyses used for clinical research purposes, the societal perspective is taken. However, within health care systems or practices, the perspective of the patient or hospital may be preferred.

Diagnostic Algorithms Using Nuclear Cardiology Cost care paths are used to simulate the clinical outcomes and economic costs of the care process, which are evaluated through three clinical periods. (Source: Premier Innovations Institute.) 1. Point of care: the time at which the clinician makes the decision to use one cardiac imaging modality vs another cardiac imaging modality. 2. Episode of care: the full care process described in each care path. 3. Contract horizon: the time for a renewal of a health care plan (approximately 2–3 years).

The costs associated with a given diagnostic work-up can be accounted for by following the algorithm of care that mirrors the physician’s decisions and use of procedures or therapies within a given episode of care. For example, Fig. 3 depicts the clinical work-up for rule-out myocardial infarction (MI) in a patient with an intermediate probability of disease, which is evaluated in the Emergency Department (ED). In this case, billing codes are assigned to procedures and therapies that are given to the patient in the ED. Total cost may be accounted for by totaling costs for all billable codes included in the ED visit. A similar cost example for the acute MI setting is illustrated in Fig. 4, where predischarge stress echocardiography is employed for the purposes of risk stratification. Figure 5 uses this example of the predischarge setting for the management of patients with acute MI, comparing total costs for 100 admitted patients undergoing an array of noninvasive tests. In addition to the cost of the test, the rate of inducible (i.e., false-positive and -negative) cost is included in the total care path for this evaluation. Using this example for the management of acute MI, the use of stress Tl-201 or electrocardiography had similar charges for the evaluation of 100 hospitalized patients evaluated in the predischarge setting. A final example (see Fig. 6) is used to illustrate the evaluation of patients with suspected left ventricular dysfunction who undergo echocardiography. DECISION MODELS VS HYBRID RESOURCE USE MODELS In general, researchers have focused on two strategies for assessing the cost implications of noninvasive cardiovascular procedure use. Historically, a decision or simulation model has been employed (7–16). The work-up of a patient is put forth in the form of a classification tree or algorithm. At each juncture or branch of the care path, both the disease and event likelihoods are averaged, and average costs are estimated. In general, these approaches utilize existing published evidence (often based on meta-analytic techniques) to formulate the decision model. In the area of diagnostic testing, this often includes supplementation of published evidence from invasive or therapeutic trials, where patient populations may be dissimilar. The paucity of longitudinal or other observational data series, as well as randomized control trial data limit the development of decision models, employing diagnostic test modalities and constraining the generalizability of the findings.

290 Fig. 3. Accruing charges for a selected patient scenario of rule-out Acute MI in a patient with an intermediate probability of MI. This patient is a 52-year-old male presenting with crescendo chest pain lasting more than 30 minutes. Cardiac risk factors include smoking (1 pack per day) and hypertension. In the ED, pain is relieved with IV heparin and IV nitroglycerin. Initial electrocardiogram and cardiac enzymes were negative. At the time of evaluation, the patient is felt to be of intermediate probability, and a stress Tl-201 was performed. The nuclear test was negative for ischemia. A gastoenterology consult was obtained and the patient was discharged and scheduled for an out-patient endoscopy.

291 Fig. 4. Accruing charges for a selected patient scenario of acute MI management and predischarge risk stratification. This patient is a 72-year-old male presenting with crescendo chest pain lasting more than 30 minutes. Patient smokes 1 pack per day and has diabetes and hypertension. Patient has had a recent cerebrovascular accident. The ED evaluation revealed an initial electrocardiogram with 2 mm of anterior ST elevation. Patient is admitted to the Coronary Care Unit. Patient was given IV β blockade and Abciximab. The chest pain improved but was still present. At this time, the electrocardiogram revealed a new left bundle branch block. The patient refused CATH. A two-dimensional echocardiogram was performed and revealed minimal anterior hypokinesis. After 24 hours, he was transferred to a telemetry floor for an additional 48 hours. A predischarge stress echocardiogram was negative for ischemia, and the patient was discharged home on day 4 postinfarction.

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Fig. 5. Comparative costs used for the predischarge risk assessment of acute MI patients.

More recently, a number of centers have invested in the development of observational datasets, which include detailed clinical outcome and “big ticket” resource consumption (18–23). This strategy applies the average procedure and hospital admission costs (±1–2 standard deviations) to the observed medical service use. These items reflect improved precision of accrued costs, but often lack detail regarding medical therapy or indirect costs, as is often seen in therapeutic trials (24).

CURRENT EVIDENCE Cost Savings Models Cost savings, also termed cost minimization, defines lower cost strategies of equivalent choices (i.e., given similar outcomes between a test comparison). This latter requirement for a cost savings model is frequently overlooked in many analyses. For example, health care administrators that identify tests by only their upfront test cost, not induced cost secondary to misclassification, fail to encompass all of the costs associated with a particular test choice. As such, test comparisons should include some measure of diagnostic or prognostic test performance. Numerous examples of costs savings models have been published (25–29). Berman et al. defined costs of using hierarchical noninvasive stress-testing strategies for patients with and without an abnormal rest electrocardiogram (13,19). For patients with a normal rest electrocardiogram, an exercise treadmill testing without imaging was associated with a 25% lower cost than that of direct catheterization (13,19). For patients with an abnormal rest electrocardiogram, nuclear imaging followed by catheterization in patients with ischemia had 38% lower costs than direct catheterization (13,19). In a related report from this same group

293 Fig. 6. Accruing charges for a patient scenario of suspected cardiac dysfunction in a patient with chronic ischemic heart disease. This patient is a 68-year-old male with history of previous anterior MI 2 years ago. The patient was referred to a cardiologist because of fatigue, dyspnea, and chest pain. The electrocardiogram revealed evidence of previous anterior infarction. A two-dimensional echocardiogram obtained with color and Doppler imaging revealed an left ventricular ejection fraction of 40% with moderate mitral regurgitation. Patient is started on medical therapy (digoxin, diuretics, angiotensin-converting enzyme-inhibitors, and eventually βblocker therapy).

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of investigators using cost simulation models, a selective catheterization strategy (where angiography was limited to patients with a moderate to severely abnormal SPECT scan) was associated with cost savings as high as 55% (14). Several reports have examined the cost efficiency (i.e., cost savings) of stress echocardiography (10,17). Marwick (17) examined the cost efficiency of stress echocardiography in identifying the lowest cost diagnostic strategy. This report examined diagnostic costs for four strategies: (1) direct catheterization, (2) exercise electrocardiogram followed by catheterization in women with abnormal test results, (3) stress echocardiography followed by catheterization in women with abnormal test results, and (4) a stepwise strategy of stress echocardiography in patients with an abnormal electrocardiogram and catheterization only for women with an abnormal echocardiogram. The results indicated that the stepwise strategy achieved the lowest diagnostic cost ($663 vs a range of $828–1434). Decision models have been put forth to explore the economic value of contrastenhanced echocardiography (15). In general, these models examine the added diagnostic content provided by enhanced visualization of endocardial borders with intravenous contrast agents. Currently, Food and Drug Administration approval of intravenous contrast echocardiography is limited to endocardial border delineation or left ventricular opacification. Diminished ability to visualize major myocardial segments particularly afflicts obese patients or those with lung disease. On average, this affects 10% to 15% of patients undergoing rest echocardiography and upwards of 50% of patients undergoing stress echocardiography (depending on the patient case-mix of a laboratory). As such, if these patients do not receive contrast enhancement, a suboptimal diagnosis may ensue with resultant inefficiency of care. A recent example of the cost efficiency of Optison was published revealing an 18% cost savings because of improved image quality (15). Others have noted an incremental value, with the addition of harmonic imaging alone for enhanced visualization of the myocardium (26).

Selective Testing: Gatekeeping Principles—Patient Selection for CATH One method of understanding cost models is to examine in more detail the portions of the diagnostic work-up (see Fig. 7) (20). For example, cost waste from SPECT imaging may be accounted from the false-positive (i.e., unnecessary testing) and falsenegative (i.e., downstream admissions for acute coronary syndromes) test rates. A number of prior reports have examined the advantages of using stress SPECT imaging as a gatekeeper for CATH. In economic terms, this is the principle of selective resource use, where stress SPECT imaging results further limit the decision to perform coronary angiography by excluding low-risk patients. Hachamovitch et al. were the first to report that when catheterization was limited to patients with a summed stress score greater than 8 or moderate to severe perfusion abnormalities, coronary angiography could be reduced by 17% (14). The implications for developing diagnostic cost efficiency may be illustrated by identifying the total cost savings that could be accrued by stress testing as an initial test of choice for stable chest pain patients when compared with an invasive CATH approach. This type of analysis was recently put forth by the Economics of Noninvasive Diagnosis (END) investigators (20). From the recent guidelines for stable angina from the ACC American Heart Association [AHA]/American College of Physicians/ American Society of Internal Medicine, stress cardiac imaging is the initial test of

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Fig. 7. Two- to three-year costs for varying diagnostic strategies: EMPIRE and END multicenter registries. From refs. 20 and 21.

choice for patients with Canadian cardiovascular class I or II angina (i.e., mild to moderate chest pain) (30). Based on current guidelines, catheter-based intervention is highly indicated for patients with stable symptoms and a large area of ischemic myocardium subtending a significant coronary stenosis (31). Despite the body of evidence, national practice patterns reveal a frequent use of direct catheterization. The END study compared the cost implications of using nuclear imaging as an initial diagnostic test and limiting CATH to patients with provocative ischemia only. These results reveal a 30–40% cost savings when provocative ischemia is a requisite prior to referral to catheterization (20). Translating this evidence from seven hospitals to any hospital’s population reveals that for every 1000 patients referred to CATH, the use of a stress SPECT scan could result in a savings of approximately $3 million. The primary benefit would be to exclude catheterization for patients who have normal perfusion results and would receive little therapeutic benefit from percutaneous intervention. Similar results have been reported in the Economics of Myocardial Perfusion Imaging in Europe (EMPIRE) study (21). Figure 7 depicts the cumulative evidence from the END and EMPIRE registries for patients with an intermediate coronary disease risk. We are currently expanding the evidence put forth in the END study into a disease management strategy that is currently being applied in the Clinical Outcomes of Revascularization and Aggressive Drug Evaluation (COURAGE) trial. The COURAGE trial is a 3000 patient randomized controlled trial of current maximal medical therapy vs medical therapy plus percutaneous coronary intervention (PCI) in reducing cardiac death or MI for patients with catheterization-defined coronary disease (excluding poor left ventricular dysfunction or three-vessel-left main CAD). In the COURAGE trial, nuclear imaging is being used to decide whether treated CAD patients should return to the angiographic suite. In the patient with recurrent symptoms, who is being treated medically or has had PCI, if insufficient ischemia is documented on stress SPECT imaging, then continued medical management is warranted. Conversely, for those with recurrent symptoms and worsening ischemia, reangiography and revascularization is considered appropriate.

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Cost-Effectiveness Analysis The term cost-effectiveness (CE) is often used ubiquitously to identify all forms of cost analysis. However, by definition, cost-effectiveness analysis (CEA) attempts to conjoin both the costs encumbered by a therapy or test choice with the outcome advantages or disadvantages of those choices (32–39). Incremental CE is defined as the ∆cost/∆ life years saved with varying noninvasive and invasive testing strategies. CEA is used when comparing varying test techniques defined as the marginal cost difference of one test divided by outcome differences. Namely, CEA compare the amount of resources consumed by a test in relation to its accrued benefits. The simple equation for incremental or marginal CE is CTest #1 – CTest #2/OTest #1 – OTest #2, where C is cost and O is outcome. In order for a test to be cost effective, it must optimize either the cost or outcome portion of the equation. A dominant strategy results when both cost and outcomes are improved. Although the outcome differences can be one of any significant outcomes, ranging from changes in symptoms to life years saved, the US Public Health Service has put forth standards that CEA should use the common metric of cost per life year saved (40–42). This type of analysis appears to fit with therapeutic agents, however, it does not appear to reflect the practical usage of a diagnostic test (32–39). Diagnostic tests do not save lives, per se, but identify disease or risk. Subsequent therapies and decisions of the caring physician result in an improvement in patient quality and quantity-of-life years. As such, there is no accepted standard for CE of a diagnostic test. However, there are those who have put forth incremental modeling strategies of CE that deal with the identification of disease or clinical outcome that more closely mirror actual test interpretation. One of the most simple diagnostic CE models is the cost to identify a particular outcome. In 1994, Christian and colleagues defined the cost to identify left main or threevessel CAD with stress SPECT imaging to be excessive in patients with a normal resting electrocardiogram (16). In an analysis of 5083 patients, the cost to identify cardiac death or MI was $5179, but only $3652 if testing was limited to patients at intermediate likelihood of coronary disease (14). By comparison, Christian et al. reported a cost of $20,550 to identify three-vessel or left main disease for patients with a normal rest electrocardiogram (16). In the preoperative risk evaluation, the cost to avert an inhospital event was reported to be more favorable for intermediate risk patients with cardiac risk factors or for those of advanced age (i.e., >70 years) (15). Other examples of the cost to identify a cardiac death or MI have been put forth (19,26). Recently, Marwick et al. presented an abstract on the CE of exercise echocardiography in comparison with exercise electrocardiography in 7618 patients presenting with chest pain and suspected CAD (23). These results reveal that because of an improved risk classification with echocardiography and suboptimal accuracy of electrocardiographic test results, for low-risk (<0.75% annual risk of death or infarction) to high-risk (>1% per annuum risk of death or MI) patients, the use of exercise echocardiography was costeffective. In particular, the delta cost to identify cardiac death or MI was approximately $4000–10,000 less with exercise echocardiography than electrocardiography. For example, Fig. 8 depicts the incremental diagnostic CE of varying test choices, defined as cost to identify coronary disease. In this example, test costs are depicted and also increase with higher equipment and labor costs, such as positron emission tomography or magnetic resonance imaging. However, improved sensitivity and specificity can lower overall diagnostic CE even resulting in a dominant strategy or negative cost effectiveness ratio (CER). A challenge to the use of these models is the lack of standards or thresholds for excess CE in diagnostic testing. Thus, the analysis requires subset analysis and com-

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Fig. 8. The top line, labeled “Consider Test Cost Only,” indicates that estimated cost for each procedure. However, the lower line identifies the diagnostic cost-effectiveness for each procedure as defined as the cost to identify coronary artery disease. That is, higher cost tests are generally more effective at identifying disease and as such, the upfront cost is minimized by a greater diagnostic effectiveness. TM Exercise, treadmill exercise; EBT, electron beam tomography; Echo, echocardiogram; SPECT, single photon emission computed tomography; MRI, magnetic resonance imaging; PET, postron emission tomography.

parisons in varying patient cohorts (e.g., costs in patients with a normal vs abnormal rest electrocardiogram, elderly vs younger patients, and intermediate vs low pretest risk patient subsets). Despite this limitation, one would expect that costs would rise with risk, such that high-risk patients are also higher cost patients. Conversely, lower risk patients should be lower cost patients. A modeling analysis may be constructed, whereby expected cost (i.e., low- to high-risk) may be compared with actual costs to discern areas of excess spending or if under the use of medical services. A modification to this method for calculating a clinicoeconomic model of a diagnostic care path includes the use of the following formula, where cost (loss) = waste (FP) + retest cost (FN) (3). Retest cost (FN) is defined as false-negative tests from the first testing pass multiplied by the test cost. Waste cost (FP) is defined as false-positive test from the first testing pass multiplied by the cost of CATH. Most recently, Shaw and colleagues compared costs of cardiac testing in 210 US hospitals (n = 24,967) (3). The episode of care for this analysis was 180-day costs for patients undergoing cardiac testing, including stress nuclear echocardiography, treadmill testing, as well as CATH. From this dataset (Fig. 9), the average cost to identify CAD ranged from $355 for gated SPECT with Tc-99m to $1320 for exercise electrocardiography. Similar costs were noted for contrast-enhanced stress echocardiography and Tc-99m SPECT imaging. The rationale supporting lower costs for Tc-99m imaging is the recent introduction of gated imaging that allows for the assessment of global and regional ventricular function, providing for approximately 30% improvement in test specificity (i.e., reduction in false-positives). Higher costs for exercise electrocardiography relate to lower diagnostic accuracy, including a diminished specificity.

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Fig. 9. Average cost to identify coronary disease for the assessment of suspected ischemia. Comparable costs are noted for myocardial contrast echocardiography and Tc-99m SPECT, reflecting improved image quality with these techniques. SPECT, single photon emission computed tomography; Optison, Optison stress echocardiogram; Echo, echocardiogram; ECG, electrocardiogram.

Goldman et al. published other reports on the CE of a variety of diagnostic tests in a review of economic evidence in cardiology (i.e., a league table of economic evidence) (37). This review includes only those reports that use the traditional definition of cost per life year saved. Other reviews that include updated evidence on cardiac diagnostic testing reveal that stress cardiac imaging is cost-effective for various patient subsets, including patients without a prior coronary diagnosis and are at intermediate risk, those with stable chest pain, and elderly patients (10,15,18). These results coincide with one of the general tenets of CE, where tests become more cost-effective concomitant to being more accurate in detecting disease or outcome risk. Another tenet is the high-risk CE model, where treatment of higher risk patients results in greater therapeutic benefit and improved CER. For example, the model of high-risk CE was published for preoperative risk stratification prior to vascular surgery (15). From this analysis, preoperative risk stratification, including pharmacologic stress SPECT imaging, was more cost-effective for symptomatic patients, those with an intermediate likelihood of coronary disease, and those patients over 70 years of age (15). For each of these patient subsets, the CER were less than $50,000 per life year saved (the standard threshold for economic efficiency) (32–39). In a related report on exercise echocardiography, the results revealed that echocardiography was cost-effective (i.e., lowered cost/quality-adjusted life years saved) and in some cases, dominated other test modalities, including exercise electrocardiogram and Tl-201 imaging (10). For women with definite, probable, and nonspecific chest pain, echocardiography economically dominated over Tl-201 imaging, resulting in lowered cost and improved outcome detection using the base strategy of 55-year-old women. Exercise echocardiography had incremental CER less than or equal to $40,000/life year saved, the threshold for economic efficiency when compared to other tests (10).

CONCLUSIONS Although there is an ever-increasing body of evidence on the clinical and economic value of noninvasive cardiac testing, the data are primarily composed of decision ana-

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lytic models with a smattering of observational data series. Even fewer data sets are composed of sufficiently powered series with a limited depth of detail on direct and indirect patient costs. This lack of evidence reflects the lack of funding for diagnostic studies when compared to therapeutic trials. A recent surge in funded projects by the Department of Veteran’s Affairs (e.g., the COURAGE trial) and National Institutes of Health [NIH]-National Heart, Lung, and Blood Institute (e.g., Women’s Ischemia Syndrome Evaluation) could provide additional detail on the economic value and clinical outcome (including quality-of-life data) that may enhance the level of evidence to support decision making in the area of cardiac noninvasive testing. Despite the lack of high-quality evidence, current data reveal the fact that nuclear cardiology and echocardiographic techniques are increasingly cost-efficient in selected patient subsets. Assessing both myocardial perfusion and global and regional ventricular function can be a clinically effective tool and can result in cost-effective management of patients with suspected cardiac ischemia, including intermediate likelihood patients, those patients with stable chest pain, the elderly, preoperative risk stratification, and patients postexercise treadmill testing.

REFERENCES 1. Smith S, Freeland M, Heffer S, et al. The next ten years of health spending, Health Affairs 1998;17(5):128–140. 2. http://www.acc.org/advocacy/advoc_issues/advoc_issues.htm#physicianfee; accessed March 2002. 3. Shaw LJ, Mulvagh SL, Jacobsen C, et al. Cost implications of diagnosing coronary disease. Eur Heart J 2000;21:477. 4. 2001 Drug Topics Red Book. Medical Economics, Thomson Healthcare. 5. Direct costs were extrapolated from estimates for 1997 by Thomas A. Hodgson, chief economist and acting director, Division of Health and Utilization Analysis, OAEHP, CDC/NCHS. Estimates of indirect costs were made by Thomas. J. Thom, statistician in the division of Epidemiology and Clinical Applications, NHLBJ, 1997, Bethesda, MD. 6. Kangarloo H, Ho B, Lufkin RB, et al. Effect of conversion from a fee-for-service plan to a capitation reimbursement system on a circumscribed outpatient radiology practice of 20,000 persons. Health Policy Pract 1996;201:79–84. 7. Garber AM, Solomon NA. Cost-effectiveness of alternative test strategies for the diagnosis of coronary artery disease. Ann Intern Med 1999;130:719–728. 8. Kuntz KM. Cost-effectiveness of diagnostic strategies for patients with chest pain. Ann Intern Med 1999;130:709–718. 9. Kymes S, Bruns D, Shaw LJ, Fletcher J. Anatomy of a meta-analysis: A critical review of “Exercise echocardiography or exercise SPECT imaging? A meta-analysis of diagnostic test performance.” J Nucl Cardiol 2000;7:599–615. 10. Kim C, Kwok YS, Saha S, Redberg RF. Diagnosis of suspected coronary artery disease in women: a cost-effectiveness analysis. Am Heart J 1999;137:1019–1027. 11. Shaw LJ, Dittrich HC. Use of intravenous Optison contrast echocardiography reduces downstream resource use and enhances cost savings. Acad Radiol 1998;(5 Suppl) 1:S250-1–S250-3. 12. Shaw LJ, Gillam L, Feinstein S, et al. for the Optison Multicenter Study Group. Technology assessment in the managed care era: Use of an intravenous contrast agent (FS069-Optison) to enhance cardiac diagnostic testing. Am J Managed Care 1998;4:SP169–SP176. 13. Berman DS, Hachamovitch R, Kiat H, et al. Incremental value of prognostic testing in patients with known or suspected ischemic heart disease. J Am Coll Cardiol 1995;26:639–647. 14. Hachamovitch R, Berman DS, Shaw LJ, et al. Incremental prognostic value of myocardial perfusion single photon emission computed tomography for the prediction of cardiac death: differential stratification for risk of cardiac death and myocardial infarction. Circulation 1998;97:535–543. 15. Shaw LJ, Hachamovitch R, Eisenstein E. Cost implications for implementing a selective preoperative risk screening approach for peripheral vascular surgery patients. Am J Managed Care 1997;3:1817–1827.

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16. Christian TF, Miller TD, Bailey KR, Gibbons RJ. Exercise tomographic thallium-201 imaging in patients with severe coronary artery disease and normal electrocardiograms. Ann Int Med 1994;121:825–832. 17. Marwick TH, Anderson T, Williams MJ, et al. Exercise echocardiography is an accurate and cost-efficient technique for detection of coronary artery disease in women. J Am Coll Cardiol 1995;26:335–341. 18. Shaw LJ, Miller DD, Romeis JC, et al. Prognostic value of noninvasive risk stratification and coronary revascularization in nonelderly and elderly patients referred for evaluation of clinically suspected coronary artery disease. J Am Geriatr Soc 1996;44:1190–1197. 19. Berman D, Hachamovitch R, Lewin H, et al. Risk stratification in coronary artery disease: implications for stabilization and prevention. Am J Cardiol 1997;79:10–16. 20. Shaw LJ, Hachamovitch R, Berman DS, et al. The economic consequences of available diagnostic and prognostic strategies for the evaluation of stable angina patients: an observational assessment of the value of precatheterization ischemia. Economics of Noninvasive Diagnosis (END) Multicenter Study Group. J Am Coll Cardiol 1999;33:661–669. 21. Underwood SR, Godman B, Salyani S, et al. Economics of myocardial perfusion imaging in Europe— The EMPIRE study. Eur Heart J 1999;20:157–166. 22. Shaw LJ, Heller GV, Travin MI, et al. Cost analysis of diagnostic testing for coronary artery disease in women with stable chest pain. J Nucl Cardiol 1999;6:559–569. 23. Marwick T, Shaw LJ, Vassey C. Exercise echo is more cost-effective than exercise ECG for prediction of prognosis in stable chronic CAD. Abstract for the American Heart Association’s Annual Scientific Sessions, November 10–14,2001, Anaheim, CA. 24. Weintraub WS, Mauldin PD, Becker E, et al. A comparison of the costs of and quality of life after coronary angioplasty or coronary surgery for multivessel CAD. Results from the Emory Angioplasty Versus Surgery Trial (EAST). Circulation 1995;92:2831–2840. 25. Shaw LJ. Cost effectiveness of gated and non-gated spect nuclear imaging. In: Germano G, Berman DS, (eds.), Clinical Gated Cardiac SPECT. Futura Publishing, Inc., Armonk, NY, 1999, pp. 325–338. 26. Shaw LJ, Culler SD, Becker NR. Current evidence on cost effectiveness of noninvasive cardiac testing. Subsection E. In: Pohost G, O’Rourke R, Shah P, Berman D, (eds.), Analytic Approaches to Cost Effectiveness and Outcomes Measurement in Cardiovascular Imaging, Imaging in Cardiovascular Disease. Lippincott, Williams & Wilkins, Philadelphia, PA, 2000, pp. 479–500. 27. Shaw LJ, Niyannopoulos P. Clinical and economic outcomes assessment with myocardial contrast echocardiography. Heart 1999;82(Suppl 3):IIII6–IIII21. 28. Shaw LJ, Miller DD, Berman DS, Hachamovitch R. Clinical and economic outcomes assessment in nuclear cardiology. Q J Nuc Med 2000;44:138–152. 29. Shaw LJ. Is contrast-enhanced echocardiography worth the added cost? In: Goldberg (ed.), Ultrasound Contrast Agents, 2nd ed. Martin-Duntz, Ltd., London, UK, 2001. 30. Gibbons R, Chatterjee K, Daley J, et al. ACC/AHA/ACP-ASIM guidelines for the management of patients with chronic stable angina. J Am Coll Cardiol 1999;33:2092–2197. 31. Scanlon PJ, Faxon DP, Audet AM, et al. ACC/AHA Guidelines for coronary angiography: A report of the American College of Cardiology/American Heart Association Task Force on practice guidelines. J Am Coll Cardiol 1999;33:1756–1824. 32. Schwartz JS. Economics and cost effectiveness in evaluating the value of cardiovascular therapies. Comparative economic data regarding lipid-lowering drugs. Am Heart J 1999;137:S97–S10. 33. Finkler S. Cost Accounting for Health Care Organizations: Concepts and Applications. Aspen Publishers, Gaithersburg, MD, 1994. 34. Laupacis A, Feeny D, Detsky AS, Tugwell PX. How attractive does a new technology have to be to warrant adoption and utilization? Tentative guidelines for using clinical and economic evaluations. CMAJ 1992;146:473–481. 35. Weinstein MC, Stason WB. Cost-effectiveness of coronary artery bypass surgery. Circulation 1982;66:III56–III66. 36. Mark D. Medical Economics and Health Policy Issues for Interventional Cardiology, 2nd ed. Textbook of Interventional Cardiology. W. B. Saunders Co., Philadelphia, PA, 1993. 37. Goldman L, Garber AM, Grover SA, Hlatky MA. 27th Bethesda Conference: matching the intensity of risk factor management with the hazard for CAD events. Task Force 6. Cost effectiveness of assessment and management of risk factors. J Am Coll Cardiol 1996;27:1020–1030. 38. Shaw LJ, Eisenstein EL, Hachamovitch R, et al. A primer of biostatistic and economic methods for diagnostic and prognostic modeling in nuclear cardiology: Part II. J Nucl Cardiol 1997;4:52–60.

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39. Drummond MF, Jefferson TO. Guidelines for authors and peer reviewers of economic submissions to the BMJ. The BMJ Economic Evaluation Working Party. BMJ 1996;313:275–283. 40. Siegel JE, Weinstein MC, Russell LB, Gold MR. Recommendations for reporting cost-effectiveness analyses. Panel on Cost-Effectiveness in Health and Medicine. JAMA 1996;276:1339–1341. 41. Weinstein MC, Siegel JE, Gold MR, et al. Recommendations of the Panel on Cost-effectiveness in Health and Medicine. JAMA 1996;276:1253–1258. 42. Russell LB, Gold MR, Siegel JE, et al. The role of cost-effectiveness analysis in health and medicine. Panel on Cost-Effectiveness in Health and Medicine. JAMA 1996;276:1172–1177.

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Cost-Effective Care in the Management of Conduction Disease and Arrhythmias David J. Malenka, MD and Edward Catherwood, MD, MS CONTENTS INTRODUCTION CONDUCTION DISEASE: PACEMAKERS TREATMENT OF VENTRICULAR ARRHYTHMIAS RADIOFREQUENCY ABLATION OF SUPRAVENTRICULAR TACHYCARDIAS ATRIAL FIBRILLATION ANTITHROMBOTIC PROPHYLAXIS CARDIOVERSION AND ANTIARRHYTHMIC THERAPY PRECARDIOVERSION TRANSESOPHAGEAL ECHOCARDIOGRAPHY SUMMARY OF ATRIAL FIBRILLATION CONCLUSION REFERENCES

INTRODUCTION In the treatment of arrhythmias and disease of the conduction system, physicians have an ever-growing menu of tests, drugs, and devices from which to choose. How best to use this diagnostic and therapeutic armamentarium in patient care has become an area of active study, and clinical trials to establish efficacy are now quite common. As should be the case, the major focus of these studies has been on clinical outcomes. However, as in other areas of medicine that have seen rapid growth in available technology and expanding indications for its use, there has been a growing concern about the impact of practice choices on the costs of care and whether, from a societal view, the costs are acceptable. Consequently, there has been literature emerging on the costeffectiveness (CE) of device use and treatment strategies in the management of patients with conduction disease and arrhythmias. In this chapter, we review the current literature on the cost-effective use of pacemakers and implantable cardioverter defibrillators From: Contemporary Cardiology: Cardiovascular Health Care Economics Edited by: W. S. Weintraub © Humana Press Inc., Totowa, NJ

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(ICDs), as well as what is known about cost-effective treatment strategies for the management of supraventricular arrhythmias, including radiofrequency ablation and the whole range of options for managing the most common of arrhythmias, atrial fibrillation.

CONDUCTION DISEASE: PACEMAKERS Asynchronous ventricular pacing in humans with a fully implantable device was first used for the treatment of Stokes-Adams attacks by Senning and Elmqvist in Sweden in 1958 (1). These early pioneers could hardly have imagined how dramatically the physiology and technology of pacing would change by the new millennium. Devices have become smaller and smaller, with longer battery life and an increasing assortment of options that allow a unique fit to each patient. Newer features have broadened the indications for pacing beyond the treatment of bradyarrhythmias to the treatment of abnormal physiology. Pacing has been used to treat symptomatic hypertrophic obstructive cardiomyopathy, neurocardiogenic syncope, and the long Q-T syndrome, as well as to prevent atrial fibrillation and to improve the function of patients with congestive heart failure (CHF). However, the majority of permanent pacemakers are still implanted to treat sick sinus syndrome (SSS) or atrioventricular (AV) conduction disorders (2,3). As such, the studies of CE have focused on the management of these problems. The major question in the treatment of patients with SSS or AV block, who are thought to require a pacemaker, is what mode of pacing to use—single-chamber atrial, single-chamber ventricular, or dual chamber. Compared to dual-chamber devices, single-chamber devices are inherently safer to place, associated with a longer battery life, and are less expensive. For SSS, atrial-based pacing preserves AV synchrony, maximizing cardiac output and reducing myocardial oxygen consumption (4,5), and may prevent the occurrence of atrial arrhythmias (4,6). The downside is that atrial leads are somewhat unstable (7), and some patients with SSS will develop AV block (8,) requiring an upgrade to a dual-chamber system. The benefits of single-chamber ventricular pacing for either SSS or AV block are counterbalanced by the loss of AV synchrony, the potential for developing a pacemaker syndrome (9–11), the possibility of adverse ventricular remodeling (12), and the possibility of increased mortality in comparison to atrial pacing (6,13–15). Dual-chamber devices maintain AV synchrony and help to prevent pacemaker syndrome, but when compared to single-chamber pacing, they are not just more expensive, but may be associated with more complications and no improvement in quality of life (15) or survival (in patients treated for AV block) (16). Tang et al. (17) provide a thorough review of these issues. One of the early efforts at examining CE in the use of permanent pacemakers focused not on the selection of the mode of pacing, but whether or not the empiric use of a pacemaker was indicated at all. In the absence of data from a randomized clinical trial, Beck et al. (18) developed a Markov model to study how to manage a 65-year-old man with recurrent syncope, no clear precipitating cause, but a known chronic bifascicular block. Although empiric pacing for presumed bradycardia was one therapeutic option, others included the use of antiarrhythmics to treat possible ventricular tachyarrhythmias, pacemaker placement plus the empiric use of antiarrhythmics, treatment guided by the results of electrophysiologic study (EPS), or observation for the progression of arrhythmias. Because the spectrum and risks of arrhythmias differed depending

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Table 1 Baseline Results: 65-Year-Old Man

Strategy

Expected survival Costs ($) (mo)

QualityMarginal adjusted Marginal qualitysurvival Costa adjusted (Mo) ($) survivala

MCEa,b ($/year)

Normal left ventricular function Observation Drugs Electrophysiologic testing Pacing Both pacing and drugs

3260 14,800 27,450 41,710 55,760

116.6 117.8 134.4 137.1 138.3

60.1 54.9 73.8 76.3 70.9

– 11,540 24,190 14,260 14,050

– –5.2 13.7 2.5 –5.4

– – 21,200 68,400 –

– 6930 16,900 1540 9740

– 1.5 16.5 –13.2 –11.1

– 55,400 12,300 – –

Poor left ventricular function Observation Drugs Electrophysiologic testing Pacing Both pacing and drugs

1360 8290 25,190 26,730 34,930

61.4 64.1 85.2 67.8 70.5

35.8 37.3 53.8 40.6 42.7

a

Compared with prior nondominated strategy. Final column is calculated by dividing column 5 by column 6 and multiplying by 12. (From Beck et al. (18) with permission.) b

on left ventricular function, they performed separate analyses for those with preserved ejection fractions (≥40%) vs those with depressed ejection fractions (<40%) from coronary artery disease (CAD) (see Table 1). For patients with preserved left ventricular function, the combination of pacing and drugs provided the greatest survival (138.3 months) at a cost of $55,760. However, pacing alone offered nearly the same survival (137.1 months) at substantially less cost ($41,710), and EPS provided only slightly less survival (134.4 months), but at a much lower cost ($27,450). Empiric therapy with drugs or observation alone provided much less survival. When expected survival was adjusted for quality-of-life concerns, pacing plus empiric drug use provided less quality-adjusted life expectancy (QALE) than pacing alone, and the use of drugs alone provided less than mere observation. To compare the relative costs and effectiveness of each strategy, the marginal costeffectiveness (MCE) was calculated, a measure of how much additional survival could be bought for each additional dollar expended. Because drug therapy alone and pacing plus drugs provided less QALE than continued observation, these strategies were “dominated” by others and fell out of consideration. EPS had an MCE of $21,200 per additional quality-adjusted life year (QALY) whereas that for empiric pacing was $68,400. The authors concluded that EPS seemed a prudent approach, one with an MCE similar to other accepted forms of therapy, such as coronary artery bypass grafting surgery for patients with moderate to severe angina ($20,000 per QALE) and antihypertensive drug therapy ($15,000 per QALE). For patients with poor left ventricular function, observation, empiric drug therapy, and EPS dominated the other strategies. This made sense because in this patient popu-

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lation, the likelihood that ventricular tachyarrhythmias was responsible for syncope was thought to be much higher than in patients with preserved function (45% vs 10%). If empiric drug therapy were truly an option, the MCE of EPS was a very reasonable $12,300 per additional year of QALE. Even if empiric drug therapy were not considered a reasonable option in this patient population, the MCE of testing in comparison to observation only increased to a very reasonable $15,900 per QALY (marginal cost $23,830 for 18 additional months of quality-adjusted life). A major advantage of modeling complex decision making is that it permits testing of baseline assumptions. Because the prior probability of whether the syncope was because of bradyarrhythmias or ventricular tachyarrhythmias was thought to be the “softest” data in the analysis and the one most likely to drive the decision between empiric pacing vs EPS, the authors performed a sensitivity analysis on this variable (see Fig. 1). When the probability of bradyarrhythmia was less than 38% (see Fig. 1A), EPS provided the best QALE, whereas above it, empiric pacing provided the best QALE. Below a bradyarrhythmia probability of 72%, EPS was the most cost-effective strategy (see Fig. 1B), whereas above it, pacing became more cost-effective. In fact, below 38% EPS provided both the longest QALE and was the most cost-effective. Between 38 and 72%, although empiric pacing was associated with the best QALE, the MCE of EPS was superior. Above 72%, empiric pacing was superior both in terms of longevity and MCE. The authors also examined the impact of decreasing the cost of implanting a pacemaker, an analysis with relevance to current practice. Their baseline analysis was performed using cost estimates obtained from one academic medical center in the mid-1980s, at which time a great deal of the cost of pacemakers was attributable to lengths of stay: 8 days for pacemaker implantation and 5 days for generator changes. Since then, lengths of stay have decreased dramatically. This would decrease the cost of empiric pacemaker implantation and EPS, though to a greater extent for the former. They found that because it avoided placing pacemakers in patients with nonarrhythymic syncope, EPS was always the more cost-effective strategy. However, in patients with preserved ejection fractions, the empiric strategy was associated with improved survival, though at a cost. With decreasing costs of pacemaker implantation and battery changes, the MCE of empiric pacemaker implantation could be improved, such that a 20% decrease in cost had a MCE of $17,000 per additional QALY. Using a decision-analytic format, Eagle et al. (19) performed an analysis to determine the differential costs of single- vs dual-chamber pacing. Their goal was to examine how the choice of device, the need for follow-up, and the need for generator replacement affected the cost differential at 12 years. Based on the patient populations followed in earlier studies of single-chamber ventricular pacing, many of whom were elderly and had heart disease, they estimated a 1-year mortality of 15% and an annual mortality rate thereafter of 9%. At 12 years, they estimated that dual-chamber pacing cost $5167 more than single-chamber pacing. There was a 48.5% differential in implantation costs associated with the increased cost of dual-chamber devices, a 16.9% difference in follow-up costs associated with increased frequency of monitoring of dual-chamber devices, and a 42.1% difference in the cost of generator replacement, again associated with device costs. This was not a cost-effectiveness analysis (CEA) that would account for any differences in survival and/or quality of life that might be associated with mode of pacing for SSS.

Chapter 18 / Cost of Conduction Disease and Arrhythmias Fig. 1. One-way sensitivity analyses in the patient with normal left ventricular function. The horizontal axes represent the probability of bradyarrhythmic etiology of syncope. The vertical lines illustrate the threshold probabilities. The arrows represent baseline conditions. (A) Vertical axis represents the qualityadjusted life expectancy. (B) Vertical axis represents marginal (Marg.) cost-effectiveness of different strategies. Lines represent comparisons between invasive strategies and observation. QALE, quality-adjusted life expectancy; Obs, observation; EPS, electrophysiologic study: (From ref. 18 with permission.)

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Though the data from this study was generated from experience in the early 1980s, it has applicability today. The cost of implantation plus device cost for a single-chamber device was estimated to be 26.5% less than for a dual-chamber device, much in keeping with current cost structures (B. Wolf, personal communication). The average cumulative cost over 12 years for generator replacement(s) was 52% greater for dual-chamber pacemakers, largely reflecting their calculation that patients with dual-chamber pacing were 30% more likely to require at least 1 generator replacement than patients with singlechamber pacing, an estimate in keeping with practice today (20). If survival for this patient cohort were to improve, the cost differential would only increase as the need for additional generator replacements increased. Any narrowing of the gap in the need for generator replacement would decrease the cost differential between dual- vs single-chamber pacing. This study makes explicit the additional cost associated with the decision to use dual-chamber pacing. At least in the case of AV block, where there is scant evidence that, for the average patient, dual-chamber pacing improves quality of life or survival, this may be an issue of interest to policymakers concerned with a shrinking health care dollar. Sutton et al. (21) also published a cost analysis of single- vs dual-chamber pacing, reporting that dual-chamber pacing was more cost-effective over a 10-year period. However, they based their analysis on outcomes data derived almost exclusively from retrospective studies published prior to 1995, which, in aggregate, suggested dualchamber pacing was associated with less atrial fibrillation, less stroke, less heart failure, and improved survival. Data from more recent prospective randomized trials (15,16) have, by and large, failed to support these assumptions, undermining their conclusions. Much of the literature on mode selection is retrospective and suffers from the bias introduced when selecting patients for a given device. High-quality data, including information on CE, should be forthcoming as two large trials draw to a close. The Mode Selection Trial (MOST) (22) is a single-blind study of VVIR vs DDDR pacing in patients with SSS. During 1994–1999, 2010 patients were enrolled, to be followed for an average of 3 years, with a projected end date in 2001. The study’s primary endpoint is the combination of nonfatal stroke and total mortality, with a variety of important secondary endpoints, including health-related quality of life and CE. The United Kingdom Pacing and Cardiovascular Events (UKPACE) trial (23) plans to enroll more than 2000 patients at least 70 years of age with high-grade AV block and randomize them to VVI (25%), VVIR (25%), or DDD (50%) pacing. All patients will be followed for more than 3 years, and the primary endpoint is all-cause mortality. As with MOST, quality of life and cost utility will be among the secondary endpoints. As important as these CEA may be, the knowledge gained from them will have limited usefulness until the central issue addressed by Beck et al. (18) is resolved, that of appropriateness. When should a pacemaker be placed? Figure 2 from the Dartmouth Atlas of Cardiovascular Health Care (24) shows the age-, sex-, and race-adjusted utilization rates of pacemakers across 306 hospital referral regions in the United States in 1996 for the Medicare population. Although the average national rate was 5.1 per 1000 Medicare enrollees, rates varied from 1.5 to more than 10 per 1000, a fivefold difference. Why the variation? Is it a result of patient or provider characteristics? Patient characteristics are unlikely to have a major influence on these rates, as they are age-, sex-, and race-adjusted. Furthermore, among adjacent hospital referral regions in which patient characteristics are likely to be comparable, low, average, and high pacemaker utilization rates are noted. If not patient characteristics, then provider characteristics

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309

Fig. 2. Permanent pacemaker implantation in the 1996 Medicare population. Ratio of rates in 306 hospital referral regions (HRRs) when compared to the US average of 5.1 per 1000 enrollees. Also shown are select HRRs.

must be driving the variation in rates. As with CAD (24), the supply of physicians, intensity of diagnostic testing, and availability of laboratories and operating rooms likely has some relationship to rates. However, extrapolating from studies done on other medical conditions (25), it is very likely that a good deal of the variation in rates stems from local differences when physicians believe placement of a pacemaker is appropriate. This is a reflection of the difficulty in accumulating good data on outcomes to support evidenced-based practice. The American College of Cardiology/American Heart Association guidelines for pacemaker implantation (26) rely heavily on expert opinion because data to support their recommendations are largely lacking.

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Cardiovascular Health Care Economics Table 2 Decision-Analysis Modeling Studies of Implantable Cardioverter Defibrillator (ICD) CE

Model (reference)

Population

Kupperman Survivors of cardiac arrest et al. (33) with inducible VT or VF O’Brien History of VT or VF, et al. (34) or cardiac arrest survivor Larsen et al. Recurrent sustained VT or (35) VF refractory to drug therapy Kupersmith History of VT or VF et al. (36) Owens et al. Survivors of cardiac arrest (37) at high risk for sudden cardiac death

Comparison

Cost per life Time Gain in year gained horizon life-years (1999) ($)*

ICD vs usual drug therapy

Lifetime

1.90

17,740

ICD vs amiodarone

20 years

1.70

20,326

ICD vs amiodarone

Lifetime

2.22

25,400

ICD vs EP-guided drug therapy ICD vs amiodarone

6 years

1.72

25,718

Lifetime

0.69

39,245

All prices converted to 1999 US dollars. * Inflated using the health care component of the consumer price index for Canada. EP, electrophysiologically; VT, ventricular tachycardia; VF, ventricular fibrillation. From O’Brien et al. (32) with permission.

TREATMENT OF VENTRICULAR ARRHYTHMIAS More than 300,000 sudden cardiac deaths occur every year in the United States, and most are thought to be secondary to ventricular tachyarrhythmias. Although drug therapy may be of some benefit, it is now clear that antiarrhythmics are not without risk (27), and their efficacy is limited. ICDs may not prevent lethal arrhythmias, but they do effectively terminate them, and there is now strong evidence that in comparison to medical therapy, ICDs reduce mortality (28–31). However, with costs exceeding $20,000 for the device alone, there is ongoing interest in understanding the CE of this therapy. ICDs represent new technology. The first device was implanted in 1980, and it was only in 1985 that they became Food and Drug Administration (FDA) approved. While randomized trials of medical vs device therapy were organized and initiated in the early to mid-1990s, a number of investigators used decision analysis to explore the trade-offs between medical vs device therapy in the treatment of survivors of sudden death or patients at high risk for sudden death. Table 2, reproduced from O’Brien et al. (32), summarizes these studies (33–36). Owens et al. (37) were able to incorporate data from randomized trials in their Markov model, whereas the other studies used observational data. This resulted in a noticeable difference in the estimate of expected gain in life expectancy for patients receiving an ICD. As a consequence, whereas the other studies calculated costs from $17,000–26,000 per QALY, Owens et al. calculated a value of $39,200 per QALY. Regardless, by contemporary benchmarks of CE this would be a reasonable value for the money (38,39). Studies based on observational data have now been replaced by information from randomized trials. There is little doubt that ICDs provide a significant survival benefit when compared to other treatments, but at what cost? The Multicenter Automatic Defibrillator Implantation Trial (MADIT) (40) compared ICD to conventional therapy in 196 patients with asymptomatic, but inducible,

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311

ventricular tachycardia, a prior myocardial infarction (MI) and ejection fraction less than 35%. Of these, 181 patients from the United States were used in the analysis of CE. All patients were followed out to 4 years. Average life expectancy in the ICD group was 3.66 years in comparison to 2.80 years for conventional therapy (p < 0.009). As expected, initial costs (in 1995 dollars) for the ICD group ($44,600) was higher than for conventional therapy ($18,900), but average monthly costs in survivors over the subsequent months was higher in the conventional group ($1915 vs $1384). The investigators reported the incremental cost-effectiveness ratio (CER) for patients randomized to ICDs, rather than conventional therapy, as $27,000, representing the extra cost incurred to save 1 year of life during those 4 years of follow-up. When MADIT started, ICDs were implanted via a thoracotomy, and 54% of the study cohort had such transthoracic implants. Although transvenous devices were more expensive, avoiding an operation made the costs of the initial hospitalization less expensive by $8800. Assuming all implants were transvenous decreased the incremental CER to $22,800 per year of life saved. Extrapolating survival out to 8 years further decreased this ratio to $16,900 per year of life saved. The Canadian Implantable Defibrillator Study (CIDS) (41) randomized 659 survivors of resusciated ventricular tachycardia or fibrillation or unmonitored syncope to ICD or amiodarone. Over an average follow-up of 6.3 years, annual mortality decreased from 10.2% with conventional therapy to 8.3% with an ICD (p = 0.142) and life expectancy increased from 4.65 years in the amiodarone group to 4.91 years in the ICD group. Over 6.3 years, the average cost (in 1999 dollars) for the amiodarone group was $25,090 when compared to $57,015 for the ICD group. Therefore, the incremental CER in this study was $138,803 per year of life gained. Extrapolating costs and survival to 12 years, CE ranged from approximately $65,000 to $97,500 per life year gained, depending on the assumptions about survival from 6.3 to 12 years. Other studies had suggested that sicker patients with lower ejection fractions benefited the most from an ICD (42–44). In CIDS, the CER for an ICD in patients with ejection fractions less than or equal to 35% dropped to $70,515. In those with ejection fractions greater than 35%, ICD offered no mortality benefit, and amiodarone was less expensive, making drug therapy the more cost-effective choice. The Antiarrhythmics Versus Implantable Defibrillators (AVID) trial (45) randomized 1008 survivors of ventricular arrhythmias to an ICD or antiarrhythmic therapy with amiodarone or sotalol. At 3 years of follow-up, ICD therapy was associated with an average increase in life expectancy of 0.21 years. Total per-patient charges at 3 years were $85,522 for the ICD patients vs $71,421 for medically treated patients, for a CER of $66,677. This ratio was lower for patients with ventricular fibrillation, ejection fractions of 35% or less, and 70 years of age or older (range $55,163–$65,041) and higher for those with ventricular tachycardia, less than 70 years of age, and coronary artery disease (range $72,917–$82,889). With ICD therapy providing essentially no survival benefit in subjects with ejection fractions of more than 35% (0.02) years, the CER for this patient population was extremely high ($536,106). The CER was sensitive to the length of the initial hospitalization. Although the average length of stay for the study was 13–14 days, it had decreased to 10 days by the end of the study. The authors calculated that with a 10-day length stay the overall CER would decrease to $47,834. What conclusions can be drawn from this data? The MADIT study of primary prevention had a CER for ICD that is comparable to many other accepted treatments (32,38,39), suggesting this technology should be adopted. The data from the secondary

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prevention trials is less persuasive. The CER from CIDS is high and beyond current standards for cost-attractive therapies, whereas the CER from AVID makes ICD therapy, at best, moderately cost-effective, although when used in some patient populations, such as those with lower ejection fractions, the CER becomes more attractive. The fact that survival was three times greater in MADIT than in the secondary prevention trials explains why ICDs appear reasonably cost-effective in primary prevention. These trials are not the last word. Results may change with length of follow-up because the initial expense of an ICD is dispersed over a longer period of time, during which survival benefit may accrue. Technology is changing. ICDs may become less expensive and may have longer battery lives, decreasing their costs. None of these studies account for the differences in quality of life. If quality of life is better with an ICD than with drug therapy (46), the cost per QALY gained will decrease for ICD. Clearly, more work is needed.

RADIOFREQUENCY ABLATION OF SUPRAVENTRICULAR TACHYCARDIAS Supraventricular tachycardias (SVTs) are a common problem. In the early 1990s, there were an estimated 570,000 people in the United States with paroxysmal SVTs (from atrioventricular node re-entry, pre-excitation, or a concealed bypass tract), with 89,000 new cases per year (47). Symptomatic patients may have multiple episodes each year that diminish their quality of life (48) and incur medical costs. Treatment of these symptomatic patients has evolved from medical management to surgery and, more recently, to radiofrequency ablation (RFA). In RFA, a catheter is used to interrupt conduction through part of a reentrant pathway by destroying the tissue in that area with heat. In experienced hands, the technique is very effective (49–52) with more than a 90% success rate. Compared to medical management, a successful RFA results in improved functional health (53,54), although not without some upfront risks (51,55,56). Hogenhuis et al. (57) used Markov modeling to examine the CE of RFA in patients with Wolff-Parkinson-White Syndrome (WPW). They considered four types of WPW patients: (1) cardiac arrest survivors, (2) patients with a history of paroxysmal SVT or atrial fibrillation who were hemodynamically compromised with their arrhythmia, (3) patients with a history of paroxysmal SVT or atrial fibrillation who were not hemodynamically compromised, but were symptomatic with their arrhythmia, and (4) asymptomatic patients with a history of paroxysmal SVT or atrial fibrillation. Five management strategies were considered: (1) observation alone, (2) observation until cardiac arrest occurs, then initiating therapy, (3) noninvasively guided drug therapy, (4) RFA, with a second attempt if the first fails (to look for a second pathway), and (5) surgical ablation. If the RFA failed, patients could be observed, undergo surgical ablation, or be treated with drugs. The results of their baseline analysis are summarized in Table 3, which shows the MCER per QALY. For survivors of a cardiac arrest, RFA dominated all other strategies (being less expensive and offering greater life expectancy than the alternatives). For patients with paroxysmal SVT complicated by hemodynamic compromise, RFA followed by surgery had a favorable MCE ratio of $770–2800 for those younger than 60 years of age. For those 60 years old or older, this strategy was dominated by RFA with no treatment for failure because of their higher risk of surgical complications. However, avoiding surgery, but using drug therapy for this group had a reasonable MCE of $8400 per

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313

Table 3 Baseline Discounted MCE Ratios of Nondominated Strategies for WPW Patient Subsets Defined by Age and History of Symptoms Age, y Presentation Cardiac arrest PSVT/AF with HC

PSVT/AF without HC

Asymptomatic (delta wave only)

Strategy RFA, surgery for failure RFA, no preventive Tx for failure RFA, surgery for failure RFA, drug Tx for failure Observation RFA, no Tx for failure RFA, surgery for failure RFA, drug Tx for failure Observation RFA, no Tx for failure RFA, drug Tx for failure Drug therapy

20

40

60

770 60,000

2800 36,000

D 8400

6600 ED 55,000

9900 ED 62,500

19,000 D 74,000

33,000 174,000 D

52,000 230,000 D

D 540,000 5,500,000

Ratios are based on dollars per QALY gained. Discount rate is 5%. WPW, Wolff-Parkinson-White syndrome; RFA, radiofrequency ablation; PSVT/AF, paroxysmal supraventricular tachycardia or atrial fibrillation; HC, hemodynamic compromise; Tx, treatment; D, dominated (i.e., offering less life expectancy at a greater cost); ED, dominated by extended dominance (i.e., offering more life expectancy but at a higher MCE ratio than the next nondominated strategy). From Hogenhuis et al. (57).

QALY gained. This was not the case for younger patients, whose MCE ratio for such drug therapy was as much as four to eight times as high. For patients who were symptomatic from their paroxysmal SVT but had no hemodynamic compromise, RFA with no attempted treatment for failure had favorable MCE ratios, ranging from $6600–19,000. Given the expense and the risk, it made no sense to consider surgical ablation in such mildly symptomatic patients. In the truly asymptomatic patient with WPW, RFA had a moderately high MCE ratio ($33,000–52,000), but was dominated by observation in those 60 or older. Any use of drugs in these asymptomatic WPW patients had an unreasonable MCE ratio. The findings of this model were sensitive to the cost of RFA and the annual incidence of paroxysmal SVT in the mildly symptomatic. Using decision analysis, Cheng et al. (58) examined RFA as a treatment for symptomatic patients with paroxysmal supraventricular tachycardia (PSVT), excluding those with WPW, comparing it to chronic drug therapy and to episodic therapy taken at the time of symptoms. Some of their data were derived from a cohort of Kaiser Permanente patients with PSVT referred for RFA. These symptomatic patients on chronic medical therapy had an average of 4.6 unscheduled emergency room or physician visits per year. Assuming drug therapy reduced symptoms to 40% of their frequency before treatment, the authors estimated untreated patients would have 11.5 visits per year. Using data from high-volume centers, RFA was thought to be successful in 97% of atrioventricular reciprocating tachycardia (AVRTs) (65% of patients) and 93% of concealed pathways (30% of patients, 5% of patients would have arrhythmias untreatable by RFA), with recurrence rates of 5 and 8%, respectively. In their baseline analysis, Cheng et al. (58) assumed drug therapy consisted of relatively inexpensive generic metoprolol, biasing the analysis against RFA. Hospitalization costs were estimated from published data and from the

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Fig. 3. The analysis includes all costs of medical therapy, including drug costs, physician visits, and treatment in emergency departments. Circles represent a monthly drug cost of $10, squares represent a monthly drug cost of $50, and triangles represents a monthly drug cost of $00. The vertical axis represents the number of arrhythmic events per year the patient experiences while receiving long-term drug therapy. The horizontal axis shows the number of years after RFA until the cumulative cost of that strategy would be less than the cost of the long-term drug strategy. Costs are recouped more quickly for patients who have more frequent arrhythmic events and for patients whose drug therapy is more expensive. The time to recoup costs for the base-case analysis is shown by the arrow. (From ref. 58 the permission.)

1997–1998 experience of a 60-patient cohort treated at a major academic hospital. Professional fees were estimated using the 1998 National Physician Fee Schedule Relative Value File, and wholesale drug costs were based on the Red Book. All costs were adjusted to 1999 dollars. One difference between this decision analysis and others that have been mentioned was that data on utilities, how patients valued their state of health before and after RFA when compared to ideal health, was available and incorporated (48). This is important because RFA for PSVT does not prolong life, but may improve its quality. In fact, in the published study, the median utility for health status before RFA was 0.8333, increasing to 0.983 after successful RFA. In the baseline case, RFA was both less expensive (lifetime costs of $61,880 vs $89,820) and more effective (QALE 21.66 years vs 18.46 years) than long-term medical therapy. As might be expected, the break-even point between the costs of RFA vs chronic medical therapy was inversely proportional to the frequency of symptomatic episodes and the cost of drugs (Fig. 3). The less frequent the symptoms, the less a patient had to gain in terms of quality of life from RFA. Even if the quality of life increased by only 1% with RFA in comparison to medical management, as long as the annual cost of chronic drug therapy (including office and emergency room visits) was at least $500 (compared to $1900 in the baseline analysis), RFA dominated as the strat-

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egy of choice. When the analysis was further biased against RFA by increasing its complication rate threefold, and decreasing its efficacy to 0.85 for AVRT and 0.75 for concealed pathways, RFA was again the dominant strategy. Based on this analysis and the analysis by Hogenhuis et al., it appears that RFA is a cost-effective approach to the management of PSVT. RFA seems the preferred strategy across a wide range of assumptions and patient characteristics, including utilities, efficacy, and cost.

ATRIAL FIBRILLATION Comprehensive management of atrial fibrillation requires a multifaceted approach directed at controlling symptoms, protecting the patient from thromboembolism, and, if appropriate, considering options to restore sinus rhythm (59,60). Numerous randomized trials have demonstrated the efficacy of antithrombotic therapy in reducing the risk of embolic events. Likewise, therapeutic strategies using pharmacological or electrical cardioversion and antiarrhythmic prophylaxis exist for favorably modifying symptoms by restoring and maintaining sinus rhythm. The risks and benefits of various treatment options are highly dependent on patient-specific features. This section reviews the major findings and limitations of CE studies published over the past decade that evaluated various aspects of treatment strategies for atrial fibrillation (61). We focus on the economic attractiveness of antithrombotic prophylaxis, strategies using antiarrhythmic agents, and the role of transesophageal echocardiographic screening prior to cardioversion.

ANTITHROMBOTIC PROPHYLAXIS Multiple randomized trials of antithrombotic therapy in atrial fibrillation have confirmed the dramatic benefit of warfarin, and lesser efficacy of aspirin, in reducing the risk of ischemic stroke. A recent meta-analysis (62) of these trials demonstrated that adjusted-dose warfarin reduced ischemic stroke by 62% (95% confidence interval [CI] 48–72%) when compared to placebo. In contrast, aspirin reduced stroke by 22% (95% CI 2–38%). These findings serve as the foundation for present practice recommendations (59,63) on antithrombotic therapy. The risk of thromboembolism is dependent on several patient-specific features and the duration of the arrhythmia. The frequency is low in younger individuals and in those without evidence of cardiovascular disease (i.e., no history of hypertension, left ventricular dysfunction, or CAD). Similarly, the risk is low if the interval from onset of atrial fibrillation to the restoration of sinus rhythm is less than 48 hours. Conversely, the risk is increased in those with persistent arrhythmia, a history of remote stroke, or with significant organic heart disease. Several scoring systems (64,65) have been proposed to predict the annual thromboembolic risk without treatment. Gage and associates (66) used a Markov model to analyze three treatment strategies—warfarin, aspirin, or no therapy—over a 10-year period in a hypothetical cohort of 65-year-old patients with atrial fibrillation at varying risk for stroke. This model accounted for the severity of stroke, risk of bleeding with warfarin or aspirin, and the annual direct costs of treatment. Health states were quality-adjusted, and the hypothetical cohort was assumed to remain in atrial fibrillation. They concluded that treatment with warfarin is cost-effective in patients who are at moderate (3.6% per year, MCE ratio $8500 per QALY) to high (5.3% per year) risk of stroke. Indeed, treatment with

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warfarin in the high-risk group was cost saving; the warfarin strategy dominated (more effective and less expensive) the aspirin and no treatment options. In contrast, the CER for warfarin when compared to aspirin ($394,000 per QALY) was not economically attractive in patients at low risk of stroke. In a similar model, Eckman and associates (67), as part of the Fifth American College of Chest Physicians Consensus Conference on Antithrombotic Therapy, modeled the CE of treating a prototypical 69-year-old patient with an estimated annual stroke risk of 4.5% without therapy; warfarin efficacy was 68%. Warfarin treatment was clearly cost-effective with an attractive MCE ratio of $4500 per QALY. A major limitation of these CE investigations is the assumption that the benefit of warfarin anticoagulation and jeopardy for bleeding events in clinical practice is comparable to that found in the randomized trials. The randomized trials had significant exclusion criteria (10–40% of screened patients were excluded because of contraindications to anticoagulation), and the protocols demanded close prothrombin time monitoring and medical supervision. Optimal warfarin efficacy depends on maintaining therapeutic international normalized ratio values (68–70). Accordingly, either suboptimal or excessive anticoagulation would increase the incremental CER at any age or baseline stroke risk.

CARDIOVERSION AND ANTIARRHYTHMIC THERAPY For patients without refractory symptoms or hemodynamic compromise from atrial fibrillation, the decision to offer pharmacologic or direct current cardioversion vs chronic rate control assumes that the benefit of sinus rhythm justifies the associated costs, inconvenience, potential for drug toxicity, and procedural risks. Several antiarrhythmic agents are effective in helping restore and/or maintain sinus rhythm: ibutilide (71–73), quinidine (74), procainamide (75), disopyramide (76), propafenone (77), flecainide (78), dofetilide (79,80), or amiodarone (81–83). The choice of antiarrhythmic agent depends on physician experience (84) and the patient’s cardiovascular profile (85). Patients with paroxysmal atrial fibrillation, if not in need of urgent direct current cardioversion, can be offered several therapeutic alternatives: early electrical cardioversion using brief general anesthesia, pharmacological cardioversion with one of several drugs, or “watchful waiting” (with or without telemetry), given the possibility of spontaneous cardioversion (86,87). The various approaches for acute management have been recently reviewed (88). To our knowledge, there is no primary CE data comparing the multiple options available to patients with new-onset atrial fibrillation. Murdock and colleagues (89) performed a prospective randomized trial of two strategies for persistent atrial fibrillation or flutter (>72 hours, but <90 days): (1) intravenous ibutilide, followed by direct current cardioversion if sinus rhythm was not restored or (2) direct current cardioversion without antecedent ibutilide. Exclusion criteria included CHF, unstable angina, hemodynamic instability, or prior use of class I or III antiarrhythmic agents. The primary endpoint was the successful termination of the arrhythmia. In this small study, only 24% of 17 patients cardioverted with the ibutilide infusion. The length of hospital monitoring and pharmaceutical costs made the ibutilide strategy less economically attractive. However, other investigators (73,90) have reported that ibutilide facilitates successful electrical cardioversion. Importantly, it is possible that ibutilide would be more cost-effective in atrial fibrillation or flutter of shorter duration given its greater efficacy in this setting (91).

317 Fig. 4. Simplified schematic of Markov decision-analytic model. The square at left represents the decision to follow one of the treatment strategies. The M indicates the Markov process that leads to one of several health states. *Health states in the figure are simplified and each represents multiple states in the actual model (e.g., “disabled in NSR” includes patients in sinus rhythm with disability owing to stroke, intracerebral hemorrhage, or chronic pulmonary toxicity from amiodarone). Circles represent chance events, which may occur during each cycle, resulting in continued good health, one of several temporary or permanent disabling events, or death. NSR, normal sinus rhythm; AF, atrial fibrillation; CV, cardioversion; Mod-Sev, moderate to severe; CNS, central nervous system (intracranial); nonCNS, extracranial. (Reproduced with permission from ref. 92.)

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Patients with persistent (days to months) atrial fibrillation represent a more challenging subgroup because of their atrial remodeling, lower rate of successful cardioversion, need for therapeutic anticoagulation before and after cardioversion, and frequent arrhythmia relapse. Catherwood and colleagues (92) reported a Markov model (see Fig. 4) that analyzes the CE of 8 treatment strategies in a hypothetical cohort of 70-yearold patients with persistent atrial fibrillation and limited symptoms (utility for atrial fibrillation on aspirin = 0.998, on warfarin = 0.987) at varying risk for stroke. The approaches included rate control and either warfarin or aspirin, cardioversion followed by either warfarin or aspirin on relapse, or cardioversion with either immediate or delayed (until arrhythmia relapse) use of amiodarone or quinidine therapy. This model assumed patients holding sinus rhythm for at least 1 month following cardioversion, with or without antiarrhythmic prophylaxis, would take aspirin until relapse of atrial fibrillation— warfarin was restarted on relapse. They found that initial direct current cardioversion without antiarrhythmic drugs dominated those strategies that started amiodarone or quinidine immediately. The use of long-term amiodarone treatment to maintain sinus rhythm was cost-effective for patients at moderate or high risk of stroke (see Table 4). The sensitivity analysis demonstrated that the MCE of this strategy in comparison to long-term warfarin prophylaxis and rate control was substantially influenced by the baseline risk of ischemic stroke without therapy and other variables (see Fig. 5). Eckman and associates (93) performed a CEA of 19 strategies for managing atrial fibrillation. The approaches for their hypothetical 65-year-old man included various combinations of cardioversion, antiarrhythmic therapy (amiodarone, quinidine, sotalol), and antithrombotic agents (warfarin or aspirin). The authors assumed an annual stroke risk without therapy of 4.5% in atrial fibrillation, and 1.7% in sinus rhythm. Following successful cardioversion, patients were maintained on the antiarrhythmic agent and either warfarin or aspirin. The study found that cardioversion, followed by the use of amiodarone and warfarin together, was the most effective strategy, gaining 2.3 QALYs when compared to no therapy. However, this strategy was expensive in comparison with amiodarone and aspirin while the patient remained in sinus rhythm (see Table 4). The Markov models by Eckman et al. (93) and Catherwood et al. (92) provide complementary information. Both analyses concluded that amiodarone is a cost- effective drug to maintain sinus rhythm, particularly in patients with atrial fibrillation at a moderate-to-high risk of stroke. Although amiodarone has numerous potential side effects (82,94), including serious pulmonary toxicity (95), its low proarrhythmia risk and superior antiarrhythmic efficacy yielded more benefit and less cost than strategies with quinidine or sotalol. Furthermore, both studies assumed that a major benefit from the maintenance of sinus rhythm would be a reduction in the risk of ischemic stroke. This advantage, although intuitively reasonable (96), has not been proven prospectively. As with the annual stroke risk on no therapy, the risk of stroke in sinus rhythm may vary substantially with the severity of the underlying cardiovascular disease (97). Indeed, the absolute reduction in ischemic stroke from maintaining sinus rhythm might actually be greatest in those at highest baseline risk. These and other issues are being investigated by the Atrial Fibrillation Follow-up Investigation of Rhythm Management trial (98). A controversial issue in the use of antiarrhythmic therapy is whether hospitalization is required to provide telemetry-guided initiation of antiarrhythmic drugs. Since the publication of the Cardiac Arrhythmia Suppression Trial (27), physicians are justifiably

Table 4 CE of Strategies in Atrial Fibrillation: Restoration of Sinus Rhythm Source Eckman et al. (93)

319

Catherwood

et al. (92)

Baseline Stroke age risk %/yra 65

70

4.5

5.3

3.6

1.6

a

Preferred strategy CV and aspirin; warfarin and rate control on relapse CV, amiodarone and aspirin; warfarin and rate control on relapse CV, amiodarone and warfarin CV and aspirin; repeat CV, amiodarone and aspirin on relapse; warfarin on final relapse CV and aspirin; repeat CV, amiodarone and aspirin on relapse; warfarin on final relapse CV and aspirin; aspirin on relapse

Comparison strategy CV with daily aspirin; aspirin on relapse

11,500

CV and aspirin; warfarin on relapse

35,900

CV, amiodarone and aspirin; warfarin on relapse CV and aspirin; warfarin on relapse

98,300

CV and aspirin; warfarin on relapse

19,400

All other strategies examined (including those with warfarin or antiarrhythmic drugs)

CV and aspirin; aspirin with rate control on relapse dominantb

Annual rate of stroke with no therapy. More effective and less expensive than comparison strategies. CV, cardioversion. See text for details on treatment strategies. Modified and reprinted with permission from Teng et al. (61). b

MCE ratio ($/QALY)

9600

320 Fig. 5. Tornado diagram of variables with significant influence on CE. Variables identified in the sensitivity analysis which substantially impact on the incremental CE when evaluated across the range of estimates for each variable. The analysis is for the cardioversion alone, repeat cardioversion, and amiodarone on relapse strategy in comparison to cardioversion with warfarin on relapse. The analysis was performed at base-case estimates for moderate (open bars, 3.6% per year) and high (bars with diagonal lines, 5.3% per year) risk of ischemic stroke with no therapy. Dominated implies that the incremental CER reaches the threshold value, beyond which the comparison strategy is less costly and more effective. QALY, quality-adjusted life-year. (Reproduced with permission from ref. 92.)

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concerned that antiarrhythmic therapy for atrial fibrillation may provoke other lifethreatening arrhythmias (99–101). In order to limit these events, some authorities advise routine telemetry-guided initiation of class I or class III agents for most patients with atrial fibrillation. However, this costly and inconvenient surveillance might not be necessary in the absence of significant organic heart disease (102,103). Simons and colleagues (104) developed a decision-analytic model comparing telemetry-guided ambulatory initiation of antiarrhythmic agents for supraventricular arrhythmias. With data from 57 high-quality studies involving more than 2800 patients and commonly used antiarrhythmic agents, they calculated a 0.63% (range 0.2–1.2%) cardiac arrest or potentially lethal (e.g., sustained or polymorphic ventricular tachycardia) event rate during 72 hours of monitoring. The authors assumed that these events would be reversible on telemetry, but uniformly lethal in an out-patient setting. The cost per year of life saved with in-patient antiarrhythmic initiation for a hypothetical 60-year-old patient was $19,800. They found attractive incremental CER ($18,000–70,000 per year of life saved) for various baseline ages, with and without underlying structural heart disease. Fortunately, more recent studies suggest a lower frequency of proarrhythmia with more judicious antiarrhythmic dosing, attention to electrolyte imbalance, and better profiling of the extent of organic heart disease (103,105).

PRECARDIOVERSION TRANSESOPHAGEAL ECHOCARDIOGRAPHY Careful prophylactic anticoagulation several weeks before and after cardioversion in patients with persistent atrial fibrillation substantially lowers thromboembolic risk (70,106). However, this approach requires a delay in restoring sinus rhythm and a longer period of anticoagulation with its associated inconvenience and bleeding risk. Therefore, alternative strategies using transesophageal echocardiography (TEE) to expedite cardioversion have been proposed (107). TEE offers superior visualization of the atrial chambers, particularly the left atrial appendage—a common location for clot formation (108–110). One management approach for hospitalized patients involves performing a TEE and, if negative for clot, proceeding to early cardioversion. However, it is recognized that atrial mechanical function may not recover immediately following cardioversion; thus, systemic anticoagulation must be continued for at least 1 month following restoration of sinus rhythm. Seto and associates (111) used a decision-analytic model to calculate the CE of 3 strategies for a hypothetical cohort of 70-year-old patients hospitalized with persistent atrial fibrillation: (1) conventional therapy that includes transthoracic echocardiography (TTE) and 1 month of warfarin before cardioversion; (2) TTE and, if negative, TEE screening; and (3) initial TEE. In strategies 2 and 3, cardioversion was promptly performed if the TEE failed to demonstrate atrial clot. Complications explicitly defined in the model included hemorrhage from warfarin, embolic stroke and, for those undergoing TEE, esophageal perforation. The authors found the strategy of initial TEE to be less expensive ($2800 vs $3100) and minimally more effective (8.49 QALY vs 8.48 QALY) than the conventional approach. The analysis by Seto and colleagues (111) supports the utility of TEE for selected patients being considered for early cardioversion. However, several important caveats deserve emphasis. First, the performance of TEE is a learned skill that requires highquality equipment and technical competence to safely obtain valid information. Second, the hypothetical cohorts consisted of patients hospitalized with persistent atrial

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fibrillation. It is unclear whether the advantage of initial TEE would apply to compensated ambulatory patients. Third, the authors assumed that TEE screening reduces the 1 month risk of stroke—relative risk 0.625 (0.5%/0.8%, TEE-guided/conventional therapy). If the relative risk was greater than 0.95, the strategy with initial TEE would no longer be favored. Recently published results from the Assessment of Cardioversion Using Transesophageal Echocardiography (ACUTE) multicenter trial (112) showed a combined stroke or transient ischemic attack (TIA) rate of 0.81% for the TEE strategy and 0.5% for conventional anticoagulation. However, there were more hemorrhagic events (5.5% vs 2.9%) with the conventional approach. Although cardiac mortality at 8 weeks was similar, there was a worrisome trend toward higher all-cause mortality (2.4% vs 1%, p = 0.06) with the TEE strategy. These data strongly suggest that TEE screening may offer benefit to some patients at higher bleeding risk, but not the overall superior effectiveness described by Seto et al.

SUMMARY OF ATRIAL FIBRILLATION Clinicians have an array of therapeutic options for the comprehensive management of patients with atrial fibrillation. Initial treatment decisions are directly related to patientspecific features: severity of symptoms, hemodynamic stability, extent of underlying cardiovascular disease, duration of the arrhythmia, and eligibility for various approaches. Treatment with warfarin offers dramatic reductions in morbidity and mortality from ischemic stroke. Furthermore, despite a risk for bleeding complications, antithrombotic therapy has been shown to be cost-effective across a spectrum of patient ages and baseline stroke risk. However, debate continues regarding the duration and type of antithrombotic prophylaxis for patients restored to and maintaining sinus rhythm. Decision-analytic models support the concept that restoring and maintaining sinus rhythm can provide an improved quality-adjusted survival. Low-dose amiodarone with daily aspirin appears cost-effective when compared to warfarin and rate control for patients at a moderate- to high-risk for stroke. Despite concerns of pulmonary and other toxicities, amiodarone provides superior efficacy to quinidine and sotalol in maintaining sinus rhythm, a finding supported by recent results from the Canadian Trial of Atrial Fibrillation (113). Telemetry-guided initiation of antiarrhythmic agents is cost-effective and indicated for most patients with atrial fibrillation and significant structural heart disease. However, patients with limited or no detectable structural heart disease might be offered ambulatory antiarrhythmic therapy if close monitoring can be assured (103). Expediting early cardioversion in hospitalized patients with atrial fibrillation through the broad application of TEE contrasts costs and procedural risks with delays in decision making and longer periods of anticoagulation. In light of current knowledge, TEE screening should be considered an alternative, but not necessarily superior, strategy when compared to prophylactic warfarin and delayed cardioversion in hemodynamically stable patients without heightened bleeding risk. A number of electrophysiologic procedures and devices in various stages of refinement may change the landscape for treating atrial fibrillation (114,115). These include atrioventricular node ablation or modification (116,117), RFA or nonsurgical MAZE procedures (118), and implantable atrial defibrillators (119). For patients who are committed to long-term anticoagulation with difficulty controlling ventricular rates, atrioventricular node ablation and permanent ventricular pacing improve physical performance and quality of life (117). Unfortunately, there is no CE data to guide the application of these evolving technologies in the overall management of atrial fibrillation.

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A number of other unanswered questions remain: Is low-molecular-weight heparin as effective as warfarin in the prevention of thromboemboli? Is there an antithrombotic agent with superior efficacy and less bleeding risk than warfarin? Should patients with lifestyle-limiting symptoms from persistent atrial fibrillation undergo serial drug trials to restore and maintain sinus rhythm or proceed sooner to atrioventricular node ablation and permanent pacing? Should dofetilide replace amiodarone as the antiarrhythmic of first choice in patients with significant left ventricular dysfunction? What is the optimal approach to the frequent problem of atrial fibrillation occurring after coronary bypass surgery? Is the CE of treatment different when directed by electrophysiologists, general cardiologists, internists, or primary care physicians? Research into these and other therapeutic dilemmas is essential given the increasing prevalence of atrial fibrillation in this century (120,121). For now, physicians managing patients with this common arrhythmia must integrate the spectrum of patient-specific features and preferences with an understanding of the benefits and risks of available treatment options.

CONCLUSION Concerns about costs will be a recurrent issue in health care for some time to come, and the practice of electrophysiology will be a prime target for scrutiny. With expensive devices and drugs, patients, payers, and policymakers will demand cost-effective care as electrophysiologists make choices about treatment. As has been reviewed in the chapter, some insights are available from CE studies, and the results from ongoing randomized trials will provide even more data on which to base decisions. However, with time, the technology will change, and new studies will be needed. It is important that investigators recognize that information on costs should be an integral part of all new studies. Without an explicit understanding of costs and efficacy, it will be difficult to identify the optimal role for new treatment modalities in the future.

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38. Laupacis A, Feeny D, Detsky AS, et al. How attractive does a new technology have to be to warrant adoption and utilization? Tentative guidelines for using clinical and economic evaluations. CMAJ 1992;146:473–481. 39. Chapman RH, Stone PW, Sandberg EA, et al. A comprehensive league table of cost-utility ratios and a sub-table of “panel-worthy” studies. Med Decis Making 2000;20:451–467. 40. Mushlin AI, Hall WJ, Zwanziger J, et al. The cost-effectiveness of automatic implantable cardiac defibrillators: results from MADIT. Multicenter Automatic Defibrillator Implantation Trial. Circulation 1998;97:2129–2135. 41. O’Brien BJ, Connolly SJ, Goeree R, et.al. Cost-effectiveness of the implantable cardioverter-defibrillator: results from the Canadian Implantable Defibrillator Study (CIDS). Circulation 2001;103:1416–1421. 42. Domanski MJ, Sakseena S, Epstein AE, et al. Relative effectiveness of the implantable cardioverterdefibrillator and antiarrhythmic drugs in patients with varying degrees of left ventricular dysfunction who have survived malignant ventricular arrhythmias. J Am Coll Cardiol 1999;34:1090–1095. 43. Moss AJ. Implantable cardioverter defibrillator therapy: the sickest patients benefit the most. Circulation 2000;101:1638–1664. 44. Connolly SJ, Hallstrom AP, Cappato R, et al. Meta-analysis of the implantable caradioverter defibrillator secondary prevention trials. Eur Heart J 2000;21:2071–2078. 45. Larsen G, Hallstrom A, McAnulty J, et al. Cost-effectiveness of the implantable cardioverter-defibrillator versus antiarrhythmic drugs in survivors of serious ventricular tachyarrhythmias. Results of the Antiarrhythmics Versus Implantable Defibrillators (AVID) economic analysis substudy. Circulation 2002;105:2049–2057. 46. Irvine J, Dorian P, Smith J, et al. Quality of life comparisons between the implantable cardioverter defibrillator and amiodarone. Psychosom Med 1999;61 (Abstract):114. 47. Orejarena LA, Vidaillet H, DeStefano F, et al. Paroxysmal supraventricular tachycardia in the general population. J Am Coll Cardiol 1998;31:150–157. 48. Larson MS, McDonald K, Young C, et al. Quality of life before and after radiofrequency catheter ablation in patients with drug refractory atrioventricular nodal reentrant tachycardia. Am J Cardiol 1999;84:471–473, A9. 49. Kugler JD, Danford DA, Houston K, Felix G. Radiofrequency catheter ablation for paroxysmal supraventricular tachycardia in children and adolescents without structural heart disease. Pediatric EP Society, Radiofrequency Catheter Ablation Registry. Am J Cardiol 1997;80:1438–1443. 50. Basta MN, Krahn AD, Klein GJ, et al. Safety of slow pathway ablation in patients with atrioventricular node reentrant tachycardia and a long fast pathway effective refractory period. Am J Cardiol 1997;80:155–159. 51. Scheinman MM. NASPE Survey on Catheter Ablation. Pac Clin Electrophysiol 1995;18:1474–1478. 52. Calkins H, Yong P, Miller JM, et al. Catheter ablation of accessory pathways, atrioventricular nodal reentrant tachycardia, and the atrioventricular junction: final results of a prospective, multicenter clinical trial. The Atakr Multicenter Investigators Group. Circulation 1999;99:262–270. 53. Bubien RS, Knotts-Dolson SM, Plumb VJ, Kay GN. Effect of radiofrequency catheter ablation on health-related quality of life and activities of daily living in patients with recurrent arrhythmias. Circulation 1996;94:1585–1591. 54. Lau CP, Tai YT, Lee PW. The effects of radiofrequency ablation versus medical therapy on the quality-of-life and exercise capacity in patients with accessory pathway-mediated supraventricular tachycardia: a treatment comparison study. Pac Clin Electrophysiol 1995;18:424–432. 55. Chen SA, Chiang CE, Tai CT, et al. Complications of diagnostic electrophysiologic studies and radiofrequency catheter ablation in patients with tachyarrhythmias: an eight-year survey of 3966 consecutive procedures in a tertiary referral center. Am J Cardiol 1996;77:41–46. 56. Thakur RK, Klein GJ, Yee R, Guiraudon GM. Complications of radiofrequency catheter ablation: a review. Can J Cardiol 1994;10:835–839. 57. Hogenhuis W, Stevens SK, Wang P, et al. Cost-effectiveness of radiofrequency ablation compared with other strategies in Wolff-Parkinson-White syndrome. Circulation 1993;88:II437–II446. 58. Cheng CH, Sanders GD, Hlatky MA, et al. Cost-effectiveness of radiofrequency ablation for supraventricular tachycardia. Ann Intern Med 2000;133:864–876. 59. Fuster V, Ryden L. ACC/AHA/ESC guidelines for the management of patients with atrial fibrillation: Executive summary. J Am Coll Cardiol 2001;38:1231–1265. 60. Falk R. Atrial fibrillation. N Engl J Med 2001;344:1067–1078.

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61. Teng M, Catherwood E, Melby D. Cost Effectiveness of Therapies for Atrial Fibrillation. Pharmacoeconomics 2000;18:317–333. 62. Hart R, Benavente O, McBride R, Pearce L. Antithrombotic therapy to prevent stroke in patients with atrial fibrillation: a meta–analysis. Ann Intern Med 1999;131:492–501. 63. Albers G, Dalen J, Laupacis A, et al. Antithrombotic therapy in atrial fibrillation. Chest 2001;119:194S-206S. 64. Gage B, Waterman A, Shannon W, et al. Validation of clinical classification schemes for predicting stroke: results from the National Registry of Atrial Fibrillation. J Am Med Ass 2001;285:2864–2870. 65. Kamath S, Lip G. Risk stratification for thromboprophylaxis in atrial fibrillation. Cardiac Electrophysiol Rev 2001;5:171–178. 66. Gage BF, Cardinalli AB, Albers GW, Owens DK. Cost-effectiveness of warfarin and aspirin for prophylaxis of stroke in patients with nonvalvular atrial fibrillation [see comments]. JAMA 1995;274:1839–1845. 67. Eckman MH, Levine HJ, Salem DN, Pauker SG. Making decisions about antithrombotic therapy in heart disease: decision analytic and cost-effectiveness issues. Chest 1998;114:699S-714S. 68. Hylek EM, Skates SJ, Sheehan MA, Singer DE. An analysis of the lowest effective intensity of prophylactic anticoagulation for patients with nonrheumatic atrial fibrillation. N Engl J Med 1996;335:540–654. 69. Group TEAFTS. Optimal oral anticoagulation therapy in patients with nonrheumatic atrial fibrillation and recent cerebral ischemia. N Engl J Med 1995;333:5–10. 70. Hirsh J, Dalen JE, Deykin D, et al. Oral anticoagulants. Mechanism of action, clinical effectiveness, and optimal therapeutic range. Chest 1995;108:231S–246S. 71. Ellenbogen KA, Clemo HF, Stambler BS, Wood MA, VanderLugt JT. Efficacy of ibutilide for termination of atrial fibrillation and flutter. Am J Cardiol 1996;78:42–45. 72. Foster RH, Wilde MI, Markham A. Ibutilide. A review of its pharmacological properties and clinical potential in the acute management of atrial flutter and fibrillation. Drugs 1997;54:312–330. 73. Oral H, Souza JJ, Michaud GF, et al. Facilitating transthoracic cardioversion of atrial fibrillation with ibutilide pretreatment [see comments]. N Engl J Med 1999;340:1849–1854. 74. Grace AA, Camm AJ. Quinidine. N Engl J Med 1998;338:35–45. 75. Volgman AS, Carberry PA, Stambler B, et al. Conversion efficacy and safety of intravenous ibutilide compared with intravenous procainamide in patients with atrial flutter or fibrillation. J Am Coll Cardiol 1998;31:1414–1419. 76. Lundstrom T, Ryden L. Chronic atrial fibrillation. Long-term results of direct current conversion. Acta Med Scand 1988;223:53–59. 77. Reimold SC, Maisel WH, Antman EM. Propafenone for the treatment of supraventricular tachycardia and atrial fibrillation: a meta-analysis. Am J Cardiol 1998;82:66N–71N. 78. Naccarelli GV, Dorian P, Hohnloser SH, Coumel P. Prospective comparison of flecainide versus quinidine for the treatment of paroxysmal atrial fibrillation/flutter. The Flecainide Multicenter Atrial Fibrillation Study Group. Am J Cardiol 1996;77:53A–59A. 79. Falk RH, Pollak A, Singh SN, Friedrich T. Intravenous dofetilide, a class III antiarrhythmic agent, for the termination of sustained atrial fibrillation or flutter. Intravenous Dofetilide Investigators [see comments]. J Am Coll Cardiol 1997;29:385–390. 80. Norgaard BL, Wachtell K, Christensen PD, et al. Efficacy and safety of intravenously administered dofetilide in acute termination of atrial fibrillation and flutter: a multicenter, randomized, doubleblind, placebo-controlled trial. Danish Dofetilide in Atrial Fibrillation and Flutter Study Group. Am Heart J 1999;137:1062–1069. 81. Zarembski DG, Nolan PE, Jr., Slack MK, Caruso AC. Treatment of resistant atrial fibrillation. A meta-analysis comparing amiodarone and flecainide. Arch Intern Med 1995;155:1885–1891. 82. Podrid PJ. Amiodarone: reevaluation of an old drug. Ann Intern Med 1995;122:689–700. 83. Opolski G, Stanislawska J, Gorecki A, et al. Amiodarone in restoration and maintenance of sinus rhythm in patients with chronic atrial fibrillation after unsuccessful direct-current cardioversion. Clin Cardiol 1997;20:337–340. 84. Stafford RS, Robson DC, Misra B, et al. Rate control and sinus rhythm maintenance in atrial fibrillation: national trends in medication use, 1980–1996. Arch Intern Med 1998;158:2144–2148. 85. Reiffel JA. Selecting an antiarrhythmic agent for atrial fibrillation should be a patient-specific, datadriven decision. Am J Cardiol 1998;82:72N–81N. 86. Danias P, Caulfield T, Weigner M, et al. Likelihood of spontaneous conversion of atrial fibrillation to sinus rhythm. J Am Coll Cardiol 1998;31:588–592.

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87. Kowey PR, Marinchak RA, Rials SJ, Filart RA. Acute treatment of atrial fibrillation. Am J Cardiol 1998;81:16C–22C. 88. Naccarelli GV, Dell’Orfano JT, Wolbrette DL, et al. Cost-effective management of acute atrial fibrillation: Role of rate control, spontaneous conversion, medical and direct current cardioversion, transesophageal echocardiography, and antiembolic therapy. Am J Cardiol 2000;85:36D–45D. 89. Murdock DK, Schumock GT, Kaliebe J, et al. Clinical and cost comparison of ibutilide and directcurrent cardioversion for atrial fibrillation and flutter. Am J Cardiol 2000;85:503–506, A11. 90. Li H, Natale A, Tomassoni G, et al. Usefulness of ibutilide in facilitating successful external cardioversion of refractory atrial fibrillation. American J Cardiol 1999;84:1096–1098, A10. 91. Murray KT. Ibutilide. Circulation 1998;97:493–497. 92. Catherwood E, Fitzpatrick WD, Greenberg ML, et al. Cost-effectiveness of cardioversion and antiarrhythmic therapy in nonvalvular atrial fibrillation [see comments]. Ann Intern Med 1999;130:625–636. 93. Eckman MH, Falk RH, Pauker SG. Cost-effectiveness of therapies for patients with nonvalvular atrial fibrillation. Arc Intern Med 1998;158:1669–1677. 94. Vorperian VR, Havighurst TC, Miller S, January CT. Adverse effects of low dose amiodarone: a meta-analysis. J Am Coll Cardiol 1997;30:791–798. 95. Jessurun GAJ, Crijns HJGM. Amiodarone pulmonary toxicity. Br Med J 1997;314:619–620. 96. Inoue H, Atarashi H. Risk factors for thromboembolism in patients with paroxysmal atrial fibrillation. Am J Cardiol 2000;86:852–855. 97. Pauker S, Eckman M. Finding what you seek: analyzing therapies for nonvalvular atrial fibrillation. Ann Intern Med 1999;130:690–691. 98. Study PaSCotA. Atrial Fibrillation Follow-Up Investigation of Rhythm Managemen-The AFFIRM Study Design. Am J Cardiol 1997;79:1198–1202. 99. Maisel WH, Kuntz KM, Reimold SC, et al. Risk of initiating antiarrhythmic drug therapy for atrial fibrillation in patients admitted to a university hospital. Ann Intern Med 1997;127:281–284. 100. Friedman PL, Stevenson WG. Proarrhythmia. Am J Cardiol 1998;82:50N–58N. 101. Flaker GC, Blackshear JL, McBride R, et al. Antiarrhythmic drug therapy and cardiac mortality in atrial fibrillation. Journal Am Coll Cardiology 1992;20:527–532. 102. Chung MK, Schweikert RA, Wilkoff BL, et al. Is hospital admission for initiation of antiarrhythmic therapy with sotalol for atrial arrhythmias required? Yield of in-hospital monitoring and prediction of risk for significant arrhythmia complications. J Am Coll Cardiol 1998;32:169–176. 103. Pinski SL, Helguera ME. Antiarrhythmic drug initiation in patients with atrial fibrillation. Prog Card Dis 1999;42:75–90. 104. Simons G, Eisenstein E, Shaw L, et al. Cost-effectiveness of inpatient initiation of antiarrhythmic therapy for supraventricular tachycardias. Am J Cardiol 1997;80:1551–1557. 105. Marcus FI. Risks of initiating therapy with sotalol for treatment of atrial fibrillation. J Am Coll Cardiol 1998;32:177–180. 106. Mayet J, More RS, Sutton GC. Anticoagulation for cardioversion of atrial arrhythmias. Eur Heart J 1998;19:548–552. 107. Grimm R, Stewart W, Black I, et al. Should all patients undergo transesophageal echocardiography before electrical cardioversion of atrial fibrillation. J Am Coll Cardiol 1994;23:533–541. 108. Black I, Fatkin D, Sagar K, et al. Exclusion of atrial thrombus by transesophageal echocardiography does not preclude embolism after cardioversion of atrial fibrillation. Circulation 1994;89:2509–2513. 109. Manning W, Silverman D, Keighley C, et al. Transesophageal echocardiographically facilitated early cardioversion from atrial fibrillation using short-term anticoagulation: Final results of a prospective 4.5-year study. J Am Coll Cardiol 1995;25:1354–1361. 110. Manning W, Weintraub R, Waksmonski C, et al. Accuracy of transesophageal echocardiography for identifying left atrial thrombi. Ann Intern Med 1995;123:817–822. 111. Seto TB, Taira DA, Tsevat J, Manning WJ. Cost-effectiveness of transesophageal echocardiographicguided cardioversion: a decision analytic model for patients admitted to the hospital with atrial fibrillation. J Am Coll Cardiol 1997;29:122–130. 112. Klein A, Grimm R, Murray D, et al. Use of transesophageal echocardiography to guide cardioversion in patients with atrial fibrillation. N Engl J Med 2001;344:1411–1420. 113. Roy D, Talajic M, Dorian P, et al. Amiodarone to prevent recurrences of atrial fibrillation. N Engl J Med 2000;342:913–920. 114. Guerra P, Lesh M. The role of nonpharmacologic therapies for the treatment of atrial fibrillation. J Card Electrophysiol 1999;10:450–460.

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115. Cannom DS. Atrial fibrillation: nonpharmacologic approaches. Am J Cardiol 2000;85:25D–35D. 116. Narasimhan C, Blanck Z, Akhtar M. Atrioventricular nodal modification and atrioventricular junctional ablation for control of ventricular rate in atrial fibrillation. J Card Electrophysiol 1997;9:S146–S150. 117. Brignole M, Menozzi C, Gianfrachi L, et al. Assessment of atrioventricular junction ablation and VVIR pacemaker versus pharmacological treatment in patients with heart failure and chronic atrial fibrillation. Circulation 1998;98:953–960. 118. Haissaguerre M, Shah D, Jais P. Role of catheter ablation for atrial fibrillation. Curr Opin Cardiol 1997;12:18–23. 119. Lau C, Tse H, Lok N, et al. Initial experience with an implantable human atrial defibrillator. PACE 1997;20:220–225. 120. Ezekowitz M. Atrial fibrillation: the epidemic of the new millenium. Ann Intern Med 1999;131:537–538. 121. Kowey PR, Marinchak RA, Rials SJ, et al. Atrial fibrillation trials: will they teach us what we need to know? Am J Cardiol 1998;82:86N–91N.

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Comparing Cost-Utility Analyses in Cardiovascular Medicine Wolfgang C. Winkelmayer, MD, ScD, MPH, David J. Cohen, MD, MSc, Marc L. Berger, MD, and Peter J. Neumann, ScD CONTENTS INTRODUCTION METHODS RESULTS DISCUSSION REFERENCES

INTRODUCTION Cost-utility analysis (CUA) is a special case of cost-effectiveness analysis (CEA). In CUA, time spent in a certain health state is weighed by the relative preference (utility) people have to be in that health state, where 0 equals death, and 1 equals perfect health. If the preference weights are assessed using standard methods, such as the time trade-off technique, standard gamble, or a generic utility assessment tool, then comparisons between cost-utility ratios (CUR) are congruent with welfare economic theory. Results of CUAs are expressed in currency units per quality-adjusted life year (QALY). The appeal of CUA is that results are easily interpreted, allowing simple comparisons among alternative interventions, offering a useful aid in making allocation decisions to maximize the value of limited resources. Given continuing development of valuable and costly new medical technologies, CUA seems poised to become an increasingly useful tool in choosing among competing diagnostic and therapeutic alternatives. Comparisons among alternative interventions using CUR are facilitated by construction of a “league table,” consisting of ratios for several types of interventions (1). League tables include a description of the intervention, the target population, and the CUR. Sometimes they also include information on the methodology of the study in which the ratio was estimated. Therefore, ratios can be compared with consideration given to discrepancies in research methods. However, the mechanistic use of such From: Contemporary Cardiology: Cardiovascular Health Care Economics Edited by: W. S. Weintraub © Humana Press Inc., Totowa, NJ

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league tables has been criticized, because studies of very different quality were often inappropriately compared head to head. In 1993, the US Public Health Service convened the Panel on Cost-Effectiveness in Health and Medicine, a group of 13 scientists and scholars in CEA (2). In 1996, the Panel reported standards on the conduct and reporting of CEA, with the goal to enhance quality and comparability between studies (3–5). Chapman et al. have since distinguished between “Panel-worthy” and other CUAs, thus responding to some of the earlier criticisms of such league tables (6,7). To our knowledge, no systematic summary or league table has been published that allows comparison of peer-reviewed CUR by category of cardiovascular disease, such as ischemic heart disease or cerebrovascular disease. Therefore, we built on previous work at our institution to evaluate in further detail all CUAs that pertained to the field of cardiovascular medicine (6). Our objective was to present a league table of published CUR for health interventions specific to cardiovascular disease, providing a useful reference source for estimates of the cost per QALY of these clinical interventions. A secondary purpose was to report on whether a particular CUR was estimated using study methods fitting the recommendations made by the US Panel on Cost-Effectiveness in an attempt to gauge the comparability of ratios.

METHODS We recently developed a comprehensive database of CUAs published in the medical literature through the end of 1997 (6). For this chapter, we compiled the subset of all cardiovascular disease-related CUAs. Details of the data collection and auditing methods are described elsewhere (8). Briefly, we conducted a literature search for all qualityadjusted CEAs published in English, using the MEDLINE, CancerLit, EconLit, Current Contents (all editions), and HealthStar databases. We retrieved all CUAs that were published in full text, and two trained abstractors independently retrieved data from each article on the reporting and methods used, including study perspective, measurements of effectiveness and quality of life, costs, discounting methods, and whether incremental analyses and sensitivity analyses were performed. The readers then convened for a consensus audit to resolve discrepancies. Methods used in each article were compared with those recommended in the report of the US Panel on Cost-Effectiveness in Health and Medicine (3). Our abstractors made assessments as to whether a study followed methodologies consistent with certain recommendations made by the Panel (1). We compiled a league table that consisted of a description of the intervention, baseline comparator, target population, the cost-effectiveness ratio (CER), and an indicator representing whether the study’s methods were found to be consistent with selected Panel recommendations (reference case recommendations: adoption of a societal perspective, preference weights from community or patients, use of net costs, appropriate incremental comparisons, and discounting of costs and QALYs at the same rate). Where necessary, CUR were first converted into US dollars (9), then inflated into 1998 US dollar values using the published US consumer price index (CPI) (10). CURs were categorized by type of intervention: care delivery, medical device, diagnostic procedure, health education/counseling, medical procedure, pharmaceutical therapy, screening, and surgical procedure. Results are also sorted by the following subcategories within cardiovascular disease: abdominal aortic aneurysm, arrhythmia, congestive heart failure (CHF), deep venous thrombosis/pulmonary embolism, hypertension,

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coronary artery disease (CAD), peripheral artery disease, cerebrovascular disease, heart transplantation, and valvular disorder.

RESULTS For the entire database, we identified 228 CUA articles, of which 58 (25.4%) dealt with interventions for cardiovascular diseases. Each study could contain assessments of more than just one intervention or strategy. These 58 articles contained CURs for a total of 229 interventions. Most articles were published in the last 3 years of observation (1995–1997), indicating that CUAs have recently become more prevalent in the peer reviewed medical literature (see Table 1). Eighteen articles (31%) appeared in journals focusing on various aspects of cardiovascular medicine, and 14 (24.1%) were published in prominent general medical journals (see Table 1). However, nearly half of the studies appeared in other journals. More than 70% of all studies were done in and based on data from the United States (see Table 1). Sponsorship came most frequently from governmental agencies (55.2%) or foundations (31%). Only 10% of studies were funded by the pharmaceutical or medical device industry (see Table 1). By far the most frequently evaluated disease category was CAD (21 studies; 87 CURs), and 49 of those CURs were attributable to surgical procedures (see Table 2). Other frequently assessed disease categories were cerebrovascular disease (12 studies; 36 CURs), hypertension (7 studies; 37 CURs), arrhythmia (7 studies; 27 CURs), peripheral artery disease (5 studies; 17 CURs), and aortic aneurysm (4 studies; 13 CURs). There were fewer than 4 studies or 10 CURs each for the categories of valvular disease, CHF, heart transplantation, and deep venous thrombosis/pulmonary embolism. Most of the ratios came from assessments of surgical procedures (79 CURs), pharmaceutical interventions (56 CURs), screening programs (24 CURs), and diagnostic procedures (21 CURs, see Table 2). All other intervention categories were represented by less than 20 ratios each. When evaluating the distribution of CURs by disease category, lower CER were found in interventions pertaining to aortic aneurysm, peripheral artery disease, and hypertension in comparison to interventions in CAD, cerebrovascular disease, and arrhythmia (see Fig. 1). Important quality aspects of CUAs are discounting of costs and effects, disclosure of perspective, and sensitivity analyses on key parameters to test the robustness of the findings. Most of the studies found in our survey were compliant with such quality indicators (see Table 3). The perspective of economic evaluations is of paramount importance for the users of such studies. The broadest perspective, and the one recommended by the Panel, is the societal one. This specific view considers all costs, including such components as patients’ and their caregivers’ time, or out-of-pocket costs related to their treatment or illness. Fewer than 25% of the studies reviewed here were conducted from the societal perspective (see Table 3); however, there are indications that this specific perspective is becoming more prevalent (6). Discounting is the methodology that reflects the fact that decision makers prefer a certain sum of money now, rather than in the future. It has been pointed out that it is necessary for purposes of internal consistency to discount future benefits at the same discount rate used for discounting of costs (11). Of studies in this survey, 80%

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Cardiovascular Health Care Economics Table 1 Article Characteristics (N = 58) Article Characteristic Year of publication Pre-1990 1990 1991 1992 1993 1994 1995 1996 1997 Country of study United States United Kingdom Canada Sweden Netherlands Australia Finland Ireland New Zealand Journal Cardiovascular specialty journals Circulation J Vasc Surg Stroke J Am Coll Card Eur Heart J Am J Cardiol J Hypertens Eur J Vasc Endov Surg Ann Thor Surg General medical journals Ann Int Med JAMA N Engl J Med BMI Other journals Sponsorship* Government Foundation Industry Health care organization Could not be determined * Not mutually exclusive.

No.

%

7 5 3 1 5 6 10 10 11

12.1 8.3 5.0 1.7 8.3 10.3 17.2 17.2 19.0

41 5 3 2 2 2 1 1 1

70.7 8.3 5.2 3.4 3.4 3.4 1.7 1.7 1.7

4 3 3 2 2 1 1 1 1

6.9 5.2 5.2 3.4 3.4 1.7 1.7 1.7 1.7

5 5 3 1 26

8.6 8.6 5.2 1.7 44.8

32 18 6 2 18

55.2 31.0 10.3 3.4 31.0

Table 2 Disease Category and Type of Intervention (Number of Studies) Care delivery

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CAD Cerbrovascular disease Hypertension Arrhythmia Peripheral artery disease Aortic aneurysm Deep venous thrombosis/ Pulmonary embolism CHF Cardiac transplantation Valvular disease Total

Device Diagnostic

1

3 3

Health education

Medical procedure

3

2

1 1 1

2 1 1

Pharmaceutical Screening Surgical Total 8 2 4

3 2

3 1 2 1

1

3

5 4 1 2 1 1

21 12 7 6 5 4 1

1 1 16

2 1 3 58

1

4

6

4

5

2 18

7

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Abbreviations:

AA – aortic aneurysm; PAD – peripheral artery disease; HTN – hypertension; CAD – coronary artery disease; CVD – cerebrovascular disease ARRH – arrhythmia.

*(disease categories with <10 CURs not shown)

Fig. 1. Distribution of cost-utility ratios by disease category.

Table 3 Methodological Characteristics (N = 58) Perspective Society Health care system Unclear Discounting Costs QALYs Both Sensitivity analyses Costs Effectiveness Quality of life Discount rate Not performed Cost in US $/QALY (of all 229 ratios) <20,000 20,000–50,000 50,000–100,000 >100,000 Dominated

13 44 1

22.4 75.9 1.7

48 51 46

82.8 87.9 79.3

32 46 35 31 7

55.2 79.3 60.3 53.4 12.1

108 41 28 27 25

47.2 17.9 12.2 11.8 10.9

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Fig. 2. Time trends in “Panel-worthiness” cost-utility analyses.

complied with the recommendations regarding proper discounting of costs and QALYs, and more than 50% of the studies performed sensitivity analyses on the discount rate (see Table 3). Although the number of studies that satisfied the reference case recommendations of the US Panel has also grown recently, more than 75% of all studies overall did not follow the Panel recommendations, which may limit their validity and comparability (see Fig. 2). In the league table, we pointed out if studies were fully compliant with these selected recommendations of the Panel on Cost-Effectiveness in Health and Medicine, indicating a degree of methodological quality. The league table for the 229 interventions is provided in the appendix. The appendix presents, by category of disease, a description of the intervention and its comparator, the estimated CUR, and an indication of whether the study met selected Panel criteria. A large degree of variation exists, ranging from cost-saving interventions to one specific education program with an estimated cost per QALY of $8.9 million. Strategies are noted as being dominated if they are less effective, but more costly than their alternatives. The full league table of all 647 CURs of strategies and interventions from all fields of medicine is publicly available on the Web (12).

DISCUSSION To the best of our knowledge, this chapter constitutes the most comprehensive survey thus far of cost-utility studies that pertain to cardiovascular medicine. We assembled a table of all peer-reviewed CURs published through 1997 regarding interventions designed to aid in the diagnosis and treatment of cardiovascular disease, categorized by the type of intervention and type of disease. This table can serve as an aid to health care decision makers in resource allocation. Researchers can also take advantage of this league table to consider which areas of cardiovascular disease intervention remain understudied. Also, the fact that a relatively small proportion of studies fulfilled the Panel criteria opens up opportunities to re-evaluate such interventions using recommended methodology and to improve the quality of future evaluations. CUAs are a small, but rapidly expanding, subset of economic evaluations and have

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been reported in a variety of clinical and nonclinical journals (13). In previous studies, we have found that the quality of CUAs is highly variable, but may be improving (6,8). This may be in response to the publication of guidelines for the conduct of such studies and to the establishment of study format requirements by the editorial boards of important medical journals (3,4,5,14). These is no agreement on the CUR threshold that warrants the adoption and implementation of an intervention. Laupacis et al. observed that ratios below Canadian $20,000 per QALY were almost universally accepted as being appropriate ways of using society’s and the health care system’s resources. In contrast, procedures above Canadian $100,000 per QALY are often regarded as economically unattractive. Between Canadian $20,000–100,000 per QALY, the situation is less clear-cut, and some of those technologies may not be regarded as being cost-effective (15). Following this rule, it seems that most cardiovascular interventions are in the range that should almost certainly warrant implementation, and only few interventions in our survey are above the arbitrary threshold that may indicate cost-ineffectiveness (11.8%). For example, routine coronary angiography and treatment guided by results in certain age groups of patients in the convalescence phase of acute myocardial infarction (MI) with negative exercise tolerance test, left ventricular ejection fraction ≥ 50%, and mild or no angina, provides a statistical survival benefit, but only at the incremental cost of US $960,000 per QALY when compared to a regimen of initial medical therapy without angiography (see appendix). However, quite a few results (30.1%) are in the range of values where a decision whether or not to adopt a technology is not straightforward. For example, one-time Doppler ultrasound screening for carotid stenosis in asymptomatic 60-year-old men costs an incremental US $56,000 per QALY in comparison to the no-screening strategy (see appendix). Despite several recent CUA articles in the medical literature on the CE of various approaches to diagnosis and treatment of disease, it remains unclear how CUA is actually used by health care decision makers. Some survey results suggest that decision makers are generally aware of CUA and use it in at least some cases, secondary to clinical factors (16–18). Nonetheless, it seems plausible that many important decision makers remain unaware of much of the literature on CE, especially because most of the studies found in this survey did not appear in the journals that are generally read by the medical community caring for patients with cardiovascular diseases. We hope that summary reports such as this one will simplify the task of becoming conversant in the state of CEA for treatment of cardiovascular disease. This study has several limitations. Reporting biases affect the conclusions that can be drawn regarding which types of intervention are cost-effective relative to any set of competing clinical alternatives. The league table is limited to those CURs published in the peer-reviewed literature, and this sample is almost certainly not a random representation of the population of possible clinical interventions for cardiovascular disease. For example, investigators may tend to disproportionately report findings for interventions that are inexpensive or cost-effective. Alternatively, they may be drawn specifically to analyses of interventions with the largest budget impacts. Furthermore, studies that reported effectiveness without adjustment for the quality of the gained life years are per definition not captured in this analysis. Important examples are the CEA of the Scandinavian Simvastatin Survival Study (19) or the evaluation of thrombolytic therapy with tissue plasminogen activator vs streptokinase in acute MI (20).

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Furthermore, this compilation of studies does not distinguish between analyses of low-cost, low-impact strategies (e.g., using an educational video) and high-cost, highimpact interventions (e.g., coronary bypass surgery); studies with small differences in cost and outcome are included, as are studies with large differences in outcome and cost, which influences the stability of the calculated ratios. It is beyond the scope of this study to evaluate and describe the specific sources of the input values for utility, survival, and cost for each study. Such information would contain important quality indicators, such as whether and how utilities were assessed, whether cost and outcome parameters came from randomized clinical trials, how the costing was done, and whether sensitivity analyses were performed over reasonable ranges of those parameters. Finally, because of the time and resource intensity of such a comprehensive review, the results presented here lag behind the actual information that is available to date, and so this collection does not contain any CUAs that were published after 1997. Keeping these limitations in mind, the present league table can be a valuable reference for clinicians and scientists in the field of cardiovascular disease. It seems certain that the number of such economic evaluations will increase rapidly as novel interventions reach the market and clinical practice, and as health care costs continue to grow. Economic evaluations are only one contribution to the complexities of the medical decision-making process, and decision algorithms cannot substitute for the decisions of well-trained professionals. However, it is important that clinicians are comfortable in reading and understanding such studies to make optimal use of this growing literature in their daily practice. The present league table is a significant aid in accomplishing this objective.

ACKNOWLEDGMENTS Funding provided through a joint award from the National Science Foundation and Merck & Co., Inc., under the Joint NSF/Private Research Opportunity Initiative (SBR9730448).

APPENDIX: LEAGUE TABLE OF 229 INTERVENTIONS IN CARDIOVASCULAR MEDICINE Ref. no.

Intervention vs comparator in target population

Aortic Aneurysm 21 Ultrasonic screening and treatment for abdominal aortic aneurysm in men 60 years old vs no screening program In general population of men 60 years old 21 Ultrasonic screening and treatment for abdominal aortic aneurysm in women 60 years old vs no screening program In general population of women 60 years old 21 Screening and treatment for abdominal aortic aneurysm using ultrasonic scanning of the lower abdominal aorta vs no screening program In general population of men 65 years old 21 Screening and treatment for abdominal aortic aneurysm using ultrasonic scanning of the lower abdominal aorta vs no screening program In general population of women 65 years old

$/QALY

Panelworthy

$950

No

$1100

No

$1200

No

$1400

No

338 Ref. no.

Cardiovascular Health Care Economics Intervention vs comparator in target population

21 Screening and treatment for abdominal aortic aneurysm using ultrasonic scanning of the lower abdominal aorta vs no screening program In general population of men 70 years old 21 Screening and treatment for abdominal aortic aneurysm using ultrasonic scanning of the lower abdominal aorta vs no screening program In general population of women 70 years old 21 Screening and treatment for abdominal aortic aneurysm using ultrasonic scanning of the lower abdominal aorta vs no screening program In general population of men 75 years old 21 Screening and treatment for abdominal aortic aneurysm using ultrasonic scanning of the lower abdominal aorta vs no screening program In general population of women 75 years old 21 Ultrasonic screening and treatment for abdominal aortic aneurysm in men 80 years old vs no screening program In general population of men 80 years old 22 Ultrasound screening for abdominal aortic aneurysms, and surgical repair vs no screening for abdominal aortic aneurysms In asymptomatic men 68–72 years old 21 Ultrasonic screening and treatment for abdominal aortic aneurysm in women 80 years old vs no screening program In general population of women 80 years old 23 Surgery for 4 cm abdominal aortic aneurysm on diagnosis in 60-year-old men vs Ultrasound follow-up at 6-month intervals, withholding surgery unless the abdominal aortic aneurysm reaches 5 cm diameter (“watchful waiting”) In 60-year old men with 4-cm diameter abdominal aortic aneurysms 24 Routine use of intraoperative autologous transfusion device vs no use of intraoperative autologous transfusion device In patients undergoing elective infrarenal aortic reconstruction for abdominal aortic aneurysm Arrhythmia 25 Treansesophageal echocardiography guided cardioversion vs Conventional therapy, treansesophageal echocardiography plus warfarin for 1 month before cardioversion In 70 year-old patients admitted to hospital with atrial fibrillation 26 ICU treatment vs standard ward treatment In patients admitted to a general hospital for cardiac arrest 27 Pacemaker implantation for atrioventricular heart block vs no implantation In cardiac patients 27 Pacemaker implantation for atrioventricular heart block vs no implantation In cardiac patients

$/QALY

Panelworthy

$1600

No

$1800

No

$2000

No

$2400

No

$2700

No

$3000

No

$3300

No

$20,000

No

$130,000

No

Cost-saving No

$1500

No

$1700

No

$1700

No

Chapter 19 / CUA in Cardiovascular Medicine Ref. no.

Intervention vs comparator in target population

28 RFA with observation if fail vs Observation In 40 year-old with WPW syndrome with history of PSVT 29 ICD-only regimen with relative risk reduction 40% vs amiodarone to ICD regimen (crossover to ICD if severe drug toxicity or resuscitated from ventricular fibrillation tachycardia) In 57-year-old patients at intermediate risk for ventricular fibrillation, ventricular tachycardia, or nonarrhythmic cardiac death 29 ICD-only regimen with relative risk reduction 40% vs amiodarone to ICD regimen (crossover to ICD if severe drug toxicity or resuscitated from ventricular fibrillation tachycardia) In 57-year-old survivors of cardiac arrest at high risk for ventricular fibrillation, ventricular tachycardia, or nonarrhythmic cardiac death 29 ICD-only regimen with relative risk reduction 40% vs amiodarone-only regimen In 57-year-old patients at intermediate risk for ventricular fibrillation, ventricular tachycardia, or nonarrhythmic cardiac death 29 ICD-only regimen with relative risk reduction 40% vs amiodarone-only regimen In 57-year-old survivors of cardiac arrest at high risk for ventricular fibrillation, ventricular tachycardia, or nonarrhythmic cardiac death 30 Change from 1-tier to 2-tier system by adding providers of basic and advanced life support in pump vehicles as 1st tier vs no change In cardiac arrest victims in community 29 Amiodarone to ICD regimen (crossover to ICD if severe drug toxicity or resuscitated from ventricular fibrillation tachycardia) with relative risk reduction 40% vs amiodarone-only regimen In 57-year-old survivors of cardiac arrest at high risk for ventricular fibrillation, ventricular tachycardia, or nonarrhythmic cardiac death 29 Amiodarone to ICD regimen (crossover to ICD if severe drug toxicity or resuscitated from ventricular fibrillation tachycardia) with relative risk reduction 40% vs amiodarone-only regimen In 57-year-old patients at intermediate risk for ventricular fibrillation, ventricular tachycardia, or nonarrhythmic cardiac death 30 Response time improvement in 2-tier EMS system by adding more EMS providers in pump vehicles to 1st tier vs Current EMS providers In cardiac arrest victims in community 28 RFA with drug therapy if fail vs. RFA with observation if fail In 40-year-old with WPW syndrome with history of PSVT 29 ICD-only regimen with relative risk reduction 20% vs amiodarone to ICD regimen (crossover to ICD if severe drug toxicity or resuscitated from ventricular fibrillation tachycardia) In 57-year-old survivors of cardiac arrest at high risk for ventricular fibrillation, ventricular tachycardia, or nonarrhythmic cardiac death

339 $/QALY

Panelworthy

$12,000

No

$38,000

Yes

$39,000

Yes

$39,000

Yes

$40,000

Yes

$45,000

Yes

$59,000

Yes

$59,000

Yes

$60,000

Yes

$75,000

No

$76,000

Yes

340 Ref. no.

Cardiovascular Health Care Economics Intervention vs comparator in target population

$/QALY

29 ICD-only regimen with relative risk reduction 20% vs amiodarone $79,000 to ICD regimen (crossover to ICD if severe drug toxicity or resuscitated from ventricular fibrillation tachycardia) In 57-year-old patients at intermediate risk for ventricular fibrillation, ventricular tachycardia, or nonarrhythmic cardiac death 29 ICD-only regimen with relative risk reduction 20% vs $80,000 amiodarone-only regimen In 57-year-old survivors of cardiac arrest at high risk for ventricular fibrillation, ventricular tachycardia, or nonarrhythmic cardiac death 29 ICD-only regimen with relative risk reduction 20% vs $82,000 amiodarone-only regimen In 57-year-old patients at intermediate risk for ventricular fibrillation, ventricular tachycardia, or nonarrhythmic cardiac death 29 Amiodarone to ICD regimen (crossover to ICD if severe drug $96,000 toxicity or resuscitated from ventricular fibrillation tachycardia) with relative risk reduction 20% vs amiodarone-only regimen In 57-year-old survivors of cardiac arrest at high risk for ventricular fibrillation, ventricular tachycardia, or nonarrhythmic cardiac death 30 Change from 1-tier to 2-tier system by adding providers of basic $110,000 and advanced life support in ambulances as 1st tier vs no change In cardiac arrest victims in community 29 Amiodarone to ICD regimen (crossover to ICD if severe drug $150,000 toxicity or resuscitated from ventricular fibrillation tachycardia) with relative risk reduction 20% vs amiodarone-only regimen In 57-year-old patients at intermediate risk for ventricular fibrillation, ventricular tachycardia, or nonarrhythmic cardiac death 30 Response time improvement in 2-tier EMS system by adding more $180,000 EMS providers in ambulances to 1st tier vs. Current EMS providers In cardiac arrest victims in community 31 CPR vs no CPR $240,000 In patients with cardiac arrest 30 Response time improvement in 1-tier EMS system by adding $420,000 more EMS providers in ambulances vs Current EMS providers In cardiac arrest victims in community 28 RFA with surgical ablation if fail vs RFA with observation if fail Dominated In 40 year-old with WPW syndrome with history of PSVT 28 Observation with RFA for cardiac arrest survivors vs RFA with Dominated drug therapy if fail In 40-year-old with WPW syndrome with history of PSVT 28 Surgical ablation vs RFA with drug therapy if fail Dominated In 40-year-old with WPW syndrome with history of PSVT CAD 32 Mobile unit (well-equipped emergency vehicle with trained personnel) vs diet low in cholesterol and saturated fat and high in polyunsaturated fat In 30-year-old male at risk for heart attack

Panelworthy Yes

Yes

Yes

Yes

Yes

Yes

Yes

No Yes

No No

No

Cost-saving No

Chapter 19 / CUA in Cardiovascular Medicine Ref. no.

Intervention vs comparator in target population

33 Voluntary regular-exercise regimen vs no regular-exercise regimen In 35-year-old men 34 Low osmolality contrast media vs high-osmolality contrast media In high-risk patients undergoing cardiac angiography 35 Thrombolytic therapy with streptokinase within 3 hours of MI vs traditional hospital care (with aspirin but no streptokinase) In 70-year-old patients admitted to a coronary care unit with MI 36 Niacin, stepped care vs niacin In 35-year-old males at high risk for CHD: LDL >/= 190 HDL < 35, cigarette smokers, DBP >/= 105 37 Intravenous thrombolytic therapy using rt-PA after suspected AMI vs Usual treatment with no intravenous thrombolytic therapy In patients between 18 and 75-year-old with clinically suspected AMI and major symptom onset w/in 5 hours 27 CABG for patients with severe angina and triple vessel disease vs medical management In patients with severe angina and triple-vessel disease 27 CABG vs medical management In patients with moderate angina and left main vessel disease 38 Captopril therapy vs no captopril In 80-year-old patients surviving MI 39 Thrombolytic therapy with intracoronary streptokinase vs conventional therapy In patients with electrocardiographic evidence of MI and a duration of symptoms not exceeding 4 hours 40 Aortocoronary bypass operation vs medical management In 50-year-old male patients with CAD of 3 arteries, LAD involved, 3 operable, class III or IV angina, normal hrt. fxn., positive postexercise ECG 27 CABG vs medical management In patients with severe angina and double-vessel disease 38 Captopril therapy vs no captopril In 70-year-old patients surviving MI 27 PTCA for patients with severe angina and one-vessel disease vs medical management In patients with severe angina and one vessel disease 27 CABG vs medical management In patients with moderate angina and triple vessel disease 27 CABG vs medical management In patients with mild angina and left main vessel disease 40 Aortocoronary bypass operation vs medical management In 40 year-old male patients with CAD of 3 arteries, LAD involved, 3 operable, no angina, moderate hrt. fxn. impairment, positive postexercise ECG 41 CABG for left main vessel disease and good ventricular function vs medical management of angina In 55-year-old males with CAD considered operable by angiographic and clinical characteristics, with moderately severe angina

341 $/QALY

Panelworthy

Cost-saving

No

$690

No

$1200

No

$1500

Yes

$1800

No

$3200

No

$3300

No

$4300

Yes

$4800

Yes

$5600

No

$5700

No

$5900

Yes

$6000

No

$6000

No

$6300

No

$6700

No

$6800

No

342 Ref. no.

Cardiovascular Health Care Economics Intervention vs comparator in target population

$/QALY

Panelworthy

40 Aortocoronary bypass operation vs medical management In 50 year-old male patients with CAD of 1 artery, LAD involved, 1 operable, class III or IV angina, severe hrt. fxn. impairment, positive postexercise ECG 42 Rehabilitation program (exercise and counseling) vs usual community care In eligible patients with a diagnosis of AMI who were moderately anxious or depressed while in hospital 40 Aortocoronary bypass operation vs medical management In 55-year-old male CAD of 2 arteries, LAD involved, 2 operable, class III or IV angina, moderate hrt. fxn. impairment, positive postexercise ECG 27 PTCA vs medical management In patients with moderate angina and one-vessel disease 40 Aortocoronary bypass operation vs medical management In 60-year-old male CAD of 3 arteries, LAD involved, 3 operable, class III or IV angina, moderate hrt. fxn. impairment, positive postexercise ECG 40 Aortocoronary bypass operation vs medical management In 60-year-old male CAD of 3 arteries, LAD involved, 2 LAD operable, class III or IV angina, moderate hrt. fxn. impairment, positive postexercise ECG 27 CABG for patients with moderate angina and double vessel disease vs medical management In patients with moderate angina and double-vessel disease 40 Aortocoronary bypass operation vs medical management In 50-year-old male patients with CAD of 2 arteries, no LAD involved, 2 operable, class III or IV angina, severe hrt. fxn. impairment, positive postexercise ECG 38 Captopril therapy vs no captopril In 60-year-old patients surviving myocardial infarction 40 Aortocoronary bypass operation vs medical management In 50-year-old male patients with CAD of 2 arteries, LAD involved, 1 LAD operable, no angina, normal hrt. fxn., positive postexercise ECG 41 CABG for three-vessel disease and good ventricular function vs medical management of angina In 55-year-old males with CAD considered operable by angiographic and clinical characteristics, with moderately severe angina 40 Aortocoronary bypass operation vs medical management In 50-year-old male patients with CAD of 1 artery, no LAD, 1 operable, class III or IV angina, moderate hrt. fxn. impairment, positive postexercise ECG 36 Niacin, stepped care (40) vs niacin, stepped care 20 In 35-year-old males at high-risk for CHD: LDL >/= 190 HDL < 35, cigarette smokers, DBP >/= 105 43 PTCA vs conservative treatment In 55-year-old men with severe angina from CAD with type A lesions

$7000

No

$8100

No

$8300

No

$8400

No

$9200

No

$9900

No

$9900

No

$11,000

No

$11,000

Yes

$13,000

No

$13,000

No

$14,000

No

$14,000

Yes

$15,000

No

Chapter 19 / CUA in Cardiovascular Medicine Ref. no.

Intervention vs comparator in target population

40 Aortocoronary bypass operation vs medical management In 50-year-old male patients with CAD of 3 arteries, LAD involved, 2 operable, class I or II angina, severe hrt. fxn. impairment, negative postexercise ECG 27 CABG vs medical management In patients with mild angina and triple vessel disease 33 Regular-exercise regimen vs no regular-exercise regimen In all 35-year-old men 44 Prophylactic captopril therapy vs no prophylactic captopril therapy In patients with impaired left ventricular function after a treated MI 45 Initial stent vs angioplasty In 55 year-old male with symptomatic single-vessel coronary disease 46 Video vs routine care In Australian GP patients with high risk of CVD (DBP > 95 or TC > 6.5), male 40 Aortocoronary bypass operation vs medical management In 45-year-old male patients with CAD of 3 arteries, LAD involved, 1 LAD operable, class I or II angina, normal hrt. fxn., positive postexercise ECG 27 PTCA for patients with mild angina and one vessel disease vs medical management In patients with mild angina and one-vessel disease 41 Coronary angiography for all disease levels (followed by surgery if indicated) vs medical management of angina In 55-year-old males with CAD considered operable by angiographic and clinical characteristics, with moderately severe angina 27 CABG vs medical management In patients with severe angina and one-vessel disease 27 CABG for patients with moderate angina and one-vessel disease vs medical management In patients with moderate angina and one-vessel disease 41 CABG for two-vessel disease and good ventricular function vs medical management of angina In 55-year-old males with CAD considered operable by angiographic and clinical characteristics, with moderately severe angina 27 CABG for patients with mild angina and double vessel disease vs medical management In patients with mild angina and double-vessel disease 47 Thrombolytic therapy with tPA vs thrombolytic therapy with SK In patients presenting within 6 hours after onset of symptoms of AMI 45 Angioplasty with stenting for restenosis vs angioplasty In 55 year-old male with symptomatic single-vessel coronary disease 34 Low-osmolality contrast media vs high-osmolality contrast media In low-risk patients undergoing cardiac angiography 40 Aortocoronary bypass operation vs medical management In 50-year-old male patients with CAD of 2 arteries, no LAD involved, 1 operable, class III or IV angina, moderate hrt. fxn. impairment, negative postexercise ECG

343 $/QALY

Panelworthy

$16000

No

$16,000

No

$17,000

No

$17,000

No

$18,000

No

$24,000

No

$27,000

No

$27,000

No

$28,000

No

$28,000

No

$30,000

No

$31,000

No

$31,000

No

$32,000

Yes

$36,000

No

$38,000

No

$40,000

No

344 Ref. no.

Cardiovascular Health Care Economics Intervention vs comparator in target population

$/QALY

Panelworthy

40 Aortocoronary bypass operation vs medical management In 50-year-old male patients with CAD of 1 artery, LAD involved, 1 operable, class I or II angina, moderate hrt. fxn. impairment, negative postexercise ECG 36 Stepped care 20, stepped care 40 vs niacin, stepped care (40) In 35-year-old males at high-risk for CHD:LDL >/= 190 HDL < 35, cigarette smokers, DBP >/= 105 48 Routine coronary angiography and treatment guided by results vs initial medical therapy without angiography In 35–85-year-old patients in the convalescent phase of AMI, with inducible MI and history of AMI before current AMI (except for 35–44-year-old women with LVEF ≥ 0.50 and 75–84-year-old patients with LVEF 0.20–0.49) 48 Routine coronary angiography and treatment guided by results vs initial medical therapy without angiography In 45–74-year-old men and 75–84-year-old women with mild angina and CHF, and 45–84-year-old men and 65–84-year-old women with no angina and no CHF, all in the convalescent phase of AMI with negative exercise tolerance test, LVEF ≥ 0.50, and prior AMI 41 CABG for one-vessel disease and good ventricular function vs medical management of angina In 55-year-old males with CAD considered operable by angiographic and clinical characteristics, with moderately severe angina 49 Thrombolytic therapy with tPA regimen and aspirin vs thrombolytic therapy with SK and aspirin In 80-year-old man presenting after the onset of symptoms with a definite average-sized AMI 43 PTCA vs conservative treatment In 55-year-old men with mild angina, three-vessel coronary artery disease and type A lesions 43 PTCA vs conservative treatment In 55-year-old men with mild angina, depressed ventricular function, 3-vessel CAD (with PTCA only partially effective) and type A lesions 38 Captopril therapy vs no captopril In 50-year-old patients surviving MI 33 Coercive regular-exercise regimen vs voluntary regular-exercise regimen In 35-year-old men 48 Routine coronary angiography and treatment guided by results vs initial medical therapy without angiography In 75–84-year-old patients in the convalescent phase of AMI, with inducible MI, history of AMI before current AMI, LVEF 0.20–0.49, and CHF 43 PTCA vs Conservative treatment In 55-year-old men with mild angina, normal ventricular function, two-vessel CAD and type A lesions 43 CABG vs PTCA In 55-year-old men with three-vessel CAD and type A lesions with severe angina and normal ventricular function

$41,000

No

$46,000

Yes

$48,000

Yes

$53,000

Yes

$54,000

No

$55,000

No

$56,000

No

$73,000

No

$73,000

Yes

$74,000

No

$85,000

Yes

$87,000

No

$90,000

No

Chapter 19 / CUA in Cardiovascular Medicine Ref. no.

Intervention vs comparator in target population

43 PTCA vs conservative treatment In 55-year-old men with mild angina, normal ventricular function, three-vessel CAD (with PTCA only partially effective) and type A lesions 43 PTCA vs conservative treatment In 55-year-old men with mild angina, depressed ventricular function and one-vessel CAD with LAD involvement and type A lesions 43 PTCA vs conservative treatment In 55-year-old men with mild angina, normal ventricular function and one-vessel CAD with LAD involvement and type A lesions 43 PTCA vs conservative treatment In 55-year-old men with mild angina, depressed ventricular function, two-vessel CAD and type A lesions 43 CABG vs PTCA In 55-year-old men with three-vessel CAD and type A lesions with mild angina and normal ventricular function 43 PTCA vs conservative treatment In 55-year-old men with mild angina, normal ventricular function and one-vessel CAD with no LAD involvement and type A lesions 43 PTCA vs conservative treatment In 55-year-old men with mild angina, depressed ventricular function and one-vessel CAD with no LAD involvement and type A lesions 46 Video + self help vs routine care In Australian GP patients with >= 1 modifiable CVD risk factors, male 43 CABG vs PTCA In 55-year-old men with two-vessel CAD and type A lesions with severe angina and normal ventricular function 50 Autologous blood donation vs allogeneic blood donation In patients undergoing CABG 51 Preoperative autologous donation of 2 U vs no preoperative autologous donation In patients undergoing primary, elective CABG 48 Routine coronary angiography and treatment guided by results vs initial medical therapy without angiography In 35–84-year-old patients in the convalescent phase of AMI, with negative exercise tolerance test, LVEF ≥ 0.50, and mild or no angina (except for 45–74-year-old men and 75–84-year-old women with mild angina and CHF, and 45–84-year-old men and 65–84-year-old women with no angina and no CHF, all with prior AMI) 51 Preoperative autologous donation of 3 U vs preoperative autologous donation of 2 U In patients undergoing primary, elective CABG 51 Preoperative autologous donation of 4 U vs preoperative autologous donation of 3 U In patients undergoing primary, elective CABG graft surgery 51 Preoperative autologous donation of 5 U vs preoperative autologous donation of 4 U In patients undergoing primary, elective CABG

345 $/QALY

Panelworthy

$92,000

No

$98,000

No

$100,000

No

$100,000

No

$110,000

No

$120,000

No

$120,000

No

$120,000

No

$480,000

No

$570,000

No

$590,000

No

$960,000

Yes

$1,100,000

No

$1,600,000

No

$2,600,000

No

346 Ref. no.

Cardiovascular Health Care Economics Intervention vs comparator in target population

46 Video + self help vs routine care In Australian GP patients with >= 1 modifiable CVD risk factors, female 40 Aortocoronary bypass operation vs medical management In 50-year-old male patients with CAD of 2 arteries, LAD involved, 2 operable, class I or II angina, severe hrt. fxn. impairment, negative post-exercise ECG 27 CABG vs medical management In patients with mild angina and one-vessel disease 43 CABG vs PTCA In 55-year-old men with angina from one-vessel CAD and type A lesions 43 CABG vs PTCA In 55-year-old men with three-vessel CAD and type A lesions with angina and depressed ventricular function 46 Video vs routine care In Australian GP patients with >= 1 modifiable CVD risk factors, male 46 Video vs routine care In Australian GP patients with >= 1 modifiable CVD risk factors, female 46 Video vs routine care In Australian GP patients with high risk of CVD (DBP > 95 or TC > 6.5), female 46 Video + self help vs video In Australian GP patients with high risk of CVD (DBP > 95 or TC > 6.5), male 46 Video + self help vs video In Australian GP patients with high risk of CVD (DBP > 95 or TC > 6.5), female CHF 26 ICU treatment vs standard ward treatment In patients admitted to a general hospital for pulmonary edema 52 Epoprostenol and best usual care vs best usual care alone In patients with severe CHF (in a phase III clinical trial) Deep Venous Thrombosis/Pulmonary Embolism 53 Vena caval filter vs anticoagulation therapy In lung cancer patients with acute deep venous thrombosis 53 Vena caval filter vs anticoagulation therapy In lung cancer patients who have survived acute pulmonary embolism 53 Anticoagulation therapy vs observation In lung cancer patients with acute deep venous thrombosis 53 Anticoagulation therapy vs observation In lung cancer patients who have survived acute pulmonary embolism Hypertension 54 Hypertension treatment vs no treatment In 45–69-year-old men in Sweden

$/QALY

Panelworthy

$8,900,000

No

Dominated

No

Dominated

No

Dominated

No

Dominated

No

Dominated

No

Dominated

No

Dominated

No

Dominated

No

Dominated

No

$2700

No

Dominated

No

Cost-saving No Cost-saving No Cost-saving No Cost-saving No

$10

No

Chapter 19 / CUA in Cardiovascular Medicine Ref. no.

Intervention vs comparator in target population

54 Hypertension treatment vs no treatment In 45–69-year-old women in Sweden 54 Hypertension treatment vs no treatment In 45–69-year-old men in Sweden 54 Hypertension treatment vs no treatment In 45–69-year-old men in Sweden 54 Hypertension treatment vs no treatment In 45–69-year-old women in Sweden 54 Hypertension treatment vs no treatment In >70-year-old men in Sweden 54 Hypertension treatment vs no treatment In >70-year-old women in Sweden 54 Hypertension treatment vs no treatment In >70-year-old women in Sweden 54 Hypertension treatment vs no treatment In >70-year-old men in Sweden 54 Hypertension treatment vs no treatment In 45–69-year-old women in Sweden 54 Hypertension treatment vs no treatment In >70-year-old women in Sweden 54 Hypertension treatment vs no treatment In >70-year-old men in Sweden 54 Hypertension treatment vs no treatment In <45-year-old men in Sweden 54 Hypertension treatment vs no treatment In <45-year-old men in Sweden 54 Hypertension treatment vs no treatment In <45-year-old women in Sweden 54 Hypertension treatment vs no treatment In <45-year-old men in Sweden 54 Hypertension treatment vs no treatment In <45-year-old women in Sweden 54 Hypertension treatment vs no treatment In <45-year-old women in Sweden 55 Antihypertensive medication treatment vs no antihypertensive treatment In 20-year-old male patients with essential hypertension (pretreatment diastolic blood pressure of 110 mm Hg) 56 Hypertension screening and therapy vs no screening In asymptomatic 60-year-old men in the United States 55 Antihypertensive medication treatment vs no antihypertensive treatment In 60-year-old female patients with essential hypertension (pretreatment diastolic blood pressure of 110 mm Hg) 57 Hypertension identification and follow-up program vs no hypertension program In inhabitants of North Karelia, Finland 56 Hypertension screening and therapy vs no screening In asymptomatic 60-year-old men in the United States 56 Hypertension screening and therapy vs no screening In asymptomatic 40-year-old men in the United States

347 $/QALY

Panelworthy

$20

No

$20

No

$20

No

$30

No

$30

No

$30

No

$30

No

$30

No

$30

No

$30

No

$30

No

$80

No

$90

No

$100

No

$110

No

$140

No

$190

No

$9500

No

$12,000

No

$14,000

No

$14,000

No

$17,000

No

$22,000

No

348 Ref. no.

Cardiovascular Health Care Economics Intervention vs comparator in target population

55 Antihypertensive medication treatment vs no antihypertensive treatment In 20-year-old female patients with essential hypertension (pretreatment diastolic blood pressure of 110 mm Hg) 56 Hypertension screening and therapy vs no screening In asymptomatic 40-year-old women in the United States 58 Treatment with antihypertensive medication vs no antihypertensive treatment In 60-year-old men with mild-to-moderate hypertension (pretreatment DBP = 110 mm Hg) in New Zealand 56 Hypertension screening and therapy vs no screening In asymptomatic 20-year-old men in the United States 55 Antihypertensive medication treatment vs no antihypertensive treatment In 60-year-old male patients with essential hypertension (pretreatment diastolic blood pressure of 110 mm Hg) 59 Propranolol vs no initial antihypertensive therapy In persons in the US population 35–64-year-old without the diagnosis of coronary heart disease with essential hypertension (>95 mm Hg) 58 Treatment with antihypertensive medication vs no antihypertensive treatment In 60 year-old women with mild-to-moderate hypertension (pretreatment DBP = 110 mm Hg) in New Zealand 60 Individual utility assessment of trial of drug therapy vs no individualized utility assessment In mild hypertensive patients 56 Hypertension screening and therapy vs no screening In asymptomatic 20-year-old women in the United States 59 Captopril vs propranolol In persons in the US population 35–64-year-old without the diagnosis of coronary heart disease with essential hypertension (>95 mm Hg) 58 Treatment with antihypertensive medication vs no antihypertensive treatment In 30-year-old men with mild-to-moderate hypertension (pretreatment DBP = 90 mm Hg) in New Zealand 58 Treatment with antihypertensive medication vs no antihypertensive treatment In 30-year-old women with mild-to-moderate hypertension (pretreatment DBP = 90 or 100 mm Hg) in New Zealand 58 Treatment with antihypertensive medication vs no antihypertensive treatment In 40-year-old women with mild-to-moderate hypertension (pretreatment DBP = 90 mm Hg) in New Zealand Peripheral Artery Disease 61 PTA-PTA for patients with: stenosis, claudication or rest pain, and vein or PTFE above-the-knee graft; stenosis, necrosis and PTFE above-the-knee graft; or occlusion and claudication with any type of graft vs no treatment In 65-year-old male patients with femoropopliteal lesions < 10 cm requiring revascularization

$/QALY

Panelworthy

$24,000

No

$32,000

No

$33,000

No

$40,000

No

$47,000

No

$48,000

No

$57,000

No

$58,000

Yes

$61,000

No

$150,000

No

Dominated No

Dominated No

Dominated No

Cost-saving Yes

Chapter 19 / CUA in Cardiovascular Medicine Ref. no.

Intervention vs comparator in target population

61 Percutaneous transluminal angioplasty with PTFE below-the-knee graft for 65-year-old male patients with stenotic femoropoplitieal lesions < 10 cm vs All other strategies (NoTx-NoTx, PTA-NoTx, PTA-PTA, PTA-BS, BS-NoTx, BS-Rev) In 65-year-old male patients with femoropopliteal lesions < 10 cm requiring revascularization 61 PTA-BS for patients with: stenosis, rest pain, and vein graft vs PTA-PTA In 65-year-old male patients with femoropopliteal lesions < 10 cm requiring revascularization 61 PTA-BS for patients with: occlusion, claudication, and vein graft vs PTA-PTA In 65-year-old male patients with femoropopliteal lesions < 10 cm requiring revascularization 61 BS-Rev for patients with: occlusion, claudication, and PTFE below-the-knee graft vs no treatment In 65-year-old male patients with femoropopliteal lesions < 10 cm requiring revascularization 62 Secondary use of laser-assisted angioplasty with the Nd/YAG laser vs conventional guidewire angioplasty In patients with peripheral vascular occlusions and rest pain/ulceration 26 ICU treatment vs standard ward treatment In patients admitted to a general hospital for vascular surgery 61 PTA-BS for patients with: occlusion, claudication, and PTFE above-the-knee graft vs PTA-PTA In 65-year-old male patients with femoropopliteal lesions < 10 cm requiring revascularization 62 Secondary use of laser-assisted angioplasty with the Nd/YAG laser vs conventional guidewire angioplasty In patients with peripheral vascular occlusions and claudication 61 PTA-BS for patients with: occlusion, claudication, and PTFE below-the-knee graft vs PTA-PTA In 65-year-old male patients with femoropopliteal lesions < 10 cm requiring revascularization 61 PTA-BS for patients with: stenosis, necrosis, and PTFE above-the-knee graft vs PTA-PTA In 65-year-old male patients with femoropopliteal lesions < 10 cm requiring revascularization 61 PTA-BS for patients with: stenosis, claudication, and vein graft vs PTA-PTA In 65-year-old male patients with femoropopliteal lesions < 10 cm requiring revascularization 63 Magnetic resonance angiographic preoperative evaluation vs conventional angiographic preoperative evaluation In patients with limb-threatening peripheral vascular disease 61 PTA-BS for patients with: stenosis, rest pain, and PTFE above-the-knee graft vs PTA-PTA In 65-year-old male patients with femoropopliteal lesions < 10 cm requiring revascularization

349 $/QALY

Panelworthy

Cost-saving Yes

$370

Yes

$1400

Yes

$2400

Yes

$3100

No

$3200

No

$3500

Yes

$5100

No

$8100

Yes

$12,000

Yes

$27,000

Yes

$30,000

Yes

$40,000

Yes

350 Ref. no.

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61 PTA-BS for patients with: stenosis, claudication, and PTFE above-the-knee graft vs PTA-PTA In 65-year-old male patients with femoropopliteal lesions < 10 cm requiring revascularization 24 Routine use of intraoperative autologous transfusion device vs no use of intraoperative autologous transfusion device In patients undergoing elective infrarenal aortic reconstruction for aortoiliac occlusive disease 61 PTA-PTA for patients with: stenosis, necrosis, and vein graft, or occlusion, rest pain, or necrosis, and any type of graft. PTA-BS for patients with: stenosis and any other indication, and PTFE below-the-knee graft; or occlusion, rest pain or necrosis, and any type of graft. BS-Rev for patients with: stenosis (all groups); or occlusion, claudication, and any type of graft vs another strategy In 65-year-old male patients with femoropopliteal lesions < 10 cm requiring revascularization Cerebrovascular Disease 64 Warfarin vs ASA In high-risk stroke—65-year-old with NVAF 64 Warfarin vs no therapy In high risk stroke-65-year-old with NVAF 64 Warfarin vs no therapy In medium risk stroke—65-year-old with NVAF 65 Carotid endarterectomy vs observation In symptomatic 65-year-old at risk for stroke 65 Carotid endarterectomy vs ASA In symptomatic 65-year-old at risk for stroke 66 Multiple screens with ultrasound then magnetic resonance angiography if needed vs wait and see; test with ultrasound then magnetic resonance angiography In patients with asymptomatic neck bruits 67 Embolization and surgery vs no treatment In patients with intraparenchymal cerebral arteriovenous malformations (AVMs), with a mean age of 34 years and complete cure by treatment 68 Carotid endarterectomy plus medical management vs medical management In 67-year-old patients with asymptomatic >= 60% internal carotid artery (ICA) stenosis 64 Warfarin vs ASA In medium risk stroke-65-year-old with NVAF 69 Duplex sonography with carotid endarterectomy if 300 cm/s or higher velocity vs duplex sonography with carotid endarterectomy if 400 cm/s or higher velocity In symptomatic patients suspected to have carotid disease 70 Selective-transesophageal diagnostic strategy (transesophageal echocardiography done only in patients who have had stroke and a history of cardiac problems) vs treat none (no imaging or anticoagulation) In 65-year-old patients in normal sinus rhythm with new-onset stroke

$/QALY

Panelworthy

$80,000

Yes

$600,000

No

Dominated

Yes

Cost-saving Yes Cost-saving Yes Cost-saving Yes Cost-saving No Cost-saving No $5300

No

$7800

No

$8300

No

$8800

Yes

$9200

No

$9300

Yes

$10,000

No

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Intervention vs comparator in target population

$/QALY

Panelworthy

69 Duplex sonography with carotid endarterectomy if 250 cm/s or higher velocity vs duplex sonography with carotid endarterectomy if 300 cm/s or higher velocity In symptomatic patients suspected to have carotid disease 67 Surgery alone vs no treatment In patients with intraparenchymal cerebral arteriovenous malformations (AVMs), with a mean age of 34 yrs. and complete cure by treatment 64 Warfarin vs no therapy In low risk stroke-65 year-old with NVAF 71 Angiography vs no angiography In 40 years old with ruptured cerebral aneurysms or av malformations 70 All-transesophageal diagnostic strategy (transesophageal echocardiography done in all patients who have had stroke) vs selective-transesophageal diagnostic strategy In 65-year-old patients in normal sinus rhythm with new-onset stroke 69 Duplex sonography/magnetic resonance angiography with carotid endarterectomy if positive (250 cm/s or higher for sonography and 70% or greater stenosis for angiography) vs duplex sonography with carotid endarterectomy if 250 cm/s or higher velocity In symptomatic patients suspected to have carotid disease 72 Prompt elective surgical repair for all patients vs expectant management In 40-year-old patients with asymptomatic, unruptured, intracranial aneurysms 72 Prompt elective surgical repair for all patients vs expectant management In 30-year-old patients with asymptomatic, unruptured, intracranial aneurysms 72 Prompt elective surgical repair for all patients vs expectant management In 50-year-old patients with asymptomatic, unruptured, intracranial aneurysms 72 Prompt elective surgical repair for all patients vs expectant management In 60-year-old patients with asymptomatic, unruptured, intracranial aneurysms 73 One-time Doppler ultrasound screening vs no screening In asymptomatic 60-year-old men with a high prevalence of >= 60% carotid stenosis and risk factors such as MI, bruit, or peripheral vascular disease 74 Ticlopidine vs ASA In 65-year-old with high risk of stroke 72 Prompt elective surgical repair for all patients vs expectant management In 70-year-old patients with asymptomatic, unruptured, intracranial aneurysms 73 One-time Doppler ultrasound screening vs no screening In asymptomatic 60-year-old men with a low prevalence of >= 60% carotid stenosis (representative of general population)

$10,000

No

$11,000

No

$15,000

Yes

$20,000 $21,000

No Yes

$25,000

No

$26,000

No

$27,000

No

$28,000

No

$32,000

No

$38,000

Yes

$48,000

No

$50,000

No

$56,000

Yes

352 Ref. no.

Cardiovascular Health Care Economics Intervention vs comparator in target population

$/QALY

Panelworthy

69 Duplex sonography/magnetic resonance angiography with carotid endarterectomy if positive (400 cm/s or higher for sonography and 70% or greater stenosis for angiography) vs duplex sonography/ magnetic resonance angiography with carotid endarterectomy if positive (250 cm/s or higher for sonography and 70% or greater stenosis for angiography) In symptomatic patients suspected to have carotid disease 75 Screening for carotid disease, with carotid endarterectomy if positive vs no screening for carotid endarterectomy In 65-year-old men with no symptoms of carotid disease 64 Warfarin vs ASA In medium risk stroke-65 year-old with NVAF 73 Annual Doppler ultrasound screening vs one-time Doppler ultrasound screening In asymptomatic 60-year-old men with a low prevalence of >= 60% carotid stenosis (representative of general population) 73 Annual Doppler ultrasound screening vs one-time Doppler ultrasound screening In asymptomatic 60-year-old men with a high prevalence of >= 60% carotid stenosis and risk factors such as MI, bruit, or peripheral vascular disease 70 Treat-all diagnostic strategy (no imaging done, all patients receive anticoagulants) vs treat none (no imaging or anticoagulation) In 65-year-old patients in normal sinus rhythm with new-onset stroke 70 Selective-transthoracic diagnostic strategy (transthoracic echocardiography done in all patients who have had stroke and a history of cardiac problems) vs selective-transesophageal diagnostic strategy In 65-year-old patients in normal sinus rhythm with new-onset stroke 70 Selective-sequential-1 diagnostic strategy (transthoracic echocardiography done in patients who have had stroke and a history of cardiac problems, transesophageal echocardiography done in patients with negative findings on transthoracic echocardiography, and no echocardiography done in patients who do not have a cardiac history) vs selective-transesophageal diagnostic strategy In 65-year-old patients in normal sinus rhythm with new-onset stroke 70 All-transthoracic diagnostic strategy (transthoracic echocardiography done in all patients who have had stroke) vs selective-transesophageal diagnostic strategy In 65-year-old patients in normal sinus rhythm with new-onset stroke 70 Selective-sequential-2 diagnostic strategy (transthoracic echocardiography done in all patients who have had stroke and who have a history of cardiac problems, and transesophageal echocardiography done in patients who have negative findings on transthoracic echocardiography and all patients who do not have a history of cardiac problems) vs all-transesophageal diagnostic strategy In 65-year-old patients in normal sinus rhythm with new-onset stroke

$93,000

No

$130,000

Yes

$410,000

Yes

Dominated Yes

Dominated Yes

Dominated Yes

Dominated Yes

Dominated Yes

Dominated Yes

Dominated Yes

Chapter 19 / CUA in Cardiovascular Medicine Ref. no.

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353 $/QALY

70 All-sequential diagnostic strategy (transthoracic echocardiography Dominated done in all patients who have had stroke, and transesophageal echocardiography done in patients who have negative findings on transthoracic echocardiography) vs all-transesophageal diagnostic strategy In 65-year-old patients in normal sinus rhythm with new-onset stroke Heart Transplantation 76 Heart transplantation program vs optimal conventional treatment In patients needing heart transplants in the Netherlands Valvular Disease 77 Anticoagulation therapy with target international normalized ratio of 2.5–3.5 vs no anticoagulation therapy In 35-year-old woman with prosthetic aortic valve 27 Valve replacement for aortic stenosis vs no valve replacement In cardiac patients 78 Discontinued preoperative warfarin requiring 1 added day of heparin/hospitalization vs no additional days of hospitalization In 35-year-old woman with ball valve in the aortic position undergoing non-heart surgery 78 Discontinued preoperative warfarin requiring 2 added days of heparin/hospitalization vs discontinued preoperative warfarin requiring 1 added day of heparin/hospitalization In 35-year-old woman with ball valve in the aortic position undergoing non-heart surgery 78 Discontinued preoperative warfarin requiring 3 added days of heparin/hospitalization vs discontinued preoperative warfarin requiring 2 added days of heparin/hospitalization In 35-year-old woman with ball valve in the aortic position undergoing non-heart surgery

Panelworthy Yes

$46,000

No

$760

No

$2200

No

$250,000

No

$540,000

No

$1,600,000

No

Note: AMI, acute myocardial infarction; BS, bypass surgery; CABG, coronary artery bypass grafting; CPU, cardiopulmonary resuscitation; CHF, congestive heart failure; ECG, electrocardiogram; EMS, = emergency medical services; GP, general practitioner; hrt fxn, heart function; ICD, implantable cardioverter defibrillator; ICU, intensive care unit; LVEF, left ventricular ejection fraction; NoTx, no treatment; NSAID, nonsteroidal anti-inflammatory drugs; PSVT, paroxyxmal supraventricular tachycardia; PTA, percutaneous transluminal angioplasty; PTCA, percutaneous transluminal coronary angioplasty; PTFE, = polytetrafluorethylene graft; RFA, radiofrequency ablation; SK, streptokinase; tPA, tissue plasminogen activator

REFERENCES 1. Tengs TO, Adams ME, Pliskin JS, et al. Five-hundred life-saving interventions and their cost-effectiveness. Risk Analysis 1995;15:369–390. 2. Gold MR, Siegel JE, Russell LB, Weinstein MC. Cost-Effectiveness in Health and Medicine. Oxford University Press, Oxford, England, 1996. 3. Russell LB, Gold MR, Siegel JE, et al. The role of cost-effectiveness analysis in health and medicine. JAMA 1996;276:1172–1177. 4. Weinstein MC, Siegel JE, Gold MR, et al. Recommendations of the Panel on Cost-Effectiveness in Health and Medicine. JAMA 1996;276:1253–1258.

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5. Siegel JE, Weinstein MC, Russell LB, Gold MR. Recommendations for reporting cost-effectiveness analyses. JAMA 1996;276:1339–1341. 6. Chapman RH, Stone PW, Sandberg EA, et al. A comprehensive league table of cost-utility ratios and a sub-table of “Panel-worthy” studies. Med Decis Making 2000;20:451–467. 7. The CEA Registry: standardizing the methods and practices of cost-effectivenss analysis. Available at www.hsph.harvard.edu/cearegistry/ (accessed 3/25/03). 8. Neumann PJ, Stone PW, Chapman RH, et al. The quality of reporting in published cost-utility analyses, 1976–1997. Ann Intern Med 2000;132:964–972. 9. Federal Reserve Bank of St. Louis. Monthly exchange rates series; extracted March 17, 1999. Available from: http//www.stls.frb.org/fred/data/exchange.html 10. US Department of Labor, Bureau of Labor Statistics. Consumer Price Index—All Urban Consumers. Series ID: CUUR0000SA0; extracted March 17, 1999. Available from: http://146.142.4.24/cgibin/surveymost?bls. 11. Weinstein MC, Stason WB. Foundations of cost-effectiveness analysis for health and medical practices. N Engl J Med 1977;296:716–721. 12. Comprehensive league table of cost-utility analyses published through 1997, with ratios converted to 1998 U.S. dollars. Available at www.hsph.harvard.edu/cearegistry/panel_worthy.pdf (accessed 3/25/03). 13. OHE-IFPMA Database Limited: OHE Briefing: The Health Economic Evaluations Database – Trends in Economic Evaluation [report]. OHE-IFPMA, London, England, briefing no. 36, 1998. 14. Kassirer J, Angell M. The Journal’s Policy on Cost-effectiveness Analyses. N Engl J Med 1994;331:669–670. 15. Laupacis A, Feeny D, Detsky A, Tugwell TX. How attractive does a new technology have to be to warrant adoption and utilization? Tentative guidelines for using clinical and economic evaluations. Can Med Assoc J 1992;146:473–481. 16. Zellmer W. Comments of the American society of health-system pharmacists. Presentations at the Food and Drug Administration hearing, “Pharmaceutical marketing and information exchange in managed care environments,” Silver Spring, MD, October 19, 1995. 17. Sloan FA, Whetten-Goldstein K, Wilson A. Hospital pharmacy decisions, cost-containment, and the use of cost-effectiveness analysis. Social Sci Med 1997;45:523–533. 18. Luce BR, Lyles CA, Rentz AM. The view from managed care pharmacy. Health Affairs 1996;15:168–176. 19. Johannesson M, Jonsson B, Kjekshus J, et al. Cost effectiveness of simvastatin treatment to lower cholesterol levels in patients with coronary heart disease. Scandinavian Simvastatin Survival Study Group. New Engl J Med 1997;336:332–336. 20. Mark DB, Hlatky MA, Califf RM, et al. Nelson CL. Cost effectiveness of thrombolytic therapy with tissue plasminogen activator as compared with streptokinase for acute myocardial infarction. New Engl J Med 1997;332:1418–1424. 21. Russell JG. Is screening for abdominal aortic aneurysm worthwhile? Clin Radiol 1990;41:182–184. 22. St. Leger AS, Spencely M, McColloum CN. Screening for abdominal aortic aneurysm: a computer assisted cost-utility analysis. Eur J Vasc Endov Surg 1996;11:183–190. 23. Katz DA, Cronenwett JL. The cost-effectiveness of early surgery versus watchful waiting in the management of small abdominal aortic aneurysms. J Vasc Surg 1994;19:980–990. 24. Huber TS, McGorray SP, Carlton LC. Intraoperative autologous transfusion during elective infrarenal aortic reconstruction: A decision analysis model. J Vasc Surg 1997;25:984–994. 25. Seto TB, Taira DA, Tsevat J. Cost-effectiveness of transesophageal echocardiographic-guided cardioversion: a decision analytic model for patients admitted to hospital with atrial fibrillation. J Am Coll Cardiol 1997;29:122–130. 26. Kerridge RK, Glasziou PP, Hillman KM. The use of “quality-adjusted life years” (QALYs) to evaluate treatment in intensive care. Anaesth Intens Care 1995;23:322–331. 27. Williams A. Economics of coronary artery bypass grafting. BMJ Clin Res Ed 1985;291:326–329. 28. Hogenhuis W, Stevens SK, Wang P. Cost-effectiveness of radiofrequency ablation compared with other strategies in Wolff-Parkinson-White syndrome. Circulation 1993;88:II437–II446. 29. Owens DK, Sanders GD, Harris RA. Cost-effectiveness of implantable cardioverter defibrillator relative to amiodarone for preventino of sudden cardiac death. Ann Intern Med 1997;126:1–12. 30. Nichol G, Laupacis A, Stiell IG. Cost-effectiveness analysis of potential improvements to emergency medical services for victims of out-of hospital cardiac arrest. Ann Emerg Med 1996;27:711–720. 31. Lee KH, Angus DC, Abramson NS. Cardiopulmonary resuscitation: What cost to cheat death? Crit Care Med 1996;24:2046–2052.

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60. Nease RF Jr., Owens DK. A method for estimating the cost-effectiveness of incorporating patient preferences into practice guidelines. Med Decis Making 1994;14:382–392. 61. Hunink MG, Wong JB, Donaldson MC. Revascularization for femoropopliteal disease: A decision and cost-effectiveness analysis. JAMA 1995;274:165–171 62. Sculpher M, Michaels J, McKenna MA. A cost-utility analysis of laser-assisted angioplasty for peripheral arterial occlusions. Int J Technol Assess Health Care 1996;12:104–125. 63. Yin D, Baum RA, Carpenter JP. Cost-effectiveness of MR angiography in cases of limb-threatening peripheral vascular disease. Radiology 1995;194:757–764. 64. Gage BF, Cardinalli AB, Albers GW. Cost-effectiveness of Warfarin and Aspirin for prohylaxis of stroke in patients with non valvular atrial fibrillation. JAMA 1995;274:1839–1845. 65. Nussbaum ES, Heros RC, Erickson DL. Cost-effectiveness of carotid endarterectomy. Neurosurg 1996;38:237–244. 66. Obuchowski NA, Modic MT, Magdinec M. Assessment of the efficacy of noninvasive screening for patients with asymptomatic neck bruits. Stroke 1997;28:1330–1339. 67. Jordan JE, Marks MP, Lane B. Cost-effectiveness of endovascular therapy in the surgical management of cerebral arteriovenous malformations. Am J Neuroradiol 1996;17:247–254. 68. Cronenwett JL, Birkmeyer JD, Nackman GB. Cost-effectiveness of carotid endarterectomy in asymptomatic patients. J Vasc Surg 1997;25:298–309. 69. Kent KC, Kuntz KM, Patel MR. Perioperative imaging strategies for carotid endarectomy: an analysis of morbidity and cost-effectiveness in symptomatic patients. JAMA 1995;274:888–893. 70. McNamara RL, Lima JA, Whelton PK. Echocardigraphic identification of cardiovascular sources of emboli to guide clinical management of stroke: A cost-effectiveness analysis. Ann Intern Med 1997;127:775–787. 71. Kallmes DF, Kallmes MH. Cost-effectiveness of angiography performed during surgery for ruptured intracranial aneurysms. Am J Neuroradiol 1997;18:1453–1462. 72. King JT, Glick HA, Mason TJ. Elective surgery for asymptomatic, unruptured, intracranial aneurysms: A cost-effectiveness analysis. J Neurosurg 1995;83:403–412. 73. Derdeyn CP, Powers WJ. Cost-effectiveness of screening for asymptomatic carotid atherosclerotic disease. Stroke 1996;27:1944–1950. 74. Oster G, Huse DM, Lacey MJ. Cost-effectiveness of ticlopidine in preventing stroke in high-risk patients. Stroke 1994;25:1149–1156. 75. Lee TT, Solomon NA, Heidenreich PA. Cost-effectiveness of screening for carotid stenosis in asymptomatic patients. Ann Intern Med 1997;126:337–346. 76. van Hout B, Bonsel G, Habbema D. Heart transplantation in the Netherlands: Costs, effects and scenarios. J Health Econ 1993;12:73–93. 77. Eckman MH, Levine HJ, Pauker SG. Effect of laboratory variation in the prothrombin-time ratio on the results of anticoagulant therapy. N Engl J Med 1993;329:696–702. 78. Eckman MH, Beshansky JR, Durand-Zaleski I. Anticoagulation for noncardiac procedures in patients with prosthetic heart valves. Does low risk mean high cost? JAMA 1990;263:1513–1521.

20

Beyond Heart Disease Cost-Effectiveness as a Guide to Comparing Alternate Approaches to Improving the Nation’s Health

Tammy O. Tengs, ScD and Nicholas P. Emptage, MA CONTENTS INTRODUCTION USE OF CEA IN HEALTH POLICY INTERNATIONAL USE OF CEA CE IN OTHER POLICY CONTEXTS COMPARING CE RATIOS WHERE TO FIND CE INFORMATION CONCLUSIONS REFERENCES

INTRODUCTION In the past two decades, there has been an explosion of interest in cost-effectiveness analysis (CEA). In 1980, there were 50 articles published with “cost-effectiveness” in the title; in 2000, there were more than 400. Given that national health spending increased 6.9% to $1.3 trillion in 2000—the largest 1-year percent increase in the last decade—it is no wonder that this analytical tool is considered essential. CEA can be used to help understand which medical and public health interventions offer good value for money and which do not. In addition to being of increasing importance within cardiovascular medicine, CEA is now widely used throughout the health care system and beyond. Directors of public health authorities use CEA to evaluate the economic efficiency of preventive interventions. Regulatory agencies use CEA to compare different regulatory approaches and to determine the optimal level of stringency. The World Health Organization (WHO) uses CEA to evaluate and compare health improvement opportunities in developing nations. Decision makers recognize that because resources are limited, it makes intuitive sense to invest first in medical and public health interventions that offer good value for money before proceeding to those that may not. From: Contemporary Cardiology: Cardiovascular Health Care Economics Edited by: W. S. Weintraub © Humana Press Inc., Totowa, NJ

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USE OF CEA IN HEALTH POLICY The Oregon Plan Perhaps the most notable effort to use cost-effectiveness (CE) as a criterion for allocating health resources was the state of Oregon’s plan to ration Medicaid funds by covering only treatments with demonstrated CE. The impetus for the Oregon Plan came in 1987 when the state was forced to end Medicaid reimbursement for transplant surgeries after facing a $48 million need with a $21 million budget. The state recognized that it had a choice to make—the state could provide expensive surgeries for a small number of people or basic health care for many more. Oregon chose the latter. In retrospect, the outcome of this bold decision was predictable. Shortly after the state declined to pay for soft-tissue transplants, Coby Howard, a young boy, died without receiving needed surgery. This highly publicized story intensified the debate over allocation of health care funds in the state. It became clear that transplants were not the only example of high-cost medical care. This debate about the cost and efficiency of medical care, along with the efforts of grassroots organizations and academics, ultimately led to the passage of the Oregon Basic Health Services Act in 1989. The Health Services Act created the Oregon Health Services Commission, charging it with producing a list of medical services to be covered with a limited Medicaid budget. The Commission created a list ranking “condition/treatment pairs” by their own estimate of CE. Their intent was to draw a line in this list at the point where the Medicaid budget was exhausted and reimburse only those treatments above the line (1). Oregon sought to use those resources saved from not covering treatments lower on the list to extend coverage to all residents below the poverty level. An avalanche of public criticism greeted the unveiling of the original 1990 list. This list contained many counterintuitive rankings and appeared to deny coverage for critical treatments, while covering seemingly less important care. For example, dental caps were given a higher ranking than appendectomy and surgery for ectopic pregnancy (2). Also omitted were treatments for several types of cancer, viral pneumonia, acute viral hepatitis, chronic bronchitis, back injuries, aseptic meningitis, perinatal digestive disorders, uncomplicated gallstones, and traumatic brain injury (3). Routine care, such as treatments for nonfatal viral infections, upper respiratory infections, colds, burns, laryngitis, and mononucleosis, did not appear on the list. Academics noted flaws in the methodology used to create the list (4). In fact, Tengs et al. (5) found essentially no correlation between Oregon’s CE estimates and more rigorous estimates from the economic literature. In response to public criticism, the original 1990 list was withdrawn and revised. The next list, produced in 1991, did not use CE as a ranking criterion. Instead, condition/ treatment pairs were grouped into broader categories, and these categories were ranked according to their importance. Within each category, condition/treatment pairs were ranked not by CE, but by net benefit, a measure of the health gains from the intervention. The 1991 list was submitted for waiver approval to the Health Care Financing Administration (HCFA), but was rejected by the Bush administration, which felt that Oregon’s use of quality-of-life valuations violated the Americans with Disabilities Act (ADA). HCFA argued that the act of assigning a lower quality of life to a disabled health state was tantamount to assuming that the life of those who were disabled was worth less than those who were not disabled (6). Furthermore, in a few instances, Oregon had indeed

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ranked treatments provided to nondisabled persons higher than the same treatments provided to disabled persons. For example, alcoholism is considered a disability, and liver transplants for biogenic cirrhosis were covered, whereas transplants for alcoholic cirrhosis were not (1). A revised list was produced in 1992. In this version, quality-of-life valuations were not used to rank the list. Disallowed from considering quality of life, it was pointless to distinguish between medical conditions, and so all outcomes of medical care were divided into three categories: symptomatic, asymptomatic, and death. By 1992, the Clinton administration was in office, and they also rejected the list. Their reason was that many disabled persons could not, by definition, achieve an asymptomatic state and, thus, this list also violated the ADA. In 1993, the final revised list was released. Unable to consider quality of life or even symptom alleviation, the list was ordered primarily by the extent to which the treatment improved 5-year survival. After ranking the list, the 11-member Commission moved condition/treatment pairs around by hand. This fourth list, produced in 1993, was finally accepted by the Clinton administration and is in use today. The problems encountered by the architects of the Oregon Plan illustrate some of the practical and political difficulties associated with using CE to prioritize coverage for medical services. Because of the lack of CE data, the Commission elected to gather its own data and used CE methods that departed from rigorous CEA. Furthermore, the Oregon experience highlighted a fundamental conflict between CEA and the altruistic instinct to rescue endangered life, the so-called Rule of Rescue (7). This argument maintains that we should not deny treatment to individuals in need, regardless of cost (2). Of course, such an allocation scheme, because it effectively ignores resource consumption, would be unlikely to maximize health gains given limited resources and, thus, is not without its own ethical difficulties.

INTERNATIONAL USE OF CEA Internationally, CEA has been used in various contexts. In Sweden, for example, a number of economic analyses have been performed (8). In Swedish county councils (regional political authorities), most economic evaluations have focused on capitalintensive technologies, because they directly affect explicit budget appropriations. In this context, an economic analysis of extracorporeal shock-wave lithotripsy (ESWL) showed that it was safe, effective, and cost-effective, and today there are approximately 25 ESWL units in Sweden (9). In both the United Kingdom and The Netherlands, costutility analyses played an important role in preserving funding for heart transplants (10,11). CE considerations also influenced The Netherlands to include liver transplants in its national insurance package for some indications and affected the decision to initiate a breast cancer screening program (11). In Germany, CEA plays a small, but increasingly important, role in influencing which pharmaceuticals or medical services are covered by the sickness fund (12). In addition, the launch of the international Disease Control Priorities Project was recently announced (13). The project is a 3-year effort to assess disease control priorities and produce science-based CE and other economic analyses to inform health policy in developing countries. A joint project of the Fogarty International Center of the National Institutes of Health, the WHO, and The World Bank, the project is funded by a $3.5 million grant by the Bill & Melinda Gates Foundation (14–16).

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CE IN OTHER POLICY CONTEXTS Beyond medicine, CEA is used to assess a variety of public health measures, regulations, and laws. For example, the Food and Drug Administration (FDA) used CEA to inform their decision about whether to fortify the nation’s food supply with folic acid to reduce the incidence of children born with neural tube defects (17,18). A rigorous economic analysis found that fortifying the national food supply at 0.70 mg/100 g of grain yielded higher population health gains and saved money relative to lower levels of fortification or encouraging women to voluntarily take folic acid supplements. Interestingly, the FDA ultimately decided on a lower level of fortification, 0.14 mg, because of the risk of pernicious anemia in the elderly associated with increased folic acid consumption (Russell LB, personal communication). As another example, researchers at the Centers for Disease Control (CDC) conducted an analysis of the CE of hepatitis B immunization (19) which influenced many states to begin vaccination programs to target adolescents (Riggs TL, personal communication). Finally, federal agencies, such as the National Highway Traffic Safety Administration (NHTSA), Environmental Protection Agency (EPA), Occupational Safety and Health Administration (OSHA), and Consumer Product Safety Commission, perform regulatory impact analyses of major rules (20,21). For example, NHTSA performed a CEA to assess the value of requiring automobile manufacturers to install shoulder belts and airbags in addition to lap belts (22) and front-disk vs dual-master braking systems (23). OSHA has conducted analyses of reducing occupational exposure to methylene chloride (24), asbestos (25), and increasing underground construction safety standards (26). An economic analysis also featured prominently in the Bush administration’s decision whether the EPA should set maximum arsenic levels in drinking water at 3, 5, 10, or 20 µg/L. Executive Order 12291, issued by the Reagan administration, requires cost–benefit analyses for all proposed rules (27). This order required that all regulations justify their costs and undergo review by the Office of Management and Budget (OMB). Executive Order 12866, issued by President Clinton, however, states that only “economically significant” regulations (costing $100 million or more) must be submitted for OMB review (28). In addition to its use in setting regulation, CEA have been used in lawmaking. For example, in the 1970s economic analysis prominently featured in the debate over whether to pass a nationwide 55-mph speed limit (29,30). In his analysis of this change in the law, Kamerud (31) considered costs, such as enforcement and compliance, productivity losses, travel time in buses and commercial vehicles, and the value of passenger time. He compared these with benefits, such as lives saved, averted costs of accidents, fuel savings, and reduced vehicle wear. The conclusions were that the 55-mph speed limit was cost beneficial on most highways, with the exception of rural interstates.

COMPARING CE RATIOS CE can inform different kinds of decisions, including the most efficient intensity or periodicity of a particular intervention, the best choice among multiple interventions for the same medical condition, and the mix of interventions that will lead to the greatest gains in societal health. As an example, CEA was used in the debate among cancer specialists over the optimal frequency of pap smear screening for cervical cancer (32). Building on the work of

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Eddy (33), Fahs et al. (34) compared the CE of screening annually, every 3 years, every 5 years, or just once in a lifetime at age 65. They found that, when compared to screening once at 65, screening every 5 years cost $1500 per life year saved. Furthermore, in comparison to screening every 5 years, screening every 3 years cost $6000 per life year. The CE of annual screening when compared to screening every 3 years, however, cost nearly $40,000 per life year. Clearly, CE varies with the intensity of an intervention. CE information is also useful for comparing alternate treatments for the same disease. For example, Leung et al. (35) compared the CE of Paclitaxel, Docetaxel, and Vinorelbine chemotherapy for anthracycline-resistant breast cancer. As another example, Cromwell et al. (36) compared group intensive counseling with individual intensive counseling for smoking cessation. Finally, the combined results of multiple CE studies may be useful for optimizing a portfolio of health improvement interventions. For example, using linear and integer programming techniques, Tengs and Graham (37) found that the United States could save 60,000 lives (or 636,000 life years) annually at no additional cost by shifting resources away from interventions that were cost-ineffective to those that were cost-effective.

WHERE TO FIND CE INFORMATION There are a number of disease-specific reviews of CE information, and more are appearing in journals every day. Holloway et al. (38) reviewed 26 CEAs related to stroke. In 1994, CDC researchers reviewed CE studies of AIDS prevention and treatment (39). Miller and Levy (40) reviewed 84 cost-utility studies of interventions aimed at injury prevention. Although condition-specific reviews are potentially very useful, other authors have taken a broader approach and have considered the relative value of interventions for all diseases and across all sectors of society. For example, Tengs et al. (41) reviewed the CE of more than 500 life-saving interventions. In addition to the previous reviews, published as journal articles, other authors have taken the approach of creating databases of CE information. The Centers for Disease Control and Prevention have made public an online bibliography of CE and cost–benefit studies, containing information on more than 3000 economic studies (42,43). A database of CE studies was commissioned by the UK Department of Health and compiled at the University of York. Currently, the Register of Cost-Effectiveness studies is contained within the National Health Service’s Economic Evaluations Database (44). The International Federation of Pharmaceutical Manufacturers’ Office of Health Economics has also made a large bibliography of CE studies available on CD-ROM or with temporary guest access on the Web (45). Finally, the Health Priorities Research Group at the University of California, Irvine, has compiled a database of published CE studies of cancer interventions (46). This database, being the largest of its kind, includes data on the CE of more than 1000 interventions aimed at cancer prevention, screening, and treatment. It is available free-of-charge over the Internet.

CONCLUSIONS CEA has been used in a wide array of medical and policy contexts. Arguably, it is one of the most useful tools in existence for making difficult choices in the face of limited resources. Although some subjectivity and inconsistency is inherent in all types of research and decision-making processes, CEA have a certain transparency to them.

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Furthermore, the “rule” for making decisions is clear—adopt the technology if it the health gains are large enough to warrant the costs; otherwise, do not adopt the technology. Decisions based on political or other considerations are more likely to be prone to biases that may not lead to maximal health gains. Most advocates of the CEA approach acknowledge its weaknesses and do not suggest that CE be the only criterion used to allocate health resources (39,49). Physicians must uphold their professional duty to give their patients the best health care possible, and health plans and government policymakers must always pay close attention to concerns beyond those that can be captured in a CE ratio. However, CE ratio are a uniquely valuable tool because they combine a myriad of factors, such as the incidence of disease, treatment efficacy, medical costs, and survival rates into a single, easily interpretable figure. Furthermore, recent steps taken to standardize methods and make CE data available on the Internet will increase the use of this information in medical and public health decisions. We expect that this will ultimately lead to greater population health gains at lower cost.

REFERENCES 1. Tengs TO. An evaluation of Oregon’s Medicaid rationing algorithms. Health Econ 1996;5:171–181. 2. Hadorn DC. Setting health care priorities in Oregon: Cost-effectiveness meets the rule of rescue. J Am Med Assoc 1991;265:2218–2225. 3. Ferrara PJ. Power to the People—Positive Alternatives to the Oregon Health Plan Health Care Policy Insight, vol. 3. Cascade Policy Institute, Portland, OR, 1994. 4. Eddy DM. Oregon’s methods: Did cost-effectiveness analysis fail? J Am Med Assoc 1991;266:2135–2141. 5. Tengs TO, Meyer G, Siegel JE, et al. Oregon’s Medicaid ranking and cost-effectiveness: Is there any relationship? Med Decis Making 1996;16:99–107. 6. Orentlicher D. Rationing and the Americans with Disabilities Act. J Am Med Assoc 1994;271:308–314. 7. Jonsen A. Bentham in a box: Technology assessment and health care allocation. Law Med Health Care 1986;14:172–174. 8. Ramsberg JA, Sjoberg L. The cost-effectiveness of lifesaving interventions in Sweden. Risk Anal 1997;17:467–478. 9. Jonsson B. Economic evaluation of medical technologies in Sweden. Social Science Med 1997;45:597–604. 10. Drummond M, Cooke J, Walley T. Economic evaluation under managed competition: Evidence from the U.K. Soc Sci Med 1997;45:583–595. 11. Elsinga E, Rutten F. Economic evaluation in support of national health policy: The case of the Netherlands. Soc Sci Med 1997;45:605–620. 12. Schulenburg, JM. Economic evaluation of medical technologies: From theory to practice – The German perspective. Soc Sci Medicine 1997;45:621–633. 13. Disease Control Priorities Project website. Available at: www.nih.gov/fic/dcpp. Accessed September 5, 2002. 14. Jamison DT, Mosley WH, Measham AR, Bobadilla JL. Disease Control Priorities in Developing Countries. Oxford University Press, New York, NY, 1993. 15. Murray CJ, Evans DB, Acharya A, Baltussen RM. Development of WHO guidelines on generalized cost-effectiveness analysis. Health Econ 2000;9:235–251. 16. Murray CJ, Evans DB, Acharya A, Baltussen RM. Development of WHO guidelines on generalized cost-effectiveness analysis. Health Econ 2000;9:235–251. 17. Food and Drug Administration. Food standards: Amendment to the standards of identity for enriched grain products to require addition of folic acid. Fed Reg 1993;58:53,305–53,312. 18. Kelly AE, Haddix AC, Scanlon KS, et al. Appendix B: Cost-effectiveness of strategies to prevent neural tube defects. In: Gold MR, Siegel JE, et al. (eds.) Cost-Effectiveness in Health and Medicine. Oxford University Press, New York, NY, 1996, pp. 313–348. 19. Margolis HS, Coleman PJ, Brown RE, et al. Prevention of hepatitis B virus transmission by immunization. An economic analysis of current recommendations. J Am Med Assoc 1995;274:1201–1208.

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20. Morrall JF. A review of the record. Regulation 1986:25–34. 21. Travis CC, Richter SA, Crouch EAC, et al. Environ Sci Technol 1987;21:415–420. 22. National Highway Traffic Safety Administration, Plans and Programs, Office of Planning and Analysis. Final regulatory impact analysis amendment to FMVSS no. 208 passenger care front seat occupant protection, Washington, DC, 1984. 23. Kahane, CJ. An evaluation of side structure improvements in response to federal motor vehicle safety standard 214. Office of Program Evaluation, National Highway Traffic Safety Administration, Washington DC, 1982. 24. Occupational Safety and Health Administration. Final economic and regulatory flexibility analysis for OSHA’s standard for occupational exposure to methylene chloride. Office of Regulatory Analysis, Occupational Safety and Health Administration, Washington, DC, contract no. H-071B, 1996. 25. Occupational Safety and Health Administration. Final regulatory impact and regulatory flexibility analysis of the revised asbestos standard. Office of Regulatory Analysis, US Department of Labor, Occupational Safety and Health Administration, Washington, DC, 1986. 26. Occupational Safety and Health Administration. Underground construction; Final rule. Fed Reg 1989;54:23,824–23,857. 27. Executive Office of the President. Executive Order 12291 of February 17, 1981. Fed Reg 1981;46:13,193–13,198. 28. Executive Office of the President. Executive Order 12866 of September 30, 1993. Fed Reg 1993;58:1925–1933. 29. Castle GH. The 55 mph speed limit: a cost/benefit analysis. Traffic Engineering 1976;11–14. 30. Forester TH, McNown RF, Singell LD. A cost-benefit analysis of the 55 MPH speed limit. Southern Econ J 1984;50:631–641. 31. Kamerud, DB. Benefits and costs of the 55 mph speed limit: New estimates and their implications. J Pol Anal Manage 1988;7:341–352. 32. Schulman KA, Yabroff KR. Measuring the cost-effectiveness of cancer care. Oncology 1995;9:523–538. 33. Eddy DM. Screening for cervical cancer. Ann Intern med 1990;113:214–226. 34. Fahs MC, Mandelblatt J, Schechter C, Muller C. Cost effectiveness of cervical cancer screening for the elderly. Ann Intern Med 1992;117:520–527. 35. Leung PP, Tannock IF, Oza AM, et al. Cost-utility analysis of chemotherapy using paclitaxel, docetaxel, or vinorelbine for patients with anthracycline-resistant breast cancer. J Clin Oncol 1999;17:3082–3090. 36. Cromwell J, Bartosch WJ, Fiore MC, et al. Cost-effectiveness of the clinical practice recommendations in the AHCPR guideline for smoking cessation. J Am Med Assoc 1997;278:1759–1766. 37. Tengs TO, Graham JD. The opportunity costs of haphazard social investments in life-saving. In: Hahn RW (ed.) Risks, Costs, and Lives Saved: Getting Better Results from Regulation. Oxford University Press, New York, NY, 1996, pp. 167–182. 38. Holloway RG, Benesch CG, Rahilly CR, Courtright CE. A systematic review of cost-effectiveness research of stroke evaluation and treatment. Stroke 1999;30:1340–1349. 39. Holtgrave DR, Qualls NL, Graham JD. Economic evaluation of HIV prevention programs. Annu Rev Public Health 1996;17:467–488. 40. Miller TR, Levy DT. Cost-outcome analysis in injury prevention and control: Eighty-four recent estimates for the United States. Med Care 2000;38:562–582. 41. Tengs TO, Adams ME, Pliskin JS, et al. Five-hundred life-saving interventions and their cost-effectiveness. Risk_Anal 1995;15:369–390. 42. Elixhauser A, Luce BR, Taylor WR, Reblando J. Health care CBA/CEA: An update on the growth and composition of the literature. Med Care 1993;31(Suppl):JS1–JS11. 43. Friede A, Taylor WR, Nadelman L. On-line access to a cost-benefit/cost-effectiveness analysis bibliography via CDC WONDER. Med Care. 1993;31(Suppl 7):S12–S17. 44. National Health Services, Economic Evaluations Database. Available at: http://agatha.york.ac.uk/welcome.htm. Accessed on September 11, 2002. 45. Office of Health Economics. Bibliography of cost-effectiveness studies. Available at: http://www.oheheed.com/. Accessed on September 11, 2002. 46. Tengs TO. Cost-effectiveness Database [database online]. Health Priorities Research Group, University of California, Irvine, Irvine, CA, 2002. Available at: http://www.hprg.uci.edu. Accessed February 27, 2003. 47. Eddy DM. Cost-effectiveness analysis: A conversation with my father. J Am Med Assoc 1992;267:1669–1675.

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Using Economic Studies for Policy Purposes Rajiv Shah, MD and Kevin G. M. Volpp, MD, PhD CONTENTS INTRODUCTION ECONOMIC STUDIES AND THE GROWTH OF THE US HEALTH CARE SYSTEM USING COST–BENEFIT ANALYSIS TO APPROXIMATE MARKET INTERACTIONS REAL-WORLD APPLICATIONS OF ECONOMIC ANALYSIS IN POLICY DECISION MAKING EXAMPLES OF OTHER ECONOMIC STUDIES THAT CAN INFLUENCE POLICYMAKING SUMMARY REFERENCES

INTRODUCTION Economics is the study of how to best allocate limited resources. Economics assumes that resources are inherently limited, and decision makers must choose how to employ these resources between alternative uses. Ever since Adam Smith described an economic market as an “invisible hand,” which automatically allocates resources efficiently, economists have believed that competitive markets—with their inherent rules of supply and demand—should help guide resource allocation. Market incentives drive the quantity, price, and allocation of goods and services, and if the market meets certain competitive conditions, it will allow people who most value particular goods and services to obtain them at fair prices. These incentives ensure that producers create high-quality goods and services efficiently—at the lowest possible prices for a given quality product. Goods and services continue to be traded as long as trades make all parties better off. This exchange of resources would continue until no one could be made better off without making someone else worse off—an efficient outcome by economic standards. The past century of American medicine has witnessed tremendous growth in medical capabilities and the development of a large, modern health care system. In 1998, From: Contemporary Cardiology: Cardiovascular Health Care Economics Edited by: W. S. Weintraub © Humana Press Inc., Totowa, NJ

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Americans invested $1.15 trillion—or 13.5% of total gross domestic product (GDP)— in health care, and how we allocate these resources determines whether or not they are used to maximize the health benefit of our investment (1). Should the rules of the marketplace, the rules of supply and demand, determine the quantity and price of services offered? Will market allocation in health care achieve efficient outcomes, or can we employ current resources differently to make more people better off without making anyone worse off? Many of these questions regarding health care resource utilization fall under the rubric of economics analyses applied to health care. Although the rules of economics clearly apply, in some form, to the US health care system, this system has never been a simple, laissez-faire competitive market for goods and services. Because we have been unable to rely on competitive markets to allocate resources efficiently, other decision makers have had to step in and assume the role theoretically reserved for the market’s “invisible hand.” Economic analyses have been conducted to aid decision makers, including government policymakers, private payers, providers, and patients in this effort. In the context of clinical medicine, a certain type of economic analysis, cost-effectiveness analysis (CEA), has been employed to evaluate the relative merits and expense of specific medical interventions. The methods and results of such studies have been described throughout this book. These studies intend to influence resource allocation by promoting medical interventions that yield the most benefit per dollar—allowing the system to maximize the total benefit generated from a limited supply of dollars and achieve an economically efficient outcome. This chapter discusses some of the challenges inherent in using economic studies in medicine to meet this goal. In this chapter, we describe how economic studies are translated into policy, and whether these studies do, in fact, allow the system to approximate economic efficiency. In order to address this question, we present a brief history of efforts to allocate resources effectively in modern American health care and describe how economic studies could be used in resource allocation decisions. We discuss how cost–benefit studies can serve as a guide to help policymakers replace the market allocation system, as long as costs and benefits can be measured accurately and compared. However, most cost–benefit analyses require interpreting the benefit of a medical intervention in dollar terms, and this has proven to be a difficult and controversial task. As a result, most economic studies in medicine describe the outcomes of an intervention in terms of clinical effectiveness, not benefits translated into dollars. These cost-effectiveness (CE) studies are less controversial, but it can be difficult to translate the results into policy, as comparisons between dissimilar alternatives can be challenging. Nevertheless, some important policymakers have tried, and we discuss one important application of these economic concepts, the Oregon health care coverage experiment. In the process, we will provide a framework for understanding why and how economic studies should be used in policy decision making, and how reality differs from this theoretical ideal.

ECONOMIC STUDIES AND THE GROWTH OF THE US HEALTH CARE SYSTEM A System Designed to Promote Perceived Technical Quality In theory, the underlying rationale for economic analysis of costs and benefits is simple. Because resources for all goods and medical services are inherently limited, choices must be made between their alternative uses. By better understanding the trade-offs that

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are being made in terms of costs and benefits, collectively, we hope to make better decisions that enable us to achieve greater value for the money spent. When compared to existing practices, health care technologies that allow us to achieve greater value—either by lowering costs, improving benefits, or both—should be incorporated into the progress of medical care. Interventions that fail to meet this standard would not represent a better use of limited resources and should not be incorporated into medical care. Although taken for granted in the competitive market, this prescription has never defined the US health care system. Modern American medicine is the result of tremendous strides in technological ability, a financing structure characterized by third-party payment and government involvement, as well as decades of relative provider autonomy. In this environment, providers determined which technologies were most appropriate to use, while benefiting financially from higher technology approaches to patient care. Patients, usually covered by a third-party payer, did not encounter the full cost care when they utilized services and, naturally, wanted any intervention that could improve their health status—whether or not its expected benefit justified its cost. As a result, new technologies have often been adopted, disregarding whether or not that technology enabled consumers to achieve greater health benefit for the money spent. For decades, the insurers and employers that paid the bills allowed this system to flourish. In 1982, one prominent observer of American health care noted, The dynamics of the system in everyday life are simple to follow. Patients want the best medical services available. Providers know that the more services they give and the more complex the services are, the more they earn and the more they are likely to please their clients. Besides, physicians are trained to practice medicine at the highest level of technical quality without regard to cost. Hospitals want to retain their patients, physicians, and community support by offering the maximum range of services and the most modern technology, often regardless of whether they are duplicating services offered by other institutions nearby. Though insurance companies would prefer to avoid the uncertainty that rising prices create, they have generally been able to pass along the costs to their subscribers, and their profits increase with the total volume of expenditures. No one in the system stands to lose from its expansion. Only the population over whom the insurance costs and taxes are spread has to pay, and it is too poorly organized to offer resistance (2).

Because there was relatively little meaningful data on clinical effectiveness, technical sophistication and perceptions of technical quality were used as a proxy for health care effectiveness by both providers and consumers. Whereas cardiovascular medicine has been guided by several carefully designed, multicenter randomized controlled trials (RCTs), most of medical practice lags behind cardiology in data-driven practice (3). In fact, even when available, good data do not necessarily drive decision making. Although most physicians acknowledge that staying current with the latest research findings is a laudable goal, most practicing clinicians base their decisions on what they observed in medical school and their own prior experience (4). Consumers often remain unable to access clinically relevant data—few patients read medical journals, process RCT results, and are able to judge the quality of care they receive. As a result, both providers and consumers rely on observable substitutes, such as technical sophistication, in their attempt to determine which treatment approaches are better. Uncertainty among consumers about health care quality has been a long-recognized distinctive characteristic of medical markets (5). Because providers have specialized

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knowledge that develops over years of education and training, they have much more information than their patients. Even when patients are presented with objective information about effectiveness and outcomes, their state of ill health may render them unable to make reasoned judgments about whether or not a treatment is appropriate and delivered in a high-quality manner. Today, some insurers have started collecting validated, risk-adjusted information on the quality of care at different providers, but these efforts are generally not rigorous, systematic, or broadly disseminated. Even as this information improves, it is unclear if patients will be able to use the information effectively in their interactions with the health care system. The lack of readily accessible measures of technical quality and the payment incentives that existed in much of the country until the mid-1980s created a system built around trying to maximize perceived technical quality, not around maximizing the aggregate health benefit achieved with limited resources.

Cost Growth as an Impetus for Change As the ever-expanding quest to provide more technically sophisticated medical care drove up costs, leading insurers and employers took up the task of cost containment. In 1960, Americans spent 5.1% of GDP output on health care. By 1998, that proportion grew to 13.5% (6). Medical care cost inflation led nearly all sectors of the US economy, as health care costs grew by 4.5% per year in real terms (in nominal terms, the annual rate of cost growth was often in double digits). There are multiple explanations for the high level of health care costs—third-party payment accounts for between 75% and 80% of all health care spending, the population is aging, administrative costs are high when compared to other health care systems, and care that is of marginal benefit, but high cost (particularly at the end of life), is routinely provided to patients. However, most analysts agree that much of the rate of cost growth from year to year is caused by the adoption and utilization of new medical technologies and treatments (7). Since the mid-1980s, governments, employers, and insurers have sought to achieve control over these escalating costs by, in theory, creating a system oriented around cost-effectiveness as opposed to technical quality. Medicare led these policy efforts by changing its reimbursement structure, attempting to change its benefit design to achieve value-oriented use of health care resources. Large employers and private insurers also took up the responsibility for driving the cost-effective use of resources—often by promoting managed care. In the process, both governments and the private sector sought to expand the use of cost-effectiveness analysis (CEA) in guiding the allocation of resources. Understanding what happened with each of these efforts will help set the stage for how CEA is used in health care policymaking today.

Efforts to Control Costs and Create a System Oriented Around CE GOVERNMENT ATTEMPTS TO INTRODUCE CEA INTO POLICYMAKING Since the federal government created Medicare by passing The Social Security Amendments of 1965, Medicare has played a dominant role in determining which technologies are adopted as the medical field moves forward. Until the mid-1980s, Medicare determined which benefits it would cover, paying providers based on the costs they incurred in providing those benefits to patients, in addition to compensation for their time. Blue Cross/Blue Shield and other private payers mimicked Medicare’s benefit package and its cost-plus reimbursement strategy (8). As a result, the design of

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Medicare’s benefit package—identifying which treatments Medicare would cover— determined what new technologies were adopted nationwide. Ideally, Medicare’s benefits package should have resulted from well-informed federal government approximations of the CE of various medical treatments. Under this scenario, the Health Care Financing Administration (HCFA), which administers Medicare, would predict the value of a particular treatment to Medicare beneficiaries, compare this value to the treatment’s cost, and determine whether or not to pay for a particular therapy based on this cost–benefit analysis or CEA. If, on average, the additional benefit of a specific treatment regime exceeded its added cost, it would be included in the benefits package. If several alternative treatments met this condition, use of the most cost-effective treatment should be encouraged. However, CEA was rarely used in this manner to determine Medicare’s benefit design and drive the efficient use of health care resources. Although Medicare tried to utilize CEA in making coverage decisions, even utilizing an economic studies organization called the Office of Technology Assessment, it largely covered whatever technologies providers determined were the best approach for treatment (9). In fact, under the cost-plus reimbursement system, providers could determine which technologies to use to provide a particular treatment, and Medicare would pay higher reimbursements for more technically sophisticated techniques. Even when the government tried to use economic studies to guide policymaking, many benefit package design issues were driven by politics rather than CE. One revealing example occurred when the Agency for Health Care Policy and Research (AHCPR) actively tried to implement the results of an important CE study the agency had commissioned. The study found that surgeries to alleviate lower back pain from herniated disks yielded little or no positive health outcomes, yet were routinely performed by back surgeons. The study recommended that physicians use more prudence in prescribing this dangerous surgery, and HCFA tried to lower its reimbursements for this treatment in an effort to discourage its use. In response, a group of surgeons who would have encountered financial losses from the policy change organized an effective lobbying effort to fight the agency. Senator Patrick Moynihan called HCFA’s action a “sin against God” from the floor of the US Senate. In the end, HCFA was unable to make its proposed reimbursement change, and the AHCPR had its funding cut by Congress by more than 21% (10). Examples of the political process trumping a more scientific application of CEA extend well beyond Medicare. Today, everything from mandatory bone mass measurement for osteoporosis to direct access to specialists is debated in Congress as potential mandated benefits. These mandated benefits are also politically popular at the state level. In 1970, there were 48 insurance benefits required by state governments. By 1991, the number of these insurance regulations increased to 991 (11). Because mandated benefits are the result of political debates, the results—whether high reimbursement rates for back surgeons or coverage for chiropractic care at the state level—are rarely directly recommended by cost–benefit analyses. PRIVATE SECTOR EFFORTS TO ENCOURAGE COST-EFFECTIVE USE OF HEALTH CARE RESOURCES Since the early 1980s, many private plans have attempted to adopt new, more innovative techniques to control health care expenditures while maintaining or improving

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health outcomes. Confronted with the need to allocate limited resources efficiently, private insurers began replacing fee-for-service reimbursement with financial and administrative managed care practices designed to promote a more cost-effective allocation of health care resources. The first insurers to utilize such practices were health maintenance organizations (HMOs), which often utilized primary care physicians as gatekeepers, essentially asking them to consider the cost of care when determining whether or not a patient should obtain specialized treatment. Since then, managed care organizations have evolved. Today, they utilize a variety of financial and administrative mechanisms to help guide health care resource utilization (12). Financial mechanisms include beneficiary copayments, capitated (or fixed prepayments) payments to physicians, and provider withholds or bonuses. Administrative mechanisms include treatment guidelines, institutional review, electronic patient pathways, and a variety of other care management activities. Ideally, health plans would make coverage and resource allocation decisions based on trade-offs between the costs and benefits of different types of care, then use managed care tools and incentives to implement the decisions. Because managed care plans are not a direct part of the political process, they were thought to be less susceptible to the special interest lobbying that often impeded Medicare’s cost-containment efforts. As a result, these new managed care plans were expected to help reduce the rate of health care cost growth, creating a more rational environment for evaluating and adopting new medical technologies. In fact, managed care did help contain costs. As more people moved into managed care plans, overall health care cost growth fell. Although relatively rare in the early 1990s, managed care has become the predominant form of private insurance coverage. In 2000, nearly 60% of all Americans, including nearly 89% of all privately insured nonelderly citizens, were in health plans that actively managed their care (13). Between 1993 and 1997, as managed care penetration grew, real health care cost growth fell from 4.5% to 2% annually. Compounded, this seemingly modest drop in cost growth means that we now spend 12–14% less on health care than we otherwise would—a savings of more than $160 billion in 1998 (14). Although there have been few rigorous outcome studies, there is no clear evidence that managed care achieved these savings by decreasing the quality of care (15). For instance, outcomes among heart attack patients in Massachusetts did not vary across payer type. Patients in managed care plans experienced the same likelihood of receiving key interventions (e.g., coronary catheterization) as patients with traditional indemnity coverage (12). Managed care organizations appear to have achieved cost savings by aggregating purchasing power to win lower unit prices from doctors and hospitals, although it can be difficult to measure risk selection and be certain of the comparability of populations. Close examination of managed care decision making indicates that despite the stated desire of managed care organizations to lower costs without sacrificing clinical outcomes, systematic CEA was not aggressively used in the plans’ benefit design. Their guidelines, utilization review regulations, and other care management processes are often standardized, and many plans do not disclose how they make their coverage decisions. Some of the largest plans, despite having access to the treatment costs and health outcomes within their plans, refer new technology approval decisions to expert medical panels that usually discuss medical efficacy without considering costs. In fact, many insurers ask these expert panels to provide medical, not financial, opinions and,

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subsequently, note that their medical experts have records of approving most newly evaluated technologies (16). The private sector also proved to be less immune to political pressure and public opinion than originally thought, especially when trying to pare away benefits that covered expensive and ineffective, or at least unproven, medical therapies. As patients witnessed insurers trying to limit therapies their providers often recommended, they became angry and sought political recourse. The ensuing managed care backlash brought health insurance regulation to the forefront of the legislative agenda, and politics again overwhelmed economic analysis in driving government policy. The politically charged furor over the practice of allowing only a 1-day stay for a mother who underwent a normal delivery is often cited as a starting point for this backlash (17,18). Legislation at both the state and federal level requiring insurers to pay for a minimum 2-day stay for uncomplicated vaginal deliveries, and a minimum 4-day stay for Cesarean sections, generated widespread bipartisan support, culminating in the passage of The Newborns’ and Mothers’ Health Protection Act of 1996, which mandated these minimum stays nationwide (19). HMOs bore much of the brunt of the criticism, although it became clear that 1-day stays had become common among all insurers (20). Furthermore, 1-day stays for normal vaginal deliveries were never proven to have any worse outcomes than longer stays. As Jerome Kassirer, the former editor of the New England Journal of Medicine, put it, “regardless of whether HMOs are to blame for the trend toward drive-through deliveries, it was clear that both the push towards 1-day stays and society’s response of mandated minimum stays have taken place largely in the absence of relevant data on the impact of these changes on quality of care” (21). When making policy and coverage decisions, private insurance plans do not appear to prioritize CEA. Instead, they continue to offer similar benefits packages, and when they try to develop restrictions—even restrictions that might be justified by effectiveness data—they confront the same politicized legislative environment that has undone government efforts to rationally reduce costs. More significantly, the managed care backlash has convinced many plans to abandon efforts to contain costs through traditional managed care techniques. At least one major insurer, United Health Care, has announced that it will cease using financial and administrative techniques designed to encourage provider restraint in the provision of care. In doing so, it seems to be returning to the days of relative provider and patient autonomy (22). Whereas managed care did achieve a one-time cost savings when compared to other forms of coverage, it does not appear to have changed the underlying rate of growth, or how the health care system adopts and integrates new technologies. Although some research indicates that areas with higher managed care penetration are slower to adopt certain technologies (e.g., MRIs) this trend does not appear to be correlated with lower rates of overall cost growth (23). In fact, after experiencing a one-time cost savings, health care costs appear to have returned to their traditionally high real rates of growth. In 2001, total health care costs rose between 4% and 6% in real terms, and private health plans experienced nominal average cost increases of 10–13% (24). With the return of double-digit health care cost increases, a looming Patients’ Bill of Rights, and with many managed care firms returning to the days of relative patient and provider autonomy, the prospects seem dim for private insurers to promote CEA as the standard for determining which technologies to adopt and use. Public sector policymakers do not seem to be offering many solutions either. A closer look at the economic

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tools that were supposed to drive decision making and achieve a lower, rational cost structure for American health care will help us identify why these efforts—by both private and public sector decision makers—have proven to be so difficult to implement.

USING COST–BENEFIT ANALYSIS TO APPROXIMATE MARKET INTERACTIONS Our brief overview of recent history indicates that cost–benefit analysis has not been used effectively to design and operate the US health care system. Both the government and the private sectors have tried to use CEA to change existing resource allocation and achieve more value for the health care dollar, but neither has been particularly successful. The following section describes the theoretical role that cost–benefit evaluations, including CEA, play in health care and the principles behind their use in health policy decision making.

Cost–Benefit Analysis is a Substitute for Marketplace Decision Making In teaching cost–benefit analysis and CEA, one prominent economist joked, “The market is a giant cost–benefit calculator that runs without batteries” (Pauly MV, personal communication). A private market brings buyers and sellers together, allowing them to engage in mutually advantageous exchanges. Buyers signal their preferences for certain types of products by purchasing the things they want, and producers profit by meeting this consumer demand efficiently. Because buyers and sellers undertake market behavior voluntarily, any exchange indicates that both parties are better off having made the trade. Trades—usually money for goods and services—continue to occur until no one could be made better off without making someone else worse off. Once this condition is achieved, a market is said to be efficient. This condition has a specific name, Pareto efficiency, and it represents a minimum efficiency condition that every market strives to achieve. Consider the market for food, or specifically, groceries. Individuals, based on their preferences for different types of food items, go to the store and purchase the type of food they want. Consumers choose the flavors, colors, quantities, and brands they prefer, and they factor the price of various food items into their decision making. They also consider how much of their total income they want to spend on food, because they know that money saved at the grocery store can be used on other things they want. Producers of food, each competing with each other, provide the items they think consumers want at the combination of quality and price that will allow the product of sales quantity multiplied by unit profitability as large as possible. They gather data on sales and consumer preferences, adjusting their products and selections accordingly. Consumers do not buy items that they value to be less than the prices given, e.g., the benefit of the groceries must exceed their cost for each customer in the check-out lane. In fact, consumers choose the precise bundles of products that maximize their benefit for the cost of the total grocery bill—each consumer has acted as his or her own cost–benefit calculator. In order for a market to function, “as millions of individual cost–benefit calculators operating without batteries,” four conditions need to be met. Assuming that the motives of producers and consumers are driven by self-interest (producers seek profits, and consumers seek to use their money to maximize their well-being), these conditions allow for what economists call a competitive market that may yield a Pareto-efficient

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outcome. First, numerous firms and consumers must characterize the market so that no single firm has undue influence over output levels, and no single consumer can dramatically increase or decrease the total demand for a product. Firms must be able to enter the market freely and to offer their goods and services. Second, firms must produce a homogenous commodity so that any one firm’s product is a perfect substitute for another firm’s product. This allows consumers to understand and compare product quality and price. Third, information regarding price and quality must be “perfect.” Consumers must know the price and quality of each firm’s product, and producers must be able to observe consumer-purchasing behavior. Fourth, transactions—exchanging money for goods or choosing one firm over another—must be costless. If a consumer had to leave a store to go another one across town in order to obtain a substitute product, this would entail a cost that affects consumer decision making. In health care, these conditions are rarely met. 1. Providers are unable to freely enter the market and offer goods and services. Government and medical boards both require licensing and credentialing of providers before they can supply goods and services. Institutional providers, including hospitals, must often obtain certificates of need before they can enter a market. Furthermore, because many services require such a high level of fixed investment and specialized training, there are often not multiple providers of a similar service that compete for business (25). 2. Firms rarely produce homogenous commodities that are perfect substitutes across firms—at least from the patient’s perspective. Even small differences in how medical treatments are provided can cause patients to think that the treatments are sufficiently different from one another. 3. Information certainly is not perfect. Uncertainty dominates the medical interaction, as knowledgeable providers always remind patients that no outcome is guaranteed, and there is always the chance that something could go wrong. Statistically valid comparisons of risk-adjusted outcomes between providers are rarely available. The asymmetric information between patients and providers further leads patients to defer to providers in making decisions, thus, impeding consumers’ ability to objectively learn about all of the possible providers for a certain treatment, their relative merits, and their associated costs (26). 4. Transactions usually are not costless. A patient cannot easily go to one provider for a diagnosis, then shop among providers for ideal treatment. Although certain types of care are amenable to such behavior, a patient could not sustain such a decision-making process for most acute medical needs (27).

As a result, the market mechanism does not seem to work in health care as it might in many other sectors of the economy. Because consumers rarely pay the full cost of care at the point of service, health care consumers are unlikely to operate as a “cost–benefit calculator.” To the extent that patients are insulated by insurance from the full cost of services, patients are likely to pursue any possible treatments that provide net benefits, while largely ignoring costs. Furthermore, for certain states of ill health (i.e., a heart attack patient who may need a catheterization), even if every condition for market efficiency is met, a patient may simply be physically or emotionally unable to calculate the costs and benefits of a particular course of action. When market conditions are met, as in the case of an individual purchasing food items in the grocery store, the market mechanism—driven by each consumer behaving as his or her own cost–benefit calculator—will result in an economically efficient outcome. When these conditions are not met, as in health care, a consumer-driven compet-

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itive market cannot be relied on to achieve the economically efficient outcome. Decision makers cannot rely on consumer-purchasing behavior to result in optimal resource allocation and must choose the set of actions that maximizes the benefits obtained for a given resource investment. To help make these decisions, the policymaker must find a substitute for the “millions of cost–benefit calculators” that would otherwise exist in the form of consumer decision making. Cost–benefit analysis or CEA is this substitute.

Principles to Guide Decision Making Based on CEA PRINCIPLE 1: COSTS SHOULD REPRESENT MARGINAL SOCIAL OPPORTUNITY COSTS To the average person, “cost” means money, but in economic analyses, cost has a slightly different interpretation. Economic cost represents the total value of opportunities missed because resources were used in a particular way. Consider a cardiologist trying to determine if he or she should spend more of his or her time seeing patients in the office rather than conducting catheterization procedures. Assume that there is a backlog of patients awaiting catheterization, a backlog of patients awaiting office visits, and that the cardiologist’s time is limited. In economics, cost refers to an opportunity lost, referring to the value of resources used if they had been deployed in their highest-valued alternative. If the cardiologist is paid five times as much for the catheterization than for an extended office visit, the opportunity cost of a 30-minute office visit is the income that cardiologist could have earned spending that 30-minute performing the catheterization. To an accountant, the cost may be the average income the cardiologist makes every half-hour, and to a patient, the cost may be the difference in the copay between an office visit and a procedure, but neither of these numbers represents the cardiologist’s true opportunity costs. To aid policymaking, economic approximations of the costs and benefits of a decision must include the value of all resources used, not just the private costs borne by the decision maker. For example, the costs of a 30-minute office visit will include the cost of a receptionist’s time and whatever other inputs are used to see the patient. On the other hand, the costs of a catheterization would include the costs of other aides and technicians, the amortized costs of the equipment used, as well as costs of the materials consumed during the procedure. Even though these costs are borne by the hospital, not the cardiologist, they help constitute the social costs of the procedure and should be included in an economic evaluation. Finally, in comparing two uses of one resource, the marginal cost, or the difference in total social opportunity costs between one intervention and another, is the significant cost to understand. Because policymaking involves choosing between alternatives, the marginal cost must be used to guide decision making. In the case of our sample cardiologist, the marginal cost between an office visit and a procedure would be the value of the additional resources required to produce the procedure when compared to those required to produce the office visit. In practice, accounting for marginal social opportunity costs in health care can be difficult. Cost Measurement and Data Limitations. Data restrictions often require economic studies to use approximations of costs or simply make assumptions about the opportunity cost of various items used to produce health care. Most CEA, particularly those described in this book, involve adding cost analyses to RCT. These efforts are often restricted or biased by a hospital’s cost accounting system, and the results are not

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often adequate representations of true opportunity costs. For example, a hospital might claim the cost associated with 1 extra in-patient day is $800. Although this might be the accounting cost of maintaining the room, if there is no one else who would use the room if it were empty, the true opportunity cost of that in-patient day may be significantly less than the accounting cost. Time Horizon for Cost Analysis. The appropriate time horizon for a cost analysis has been the subject of much debate. Should an analysis include costs incurred or saved in the distant future as a result of an intervention in the present? Doing so often changes the outcome of the cost analysis dramatically (28). Furthermore, the results may be counterintuitive and unappealing. Recently, Philip Morris sponsored research presented to the Czech Government, which illustrated how smoking actually reduces long-term health care costs because patients die earlier and use fewer health care resources over the course of their lifetime (29). PRINCIPLE 2: BENEFITS SHOULD BE REPRESENTED BY THE CONSUMER’S WILLINGNESS TO PAY (WTP) Economists believe that estimating a consumer’s WTP is the most accurate way to measure the benefits that consumer would receive from a particular good or service. Given reasonably good information, WTP should include that consumer’s analysis of available complementary or substitutable products, personal preferences and tastes, and level of risk aversion. WTP should also include any intangible benefits the consumer would enjoy. In theory, a consumer would express a WTP for a certain medical intervention, and that dollar value would describe the consumer’s value for the health that medical intervention might restore. However, WTP has not been an effective measure of benefit in economic analyses of medical interventions, and measuring benefits in dollars has always been difficult and controversial. As a result, cost–benefit studies have not become commonplace in medical economics. Instead of trying to capture the value of an outcome, economic studies in medicine tend to be CE studies that describe benefits in terms of clinical effectiveness. Although less controversial and more commonplace, this focus on CE has limited the use of these studies in policy decision making, because the results may be more difficult to compare between possible interventions or treatments. Multiple conceptual and practical limitations impede WTP measurement and limit the ability to perform cost–benefit studies in medicine. First, quality of care—a critical part of a treatment’s benefit—is rarely well-defined or observable to the consumer. In this environment, consumers substitute other metrics, most commonly price or technical sophistication, for outcomes data. Because our health care system developed under incentives to prioritize perceived technical quality, providers and consumers continue to believe that treatments with a higher perceived technical quality should have higher values. Other metrics used as a substitute for often unobservable quality or outcomes information include the status of the provider institution, the amenities offered as part of the care experience, and the WTP of larger, presumably more capable, consumers of services (e.g., private or public insurance plans). Each of these are poor substitutes for the technical quality of care that skew WTP estimates. Nonprofit Provider Status. Although for-profit hospitals are believed to engage primarily in profit maximization, many consumers (and economists) believe that nonprofit hospitals may have a greater preference for providing high-quality care (30,31).

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Presumably, a nonprofit institution is more willing to trade net revenues and profits for higher quality (32,33). Amenities Offered. Because patients are unable to observe the true quality of the medical care they receive, they often resort to making educated guesses about quality based on nonmedical amenities that are easy to observe. In the in-patient setting, this may include the attractiveness of the patient room and the availability of high-quality guest services. In the out-patient setting, it may include the appearance of the waiting room, waiting times, and accessibility of ancillary service staff. Although not necessarily correlated with good outcomes, these amenities are observable, often creating impressions about quality that consumers use to determine WTP. Perceived Technical Quality: A Third-Party Payer’s WTP. In any environment where consumers cannot genuinely observe quality, they often substitute price or perceived technical sophistication for quality and value. A prominent economist once illustrated this phenomenon in the market for used cars. Confronted with a situation in which they were unable to look under the hood, study, and understand the reliability and quality of a used car, buyers naturally assume that cars being sold at higher prices must be better vehicles (34). Similarly, higher priced medical treatments are assumed to be better in an environment where meaningful outcomes data are unavailable (35). Consumers may look to third-party payers, perceived to be large organizations with access to extensive information about patient outcomes, and substitute a third-party payer’s WTP for their own. These payers have traditionally paid more for more technically sophisticated treatments, a result of the old cost-plus reimbursement system that defined American health care for decades. Patients naturally exhibit similar WTP behavior. Second, the task of attaching financial values to various states of health is a controversial exercise for consumers, economic analysts, or policymakers. Most individuals are unable to assign dollar values to years of their own life and estimating a WTP to avoid a certain state of disability is no easier. Valuing Human Life. Because the benefit of a medical intervention may involve extending a person’s life for a number of years, the WTP approach requires that the patient place a dollar value on years of his or her own life. Economists have used a variety of techniques to place a value on human life, with some consensus in the literature that the estimate generated by William Nordhaus of Yale University—$3 million for the average value of avoiding 1 death—is conservative, but legitimately derived (36). The Environmental Protection Agency, in its Particulate Matter and Ozone Standards Regulatory Impact Assessment, estimated the value of a human life at $4.8 million (37). These estimates are generally imputations based on how much money our society appears to be willing to spend to save a human life as reflected by proxies such as air traffic control or motor vehicle safety. They are not surveys of how much an individual values his or her own life, which would presumably be much higher estimates. Valuing States of Health. If not saving or extending life itself, a medical intervention might improve the state of health of the patient. However, placing a value on states of disability, whether through survey research or observed behavior, yields tremendous differences across individuals and creates systematic biases. Some individuals heavily discount years of disabled life, whereas others (e.g., the disabled) do not. These differences may reflect random variation in available social support systems, personal preferences, and stage of life. In addition, individuals who become disabled often have a shift in their reference point, so that their current quality of life is viewed more posi-

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tively than they would have estimated when they were healthy. Analysts and decision makers seeking to use these data must aggregate these different preferences, often earning criticisms for discriminating against a particular group of individuals. Wealth and Distributional Effects. A person’s WTP for certain goods and services is clearly tied to that individual’s income and wealth. As a result, WTP analyses are often biased in favor of wealthy individuals. Cosmetic surgeries for the wealthiest individuals may appear to represent a greater benefit than interventions that improve the quality or quantity of life for less wealthy individuals. Despite the fact that these systematic biases may accurately reflect market preferences and consumer behavior, nevertheless, they, are problematic policymakers who must make decisions on behalf of broad populations. As a result of these inherent difficulties with using WTP to capture benefits in health care, most economic studies of medical interventions do not rely on consumer WTP. Among the variety of substitutes available to analysts, two standardized measures of health benefit (quality-adjusted life years [QALYs] and disability-adjusted life years [DALYs] and standard medical analysis of effectiveness are of particular importance and are widely used. Standardized Measures of Health Benefit (QALYs and DALYs). Of the various integrated measures of health benefit, QALYs and DALYs, have received the most attention and application. As integrated measures of health benefit, these measures combine the quantity of life and quality of life gained from a medical intervention. They avoid the problem associated with asking consumers and patients to place a dollar value on years of human life, as they use years of life as their standard measure, but they still rely on asking people to value different states of health and interpret them in terms of years of life. As a result, both of these measures have been criticized as discriminatory. Because respondents without disabilities significantly devalue years of disabled life, when applied, the measures tend to place a low value on years of life with a disability. Furthermore, because the base measure is time (years of life), the measures prioritize services provided to the young. If both a young and an old person are in the same state of ill health and can be cured by the same medical intervention, the intervention will add many more total years of life to the young patient and significantly fewer years of life to the older patient. Despite having the same response to therapy, the decision maker would be guided to provide the benefit first to the young, then to the old, in an effort to maximize years of life gained by dollar spent. These potentially discriminatory issues make the use of these measures in policymaking more challenging. Medical Effectiveness. Given an inability to capture the personal benefit, either in dollars or in QALYs, of a medical intervention, many analysts resort to using standard clinical measures of effectiveness, such as 30-, 90-, or 365-day mortality rates, objective improvement in physical function (e.g., ability to walk or see), and the ability to perform activities of daily living (ADLs) as measured by standardized survey instruments. These measures of effectiveness share one characteristic—they do not assign a value to the specified functional or survival improvement to the patient. They can be objectively measured across patients, linked to outcomes in RCT, and can be used with much less controversy to demonstrate clinical effectiveness. Whereas WTP is the theoretically preferred measure of benefit, most economic studies in health care, including nearly all studies described in this book, use measures of clinical effectiveness for the reasons described previously. However, this decision comes

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with an important liability. Because CE studies explicitly avoid using a single metric, such as money, to estimate the magnitude of the benefit experienced by the recipient of care, they may not provide easily comparable data for policymakers to use in making difficult trade-offs when allocating scarce health care resources. Should an insurance plan pay for an intervention that improves 30-day mortality for heart attack patients, or an intervention that will improve vision in patients with glaucoma? What is the relative value of these two clinical improvements? Substituting clinical effectiveness for true WTP-based measures of benefit may be required, given the social and political pressures facing health policy decision makers, but it is a trade-off that comes with its own costs. PRINCIPLE 3: THE POTENTIAL COMPENSATION PRINCIPLE CAN EXPAND THE REACH OF ECONOMIC STUDIES IN GUIDING DECISION MAKING The Pareto principle—described earlier as a minimum standard of economic efficiency—embodies a decision rule that states that actions that can make at least one person better off, while making no one worse off, should always be undertaken. This rule proves to have limited application in most real policy debates. This is especially true in health care, where any decision regarding resource allocation, even if it dramatically increases the total benefit achieved with the same amount of resources, will have winners and losers. In the case of the AHCPR study on back surgery described previously, shifting resources away from back surgery and into other, more effective therapies for people with back pain (e.g., physical therapy and lifestyle modification) would have increased the health benefit achieved with the same health care dollars. However, there was still a group of people who stood to lose (e.g., namely back surgeons), and they acted aggressively to prevent such a loss. In situations where a policy decision will have winners and losers, the Pareto principle alone is unlikely to work as a guide to decision making. In these situations, the Potential Compensation Principle can help determine how a decision maker should allocate resources when a seemingly beneficial policy change will result in some people being worse off. This principle states that the policymaker should undertake an action if those who gain from the policy could hypothetically compensate those who lose and still be better off (38). This principle provides a standard to guide decision making for the more realistic scenario, where there are winners and losers that result from a policy change. However, two important attributes of this principle—its reliance on hypothetical compensation and its requirement that economic studies are comparable—limit its application in practice. Hypothetical Compensation. Although the hypothetical compensation criteria makes sense in theory, real policy decisions are made in practical, political environments in which people who would be worse off, resulting from a policy change, will try to block an intervention even if it passes the potential compensation test. The case of the back surgeons responding to the AHCPR CE study is one glaring example, but nearly every policy decision will have a related narrative. Suppose a hospital uses the potential compensation principle and decides to invest in a new catheterization laboratory, instead of a new neonatal intensive care unit (NICU), based on data indicating that having the new catheterization laboratory would yield a greater overall increase in patient volume and improved outcomes than the NICU (for the same cost). The cardiologists and their patients would be the winners, whereas the neonatologists would lose an opportunity to improve the volume and quality of their service. However, the cardiology patients whose lives have been saved would not be able to transfer health gained, or the money value of

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that health gain, to the neonates who may have otherwise been saved, nor is it likely that there would be a transfer of funds between the physician groups. Compensation is not only hypothetical, it is often impossible, and the hospital policymakers can expect the neonatalogists to do everything possible to change the decision. Outcome Comparability. Health care policymakers who rely on CE studies, instead of much more difficult cost–benefit evaluations, will confront the difficult task of comparing various outcomes and trying to determine which one represents a greater benefit to patients and society. As previously discussed, using different measures of medical effectiveness—the standard for most RCTs—makes outcome comparability across different types of interventions difficult or impossible. What is the relative value of improved survival for heart attack patients when compared to a significant qualityof-life improvement for the blind, disabled, or for premature infants? How can these different clinical outcomes be compared? Without a standard metric for comparison, discussions of hypothetical compensation are even less feasible. PRINCIPLE 4: THE SOCIAL WELFARE PERSPECTIVE SUPPORTS RESPONSIBLE DECISION MAKING When policymakers allocate goods and services, instead of the marketplace, the perspective of that policymaker will be vitally important to the outcome achieved. Ideally, the policymaker will weigh all social costs and all social benefits of a particular intervention. This practice is referred to as taking the social welfare perspective, allowing decisions to be made that are optimal for society as a whole by coming closest to replicating a competitive market outcome. In health care, there are a variety of policymakers, including governments, public and private insurers, providers, and patients. Often, policymakers will take a more narrow perspective than the social welfare principle when making decisions. For instance, when making policy decisions, many payers—whether public insurers, private insurers, or employers—naturally use their own perspective, which is not necessarily in society’s best interest. The paradox of discounting provides an important illustration. The Paradox of Discounting. Taking a social welfare perspective allows a policymaker to evaluate the stream of costs and benefits to society as a whole over time. The paradox of discounting occurs when a payer evaluates its costs, which usually accrue over the short term, but discounts many benefits that accrue over a much longer timeframe. Consider a payer evaluating a preventive medical intervention, such as smoking cessation. The costs of the intervention accrue to the payer in the short term, whereas the benefits of the intervention (which may include savings on future health care costs) may not even be counted by an insurer who does not think that patient will stay in its insurance pool for the length of time needed to reap the benefit of reductions in future medical expenditures. An analysis conducted from the social welfare perspective would have calculated the present value of the savings, but from the perspective of the insurer, these savings do not count. Payors that do not benefit from future cost savings will be inclined to make shorter term decisions, burdening society (presumably, federal programs such as Medicare), with the future implications of decision making that from a societal standpoint is suboptimal. Much of this chapter is dedicated to the policy decision making of government bodies and large payers. It is worth noting that other stakeholders in health care (e.g., patients and providers) also have a use for economic cost–benefit and CE studies. Patients have options between treatment strategies that may yield different outcomes, therefore, they

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play an important role in cost–benefit evaluations and health care decision making. For example, men with prostate cancer now have three effective options for treatment— surgery and two different types of radiation treatment—and each option has its own unique benefits and disadvantages (i.e., unique side-effect profiles). Only the individual patient can evaluate the personal costs and benefits of these three treatment strategies, and the patient should be encouraged to make informed choices after such an evaluation. More often, patients turn to clinicians to help guide them through the decision making process, and clinicians have long relied on clinical effectiveness data when available. Traditionally, clinicians have not been responsible for the costs of the care they prescribe to their patients, and many clinicians continue to feel that a doctor should be an advocate for their patient and fight for any treatment that may improve the patient’s state of health. According to this school of thought, health care rationing decisions are important, but the patient’s doctor should not make them at the patient’s bedside, as this violates a central tenant of the physician–patient relationship—trust (39). Other physicians note that an unwillingness to make difficult cost–benefit trade-offs at the bedside simply abdicates this responsibility to the larger and, often more powerful, insurer or payer (40). Although this debate will continue, more physicians are increasingly aware of cost considerations when they make decisions. PRINCIPLE 5: ECONOMICALLY EFFICIENT OUTCOMES ARE NOT NECESSARILY EQUITABLE OR PREFERRED This book is concerned with the economics of cardiovascular medicine, and as we have described optimal resource allocation we have used the language and definitions of economic efficiency. Pareto optimal outcomes, or even outcomes that are consistent with the potential compensation principle, define outcomes in which resources are used to maximize some benefit—in this case, the collective health benefit of various medical interventions. It is important to note that this does not imply that society, or our health care system, should focus entirely on maximizing the health benefit created by our investment in health care. Other goals, such as ensuring a better distribution of resources and health outcomes across the entire population of US citizens, may be important social goals to consider as important alternatives to a singular focus on economic efficiency.

The Challenge of Making Group Decisions Cost–benefit analysis or CEA plays its most important role when it allows policymakers to make informed decisions in using limited resources to achieve the greatest benefit. However, the very fact that policymakers, and not consumers themselves, largely guide the allocation of resources, is bound to lead to controversial decisions. By using a systematic approach based soundly on economic principles, policymakers, although unable to please everyone, may at least be able to make consistent and socially responsible decisions.

REAL-WORLD APPLICATIONS OF ECONOMIC ANALYSIS IN POLICY DECISION MAKING Oregon: A Public Attempt to Use CEA Although not focused on cardiovascular services, the efforts by the Oregon legislature to use CE thinking to set health care priorities contain a number of important lessons in

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the challenges of CE for systematic resource allocation. In the late 1980s and early 1990s, the state of Oregon tried to allocate their limited health care budget to achieve the most benefit for their population. There were two significant problems that the legislation was trying to address. Of the 320,000 individuals under the poverty line in Oregon at that time, approximately only 200,000 were covered by the state’s Medicaid program. The second was the pervasive sense that some low-benefit, high-cost services were covered under Medicaid, whereas other high-benefit services were not. In a report written by John Kitzhaber, at that time the president of the Oregon State Senate and a physician, regarded as the architect of the plan, attributes the crisis in health care costs and the large number of the uninsured to “the lack of a national health policy and … the lack of any rational and accountable process of health care resource allocation.” He wrote, “If we accept the fact that the health care budget, like any other budget, is ultimately finite, it follows that an explicit decision to allocate money for one set of services means that an implicit decision has also been made not to spend money on other services. That, in essence, constitutes the rationing of health care, and legislative bodies do it every budget cycle. But it is rationing done implicitly, and for which there is no accountability” (41). One of the galvanizing events behind the Oregon experiment came as a result of the legislature’s decision in June 1987 to stop funding pancreas, liver, heart, and bone marrow transplants through its Medicaid system because of budget shortfalls. At the time, the legislature had a choice of discontinuing coverage for expensive transplants that would affect approximately 30 individuals a year or basic health care for 5700 women and children (42). Months later, the national news media described an unfortunate result of this change in policy. A 7-year-old named Coby Howard died of acute lymphocytic leukemia, when the Medicaid program refused to pay for him to receive a bone marrow transplant. His mother tried valiantly to raise the required $100,000, but could only raise $80,000 before her son died. The horror of watching the little boy die because he did not receive a medical procedure that would have been available to him a few months previously—and which was readily available to many other privately insured children—created an uproar and highlighted the need to improve health care resource allocation in Oregon. In response, the state legislature passed the Oregon Basic Services Act in July 1989, creating the Oregon Health Services Commission—a group charged with the responsibility to produce a ranked list of services to be used as the package of basic services to be covered by Medicaid. The Basic Services Act also tried to expand Medicaid coverage to include all Oregon residents living below the poverty line and establish employer mandates to require workplace coverage, but it was their novel approach to rational health care resource allocation that earned national attention. We focus here on the resource allocation efforts. Initially, the Health Services Commission set about trying to estimate benefits and costs of each clinical service, then once the ratio of costs to benefit were calculated, ranking services in order of their cost–benefit ratios. Benefits were calculated by estimating the probabilities of different outcomes with or without treatment and multiplying that by the duration of effective treatment. The probabilities of different outcomes and estimation of benefits were created by panels of physician specialists who were instructed to use the literature where possible with the use of clinical judgment to fill in the gaps of the literature. When released in May 1990, the initial draft of the priority list was widely criticized. Many of the resulting rankings were clinically counter-intuitive and politically

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unpopular. For example, dental caps for pulp exposure were assigned a higher priority than surgery for ectopic pregnancy, and splints for temperomandibular joints ranked higher than appendectomies for appendicitis (43). In the ensuing firestorm, the commission made a major philosophical change by deciding to no longer consider costs and cost–benefit ratios in the decision-making process. Services were grouped into 117 major categories, such as “treatment of acute life-threatening conditions where treatment prevents death with a full recovery and return to previous health state.” These categories were ranked based on perceived value to society, value to an individual, and whether they are considered essential to health care. Within each category, services were ranked based on the revised estimate of net benefits (excluding any consideration of cost), and the commissioners examined each set of rankings to confirm that there were no further counterintuitive results. In the end, the Commission ranked 709 services in order of priority. The state legislature reviewed actuarial data on the cost of covering each of the services on the list, identified the top 587 services as “basic,” and mandated coverage for each of these. Limiting coverage to this set of 587 “basic” items allowed the legislature to then expand Medicaid coverage to the 40% of patients under the poverty line who were previously uninsured (42). When the list was initially submitted by the Oregon legislature to the federal government for approval in 1991 (a waiver from the HCFA was required), it was rejected as being inconsistent with the Americans with Disabilities Act of 1990. Revisions had to be made to address the perception that lives of those with disabilities were undervalued, and at that point, agreement was given by the HCFA for the list to take effect on February 1994 (44). Oregon’s original attempt to use cost–benefit analysis to allocate limited health care dollars in a way that created the most benefit for a given amount of resources appears to have failed. This failure leads to a critical question. Is cost–benefit analysis or CEA conceptually flawed as a tool for public resource allocation? Or, alternatively, did the state of Oregon fail to properly implement the correct conceptual tool? Eddy (45) provides a cogent analysis of where Oregon went wrong. The first problem identified by Eddy was the use of ICD-9 and CPT-4 codes for identifying treatment and procedure pairs. Although necessary for administrative purposes, these codes cannot give any information about severity of illness within the particular code. In addition, to limit the total number of codes to a reasonable number, several codes had to be combined, further limiting their accuracy and specificity. Second, estimating the costs and the benefits proved to be difficult, often inaccurate, and ultimately controversial. There were also problems with the estimation of cost, as treatments ranging from medical treatment of tinea pedis to percutaneous transluminal coronary angioplasty for heart attacks were all assigned the same cost of $98.51. Benefits were not precisely estimated. For example, the category “trouble speaking” ranged from a mild lisp to total mutism. For 55 disparate medical conditions, obtaining a biopsy was estimated to yield the same exact benefit. Although the Oregon system was created in order to determine what volume of different services could be offered with a particular amount of resources, the method used to elicit and list preferences ranked individual services against each other on an apparent one-to-one basis (46). Treating 105 patients with dental caps at $38 each cost the same as treating 1 patient with surgery for ectopic pregnancy at $4015. The initial priority list, by ranking ectopic pregnancy below dental caps, implied that a

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single dental cap was more important to cover than surgery for ectopic pregnancy—a potentially life-saving therapy. However, the implicit calculation behind the relative rankings actually implied that 105 dental caps were approximately equal to 1 case of surgery for ectopic pregnancy. Critics argued that the Oregon experience proved the assertion that CEA is incapable of including the value society places on saving the life of an identifiable individual (39), such as Coby Howard. However, the method used to derive the weights, or relative values, for various states of well-being (or quality of life) could have been adjusted to address this discrepancy. Oregon citizens were asked to describe how different types of symptoms and health states would affect their individual quality of life. This resulted in a system that reflected individual patient preferences among different treatments, as opposed to an appropriate way to allocate treatments across people. In terms of allocation, the state could have asked, “If you had to give up performing life-saving surgery on one woman with an ectopic pregnancy, how many patients would need to get dental caps in order to make the sacrifice worthwhile?” Asking the question this way would allow societal utility in saving lives and the “rule of rescue,” or societal preferences for saving the lives of identified individuals, to be incorporated into allocation decisions (40). CHALLENGES TO ADDRESS IN REAL-LIFE APPLICATIONS OF CEA Several issues emerge as challenges to address in any such further endeavors. Incomplete Evidence. Oregon’s experience highlights the fact that policymakers and their constituents do not have the full information required to make perfect resource allocation decisions. Difficult decisions need to be made with incomplete evidence. It is much easier to make direct comparisons between two medications evaluated in a head-to-head clinical trial than to compare the effectiveness of different kinds of interventions. Even when decision makers demonstrate the political will to try to determine the benefits of various medical interventions, the controversies in the approach—whether using WTP or simply rank ordering items—are unavoidable. Much research needs to be done to generate more cogent data on effectiveness with which to make decisions. Meanwhile, policymakers and legislatures must “make do” using noncomparable data and incomplete evidence to inform their policy decisions. Grouping of Heterogeneous Conditions. Grouping together conditions may be necessary to achieve a reasonably parsimonious list, but heterogeneous conditions can be difficult or inappropriate to assess as uniform entities. Treatments and indications have to be relatively narrowly defined for the interpretation of data on effectiveness and decisions between treatments to make sense. Defining Benefits. Defining a measure of benefit is difficult because the nature of benefits may differ between different conditions (e.g., treating a cold vs a life-threatening illness). Whereas reducing mortality may be important in treating cancer cases, it is less relevant in situations in which quality of life is more significant. For whatever system is used, it is important to measure benefits to reflect these differences. Assigning relative values for life-saving treatments and less critical interventions is difficult, but can be directly elicited from survey respondents. Hold Old and New Programs and Treatments to the Same Standard. Even when the status quo represents a blatantly flawed system, change is difficult and controversial. Old treatments and programs are rarely held up to the same standards as new treatments and programs, and insurers routinely cover hundreds of treatments from a time period

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in which the standards of evidence and CE were lower (46). If the old Oregon plan had covered everyone under the poverty line for services 1–587, it is hard to imagine that anyone would suggest covering an additional 122 services at the bottom of a priority list in exchange for discontinuing coverage of 40% of those under the poverty line. It does not make sense to have strict standards for coverage of new standards, but not apply the same standards to services already covered. The Oregon plan to shift resources from covering low-priority services for roughly 60% of those under the poverty line to covering higher priority services for all under the poverty line, although often attacked for rationing services, was a far more justifiable type of rationing that what had been occurring under that status quo (47). But even when these policy reforms are justifiable by rational standards, nearly every policy change has winners and losers, making resistance to change difficult to overcome. Identifiable vs Statistical Rationing. Perhaps the biggest challenge of all is making rationing explicit. As the architects of the Oregon plan point out, the Medicaid system rations care by denying any coverage to many residents who live in poverty. There is no mechanism for shifting resources from low-yield expensive services, such as intensive care units for patients with terminal disease, to basic services or high-yield preventive services for others. This implicit rationing and lack of transferability of resources is true of our hodgepodge system of financing and insurance in general. In addition, as the outcry over the death of Coby Howard showed, it is very difficult to deny services to an identified person, even if a far greater number of unnamed patients stand to gain access to basic services in return. This is a difficult problem, but one that on some level must be addressed if we are to more rationally allocate resources.

EXAMPLES OF OTHER ECONOMIC STUDIES THAT CAN INFLUENCE POLICYMAKING Much of the clinical CE data in cardiovascular medicine has been generated through RCTs with a cost component, but there are many areas of medicine and health care policy where RCTs have not or cannot be performed. Increasingly, in these situations economic tools besides CEA are being used to generate data from observational studies. Observational studies can substitute for RCTs when a trial has not been or cannot be done, or these observational studies can complement data from a RCT. An advantage to using observational studies is that they often better reflect effectiveness (the performance of a certain intervention in actual practice in the population), instead of efficacy (a measure of theoretical effectiveness identified in strictly controlled settings). The population tends to be more generalizable, including women and the elderly, and care is generally provided at sites other than the academic medical centers where clinical trials tend to be done. The biggest challenge with observational studies is that patients are not randomly assigned to different treatments, so those who receive treatments may have different characteristics than those who do not. Known as confounding variable bias, this phenomenon can skew our interpretation of effectiveness and comparative outcomes. Although we can control for differences that we observe using regression analysis, differences between patient groups may be nonobservable and could easily bias the results of an unassuming study.

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Various statistical and econometric tools have been developed to make the problem of confounding variables, or observed and unobserved differences in patient groups, less significant in interpretation of results (48). Examples are the work done using instrumental variables by McClellan et al. (49), the use of hierarchical models (50), and natural experiment designs that seek to minimize intergroup differences by using similar cohorts based on units of geography, then tracking measurable changes over time (51). As the application of these techniques continues to spread in studying the effects of health care programs and clinical interventions, the ability to effectively use the pool of data generated by observational studies will significantly increase. The use of existing sources of secondary data for observational studies is going to play an increasingly important role in helping us understand the effects of different policy interventions. In such settings, it is unlikely that RCTs will be done, and econometric techniques will help to address of the underlying problems with patient selection issues that are present in nonrandomized controlled trials.

SUMMARY Within cardiology, the economic component of RCTs has demonstrated that many new treatments that are clinically effective also may be highly cost-effective, facilitating their adoption in clinical practice. But much of medicine outside of cardiology remains without good data on effectiveness and CE and tends to rely on health care quality indicators that are not related to health outcomes. Prioritizing effectiveness (and CE) will lead to the development of measures and data that are helpful, but direct comparison between different types of interventions and use of this data for resource allocation will continue to be challenging. Even with appropriate CE data, policymakers will continue to have a hard time evaluating and comparing the social benefit associated with various medical interventions, as well as navigating the complex ethical and political obstacles to implementation. Until analysts, clinicians, patients, and policymakers become comfortable with the need for cost–benefit studies—and the underlying premise that resources are limited and difficult choices must be made. The application of economic studies in medicine, although important, will continue to be limited. Implementing cost–benefit thinking is always to likely be difficult and controversial. As the Oregon health insurance experiment shows, there is much societal resistance to making such decisions explicit. Nevertheless, by integrating clinical effectiveness, expert opinion, cost–benefit thinking, and political reality, Oregon was able to define a benefits package and extend coverage to a larger proportion of its poor population. Perhaps this indicates that if society does come to terms with the reality that difficult decisions must be made between alternative uses of resources, policymakers can find a way to make more explicit rational resource allocation decisions. Although the conceptual and practical challenges that often have prevented cost–benefit analysis and CEA, as taught in classrooms, from taking center stage in day-to-day resource allocation decisions are significant, they are not insurmountable. Because of many factors, including lack of readily available, meaningful data on provider and service effectiveness and the role of insurance in shielding consumers from the true cost of services, consumer behavior alone will be unable to drive effective resource allocation in health care. As long as this remains true, policymakers and insurance company executives will implicitly or explicitly, decide how we use our

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health care resources. Until policymakers have the economic tools and political courage to make these decisions explicitly, implicit rationing of services and people will continue, with an outcome that, although less painful to achieve, likely provides us with less net benefit for our health care dollar.

REFERENCES 1. National Health Statistics Group, Office of the Actuary, Health Care Financing Administration, 1999. National health expenditures, 1998. Health Care Financ Rev 1999;21:2. 2. Starr P. End of a mandate. The Social Transformation of American Medicine, Basic Books, NY, 1982, p. 386. 3. Mark DB. Medical Economics in Cardiovascular Medicine. Textbook of Cardiovascular Medicine (Ed. Topol) 1998, pp. 1033–1060. 4. Sackett DL, et al. Evidence-Based Medicine: How to Practice and Teach EBM. 2nd ed. Churchill Livingstone, NY, 2000. 5. Arrow KJ. Uncertainty and the Welfare Economics of Medical Care. Am Econ Rev 1964:941–973. 6. National Health Statistics Group, Office of the Actuary, Health Care Financing Administration, 1999. National Health expenditures, 1998. Health Care Financ Rev 1999;21:2. 7. Weisbrod BA. The health care quadrilemma: an essay on technological change, insurance, quality of care, and cost containment. J Econ Literature 1991;29:523–552. 8. Starr P. The triumph of accomodation. The Social Transformation of American Medicine, Basic Books, NY, 1982, p. 290. 9. Goodman JC, Musgrave GL. The liberal years. Patient Power: Solving America’s Health Care Crisis, Basic Books, NY, 1992, p. 356. 10. Iglehart J. Health Policy Report: The American Health Care System – Expenditures. N Eng J Med 1990;340:70–76; Reinhardt U. Keynote Address. Academy of Health Services Research and Policy. Atlanta, GA, June 2001. 11. Goodman JC, Musgrave GL. Patient Power: Solving America’s Health Care Crisis, Citing Health Benefits Letter, 1992, p. 324. 12. Cutler D, McClellan M, Newhouse JF. How does managed care do it? The RAND J Econ 2000;31. 13. Health Care Financing Administration and the US Census Bureau, Managed Care Statistics, 2001. (http://www.mcareol.com/factshts/factnati.htm). (Accessed 8/1/01.) 14. Health Care Financing Administration and the U.S. Census Bureau, Managed Care Statistics, (http://www.mcareol.com/factshts/factnati.htm), National Health Statistics Group, Office of the Actuary, Health Care Financing Administration, 1999. National Health Expenditures, 1998. Health Care Financ Rev 2001;21:2. 15. Miller RH, Luft HS. Managed care performance: is quality of care better or worse? Health Affairs 1997;16:7–25. 16. Rettig RA. Medical Innovation Duels Cost Containment. Health Affairs 1994;13:3. 17. Ginzberg E, Ostow M. Managed Care—A Look Back and a Look Ahead. N Eng J Med 1997;336:1018–1020. 18. McCullough M. Effects to Fix Managed Care May Add to Costs, Experts Say. Philadelphia Inquirer PA, January 29, 1997, A3. 19. Passel P. Economic Scene: When Politicians Seek to Please on Medical Benefits. New York Times, NY, October 10, 1996, D2. 20. Volpp KGM, Bundorf KM. Consumer protection and the HMO backlash: are HMOs to blame for drive-through deliveries? Inquiry 1999;36:101–109. 21. Kassirer JP. Practicing medicine without a license – the new intrusions by congress. N Eng J Med 1997;336:1747. 22. www.unitedhealthcare.com. (Accessed 8/1/01.) 23. Baker LC. Working Paper, no. W8020. National Bureau of Economic Research November, 2000. 24. Health Inflation News, Health and Medicine 3/31/01; MostChoice.com. Employers to Face Double Digit Health Care Cost Increases for Third Consecutive Year. MostChoice.com (Business Wire) July 16, 2000. 25. Phelps CE. Health Econ 1992;2–16. 26. Arrow KJ. Uncertainty and the welfare economics of medical care. Am Econ Rev 1963;53:941–973.

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27. Phelps CE. Health Econ 1992;2–16, 85–154. 28. Meltzer D. Accounting for future costs in medical cost-effectiveness analysis J Health Econ 1997;16:33–64. 29. Swoger K. Report Says Smoking Has Benefits. The Prague Post, June 27, 2001. (Citing Arthur D. Little study paid for by Philip Morris, Inc.) 30. Newhouse JP. Toward a theory of nonprofit institutions: an economic model of the hospital. Am Economic Rev 1970;60:64–74. 31. Weisbrod BA. Rewarding performance that is hard to measure: the role of nonprofit Organizations. Science 1989;244:541–546. 32. Sloan FA, Steinwald B. Insurance, Regulation, and Hospital Costs. Lexington Books, Lexington, 1980. 33. Pauly MV. Lessons from health economics: nonprofit firms in medical markets. Am Econ Rev 1987;77:257–262. 34. Akerlof GA. The market for lemons: qualitative uncertainty and the market mechanism. Quarterly J Econ 1970;83:488–500. 35. McClellan M. Uncertainty, health-care technologies, and health-care choices. American Econ Rev 1995;85:38–44. 36. Hand L. “What’s A Human Life Worth?” The Scientist 2000;14(1):6. 37. Environmental Protection Agency, Particulate Matter and Ozone Standards Regulatory Impact Assessment (http://www.eba-nys.org/eba/rcba.html) 38. Danzon P. Health Care Cost-Benefit Course Lecture. 1998. 39. Ubel P. Physician’s Duties in an Era of Cost Containment: Advocacy or Betrayal? Journal of the American Med Assoc 1999;282:1675. 40. Ubel P. Physician’s Duties in an Era of Cost Containment: Advocacy or Betrayal? J Am Med Assoc 1999;282:1675. 41. Kitzhaber J. The Oregon Health Plan 1992, Oregon State Senate, State Capitol, Salem 42. Eddy DM. What’s going on in Oregon? JAMA 1991;266:417–420. 43. Hadorn DC. Setting health care priorities in Oregon. Cost-effectiveness meets the rule of rescue. JAMA 1991;265:2218–2225. 44. Bodenheimer T. The Oregon Health Plan – Lessons for the Nation. N Eng J Med 1997;337:651–655. 45. Eddy DM. Oregon’s methods. Did cost-effectiveness analysis fail? JAMA 1991;266:2135–2141. 46. Eddy DM. From theory to practice: three battles to watch in the 1990s. JAMA 1993;270:520–526. 47. Eddy DM. Oregon’s plan: should it be approved? JAMA 1991;266:2439–2445. 48. Newhouse JP, McClellan M. Econometric in outcomes research. Ann Rev Public Health 1998;19:17–34. 49. McClellan M, McNeil BJ, Newhouse JP. Does more intensive treatment of acute myocardial infarction reduce mortality? JAMA 1994;272:859–866. 50. Normand SLT, Glickman ME, Gatsonis CA. Statistical methods for profiling providers of medical care: issues and applications. J Am Stat Assoc 1997;92:803–814. 51. Milyo, Jeffrey, Waldfogel, Joel. The Effect of Price Advertising on Prices: Evidence in the Wake of 44 Liquormart Am Econ Rev 1999;89:1081–1096.

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Medicare, the Aging of America, and the Balanced Budget Paul Heidenreich, MD, MS CONTENTS MEDICARE OVERVIEW TRADITIONAL MEDICARE MEDICARE MANAGED CARE THE 1997 BALANCED BUDGET ACT IMPACT OF ANTIFRAUD AND ANTI-ABUSE POLICIES MITIGATION OF THE BALANCED BUDGET ACT IMPACT OF THE AGING OF AMERICA ON THE MEDICARE PROGRAM IMPACT OF AGING VS TECHNOLOGICAL CHANGE AGING AND DISABILITY IMPACT OF CARDIAC DISEASE ON THE MEDICARE PROGRAM IMPACT OF MEDICARE REIMBURSEMENT ON THE PRACTICE OF CARDIOLOGY THE FUTURE OF MEDICARE REFERENCES

MEDICARE OVERVIEW Medicare is the United States’ health insurance program for people over the age of 65 (85%), those with certain disabilities (10%), and those with end-stage renal disease requiring dialysis or transplant (5%). In 1965, it was established with Title XVIII of the Social Security Act to provide elderly Americans access to health care, regardless of their socioeconomic status, and now enrolls approximately 35 million elderly (see Fig. 1). In 1999, Medicare spent $214 billion, which accounts for 18% of all national health care expenditures and 39% of all US public health care spending. For comparison, private health insurance accounted for $401 billion in expenditures, whereas Medicaid expenditures were $188 billion. There are two parts to coverage in Medicare, Part A and Part B. Part A covers care provided by hospitals, skilled nursing facilities, critical access hospitals (small facilities that give limited out-patient and in-patient services to people in rural areas) hospital providers, and some home-health providers. Funding for Part A is through a 2.9% payroll tax that goes to the Medicare Hospital Insurance Trust Fund. Patients do not pay a From: Contemporary Cardiology: Cardiovascular Health Care Economics Edited by: W. S. Weintraub © Humana Press Inc., Totowa, NJ

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Fig. 1. Enrollment of persons 65 years of age and older in Medicare has increased from steadily during the last 30 years. Over 90% of persons have both hospital and supplementary insurance. Source: Centers for Medicare and Medicaid Services.

Medicare Part A premium if they or their spouse paid taxes to Medicare (i.e., paid Social Security taxes) during prior employment for at least 40 quarters. However, there are significant deductibles and high copayments for prolonged hospital stays. The patient pays a deductible equal to the cost of the first day of hospitalization. Medicare pays for days 2–60 in full. Days 61–90 are paid by Medicare with a copayment equal to 25% of the deductible. Days 91–150 are covered (with a copayment equal to 50% of the deductible), as long as the lifetime reserve days (60) have not been exhausted. After 150 days, the patient is responsible for 100%. Early re-admissions for the same illness are considered part of the initial hospitalization; thus, a second deductible is not required. People that have not paid taxes into the Medicare system can still receive Part A coverage by paying a monthly premium ($319 in 2002). The imminent insolvency of the Trust Fund (projected to have been exhausted by 2001) was one of the factors leading to the Balanced Budget Act of 1997. Owing in part to this Act, and the continued economic growth, the solvency of the Trust Fund was extended to 2015 in 1999 and recently to 2025 (1). Part B, also referred to as Supplemental Medical Insurance, covers “medically necessary” physician services, out-patient hospital care, laboratory tests, medical equipment, some services of other providers (e.g., physical and occupational therapists), and some home health care (1). US residents age 65 and older are entitled to Part B benefits even if they are not eligible for Part A benefits. Disabled persons, and those with endstage renal disease, are also eligible. Most patients pay a Medicare Part B premium of approximately $54 per month, with a $100 deductible (1). A 20% copayment is required for physician services and durable medical equipment once the deductible is paid. Home-health and clinical laboratory services are fully covered. Patient premiums cover one-fourth of the cost of Part B, with the remainder funded by general federal revenues. Because funding automatically increases as expenditures increase, insolvency is not an issue for Part B. In response to the large copayments and deductibles, beneficiaries (87%) often have “Medigap” policies, either through their former employer (33%) or private insurers (33%) (1). Those elderly who are eligible for Medicaid have many of the Medigap costs covered by these state-run plans. However, even with Medigap coverage, which pays 11.5% of the elderly’s health care expenditures, another 15% are paid out-of-pocket (2). Of the elderly’s combined yearly income and medical benefits, they spend 35% on health care, with Medicare paying approximately half this amount (3). By 2020, 52% of the combined income and health benefits are expected to go for health care (3).

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Table 1 Reimbursement for Heart Failure (DRG 127) for Three Different Hospitals*

Base DRG payment ($) Adjustments ($) Wage index Case weight Indirect medical education Disproportionate share of indigent patients Total ($)

Large urban, teaching San Francisco, CA

Large urban, nonteaching San Francisco, CA

Nonurban, nonteaching Peoria, IL

4151

4151

3903

1456 73 2734 1676

1456 73 0 289

–328 47 0 0

10,090

5969

3642

* For inlier (as opposed to outlier) patients. DRG, diagnostic-related groups.

No part of the Medicare program covers prescription drugs, and this has been one of the prominent issues for most Medicare reform proposals. However, patients enrolled in Medicare-managed care may receive drug coverage at the discretion of the managed care plan. Other common services that are not covered by traditional Medicare include long-term nursing care, dental care, eyeglasses, and hearing aids.

TRADITIONAL MEDICARE The majority of beneficiaries have Medicare fee-for-service insurance. This plan was initially similar to private indemnity insurance plans. However, in an effort to control costs, Medicare sets prices for physician and hospital care using increasingly complex rules based on type of service, setting, and geographic location.

Hospital Reimbursement In 1984, hospital payments were bundled into diagnostic-related groups (DRGs, n = 511). In an effort to remove incentives to provide more care, hospitals receive a fixed reimbursement for each patient based on the DRG. However, many DRGs are classified based on services provided (e.g., bypass surgery and valve replacement), so incentives for more care still exist. Each DRG has a relative weight attached, which corresponds to the cost of treatment. Adjustments are made if the hospital is large and urban (higher reimbursement) for local wage index (for each metropolitan statistical area), indirect medical education, disproportionate share of indigent patients, and outlier (high-cost) patients. Hospitals are reimbursed for services provided, operating expenses, capital equipment, and medical education. Given the large variation in cost of treating patients and training physicians, the payment for any given DRG can vary widely across hospitals (Table 1).

Physician Reimbursement Physicians are paid based on a relative value system that has three divisions: work, practice expense, and malpractice. Each of these divisions has a technical and professional component. This allows for physicians practicing in a facility to bill the

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professional component, whereas the facility (hospital) that owns the equipment and provides staff can bill for the technical component. Approximately 55% of payments are assigned to work units, 42% to practice expense units, and 3% to malpractice units. The total relative value unit (RVU) is then converted to dollars by multiplying by a conversion factor set each year by Congress. In 2001, this value was $38.26. As an example, a typical transthoracic echocardiogram would include three codes: 2D echo 93307, spectral Doppler 93320, and color Doppler 93325. Each procedure has a code based on the Current Procedural Terminology (CPT) code developed by the American Medical Association. The combined technical component is $342.81 (8.96 RVUs × $38.26 per RVU), and the professional component is $86.47 (2.26 RVUs × $38.26 per RVU). Costs are also adjusted by geographic area. Based on estimates of local cost of practice these adjustments lead to payments roughly 30% higher in San Francisco, California than in Arkansas or Montana.

MEDICARE MANAGED CARE Medicare began paying for managed care in 1982, following the Tax Equity and Fiscal Responsibility Act (TEFRA). Prior to the Balanced Budget Act, there were three types of managed care plans available through Medicare: cost, risk, and health care prepayment plans. Risk plans remain the most common. These plans are paid a fixed amount per member per month that was 95% of traditional Medicare’s average payment in the enrollee’s county of residence (adjusted average per capita cost [AAPCC]). The plans are assumed to be able to make a profit and still save the government 5%. Initially, adjustments were made only for the enrollee’s age, gender, and institutional status (hospitalized in a chronic care facility). Plans are allowed to charge a small premium. If plans can provide services at a lower cost than the AAPCC payment, they could eliminate the premium or provide additional services (4). The cost plan beneficiaries may obtain care outside the plan, where Medicare pays fee-for-service rates with deductibles and coinsurance. The health care prepayment plans cover a limited number of Medicare benefits and usually do not cover Part A services. Both cost and health care prepayment plans are being phased out as part of the Balanced Budget Act. Enrollment in managed care plans was initially slow, with only 3% enrolled by 1990. However by 1999, 16% of Medicare beneficiaries were enrolled in managed care plans (4). Approximately 75% of all beneficiaries have the option of joining a managed care plan because they live in areas where plans offer service. Managed care organizations grew in large urban areas where the AAPCC payment was high. For example, the AAPCC for Dade county Florida was $9000 per person when compared to several counties in Nebraska, where the AAPCC was less than $2800. Competition between plans led to additional services for the elderly, such as prescription drug benefits, as managed care organizations tried to lure customers. However, the payment system introduced incentives for care that was not in the interest of Medicare or its beneficiaries. Because of the limited-risk adjustment (age, gender, and institutional status), managed care plans could increase profits by selecting the healthiest adults of any given age. Several studies have now suggested that this has occurred. A study by the Physician Payment Review commission from 1989 to 1994 found that new managed care patients spent 38% fewer days in the hospital in comparison with a control group that did not join a Medicare-managed care plan (4,5). Those leaving the plan spent 42% more time in the hospital during the subsequent 6 months than did a

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control group. The mortality rate for those joining managed care plans was 25% below the mortality of the traditional fee-for-service plan. In a study of 4576 enrollees of 863 managed care plans, those enrolled in managed care were less likely to report poor health, functional impairment, or heart disease (6). Another concern of enrolling Medicare beneficiaries in managed care is that appropriate, but expensive, care will be withheld. In a study of appropriateness of coronary angiography after myocardial infarction (MI), Guadagnoli et al. found that only 37% of Medicare-managed care enrollees received angiography, despite having an American College of Cardiology (ACC)/American Heart Association Class 1 indication (procedure is useful and effective). This was significantly lower than for Medicare beneficiaries enrolled in traditional fee-for-service (46% angiography use, p < 0.001) (7). Other studies have found that less expensive, but effective, treatments are more often used in managed care than in fee-for-service patients (8,9). Thus, the overall effect of managed care on mortality is unclear. The effects of managed care may not be limited to enrollees. Fee-for-service patients residing in areas with high levels of managed care activity are often treated like other managed care patients (less resources used and more inexpensive appropriate treatments) and less like fee-for-service patients from areas with little managed care activity (10–12). Thus, as long as managed care continues to grow in the United States, it is likely that more Medicare patients will be affected, regardless of the popularity of Medicare-managed care plans.

THE 1997 BALANCED BUDGET ACT Background Although the overall impetus for the 1997 Balanced Budget Act was rising expenditures, there were several specific indications cited by the Medicare Payment Advisory Commission, which reports to Congress on Medicare-related issues (13,14). The first was the cost of the Medicare program that was growing by more than 8% per year. As noted previously, the Medicare Part A Trust Fund for in-patient treatment was expected to be insolvent by 2001. Projections of an aging population, without a corresponding increase in payroll taxes, indicated that costs had to be reduced. Second, expenditures for home health and nursing hope services were increasing dramatically (>30% per year for home health). It was felt that these costs could be controlled using a cost-based system similar to the DRG system for acute hospital care. Third, estimates indicated that managed care providers were being overpaid for services provided. The provisions of the Balanced Budget Act affected physicians, hospitals, long-term caregivers, and managed care plans. The goal was to save $110–170 billion over 5 years. These savings were expected to extend the life of the Part A Trust Fund through 2007, thus, the Balanced Budget Act was not considered a long-term fix for the Medicare program. The Act also wished to address the large variation in reimbursements across regions for the same services. Medicare spending has varied more than twofold between different regions without a clear difference in health outcomes (15,16).

Impact on Physicians The impact of the Balanced Budget Act on physicians may be considered minor in comparison to the impact on hospitals. The Act did affect procedure-oriented and

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clinic-based physicians differently in an attempt to reduce the large differences in reimbursement. The Act created a single conversion factor for all services, instead of three separate factors, for primary care, surgical, and other nonsurgical services. In addition, the system for updating physicians payments was changed to a sustainable growth rate system (SGR). In the past, changes in reimbursement were based on historical volume and intensity of services. The new SGR system forced any changes to be based on the gross domestic product (GDP), such that an increase in physician payments had to parallel real economic growth (increases were limited to –7%–3% of the Medicare Economic Index) (17). The Balanced Budget Act aided specialty physicians slightly by delaying the implementation of new methods of determining practice expense (14,18). When the resource-based relative value system (RBRVS) was initiated in 1992, practice expense values were based on prior average charge allowed. In order to make the practicerelated RVUs reflect actual expense rather than historical charges, Congress passed legislation in 1994 to require the Health Care Financing Administration (now the Center for Medicare and Medicaid Services [CMS]) to develop rules. If implemented, the initial rules would have led to dramatic reductions in payments for specialty physicians. In response to concerns for their accuracy and fairness, the Balanced Budget Act delayed implementation, then required a phase in over 4 years. However, in return for the delay, a $390 million downpayment was given for primary care services. The Act helped physicians not participating in Medicare by allowing beneficiaries to pay these physicians for services at rates above Medicare’s limits.

Impact on Hospitals The Balanced Budget Act had perhaps its greatest impact on hospitals, with cuts in both operating payments and capital expenses (14). No increase in operating expenses was planned for 1998, and only limited increases were planned for 1999–2002. (17). By 2003, payments were to return to normal. Because Medicare was thought to be overpaying for capital expenses, these were reduced by 18% to more accurately reflect the true hospital costs. The disproportionate share adjustment (for hospitals serving low-income patients) was reduced by 1% in 1998 and 5% in 2002. The disproportionate share calculation was also changed to better reflect true costs. The old calculation was based only on the number of state Medicaid recipients.

Impact on Teaching Hospitals Teaching hospitals were particularly hit hard by the Balanced Budget Act reductions in payments for medical education (19,20). Direct medical education payments, previously based on the number of residents, was capped at the 1996 level. This was intended to stop the incentive to train more physicians. Indirect medical education payments based on teaching intensity were reduced from 7.7% per 10% rise in teaching intensity to 5.5% for each 10% rise. Thus, large teaching hospitals have seen a greater percentage drop in Medicare reimbursements than small nonteaching hospitals. The impact of the Balanced Budget Act on teaching hospitals has been evaluated by the Association of American Medical Colleges (AAMC) (21). They estimated that the median teaching hospital will see a fall in cumulative revenue of $45.8 million by 2002 in comparison to what they would have received without the Balanced Budget Act. The median total margin (revenues–expenses) was reduced by half to 1% by 2002. Of

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course, not all hospitals are at the median. Approximately 100 teaching hospitals were operating at a loss by 2002 (negative margins).

Impact on Managed Care The balanced budget act also affected Medicare-managed care by creating Medicare+Choice (M+C). Prior to the Act, HMO plans were the only ones offered through Medicare. The Balanced Budget Act increased beneficiaries’ choices, allowing contracts with preferred provider organizations (PPOs) and provider-sponsored organizations (PSOs). Previously, plans could be paid on cost or through capitation (risk plans). The Balanced Budget Act intended to eliminate the cost plans by 2002. The M+C plans, also called coordinated care plans, offer point-of-service (POS) options that provide some coverage for out-of-network care. The goals were to expand choice of providers, improve the equity in payments to managed care plans, and improve quality and performance measures (22). Although enrollment in Medicare-managed care increased in 1998, it peaked in 1999 (6.3 million members) and has declined in 2000 and 2001, despite an increase in the number of Medicare beneficiaries. Between 1997 and 2000, 44% of managed care contractors (usually with few enrollees) terminated their participation. Of those beneficiaries forced to leave in their managed care plan in 1999, 40% paid higher premiums, 22% had to find a new physician, and 8% had no alternative to traditional Medicare (23). The risk adjustment for payments to managed care plans were modified by the Act to include diagnoses from the prior year’s in-patient hospitalizations, in addition to age, gender, and institutional status (24). By 2004, CMS intends to implement a multiple-site risk-adjustment system that will include out-patient diagnoses (24). These changes are intended to limit incentives for managed care patients to enroll only healthy patients.

Medical Savings Accounts (MSAs) The balanced budget act created a demonstration project of MSAs for 390,000 enrollees (25). MSA plans typically offer complete coverage beyond a high deductible (often more than $5000 per year). Medicare would pay the monthly premium and deposit money into the account to be used toward the deductible and other services not covered by the plan. MSAs have not been popular with providers, and as of 2000, no plan had applied to offer an MSA. At the request of Congress, the Medicare Payment Advisory Commission evaluated the program and concluded that marketing such a complex health care product to a risk averse population was not going to be successful. The project is intended to end in 2003 (25).

IMPACT OF ANTIFRAUD AND ANTI-ABUSE POLICIES Although the Balanced Budget Act is considered the major factor for the slower growth in Medicare expenditures in the late 1990s, increased enforcement of antifraud and abuse laws has made a considerable contribution. At the same time as the Balanced Budget Act was taking effect, the antifraud efforts by the US Department of Health and Human Services were accelerating. In 1998, the severity of illness Medicare Case-Mix Index based on coding of in-patient admission fell for the first time (4). Downcoding of DRGs (coding similar admissions to lower paying DRGs) was also observed for the first time (4).

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Enforcement of the antifraud laws are not without controversy (26). Using the 1863 False Claims Act, private whistleblowers can initiate a claim and if successful, receive a substantial fraction of any settlement or payment. Since the late 1980s, approximately $2 billion has been collected from health care providers using the False Claims Act, and payments to whistle blowers have exceeded $20 million. (27). The potential damages are immense, at up to $10,000 per false claim (individual bill) to Medicare. In addition, Medicare may suspend payment of all claims from a given provider once an allegation of fraud has been made. Thus, there is a strong incentive to settle cases quickly. Although funding for enforcement agencies has increased (e.g., through the Health Insurance Portability and Accountability Act [HIPAA]), their budgets remain modest when compared with the legal resources of large health care providers. A backlash by providers almost led to legislation in 1998 that would have greatly weakened the False Claims Act. In response, enforcement agencies have tried to limit their discretion in using the False Claims Act by providing guidelines for what actions should and should not be prosecuted.

MITIGATION OF THE BALANCED BUDGET ACT Because of the overwhelming response of health care providers, two pieces of legislation were passed in 1999 and 2000 to give back some of the cuts in reimbursement. However, many of the provisions are temporary or small in comparison to the cuts made in the Balanced Budget Act.

Balanced Budget Refinement Act (1999) Because so few people were entering managed care plans, the Balanced Budget Refinement Act (BBRA) extended the conversion of cost-to-risk-based plans until 2004. A bonus (5% for the first year, 3% for the second year) was established for managed care plans that entered an underserved area. Indirect medical education payments were further reduced, although the reduced disproportionate share adjustment was limited in 2001 and 2002 (14). Direct medical education payments were increased for those teaching hospitals, with costs below 85% the national average, whereas those above 140% of the national average were not expected to receive an increase until at least 2003.

Benefits Improvement and Protection Act (2000) Additional adjustments to the Balanced Budget Act were made through the Medicare, Medicaid, and State Children’s Health Insurance Program (SCHIP) Benefits Improvement and Protection Act of 2000 (BIPA). This act provided coverage for screening for certain cancers and glaucoma and increased reimbursement for rural health care, including payments for telemedicine. The act delayed scheduled reductions in operating payments to hospitals until after 2001 and delayed the complete phase in of indirect medical education adjustments until 2003. Cuts in payments for hospitals serving a disproportionate share of low-income Medicare beneficiaries were reduced. Similar delays in scheduled reductions in reimbursements were provided for home health care. In an attempt to increase managed care coverage in less populated areas, the minimum payment per beneficiary per year was increased to $415 for 2001. (The payment for patients residing in a metropolitan statistical area with more than 250,000 people was $525 in 2001.) There were several components of the BIPA that were directly related to heart disease. Demonstration projects for disease management programs for severely chronically ill

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Fig. 2. The median age of US residents continues to increase. Source: US Bureau of the Census, Statistical Abstract of United States.

Fig. 3. The projected aging of America. Beginning in 2010 there will be a significant increase in the fraction of the US population that is over 65. Those over 85 will increase from1.3% to 1.7% of the US population over the next 20 years. Source: US Bureau of the Census, Statistical Abstract of United States.

Medicare beneficiaries (heart failure, coronary disease, or diabetes) were instituted, and a study was initiated to determine the appropriate qualifying diagnoses for cardiac rehabilitation and the appropriate reimbursement for services.

IMPACT OF THE AGING OF AMERICA ON THE MEDICARE PROGRAM America is getting older, as shown by the increase in median age of US residents of 8 years during the last three decades (see Fig. 2). The number of Medicare beneficiaries is projected to increase markedly as the baby boomers reach age 65. Currently, there are approximately 35 million people 65 years or older in the United States. This number is expected to increase by 54% over the next 20 years. Although this will clearly increase the cost of the Medicare program, resources will also increase as the number of workers increases. Unfortunately, the elderly are increasing at a faster rate than are younger age groups. Currently, those 65 years or older make up 12.6% of the US population (see Fig. 3). This is expected to increase to 16.6% by 2020 and to 20.3% by 2050. The number of very old (>85 years) will also increase from 1.6% to 2.1% of the US population over the next 20 years. Those

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younger than 65 already pay over 90% of the cost of the Medicare program through the 2.9% payroll tax. Currently, the elderly rely on younger Americans for half of their combined income and health care expenditures (3). The relative increase in the elderly population will mean that working people will have to pay more per person to maintain the current level of benefits. Improved survival of the elderly may further increase the number of Medicare beneficiaries. For those age 65 at 1900, estimated life expectancy was 76.7 years, which increased to 78.9 years by 1950, a 19% increase subsequent survival. By 1990, life expectancy had increased another 20% to 81.8 years for patients aged 65 (16). Although a 20% increase in life expectancy every 40–50 years is unlikely to be sustained, even a modest continued increase in life expectancy will create increased strain for the Medicare program. Although the projected increase in the US elderly will be substantial, the financial strain may not be as great as in other developed countries. Currently, the percentage of those over age 65 for Japan (17%), Germany (16%), the United Kingdom (16%), France (16%), and Canada (13%) are all greater than the fraction of US elderly (12.5%) (28,29). By 2020, 16.6% of the US population will be elderly, but this will be much less than the projected 26% elderly fraction in Japan and 22% in Germany (28,29). However, when the increase in elderly is multiplied by the spending per patient (highest in the United States), the financial impact of the aging population may still be greatest in the United States.

IMPACT OF AGING VS TECHNOLOGICAL CHANGE Although demographics will have an important impact on the Medicare program, they are minor when compared to the increase in medical technology that is expected to continue indefinitely. Although the increase in the number of elderly patients has frequently been cited as the cause of the rapid growth of Medicare expenditures, the increase in those older than 65 (1.5% per year) is small in comparison to the growth of per capita health care expenditures (over 4% per year) (3). Estimates from 1990 indicate that Medicare now pays more than $55,000 per beneficiary over their lifetime, and that figure is expected to have risen by 50% by 2001 (16). The impact of new medical technology affects the cost of both diagnoses and treatment of disease. New and improved medications, surgical devices, and diagnostic tests are rarely cheaper than current alternatives. Even if diagnostic tests are improved without an increase in cost per test, they may be used more widely and, if more sensitive (troponin vs creatine kinase), may lead to more treatment (e.g., IIb/IIIa glycoprotein inhibitors for acute coronary syndromes [ACS]). Slowing the continued development of technology will be difficult at best. Although the development and diffusion of technology could be slowed through a decrease research funds, including decreased payments to drug companies, it is unclear if potential lower rates of health improvement would be acceptable to the public.

AGING AND DISABILITY The increasing disability that occurs with aging is an important cause of increased health expenditures for the elderly. In a study using the Medicare Current Beneficiary

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Survey from 1992–1994, Cutler estimated that patients 85 years old or more cost Medicare $3400 more than those age 65–70 (30). However, when measures of disability (impairments in basic and instrumental activities of daily living [ADL, IADL]) and death during the subsequent year were included, the expenditure difference between the 85+ group and those age 65–70 was only $272. Older patients are more expensive to care for because they have more disabilities. Heart disease is an important cause of disability among the elderly, particularly for those in skilled nursing facilities. In a study from the National Nursing Home Survey, patients or caregivers cited heart disease as the cause of 20% of disability (second only to cognitive impairment) (30). For noninstitutionalized patients, arthritis is the most common-associated disease with heart disease, cited as the cause of disability in 4–5% (31). Fortunately, the level of disability among the elderly has been decreasing for at least 15 years. Both the National Long-Term Care Survey, the Medicare Current Beneficiary survey, and the National Health Interview Survey all indicate that the fraction of elderly that need assistance with ADLs has declined by 10–20% (30). In 1984, 64% of those age 85 and older required assistance in ADLs or IADLs when compared to 52% in 1999. When averaged over the last 15 years, it appears that the level of disability among the elderly has been dropping by 1% per year (30). Reasons for the decline in disability are not well-documented, however, it is likely that improved treatments, including those for heart disease, have contributed. Data from the Framingham study indicate that disability among older adults was frequently associated with the development of angina pectoris and congestive heart failure (in women) (32). Both the decline in coronary disease and the improved medical and interventional treatments over the last 20 years are likely to have reduced the level of disability among the elderly. The decline in disability has significant implications in forecasting the cost of care for the elderly. Most forecasts have assumed a constant age-specific cost, then estimated costs based on expected changes in the age distribution. Because the elderly are becoming healthier at any given age, these models overestimate the future cost of care. If technological advances and disability were held constant, then the advancing age of the population would lead to a 74% increase in per-person spending by 2050 (30). However, if disability continues to decline at 1% per year, then the increase will be only 50% above the spending in 2000.

IMPACT OF CARDIAC DISEASE ON THE MEDICARE PROGRAM Given that cardiac disease is the number one cause of death in the United States, it is not surprising that a large amount of Medicare’s resources are used in the diagnosis and treatment of cardiac disease. For both male and female elderly, death is more likely to be a result of heart disease than of cancer and cerebrovascular disease combined (see Table 2). Heart disease is a common cause for admission among Medicare beneficiaries. Heart failure has remained the number one reason for admission in this population, followed by pneumonia throughout the 1990s (see Table 3). Of the $79 billion spent on hospitalization in 1999, 28% was for cardiac-related admissions (DRGs 103–145). The fraction of Medicare payments going to treat cardiovascular disease is unchanged since 1990. Mortality from circulatory disease (ICD9 codes 390–459) and heart disease (ICD9 codes 390–429) have declined for the US elderly (see Figs. 4 and 5). The admission rate per capita and the total number of admissions have decreased in the late 1990s, following

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Cardiovascular Health Care Economics Table 2 Top 10 Causes of Death for Persons 55 and Over in 1997

Cause

Total (1000s)

Rate per 100,000 (total)

Rate per 100,000 (males)

Rate per 100,000 (females)

Heart disease Cancer Cerebrovascular disease COPD Pneumonia Diabetes Accidents Alzheimer’s disease Kidney disease Sepsis

607 383 140 94 78 47 31 22 22 18

1781 1124 412 277 227 139 92 65 64 53

1944 1415 371 326 243 143 111 49 73 53

1667 914 441 229 217 136 79 76 58 53

Rank 1 2 3 4 5 6 7 8 9 10

Source: US Census Bureau, Statistical abstract of the United States, 2000. COPD, chronic obstructive pulmonary disease.

Table 3 Top 10 Reasons for Hospitalization for Medicare Beneficiaries Age 65 and Over in 1990 and 1999 Rank

DRG

1 2

127 089

3 4

140 014

5

182

6

209

7

296

8 9

096 430

10

138

Diagnosis

1990 ADM

DRG

Diagnosis

1999 ADM

Heart failure Pneumonia with complications Angina pectoris Stroke

578,942 391,062

127 089

686,040 528,045

356,079 334,521

088 209

Gastroenteritis, esophagitis with complications Lower extremity joint reattachment Malnutrition with complications Asthma Psychoses

257,481

014

Heart failure Pneumonia with complications COPD Lower extremity joint reattachment Stroke

249,208

116

310,574

206,460

430

Pacemaker or coronary stent Psychoses

197,288 193,448

462 174

178,377

296

Cardiac conduction problems with complications

Rehab Gastrointestinal bleeding with complications Malnutrition with complications

407,328 344,684 334,767

307,906 247,782 238,407

235,047

DRG, diagnostic-related groups; ADM, admissions; COPD, chronic obstructive pulmonary disease.

a long period of increase (see Figs. 6 and 7). This parallels the trend in admissions for all causes among the Medicare population (see Figs. 8 and 9). Despite a decreasing length of stay, the hospitalization expenditures per beneficiary continued to increase until the late 1990s.

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Fig. 4. Trend in mortality due to all circulatory diseases (ICD9 390–459). Source: National Center for Health Statistics.

Fig. 5. Trends in mortality due to all heart diseases (ICD9 390–429). Source: National Center for Health Statistics.

Heart Failure Heart failure remains the primary reason for admission in the Medicare program. It remains a disease of the elderly: more than 80% of patients are 65 years of age or older. Past studies have shown that survival following a heart failure admission is poor, with less than 25% of Medicare patients surviving 6 years (33). During the 1980s and early 1990s, the rate of heart failure admissions was increasing, and dire forecasts were given for an impending epidemic of heart failure (33). Perhaps because of increased use of life-prolonging treatments, such as angiotensin-converting enzyme (ACE) inhibitors and β-blockers, recent data indicate that survival following a heart failure admission is improving (34). The death rate owing to heart failure has remained flat during the mid to late 1990s (see Fig. 10). However, a change in heart failure coding cannot be ruled out. For example, mortality due to hypertensive heart disease (ICD9 401–404) has increased among the oldest old during the 1990s (see Fig. 11). If hypertension led to heart failure prior to death the cause may be listed as hypertensive heart disease and the prevalence of heart failure will be underestimated.

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Fig. 6. Trends in total number of Medicare cardiac admissions and length of stay 1990–1999 (DRG 103-145).

Fig. 7. Trends in admission rate and hospital costs per Medicare beneficiary for cardiovascular admissions (DRG 103-145)1990–1999. Source: Centers for Medicare and Medicaid Services.

In the early 1990s, the cost per beneficiary for in-patient heart failure care was increasing at 8% per year (see Fig. 12). However, following 1997 (and the Balanced Budget Act) the admissions per capita, total admissions, and cost per beneficiary for heart failure have all fallen (see Figs. 12 and 13).

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Fig. 8. Trends in total number of Medicare admissions (all DRGs) and length of stay 1990–1999. Source: Centers for Medicare and Medicaid Services.

Fig. 9. Trends in admission rate and hospital costs per Medicare beneficiary for all admissions from any cause 1990–1999. Source: Centers for Medicare and Medicaid Services.

Coronary Artery Disease (CAD) CAD mortality has been improving for more than 20 years (see Fig. 14). In part, this results from a decrease in the incidence of acute coronary syndromes, as well as improved treatment when these syndromes do occur. The improvement in survival fol-

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Fig. 10. Trends in mortality due to congestive heart failure (ICD9 428) 1979–1998. National Center for Health Statistics.

Fig. 11. Trends in mortality due to hypertensive heart disease (ICD9 401–404). Center for Health Statistics.

Source:

Source: National

lowing MI has been documented for the Medicare population by Pashos et al. (35). They observed a 10% decrease in 30-day mortality rates (26% to 23%) from 1987 to 1990. The decline in mortality persisted at 1 year (40% vs 36%). Declines were similar for men, women, blacks, and whites. Mortality was substantial for the very old (85 years and over), as 42% had died by 3 months and 55% by 12 months. Procedure use within 90 days of an acute MI by the elderly has increased as mortality has decreased. Between 1987 and 1990, angiography use increased from 24% to 33%, coronary artery bypass grafting (CABG) increased from 8% to 11%, and percutaneous transluminal coronary angioplasty increased from 5% to 10% (35). The quality of acute MI care among Medicare beneficiaries was evaluated in the Cooperative Cardiovascular Project. Numerous studies from this project have documented that many elderly receive suboptimal treatment. Aspirin in appropriate candidates was used in only two-thirds of patients within the first 48 hours following admission (36). β-blockers were used even less frequently, despite the absence of contraindications (50% at discharge) (37).

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Fig. 12. Trends in admission rate and hospital costs per Medicare beneficiary for heart failure admissions (DRG 127) 1990–1999. Source: Centers for Medicare and Medicaid Services.

Fig. 13. Trends in total number of heart failure admissions (DRG 127) and associated length of stay for Medicare patients. Source: Centers for Medicare and Medicaid Services.

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Fig. 14. Trends in mortality due to ischemic heart disease (ICD9 410–414). Source: National Center for Health Statistics.

The relation between cost and quality of care is not well-described. In analyses using Cooperative Cardiovascular Project data, Krumholz found that only 7% of the variation of in-hospital cost could be explained by differences in patient characteristics and severity of MI (38). Initial costs were higher for patients admitted to hospitals with on-site catheterization laboratories (39). However, re-admission rates were lower for patients initially admitted to a catheterization hospital and, by 3 years, costs were not different between catheterization and noncatheterization hospitals After rising during the early 1990s, both the number of MI admissions and the cost per Medicare beneficiary began to decrease (see Figs. 15 and 16). Both the length of stay and admission rate have steadily declined during this time. The use of bypass grafting for Medicare beneficiaries increased steadily until the late 1990 when a decline in total procedures performed and cost per beneficiary was observed for the first time (see Figs. 17 and 18). Mortality (40) and length of stay (see Fig. 17) for patients undergoing bypass grafting have both dropped since the late 1980s.

Valve Replacement The dramatic reductions in hospitalizations observed for coronary disease have not been seen for valvular disease (see Fig. 19). In fact, the rate of admission for valve replacement per year increased by approximately 6% per year during the 1990s (see Fig. 20). In contrast, rates of CABG have fallen at 6% per year since 1997 (see Fig. 21). If these trends continue, more money will be spent on valve replacement than on bypass grafting by 2004. Part of the continued increase in valve disease may be because of a decrease in competing risks. Patients are living longer now that numerous life-prolonging therapies are available for coronary disease and heart failure. Similar life-prolonging treatments are not yet available for valve disease. Aortic stenosis, in particular, is common in the elderly. Of patients over 65 year of age, 2% have stenosis, and another 29% have aortic sclerosis without stenosis (41). In the past, many of these patients would have died of coronary disease, as they had a 40% increased risk of MI (41). As the overall risk of MI decreases, more patients will progress to symptomatic aortic valve disease. The outcome in elderly patients undergoing aortic valve surgery,

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Fig. 15. Trends in total number of myocardial infarction admissions (DRGs 121–123) and length of stay for Medicare patients. Source: Centers for Medicare and Medicaid Services.

Fig. 16. Trends in admission rate and hospital costs per Medicare beneficiary for acute myocardial infarction admissions (DRG 121–123) from 1990–1999. Source: Centers for Medicare and Medicaid Services.

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Fig. 17. Trends in total number of Medicare admissions for coronary artery bypass surgery and associated length of stay 1990–1999 (DRG 106,107,109). Source: Centers for Medicare and Medicaid Services.

Fig. 18. Trends in admission rate and hospital costs per Medicare beneficiary for coronary artery bypass grafting (DRG 106,107,109)1990–1999. Source: Centers for Medicare and Medicaid Services.

although not as good as in younger patients, is now acceptable for even octogenarians and better than medical treatment for patients with symptoms (42–44). Recent studies have documented that mitral valve surgery in patients age 70 and above leads to improved functional status in survivors although mortality remains high (10–30% 30day mortality in symptomatic patients with a median age 75 or greater) (45,46).

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Fig. 19. Trends in total number of Medicare admissions for cardiac valve surgery and associated length of stay 1990–1999 (DRG 104,105). Source: Centers for Medicare and Medicaid Services.

Fig. 20. Trends in admission rate and hospital costs per Medicare beneficiary for valve replacement (DRG 104–105)1990–1999. Source: Centers for Medicare and Medicaid Services.

Ventricular Arrhythmias Unlike coronary disease, death from cardiac dysrhythmias (ICD9 427) remained relatively unchanged during the 1990s (see Fig. 22). However, the use of implantable defibrillators (ICDs) in the elderly increased dramatically during the 1990s. In 1985, there were 485 ICDs placed in Medicare beneficiaries. By 1995, the number of ICDs

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Fig. 21. Trends in cost of acute hospital care per Medicare beneficiary indicate that more money is spent on bypass grafting (DRG 106,107,109) and heart failure (DRG127) than myocardial infarction(DRG 121–123) and valve replacement (DRG 104,105). Unlike the other diagnoses and procedures, the cost of valve replacement continues to increase. Source: Centers for Medicare and Medicaid Services.

Fig. 22. Trends in mortality due to cardiac dysrhythmias (ICD9 427) 1979–1998. Source: National Center for Health Statistics.

implanted per year had increased 15-fold to more than 7250 (47). The number of hospitals implanting ICDs in Medicare patients also increased from 138 to 535. Mortality during the year following surgery was higher than reported in randomized trials, but has been improving (19.3% in 1987 to 11.4% in 1994 for those over 65) (47). As with other cardiac diseases, the length of stay for patients with ventricular arrhythmia, where an ICD was placed, has decreased significantly from 27 days in 1987 to 11 days in 1995. One-year Medicare expenditures beginning with ICD admission initially rose from $48,000 in 1987 to $50,000 in 1992, before falling slightly to $46,000 in 1994

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Fig. 23. Trends in mortality due to endocarditis (ICD9 421) 1979–1998. Source: National Center for Health Statistics.

(all in 1993 dollars) (47). The mortality, length of stay, and cost have likely all decreased dramatically since the mid-1990’s as the procedure has shifted from requiring a thoracotomy to a transvenous system similar to a pacemaker. The appropriate indications for ICD implantation are still being debated. Although the use of ICDs in patients with life-threatening ventricular arrhythmias is accepted, the prophylactic use of patients at risk for their first ventricular arrhythmia remains controversial. There is a potentially huge number of candidates for prophylactic ICD placement depending on how risk is defined. If the highly restrictive inclusion criteria from the Multicenter Automatic Defibrillator Implantation Trial (48) are used, it is estimated that up to 1.7% of patients with MI will qualify (49). Assuming that 80% of the 290,000 yearly MI are new (50), then another 3900 ICDs would be implanted in Medicare beneficiaries each year for this indication alone. It is likely that many more survivors of MI (e.g., with low ejection fraction) will also benefit. Given the low perioperative morbidity now possible with transvenous implantations (51), the implantation rate among Medicare patients is likely to increase in the foreseeable future. Because implantation costs are more than $40,000 (52), a large fraction of Medicare expenditures will be devoted to ICDs in the coming decade.

Endocarditis Infective endocarditis is a highly lethal disease in the elderly (see Fig. 23) although initial symptoms are frequently less severe when compared to younger patients (53). Whether age increases mortality in infective endocarditis remains controversial (53,54). Unlike most other cardiac conditions, the hospitalization rate for endocarditis continues to slowly increase, although it remains a rare disease (1.3 admissions per 10,000 patients per year, see Fig. 24). Length of stay in the acute hospital setting has decreased dramatically (see Fig. 25), however, it is only recently that the actual cost per Medicare beneficiary has dropped. (Fig. 24) Given the steady increase in heart valve replacements, it is likely that the rate of endocarditis will continue to rise. Currently, prosthetic endocarditis develops in up to 0.6% of patients per year (55,56). If this trend continues, prosthetic endocarditis may become the most common presentation of infective endocarditis in the Medicare population.

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Fig. 24. Trends in Admission rate and hospital costs per Medicare beneficiary for endocarditis (DRG 126) 1990–1999. Source: Centers for Medicare and Medicaid Services.

Fig. 25. Trends in total number of Medicare endocarditis admissions (DRG 126) and associated length of stay 1990–1999. Source: Centers for Medicare and Medicaid Services.

IMPACT OF MEDICARE REIMBURSEMENT ON THE PRACTICE OF CARDIOLOGY Medicare will continue to influence cardiovascular care by setting price levels and establishing standards for payment. Until now, there have been few restrictions on a provider’s ability to be reimbursed for services rendered to Medicare beneficiaries. However, that is starting to change. Beginning January 2002, echocardiograms in

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South Carolina are being reimbursed by Medicare only if they are performed by either (1) an accredited sonographer (registered diagnostic cardiac sonographer) and read by a physician with level II echocardiography training or (2) performed at a laboratory accredited by the Intersocietal Commission for the Accreditation of Echocardiography Laboratories. As of July 2001, there were only three laboratories accredited to perform transthoracic echocardiography in South Carolina. Five other states have various restrictions on performance of echocardiography. Additional limitations have been proposed as part of the Medicare reform. The CMS may choose to select “centers of excellence” for bypass surgery, valve replacement, and ICD implantation. Cardiology practice groups may find it necessary to compete for Medicare patients. Ironically, payments are falling more rapidly at the high-volume teaching hospitals (potential centers of excellence) than they are at smaller community hospitals. Thus, an incentive currently exists for more procedures to be performed at the low-volume hospitals (57).

THE FUTURE OF MEDICARE Despite the implementation of the Balanced Budget Act of 1997, there remains a strong interest in continuing to reduce expenditures. Despite the Balanced Budget Act, Medicare spending per beneficiary is expected to reach $8000 per beneficiary by 2018 (in 1999 dollars) and to $12,000 by 2030 (1). Many authorities believe a meaningful reduction in expenditures can only be achieved through a decrease in services to the elderly. A frequently cited option to reducing expenditures is to reduce the cost of prescription drugs. It is estimated that the elderly fill an average of 18 prescriptions and spend more than $1000 per year on drugs (58,59). Cardiovascular medications make up a large proportion as three of the top four prescribed medication categories are cardiac-related: CAD, blood pressure, and heart failure medications. Although excess profits by pharmaceutical manufactures are frequently cited as a cause of the high cost of care, payments for drugs make up a small fraction of total health care expenditures. Fuchs estimates that even if drug company profits were cut in half, the reduction in growth of Medicare expenditures would be less than 0.1% per year (3). Several reforms have been suggested for both the benefits and financing of the Medicare program. Many of these originated from The Bipartisan Commission headed by Senator John Breaux (D-LA) and Representative Bill Thomas (R-CA) that presented their recommendations in 1999. The proposal had more republican than democratic support and did not receive enough votes to be formally presented to Congress. However, many of their ideas are still being considered for future legislation. One major change recommended by the Breaux Committee was that all managed care plans and traditional Medicare compete for Medicare beneficiaries (1). The government would pay a fixed amount, and each plan would have to provide a minimum set of benefits. Each beneficiary could choose a plan or traditional Medicare based on price as well as benefits. The beneficiary would pay 12% of the cost of the average plan, receive 80% of the savings from a plan costing less than average, and pay 100% of the cost above the average plan. Exemptions would be made for low-income and rural beneficiaries. Centers of excellence have been proposed as a way to improve quality and potentially reduce costs. Financial incentives could be put in place to draw patients to low-cost and

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high-quality centers for percutaneous coronary interventions or CABG (1). However, this would give Medicare unprecedented power as the dominant purchase of health care. Concerns have been raised that the choice of centers of excellence would be affected by politics, in addition to quality and cost (1). Other proposals to limit Medicare spending include greater copayments and increasing the eligibility age as the social security eligibility increases from 65 to 67 between 2003 and 2025. Income-specific premiums have been suggested. Critics point out that they would produce an incentive to discourage today’s workers from accumulating wealth/working to old age. Accounting measures have also been proposed for payments to teaching and public hospitals. The Breaux-Thomas proposal suggested that hospital payments for medical education and the uninsured be line items in the budget, not part of Medicare (1). Breaux-Thomas has suggested combining Part A and B into one trust fund that would receive 40% of its funding from general revenues. It is unclear how these changes would affect the growth of Medicare expenditures. In summary, the aging of a slightly healthier population will lead to financial strain for the Medicare program. However, this effect is likely to be dwarfed by the progressive march of better and more expensive technologies. Policymakers, providers, and patients will be forced like never before to examine the cost along with the effectiveness of new health care treatments.

REFERENCES 1. McClellan M. Medicare reform: fundamental problems, incremental steps. J Econ Perspectives 2000;14:21–44. 2. Medicare Payment Advisory Commission. Report to Congress: Selected Medicare Issues, June 1999. 3. Fuchs V. Medicare reform: the larger picture. J Econ Perspectives 2000;14:57–70. 4. Newhouse J. Medicare. Conference on Economic Policy During the 1990s. Kennedy School of Government, 2001. 5. Physician Payment Review Commission. Annual Report to Congress 1996. 6. Riley G, Tudor C, Chiang YP, Ingber M. Health status of Medicare enrollees in HMOs and fee-for-service in 1994. Health Care Financ Rev 1996;17:65–76. 7. Guadagnoli E, Landrum MB, Peterson EA, et al. Appropriateness of coronary angiography after myocardial infarction among Medicare beneficiaries. Managed care versus fee for service. N Engl J Med 2000;343:1460–1466. 8. Every NR, Cannon CP, Granger C, et al. Influence of insurance type on the use of procedures, medications and hospital outcome in patients with unstable angina: results from the GUARANTEE Registry Global Unstable Angina Registry and Treatment Evaluation. J Am Coll Cardiol 1998;32:387–392. 9. Carlisle DM, Siu AL, Keeler EB, et al. HMO vs fee-for-service care of older persons with acute myocardial infarction. Am J Public Health 1992;82:1626–1630. 10. Heidenreich P, McClellan M, Frances C, Baker L. The relation between managed care market share and the treatment of elderly fee-for-service patients with myocardial infarction. Am J Med 2002;112:176–182. 11. Baker LC. The effect of HMOs on fee-for-service health care expenditures: evidence from Medicare. J Health Econ 1997;16:453–481. 12. Baker LC. Association of managed care market share and health expenditures for fee-for-service Medicare patients. JAMA 1999;281:432–437. 13. Medicare Payment Advisory Commission. Report to Congress: Medicare Payment Policy, March 2000. 14. Silversmith J. The impact of the 1997 Balanced Budget Act on Medicare, Part II. Minn Med 2001;84:47–54. 15. Skinner J, Wennberg J. How much is enough? Efficiency and medicare spending in the last six months of life. In: Cutler D (ed.) The Changing Hospital Industry. University of Chicago Press; Chicago, IL, 1999, pp. 169–193.

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16. Cutler D. Walking the tightrope on medicare reform. J Econ Perspectives 2000;14:45–56. 17. Medicare Payment Advisory Commission. Report to Congress: Medicare Payment Policy, March 1999. 18. Schoenman JA, Hayes KJ, Cheng CM. Medicare physician payment changes: impact on physicians and beneficiaries. Health Aff 2001;20:263–273. 19. Dickler R, Shaw G. The Balanced Budget Act of 1997: its impact on U.S. teaching hospitals. Ann Intern Med 2000;132:820–824. 20. Iglehart JK. Support for academic medical centers—revisiting the 1997 Balanced Budget Act. N Engl J Med 1999;341:299–304. 21. Marwick C. AAMC analyzes 1997 Balanced Budget Act. Association of American Medical Colleges. JAMA 1999;281:1781–1782. www.medpac.gov. 22. Gold M. Medicare+Choice: An Interim Report Card. Health Aff 2001;20:121–138. 23. Laschober MA, Neuman P, Kitchman MS, et al. Medicare HMO withdrawals: what happens to beneficiaries? Health Aff 1999;18:150–157. 24. Medicare Payment Advisory Commission. Report to Congress: Improving Risk Adjustment in Medicare, June 1999. 25. Medicare Payment Advisory Commission. Report to Congress: Medicare Savings Accounts and the Medicare Program, November 2000. 26. Stanton T. Fraud And Abuse Enforcement in Medicare: Finding Middle Ground. Health Aff 2001;20:28–42. 27. Slade S. The False Claims Act and Health Care Fraud: How Far Does the Act Reach? 2000. www.fraudbuster.com/page10.html. 28. Anderson G, Hussey P. Health and Population Aging: A Multinational Comparison. The Commonwealth Fund, New York, NY, 1999. 29. Reinhardt U. Health Care for the Aging Baby Boom: Lessons from Abroad. J Econ Perspectives 2000;14:71–83. 30. Cutler D. Declining Disability Among the Elderly. Health Affairs 2001;20:11–27. 31. Stuck AE, Walthert JM, Nikolaus T, et al. Risk factors for functional status decline in community-living elderly people: a systematic literature review. Soc Sci Med 1999;48:445–469. 32. Pinsky JL, Jette AM, Branch LG, et al. The Framingham Disability Study: relationship of various coronary heart disease manifestations to disability in older persons living in the community. Am J Public Health 1990;80:1363–1367. 33. Croft JB, Giles WH, Pollard RA, et al. Heart failure survival among older adults in the United States: a poor prognosis for an emerging epidemic in the Medicare population. Arch Intern Med 1999;159:505–510. 34. Heidenreich P, Kagay K, McClellan M. Trends in survival following a new admission for heart failure. J Am Coll Cardiol 2001;37(Abstract) 502A. 35. Pashos CL, Newhouse JP, McNeil BJ. Temporal changes in the care and outcomes of elderly patients with acute myocardial infarction, 1987 through 1990. JAMA 1993;270:1832–1836. 36. Krumholz HM, Radford MJ, Ellerbeck EF, et al. Aspirin in the treatment of acute myocardial infarction in elderly Medicare beneficiaries. Patterns of use and outcomes. Circulation 1995;92:2841–2847. 37. Krumholz HM, Radford MJ, Wang Y, et al. National use and effectiveness of beta-blockers for the treatment of elderly patients after acute myocardial infarction: National Cooperative Cardiovascular Project. JAMA 1998;280:623–629. 38. Krumholz HM, Chen J, Murillo JE, et al. Clinical correlates of in-hospital costs for acute myocardial infarction in patients 65 years of age and older. Am Heart J 1998;135:523–531. 39. Krumholz HM, Chen J, Murillo JE, et al. Admission to hospitals with on-site cardiac catheterization facilities: impact on long-term costs and outcomes. Circulation 1998;98:2010–2016. 40. Weintraub WS, Craver JM, Jones EL, et al. Improving cost and outcome of coronary surgery. Circulation 1998;98(19 Suppl):II23–II28. 41. Otto CM, Lind BK, Kitzman DW, et al. Association of aortic-valve sclerosis with cardiovascular mortality and morbidity in the elderly. N Engl J Med 1999;341:142–147. 42. Morell VO, Daggett WM, Pezzella AT, et al. Aortic stenosis in the elderly: result of aortic valve replacement. J Cardiovasc Surg (Torino) 1996;37(6 Suppl 1):33–35. 43. Mullany CJ. Aortic Valve Surgery in the Elderly. Cardiol Rev 2000;8:333–339. 44. Olsson M, Granstrom L, Lindblom D, et al. Aortic valve replacement in octogenarians with aortic stenosis: a case-control study. J Am Coll Cardiol 1992;20:1512–1516. 45. Goldsmith I, Lip GY, Kaukuntla H, Patel RL. Hospital morbidity and mortality and changes in quality of life following mitral valve surgery in the elderly. J Heart Valve Dis 1999;8:702–707.

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46. Grossi EA, Zakow PK, Sussman M, et al. Late results of mitral valve reconstruction in the elderly. Ann Thorac Surg 2000;70:1224–1226. 47. Hlatky M, McDonald K, Saynina O, Garber A, McClellan M. Utilization and Outcomes of the Implantable Cardioverter Defibrillator, 1987–1995. Am Heart J. 48. Moss AJ, Hall WJ, Cannom DS, et al. Improved survival with an implanted defibrillator in patients with coronary disease at high risk for ventricular arrhythmia. Multicenter Automatic Defibrillator Implantation Trial Investigators. N Engl J Med 1996;335:1933–1940. 49. Every NR, Hlatky MA, McDonald KM, et al. Estimating the proportion of post-myocardial infarction patients who may benefit from prophylactic implantable defibrillator placement from analysis of the CAST registry. Cardiac Arrhythmia Suppression Trial. Am J Cardiol 1998;82:683–685, A8. 50. Heidenreich PA, McClellan M. Trends in treatment and outcomes for acute myocardial infarction: 1975–1995. Am J Med 2001;110:165–174. 51. Zipes DP, Roberts D. Results of the international study of the implantable pacemaker cardioverterdefibrillator. A comparison of epicardial and endocardial lead systems. The Pacemaker-CardioverterDefibrillator Investigators. Circulation 1995;92:59–65. 52. Owens DK, Sanders GD, Harris RA, et al. Cost-effectiveness of implantable cardioverter defibrillators relative to amiodarone for prevention of sudden cardiac death. Ann Intern Med 1997;126:1–12. 53. Selton-Suty C, Hoen B, Grentzinger A, et al. Clinical and bacteriological characteristics of infective endocarditis in the elderly. Heart 1997;77:260–263. 54. Gagliardi JP, Nettles RE, McCarty DE, et al. Native valve infective endocarditis in elderly and younger adult patients: comparison of clinical features and outcomes with use of the Duke criteria and the Duke Endocarditis Database. Clin Infect Dis 1998;26:1165–1168. 55. Agnihotri AK, McGiffin DC, Galbraith AJ, O’Brien MF. The prevalence of infective endocarditis after aortic valve replacement. J Thorac Cardiovasc Surg 1995;110:1708–1720. 56. Glower DD, Landolfo KP, Cheruvu S, et al. Determinants of 15-year outcome with 1119 standard Carpentier-Edwards porcine valves. Ann Thorac Surg 1998;66(6 Suppl):S44–S48. 57. Roddy SP, O’Donnell TF, Jr., Wilson AL, et al. The Balanced Budget Act: potential implications for the practice of vascular surgery. J Vasc Surg 2000;31:227–236. 58. Davis M, Poisal J, Chulis G, et al. Prescription drug coverage, utilization, and spending among Medicare beneficiaries. Health Aff (Millwood) 1999;18:231–243. 59. Smith S, Heffler S, Freeland M. The next decade of health spending: a new outlook. The National Health Expenditures Projection Team. Health Aff 1999;18:86–95.

Afterword The Future of Economics in Cardiovascular Care and Research

William S. Weintraub, MD

“I never think of the future. It comes soon enough.” — Albert Einstein

In this book, contemporary methods for health care economic evaluations have been presented, including both chapters on methods and chapters on economic studies in various areas of cardiovascular medicine. Where will the field go from here? There are a number of forces in society, that seem likely to drive the future of health care economic studies. The first is the changing demographic character of the United States and much of the industrialized world, as outlined in Chapter 22 by Dr. Heidenreich. As the population ages, and the need for services grow relative to the working population, there will be demand for greater economic accountability, which will increase the demand for economic studies for all medical services. Innovative new diagnostic and therapeutic advances will also certainly continue. Most of these advances will cost money. Unless more money becomes available overall, there will be increasing competition between medical services to show clinical and economic benefit. A demand for quality will also continue. Although there has been concern that too much focus on cost would drive down the quality of care, these concerns are perhaps overblown. Through its various stakeholders, society will continue to demand high-quality medical services at fair value. Although third-party payers may naturally be more concerned about price than quality, the payers do not exist independently of the population that will demand quality service. This being true, there still remains grave reason for concern. Quality is not uniform; the underinsured in the United States who do not get adequate quality of care remain a major problem. In addition, at times, payers and providers will act out of economic concerns independent of concerns for quality. Nonetheless, the demand for services of quality at a good value, and the willingness of providers and third party payers to meet that challenge, will continue to drive medical care in the future. Methodologic advances in economic studies can be expected to continue. Measurement of health status has long been a major field. In recent years, health status measures are being applied more widely, both in clinical trials and observational studies. From: Contemporary Cardiology: Cardiovascular Health Care Economics Edited by: W. S. Weintraub © Humana Press Inc., Totowa, NJ

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Formal health status measures may also become a part of more routine clinical care. The measurement of utility will likely remain within the province of investigation. Improved methods to assess utility, whether from improved surveys or more readily performed direct patient preference measures, should be expected. Increased validation of utility measures should also become available. Cost measurement should also improve as hospital and payer accounting improve, and as information becomes more readily available. This will need to be coupled with patients’ concern over privacy. Although privacy is a major concern, particularly in the United States, perfect privacy is also not possible, and will need to be compromised to some extent to permit medical care to be delivered, as well as to permit clinical research to be conducted so that medical care can be improved. Cost-effectiveness (CE) methods will also continue to improve. The Unites States Public Health service recommendations has greatly improved the field by establishing a set of standards for CE studies (1). Although it may be difficult for studies to meet these standards, and often extrapolations beyond measurements that are actually made in studies will be necessary, the field still largely benefits from greater uniformity of approach, fostering more meaningful studies, and allowing greater interpretability across studies. Technical improvements in cost-effectiveness analysis (CEA), as outlined in Chapter 9 by Drs. Mahoney and Chu, are also offering a richer perspective in the recent literature. An emphasis on estimation, rather than hypothesis testing, offers deeper insight into the relationship between cost and outcome (2). An increasing emphasis on Bayesian approaches to economic studies will allow a greater ability to interpret the impact of studies on underlying populations given prior knowledge, rather that the frequentist approach, which offers an interpretation of studies given the results in a sample drawn from the underlying population with no prior knowledge (2–5). Increasingly CEA will be based on more firm data. The data may be observational or from randomized trials. CE studies are, by design, a comparison of two or more alternatives. Nonrandomized comparisons will be limited by selection bias, whereby one service or therapy is chosen over another because of the perception that it is in some way better. Attempts to overcome selection bias will continue to be made with multivariate analysis (6,7). In the future, more comparisons are likely to be made using propensity scores, in which the propensity to have one therapy or service over the alternative is predicted using multivariate analysis (8,9). The propensity to have one therapy can then be used to correct both outcome and potentially cost. However, standard multivariate analysis and propensity scores suffer from the inability to remove selection bias because of unmeasured confounders. In principle, this can be overcome by the use of an instrumental variable. An instrumental variable is one that affects selection, but not outcome (10,11). However, it is difficult to find instrumental variables that can be used to remove selection bias, except for one particular kind. The instrumental variable that most successfully overcomes selection bias, and is thus used most commonly to remove it, is randomization. Overcoming selection bias is the only thing that randomization accomplishes, and often with the downsides of increasing the cost, difficulty, and duration of studies, often limiting their generalizability. Nonetheless, the attractiveness of randomized clinical trials in overcoming selection remains highly attractive. In recent years, economic studies are much more commonly coupled to randomized trials. This will certainly continue and, hopefully, with improved data gathering to permit improved, more robust economic studies to emerge from clinical trials.

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Much will depend on the quality of information. Information systems in medicine continue to lag other industries in quality and investment. There are many reasons for this, largely beyond the scope of this book. However, information systems in medicine are gradually improving, with better standards for data collection (12,13) and improved ability for routine daily transactions. Improved information systems will be a boon to clinical and economic research and should also improve quality of care, hopefully with the almost unheard of cost savings. Of course, the use of information systems will themselves be a subject of economic evaluation. Yogi Berra somewhat famously said, “The future ain’t what it used to be.” This likely does not correctly characterize health care economics. Economics studies have evolved quite rapidly over the last several decades. This process seems likely to accelerate in the future, offering increased ability to understand the value of medical care.

REFERENCES 1. Gold MR, Siegel JE, Russell LB, Weinstein MC (eds.). Cost-Effectiveness in Health and Medicine. Oxford University Press, NY, 1996. 2. Briggs AH, O’Brien BJ, Blackhouse G. Thinking Outside the Box: Recent Advances in the Analysis and Presentation of Uncertainty in Cost-Effectiveness Studies. Annual Rev. of Pub Health 2002;23:377–401. 3. Briggs A. A Bayesian approach to stochastic cost-effectiveness analysis. Health Econ 1999;8:257–261. 4. Briggs AH. A Bayesian approach to stochastic cost-effectiveness analysis: an illustration and application to blood pressure control in type 2 diabetes. Int J Technol Assess Health Care 1001;17:69–82. 5. Fryback DG, Chinnis JO Jr, Ulvila JW. Bayesian cost-effectiveness analysis: an example using the GUSTO trial. Int J Technol Assess Health Care 2001;17:83–97. 6. Mark DB, Nelson CL, Califf RM, et al. Continuing evolution of therapy for coronary artery disease. Initial results from the era of coronary angioplasty. Circulation 1994;89:2015–2025. 7. Weintraub, WS, Stein B, Kosinski A, et al. Outcome of coronary bypass surgery versus coronary angioplasty in diabetics with mutlivesel coronary artery disease. J Am Coll Cardiol 1998;31:10–19. 8. Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika 1983;70:41–55. 9. Rosenbaum PR, Rubin DB. Reducing bias in observational studies using subclassification on the propensity score. J Am Stat Assoc 1984;79:516–524. 10. Harris KM, Remler DK. Who is the marginal patient? Understanding instrumental variables estimates of treatment effects. Health Serv Res 1998;33:1337–1360. 11. Bound J, Jaeger DA, Baker RM. Problems with instrumental variables estimation when the correlation between the instruments and the endogenous explanatory variable is weak. J Am Stat Assoc 1995;90:443–450. 12. McDonald CJ. Quality measures and electronic medical systems. J Am Med Assoc 1999;282:1181–1182. 13. McDonald CJ, Overhage JM, Tierney WM, et al. The Regenstrief Medical Record System: A quarter century experience. Int J Med Inform 1999;54:225–253.

Index

421

INDEX A AAPCC, 392 Abciximab, 176, 209 Absorbing cost center (ACC), 35 ACC, 35 ACE inhibitors. See Angiotensin-converting (ACE) inhibitors ACME, 189 ACS. See Acute coronary syndromes (ACS) ACTION-HF, 265 ACUTE, 322 Acute coronary syndromes (ACS), 173–182 antiplatelet therapy, 178–179 antithrombin therapy, 180 complications, 174 cost components, 174 invasive early management, 213–214 primary percutaneous coronary reperfusion, 176–178 reperfusion therapy, 175–176 risk stratification, 174 secondary prevention, 180–182 Acute myocardial infarct (AMI) comparative costs, 292 predischarge risk assessment, 292 primary angioplasty vs reperfusion, 209– 211 stenting vs percutaneous transluminal coronary angioplasty, 211–213 Adjunctive glycoprotein IIB/IIIA inhibition percutaneous coronary interventions, 207–209 Adjunctive pharmacotherapy percutaneous coronary interventions, 207 Adjusted average per capita cost (AAPCC), 392 Administrative data estimating hospital costs, 8–10 ADMIRAL, 178 Advisory Committee to Improve Outcomes Nationwide in Heart Failure (ACTION-HF), 265

AFCAPS/TexCAPS, 160 African Americans congestive heart failure, 264 Aging of America, 397 disability, 398–399 Medicare, 397–398 technological change, 398 Alberta case-cost data, 37 Allocated costs, 5 Alpha-blockers, 166 AMA, 48 Ambulatory Payment Category, 16 Amenities, 376 American Heart Association cardiovascular disease and stroke costs, 71–72 American Medical Association (AMA), 48 AMI. See Acute myocardial infarct (AMI) Amiodarone, 318 Angina Pectoris Quality of Life Questionnaire (APQLQ), 90 Angioplasty Compared to Medicine study (ACME), 189 Angiotensin-converting (ACE) inhibitors, 165, 182, 401 congestive heart failure, 266–270 Angular transformation, 139–140 Annual Hospital Survey Canadian Institute for Health Information, 33–34 Annual Medicare Cost Reports, 9 Anti-abuse policies, 395–396 Antiarrhythmic agents congestive heart failure, 272–273 tornado diagram, 320 Antiarrhythmics vs Implantable Defibrillators (AVID), 311 Antifraud, 395–396 Antiplatelet therapy acute coronary syndromes, 178–179 421

weintraub_Index_Final

421

5/12/03, 9:25 AM

422

Index

Antithrombotic prophylaxis, 315–316 Aortic aneurysm interventions, 337–338 Aortic valve replacement (AVR), 250–251 APQLQ, 90 Argentine Randomized Trial of Percutaneous Transluminal Coronary Angioplasty vs Coronary Artery Bypass Surgery in Multivessel Disease (ERACI-I), 227 Argentine Randomized Trial of Percutaneous Transluminal Coronary Angioplasty vs Coronary Artery Bypass Surgery in Multivessel Disease (ERACI-II), 230 Arrhythmia interventions, 338–340 Arterial Revascularization Therapy Study (ARTS), 194, 229 ARTS, 194, 229 Aspirin, 182, 315 acute coronary syndromes, 178–179, 180 ASSENT 2, 176 ASSENT 3, 176 Assessment of Cardioversion Using Transesophageal Echocardiography (ACUTE), 322 Asynchronous ventricular pacing, 304 Atrial fibrillation, 315 postoperative CABG, 240 prophylactic anticoagulation, 321 strategies, 319 Atrioventricular conduction, 304 Atrioventricular reciprocating tachycardia (AVRT), 313 Automated utility assessment interviews Internet, 107 Average cost per hire, 76 Average daily benefits, 76 Average daily wages, 76 AVR, 250–251 AVRT, 313 B Balanced Budget Act of 1997, 393–395 mitigation of, 396–397 Balanced Budget Refinement Act of 1999, 396 Balloon angioplasty vs brachytherapy, 207 vs coronary stents, 198

weintraub_Index_Final

422

BARI, 192, 225, 234 implications, 230–231 Bayesian framework, 146–147 Benefit Evaluation of Direct Coronary Stenting (BET), 199 Benefits defining, 383 Benefits Improvement and Protection Act of 2000, 396–397 State Children’s Health Insurance Program, 396 BET, 199 Beta-blockers, 165, 182, 401 acute coronary syndromes, 180–181 congestive heart failure, 270–271 cost, 165 Bias, 336 Bivalrudin acute coronary syndromes, 180 BLS, 75 Blue Cross and Blue Shield, 48–49 BOAT, 204 Bootstrap cost and effect differences, 135 example of, 134 histogram, 127 sampling distributions, 135 Bootstrap confidence limits cost difference, 126 Bootstrap distribution cost and effect differences TACTICS-TIMI, 138 Brachytherapy vs balloon angioplasty, 207 in-stent restenosis, 206–207 Breaux-Thomas proposal Medicare reform, 414 Bureau of Labor Statistics (BLS), 75 Bypass Angioplasty Revascularization Investigation (BARI), 192, 225, 234 implications, 230–231 Bypass surgery clinical outcomes, 224 vs coronary angioplasty, 224–228 economic endpoints, 224–225 vs percutaneous coronary revascularization, 193 C CABG. See Coronary artery bypass graft (CABG)

5/12/03, 9:25 AM

Index

423

CAD. See Coronary artery disease (CAD) CADILLAC, 178, 213 Calcium antagonist cost, 165 Calcium channel blockers (CCB), 166 Calculations and formulas indirect costs, 76 Canada cardiac costing examples, 41–43 cholesterol studies, 164 Canadian Cardiovascular Society Classification system (CCAC), 90, 108 Canadian health care resources Canadian cardiac costing, 41–43 case-costing, 35–38 estimating costs, 31–43 gross-cost estimates, 35–38 microcost estimates, 36 nonstandardized sources for resource costs, 32–33 pharmaceutical products, 40–41 physician services, 39–40 standard in-patient cost lists, 36 standardized sources for resource costs, 33–35 Canadian Implantable Defibrillator Study (CIDS), 41–43, 311 unit costs, 42 Canadian Institute for Health Information (CIHI), 32, 33–34 National Grouping Categories Report, 40 Canadian pharmaceutical products, 40–41 hospital pharmacies, 41 IMS HEALTH Canada database, 41 pharmacy dispensing fees and markups, 41 provincial drug benefit formularies, 41 Canadian physician services cost estimates, 39–40 provincial fee schedules, 39–40 reimbursement, 39 CAPRIE, 179 Captopril, 268 Cardiac care costs hospitalization declining, 27 US Department of Veterans Affairs, 15– 29 Cardiac Health Profile (CHP), 90 Cardiovascular disease costs

weintraub_Index_Final

423

American Heart Association, 71–72 disease-specific measures, 86, 89–91 indirect costs, 71–72 medical literature database cost-utility analysis, 330 Medicare, 399–411, 412–413 Cardiovascular interventions league table, 337–353 Cardiovascular services EUH, 50–52 Cardioversion, 316–321 CARE, 182 Care costs cost effective analysis, 157–169 Case-mix group (CMG), 34 CATH patient selection for, 294–295 Catheterization estimated average cost, 288 CAVEAT, 189 CCAC, 90, 108 CCB, 166 CDC, 360, 361 CE. See Cost effectiveness (CE) CEA. See Cost effective analysis (CEA) Center for Medicare and Medicaid Services (CMS), 394 Centers for Disease Control (CDC), 360, 361 Central limit theorem, 125–126 CER, 112 specification, 152–153 Cerebrovascular disease interventions, 350–353 Change impetus cost growth, 368 CHF. See Congestive heart failure (CHF) Cholesterol, 159–165 lowering, 161, 162 Cholesterol and Recurrent Events (CARE), 182 Cholesterol drugs cost, 163 CHP, 90 CHQ, 89, 91 Chronic Heart Failure Questionnaire (CHQ), 89, 91 Chronic Obstructive Pulmonary Disease (COPD), 89 CIDS, 41–43, 311 unit costs, 42 CIHI, 32, 33–34

5/12/03, 9:26 AM

424

Index

National Grouping Categories Report, 40 Clinical department relative value units, 56 Clinical Outcomes of Revascularization and Aggressive Drug Evaluation (COURAGE), 295 Clinical trials, 91–93 cost effective analysis, 123–153 cost effectiveness heterogeneity and stratified analyses, 147 multinational studies, 151–152 health status assessment implementation in, 95–97 Clopidogrel acute coronary syndromes, 178–179 Clopidogrel vs Aspirin in Patients at Risk of Ischemic Events (CAPRIE), 179 CMG, 34 CMS, 394 Comorbid conditions Decision Support System, 19 Computer-based utility assessment, 106–107 Conduction disease, 304–309 Confidence box, 130–132 Congestive heart failure (CHF), 165, 259– 278, 304 ACE inhibitors, 266–270 antiarrhythmic agents, 272–273 beta-blockers, 270–271 CEA, 266 deaths, 261 defined, 260 digoxin, 271 disease management programs, 275–278 disease-specific measures, 91 diuretics, 272 economic burden, 262–265 epidemiology, 260–262 hospital discharge by age, 263 implantable defibrillators, 272–273 interventions, 346 Medicare, 401–402 pacemakers, 272–273 prevalence, 260–261 reimbursement for, 391 transplantation, 273–275 treatment, 265–266 ventricular assist devices, 273–275 Consumer Product Safety Commission, 360 Content validity, 85

weintraub_Index_Final

424

Contrast-enhanced echocardiography decision models, 294 Controlled Abciximab and Device Investigation to Lower Late Angioplasty Complications (CADILLAC), 213 Controlled clinical trials, 73 COPD, 89 Coronary angioplasty vs bypass surgery, 224–228 clinical outcomes, 224 cumulative cost, 226 economic endpoints, 224–225 randomized trials implications, 230–231 single-vessel disease, 188–191 Coronary Angioplasty vs Excisional Atherectomy Trial (CAVEAT), 189 Coronary artery bypass graft (CABG), 191 costs determination, 234–236 geographic variation in, 238–239 postoperative complications, 239–240 predictors, 236–238 reduction, 244–245 direct costs, 234–236 in-patient costs, 235 long-term costs after, 241 vs medicine cost-effectiveness, 233–245 mortality, 250 off-pump, 237–238 Coronary artery disease (CAD) decision analysis, 101–103 diabetes, 167 health status domains, 82 interventions, 340–346 Medicare, 403–406 primary prevention of CE of, 159–176 Coronary artery surgery costs, 233–245 Coronary heart disease indirect cost of, 72 Coronary Heart Disease Policy Model, 179 Coronary revascularization, 188 Coronary stents, 195–199 vs balloon angioplasty, 198 direct vs conventional with predilatation, 199 provisional, 199–202 provisional vs universal, 201

5/12/03, 9:26 AM

Index

425

Cost analysis time horizon, 375 Cost and effect differences bootstrap distribution TACTICS-TIMI, 138 correlation, 136 Cost and effects ICER, 136 Cost-benefit analysis marketplace decision making, 372–374 Cost-benefit calculator, 373 Cost data analysis statistical considerations, 124–127 Cost difference bootstrap confidence limits, 126 Cost Distribution Report, 16 Cost distributions characteristics, 124 Cost effective analysis (CEA), 107, 111–120, 329 Bayesian framework, 146–147 care costs, 157–169 clinical trials, 123–153 decision making guide, 374–380 defined, 111 discount rate, 7 evidence levels grading, 160 future of, 418 international use, 359 perspective, 119–120 societal, 119–120 policymaking, 368–372 real-life applications challenges, 383–384 time effects, 7 time horizon, 116–118 unique aspect, 111 Cost effectiveness (CE) clinical trials heterogeneity and stratified analyses, 147 measuring benefits, 148–149 multinational studies, 151–152 coronary artery disease primary prevention of, 159–176 future of, 418 information sources, 361 measuring of challenge of, 114–116 ratios

weintraub_Index_Final

425

comparison, 360–361 confidence intervals, 130–132 Cost-effectiveness acceptability, 1, 249–256 curve, 140–141 uncertainty, 141–143 VA, 16–18 Cost-effectiveness plane, 128–130 Cost effectiveness ratio (CER), 112 specification, 152–153 Cost function VA, 17 Cost growth change impetus, 368 Cost minimization, 292 Cost regression Decision Support System, 23 Costs combining with effects, 128–140 by hospital type in Decision Support System, 22–23 measurement, 374–375 nonparametric approaches to comparing, 124–127 parametric approaches to comparing, 124 Cost savings models, 292–294 Cost-utility, 149 Cost-utility analysis (CUA), 104, 329 cardiovascular disease medical literature database, 330 methods, 330–331 results, 331–335 Cost-utility ratios (CUR), 329 COURAGE, 295 Covariates adjusting for, 126–127 CPT, 16, 45 classification categories, 51 modifiers, 51 CPT-4, 49, 382 CUA. See Cost-utility analysis (CUA) CUR, 329 Current Procedural Terminology (CPT), 16, 45, 48 classification categories, 51 modifiers, 51 Current Procedural Terminology Fourth edition (CPT-4), 49, 382 D DAD Canadian Institute for Health Information, 34

5/12/03, 9:26 AM

426

Index

DALYS, 377 DANAMI 2, 177 DART, 204 DASI, 88, 90, 108 Data administrative estimating hospital costs, 8–10 cost analysis, 124–127 hospital billing issues with, 58–59 physician costs, 57–58 limitations, 374–375 Medicare Decision Support System, 19 physician billing, 48–49 resource-based relative value scale, 55–57 reimbursement estimating hospital costs, 8–10 Days of work lost, 76 DCA, 202–204 vs conventional percutaneous transluminal coronary angioplasty, 203 DCCT, 167 Death causes in elderly, 400 rate, 76 Decision analysis coronary artery disease, 101–103 Decision making Bayesian framework, 146–147 social welfare, 379–380 Decision models vs hybrid resource use models, 289–292 prototype, 102 Decision Support System (DSS), 16, 17–20, 23 comorbid conditions, 19 cost regression, 23 inflation, 19 Medicare data, 19 results, 20–26 site survey, 18 VA cost and utilization data, 18–19 Deep-chest infection CABG, 240 Deep venous thrombosis interventions, 346 DESTIN, 202 Diabetes, 166–167 coronary artery disease, 167

weintraub_Index_Final

426

Diabetes Control and Complications Trial (DCCT), 167 Diabetes Mellitus Insulin Glucose Infusion in Acute Myocardial Infarction (DIGAMI), 166 Diagnosis-related groups (DRG), 8, 50, 57, 286 Diagnostic tests assessment, 286–292 CEA, 296–298 costs of, 286–289 estimated average cost, 288 Diet modification hypertension, 165 DIGAMI, 166 Digoxin congestive heart failure, 271 Dilation vs Rotational Ablation Trial (DART), 204 Directional atherectomy (DCA), 202–204 vs conventional percutaneous ransluminal coronary angioplasty, 203 Direct measurement VA, 16 Direct observation, 74 physician costs, 59–60 Disability, 67 aging, 398–399 Disability-adjusted life expectancy (DALYS), 377 Discharge Abstract Database (DAD) Canadian Institute for Health Information, 34 Discount factor, 6 Discounting, 331 paradox, 379–380 Discount rate, 118 on CEA, 7 Disease productivity, 67 Disease Control Priorities Project, 359 Disease management, 94 congestive heart failure, 275–278 Disease-specific measures cardiovascular disease, 89–91 of health status, 84–85 Distal protection devices, 205–206 Diuretics congestive heart failure, 272 cost, 165 Dobutamine supply costs, 287

5/12/03, 9:26 AM

Index

427

Doppler Endpoint Stenting International Investigation Coronary Flow Reserve (DESTIN), 202 DRG, 8, 50, 57, 286 Drive-through deliveries, 371 DSS. See Decision Support System (DSS) Dual-chamber devices, 304 Dual-chamber pacing, 306 Duke Activity Score Index (DASI), 88, 90, 108 Duke Cardiovascular database, 151, 208 Durable equipment costs congestive heart failure, 262 E Early retirement, 65 EAST, 28, 192, 226, 234 implications, 230–231 Economically efficient outcomes, 380 Economic analysis in policy decision making real-world applications, 380–384 Economic Evaluations Database National Health Service, 361 Economics future of, 417–419 Economics of Myocardial Perfusion Imaging in Europe (EMPIRE), 295 Economics of Noninvasive Diagnosis (END), 294–295 Efficacy and Safety of Subcutaneous Enoxaparin Non-Q Wave Coronary Events (ESSENCE), 42–43 coefficients of regression model, 43 drug costs, 43 Elderly causes of death, 400 hospitalization, 400 ELITE, 268 Emory Angioplasty vs Surgery Trial (EAST), 28, 192, 226, 234 implications, 230–231 Emory University Hospital (EUH), 253 cardiovascular services, 50–52 analysis results, 52–55 EMPIRE, 295 Employer perspective indirect costs, 68–69 END, 294–295 Endocarditis Medicare, 411 End-stage renal disease (ESRD), 167

weintraub_Index_Final

427

Enhanced Suppression of the Platelet IIb/ IIIA Receptor with Integrilin Trial (ESPIRIT), 208 Enoxaparin acute coronary syndromes, 180 Environmental Protection Agency (EPA), 360 EPA, 360 EPIC, 207 EPILOG, 189 EPISTENT, 198 EQ-5D, 108 Equivalent annual cost, 5 ERACI-I, 227 ERACI-II, 230 ESPIRIT, 208 ESRD, 167 ESSENCE, 42–43 coefficients of regression model, 43 drug costs, 43 Estimating costs Canadian health care resources, 31–43 Estimating hospital costs administrative data, 8–10 hospital episode type, 7–8 practical approaches, 6–11 reimbursement data, 8–10 study-specific utilization data, 10 theoretical discussion, 4–6 variables, 12 EUH, 253 cardiovascular services, 50–52 analysis results, 52–55 EuroQOL, 108 Evaluation of cTE3 for the Prevention of Ischemic Complications (EPIC), 207 Evaluation of Losartan in the Elderly (ELITE), 268 Evaluation of Platelet IIb/IIIa Inhibitor for Stenting (EPISTENT), 198 Evaluation of PTCA to Improve Long-term Outcome by cF7E3 Glycoprotein receptor blockade (EPILOG), 189 EXCEL, 164 Expanded Clinical Evaluation of Lovastatin (EXCEL), 164 F False Claims Act, 396 FDA CEA, 360

5/12/03, 9:26 AM

428

Index

Fee-per-service Canadian physician services, 39 Fieller’s theorem, 132–133 comparison of, 134 Fixed costs, 4 Floor phenomenon, 93 Food and Drug Administration (FDA) CEA, 360 Framingham study, 399 Friction cost method measuring indirect costs considerations and limitations, 71 Functional range, 93 G Gatekeeping principles, 294–295 Generic measures of health status, 83–84 Germany cost effective analysis, 359 GISSI-1, 175 Global cost-to-charge ratios, 234 Global Utilization of Streptokinase and tPA for Occluded Arteries (GUSTO), 88, 152, 176 Global Utilization of Streptokinase and tPA for Occluded Arteries (GUSTO V), 176 Glycoprotein IIB/IIIA inhibition acute coronary syndromes, 178–179 adjunctive percutaneous coronary interventions, 207–209 Gross-costing, 31 Canadian health care resources, 35–38 Group decisions challenge, 380 Guardwire balloon occlusion catheter, 205 GUSTO, 88, 152, 176 GUSTO V, 176 H HCFA, 369 HCPCS, 16 HDL-C, 160 HDL cholesterol (HDL-C), 160 Health valuing, 376–377 Health benefit standardized measures, 377 Health care costs projected population age, 263

weintraub_Index_Final

428

Health Care Financing Administration (HCFA), 369 Health care resources private sector, 369–372 Health Insurance Association of America (HIAA), 50 Health Insurance Portability and Accountability Act (HIPAA), 396 Health maintenance organizations (HMO), 370 Health policy, 358–360 Health states assigning utilities to, 105–106 Health status defining, 82–83 instruments, 83–89 measures, 83–89 applications, 91–94 disease-specific, 84–85 generic, 83–84 required attributes, 85–89 Health status assessment, 82–97 in clinical trials, 95–97 handling missing data, 96–97 Health status domains coronary artery disease, 82 Health status questionnaire, 107 Health utilities vs health values, 105–106 Health Utility Index, 108 Health value vs health utilities, 105–106 Heart disease, 399 Heart failure. See Congestive heart failure (CHF) Heart Outcomes Prevention Evaluation (HOPE), 181 Heart transplantation, 353 Heparin acute coronary syndromes, 180 Heparin-coated Palmaz-Schatz stent, 196 Heterogeneous conditions grouping, 383 HIAA, 50 HIPAA, 396 Hirudin acute coronary syndromes, 180 Historical cost-based reimbursement system, 4 HMG-CoA reductase inhibitor, 160 HMO, 370

5/12/03, 9:26 AM

Index

429

HOPE, 181 HOPPS, 285 Hospital billing data issues with, 58–59 physician costs, 57–58 Hospital cost estimates future, 12–13 standardization, 13 Hospital costs congestive heart failure, 262 estimating administrative data, 8–10 reimbursement data, 8–10 study-specific utilization data, 10 Hospital expenditures total health care expenditures, 2 Hospital factors influencing CABG costs, 238 Hospitalization elderly, 400 Hospital Outpatient Prospective Payment System (HOPPS), 285 Hospital pharmacies Canadian pharmaceutical products, 41 Hospital referral regions (HRR) permanent pacemaker implantation, 309 Hospital reimbursement Medicare, 391 Hospitals Balanced Budget Act of 1997 impact on, 394 Hospital-specific microcost-accounting information, 10–11 Hospital stay decreasing lengths, 264 Hospital survival improving, 264 HOT Study, 167 Hours of training for new hires, 76 Household income losses associated with ischemic heart disease, 72 HRR permanent pacemaker implantation, 309 Human capitol method measuring indirect costs, 69–70 considerations and limitations, 70 Human life valuing, 376 3-hydroxy-3-methyl-glutaryl-coenzyme A (HMG-CoA) reductase inhibitor, 160

weintraub_Index_Final

429

Hypertension, 165–166 congestive heart failure, 261 diet modification, 165 interventions, 346–348 physical exercise, 165 Hypertension Optimal Treatment (HOT) Study, 167 Hypertrophic obstructive cardiomyopathy, 304 Hypothetical compensation, 378–379 Hypothetical decision tree single-vessel angioplasty, 103 I ICD, 409–411 decision-analysis modeling studies, 310 ICD-9, 57, 382 ICER, 112, 128, 148, 149 cost and effects, 136 data for, 113 importance of, 112–113 quality-adjusted life years, 115–116 TACTICS-TIMI, 138 Identifiable rationing vs statistical rationing, 384 IMPACT, 107 Implantable cardioverter defibrillator (ICD), 409–411 decision-analysis modeling studies, 310 Implantable defibrillators congestive heart failure, 272–273 IMS HEALTH Canada database, 41 Incomplete evidence, 383 Incremental cost per coronary event averted, 116 Incremental cost effectiveness ratio (ICER), 112, 128, 148, 149 cost and effects, 136 data for, 113 importance of, 112–113 quality-adjusted life years, 115–116 TACTICS-TIMI, 138 Indirect costs, 5 calculations and formulas, 76 cardiovascular disease, 71–72 components of, 64, 65 contributors to, 66–67 data and measurement, 72–73 employer perspective, 68–69 estimating, 74–77 measuring, 69–71

5/12/03, 9:26 AM

430

Index

friction cost method, 70–71 human capitol method, 69–70 perspective, 67–69 secondary data sources of, 74–75 study guidelines, 73–74 work loss, 64–66 Indirect health care costs, 63–78 Individual patient care, 95 Inflation, 6 Decision Support System, 19 Information quality, 419 In-stent restenosis brachytherapy, 206–207 International Classification of Diseases-9th Revision (ICD-9), 57, 382 International Federation of Pharmaceutical Manufacturers Office of Health Economics, 361 Internet automated utility assessment interviews, 107 Interpretability, 88–89 Ischemic heart disease household income losses associated with, 72 ISIS-2, 175 J Joint National Committee on Prevention, Detection, Evaluation and Treatment of High Blood Pressure (NC VI), 165 K Kansas City Cardiomyopathy Questionnaire (KCCQ), 91 KCCQ, 91 L LDL-cholesterol, 181 League table, 329, 330, 335, 336, 337–353 Left ventricular EF (LVEF), 255 Left ventricular function sensitivity analyses, 307 Life expectancy, 112 for medical therapy vs surgery, 117 Lifetime analysis, 118 LiHFE, 91 Living with Heart Failure Questionnaire (LiHFE), 91 Long Q-T syndrome, 304 Long-term assets, 5

weintraub_Index_Final

430

Loss of productivity calculation, 76 Loss to the workforce calculation, 76 Lost productivity, 66 Lost wages method, 69–70 Lost-work output, 66 Lovastatin, 164 cost, 163 Low-density lipoprotein (LDL)-cholesterol, 181 LVEF, 255 M MADIT, 310, 311–312, 411 Managed care, 370 Balanced Budget Act of 1997 impact on, 395 Medicare, 392–393 Marginal CER importance of, 112–113 Marginal cost-effectiveness (MCE), 305 Marginal cost effectiveness ratio (CER) importance of, 112–113 Marginal social opportunity costs, 374–375 Market imperfections, 3 Market interactions, 372–388 Marketplace decision making cost-benefit analysis, 372–374 Markov decision-analytic model schematic, 317 Markov model, 149 Mayo clinic trial, 209 MCE, 305 Medical decision making, 101–103 Medical effectiveness, 377–378 Medical Savings Accounts (MSA), 395 Medical therapy vs surgery survival curves, 117 Medicare, 389–391 aging, 397–398 allowable charges, 287 cardiac disease, 399–411 cardiology, 412–413 coronary artery disease, 403–406 data Decision Support System, 19 endocarditis, 411 future, 413–414 heart failure, 401–402

5/12/03, 9:26 AM

Index

431

managed care, 392–393 myocardial infarct, 404 Part A, 57, 390, 393 Part B, 57, 390 relative value resource inputs for physician services, 49 valve replacement, 406–408 ventricular arrhythmias, 409–411 website, 57 Medicare Current Beneficiary survey, 399 Medicare Fee Schedule (MFS), 45, 48 Medicare Health Care Procedures Coding System (HCPCS), 16 Medicare Payment Advisory Commission (MEDPAC), 48–49, 393 Medigap policies, 390 MEDPAC, 48–49, 393 Metoprolol, 313 MFS, 45, 48 MI hospital stays hospital stay cost random-effects regression, 24 Medicare, 404 Microcosting, 8, 31, 33 Canadian health care resources, 36 MITI, 177, 211 Mitral valve replacement (MVR), 250–253 vs repair, 253–256 CEA, 256 Mitral valve surgery quality of life following, 255–256 Mode Selection Trial (MOST), 308 Monte Carlo simulation, 149 Morbidity, 65 Mortality, 65 MOST, 308 MSA, 395 Multicenter Automatic Defibrillator Implantation Trial (MADIT), 310, 311–312, 411 Multidimensionality, 85 Multiple disease outcomes integrating, 104 Multivessel disease percutaneous vs surgical revascularization, 191–195 MVR, 250–253 vs repair, 253–256 CEA, 256

weintraub_Index_Final

431

Myocardial infarct (MI) hospital stays hospital stay cost random-effects regression, 24 Medicare, 404 Myocardial Infarction Triage Intervention (MITI), 177, 211 N National Center for Health Statistics (NCHS), 75 National Cholesterol Education Program (NCEP) Adult Treatment Panel III (ATP III) guidelines, 158 National Health Interview Survey (NHIS), 75, 399 National Health Service Economic Evaluations Database, 361 National Highway Traffic Safety Administration (NHTSA), 360 National List of Provincial Costs for Health Care, 36 National Long-Term Care Survey, 399 National Nursing Home Survey, 399 National Registry of Myocardial Infarction (NRMI), 177 NCEP Adult Treatment Panel III (ATP III) guidelines, 158 NCHS, 75 NC VI, 165 Net health benefit (NHB), 141 Netherlands cost effective analysis, 359 Net monetary benefit (NMB), 141–143 curves, 144 Net present value (NPV), 118, 150 Neurocardiogenic syncope, 304 Newborns’ and Mothers’ Health Protection Act of 1996, 371 New York Heart Association (NYHA) functional classification system, 89–91, 255 NHB, 141 NHIS, 75, 399 NHTSA, 360 Nicotine patch, 168 NMB, 141–143 curves, 144 Nonfederal US hospital costs, 1–13 estimating problems, 3–4

5/12/03, 9:26 AM

432

Index

Noninvasive cardiac testing, 285–299 nuclear cardiology diagnostic algorithms, 289–292 Nonparametric bootstrap, 125–126, 133–134 Nonparametric cost comparison costs, 124–127 Nonprofit provider, 375–376 Normal approximation, 133 NPV, 118, 150 NRMI, 177 Nuclear cardiology diagnostic algorithms, 289–292 Number of deaths calculation, 76 Nursing home costs congestive heart failure, 262 NYHA functional classification system, 89–91, 255 O Observational cross-sectional survey, 73 Observational studies, 384–385 OCBAS, 200 OCCP, 34–35 case-cost data, 38 Occupational Safety and health Administration (OSHA), 360 Off-pump coronary artery bypass graft (CABG), 237–238 Ontario Case-Costing Project (OCCP), 34– 35 case-cost data, 38 Ontario Schedule of Benefits, 39–40 CIDS, 42 Opportunity cost, 3 Optimal Coronary Balloon Angioplasty with provisional Stenting vs Primary Stenting (OCBAS), 200 Oregon Basic Health Services Act in 1989, 358, 381 Oregon Plan, 358–359, 380–383 OSHA, 360 Outcomes comparability, 379 Overhead costs, 5 P Pacemakers, 304–309 congestive heart failure, 272–273 PAMI-STENT, 178, 211–213 Parametric cost comparison costs, 124

weintraub_Index_Final

432

Pareto efficiency, 372 Pareto principle, 378 Paroxysmal atrial fibrillation, 316 Paroxysmal supraventricular tachycardias (PSVT), 313 Patients’ Bill of Rights, 371 Patient self-report of indirect costs, 74 Perceived technical quality, 376 Percentile method, 133 PercuSurge GuideWire, 205 Percutaneous coronary interventions adjunctive glycoprotein IIB/IIIA inhibition, 207–209 adjunctive pharmacotherapy, 207 brachytherapy in-stent restenosis, 206–207 coronary angioplasty for single-vessel disease, 188–191 coronary artery bypass graft, 191 coronary revascularization, 188 cost effectiveness, 187–215 distal protection devices, 205–206 newer devices, 195–205 rheolytic thrombectomy, 205 Percutaneous revascularization vs bypass surgery, 193 vs surgical revascularization multivessel disease, 191–195 Percutaneous transluminal coronary angioplasty (PTCA) cost, 188 randomized clinical trials, 192 Peripheral artery disease interventions, 348–350 Permanent pacemaker implantation hospital referral regions, 309 Per-use cost, 5 Pharmacological stress tests supply costs, 287 Pharmacotherapy adjunctive percutaneous coronary interventions, 207 Physical exercise hypertension, 165 Physician billing data, 48–49 resource-based relative value scale, 55–57 Physician costs calculating by data types, 47 conceptual overview, 46–57

5/12/03, 9:26 AM

Index

433

congestive heart failure, 262 direct observation, 59–60 estimating, 48 hospital billing data, 57–58 physician billing data, 48–49 resource-based relative value scale application to, 48–49 Physician Payment Review commission, 392 Physician reimbursement Medicare, 391–392 Physicians Balanced Budget Act of 1997 impact on, 393–394 Physician services Canadian. See Canadian physician services relative value units, 56 VA, 17 Plasminogen activator vs streptokinase, 336 Platelet glycoprotein IIB/IIIa in Unstable Angina: Receptor Suppression Using Integrilin Therapy (PURSUIT) trial, 151, 179 Policymaking CEA, 368–372 Postoperative atrial fibrillation CABG, 240 Potential Compensation Principle, 378 Pravastatin, 160, 161, 164 Precardioversion transesophageal echocardiography, 321–322 Prevalence of disease, 76 Primary angioplasty vs reperfusion AMI, 209–211 Primary percutaneous coronary reperfusion acute coronary syndromes, 176–178 Primary prevention economic analysis, 158–159 PRISM-PLUS, 179 Private sector health care resources, 369–372 Productivity, 65 disease, 67 Projected population age health care costs, 263 Prophylactic anticoagulation atrial fibrillation, 321 Prospective hospital resources, 3 Prospective Randomized Study of Ventricular Failure and Efficacy of Digoxin (PROVED), 271

weintraub_Index_Final

433

PROVED, 271 Providers influencing CABG costs, 238 Provincial case-costing initiatives, 34–35 Provincial drug benefit formularies Canadian pharmaceutical products, 41 Provincial fee schedules Canadian physician services, 39–40 Pseudo-bill VA, 16–17 PSVT, 313 PTCA cost, 188 randomized clinical trials, 192 Pulmonary embolism interventions, 346 PURSUIT trial, 151, 179 Q QALE, 305, 329 QALY, 105, 114, 148, 159, 249, 377 in incremental cost effectiveness ratio, 115–116 for multiple health states over time, 114– 115 QLMI, 90 Quality-adjusted life expectancy (QALE), 305, 329 Quality-adjusted life years (QALY), 105, 114, 148, 159, 249, 377 in incremental cost effectiveness ratio, 115–116 for multiple health states over time, 114– 115 Quality of care, 94–95 Quality of life assessment, 82 Quality of Life After Myocardial Infarction (QLMI), 90 Quality of Well Being Scale, 107 R RADIANCE, 271 Radiofrequency ablation (RFA), 312 Randomized Assessment of Digoxin and Inhibitors of Angiotensin Converting Enzyme (RADIANCE), 271 Randomized controlled trials (RCT), 11–12 Randomized Efficacy Study of Tirofiban for Outcomes and Restenosis (RESTORE), 189

5/12/03, 9:26 AM

434

Index

Randomized Intervention Treatment of Angina (RITA-1), 226 RAPPORT trial, 178 RBRVS See Resource-based relative value scale (RBRVS) RCT, 11–12 RDRG, 34 Reduced productivity, 67 Refined diagnosis-related groups (RDRG), 34 Refinement group numbers (RGN), 34 Register of Cost-Effectiveness studies, 361 Regression analysis Decision Support Analysis, 26 Reimbursement data estimating hospital costs, 8–10 Relative value units (RVU), 48 clinical department, 56 physician service, 56 Relevant cost vs total cost, 5–6 Reliability, 87 Renin-angiotensin-aldosterone system, 266 Reperfusion therapy acute coronary syndromes, 175–176 Resource-based relative value scale (RBRVS), 45, 394 application to physician costing, 48–49 development, 48 physician billing data, 55–57 Resource intensity weights (RIW), 34 Responsiveness, 87–88 RESTORE, 189 Reteplase cost, 173 Retransformation, 127 RFA, 312 RGN, 34 Rheolytic thrombectomy, 205 Right ventricular EF (RVEF), 255 RITA-1, 226 RIW, 34 Rotational atherectomy, 204–205 RVEF, 255 RVU, 48 clinical department, 56 physician service, 56 S SAFER, 205 SAQ, 90, 93, 104

weintraub_Index_Final

434

SAVE, 181, 268 Scandinavian Simvastatin Survival Study, 336 SCHIP Benefits Improvement and Protection Act of 2000, 396 SEAM, 35 Seattle Angina Questionnaire (SAQ), 90, 93, 104 Selective testing, 294–295 SEM, 88 Sensitivity analysis, 241 SEQOL, 225 Severity of illness, 382 Sheffield risk and treatment table, 163 Sick sinus syndrome (SSS), 304 Simultaneous equation allocation method (SEAM), 35 Simvastatin, 161–162, 181–182 Single-chamber pacing, 306 Single-vessel angioplasty hypothetical decision tree, 103 Single-vessel bypass surgery, 224 Sinus rhythm restoration, 319 Smearing estimate, 127 Smoking cessation, 168 studies, 169 Social welfare decision making, 379–380 Societal perspective in cost effective analysis, 119–120 Societal vs patient perspectives assigning utilities, 108 Society of Thoracic Surgeons Database, 253 SoS, 194 SPECT estimated average cost, 288 SSS, 304 Standard error of measurement (SEM), 88 Standard Gamble, 103, 105, 108 Standard in-patient cost lists Canadian health care resources, 36 Standards old vs new programs, 383–384 State Children’s Health Insurance Program (SCHIP) Benefits Improvement and Protection Act of 2000, 396 Statin therapy, 161, 165, 182 Statistical rationing vs identifiable rationing, 384

5/12/03, 9:26 AM

Index

435

Stenting vs percutaneous transluminal coronary angioplasty (PTCA) AMI, 211–213 Stent or Surgery study (SoS), 194 Stents. See Coronary stents Stokes-Adams attacks, 304 Streptokinase, 173 vs plasminogen activator, 336 STRESS, 196 Stress echocardiography estimated average cost, 288 Stress Restenosis Study (STRESS), 196 Stroke costs American Heart Association, 71–72 Study of Economics and Quality of Life (SEQOL), 225 Study population, 76 Supplemental Medical Insurance, 57, 390 Supraventricular tachycardias (SVT), 312 Surgery vs medical therapy survival curves, 117 Surgical revascularization vs percutaneous revascularization multivessel disease, 191–195 Surrogate outcomes, 92 Survival and Ventricular Enlargement (SAVE), 181, 268 Survival curves for medical therapy vs surgery, 117 SVG Angioplasty Free of Emboli Randomized (SAFER), 205 SVT, 312 Sweden cost-effective analysis, 359 T TACTICS-TIMI, 124, 125 Tax Equity and Fiscal Responsibility Act (TEFRA), 392 Taylor series method, 132 comparison of, 134 TCC, 35 Teaching hospitals Balanced Budget Act of 1997 impact on, 394–395 Technical quality, 366–368 Technological change aging, 398 TEE, 321

weintraub_Index_Final

435

TEFRA, 392 Tenecteplase, 176 cost, 173 Therapeutic lifestyle changes (TLC), 160 Thrombolysis in Myocardial Infarction (TIMI), 124 Thrombolytic therapy acute coronary syndromes, 175–176 Time effects, 6 on CEA, 7 Time horizon for CEA, 116–118 Time Trade-Off method, 105, 108 Tissue plasminogen activator (t-PA), 176, 209 cost, 173 TLC, 160 Top-down approach, 234 Total cost vs relevant cost, 5–6 T-PA, 176, 209 cost, 173 Traditional medicine, 391–392 Transesophageal echocardiography (TEE), 321 Transient cost center (TCC), 35 Transplantation congestive heart failure, 273–275 Transthoracic echocardiography (TTE), 321 Treat Angina with Aggrastat and Determine Cost of Therapy with an Invasive or Conservative Strategy (TACTICS)Thrombolysis in Myocardial Infarction (TIMI), 124, 125, 149, 150 bootstrap distribution, 138 incremental cost-effectiveness ratio, 138 quality-adjusted life years, 148 Trial based cost effectiveness studies power and sample size calculations for, 143–146 Triglyceride, 160 TTE, 321 U UB-92, 8, 9 UK Department of Health, 361 UKPACE, 308 UKPDS41, 166 United Kingdom cholesterol studies, 164 cost effective analysis, 359

5/12/03, 9:26 AM

436

Index

United Kingdom Pacing and Cardiovascular Events (UKPACE), 308 United Kingdom Prospective Diabetes Study Group (UKPDS41), 166 US Department of Veterans Affairs (VA) cardiac care costs, 15–29 cost and utilization data Decision Support System, 18–19 cost-effectiveness analyses (CEA), 16–18 cost function, 17 direct measurement, 16 physician services, 17 pseudo-bill, 16–17 US physician costs, 45–61 Utilities, 103–105 ascertaining, 106–108 assigning to health states, 105–106 attributes, 104 defining, 103 societal vs patient perspectives, 108 Utility assessment, 101–108 U-Titer, 107 V VA cardiac care costs, 15–29 cost and utilization data Decision Support System, 18–19 cost-effectiveness analyses (CEA), 16–18 cost function, 17 direct measurement, 16 physician services, 17 pseudo-bill, 16–17 Validity, 87 Valve replacement Medicare, 406–408 Valvular disease intervention, 353

weintraub_Index_Final

436

Valvular surgery CEA, 251–253 trends in, 250–251 VANQWISH, 213 Variable costs, 4 VEGAS, 205 Vein Graft AngioJet Study (VEGAS), 205 Ventricular arrhythmias Medicare, 409–411 treatment of, 310–312 Ventricular assist devices congestive heart failure, 273–275 Veterans Affairs. See US Department of Veterans Affairs (VA) Veterans’ Heart Failure Trial (V-HeFT-II), 268 V-HeFT-II, 268 W Warfarin, 315 Wealth, 377 West of Scotland Coronary Prevention Study (WOSCOPS), 160, 164 Wilcoxon rank sum, 124–125 Willingness to pay (WTP), 375–378 Wolff-Parkinson-White Syndrome (WPW), 312 MCE ratios, 313 Worker loss, 66 Worker replacement, 65, 66 data for, 75 Work loss, 64–66, 65, 67 data for, 75 WOSCOPS, 160, 164 WPW, 312 MCE ratios, 313 WTP, 375–378

5/12/03, 9:26 AM

Contemporary Cardiology ™ Christopher P. Cannon,

MD,

Series Editor

Cardiovascular Health Care Economics Edited by

William S. Weintraub, MD Emory University School of Medicine, Atlanta, GA

Given the limited ability of government to fund health care and the staggering cost of cardiovascular disease to society—an estimated $351.8 billion for 2003 in direct medical costs and lost productivity—critical choices must be made to reduce the burden of cardiovascular health care and minimize its collateral losses. In an illuminating synthesis of methodological and clinical studies, Cardiovascular Health Care Economics shows how costs can be established, how the value of clinical outcomes can be assessed, and how difficult choices can be rationally made. In the methodological chapters, well-known experts review the conceptual and practical issues involved in estimating and interpreting health care costs, making health status and utility assessments, and statistically analyzing cost-effectiveness and clinical trials. The clinical chapters apply these methods to the major clinical areas of cardiology—primary prevention of coronary artery disease, acute coronary syndromes, angioplasty vs coronary bypass surgery, CABG vs medicine, congestive heart failure, arrhythmias, and cardiac surgery. Additional chapters consider the use of economic studies for policy purposes and the future of Medicare under a balanced budget in an aging America. Comprehensive and timely, Cardiovascular Health Care Economics offers today’s cardiologists, administrators, policymakers, and investigators an enlightening introduction and up-to-date reference to cardiovascular health care economics, as well as a sound basis for making the good choices that will assure continued access to high-quality health care in the decades to come. Features • Detailed introduction to cardiovascular health care economics • Reviews of techniques to establish and interpret health care costs and cost-effectiveness

• Comprehensive economic reviews of the major clinical areas in cardiology • Unique and detailed collection of data on cardiovascular health care economics

Contents Part I. Methods. Nonfederal US Hospital Costs. Estimating the Costs of Cardiac Care Provided by the Hospitals of the US Department of Veterans Affairs. Estimating the Costs of Health Care Resources in Canada. US Physician Costs: Conceptual and Methodological Issues and Selected Applications. Indirect Health Care Costs: An Overview. Health Status Assessment. Utility Assessment. Introduction to Cost-Effectiveness Analysis. Cost-Effectiveness Analysis Alongside Clinical Trials: Statistical and Methodological Issues. Part II. Clinical Applications. Costs of Care and Cost-Effectiveness Analysis: Primary Prevention of Coronary Artery Disease. Economics of Therapy for Acute Coronary Syndromes. Cost-Effectiveness of Percutaneous Coronary Interventions. Economic Comparisons of Coronary Angioplasty and Coronary Bypass Surgery. Costs of Coronary Artery Surgery and

Cost-Effectiveness of CABG vs Medicine. Costs of Care and CostEffectiveness Analysis: Other Cardiac Surgery. Congestive Heart Failure. Current Economic Evidence Using Noninvasive Cardiac Testing. Cost-Effective Care in the Management of Conduction Disease and Arrhythmias. Comparing Cost-Utility Analyses in Cardiovascular Medicine. Beyond Heart Disease: Cost-Effectiveness as a Guide to Comparing Alternate Approaches to Improving the Nation’s Health. Using Economic Studies for Policy Purposes. Medicare, the Aging of America, and the Balanced Budget. Afterword: The Future of Economics in Cardiovascular Care and Research. Index.

90000

Contemporary Cardiology™ CARDIOVASCULAR HEALTH CARE ECONOMICS ISBN: 0-89603-874-2 E-ISBN: 1-59259-398-4 humanapress.com

9 780896 038745