Children of Addiction: Research, Health, and Public Policy Issues(Garland Reference Library of Social Science)

CHILDREN OF ADDICTION

CHILDREN OF ADDICTION RESEARCH, HEALTH, AND PUBLIC POLICY ISSUES EDITED BY

HIRAM E.FITZGERALD BARRY M.LESTER BARRY S.ZUCKERMAN

ROUTLEDGEFALMER NEW YORK & LONDON/2000

Published in 2000 by RoutledgeFalmer 29 West 35th Street New York, NY 10001 Published in Great Britain by RoutledgeFalmer 11 New Fetter Lane London EC4P 4EE This edition published in the Taylor & Francis e-Library, 2003. RoutledgeFalmer is an imprint of the Taylor & Francis Group Copyright © 2000 by Hiram E.Fitzgerald, Barry M.Lester, and Barry S.Zuckerman All rights reserved. No part of this book may be reprinted or reproduced or utilized in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publisher. Library of Congress Cataloging-in-Publication Data Children of addiction: research, health, and public policy issues/edited by Hiram E. Fitzgerald, Barry M.Lester, Barry S.Zuckerman. p. cm.—(Garland reference library of social science; 1486. Source books on education) Includes bibliographical references and index. ISBN 0-8153-3899-6 (alk. paper) 1. Children of alcoholics. 2. Children of narcotic addicts. 3. Alcoholics— Family relationships. 4. Parents—Drug use. I. Fitzgerald, Hiram E. II. Lester, Barry M. III. Zuckerman, Barry S. IV. Garland reference library of social science; v. 1486. V. Garland reference library of social science. Source books on education (Unnumbered) HV5132 .C47 2000 362.29'13–dc21 00–024777 ISBN 0-203-90460-5 Master e-book ISBN

ISBN 0-203-90464-8 (Adobe eReader Format)

Contents

Preface Contributors List of Tables and Figures Chapter 1

Chapter 2

Chapter 3

Chapter 4

Chapter 5

vii xi xiii

Are There Dose Effects of Prenatal Cocaine Exposure on Children’s Bodies and Brains? Deborah A.Frank, Marilyn Augustyn, Mark Mirochnick, Tripler Pell, and Barry S.Zuckerman

1

Prenatal Cocaine Exposure and Child Outcome: From Research to Public Policy Linda L.LaGasse and Barry M.Lester

29

Parenting and Parent-Child Relationships in Families Affected by Substance Abuse Sydney Lynn Hans

45

Assessing Vulnerability to Moderate Levels of Prenatal Alcohol Exposure Sandra W.Jacobson and Joseph L.Jacobson

69

The Teratologic Model of the Effects of Prenatal Alcohol Exposure Nancy L.Day and Gale A.Richardson

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v

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Chapter 6

Contents

The Clinical and Social Ecology of Childhood for Children of Alcoholics: Description of a Study and Implications for a Differentiated Social Policy Robert A.Zucker, Hiram E.Fitzgerald, Susan K.Refior, Leon I.Puttler, Diane M.Pallas, and Deborah A.Ellis

109

Chapter 7

American Indian Children of Alcoholics Paul Spicer and Candace Fleming

143

Chapter 8

Alcohol and Drug Use among African-American Youth H.Elaine Rodney

165

Chapter 9

Substance Use and Abuse Outcomes in Children of Alcoholics: From Adolescence to Young Adulthood Laurie Chassin and Aaron Belz

193

Author Index

217

Subject Index

231

Preface

Proceedings of the Third Society for Research on Child Development Round Table: Children of Addiction

Alcoholism is the most common form of substance abuse in the United States, with at least 28 million children exposed prenatally or postnatally to alcoholabusing parents or other caregivers. Of the 7 million children under 18 who are exposed to alcohol, approximately 679,000 are younger than 2 years of age, and 1,555,000 are between 2 and 5 (cf. Fitzgerald, Puttier, Mun, & Zucker, 2000). Recent epidemiological analyses indicate that prevalence for an alcohol dependence diagnosis as defined by the American Psychiatric Association’s Diagnostic and Statistical Manual of Mental Disorders, 4th edition (1994) decreases with age at drinking onset. The later the onset, the less likely that the individual will ever have a diagnosis of alcohol dependence. Prevalence rates have increased in the past decade at the same time that the age of first use has decreased to about 12 (Grant & Dawson, 1997). When one adds prenatal and postnatal exposure to cocaine, opioids, and other drugs into the substance abuserisk equation, one is forced to conclude that exposure to substance-abusing parents and/or other adults is a setting event that places children at high risk for the intergenerational transmission of substance abuse and related forms of psychopathology (Fitzgerald, Davies, & Zucker, in press; Zucker, Fitzgerald, & Moses, 1995). The third SRCD Round Table brought together individuals from nine major research centers, each of which focuses on some aspect of the intergenerational impact of exposure to substance-abusing parents. The first five chapters address issues related to prenatal exposure to alcohol and/or other drugs and its impact of postnatal growth and development. In Chapter 1, Deborah Frank, Marilyn Augustyn, Mark Mirochnic, Tripler Pell, and Barry Zuckerman at Boston Medical Center review evidence that links prenatal exposure to cocaine to brain and behavioral outcomes. They point out that tracing the route from prenatal exposure to postnatal outcome is difficult vii

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because a standardized method for quantifying prenatal exposure is not yet in use, many effects may be dose dependent, and exposure is always linked to the individual characteristics of the mother-fetus dyad (i.e., exposure takes place in a dynamic system). One way to try to understand diversity in research outcomes is to identify normative elements across studies that vary in sampling characteristics, sample sizes, cultural contexts, and research methodologies. In Chapter 2, Linda LaGasse and Barry Lester at Women’s and Infants’ Hospital and Brown University describe a database that has been constructed to code all published studies of cocaine exposure in an effort to isolate normative trends as well as identify critical individual differences that may better predict the boundaries of the route from prenatal exposure to postnatal outcome. Such databases allow for meta-analytic scans that dampen the methodological limitations of individual studies, while simultaneously providing sufficient statistical power to identify the critical predictors of postnatal outcomes. Such findings can then lead to replication studies testing specific hypotheses that emerge from such meta-analyses. In Chapter 3, Sydney Hans discusses her research at the University of Chicago that focuses on the quality of parenting given to infants who are exposed prenatally to opioids. She raises crucial issues related to parenting. For example, do infants who have been exposed prenatally to drugs differ from nonexposed infants with regard to the quality of parenting they receive postnatally? Stressing the biology-environment transactions now recognized by all developmental sciences, Hans attempts to tease out the relative contributions to infant outcomes made by prenatal exposure to drugs and postnatal exposure to the caregiving environment. In Chapter 4, Sandra and Joseph Jacobson at Wayne State University address issues related to what traditionally has been referred to as fetal alcohol effects. The work they describe homes in on a critical question: Do levels of exposure that are insufficient to produce fetal alcohol syndrome influence the etiology of such problems as attention deficit, learning disorder, language delay, and poor self-regulation that often are difficult to diagnose during the birth-to-3 age period? Moreover, if such effects can be linked to prenatal exposure to alcohol, what are the critical variables that modulate the expression of such social, cognitive, and behavior problems? In the final chapter to address prenatal exposure, Nancy Day and Gale Richardson at the Univeristy of Pittsburgh School of Medicine discuss the implications of the teratologic model for research with humans and especially with children. The teratologic model posits direct effects from prenatal exposure. In contrast, a developmental model accepts both direct and indirect effects due to the dynamic system in which fetal exposure to teratogens takes place. Their work suggests that both the teratologic and developmental models help us understand the effects of prenatal exposure on prenatal and postnatal development. The final four chapters address issues related to the etiology of alcoholism. In Chapter 6, Robert Zucker, Hiram Fitzgerald, Susan Refior, Leon Puttier,

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Diane Pallas, and Deborah Ellis address a wide range of issues related to the social ecology of the alcoholic environment in which children are reared and their risk for various pathological outcomes. Drawing on data from the Michigan State University-University of Michigan Longitudinal Study, they raise important questions concerning etiologic models of psychopathology, at least with respect to familial alcoholism. What are the longterm consequences of differential patterns of exposure to parental alcoholism and co-active forms of psychopathology? What role do such co-active forms of parental psychopathology as antisocial personality and depression play in structuring risk for the intergenerational transmission of substance abuse and psychopathology? How does one factor the dynamics of developmental change into etiologic models of psychopathology? Such questions are especially relevant for populations for whom etiologic issues are poorly understood. In Chapter 7, Paul Spicer and Candace Fleming, investigators at the National Center for American Indians and Alaska Natives, draw attention to a wide range of issues related to alcohol abuse and dependence among American Indians. They note that established epidemiologic morbidity and mortality rates for alcohol-specific effects among American Indians has detracted from studies of within-tribe and between-tribe variation in such risk factors. Moreover, insufficient attention to etiology has detracted from identifying etiologic factors that are common to all populations, such as parental violence and spouse and/or child abuse, and those that may be specific to American Indian cultures. From their anthropological perspective, preventive intervention programs directed at American Indians must be informed by the cultural context of Indian drinking, while simultaneously taking into account the destructive effects of alcohol abuse and individual and family life. In Chapter 8. Elaine Rodney describes her research at Prairie View A & M University and in the Midwest on alcohol and drug use among AfricanAmerican youth. Her extensive studies of alcohol and drug use among AfricanAmerican children, youth, and young adults provide additional support for the necessity of taking cultural variation into account in etiologic studies. Finally, in Chapter 9. Laurie Chassin and Aaron Belz, drawing on data from the Adult and Family Development Project at Arizona State University, illustrate the significant role that alcoholism plays in risk for alcohol and drug use independent of associated forms of parental psychopathology. Moreover, they stress the fact that the dynamics of alcoholism show great variation from one individual to another and from one family to another. This heterogeneity challenges investigators to identify the critical mediators and moderators that affect developmental pathways that either maintain risky environments and/or risky behaviors (Fitzgerald et al., 2000) and lead to poor outcomes or provide a resilience structure that substantially reduces risk for intergenerational problems related to alcohol use. Readers searching for definitive answers concerning the etiology of alcoholism will find few of them in this volume. Indeed, a key purpose of the

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round tables was to identify the questions that should structure the research, health care, and policy agenda for children of poverty (Fitzgerald, Lester, & Zuckerman, 1995), children of color (Fitzgerald, Lester, & Zuckerman, 1999), and children of addiction (current volume). The round tables were endorsed by the Executive Committee of the Society for Research in Child Development through its Liaison to Pediatrics initiative. They were financially sponsored by the Irving B.Harris Foundation, an organization that has provided broad support to issues related to research, health, and policy impacts on infants, children, and their families. We are deeply indebted to both the SRCD and the Irving B.Harris Foundation for the success of these round tables. Finally, we also thank the participants in this final round table discussion for their dedication to the task and for their amazing stamina through 9 hours of discussion. Hiram E.Fitzgerald Barry M.Lester Barry S.Zuckerman August, 1999 REFERENCES Fitzgerald, H.E., Lester, B.M., & Zuckerman, B. (1995). Children of poverty: research, health, and policy issues. New York: Garland. Fitzgerald, H.E., Lester, B.M., & Zuckerman, B. (1999). Children of color: research, health, and policy issues. New York: Garland. Fitzgerald, H.E., Davies, O.H., & Zucker, R.A. (in press). Growing up in an alcoholic family: Structuring pathways for risk aggregation and theory-driven intervention. In R.MacMahon & R.dev.Peters (Eds.), Thirtieth Banff conference on behavior science: children of disordered parents. New York: Kleuwer. Fitzgerald, H.E., Puttier, L.I., Mun, E.-Y., & Zucker, R.A. (2000). Prenatal and postnatal exposure to parental alcohol use and abuse. In J.D.Osofsky & H.E. Fitzgerald (Eds.), WAIMH Handbook of infant mental health: Vol. 4 Infant mental health in groups at high risk (pp. 124–159). New York: Wiley. Grant, B.F., & Dawson, D.A. (1997). Age of onset of alcohol use and its association with DSM-IV alcohol abuse and dependence: Results from the National Longitudinal Alcohol Epidemiologic Survey. Journal of Substance Abuse, 9, 103–110. Zucker, R.A., Fitzgerald, H.E., & Moses, H, (1995). Emergence of alcohol problems and the several alcoholisms: A developmental perspective on etiologic theory and life course trajectory. In D.Cicchetti & D.Cohen (Eds.), Manual of developmental psychopathology: Vol. 2. Risk, disorder, and adaptation (pp. 677–711). New York: Wiley.

Contributors

Marilyn Augustyn Boston Medical Center Aaron Belz, Ph.D. Arizona State University Laurie Chassin, Ph.D. Arizona State University Nancy L.Day, MPH, Ph.D. University of Pittsburgh School of Medicine Deborah A.Ellis, Ph.D. Wayne State University Hiram E.Fitzgerald, Ph.D. Michigan State University Candace Fleming, Ph.D. National Center for American Indians & Alaska Natives Deborah A.Frank, M.D. Boston Medical Center Sydney Lynn Hans, Ph.D. University of Chicago Sandra W.Jacobson, Ph.D. Wayne State University Joseph L.Jacobson, Ph.D. Wayne State University xi

xii

Linda L.LaGasse, Ph.D. Women’s & Infants’ Hospital Barry M.Lester, Ph.D. Women’s & Infants’ Hospital Mark Mirochnick, M.D. Boston Medical Center Diane M.Pallas, MA Central Michigan University Tripler Pell, M.Sc. Boston Medical Center Leon I.Puttier, Ph.D. University of Michigan Susan K.Refior, MSW University of Michigan Gale A.Richardson, Ph.D. University of Pittsburgh School of Medicine H.Elaine Rodney, Ph.D. Prairie View A & M University Paul Spicer, Ph.D. National Center for American Indians & Alaska Natives Robert A.Zucker, Ph.D. University of Michigan Barry S.Zuckerman, MD Boston University School of Medicine

Contributors

Tables and Figures

Table 1.1 Table 1.2 Figure 2.1 Figure 2.2 Table 2.1 Table 2.2 Table 3.1 Table 3.2 Table 3.3 Table 3.4 Table 3.5 Table 4.1 Table 4.2 Table 4.3 Table 4.4 Figure 4.1

Table 4.5

Research Implications for Selected Interview Formats for Assessing Alcohol and Illicit Substance Use Human Studies Addressing Cocaine Dose Effects of Prenatal Exposure Systems approach to the effects of prenatal cocaine Sample size of exposed and comparison groups Neurobehavioral Measures Societal Burden of Prenatal Cocaine Exposure Parenting Behavior of Opioid and Comparison Group Mothers over the First 2 Years of Life Psychosocial Risk Factors for Opioid and Comparison Groups Parenting Behavior Correlated with Maternal Drug Use and Psychosocial Risk Factors for Entire Sample Correlations between Parenting Behavior and Child Development at 24 Months for Opioid Group Only Caregiving Patterns of Children at Age 10 Effects of Pregnancy Drinking on the Bayley Scales Effects of Maternal Drinking on Bayley Scale Scores Number of Infants Performing in Bottom 10th Percentile by Pregnancy Drinking Level Drinking Levels in Ounces of Absolute Alcohol per Day and Standard Drinks per Day Dose-response relations for Bayley MDI and PDI, adjusted for potential confounders. Group ns are shown in parentheses. Effects of Drinking on Infant Cognitive Outcome xiii

6 13 31 34 35 38 50 51 53 54 56 70 71 72 74

75 77

xiv

Figure 4.2

Table 4.6 Table 4.7 Figure 4.3 Table 4.8 Table 4.9

Table 6.1 Table 6.2

Table 6.3

Table 6.4 Figure 6.1 Figure 6.2 Table 6.5

Table 6.6

Figure 9.1

Tables and Figures

Dose-response relations for two measures of speed of processing, adjusted for potential confounders. Group ns are shown in parentheses. Thresholds at Which Neurobehavioral Effects Were Seen for the Seattle Cohort Thresholds at Which Neurobehavioral Effects Were Seen for the Detroit Cohort Number of women drinking alcohol at least 1 day per week. Drinking Patterns during Pregnancy Relation of Pregnancy Drinking to Incidence of Functional Impairment in Infants Born to Younger and Older Mothers Lifetime (and 12-Month) Prevalence of DSM-III-R Disorders (Estimated U.S. Population Rates) Estimated Number and Percentage of Children in the Household Who Had One or More Parents Dependent on Alcohol, by Children’s Ages Estimated Number and Percentage of Children in the Household Who Had One or More Parents Dependent on Alcohol, and/or Illicit Drugs, by Children’s Ages Design and Basic Protocol of the Michigan State University-University of Michigan Longitudinal Study Recruitment flow for court alcoholic protocol (involving six district courts in four counties). Recruitment flow for community canvass to locate control and community alcoholic families. Social Visibility of Parental Alcoholism and Family Psychosocial Adaptation during the Early Child-Rearing Years (Children at Ages 3 to 5) Correlations Between Census Tract Indicators of Economic Attainment and Social Disorganization and Alcoholism Discovery Rates in Those Tracts. Variations as a Function of Social Visibility of the Alcoholism. Growth model predicting adolescent’s initial substance use (intercept) and growth over time in substance use (slope). Standardized path coefficients are shown.

79 80 81 82 83

85 111

112

113 120 121 122

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CHAPTER 1

Are There Dose Effects of Prenatal Cocaine Exposure on Children’s Bodies and Brains? DEBORAH A.FRANK MARILYN AUGUSTYN MARK MIROCHNICK TRIPLER PELL BARRY S.ZUCKERMAN

Over the past decade, the study of the possible effects of prenatal cocaine exposure on child outcome has become increasingly sophisticated. The field emerged in the midst of a national controversy over “crack babies” and “pregnant addicts.” Popular opinion predicted the emergence of a “biologic underclass” of cocaine-exposed children. In large part, the public’s early apprehensions stemmed from the social associations of illicit drug use with criminality and a variety of gender and racially biased assumptions (Daniels, 1997; Neuspiel, 1996). Throughout the 20th century, cocaine use has not only been illegal, but associated in the public mind with deviancy, violence, and uncontrolled sexuality, particularly among African Americans (Neuspiel, 1996). In the scientific world, these social meanings were reflected in implicit assumptions that any amount of exposure to cocaine in pregnancy would have negative effects on the infant. In contrast, in the early literature on prenatal alcohol exposure, there was sustained controversy as to whether “social use” (i.e., level of use that is socially acceptable in nonpregnant adults) of alcohol in pregnancy—in the absence of maternal alcoholism—had negative effects on child outcomes (Forrest & Florey, 1991). The social associations of illicit drug use have continued to impact scientific research negatively through discriminatory public policies and increasingly punitive judicial law. For example, mothers are less likely to report drug use to researchers and health providers for fear of imprisonment and loss of child custody (Roberts, 1991). In spite of ongoing public controversy, the scientific community has gradually developed a more balanced and informed approach to the study of illicit drug exposure in utero. Investigators are now addressing the problems posed by the 1

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interaction of possible drug dose effects with a variety of pharmacologic, biologic, and social variables (Frank, Augustyn, & Zuckerman, 1998). At various levels of cocaine exposure, these variables may confound, mediate, or moderate drug effects. However, despite these advances in research, there remain intrinsic methodological constraints in human studies. For example, it is still difficult to ascertain whether cocaine and other psychoactive substance exposures have occurred at all. Even more complex is the problem of determining gestational timing of exposure and the acute and cumulative doses to which the fetus was exposed. Understanding these constraints will facilitate an informed interpretation of available data and future study designs. The goals of this chapter are twofold. First, the methodologic problems entailed in defining dose in studies of prenatal cocaine exposure in humans will be summarized. Established standards for evaluating dose of alcohol exposure will be used to illustrate the difficulties generic to the field of the effects of prenatal psychoactive substance exposure on child outcome. Second, available published data will be reviewed that link various definitions of dose of cocaine exposure with neonatal and later outcomes. IDENTIFICATION OF COCAINE DOSE A critical component of any study investigating the effects of prenatal drug use on infants is identification of drug-exposed and unexposed infants. An ideal method would not only identify all exposed infants but also reveal the magnitude and pattern of exposure. Unfortunately, ascertainment of drug use during pregnancy has proven to be difficult and complex in the absence of such a method. Currently, there are two classes of techniques available to ascertain exposure: maternal interview and biologic assays. Both techniques fall far short of an ideal method. Maternal Interview Many different methods, all with intrinsic limitations, have been used in research projects to identify human prenatal cocaine exposure. The first is ascertaining substance exposure based on maternal interviews conducted by clinicians and noted in medical records. This method has been proven to be imprecise, consistently identifying many fewer users than other methods. In two recent comparisons of maternal self-report with biologic assays, only 1/4 of mothers with positive biologic assays for illicit substances admitted prenatal drug use to obstetric clinicians (Kline, Schittini, Levin, & Susser, 1998; Ostrea, Brady, Gause, Raymundo, & Stevens, 1992). Respondents are more likely to acknowledge cocaine use under research conditions, where trained interviewers offer stringent assurances of

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confidentiality (Frank et al., 1988; Richardson, Hamel, Goldschmidt, & Day, 1993). However, even under research conditions, maternal self-report is limited. Maternal self-report is lower than actual usage because of both denial (common among all substance-abusing populations) (Saitz, Mulvey, Plough, & Samet, 1997) and maternal fears of the consequences of admitting to illicit drug use, which may include the loss of child custody (Chavkin, Breitbart, Elimah, & Wise, 1998; Roberts, 1991). Hingson et al. (1986) have demonstrated that a disproportionate number of pregnant women are reluctant to admit to illicit (as compared to toxic although legal) drug use. In that study, mothers in a research project were administered a structured interview designed to determine prenatal drug use. Those mothers who were told that their urine samples would be tested for drug and alcohol use had an increased rate of reporting marijuana use but not alcohol and tobacco use. Even when aware of urine testing, significant numbers of drug-using pregnant women will deny use. Thus, reliance on selfreport alone may cause enough misclassification of drug-exposed infants to obscure clinically important differences between drug exposed and unexposed infants (Zuckerman et al., 1989). Some investigators have found that mothers respond more readily (and possibly more accurately) to questions about substance use before the recognition of pregnancy (Sampson, Bookstein, & Barr, 1994). By tacitly implying that the mothers’ substance use ceased once pregnancy was identified, this strategy obviates any possibility of using maternal report to quantify fetal substance exposure in the later stages of pregnancy, when potential detrimental effects on fetal growth are most likely to be found (Chasnoff, Griffith, MacGregor, Drikes, & Burns, 1989). Conversely, researchers in separate samples in Detroit (Jacobson et al., 1991) and Pittsburgh (Ernhart, Morrow-Tlucak, Sokol, & Martier, 1988) found that mothers acknowledge higher levels of alcohol use when questioned 1 or more years after the index pregnancy than they reported when they were asked prospectively during the pregnancy. The levels of use reported prospectively during pregnancy were more strongly associated with infant outcome in the Detroit sample (Jacobson et al., 1991). Although these findings could be due to recall bias, they may otherwise show that rank ordering of use is a more useful tool in identifying the relationship between exposure and outcome than attempting to quantify absolute amounts of exposure. Asking about use prior to recognition of pregnancy or asking well after the child’s birth may reduce the social pressure on the mothers to minimize reported substance use. Nevertheless, this approach has uncertain implications for predictive validity when compared with repeated interviews obtained during pregnancy or even single interviews conducted immediately postpartum. Measures that were developed for quantifying alcohol use have served as the models for documenting illicit drug exposure. One measure is the QuantityFrequency (Q-F) index of Strauss and Bacon (1953), which has been modified by Mulford and Miller (1960). This tool uses the average amount per drinking

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occasion as the measure of quantity but makes no distinction between patterns of drinking behavior. Thus, two people with extremely different drinking habits (for example, one binging on specific occasions and the other drinking moderately over a prolonged period) could achieve the same average quantity. These differing patterns of use could have widely disparate implications for maternal and fetal blood alcohol levels and potential fetal effects. A second index, developed by Cahalan and Cisin (1967), is the VolumeVariability Index, which attempts to compare binge versus more frequent but less heavy drinking and holds the volume constant. The basic technique of this index entails a two-step operation: (a) to classify each respondent according to average daily volume and (b) to divide each of several daily-volume groups into subgroups according to how the daily intake varies. The latter scale is able to differentiate people who drink moderate amounts of alcoholic beverages regularly (in terms of each episode of consumption) from those who binge intermittently. This issue is further complicated when pregnant adolescents are included in the research. For adolescents, sociologic as well as physiologic criteria are used to demarcate standard measures. In one recent study (Midanik, Zahn, & Klein, 1998), pregnant adolescents younger than 20 years old were considered “at risk” if they drank five or more drinks on a single occasion at least once a month. Meanwhile, pregnant women above the age of 20 were defined as “at risk” if they drank five or more drinks on a single occasion at least twice a month. In this instance, a narrow demarcation of age significantly altered the criteria used to define “at risk” groups. In epidemiologic studies, there are two major classification systems for rank ordering alcohol use. The first, used by the Department of Health and Human Services, defines drinking as light (up to one drink per occasion), moderate (more than one to three drinks), or heavy (more than three drinks). Frequency in this scheme is classified as light (less than 15 occasions per month), moderate (15 to 21 occasions per month), and heavy (21 or more occasions). Volume is classified as light (fewer than 21 drinks per month), moderate (22 to 29 drinks per month), and heavy (30 or more drinks per month) (Ashley et al., 1994). A second classification system was developed for the 1988 National Health Interview Survey. This system used a weekly rather than monthly interval, defining light as fewer than 7 drinks per week, moderate as 7 to 13 drinks a week, and heavy as 14 or more drinks per week. Jacobson and Jacobson (1994), in a study of prenatal alcohol exposure, further subdivided these categories into “very light” (up to 3.49 drinks a week) and “very heavy” (28 or more drinks a week) consumption. In the Jacobson and Jacobson sample, while there was no increased incidence in functional impairments (test scores less than the 10th percentile) at levels of exposure below seven drinks a week, even “very light” exposure had statistically significant effects on mean Bayley Mental Developmental Index scores. This suggests that patterns of drinking that are

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not of health or functional concern for nonpregnant adults may have fetal effects. There are other measures (such as CAGE, DRUG CAGE, and TWEAK) (Russell, 1994) that do not measure frequency or quantity at all but seek only to elicit evidence of tolerance and negative health or social consequences for the adult substance user (which presumably occurs at different exposure doses for different individuals). These instruments probably detect use only at levels well above most postulated thresholds for fetal effects. A recent study (Stoler et al., 1998) demonstrated that two or more physiologic markers of alcohol use, measured in maternal blood during pregnancy, were more predictive of infant outcome than mothers’ self-report on the TWEAK (Russell et al., 1996). The lack of standardization of potency and purity of illegal drugs further detracts from the usefulness of self-report. Even if a respondent is willing within the limits of memory to make every effort to provide an accurate account of her illicit substance use during pregnancy, the substance that she has used may vary from day to day, from place to place, or from year to year. Moreover, illicit drugs may be shared among a group of users, so that the amount ingested by any one user on a given occasion is difficult to estimate. In contrast, self-report of use of legal psychoactive substances such as ounces of wine or beer or number of cigarettes permits more accurate calculations of the average amount of alcohol or nicotine to which child and mother were exposed, regardless of where or when the substance was used. There is no such standardization for purity or potency in “lines” or “rocks” of cocaine. The composition and potency of Colombian cocaine may have changed in unpredictable ways by the time it is sold on the street in Columbus, Ohio. Moreover, respondents may be completely unaware of potential contaminants that have been introduced into illegal drugs, contaminants that may have active toxic effects of their own. Regardless of the accuracy of report of the dose ingested by the mother, the dose of substance transmitted to the fetus may be difficult to estimate accurately, depending on genetic variability in maternal and fetal metabolism of the drug and physiologic variability in placental detoxification and blood flow (Polin & Fox, 1992; Potter et al., 1994; Simone, Derewlany, Oskamp, Knie, & Koren, 1994). Still, self-report should never be omitted from research on prenatal cocaine exposure because it also offers some unique advantages. First, clinical interviewing by a skilled interviewer is less biased than selective urine screening in clinical settings, which may be based on provider expectation of drug use rather than actual drug use (Chasnoff, Landress, & Barnett, 1990; Kline et al., 1997). Second, maternal report is the only way to ascertain lifetime use prior to conception or use in very early pregnancy before the pregnant woman presents herself to a health care setting. Self-report also identifies route of drug administration and whether the drug is used in a “binge” pattern (Ward, Haney, Fischman, & Foltin, 1997). Self-report can delineate patterns of simultaneous or sequential psychoactive substance use (e.g., cocaine and alcohol use), which

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also may have important physiologic implications (Perez-Reyes, 1994). Selfreport is the only method that can detect use outside the time windows when biologic markers are valid. Urine reflects use only in the past 72 hours and meconium after 20 weeks of pregnancy. Furthermore, self-report instruments are cost-effective and readily available, and in medical or mental health settings they provide an opportunity for the mother to discuss with a clinician the potential benefits of and strategies for reducing or eliminating drug consumption. There is currently no gold standard interview for estimating dose of cocaine exposure in pregnancy. Most of the standard self-report screening measures have been developed for clinical use for adult, nonchildbearing populations and are designed to identify “problem use” of alcohol or addiction to illegal drugs. These measures have been primarily validated in samples of users in advanced stages of drug or alcohol dependence who may be manifesting psychosocial or medical sequelae for the adult user. However, data on prenatal alcohol effects (Jacobson & Jacobson, 1994) suggest that negative effects of prenatal exposure to psychoactive substances may be seen on infant outcomes at levels of use far lower than those associated with DSM diagnoses of substance use disorders (Spitzer, Williams, Gibbon, & First, 1992). Table 1.1 expands on standard interview formats for ascertaining alcohol and illicit substance use. In the study of illicit drug exposure in utero, a consensus has yet to evolve in defining what is “heavy” use for a pregnant woman, definitions that may differ from those accepted for other adults. As quantities of cocaine are not nationally standardized, frequency of use is established more easily than quantity. For this reason, uniform frequency and quantity measures are more difficult to derive for cocaine than for alcohol research. Table 1.1 Research Implications for Selected Interview Formats for Assessing Alcohol and Illicit Substance Use

Are There Dose Effects of Prenatal Cocaine Exposure?

7

continued

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Table 1.1 (Continued)

Biologic Assays Supplementing interview data with biologic markers has been shown to enhance identification of newborns exposed to cocaine in utero (Chasnoff et al., 1990; Kline et al., 1997; Ostrea, Brady, Gause, Raymundo, & Stevens, 1992; Zuckerman et al., 1989). Biologic assays can be performed by using a variety of analytic techniques applied to a range of biologic substrates. Immunoassays provide rapid and sensitive methods of detecting the presence of drugs and their metabolites in biologic matrices. The most commonly used immunoassay techniques are radioimmunoassay and enzyme immunoassay.

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Radioimmunoassay is a highly sensitive test that can detect low concentrations of drugs and metabolites and has been widely used in research settings. The disadvantages of radioimmunoassay are the need for expensive equipment, the use of radiolabeled materials, and the need to batch specimens into large runs. Enzyme immunoassay (either enzyme multiplied immunoassay technique or fluorescence polarization immunoassay) has the benefit of not involving radioactive materials. It is also able to screen individual samples for panels of drugs or large numbers of samples for single drugs. However, all immunoassay techniques have the drawback of cross-reaction with other substances, and confirmatory testing with chromatographic techniques is often performed. Chromatographic techniques, such as high-performance liquid chromatography (HPLC) and gas chromatography-mass spectrometry (GCMS), are extremely specific but expensive and time-consuming to perform. They also require sophisticated equipment and highly skilled personnel. Chromatographic techniques are generally used to confirm positive tests found by other techniques and are obligatory for forensic drug testing. Many clinical studies rely solely on immunoassay techniques and do not use confirmatory testing, running the risk of false-positive results. However, while chromatographic techniques are extremely specific, false negatives may result if the methodology does not look for all the significant metabolites of the drug (Lewis, Moore, & Leitkin, 1994b). Until recently, the most widely used biologic matrix for cocaine exposure in clinical or research settings was maternal or infant urine, obtained either during pregnancy or at the time of delivery (Osterloh & Lee, 1989). The use of urine assay facilitates the identification of infant effects. In a study conducted by our research group, the use of maternal urine assay greatly enhanced the classification of users and nonusers. In fact, without maternal urine assay, the cocaine effect on infant size at birth would not have been identified because so many users would have been misclassified as nonusers (Zuckerman et al., 1989). Urine is easily obtained from adults and contains most drugs and/or their metabolites in high concentrations. Most commonly used drugs of abuse can be detected in urine for several days after use. However, a few drugs, including phenobarbital, cannabinoids, and phencyclidine, can be detected for 2 weeks or longer after use (Vega, Kolody, Hwang, & Noble, 1993). The exact duration of the length of time a drug and/or its metabolites can be detected in urine will depend on the amount and frequency of use as well as on the lower limit of detection of the assay being used (Ambre et al., 1988). Cocaine metabolites appear in maternal and infant urine for only approximately 24 to 72 hours after the last dose. Thus, if a mother does not provide a urine sample relatively close to the time of drug use, her use will not be identified (Halstead, Godolphin, Lockitch, & Segal, 1988). Despite its widespread use in screening for drug use, urine has several limitations: (a) difficulty in obtaining adequate specimens from infants; (b) varying concentrations of drugs and metabolites,

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which are dependent on the subject’s state of hydration; and (c) the relatively brief period of detection after use for most drugs. In addition, urine assays are not useful for identification of dose of drug exposure (Callahan et al., 1992). For these reasons, testing of alternative biologic matrices has been investigated, including maternal and infant hair and infant meconium. A few studies have used assays of umbilical cord blood, but the collection requires sufficiently controlled conditions in the delivery room as to obviate usefulness in large epidemiological studies (Winecker et al., 1997). Several other biologic substances have been assayed for the presence of drugs and/or metabolites, including amniotic fluid and newborn gastric aspirates (Garcia, Romero, Garcia, & Ostrea, 1996). Cocaine metabolites were detected in the amniotic fluid of three of six women admitting to third-trimester cocaine use, but amniotic fluid is too difficult to obtain to be commonly used (Casanova et al., 1994). Fetuses swallow amniotic fluid in utero, and cocaine and benzoylecgonine can be detected in gastric aspirates collected immediately after birth (Garcia et al., 1996). Use of maternal or neonatal hair (Graham, Koren, Klein, Schneiderman, & Greenwald, 1989; Koren, 1995) to identify drug exposure, whether prenatal or otherwise, has been a matter of controversy (Baumgartner, Jones, Baumgartner, & Black, 1979; Kidwell & Blank 1995). The mechanism of drug transfer into hair is not known, but it may involve transfer from the bloodstream during hair matrix formation and/or from sweat or sebaceous excretions later incorporated into the hair structure. Drugs and metabolites remain until the hair is cut. In addition to illicit drugs, hair can be assayed for nicotine and cotinine to assess tobacco exposure (Pichinci et al., 1995). Hair analysis has been shown to be more sensitive than urine assay. In a recent study of maternal hair analysis, 59.8% of study women had positive hair assays for cocaine, and only 19.9% had positive urine samples (Kline et al., 1997). Hair analysis was specific as well. More than 90% of mothers who admitted to cocaine use within 6 months of delivery had positive hair samples (Kline et al., 1997). In a direct comparison of infant urine, meconium, and hair analysis, meconium analysis by GCMS and hair analysis by radioimmunoassay were equally sensitive, detecting between 75% and 80% of cocaine-exposed infants; immunoassay of urine detected only 38% (Callahan et al., 1992). Although some researchers suggest that segmenting maternal hair is a reliable way to identify exposure over time (Kline et al., 1997), other investigators working with nonpregnant samples have found that rates of appearance of radioactive-labeled cocaine show wide individual variability (Henderson, Harkey, Zhou, Jones, & Jacob, 1996). In addition, hair assays may inadvertently create ethnic and age biases in research data, such that dose of exposure may be underestimated in fair-haired samples. Hair with high concentrations of melanin binds cocaine metabolites more readily than blond or white hair (Reid, O’Connor, & Crayton, 1994). Environmental

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contamination may be reflected in assays of adult hair, contamination that cannot be completely removed by washing techniques (Joseph, Tsai, Su, Tsao, & Cone, 1997). Cosmetic treatments of hair have also been shown to alter drug concentration in that hair (Welch, Sniegoski, Allgood, & Habram, 1993). Therefore, the use of maternal hair for identifying timing and amount of illicit cocaine exposure during pregnancy must be considered approximate at best. However, Chiriboga, Brust, Bateman, and Hauser (1999) have found that concentration of cocaine metabolites in maternal hair correlates in a doserelated fashion with neonatal neurologic findings. Fetal hair has been considered by some to be a more promising medium than maternal hair in that the issues of cosmetic interference and environmental contamination do not apply. In utero hair formation begins during the last 3 to 4 months of gestation, and drugs or metabolites deposited in hair during the third trimester can be detected for as long as 4 to 5 months after birth (Graham et al., 1989). Although hair analysis requires a sample of hair only the thickness of a pencil lead, mothers may be reluctant to allow their newborn’s hair to be cut. Infant hair does not identify exposure before the second trimester, when fetal hair begins to grow, and does not have 100% congruence with other measures. However, one study of dose effects of prenatal cocaine use has correlated concentration of cocaine metabolites in fetal hair with fetal head growth (Sallee et al., 1995). Meconium drug analysis has been shown to be more sensitive than urine drug testing in identifying infants with prenatal drug exposure (Callahan et al., 1992). Drugs and drug metabolites excreted into fetal bile are secreted directly into the intestine. Alternatively, drugs and their metabolites may be excreted into the amniotic fluid via the fetal kidney as urine, swallowed by the fetus, and then deposited in the intestine. Meconium can be collected easily and noninvasively and has higher concentrations of drugs and their metabolites than urine (Ostrea, Brady, Parks, Asensio, & Naluz, 1989). Meconium acts as a reservoir for drugs used during pregnancy and their metabolites, providing a longer window of detection than urine, although the pattern and intensity of maternal drug use needed to produce a positive meconium drug assay has not yet been precisely established (Casanova et al., 1994; Ostrea, 1994a). Meconium assay has been shown to be a more sensitive method than urine assays for detecting prenatal drug exposure. In one study of 20 infants born to mothers admitting prenatal cocaine, opiate, or cannabinoid use, meconium assays were positive for all infants, but only 37% had positive urine assays (Ostrea et al., 1989). In another study, meconium assay was positive in 77% of infants born to mothers admitting prenatal cocaine use (Mirochnick, Frank, Cabral, Turner, & Zuckerman, 1995). Because meconium can be collected noninvasively from discarded diapers, it is the best methodology for use in large-scale screening studies (Ostrea et al., 1992). Meconium assay has also

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been shown to provide a marker for the intensity of prenatal cocaine use, with higher concentrations of the cocaine metabolite benzoylecgonine in meconium correlating in a dose-response fashion with decreased fetal growth as well as impaired newborn motor activity and regulation of state (Mirochnick et al., 1995; Tronick, Frank, Cabral, Mirochnick, & Zuckerman, 1996; DelaneyBlack et al., 1996). Meconium assay for nicotine metabolites can be used to determine fetal exposure to tobacco; assay for cocaethylene, a metabolite of cocaine and alcohol, will assess concurrent fetal exposure to these drugs (Lewis et al., 1994; McCance, Price, Kosten, & Jatlow, 1995). One limitation of meconium drug testing is that exposure to alcohol alone cannot be assessed. Although some investigators have reported that drug metabolites can be excreted into meconium as early as 15 prenatal weeks (Ostrea, Knapp, et al., 1994), others have not been able to show any accumulation in infants of known users until 3 weeks prior to delivery (Casanova et al., 1994). Meconium is still a controversial medium for measuring dose of cocaine exposure; in clinical settings, the cocaine concentration may be altered by admixture of infant urine that contains cocaine metabolites from recent maternal use (Rosengren et al., 1993). Nevertheless, in several studies, meconium has proved useful for a heuristic rank ordering of exposure (Delaney-Black et al., 1996; Mirochnick et al., 1995; Tronick et al., 1996). Neither meconium assay nor other biologic markers currently available permit a precise determination of threshold and dose response or gestational timing of exposure. DOSE OF PRENATAL COCAINE EXPOSURE AND CHILD OUTCOMES IN HUMAN SAMPLES Before the construct of dose was applied in the study of prenatal cocaine effects, there was little consistency in detection of cocaine effects on any outcome except infant gestational age and size at birth (which were found to be depressed in most studies) (Frank, Augustyn, & Zuckerman, 1998; Frank, Bresnahan, & Zuckerman, 1993). Even when studies were performed with confound control and masked examiners, findings varied widely. The effects of prenatal exposure did not show consistent patterns of association with neurobehavior during the neonatal period, development and behavior during infancy and early childhood, risks of sudden infant death, central nervous system structures, or somatic abnormalities. Table 1.2 summarizes the findings of human studies that address the issue of dose of prenatal exposure to cocaine, specifically in relation to perinatal outcomes, neonatal behavior, and outcomes in infancy and early childhood. In many regards, the findings of these studies of dose have been inconsistent and difficult to interpret. In large part, this may be due to their differing methods of measuring level of exposure and whether level of exposure was identified

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by biologic markers as well as by self-report. Four studies that use the Neonatal Behavioral Assessment Scale (NBAS) (Brazelton, 1984) to assess outcome (Richardson et al., 1993; Delaney-Black et al., 1996; Eyler, Behnke, Conlon, Woods, & Wobie, 1998b; Tronick et al., 1996) utilize some estimation of dose of prenatal cocaine exposure. Each study employs a different method of dose estimation: Delaney-Black et al. (1996) perform continuous correlation with concentration of cocaine metabolites in meconium; Tronick et al. (1996) rank exposure ordinally by a combination of meconium metabolites and number of self-reported days of cocaine use; Eyler et al. (1998b) define dose as mean dollars per day spent on cocaine throughout pregnancy; and Richardson et al. (1996) define dose by the use of one or more lines of cocaine a day during the first trimester of pregnancy. There is a negative cocaine dose effect on the NBAS state regulation measure in three of these four studies (Delaney-Black et al., 1996; Tronick et al., 1996; Eyler et al., 1998b). Interestingly, two of these studies (Delaney-Black et al., 1996; Eyler et al., 1998b) found no such effect when cocaine exposure was analyzed as a dichotomous exposed-unexposed variable. This suggests that inconsistency in other studies of NBAS findings may reflect obfuscation of the cocaine effect when lighter and heavier users are considered as part of a single, homogeneous group. Table 1.2 Human Studies Addressing Cocaine Dose Effects of Prenatal Exposure

continued

14 Table 1.2 (Continued)

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continued

16 Table 1.2 (Continued)

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continued

18 Table 1.2 (Continued)

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The data on whether there is a dose effect of prenatal cocaine exposure on cranial ultrasound outcomes are more difficult to interpret, especially when varying measures of dose exposure are considered. Behnke et al. (1998) found no relationship between maternal cocaine use and cranial ultrasound findings when pregnancy dose was defined as dollars per day. This finding was present despite the fact that, in the same sample, this measure was related to birth length, head circumference, and state regulation on the NBAS. In contrast, we recently reanalyzed a data set in which there was no difference among 241 term infants in ultrasound lesions if they were categorized only as cocaine exposed or unexposed (Frank, McCarten, Cabral, Levenson, & Zuckerman, 1992). However, when level of dose exposure was considered, the findings changed. An increased risk for Grade I intraventricular hemorrhage was found only among the most heavily cocaine exposed (top quartile by days of selfreported use or concentration of cocaine metabolites in meconium), where the odds of such a hemorrhage were 3.88 (95% confidence interval 1.45, 10.35, two-tailed p=.007) compared with unexposed after covariate control (including cigarettes, alcohol, and marijuana) (Frank, McCarten, Robson, Mirochnick, Cabral, Park, & Zuckerman, 1999). The more lightly exposed did not differ from unexposed. A cocaine dose relationship with cranial ultrasound findings must be considered tentative until replicated because two available studies have contradictory results. The effects of level of prenatal cocaine exposure after the neonatal period have been explored in four reports (from three samples), all of which determine dose solely by maternal self-report (Alessandri, Bendersky, & Lewis, 1998; (Hurt et al., 1997; Jacobson, Jacobson, Sokol, Martier, & Chiodo, 1996).

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Jacobson et al. (1996) reported negative effects of heavier exposure on infant information processing measured by the Pagan Test of Infant Intelligence but not on scores on the Bayley Scales of Infant Development at 13 months. In this same sample, the investigators found higher cocaine exposure associated with more rapid responsiveness on a visual expectancy paradigm. After control for maternal behavior and medical and environmental risk, Bendersky and Lewis (1998) noted that heavily exposed (defined as 2 or more days a week of use in pregnancy), 4-month-old infants showed less joy during en face play and less ability to recover from stress than lightly or unexposed infants. In the same cohort, no dose effect was found on a novelty habituation task or on Bayley II scores at 8 months, but there was a difference between heavily cocaine-exposed children and unexposed on mental development index (MDI) scores at 18 months (Alessandri et al., 1998). In contrast, Hurt et al. (1997) did not find any relationship between quartile of days of self-reported cocaine use and Wechsler Preschool and Primary Scale of Intelligence-Revised scores at 48 months. On the other hand, available research has also revealed important consistencies, helping to substantiate the real effects (or noneffects) of cocaine exposure in utero. Currently, available human research suggests that, after confound control, there is a cocaine dose effect on neonatal size at birth. As Table 1.2 shows, the more heavily exposed infants in any sample are smaller in all parameters than those less heavily exposed, whether heavy exposure is reflected by maternal self-report or hair analysis, neonatal hair analysis, meconium, or a combination of meconium and self-report. In addition, there is consistency in the three studies that have evaluated dose effects on neonatal behavior (Chiriboga et al., 1999; Delaney-Black et al., 1996; Eyler et al., 1998b; Richardson et al., 1993; Tronick et al., 1996). Less optimal state regulation is associated with heavier cocaine exposure, whether defined by meconium assay, self-report, or a combined measure (although other behavioral findings are not consistently dose related from study to study) (Eyler et al., 1998b; Delaney-Black et al., 1996; Tronick et al., 1996). When considered together, these studies demonstrate the importance of methods of measuring dose of cocaine exposure in determining infant effects. As shown in Table 1.2, it is striking that every study that uses a biologic method of quantifying level of cocaine exposure (whether the matrix is meconium, infant hair, or maternal hair, alone or in combination with self-report) identified dose effects. However, those that used self-report without biologic markers found effects only in some instances Although biologic markers cannot be used to precisely quantify exposure for individuals, they can be used to aggregate populations into exposure groups that allow the delineation of dose effects.

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CONCLUSIONS The popular perception is that any prenatal exposure to cocaine is almost certainly associated with devastating effects on the neonate. However, these data suggest that most potentially detrimental effects, including neonatal size (Mirochnick et al., 1995), neonatal behavior (Tronick et al., 1996), and central nervous system lesions (Frank et al., 1999) of prenatal cocaine exposure occur disproportionately among the heaviest users. Although this dose effect requires further replication, it parallels findings in research on alcohol and cigarette exposure (Jacobson & Jacobson, 1994). In addition, the precision of dose quantification in humans for cocaine is not yet adequate for statements to be made regarding whether the pattern of the effect is continuous or whether there is an actual threshold beyond which effects occur. Great caution must be exercised in drawing health education and public policy conclusions from available information regarding dose. For instance, several studies define heavy users as those who use more than twice a week, but this is a not a true threshold. One cannot be sure that less frequent use would not have detrimental effects. In addition, dose may vary throughout pregnancy; most investigators find that women’s use decreases as the pregnancy progresses (Richardson et al., 1993). It is not yet known whether the primary determinant of adverse outcome is cumulative dose, which is only approximately reflected in meconium (Maynard, Amoruso, & Oh, 1991) or neonatal or maternal hair (Chiriboga et al., 1999; Koren, 1995; Lewis et al., 1994b) or maximum dose used on a single occasion, which usually occurs earlier in pregnancy and may not be reflected in available biologic markers. Even if a standardized mode of quantifying prenatal cocaine exposure develops, the phenomenon of dose response is not always straightforward. Simple linear models (the more exposure, the worse the outcomes) may not be adequate to describe the actual relationships, which may be nonlinear or even paradoxical (some is worse than none or a lot) (Decoufle & Boyle, 1997). Moreover, the possible explanations for such paradoxical effects are various, including biologic and social mechanisms. For example, higher cocaine dose may produce greater placental vasoconstriction with less metabolite transferred to the fetus (Potter et al., 1994). One could speculate that repeated use might upregulate enzymatic pathways of detoxification, decreasing effects with higher cumulative use (Bailey, 1997; Simone et al., 1994). There is also the question of a “healthy user” effect, with women with better overall health tolerating higher doses than women with other adverse health conditions that may place the fetus at risk (Decoufle & Boyle, 1997). There are also potential social mediators of paradoxical dose effects. For instance, women with better education or more economic resources may obtain more doses of an illicit drug. This phenomenon has been described in Jamaica, where, compared with

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infants of nonusers, infants of heavy marijuana users had more optimal behavioral findings at 1 month of age (Dreher, Nugent, & Hudgins, 1994). In the United States, because heavier users and their infants may be more readily detected by clinicians, they may be given preferential access to compensatory services such as maternal drug treatment or infant developmental intervention—leading to a more optimal outcome long term than infants exposed to a lower dose who receive no compensatory care (Hofkosh et al., 1995; Olds, Henderson, & Tatelbaum, 1994). As these areas evolve, defining dose and documenting biologic and social variables that confound, mediate, or moderate dose effects will continue to pose major challenges. REFERENCES Alessandri, S.M., Bendersky, M., & Lewis, M. (1998). Cognitive functioning in 8- to 18month old drug-exposed infants. Developmental Psychology, 34 (3), 565–573. Ambre, J. (1988). The urinary excretion of cocaine and metabolites in humans: A kinetic analysis of published data. Journal of Analytical Toxicology, 12 (6), 301–306. Ashley, M.J., Ferrence, R., Room, R., Rankin, J., & Single, E. (1994). Moderate drinking and health: Report of an international symposium. Canadian Medical Association Journal, 151, 809–828. Bailey, D.N. (1997). Cocaine and cocaethylene binding to human placenta in vitro. American Journal of Obstetrics and Gynecology, 177 (3), 527–531. Baumgartner, A.M., Jones, P.F., Baumgartner, A.W., & Black, T.C. (1979). Radioimmunoassay of hair for determining opiate-abuse histories. Journal of Nuclear Medicine, 20, 748–752. Behnke, M., Eyler, F.D., Conlon, M., Wobie, K., Woods, N.S., & Gumming, W. (1998). Incidence and description of structural brain abnormalities in newborns exposed to cocaine. Journal of Pediatrics, 132, 291–294. Bendersky, M., & Lewis, M. (1998). Arousal modulation in cocaine-exposed infants. Developmental Psychology, 34, 555–564. Brazelton, T.B. (Ed.). (1984). Neonatal behavioral assessment scale (2nd ed.). London: Spastics International. Burkett, G., Yasin, S., Palow, D., Lavoie, L., & Martinez, M. (1994). Patterns of cocaine bingeing: Effect on pregnancy. American Journal of Obstetrics and Gynecology, 171, 372–379. Cahalan, D., & Cisin, H. (1967). American drinking practices: Summary of finding from a national probability sample. Quarterly Journal of Studies in Alcohol, 28, 642– 656. Callahan, C.M., Grant, T.M., Phipps, P., Clark, G., Novack, A.H., Streissguth, A.P., & Raisys, V.A. (1992). Measurement of gestational cocaine exposure: Sensitivity of infants’ hair, meconium, and urine. Journal of Pediatrics, 120, 763–768.

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Casanova, O.Q., Lombardero, N., Behnke, M., Eyler, F.D., Conlon, M., & Bertholf, R.L. (1994). Detection of cocaine exposure in the neonate: Analyses of urine, meconium, and amniotic fluid from mothers and infants exposed to cocaine. Archives of Pathology and Laboratory Medicine, 118, 988–993. Chasnoff, I.J., Griffith, D.R., MacGregor, S., Drikes, K., & Burns, K.A. (1989). Temporal patterns of cocaine use in pregnancy. Journal of the American Medical Association, 261 (12), 1741–1744. Chasnoff, I.J., Landress, H., & Barnett, M. (1990). The prevalence of illicit drug or alcohol use during pregnancy and discrepancies in mandatory reporting in Pinellas County, Florida. New England Journal of Medicine, 322, 120–126. Chavkin, W., Breitbart, V., Elimah, D., & Wise, P. (1998). National survey of the states: Policies and practices regarding drug using pregnant women. American Journal of Public Health, 88, 117–119. Chiriboga, C.A., Brust, J.C.M., Bateman, D., & Hauser, W.A. (1999). Dose-response effect of fetal cocaine exposure on newborn neurologic function. Pediatrics, 103, 79–85. Coles, C.D., Platzman, K.A., Smith, L., James, M.A., & Falek, A. (1992). Effects of cocaine and alcohol use in pregnancy on neonatal growth and neurobehavioral status. Neurotoxicology and Teratology, 14, 23–33. Cone, E.J. (1990). Testing human hair for drugs of abuse. 1. Individual dose and time profiles of morphine and codeine in plasma, saliva, urine, and beard compared to drug-induced effects on pupils and behavior. Journal of Analytical Toxicology, 14, 1–7. Daniels, C.R. (1997). Between fathers and fetuses: The social construction of male reproduction and the politics of fetal harm. Journal of Women in Culture and Society, 22, 579–616. Decoufle, P., & Boyle, C. (1997). Dose response analyses of women’s alcohol use during pregnancy and children’s cognitive functioning. American Journal of Public Health, 87, 299. Delaney-Black, V., Covington, C., Ostrea, E., Jr., Romero, A., Baker, D., Tagel, M. T., Nordstrom-Klee, B., Silvestre, M.A., Angellil, M.L., Hack, C., & Long, J. (1996). Prenatal cocaine and neonatal outcome: Evaluation of dose-response relationship. Pediatrics, 98, 735–40. Dreher, M.C., Nugent, K., & Hudgins, R. (1994). Prenatal marijuana exposure and neonatal outcomes in Jamaica: An ethnographic study . Pediatrics, 93, 254–260. Ernhart, C.B., Morrow-Tlucak, M., Sokol, R.J., & Martier, S. (1988). Underreporting of alcohol use in pregnancy. Alcohol: Clinical Experimental Research, 12, 506–511. Ewing, J.A. (1984). Detecting alcoholism: The CAGE questionnaire. Journal of the American Medical Association, 252, 1905–1907. Eyler, F.D., Behnke, M., Conlon, M., Woods, N.S., & Wobie, K. (1998b). Birth outcome from a prospective, matched study of prenatal crack/cocaine use: i. Interactive and dose effects on health and growth. Pediatrics, 101 (2), 229–237.

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Eyler, F.D., Behnke, M., Conlon, M., Woods, N.S., & Wobie, K. (1998b). Birth outcome from a prospective, matched study of prenatal crack/cocaine use: ii. Interactive and dose effects on neurobehavioral assessment. Pediatrics, 101 (2), 237–241. Forrest, F., & Florey, C. (1991). Reported social alcohol consumption during pregnancy and infants’ development at 18 months. British Medical Journal, 303 (6793), 22– 26. Frank, D.A., Augustyn, M., & Zuckerman, B. (1998). Neonatal neurobehavioral and neuroanatomic correlates of prenatal cocaine exposure: Problems of dose and confounding. Annals of the New York Academy of Sciences, 846, 40–49. Frank, D.A., Bresnahan, K., & Zuckerman, B. (1993). Maternal cocaine use: Impact on child health and development. Advances in Pediatrics, 40, 65–99. Frank, D.A., McCarten, K., Cabral, H., Levenson, S.M., & Zuckerman, B. (1992). Cranial ultrasound in term newborns: Failure to replicate excess abnormalities in cocaine exposed [abstract]. Pediatric Research, 31, 247A. Frank, D.A., McCarten, K., Robson, C.D., Mirochnick, M., Cabral, H., Park, H., & Zuckerman, B. (1999). Level of prenatal cocaine exposure and neonatal ultrasound findings. Pediatrics, 104, 1101–1105. Frank, D.A., Zuckerman, B., Amaro, H., Aboagye, K., Bauchner, H., Cabral, H., Fried, L., Hingson, R., Kayne, H., & Levenson, S.M. (1988). Cocaine use during pregnancy: Prevalence and correlates. Pediatrics, 82, 888–895. Garcia, D.C., Romero, A., Garcia, G.C., & Ostrea, E.M. (1996). Gastric fluid analysis for determining gestational cocaine exposure. Pediatrics, 98, 291–293. Graham, K., Koren, G., Klein, J., Schneiderman, J., & Greenwald, M. (1989). Determination of gestational cocaine exposure by hair analysis. Journal of the American Medical Association, 262, 3328–3330. Halstead, A.C., Godolphin, G.W., Lockitch, G., & Segal, S. (1988). Timing of specimens is crucial in urine screening of drug dependent mothers and infants. Clinical Biochemistry, 21, 59–61. Henderson, G.L., Harkey, M.R., Zhou, C., Jones, R.T., & Jacob, P. (1996). Incorporation of isotopically labeled cocaine and metabolites in human hair: 1. Dose-response relationships. Journal of Analytical Toxicology, 20, 1–12. Hingson, R., Zuckerman, B., Amaro, H., Frank, D.A., Kayne, H., Sorenson, J.R., Mitchell, J., Parker, S., Morelock, S., & Timperi, R. (1986). Maternal marijuana use and neonatal outcome: Uncertainty posed by self-reports. American Journal of Public Health, 76, 667–669. Hofkosh, D., Pringle, J.L., Wald, H.L., Swital, J., Hinderliter, S.A., & Hamel, S.C. (1995). Early interactions between drug involved mothers and infants. Archives of Pediatrics and Adolescent Medicine, 149, 665–672. Hurt, H., Malmud, E., Betancourt, L., Braitman, L.E., Brodsky, N., & Giannetta, J. (1997). Children with in utero cocaine exposure do not differ from control subjects on intelligence testing. Archives of Pediatrics and Adolescent Medicine, 151, 1237– 1241. Jacobson, J.L., & Jacobson, S.W. (1994). Prenatal alcohol exposure and neurobehavioral

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development: Where is the threshold? Alcohol Health and Research World, 18, 30– 36. Jacobson, J.L., Jacobson, S.W., Sokol, R.J., Martier, S.S., & Chiodo, L.M. (1996). New evidence for neurobehavioral effects of in utero cocaine exposure. Journal of Pediatrics, 129, 581–590. Jacobson, S.W., Jacobson J.L., Sokol, R.J., Martier, S., Ager, J., & Kaplan, M.G. (1991). Maternal recall of alcohol, cocaine and marijuana use during pregnancy. Neurotoxicology and Teratology, 13, 535–540. Johnson, R.A., & Gerstein, D.R. (1998). Initiation of use of alcohol, cigarettes, marijuana, cocaine, and other substances in US birth cohorts since 1919. American Journal of Public Health, 88, 27–33. Joseph, R.E., Jr., Tsai, W.J., Su, T.P., Tsao, L.I., & Cone, E.J. (1997). In vitro characterization of cocaine binding sites in human hair. Journal of Pharmacology and Experimental Therepeutics, 282 (3), 1228–1241. Kidwell, D.A., & Blank, D.L. (1995). Mechanisms of incorporation of drugs into hair and interpretation of hair analysis data. In Hair testing for drugs of abuse: International research on standards and technology: USPHS, Washington, DC: National Institute of Drug Abuse. Kline, J., Schittini, M., Levin, B., & Susser, M. (1997). Cocaine use during pregnancy: Sensitive detection by hair assay. American Journal of Public Health, 87, 352–358. Koren, G. (1995). Measurement of drugs in neonatal hair; a window to fetal exposure. Forensic Science International, 70, 77–82. Lewis, D.E., Moore, C.M., & Leikin, J.B. (1994a). Cocaethylene in meconium specimens. Clinical Toxicology, 32, 697–703. Lewis, D.E., Moore, C.M., & Leiken, J.B. (1994b). Incorrect diagnosis of cocaine-exposed babies: A report. Neonatal Intensive Care, 7, 24–26. Maynard, E.C., Amoruso, L.P., & Oh, W. (1991). Meconium for drug testing. American Journal of Diseases and Children, 145, 650–652. McCance, E.F., Price, L.H., Kosten, T.R., & Jatlow, P.I. (1995). Cocaethylene: Pharmacology, physiology, and behavioral effects in humans. Journal of Pharmacology and Experimental Therapeutics, 274, 215–223. McLellan, A.T., Kushner, H., Metzger, D., Peters, R., Smith, I., Grissom, G., Pettinati, H., & Argeriov, M. (1992). The fifth edition of the addiction severity index. Journal of Substance Abuse Treatment, 9, 199–213. McLellan, A.T., Luborsky, L., Cacciola, J., Griffith, J., Evans, F., Barr, H.L., & O’Brian, C.P. (1985). New data from the ASI reliability and validity in three centers. Journal of Nervous and Mental Diseases, 173, 412–423. Midanik, L.T., Zahn, E.G., & Klein, D. (1998). Alcohol and drug CAGE screeners for pregnant, low-income women: The California Perinatal Needs Assessment. Alcohol Clinical Experiment Review, 22 (1), 121–125. Mirochnick, M., Frank, D.A., Cabral, H., Turner, A., & Zuckerman, B. (1995). Relation between meconium concentration of the cocaine metabolite benzolecgonine and fetal growth. Journal of Pediatrics, 126, 636–638.

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Mulford, H., & Miller, D. (1960). Drinking in Iowa: II. The extent of drinking and selected sociocultural categories. Quarterly Journal of Studies in Alcohol, 21, 26–39. Neuspiel, D.R. (1996). Racism and perinatal addiction. Ethnicity and Disease, 6, 47–55. Olds, D.L., Henderson, C.R., & Tatelbaum, R. (1994). Prevention of intellectual impairment in children of women who smoked during pregnancy. Pediatrics, 93, 228–233. Osterloh, J.D., & Lee, B.L. (1989). Drug screening in mothers and newborns. American Journal of Diseases of Children, 143, 791–793. Ostrea, E.M., Brady, M.J., Cause, S., Raymundo, A.L., & Stevens, M. (1992). Drug screening of newborns by meconium analysis: A large-scale, prospective, epidemiologic study. Journal of Pediatrics, 89, 107–113. Ostrea, E.M., Brady, M.J., Parks, P.M., Asensio, D.C., & Naluz, A. (1989). Drug screening of meconium in infants of drug-dependent mothers: An alternative to urine testing. Journal of Pediatrics, 115, 474–477. Ostrea, E.M., Knapp, D.K., Romero, A., Montes, M., & Ostrea, A.R. (1994). Meconium analysis to assess fetal exposure to nicotine by active and passive maternal smoking. Journal of Pediatrics, 124, 471–476. Ostrea, E.M, Romero, A., Knapp, K., Ostrea, A.R., Lucena, E.J., & Utarnachitt, R. B. (1994). Postmortem drug analysis of meconium in early gestation human fetuses exposed to cocaine: Clinical implications. Journal of Pediatrics, 124, 477–479. Perez-Reyes, M. (1994). The order of drug administration: Its effects on the interaction between cocaine and ethanol. Life Sciences, 55, 541–550. Pichinci, S., Pacifici, R., Altieri, I, Passa, A., Rosa, M., & Zuccaro, P. (1995). Analysis of nicotine and cotinine in human hair by high-performance liquid chromatography and comparative determination with radioimmunoassay. In Hair testing for drugs of abuse: International research on standards and technology. National Institute of Drug Abuse. Polin, R.A., & Fox, W.W. (1992). Fetal and neonatal physiology. Philadelphia: W.B. Saunders. Polygenis, D., Whatron, S., Malmerg, C., Sherman, N., Kennedy, D., Koren, G., & Einarson, T.R. (1998). Moderate alcohol consumption during pregnancy and the incidence of fetal malformations: A meta-analysis. Neurotoxicology and Teratology, 20 (1), 61–67. Potter, S., Klein, J., Valiante, G., Stack, D.M., Papageorgiou, A., Stott, W., Lewis, D., Koren, G., & Zelazo, P.R. (1994). Maternal cocaine use without evidence of fetal exposure. Journal of Pediatrics, 125, 652–654. Reid, R.W., O’Connor, F.L., & Crayton, J.W. (1994). The in vitro differential binding of benzoylecgonine to pigmented human hair samples. Journal of Toxicology-Clinical Toxicology, 32, 405–410. Richardson, G.A., Hamel, S.C., Goldschmidt, L., & Day, N.L. (1993). The effects of prenatal cocaine use on neonatal neurobehavioral status. Neurotoxicology and Teratology, 18 (5), 519–528. Roberts, D.E. (1991). Punishing drug addicts who have babies: Women of color, equality,

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and the right of privacy. Harvard Law Review, 104, 1419–1482. Rosengren, S.S., Longobucco, D.B., Bernstein, B.A., Fishman, S., Cooke, E., Boctor, F., & Lewis, S.C. (1993). Meconium testing for cocaine metabolite: Prevalence, perceptions, and pitfalls. American Journal of Obstetrics and Gynecology, 168, 1449– 1456. Russell, M. (1994). New assessment tools for drinking in pregnancy: T-ACE, TWEAK, and others. Alcohol Health Research World, 18, 55–61. Russell, M., Martier, S.S., Sokol, R.J., Mudar, P., Jacobson, S., & Jacobson, J. (1996). Detecting risk drinking during pregnancy: A comparison of four screening questionnaires. American Journal of Public Health, 86 (10), 1435–1439. Saitz, R., Mulvey, K.P., Plough, A., & Samet, J.H. (1997). Physician unawareness of serious substance abuse. American Journal of Drug and Alcohol Abuse, 23 (3): 343–354. Sallee, F.R., Katikaneni, L.P., McArthur, P.D., Ibrahim, H.M., Nesbitt, L., & Sethuraman, G. (1995). Head growth in cocaine-exposed infants: Relationship to neonate hair level. Journal of Developmental and Behavioral Pediatrics, 16, 77–81. Sampson, P.D., Bookstein, F.L., & Barr, H.M. (1994). Prenatal alcohol exposure, birthweight and measures of child size from birth to age 14 years. American Journal of Pediatric Health, 84 (9), 1421–1428. Simone, C., Derewlany, L.O., Oskamp, M., Knie, B., & Koren, G. (1994). Transfer of cocaine and benzoylecgonine across the perfused human placental cotyledon. American Journal of Obstetrics and Gynecology, 170, 1404–1410. Sokol, R., Martier, S., & Ager, J. (1989). The T-ACE questions: Practical prenatal detection of risk-drinking. American Journal of Obstetrics and Gynecology, 160 (4), 863– 870. Spitzer, R.L., Williams, J.B., Gibbon, M., & First, M.B. (1992). The structured clinical interview for DSM III-R I: History, rationale, description. Archives of General Psychiatry, 49, 624–636. Stoler, J.M., Huntington, K.S., Peterson, C.M., Peterson, K.P., Daniel, P., Aboagye, K.K., Lieberman, E., Ryan, L., & Holmes, L.B. (1998). The prenatal detection of significant alcohol exposure with maternal blood markers. Journal of Pediatrics, 133 (3), 346– 352. Strauss, R,. & Bacon, S. (1953). Drinking in college. New Haven, CT: Yale University Press. Tronick, E.Z., Frank, D.A., Cabral, H., Mirochnick, M., & Zuckerman, B. (1996). Late dose-response effects of prenatal cocaine exposure on newborn neurobehavioral performance. Pediatrics, 98, 76–83. Valente, D., Cassini, M., Pigliapochi, M., & Vanseti, G. (1981). Hair as the sample in assessing morphine and cocaine addiction. Clinical Chemistry, 27, 1952–1953. Vega, W.A., Kolody, B., Hwang, J., & Noble, A. (1993). Prevalence and magnitude of perinatal substance exposures in California. New England Journal of Medicine, 329, 850–854. Volk, R.J., Steinbauer, J.R., Cantor, S.B., & Holzer, C.E. (1997). The AUDIT as a screen

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for at-risk drinking in primary care patients of different racial/ethnic backgrounds. Addiction, 92, 197–206. Ward, A.S., Haney, M., Fischman, M.W., & Foltin, R.W. (1997). Binge cocaine selfadministration in humans: Intravenous cocaine. Psychopharmacology, 132 (4), 375– 381. Welch, M.J., Sniegoski, L.T., Allgood, C.C., & Habram, M. (1993). Hair analysis for drugs of abuse: Evaluation of analytical methods, environmental issues, and development of reference materials. Journal of Analytical Toxicology, 17, 389–398. Winecker, R.E., Goldberger, B.A., Tebbett, I., Behnke, M., Eyler, F.D., Conlon, M., Wobie, K., Karlix, J., & Bertholf, R.L. (1997). Detection of cocaine and its metabolites in amniotic fluid and umbilical cord tissue. Journal of Analytical Toxicology, 21, 97–104. Zuckerman, B., Frank, D.A., Hingson, R., Amaro, H., Levenson, S.M., Parker, S., Vinci, R., Fried, L.E., Cabral, H., Kayne, H., Timperi, R., Aboagye, K., & Bauchner, H. (1989). Effects of maternal marijuana and cocaine use on fetal growth. New England Journal of Medicine, 320 (12), 762–768.

CHAPTER 2

Prenatal Cocaine Exposure and Child Outcome From Research to Public Policy LINDA L.LAGASSE BARRY M.LESTER

For the past 15 years, the problem of cocaine use by pregnant women and its effects on developing children has been a major focus of research, treatment, and public policy in the United States. It is also an issue that has been the subject of much debate and controversy and one that has taught us some interesting lessons. It is hard to describe the sentiment that ripped through this country when cocaine use by pregnant women became identified as a public health concern. This came about because of the availability of crack, a potent and cheap form of cocaine that resulted in a resurgence of cocaine use among all levels of society, including women of childbearing age. It was immediately described as an epidemic and generated anger and disgust at these women for the “damage” they were inflicting on their unborn children. How could they? How dare they? The fact that cocaine is an illegal substance made it easier for society to act out its anger by using the courts and the child welfare system to remove infants from their mothers and prosecute mothers under child abuse and neglect statutes. We created a generation of “boarder babies,” newborn infants languishing in hospitals while placement decisions were being made. Instead of viewing substance abuse by pregnant women as a mental health problem and focusing on treatment, prevention, and intervention, society stigmatized these women and treated them as criminals. DATABASE OF PUBLISHED STUDIES Despite early reports suggesting that prenatal cocaine exposure had devastating effects on the developing child, findings from the literature continue to show an inconsistent pattern of results. Concerns about this literature led us to develop for the Robert Wood Johnson Foundation a computerized database of the published literature of studies of prenatal cocaine exposure and child 29

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development outcome in order to provide a systematic analysis and description of the characteristics and findings from these studies. The Nature of the Problem Most studies aim to determine the terotologic effects of in utero cocaine exposure on child outcome. However, we know that cocaine use occurs in a context of polydrug use, risky environments, medical problems, and comorbidity with psychiatric problems. As illustrated in Figure 2.1, all these factors influence child outcome. Thus, to summarize the overall results of prenatal cocaine exposure on neurobehavioral outcomes, we need to evaluate the methodology of each study, including whether and how the investigators dealt with confounding factors. For our database, we review studies of four categories of neurobehavior: behavior, neurological function, and physiological or neurochemical measures. We further subdivide behavioral outcomes into 15 domains (later shown in Table 2.1). The first report from this database was published in 1996 as part of a National Institute on Drug Abuse (NIDA) monograph (Lester, LaGasse, Freier, & Brunner, 1996) and was based on 50 studies that met the inclusion criteria (listed later). In our second report (Lester, LaGasse, & Brunner, 1997), the database was expanded to 76 studies that met the inclusion criteria. Our most recent review includes 101 studies in the database published from 1985 through August 1997. Method The method includes quarterly literature searches through Medline and Psyclit. Each article is reviewed to determine eligibility for inclusion in the final database, contingent on the criteria described. Information from the articles is then abstracted, coded, and entered into the database. Variables are defined that represent either characteristics of the study, such as sample size and the method of drug detection, or behavioral outcomes such as an IQ score. Summary statistics are then generated across studies. Criteria for Inclusion Seven criteria are used to identify studies to be included in the final database: (a) cocaine use during pregnancy, (b) human subjects, (c) neurobehavioral measures, (d) original research, (e) inclusion of control or comparison group, (f) statistical analysis of data, and (g) publication in a peer-reviewed or refereed journal.

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Figure 2.1 Systems approach to the effects of prenatal cocaine

Identification of Drug Use An essential part of study methodology is how drug use during pregnancy is determined. The database shows that the most common methods used to detect maternal use of cocaine during pregnancy are history (including self-report) and/ or toxicology analysis of urine, meconium, or hair. Most studies do not rely on a single index; 76% of the studies used some combination of these methods. The most common combination was urine and self-report, which was used in 65% of the studies with multiple indices. The meconium assay provides a record of drug use from approximately 20 weeks gestational age, in contrast to the urine assay, which provides only a 72-hour record of drug use. However, most of these studies were conducted before the meconium assay was in widespread use. Meconium was used in 8 studies, 7 of which also used self-report. In sum, most studies include more than one means of identification of drug use, but few include meconium analysis, which provides greater confidence not only in identifying cocaine users but also in assuring a nonusing comparison group. Confounding Factors The current cocaine database indicates there are three classes of confounding factors: polydrug use, social and demographic factors, and medical and health status. Polydrug Use It is now well established that cocaine is seldom used alone (Lester et al., 1997). The most commonly reported companion drugs of cocaine are alcohol (72% of

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cocaine-exposed groups in the 101 studies) and nicotine (73%), followed by marijuana (59%) and heroin or methadone (42%). However, other drugs such as amphetamines, central nervous system (CNS) depressants, phencyclidine (PCP), and hallucinogens are present in 2% to 19% of the cocaine-exposed groups. Only 13% of the studies reported a cocaine-only sample. Unfortunately, many studies of cocaine exposure failed to account for poly drug use, calling into question claims of unique cocaine effects. For example, 23% of the 101 studies did not mention alcohol use, 25% did not mention nicotine use, 38% did not mention opiate use, and 36% did not mention marijuana use. Social Demographics The unique effects of prenatal drug exposure on development is even more difficult to separate from the impact of disadvantaged environments. Most of the current research findings are based on predominantly low socioeconomic status (SES) families on public assistance. In addition, the typical participant in these studies is black, single, a high school graduate, and between 26 and 30 years old. Can it be assumed that this profile describes the population of cocaine users? One problem with this assumption is that the preponderance of low SES, black, single women on welfare may be more reflective of the type of samples available to the investigators, such as convenience samples from drug treatment centers or clinics, rather than samples selected from the broader population. Particular groups of women were poorly represented, including Hispanic mothers, middle-class mothers, and teenage mothers. Further, many studies fail to describe social demographic information: 22% fail to describe race, 31% do not describe educational level, and 59% fail to describe SES; even fewer describe receipt of public assistance and marital status. Finally, it should be noted that the social and demographic characteristics typically described in the cocaine studies are status or index variables of social risk (e.g., SES or race) that provide little understanding of the process by which social and environmental risk compromises development. Possible risk factors that more closely represent process or direct impact on the child that are rarely reported in this database include family composition, stability of caretakers, mental illness of caretakers, foster care, neighborhood quality, and the level of violence surrounding the child. Medical and Health Status The first generation of research studies on prenatal cocaine exposure reported devastating medical outcomes (e.g., congenital malformation, apnea, sudden infant death syndrome [SIDS], perinatal cerebral infarction), but these findings have been called into question by recent, better controlled studies (see Lutiger, Graham,

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Einarson, & Koren, 1991 for a meta-analysis of medical findings; Lester & Tronick, 1994; Mayes, Granger, Bornstein, & Zuckerman, 1992). Although cocaine is unlikely to cause severe medical problems, the most replicated findings suggest that cocaine exposure is associated with increased genitourinary malformations, shorter gestational age, smaller head circumference, shorter birth length, and lower birthweight. However, recent findings have not replicated the association of cocaine exposure with birthweight or genitourinary malformations (Bauer, 1999). Unlike polydrug and social-demographic risk factors, cocaine exposure may, in fact, contribute to greater risk of medical complications. The dilemma for researchers is whether to treat adverse medical factors as outcomes, as possible mediators of developmental outcomes (Jacobson, Jacobson, Sokol, Martier, & Chiodo, 1996), or as confounding factors that obscure the question of cocaine’s unique effect on development. Intervention Effects on Child Outcome Finally, given the high-risk nature of this population, it would be reasonable to expect that many of the infants and mothers in these studies are receiving intervention services. Although intervention may affect the outcome of the child, information about intervention provided to the child or the mother was mentioned in only 37% of the studies. Sample Size Limitations Sample size is a critical issue in interpreting the results of these studies. Most studies are based on relatively small sample sizes. Small sample sizes are problematic in two ways: (a) many observed cocaine effects are subtle and can be missed if the study does not have enough subjects (i.e., low power); (b) cocaine use occurs in a multiple-risk context that requires large samples to use multivariate statistical approaches for control of confounding and mediator variables. The relationship between subtle effects and sample size can be easily explained by effect size. Effect size is described in terms of standard deviation units and is typically classified as small (<0.5 SD), medium (0.5 to 0.75 SD) and large (>.75 SD) (Cohen, 1988). In order to detect a small effect, a minimum of 80 subjects per group is necessary. Medium effect sizes can be detected with samples of 30 to 80 subjects. Only large effects can be detected with samples of fewer than 30. As shown in Figure 2.2, only a third of the studies in the database are large enough to detect subtle cocaine effects. This suggests that there may be effects of cocaine that are real but have not been observed because of too few subjects. In addition, the sample size of many studies restricts the number of covariates that could be included in the analysis. Without the ability to control for polydrug, medical, and social factors, the potential teratogenic effects of cocaine cannot be determined.

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Figure 2.2 Sample size of exposed and comparison groups

Neurobehavioral Outcome Table 2.1 shows the neurobehavioral measures used in the studies, including the number of times each measure was used and if statistically significant cocaine effects were found. It is apparent from this table that the range of measures used has been wide, with few measures used across studies. The most frequently used measures—the Neurobehavioral Assessment Scale (NBAS), the Neonatal Intensive Care Unit Network Neurobehavioral Scale (NNNS), and stress abstinence measures—as expected, pertain to early infancy. The NBAS-NNNS does show cocaine effects in 17 of 19 studies. Abstinence or withdrawal effects were reported in 12 of 21 studies and may be related to the additional effects of opiates as a confounding variable. It is also interesting that 6 of 12 studies using measures of developmental level such as the Bayley Scales did not find cocaine effects. By contrast, measures of more subtle function such as temperament showed cocaine effects in 8 of 13 studies and attention in 7 of 8 studies. With older children, 4 of 9 language studies and 3 of 7IQ studies showed cocaine effects. Database Summary To summarize, the initial outcry about the devastating effects of prenatal cocaine exposure on child development has not been validated, but we cannot rule out subtle effects that increase vulnerability in children already immersed in highrisk environments. Our literature review indicates that our knowledge base is virtually confined to early infancy, with a striking absence of long-term followup studies.

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Table 2.1 Neurobehavioral Measures (n=101)

The neurobehavioral findings are scattered across a wide array of measures, most of which have been used in only a few studies. There is a clear need for measures to be used across studies in order to determine if there is a consistent pattern of findings. There is also a question as to how measures are selected; few appear to have been theoretically or hypothesis-driven, and some measures may be too gross to detect the subtler effects that have been attributed to cocaine. Other factors, such as high attrition rates in longitudinal studies, failure to control for examiner blindness, and intervention effects, further cloud interpretation of the findings. In sum, findings are limited and compromised by methodological problems that mitigate any conclusions about whether or how prenatal cocaine exposure affects child outcome. LESSONS FROM THE PAST As described previously (Lester & Tronick, 1994), there is a certain déjà vu associated with the study of prenatal cocaine exposure. The study of prenatal influences and insults on child development is a much-studied area, and it

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might be useful to consider cocaine exposure as a special case of this larger problem. In this way, we can learn from the past as well as define what is unique about this particular problem. The study of preterm infants provides a good model. Starting in the 1950s with the National Perinatal Collaborative Study of some 20,000 pregnancy and delivery outcomes, substantial effort was devoted to the “effects of prematurity” (Niswander & Gordon, 1972). The prevailing Zeitgeist at that time was that being born prematurely was a form of biologic insult likely to affect CNS development and the long-term outcome of the child. As supporting evidence, studies showed that premature infants were overrepresented in many populations of abnormal outcomes, including cerebral palsy and mental retardation (Lilienfeld & Parkhurst, 1951; Pasamanick & Knobloch, 1966). The second generation of studies of the effects of preterm birth told a different story. We were reminded that the evidence (even if it was true) that preterm infants were overrepresented among the handicapped population was based largely on retrospective data. Prospective longitudinal studies began to show that when preterm infants were followed from birth, most developed normally. Studies such as the Kauai study showed that, in fact, it was the environments of these children that was predictive of their developmental outcome, rather than their medical status at birth (Werner, Bierman, & French, 1971). We also learned that preterm infants were not a homogeneous group. As babies began to survive at lower and lower birthweights, the medical community distinguished between low birth weight (1,500 to 2,500 g) and very low birthweight (<1,500 g). Today we make reference to the “micro preemie” under 900 g. Smaller babies are more at risk not only because they are smaller but also because they are more prone to insult, injury, and illness. Preterm infants also are not homogeneous with respect to their behavior and development. Preterm infants show a wide range of behavioral and developmental trajectories that are multidetermined. The dynamic response of the caregiving environment to the changing behavioral organization of the infant is our best window into the long-term developmental outcome of the preterm infant. APPLICATION TO DRUG-EXPOSED INFANTS Like prematurity, drug exposure can be viewed as another potential insult or injury to the developing fetus. We do not know if and how drugs affect the fetus; we do not know the effects of polydrug use; we do not know the effects of timing, dosage, and frequency of use. In some infants, there may be true injury, in others there may be any degree of insult, and many infants may escape unscathed. It is also possible that there are effects that we simply do not know how to measure or effects that are not manifested until the child is

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older. Also, drug effects interact with other prenatal factors, such as poor nutrition or illness, which may also potentially compromise the infant. It should be noted that the vast majority of drug-exposed infants are not born prematurely. Many are born at term and are otherwise normal and healthy, and others are born at term but are growth retarded (intrauterine growth retardation [IUGR] or small for gestational age [SGA]). As with the preterm, we can say that drug-exposed infants are not a homogeneous group. They are not homogeneous with respect to how they present medically or with respect to how they present behaviorally. We have just begun to describe some of the different behavioral patterns that drug-exposed infants manifest, and it is likely that these different beginnings result in different developmental trajectories as the demands of caregiving and the environment come into play. RISK AND PROTECTIVE FACTORS From the study of preterm and other at-risk populations, we have also developed multiple risk models that should be useful in the study of drugexposed infants. The study of risk factors has become an important part of our understanding of child development. A high-risk child is one who is at greater than average risk for later deviancies in behavior because of membership in some identifiable population (Sameroff & Seifer, 1983). Such populations could include infants with anomalous experiences, including medical conditions such as low birthweight, disordered parentage, disturbed family and childrearing milieus, and disadvantaged environments. However, we also know that many children do not succumb to deprivation and that even in the face of disorganized, impoverished homes, many children appear to develop normally; such children are referred to as “resilient” (Anthony, 1987; Garmezy, 1981). This has led to the study of protective factors—that is, dispositional attributes, environmental conditions, biologic predispositions, and positive events that can act to contain the expression of deviance or pathology (Fisher, Kokes, Cole, Perkins, & Wynee, 1987; Garmezy, Masten, & Tellegen, 1984; Murphy, 1987). Our models of development are now enriched by the concepts of resilience and protective factors as the positive counterparts to the constructs of vulnerability and risk factors (Werner, 1986). In contrast to the large corpus of literature on risk factors (Koop & Drakow, 1983; Sameroff & Chandler, 1975), few studies have focused on protective factors in infants exposed to biologic insult. These models have yet to be applied to the study of drug-exposed infants. The study of drug-exposed infants is probably best viewed as a special case of the infant at risk, which suggests we should bring to the study of drugexposed infants all that we have learned from the study of highrisk infants. This includes the abandonment of preconceived biases that these infants are

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damaged and doomed to fail and that they are all alike. The long-term developmental outcome of these children is likely to be a function of how the caregiving environment responds to the behavioral constellation of the infant, with the understanding that both the behavior of the infants and the caregiving environment are making dynamic adjustments to each other, as well as being influenced by other forces. PUBLIC POLICY We now know that the effects of prenatal cocaine exposure are subtler and more complex than originally anticipated. It is critical that public understanding of this issue be addressed. There is a danger that the pendulum could swing from the public hysteria about crack babies in the mid-1980s to the other extreme, that we no longer need to worry about these children. To demonstrate the public health consequences of even subtle effects, we (Lester, LaGasse, & Seifer, 1998) conducted a formal meta-analysis of the IQ and language longterm outcome studies from the Robert Wood Johnson database and showed that these subtle effects can actually have profound consequences for the success of these children in school and the cost of special education services. The analysis was conducted on the eight studies of school-age children (ages: 4 to 11). IQ was measured in 5 studies, receptive language in 4 studies, and expressive language in 5 studies. In the meta-analysis, effects from different studies are pooled to provide a better estimate of the effect size—in this case, prenatal cocaine exposure—than can be determined by a single study (Rosenthal & Rosnow, 1991). To be included in our meta-analysis, the studies had to report means, standard deviations, and sample sizes of the exposed and comparison groups. In our meta-analysis, for each study a Z-value was computed directly from the t statistic derived from mean differences between cocaine-exposed and control cases. As shown in Table 2.2, the difference in IQ between cocaineexposed and control groups across available studies is 3.26 IQ points. This difference, although small, is statistically significant and can have a substantial impact on society. Table 2.2 Societal Burden of Prenatal Cocaine Exposure

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Early intervention and special education services are typically provided for children who score less than 2 standard deviations (SD) (or in some cases less than 1.5 SD) below the mean on standardized tests. This would correspond to IQ scores of <70 or <78. In a normal distribution (a good model for IQ scores), 2.28% of children score <70 (2 SD), and 6.68% of children will score <78 (1.5 SD). When the IQ distribution is shifted downward by 3.26 IQ points, the number of children at the low end of the distribution will increase (see Table 2.2) to 3.75%. This results in a 1.6fold increase in the number of children with IQs <70 and to 10.03% or a 1.5-fold increase in the number of children with IQs <78. These distributional changes then allow estimation of some of the costs to society associated with prenatal cocaine exposure. The National Pregnancy and Health Study (NPHS) based on maternal self-report estimates that 45,000 cocaine-exposed children are born each year (National Institute of Drug Abuse, 1996). The U.S. General Accounting Office (1990), reviewing hospital records, concluded that upward of 375,000 cocaine-exposed children are born each year. These figures predict that the number of children affected by this 3.26-point IQ difference is estimated to be between 1,688 and 14,062 at <2 SD and between 4,514 and 37,612 children at <1.5 SD. According to the U.S. National Center for Education Statistics (1996), special education services (additional services for special education) cost $6,335 per child per year. The added costs of these special educational services for the number of cocaine-exposed children with IQs below 70, based on a 3.26-point IQ difference, is $4 million to $35 million per year and $10 million to $80 million per year for children with an IQ of <78. These IQ differences may also be thought of as an effect size in which the cocaine effect is expressed in standard deviation units (SD) (Cohen, 1988). For example, the standardized IQ score has a mean of 100 and an SD of 15. A difference of 7.5 IQ points would represent an effect size of 0.50. Effect size is a useful construct because all measures are expressed in the same units (SD units); therefore, effects on different tests can be compared Table 2.2 (continued)

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even if they use different scales of measurement. As noted previously, effect sizes are graded as small (<0.5 SD), medium (0.5 to .75 SD), and large (> 0.75 SD). Table 2.2 shows the mean effect sizes, weighted for the number of children in the exposed groups, for the IQ studies, as well as the studies of receptive and expressive language. (Receptive language refers to understanding or comprehension; expressive language refers to competence in language production or competence in spoken language.) The analyses of effect sizes showed that cocaine-exposed children had significantly lower scores on tests of IQ (Z=2.61, p<.01), receptive language (Z=4.44, p<.001), and expressive language (Z=3.99, p<.001). The language studies showed a medium effect size compared with the small effect size for the IQ studies. The effect size for receptive language was more than twice that of the effect size for IQ, and expressive language showed a 1.8 times greater effect size than IQ. Table 2.2 also shows the follow-up calculations for the percent of children newly affected at <2 SD and <1.5 SD, the number of children newly affected each year, and the cost of added special education services for these children annually. These estimates are higher for the IQ effect size than for the IQ differences score because the variance for the effect size calculation is based on the children in the published studies rather than the IQ distribution in the general population. The moderate effect sizes in language result in a 2.7- to 4.3-fold increase in children who will be affected at clinically significant levels. For expressive language, this translates to between 3,636 and 68,025 children newly diagnosed annually who will need special education services, costing $17 million to $272 million per year. As these estimates are for additional costs due to cases newly diagnosed annually, the costs are underestimates because the costs and burden would be accrued, with the annual addition of children with the specific deficits identified. It is also likely that these findings are underestimates for other reasons. First, the NPHS found significant underreporting of cocaine use when maternal self-reporting (the method used to generate the lower value for this analysis) was compared with urine toxicology results. Second, the IQ distribution used in our estimates is based on the general population, but inner city children fall disproportionately in the lower end, especially as they grow older (Jensen, 1980). This is a case of double jeopardy; the additional burden of cocaine exposure is placed on children who are already destined by socioeconomic and environmental factors to cross the boundary into the range where special education services are typically provided. Third, intervention services for language disabilities may be different from those required for low IQ. Thus, services may be additive, and other services that have not yet been identified, such as those for attentional, behavioral, and emotional problems, may be required.

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CONCLUSIONS The published follow-up studies of cocaine-exposed children show that cocaine is associated with reliable decrements in cognitive development that are subtle in two ways. First, the effects are subtle in that they are “small magnitude” as shown by the IQ findings. The small magnitude of the effects is in sharp contrast to the sensationalistic reports in popular media on the effects of prenatal cocaine exposure (Mayes et al., 1992). Second, the effects are subtler functions, as suggested by the larger effect sizes for specific language abilities than for global IQ. The public health consequences of both kinds of subtle effects are substantial, indicating that we should not equate small magnitude with lack of importance. Prenatal cocaine exposure may not cause devastating brain damage, but it may result in anatomical and molecular subtle brain damage that are the basis for the cognitive and language deficits described. Nevertheless, prenatal cocaine exposure will significantly increase the number of children who will fail in school and need special education services, at estimated additional costs of up to $352 million per year. The good news is that these children are not hopelessly damaged and destined to become a burden to society. Instead, we can view them as children who can be helped to become productive members of society. Meanwhile, prevention efforts need to be directed toward ridding society of the cocaine problem and toward treatment programs for drug-using pregnant women to prevent the harm caused by cocaine use. ACKNOWLEDGMENT Supported in part by a grant from the Robert Wood Johnson Substance Abuse Policy Research Programs. REFERENCES Anthony, E.J. (1987). Risk, vulnerability, and resilience: An overview. In E.F. Anthony & B.Choler (Eds.), The invulnerable child (pp. 2–48). New York: Guilford Press. Bauer, C.R. (1999). Perinatal effects of prenatal drug exposure: Neonatal aspects. Clinics in Perinatology, 26 (1), 87–106. Cohen, J. (1988). Statistical power analysis for the social sciences (2nd ed). Hillsdale, NJ: Lawrence Erlbaum. Fisher, L., Kokes, R.F., Cole, R.E., Perkins, P.M., & Wynee, L.C. (1987). Competent children at risk: A study of well-functioning offspring of disturbed parents. In E.J.Anthony & B.Cohler (Eds.), The invulnerable child (pp. 221–228). New York: Guilford Press. Garmezy, N. (1981). Children under stress: Perspectives on antecedents and correlates of vulnerability and resistance to psychopathology: In A.I.Rabin, J.Aronoff,

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A.M.Barclay, & R.A.Zucker (Eds.), Further explorations in personality (pp. 196– 269). New York: Wiley. Garmezy, N., Masten, A., & Tellegen, A. (1984). The study of stress and competence in children: A building block for developmental psychopathology. Child Development, 55, 97–111. Jacobson, S.W., Jacobson, J.L., Sokol, R.J., Martier, S.S., & Chiodo, L.M. (1996). New evidences for neurobehavioral effects of in utero cocaine exposure. Journal of Pediatrics, 129 (4), 581–590. Jensen, A.R. (1980). Bias in mental testing, New York: Free Press. Koop, C.B., & Drakow, J.B. (1983). The developmentalist and the study of biological risk: As view of the past with an eye toward the future. Child Development, 54, 1086–1108. Lester, B.M., LaGasse, L., & Brunner, S. (1997). Data base of studies on prenatal cocaine exposure and child outcome. Journal of Drug Issues, 27 (2), 487–499. Lester, B.M., LaGasse, L., Freier, K., & Brunner, S. (1996). Studies of cocaine-exposed human infants. In C.L.Wetherington, V.L.Smeriglio, & L.P.Finnegan (Eds.), Behavioral studies of drug-exposed offspring: Methodological issues in human and animal research (NIDA Research Monograph No.) (pp. 175–210). Rockville, MD: U.S. Department of Health and Human Services. Lester, B.M., LaGasse, L., & Seifer, R. (1998). Cocaine exposure and child outcome: The meaning of subtle effects. Science, 282, 633–634. Lester, B.M., & Tronick, E.Z. (1994). The effects of prenatal cocaine exposure and child outcome: Lessons from the past. Infant Mental Health Journal, 15 (2), 107–120. Lilienfeld, A.M., & Parkhurst, E. (1951). A study of the association of factors of pregnancy and parturition with the development of cerebral palsy: A preliminary report. American Journal of Hygiene, 53, 262–282. Lutiger, B., Graham, K., Einarson, T.R., & Koren, G. (1991). Relationship between gestational cocaine use and pregnancy outcome: A meta-analysis. Teratology, 44, 405–414. Mayes, L.C., Granger, R.H., Bornstein, M.H., & Zuckerman, B. (1992). The problem of prenatals cocaine exposure: A rush to judgment. Journal of the American Medical Association, 267, 406–408. Murphy, L.B. (1987). Further reflections on resilience. In E.J.Anthony & B.Cohler (Eds.), The invulnerable child, (pp. 84–105). New York: Guilford Press. National Institute on Drug Abuse. (1996). National pregnancy and health survey: Drug uses among women delivering livebirths: 1992 (National Institute of Health Publication No. 96–3819). Rockville, MD: National Institute of Health. Niswander, K.R., & Gordon, M., (1972). The women and their pregnancies: The Collaborative Perinatal Study of the National Institute of Neurological Diseases and Stroke. Washington, DC: National Institute of Health. DHEW Publication: No. (NIH) 73–379. Pasamanick, K.R., & Knobloch, H. (1966). Retrospecitve studies on the epidemiology of reproductive causality: Old and new. Merrill-Palmer Quarterly, 12, 7–26.

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Rosenthal, R.L., & Rosnow, R.L. (1991). Essentials of behavioral research: Methods and data analysis (2nd ed.). New York: McGraw Hill. Sameroff, A.J., & Chandler, J.J. (1975). Reproductive risk and the continuum of caretaking casualty. In F.D.Horowitz (Ed.), Review of child development, Vol. 4, (pp. 187– 244). Chicago: University of Chicago Press. Sameroff, A.J., & Seifer, R. (1983). Familial risk and child competence. Child Development, 54, 1254. U.S. General Accounting Office. (1990). Report to the Chairman, committee on Finance, U.S. Senate; Drug exposed infants, A generation at risk. Washington, DC: Author. U.S. National Center for Education Statistics, (1996). Digest of education statistics (based on M.T.Moore, E.W.Strang, M.Schwartz, & M.Braddock, Patterns in special education service delivery and cost). (Washington, DC: Decision Resources, 1988). Werner, E.E. (1986). Resilient offspring of alcoholics: A longitudinal study from birth to age 18. Journal of Studies on Alcohol, 47, 34–40. Werner, E.E., Bierman, J.M., & French, F.E. (1971). The Children of Kauai. Honolulu: University of Hawaii Press.

CHAPTER 3

Parenting and Parent-Child Relationships in Families Affected by Substance Abuse SYDNEY LYNN HANS

In response to the identification of fetal alcohol syndrome in the early 1970s and the “cocaine baby” crisis of the late 1980s, a large body of research has focused on the development of young children born to women who use alcohol and drugs during pregnancy (e.g., Chandler & Lane, 1996; Hutchings, 1989; Lester, 1994; Lewis & Bendersky, 1995). For the most part, this research has explored the possible teratogenic effects of prenatal exposure to drugs or alcohol on children’s physical health and neurobehavioral development, often with an emphasis on isolating the effects of a particular drug. Assessment of postnatal environmental variables that might affect development of prenatally exposed children has become standard procedure in behavioral teratology research (Jacobson & Jacobson, 1990), but in most research designs, environmental variables have been treated primarily as potential confounding factors rather than as a primary focus for investigation and understanding. Yet, there is an important (cf. Besharov, 1994; Hawley & Disney, 1992) need to understand how environmental factors, particularly those related to quality of parenting, influence the development of children exposed prenatally to drugs and alcohol. Legislators, judges, and child welfare workers making decisions about child custody and placement need information on the parenting of substance-abusing mothers and whether it places children at risk. Physicians, psychologists, and educators making decisions about intervention and treatment strategies need information about postnatal influences that can improve developmental outcomes for children with histories of prenatal exposure. Studying how environmental variables affect children with histories of prenatal exposure is important for understanding the processes of development. All sciences of development emphasize the interactions between biologic and environmental mechanisms of development, as well as the bidirectional influences that occur between developing individuals and their caregivers during the course of daily transactions (e.g., Rutter et al., 1997; Sameroff & Chandler, 45

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1975). Even within the biologically oriented science of behavioral teratology, one of the core principles identified by Vorhees (1986) is that “the type and magnitude of a behavioral teratogenic effect depend on the environmental influences on the organism, including both prenatal and postnatal environmental factors” (p. 36). Teratologic research with animal models suggests a variety of means by which prenatal drug exposure affects offspring development. For example, maternal protectiveness is altered in female rats given drugs during pregnancy (Heyser, Molina, & Spear, 1992; Mathews & Jamison, 1982; Sechzer, Lieberman, Alexander, Weidman, & Stokes, 1986), and being raised by a mother who was administered psychoactive drugs during pregnancy can disrupt pup development regardless of the pup’s exposure history (Goodwin et al., 1992). In addition, pups exposed to drugs during gestation show lessened effectiveness at eliciting maternal response, even when cross-fostered to drug-free mothers (Mathews & Jamison, 1982; Ness & Franchina, 1990). Such findings suggest the importance of understanding in human populations the complex interplay of substance abuse, parenting, and child development. This chapter poses a number of general questions about the parenting of substance abusers and the relation of parenting to development of children with histories of prenatal drug exposure. Reference is made to literature on this topic from a variety of sources, but most information presented is drawn from the author’s longitudinal study of children born to women using opioid drugs during pregnancy. In particular, the following questions will be addressed: • Do substance-abusing women differ from other mothers in their behavior during interaction with their young children? • What factors contribute to differences in parenting behavior between substance-abusing mothers and other women? • What factors are related to variability in the parenting behavior of substance abusing women? • How do psychosocial risk factors and parenting experience relate to developmental outcome of infants prenatally exposed to drugs? • Are drug-abusing mothers able to provide continuity of care for their children over the long term? Finally, as this chapter addresses a topic about which little is known, questions for future research will be suggested. METHODS Sample The Parent Health and Child Development Project (PHCDP) is a long-term longitudinal study that was designed to assess the impact of maternal opioid use

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during pregnancy on child development. Initiated soon after the identification of fetal alcohol syndrome, the study was designed to explore teratologic hypotheses, but at every assessment age the study has included assessments of parent-child interaction and other variables related to the children’s rearing environments. The study population was selected to include children whose mothers used opioid drugs during pregnancy and a comparison group of children whose mothers did not use opioids. Seventy-seven children from the original sample of 100 participated in the middle childhood follow-up (mean age=10.0 years, range 8.9 years to 11.8 years). Details of recruitment and sources of attrition have been presented elsewhere (Bernstein & Hans, 1994; Grattan & Hans, 1996; Hans, 1989; Jeremy & Hans, 1985). Data from these 77 children are used for the present chapter. All families were African American. At the time of recruitment into the study, during the mothers’ pregnancies, all families were living in inner city neighborhoods and were of low to very low socioeconomic status. Opioid Group The opioid-group families were recruited from a special high-risk obstetrical clinic for drug-abusing women at the University of Chicago’s Lying-in Hospital from 1978 to 1982. All women who were identified in clinic records as users of methadone and/or other opioid drugs, who after face-to-face screening did not appear to have a thought disorder or mental retardation, and who consented to participate in the research for a period of 2 years were enrolled in the study. If women gave birth to more than one infant during the period of recruitment into the study, those siblings were also included in the sample. All but four of the opioid-using women were participants in low-dose methadonemaintenance programs during their pregnancies. Most of the methadonemaintained women occasionally used other drugs in addition to methadone, most commonly heroin, marijuana, and diazepam and less commonly cocaine and alcohol. The other four women were users of “Ts and blues,” a combination of Talwin (pentazocine) and an antihistamine that was widely used by intravenous drug users in Chicago in the early 1980s as an inexpensive substitute for heroin (Chasnoff, Hatcher, Burns, & Schnoll, 1983; Schnoll, Chasnoff, & Glassroth, 1985). At the time of childbirth, the opioid group contained 40 opioid-using women, 6 of whom had two children in the study. Data were available at middle childhood for 36 of the children. Comparison Group A comparison group was recruited at the same hospital from a prenatal clinic for low-income, medically low-risk mothers. Mothers were excluded from the comparison group if they reported ever having used opioid drugs, if they

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engaged in more than light consumption of alcohol during pregnancy, if they displayed symptoms of thought disorder, or if they were mentally retarded. At the time of childbirth, the comparison group included 50 women, 4 of whom had two children in the study (including one set of twins). Data at the middle-childhood assessment were available for 41 of the children. Procedure The data reported in this paper were collected over a period from pregnancy through children’s 10th birthdays. Biologic mothers participated in two research sessions during their pregnancies, when they were interviewed about their substance use, mental health, and general sociodemographic issues and were administered intelligence and neuropsychological tests. Mothers and children participated in research sessions when the children were 1, 4, 8, 12, 18, and 24 months old. At those sessions, children were administered developmental tests, mothers were interviewed, and mothers and children were videotaped together. When the children were approximately 10 years old, they were seen with their mothers or other primary caregivers for three sessions, which included neuropsychological testing of children, interviewing to derive psychiatric diagnoses of children, videotaping of child-caregiver interaction, and questioning of caregivers about demographic and family issues. RESULTS Quality of Early Parenting and Parent-Infant Interaction Other studies, reviewed by Mayes (1995), have suggested that drug-using women who raise their children show problems in interaction with their children. Fitzgerald, Kaltenbach, and Finnegan (1990) reported that, when interacting with their newborns, opioid-using women were less socially engaged and showed less positive affect than comparison women. Householder (1980) reported that opioid-using mothers of 3-month-old infants exhibited more physical activity and less emotional involvement in communicating with their infants than did comparison mothers. They seemed to enjoy the mothering role less and gazed into their infants’ eyes less often. Rodning, Beckwith, and Howard (1991) found that, in interaction with their 3- and 9-month-old infants, women who used PCP and cocaine during pregnancy differed from comparison women in their effectiveness at soothing, quality and amount of physical contact, acceptance or rejection of the baby, and sensitivity or insensitivity to baby’s communications. Bauman and Dougherty (1983) found no problems in parenting attitudes in drug-using women but did find that in actual interaction with their preschool children, drug-using mothers were more likely to use a threatening discipline

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approach and less likely to offer positive reinforcement. O’Connor, Sigman, and Kasari (1992, 1993) reported that maternal social drinking was related to poorer parenting at 1 year of age but that this association was mediated by infant irritability, presumably attributable to prenatal exposure to alcohol. Several investigators have reported no statistically significant effects of maternal drug use on mother-infant interaction but have qualified their findings in terms of limited sample size (Black, Schuler, & Nair, 1993) and possible inappropriateness of their assessment instruments (Johnson & Rosen, 1990). In the Chicago PHCDP study, we assessed parent-infant interaction at each of the postnatal assessment periods. Extensive analyses have been conducted on these assessments from child ages 4, 12, and 24 months. At each age, mothers and children were videotaped for approximately 40 minutes in a laboratory living room in a series of age-appropriate activities including free play, teaching, and caregiving (e.g., diapering, feeding). The Parent Child Observation Guides for Program Planning (PCOG) (Bernstein, Percansky, & Hans, 1987) were used to assess global aspects of the caregiver-child relationship. The PCOG instrument is based on a strengths model in which the coder reviews a series of positively worded statements about the interactant’s behavior and then codes each behavior as observed or not observed during the duration of the taped session. Item analysis of the PCOG in several samples suggests that the instrument assesses two domains of parenting behavior: sensitive responsiveness and encouragement-guidance. Sensitive responsiveness includes aspects of the mother’s behavior related to gentleness, emotional engagement, and attunement to child’s needs and interests. On the PCOG, a mother was scored as being responsive if she handled her child gently and securely, responded promptly when the child was upset, remained patient with the child, showed interest in what the child was doing, paced their behavior so as not to overstimulate the child, and followed the child’s lead into a new activity. Encouragementguidance includes aspects of the mother’s behavior related to being a positive active teacher of the child. On the PCOG, a mother was scored as showing encouragementguidance if she used language to inform the child, smiled at the child in a variety of situations, demonstrated toys and other objects to the child, and enjoyed playing with or teaching the child. Similar constructs that distinguish between the roles of the parent as a nurturer and a teacher have been found in other measures of parenting behavior (e.g., Bradley et al, 1988). At each of the ages, two PCOG raters blind to drug-group status evaluated videotapes. A different pair of raters was used at each age. Interrater reliability coefficients ranged from .75 to .85 for summary parenting scores at 4, 12, and 24 months. Disagreements between raters on individual items were resolved consensually, and agreed scores were used for analyses in this study. To reduce the number of variables used in statistical analyses, standard scores were computed for the two dimensions at each age and averaged across the three ages to provide global ratings of sensitive responsiveness and encouragementguidance across infancy and toddlerhood.

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Table 3.1 Parenting Behavior of Opioid and Comparison Group Mothers over the First 2 Years of Life

*p<.05

As we have reported previously, analyzing ages separately (Bernstein & Hans, 1994; Bernstein, Jeremy, Hans, & Marcus, 1984; Bernstein, Jeremy, & Marcus, 1986; Jeremy & Bernstein, 1984), opioid-using mothers differed from comparison mothers in terms of their parenting behavior during interaction. Based on the global ratings described previously, opioid-using mothers were less responsive to their children’s cues but did not differ from comparison mothers in terms of their encouragement and guidance (see Table 3.1). Therefore, drug-using mothers were less attuned to their infants’ cues and needs and less gentle and warm in their responses to these needs. However, they were able to display enthusiasm and provide structure and stimulation to their infants much like other socioeconomically disadvantaged mothers. Psychosocial Risk Factors and Maternal Substance Abuse Despite the concerns raised by the findings from the Chicago study and other investigations of the parenting of substance-abusing women, it may not be substance abuse per se that is the primary cause of the problems drug-abusing women have in interactions with their children. Substance abuse is associated with many other factors that might contribute to unresponsive or harsh parenting behavior. Substance-abusing women, particularly those using illicit substances, are more likely than other women to be living in poverty (Finkelstein, 1994; National Institute on Drug Abuse, 1996) and to have chaotic lifestyles that are organized around addiction and that may place them in dangerous situations (Finnegan, Dehlberg, Regan, & Rudrauff, 1981; Hutchins & DiPietro, 1997). Substance-abusing women are more likely to have symptoms of psychopathology (Griffin, Weiss, Mirin, & Lange, 1989; Hans, in press; Hesselbrock, Meger, & Keener, 1985; Kessler et al., 1996; Ross, Glaser, & Stiasny, 1988; Rounsaville et al., 1991), to have experienced childhood sexual abuse (Miller, Downs, Gondoli, & Keil, 1987; Wilsnack, Vogeltanz, Klassen, & Harris, 1997), and to have difficult or abusive relationships with male partners (Hien & Scheier, 1996; Miller, Downs, & Gondoli, 1989). In two studies, risk factors related to maternal psychosocial functioning and home environment were found to have an adverse impact on

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child development, independent of the mother’s use of substances during pregnancy (Azuma & Chasnoff, 1993; Johnson, Glassman, Fiks, & Rosen, 1987). In the Chicago PHCDP study, we assessed a variety of psychosocial factors that might affect parenting behavior. In this study, in exploring the association between substance abuse and other risk factors, we have often chosen to use cumulative risk indices that tally the number of risk factors present in a family (Bernstein & Hans, 1994; Wakschlag & Hans, 1999). Previous research has documented that cumulative risk indices are usually stronger predictors of child developmental outcomes than single risk factors (Liaw & Brooks-Gunn, 1994; Sameroff, Seifer, Barocas, Zax, & Greenspan, 1987) and are well suited to studies with relatively small sample sizes that would not support multivariate models with numerous predictors and covariates. For this chapter, a cumulative psychosocial risk index was computed from sociodemographic and psychological risk variables measured during pregnancy and children’s first year of life. The cumulative psychosocial risk index included nine sociodemographic and parental functioning variables: (a) socioeconomic status (Hollingshead & Redlich, 1958); (b) poor maternal adaptive functioning during pregnancy as assessed by Axis V of the Diagnostic and Statistical Manual of Mental Disorders (American Psychiatric Association, 1980); (c) parental antisocial behavior as reflected in the same diagnostic interview or, when the father did not participate in the study, maternal report of father antisocial behavior; (d) maternal report of psychosocial stressors as assessed by Axis IV of DSM-III; (e) maternal intelligence; (f) maternal education; (g) mother’s age at first childbearing; (h) parents’ marital status; and (i) social support received by mother for childrearing. Table 3.2 presents data on differences between opioid Table 3.2 Psychosocial Risk Factors for Opioid and Comparison Groups

continued

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Table 3.2: Psychosocial Risk Factors (continued)

and comparison groups on these nine risk factors. It also indicates the criteria used for coding each variable dichotomously into low-risk and high-risk categories and the proportion of the sample coded as high risk on each variable. When summed into a cumulative risk index, families in the present sample had between 0 and 9 risk factors, with a median of 3. Comparing the two groups of children on the individual risk factors, the opioid group was more likely to be characterized by poorer maternal adaptive functioning (t(75)=8.89, p<.001) and greater maternal psychosocial stress (t(75)=3.96, p<.001). More of the opioid-group children had at least one parent displaying antisocial behavior (X2 (1)=23.81, p<.001). The groups did not differ on demographic variables or in terms of maternal intelligence. Altogether, the opioid group had a higher number of cumulative psychosocial risk factors than the comparison group (t(75)=5.08, p<.001). In order to explore the relation of risk factors to parenting, correlation coefficients were computed between cumulative psychosocial risk and the two

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Table 3.3 Parenting Behavior Correlated with Maternal Drug Use and Psychosocial Risk Factors for Entire Sample (n=77)

*p<.05, one-tailed

parenting indices described previously (see Table 3.3). More psychoso cial risk factors were related to less maternal sensitive responsiveness and to less maternal encouragement-guidance, although the latter relation was not statistically significant. We recomputed the correlation between maternal substance abuse and maternal sensitive responsiveness, controlling for level of cumulative risk, and found that it was no longer significant (see Table 3.3). Thus, differences between substance-abusing and other women in their parenting behavior are in large part attributable to differences in other risk factors related to substance abuse, such as maternal psychopathology and psychosocial stress. Variability in Parenting among Substance-Abusing Women For clinicians who work with substance-abusing mothers, the more important research question is not whether substance-abusing women differ from other women in their parenting but what factors are related to variability among substance-abusing mothers—factors that might offer clues to strategies for targeting and designing intervention (Howard & O’Donnell, 1995). Investigators and clinicians have frequently pointed to the variability in the parenting skills of drug-using women, including their potential for good child-rearing (Finnegan et al., 1981; Johnson & Rosen, 1990). Yet, few data exist on the potential sources of variability among drug-using women in parenting skills and relationships with their children. Finnegan, et al. (1981) concluded that polydrug abuse, psychological problems, and stressful life events are strong indicators of poor parenting behavior among drug-using parents. Howard, Beckwith, Espinosa, and Tyler (1995) found that, among cocaine-using mothers with infants, signs of disturbed personality correlated with insensitive parenting. Regan, Ehrlich, and Finnegan (1987) reported that mothers on methadone maintenance with a personal history of violence or abuse are most likely to have their own children placed in foster care. Our own data (Bernstein & Hans, 1994; Bernstein et al., 1984; Jeremy & Bernstein, 1984) have found that cumulative psychosocial risks are related to

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Table 3.4 Correlations between Parenting Behavior and Child Development at 24 Months for Opioid Group Only (n=36)

parenting problems among substance-abusing mothers. Table 3.4 shows that among opioid-using women, psychosocial risks are moderately correlated with maternal sensitive responsiveness and encouragement-guidance. This finding has two possible implications for intervention: (a) that parenting interventions for substance-abusing women are most needed by and may be best targeted at mothers who have other psychosocial risk factors and (b) that interventions for substance-abusing mothers that focus on amelioration of other risk factors, particularly mental health problems, may be an effective means to improve parenting. Relation of Parenting and Psychosocial Risk Factors to Child Outcome among Children Exposed to Opioids Prenatally Data from two studies suggest that quality of parenting has an impact on the development of children born to drug-abusing parents. In a sample of mothers who used cocaine and phencyclidine (PCP) during pregnancy, infant cognitive and motor development at 6 months of age was related to maternal sensitivity (Howard et al., 1995). In a sample of infants prenatally exposed to cocaine and other drugs (Hofkosh et al., 1995), developmental outcome was related to quality of the home environment provided by the mother, especially maternal responsiveness, maternal involvement with the child, and provision of appropriate play materials. We have reported that in the Chicago PHCDP sample (Bernstein & Hans, 1994) developmental outcome at age 2 years is related to quality of interaction between mother and child during infancy for opioid-exposed children. In our study, 2-year-old children were administered the Bayley Scales of Infant Development by examiners trained to reliability and blind to information about the children’s developmental histories and mothers’ substance abuse. These scales yield two age-standardized summary variables, the Mental Development Index (MDI) and the Psychomotor Development Index (PDI), which are based on the number of test items a child successfully passes. The Bayley Scales also

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contain the Infant Behavior Record (IBR), a series of rating scales requiring clinical judgments that assess the quality of the child’s behavior during test taking. Following the work of Matheny (1980, 1983), we summed selected items from the IBR to compute a summary measure of the child’s socioemotional functioning with the examiner. These items included responsiveness to persons, responsiveness to examiner, cooperativeness, general emotional tone, and endurance (Cronbach’s alpha=0.82). Table 3.4 displays the correlations between psychosocial risk, parenting, and child outcome for the children who were exposed to drugs prenatally. Cumulative psychosocial risk factors were related to poorer mental, motor, and social development as assessed on the Bayley Scales. Maternal behavior during interaction was also related to children’s development, with maternal sensitive responsiveness particularly strongly related to children’s social engagement with the Bayley examiner. Continuity in Parenting The data reported in this chapter thus far pertain to the quality of parental interaction experienced by children of drug-using parents while in the care of their parents. In children with substance-abusing parents, a more fundamental aspect of parenting often is at question—whether that parent will be able to continue to function as a parent over time. Prospective studies of substanceabusing women and their offspring find that a high proportion of children have experienced disruptions in maternal care. Rodning et al. (1991) followed a sample of 46 infants identified at birth as having been prenatally exposed to PCP and cocaine. By the time children were 15 months of age, less than half (20) of the children were in the regular daily care of their biologic mothers, 11 were cared for by extended family members without the mothers’ involvement, and 7 were in non-kin foster care. Of the 20 cared for by their mothers, 13 lived in multiplefamily households. Fiks, Johnson, and Rosen (1985) followed from birth a sample of 57 children born to women in methadone treatment and found that only 75% of the methadone-treated women were primary caregivers of their children by the child’s third birthday, compared with 91% of women in a nondrug-using comparison group. Wilson and Lawson (Lawson & Wilson, 1980; Wilson, 1989) followed a sample of 64 children born to untreated heroin addicts and women involved in methadone treatment. A high percentage (36%) of the drug-involved women abandoned contact with their infants before their first birthdays, although these were predominantly women from the untreated heroin group. Wasserman and Leventhal (1993) found that during the first 24 months of life 20% of a group of 47 children whose mothers were cocaine dependent had experienced changes in placement, compared with 2% in a case-matched comparison group.

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In the Chicago PHCDP study (Hans, Bernstein, & Henson, in press), most drug-dependent mothers entered the study as participants in methadone maintenance programs, and most remained daily caregivers of their children throughout infancy. By age 10, however, nearly half (17,47%) of the children whose mothers used opioid drugs during pregnancy were no longer residing with their biologic mothers. This is in contrast to 20% (8) of the children from the comparison group (X2 (1)=6.713, p<.001). All but two of the children (one from the opioid group in non-kin foster care and one from the comparison group in residential treatment) were living with family members. Not all of the children in the sample residing with their mothers at age 10 years had lived with their mothers continuously throughout their lives. For the 19 children of drug-using women who were in the care of their mothers at age 10, only 13 (68%) had been in the care of their mothers throughout their lives. These 13 under the continuous care of their mothers comprise only 36% of the entire sample of children whose mothers were using drugs during pregnancy. Table 3.5 presents a cross-tabulation of the numbers of children living with their mothers at age 10 and continuously throughout their lives to age 10. The primary reasons children were no longer in the care of their mothers or had experienced disruptions in care were mostly drug related (including four children in the comparison group whose mothers became drug involved). Most disruptions in care occurred after infancy during periods when mothers became unavailable or unwilling to care for their children because of active illicit drug use, incarceration for drug-related charges, and even death from drug-related illnesses or violence. In this sample, virtually all children not being cared for by their mothers were in the custody of family members (Hans et al., in press). Thus, children born to mothers who use illicit substances are more likely than other children to be raised by caregivers other than their biologic mothers. Even for those whose mothers continue to serve in a parenting role, there are likely to be disruptions in care. Within the urban, African-American families participating in the PHCDP study, kin networks were stable enough and flexible enough to provide care for children within the family system. Table 3.5 Caregiving Patterns of Children at Age 10

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DISCUSSION In the data presented from the Chicago PHCDP study of children born to opioidaddicted women, we found that, on average, substance-abusing women showed less sensitive interaction with their children compared with other demographically similar mothers. This problematic interaction was most pronounced in aspects of parenting related to nurturing of the child: sensitivity to the child’s cues and needs, warm and gentle handling toward the child. It was less pronounced in aspects of parenting related to teaching: providing the child with stimulation, enthusiasm, structure, and guidance. Responding sensitively to their children’s cues may be particularly difficult for substance-abusing mothers because of their mental health issues—experience of stress, personality disorder, and depression—that prevent them from attending to the emotional cues of others and not only to their own internal feelings states. However, our study provided little evidence that problems in parental behavior during interaction were solely due to parental substance abuse. Substanceabusing mothers experience more risk factors than other women. Our analyses suggested that the differences between the behavior of substance-abusing and other mothers could be attributable to differences in levels of these other psychosocial risk factors. In particular, substance-abusing parents were more likely to be impacted by stress and mental health problems than other parents, even though they did not differ in demographic characteristics or intelligence. The co-occurrence of substance abuse and mental health problems is a welldocumented and complicated issue. It has been difficult to determine whether substance abuse is a response to an individual’s underlying mental health problems and stress or whether mental health problems and stressors are the consequences of pharmacological and lifestyle experiences of substance abuse (Kessler et al., 1996; Meyer, 1986). We found considerable variability of parenting behavior among different substance-abusing mothers. Just as psychosocial risk factors explained some of the differences observed between substance abusing and comparison women, psychosocial risk factors also seemed to explain some of the variability among substance-abusing mothers in their parenting behavior. Both psychosocial risk factors and mothers’ parenting behavior were related to child outcome within the group of children exposed prenatally to opioids. In particular, children’s cognitive and psychomotor developmental outcomes at 24 months of age were correlated with psychosocial risk factors, and child socioemotional functioning was correlated with maternal sensitive responsiveness, even after controlling for psychosocial risk factors. Finally, not only do children with addicted mothers experience less optimal interaction with their mothers but also they are more likely to experience separation from their mothers and even permanent loss of their mothers. Only 36% of the children born to drug-using women remained in the care of their

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biologic mothers throughout the first 10 years of their lives. Most disruptions in caregiving were directly attributable to maternal illicit drug use. The women in the Chicago PHCDP sample were in methadone treatment when recruited into the study and are similar to other urban mothers seeking substance abuse treatment. The findings from this study suggest that substance abuse treatment settings should consider interventions beyond substance abuse treatment that might benefit the women and their children. The close linkages between child outcome and parenting behavior and psychosocial risk factors suggest that substance-abusing women and their children might benefit from interventions focusing on parenting behavior, particularly aspects of parenting that aim to help the mother to be a more sensitive respondent to her child’s needs and cues. They also suggest that mental health services provided to substance-abusing mothers might improve chances for their children. A substantial literature already documents that substance abuse treatment per se is more effective when comorbid psychiatric disorders are addressed simultaneously (McLellan, 1986; Osher & Drake, 1996; Saxon & Calsyn, 1995), and it has been argued that substance abuse treatment is more likely to be effective when the parenting needs of women are addressed simultaneously (Brown, Huba, & Melchior, 1995; Finkelstein, 1994). FUTURE RESEARCH Although the present study begins to answer questions about the quality of parental interaction provided by substance-abusing women and how that parenting affects children, some of the findings need further exploration, and other aspects of parenting have not begun to be addressed by this or other studies. The following are suggested topics for further inquiry. Daily lives of children with substance-abusing parents. We need more information on the daily lives of substance-abusing women and their children in their home settings. Although laboratory research is a useful and valid source of information about mothers’ parenting behavior, it reveals information about the quality of interaction during periods of close contact, which may be relatively rare during a normal day. Observing parents in a controlled and relatively stress- and distraction-free situation in the laboratory may serve to minimize differences among families that would be apparent in their daily lives. For example, laboratory paradigms tell little about how well mothers attend to the needs of a particular child in the presence of competing demands, such as other children seeking attention, running a household, and perhaps procuring drugs or attending treatment sessions. Laboratory observations cannot provide information about many important dimensions of parenting, such as vigilant monitoring of children’s behavior and structuring of daily

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routines. Research incorporating home observations and ethnographic techniques would be valuable on this topic. Maternal psychopathology. A better understanding of how maternal mental health affects the parenting of substance-abusing women and the development of their children is also needed. In the research reported in this chapter, substance-abusing women differed most from other mothers in terms of psychopathology—their adaptive functioning, antisocial behavior, and experience of stressors. Yet few data have been presented on how differing levels and types of psychopathology and maternal stress affect the parenting of substance-abusing women. Studies thus far have suggested that less sensitive parenting is related to maternal personality disorders but not depression (Hans, Bernstein, & Henson, 1999; Howard et al., 1995) and that poor infant development is associated with more maternal psychological symptoms (Singer et al., 1997). Given the growing literature from non-substance-abusing populations that links maternal psychopathology to poor child development (e.g., Downey & Coyne, 1990; Gelfand & Teti, 1990), the topic of comorbid psychopathology in substance-abusing mothers deserves considerably greater attention. Children’s contributions to interaction. The existing body of research on the parenting of drug-using mothers lacks information on children’s contribution to parent-child interaction and on how children prenatally exposed to drugs may help determine the quality of parenting provided to them. During infancy, some children prenatally exposed to drugs may be irritable or difficult to arouse as a consequence of prenatal exposure (Jacobson, Jacobson, Sokol, Martier, & Chiodo, 1996; Jeremy & Hans, 1985; Karmel, Gardner, & Freedland, 1995; Mayes, Bornstein, Chawarska, & Granger, 1995). These characteristics may challenge parents’ abilities to read their cues and to remain patient (Beeghly & Tronick, 1994; Bernstein & Hans, 1994; Freier, 1994). The strain on parenting may in turn interfere with infants’ efforts to regulate their arousal and affect. Child abuse and neglect. We need more information about whether children of substance abusers are at risk for child abuse and neglect and, if so, which children are at the greatest risk. A small number of studies have suggested that the incidence of child abuse and neglect is high when a parent abuses alcohol or drugs (Famularo, Kinscherff, & Fenton, 1992; Hawley, Halle, Drasin, & Thomas, 1995; Jaudes, Ekwo, & Voorhis, 1995; Wasserman & Leventhal, 1993). Yet we know little about factors that might contribute to child abuse in families of addiction, such as patterns of substance use (e.g., binge drinking), mothers’ histories of abuse during their own childhood, or

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presence of a substance-abusing male in the household. We know little about whether maternal substance abuse is more associated with child neglect or abuse or about whether perpetrators are likely to be the mother or others, such as her partner, from whom she fails to protect the child (Miller, Smyth, Janicki, & Mudar, 1995). Parenting older children. More information is needed on the quality of parent-child relationships after the infancy age period. By far the largest body of information on substance abusers as mothers comes from the period of infancy. Yet the clinical “children of alcoholics” literature (Seilhamer & Jacob, 1990; Sher, 1991) suggests that, as children become older, parental substance abuse can affect family processes in profound ways. Children may be expected to fill inappropriate roles in the family, such as acting as caregivers for their parents or being scapegoated for family problems. Although some features of parenting, such as the sensitive responsiveness studied in the PHCDP study, remain at all developmental stages, new parenting challenges arise with development that might challenge substance-abusing parents. For example, it would seem that substance-abusing mothers might have particular problems with vigilant supervision and monitoring of their children’s behavior and with particular socialization issues related to their children’s use of alcohol and drugs and their engagement in other high-risk behaviors. Nonmaternal caregivers. Mothers are not the only adults caring for children born prenatally exposed to drugs. As our data from the Chicago PHCDP have shown, children of substance-abusing parents often experience changes in the adults who care for them. Even when they remain in the care of their mothers, the extended family often plays a particularly important role in their rearing. More research needs to be conducted on the positive and negative impact of changes in caregivers on children, on the children’s development of attachments to multiple caregivers, on how extended kin functions as a safety net to protect the child, on how intergenerational patterns of substance abuse may impede the supportiveness of kin networks, and on ways of minimizing negative impacts associated with caregiver changes on children and on the caregivers who bear the burden. Foster care. Numerous children born to substance-abusing women end up in the foster care system. In most urban settings, a majority of young children receiving foster care placements have a biologic parent who is involved with drugs (Barth, Courtney, Berrick, & Albert, 1994; Simms, 1991; Wulczyn, 1994). Yet, there is little empirical information to help child welfare officials make decisions concerning which substance-abusing parents can effectively raise their children, what kinds of supports are needed to enable substance-abusing mothers

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to raise their own children, and, if foster placement is deemed necessary, what kinds of placements are best for children and what kinds of supports are needed for foster parents. As was the case in the Chicago PHCDP study, most foster placements are with relatives. Yet, it remains an unanswered question whether the kin networks of substance abusers can provide good environments for child development. Preliminary data (Howard et al., 1995; Tyler, Howard, Espinosa, & Doakes, 1997) suggest that there may be few differences in quality of parenting provided by substance-abusing mothers and their relatives and little advantage to infants in being placed with family members. Parenting intervention. We need more research on effectiveness of parenting interventions for substance-abusing women. Substance-abusing women often are motivated to enter treatment out of concern for the welfare of their children, particularly during pregnancy (Colten, 1982; Straussner, 1994), and while in treatment may be particularly receptive to interventions focused on promoting the welfare of their children. Many programs, such as methadone maintenance clinics, offer short-term didactic parenting classes that focus on basic child-rearing practices. Parenting interventions with women in treatment are unlikely to be successful unless they offer mothers opportunities to explore in a supportive setting issues such as how parenting is affected by a parent’s substance abuse, adverse experiences in the parent’s past, and problems in the parent’s immediate relationships with other family members, partners, and friends (Greif & Drechsler, 1993; Luthar & Walsh, 1995; Plasse, 1995). Effective parenting intervention must also focus on the parent-child relationship. Model parenting interventions go beyond classes and support groups to include child-parent play groups and videotaping of parent-child interaction to help parents observe, evaluate, and understand their own parenting behavior (Bernstein, Percansky, & Wechsler, 1996; VanBremen & Chasnoff, 1994). Evidence is only beginning to be gathered to suggest that parenting interventions can work even with drug-dependent women. In one nurse home-visiting program (Black et al., 1994), drug-using women who were randomly assigned to receive home visitation intervention were more emotionally responsive and provided their infants with more opportunities for stimulation. As these suggestions for future research imply, the most general recommendation for future research on parenting and parent-child relationships in families of addiction is that it addresses topics of relevance to intervention and prevention. Such research should include evaluations of interventions focused on parenting in drug-using families but also studies focusing on identifying patterns of differences among families affected by substance abuse that might yield ideas for targeting services to those at greatest risk and tailoring services to meet families’ differing needs.

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ACKNOWLEDGMENTS This research was supported by grants (R18DA-01884 and R01DA-05396) from the National Institute of Drug Abuse. The author acknowledges the major contributions of Joseph Marcus, Rita Jeremy, and Carrie Patterson to the design and conception of this research and to Victor Bernstein and Linda Henson for their effort and commitment throughout the course of the study. Holly Furdyna offered valuable comments on earlier versions of the manuscript. REFERENCES American Psychiatric Association. (1980). Diagnostic and statistical manual of mental disorders, 3rd ed. (DSM=III). Washington, DC: American Psychiatric Association. Azuma, S.D., & Chasnoff, I.J. (1993). Outcome of children prenatally exposed to cocaine and other drugs: A path analysis of three-year data. Pediatrics, 92, 396–402. Barth, R.P., Courtney, M., Berrick, J., & Albert, V. (1994). From child abuse to permanency planning: Pathways of children through child welfare services. New York: Aldine De Gruyter. Bauman, P.S., & Dougherty, F.E. (1983). Drug-addicted mothers’ parenting and their children’s development. International Journal of the Addictions, 18, 291–302. Beeghly, M., & Tronick, E.Z. (1994). Effects of prenatal exposure to cocaine in early infancy: Toxic effects on the process of mutual regulation. Infant Mental Health Journal, 15, 158–175. Bernstein, V.J., & Hans, S.L. (1994). Predicting the developmental outcome of two-yearold children born exposed to methadone: The impact of social-environmental risk factors. Journal of Clinical Child Psychology, 23, 349–359. Bernstein, V.J., Jeremy, R.J., Hans, S.L., & Marcus, J. (1984). A longitudinal study of offspring born to methadone-maintained women. American Journal of Alcohol and Drug Abuse, 10, 161–193. Bernstein, V.J., Jeremy, R.J., & Marcus, J. (1986). Mother-infant interaction in multiproblem families: Finding those at risk. Journal of the American Academy of Child Psychiatry, 25, 631–640. Bernstein, V.J., Percansky, C., & Hans, S.L. (April 1987). Screening for socialemotional impairment in infants born to teenage mothers. Paper presented at the meeting of the Society for Research in Child Development, Baltimore, MD. Bernstein, V.J., Percansky, C., & Wechsler, N. (1996). Strengthening families through strengthening relationships: The Ounce of Prevention Fund developmental training and support program. In M.Roberts (Ed.), Model programs in child and family mental health (pp. 109–133). Hillsdale, NJ: Erlbaum. Besharov, D.J. (1994). When addicts have children: Reorienting child welfare’s response. Washington, DC: Child Welfare League of America and American Enterprise Institute.

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CHAPTER 4

Assessing Vulnerability to Moderate Levels of Prenatal Alcohol Exposure SANDRA W.JACOBSON JOSEPH L.JACOBSON

Fetal alcohol syndrome (FAS) is now recognized as the leading known cause of mental retardation in the United States, surpassing Down syndrome and spina bifida. It is now well established that high levels of maternal drinking during pregnancy are associated with FAS, which is characterized by intrauterine and/or postnatal growth retardation, central nervous system impairment, and distinctive craniofacial dysmorphology (Stratton, Howe, & Battaglia, 1996). Although the link between alcohol abuse during pregnancy and FAS is well accepted, there is considerable continuing controversy regarding the degree to which pregnancy drinking in nonalcoholic mothers is associated with developmental insult. A recent report by the National Academy of Sciences Institute of Medicine (Stratton et al., 1996) noted that several large prospective, longitudinal studies have reported “statistical associations between low to moderate levels of prenatal alcohol exposure…and effects on a variety of behavioral, educational, and psychological tests.” The report concluded, however, that “these statistical associations are typically weak and the estimated average effects are usually small, so these results seem to have little clinical significance for individual children.” When we began our research in collaboration with Robert Sokol in 1986, it was not clear to what extent moderate levels of alcohol exposure produce neurobehavioral deficits similar to FAS, whether these occur in a dose-dependent fashion, and whether there is a threshold below which the exposed infant is not affected. We were interested to see whether some of the newer infant assessments from developmental psychology might indicate specific domains of cognitive functioning affected by prenatal exposure. Is alcohol always a risk? The risk associated with prenatal alcohol exposure depends on many factors, including level and timing of exposure, pattern of drinking, endpoint or outcome studied, and sensitivity of the test used. This 69

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research has also led to a reexamination of how to assess thresholds and the functional significance of the effects. We would like to review what we have learned about each of these issues as they relate to effects on neurobehavioral development. LEVELS OF EXPOSURE Virtually all prospective studies on the effects of prenatal alcohol exposure have used the Bayley (1969) Scales of Infant Development to assess neurobehavioral function and developmental status in infancy. The Bayley, an apical test, reflecting multiple domains of intellectual function, provides both a Mental Development Index (MDI) and a Psychomotor Development Index (PDI) score. Effects of pregnancy drinking on the Bayley have varied considerably, depending on level and timing of exposure. Some studies of mothers who drank at moderate levels during pregnancy found deficits on the Bayley, but others failed to replicate these findings, so that at the time we began our study, the degree to which moderate levels of prenatal alcohol exposure affect infant development was still uncertain. Table 4.1 summarizes the results from five major prospective studies that preceded ours. All these studies assessed maternal drinking during pregnancy and controlled for potential confounding influences. Bayley deficits were evident in the Atlanta cohort (Smith, Coles, & Falek, 1982) in which level of drinking was very high, but when we began our research, the Seattle study (Streissguth, Barr, Martin, & Herman, 1980) was the only wellcontrolled, prospective study to find Bayley effects at moderate levels of exposure. Table 4.1 Effects of Pregnancy Drinking on the Bayley Scales

a

Smith et al., 1982. bStreissguth et al., 1980. cRichardson & Day, 1991. dGreene et al., 1991. eFried & Watkinson, 1988. fEffect was significant until controlled for smoking, gJ. Jacobson et al., 1993.

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In the Atlanta cohort (Smith et al., 1982), the poorest performance was seen in the offspring of 28 heavy drinkers who continued to drink throughout pregnancy (M=1.67 oz absolute alcohol per day; AA/day); offspring of heavy drinkers who abstained during the second and third trimesters performed poorly only on the fine motor items, demonstrating the importance of the timing of the exposure for the more cognitive neurobehavioral effects. Streissguth et al. (1980) found Bayley deficits in a moderately exposed, predominantly white, middle-class Seattle cohort (M=0.26 oz AA/day during pregnancy), but deficits were not seen in lower exposed cohorts in Cleveland (M=0.07 oz AA/day; Greene et al., 1991) or in Pittsburgh (Richardson & Day, 1999; Richardson, Day, & Taylor, 1989). Despite a substantial number of moderate to heavy drinkers in Pittsburgh during the first trimester, most reduced their drinking in the second and third trimesters (M=0.14 drinks—about 0.07 oz AA/day during the second and third trimesters). Fried and Watkinson (1988) found alcohol effects on the Bayley, but these were confounded with maternal smoking in their sample. In Detroit, we also found a relation between moderate drinking during pregnancy (M=0.23 oz AA/day) and poorer 13-month MDI performance (J.Jacobson et al., 1993). The results of the regression analyses evaluating effects of drinking during pregnancy on the Bayley are presented in Table 4.2. The betas show that, even after controlling for potential confounders, prenatal alcohol exposure was related to poorer performance on the MDI, with an effect on the PDI that was just short of statistical significance. In and of themselves, the betas appear small and of questionable clinical relevance. Additional analyses are needed to examine their functional significance. For this reason, we performed a contingency table analysis in which the bottom 10th percentile of the distribution was used to indicate “poor performance” on the MDI. This analysis showed an increased incidence of poor Table 4.2 Effects of Maternal Drinking on Bayley Scale Scores

Note: r is the Pearson correlation coefficient. ß is the standardized regression coefficient, adjusted for influence of potential confounders related to outcome at p<.10. † p<.07. *p<.05. **p<.01.

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performance above a threshold of 0.5 oz AA/day or about 1 standard drink/day during pregnancy (Table 4.3). The incidence of very poor performance on the MDI more than doubled in children whose mothers averaged at least 0.5 oz AA/day. It should be noted that a majority of these moderate-to heavy-drinking mothers were not at risk for alcohol abuse problems as indicated by their scores on the Michigan Alcoholism Screening Test (MAST; Selzer, 1971), an interview used to assess psychosocial problems related to alcohol use. These findings in an inner city, highly disadvantaged, African-American cohort confirmed the effects of moderate prenatal alcohol exposure on Bayley Scale performance originally seen by Streissguth in her predominantly white, middle-class Seattle cohort. A review of the other studies that failed to detect Bayley deficits suggests that there were enough moderate to heavily exposed infants (i.e., infants whose mothers drank at least 0.5 oz AA/day at conception and 0.25 oz AA/day across pregnancy) in their samples to detect the alcohol effect. For example, an examination of the Cleveland data (Greene et al., 1991) reveals that the sample included only 7 infants whose mothers drank above that threshold, compared with 45 in Detroit, that is, too few infants exposed in the range in which the MDI effect is seen. When we randomly deleted all but seven of the infants whose mothers drank above the 0.5 oz threshold in Detroit, the zero-order correlation of alcohol with the MDI dropped from -.17 to -.05, making it similar to the -.06 correlation reported in Cleveland. The Detroit sample had been recruited to overrepresent moderate and heavy drinkers. All women Table 4.3 Number of Infants Performing in Bottom 10th Percentile by Pregnancy Drinking Level

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reporting alcohol consumption at conception averaging at least 0.5 oz of AA/ day (about one standard drink per day) and a random sample of approximately 5% of the women who drank at lower levels or abstained were invited to participate in the study (Jacobson et al., 1991). If moderate-to-heavy drinkers had not been overrepresented in Detroit, the effects on the MDI would not have been detected. This finding addressed the first controversy—namely, the inconsistent results regarding the Bayley shown in Table 4.1—and suggested that when there are a sufficient number of moderate-to-heavily exposed children in a cohort, effects are detected at moderate levels of exposure in nonsyndromal children. These prospective studies have since shown that FAS represents the severe end of a continuum of deleterious effects of prenatal alcohol exposure also seen in many moderately exposed children as well as in children lacking the characteristic facial dysmorphic features (Mattson & Riley, 1998). SAMPLE The Detroit cohort is an economically disadvantaged sample of AfricanAmerican women and their infants. The 480 mothers in the sample were predominantly lower class, poorly educated, unmarried, and on welfare. Women were recruited at their first visit to the prenatal clinic of a large, inner city maternity hospital serving primarily (92%) African-American women. Because of a cocaine epidemic in Detroit, we were concerned that many high alcohol users would also be using cocaine. To reduce the risk that alcohol would be confounded with cocaine exposure, 78 heavy cocaine (at least 2 days/week) but low alcohol (<0.5 oz AA/day) users were also included. At each prenatal clinic visit, the mother was interviewed regarding her alcohol use during the previous 2 weeks. Day-by-day recall of a “typical week” at the time of conception was also obtained at the first clinic visit. The data were converted to ounces of absolute alcohol (AA), and the AA data obtained at each clinic visit were then averaged across visits (M=5.2) to provide a summary measure of drinking during pregnancy. Data on postpartum drinking were collected at a 13-month laboratory visit using the same procedure based on day-by-day recall applied to a “typical week” since the infant’s birth. Detailed drug use data were obtained at each prenatal visit except the first. Because of wide variability in the dosage and degree of purity of commonly used substances, exposure was summarized in terms of the average number of days per month used. As with alcohol, the antenatal drug data were averaged across prenatal visits. Any woman reporting cocaine use on 2 or more days/ week at the initial prenatal visit was considered a heavy cocaine user. A more detailed description of the alcohol and drug use interview can be found in S.Jacobson et al. (1991).

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DATA ANALYSIS AND CONFOUNDING VARIABLES Each of the outcome measures was evaluated in separate hierarchical multiple regression analyses based on AA/day during pregnancy and on AA/day at conception. Correlational analysis was used to determine which control variables needed to be included in multivariate analyses to control for potential confounding. A control variable cannot be the true cause of an observed deficit unless it is related to both exposure and outcome (Schlesselman, 1982). Control variables were selected in relation to outcome, which has the advantage of including covariates unrelated to exposure, thereby increasing precision (Kleinbaum, Kupper, & Muller, 1988). All control variables even weakly related to the outcome in question (p<.10) were entered in the first step of each regression analysis; the AA/day measure, at the second step. An alcohol-related deficit was inferred only if the effect of AA/day was significant (at p<.05), after adjustment for the effects of the control variables. DOSE DEPENDENCE Dose dependence can be evaluated by dividing AA/day into discrete groups determined a priori and performing analyses of covariance in which the relevant potential confounders are held constant. A dose-response relation is inferred if the adjusted mean scores for the discrete groups increase or decrease with increasing levels of alcohol exposure. These groups, which were based on unlogged values of AA/day, correspond to the categories of abstainer, light, moderate, and heavy drinking used in the 1988 National Institutes of Health Table 4.4 Drinking Levels in Ounces of Absolute Alcohol per Day and Standard Drinks per Day

Note. From the National Institutes of Health (NIH) 1988 National Health Interview Survey. a One standard drink=0.5 oz absolute alcohol=12 oz beer=5 oz wine=1.25 oz liquor. b These categories comprise the “heavy” category in the NIH 1968 National Health Interview Survey.

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Figure 4.1 Dose-response relations for Bayley MDI and PDI, adjusted for potential confounders. Group ns are shown in parentheses.

(NIH) National Health Interview Survey (Table 4.4). Clinical significance was assessed in contingency tables comparing the incidence of poor performance (>1 SD above or below the mean) at each level of alcohol exposure. For some outcomes, data were available for a sufficiently large number of heavily exposed infants to create a very heavy drinking group. As can be seen in Figure 4.1 (top), the effect of pregnancy drinking on the Bayley MDI was dose dependent, with data suggesting an effect even at very low levels. By contrast, the effect on the PDI was not dose dependent (Figure 4.1, bottom), with scores reduced only at the highest level of exposure, 2 oz

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AA/day, suggesting that it takes a considerably higher dose to disrupt psychomotor development than to affect infant cognitive function. PROCESSING SPEED The most consistent neurobehavioral effect of prenatal alcohol exposure in older children relates to attention. Retrospective studies (Shaywitz, Cohen, & Shaywitz, 1980) suggested a deficit in attentional function. Streissguth and Coles and their associates administered vigilance tests in their prospective studies to assess effects on sustained attention (Brown et al, 1991; Streissguth et al., 1984, 1986). The Seattle investigators found both poorer accuracy and slower reaction times in 4- and 7.5-year-old children exposed at moderate levels (Streissguth et al., 1984, 1986). These data, together with data from two other tests involving reaction time (Streissguth et al., 1984, 1986), led Streissguth to suggest an alcohol-related deficit in “speed of central processing.” In our Detroit infancy study, we included three tests that assess processing speed in infancy. Infants were administered the Pagan Test of Infant Intelligence (FTII; Pagan & Singer, 1983) at 6.5 and 12 months, a test of cross-modal transfer (Rose, Gottfried, & Bridger, 1978) at 12 months, and a reaction time test (Haith, Hazen, & Goodman, 1988; Haith & McCarty, 1990) at 6.5 months. The FTII and cross-modal test provide a measure of recognition memory (novelty preference) as well as a new measure of length of visual fixation (Colombo & Mitchell, 1990), which provides a valid measure of processing speed. Mean duration of visual fixation is defined as total duration looking time divided by number of looks. A pattern of short looks is believed to reflect more rapid processing of information and has been found to relate to superior performance on numerous infant tests and to predict higher childhood IQ (Colombo, Mitchell, and Horowitz, 1988; Colombo & Mitchell, 1990; Sigman, Cohen, Beckwith, & Parmelee, 1985). We have since shown that length of look correlates with infant reaction time (Jacobson et al., 1992). The second measure of infant processing speed came from a new test, which for the first time can assess infant reaction time (Haith et al., 1988). We adapted the Haith Visual Expectancy Paradigm (VExP), originally designed for 3-montholds, for use with 6-month-old infants (Jacobson et al., 1992). After a brief baseline of random presentations, 60 stimuli appear in a predictable left-right alternating sequence, with the entire series lasting 120.5 s. The infant’s eye movements are recorded on videotape, which is subsequently coded to determine speed of response, defined as latency between the onset of the stimulus and the time the infant’s eye begins to move toward the stimulus. In some studies that have used the visual expectancy task, a large proportion of the infants fail to complete the test. Subject attrition was minimized in the

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present study by the use of a single large, color monitor (Sony 32 x 41 cm or 12 x 16 in.) for display of the stimuli. The monitor was positioned behind a gray screen used to focus the infant’s attention and limit external distraction. The screen limited the viewing area to 20.5 x 41 cm. Attrition was also minimized by repeating the baseline stimuli for infants who failed to achieve the criterion (of at least three of five possible baseline alternations) instead of omitting them from the sample. One of the principal findings of the Detroit study is that maternal drinking during pregnancy is associated with slower, less efficient cognitive processing in infancy. By contrast to methylmercury and polychlorinated biphenyls (PCBs), for which prenatal exposure has been linked to poorer recognition memory on the Pagan test (Jacobson, Fein, Jacobson, Schwartz, & Dowler, 1985; Jacobson, Ko, et al., 1994; Gunderson, Grant, Burbacher, Pagan, & Mottet, 1986), there was no effect of prenatal alcohol on either of the novelty preference measures (Table 4.5). Instead, drinking during pregnancy was associated with a pattern of longer infant visual fixation and slower reaction times (S.Jacobson, Jacobson, Sokol, Martier, & Ager, 1993; Jacobson, Jacobson, & Sokol, 1994). Thus, the effect of alcohol on processing speed was seen in three separate domains—recognition memory, crossmodal transfer, and visual expectancy—and at two different ages—6.5 and 12 months. These data suggest a specific effect of prenatal alcohol exposure on speed of information processing rather than general delay or overall poorer performance on these tests. This pattern of effects is different for that found for heavy prenatal cocaine exposure, which was related to poorer FTII recognition memory deficits but faster reaction times on the VExP for this cohort (Jacobson, Jacobson, Sokol, Martier, & Chiodo, 1996). It is also different from the pattern found for prenatal exposure to PCBs, which was related to poorer FTII Table 4.5 Effects of Drinking on Infant Cognitive Outcome

Note, r is the Pearson correlation coefficient. ß is the standardized regression coefficient, adjusted for influence of potential confounders related to outcome at p<.10. *p<.05. **p<.01.

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recognition memory in the absence of FTII processing speed effects (Jacobson, Ko, et al., 1994; Jacobson, 1998). POSTPARTUM DRINKING The risk of overcontrol for confounding is particularly great in studies of prenatal exposure to a substance, such as alcohol, that the mother is likely to continue to use postnatally. An association between pregnancy drinking and infant cognitive function is normally interpreted as teratogenic, that is, due to a direct effect of the drug on fetal CNS development. An alternative explanation, that the observed deficit is due to the socioenvironmental concomitants of being raised by a drinking mother, can be evaluated by examining the relation of the deficit to postpartum maternal alcohol use. However, if maternal drinking during pregnancy and postpartum are highly confounded, as is likely, it may not be possible to determine the degree to which observed deficits are due to teratogenic versus socioenvironmental factors, and inclusion of both in the regression analyses may wipe out the true prenatal effects. It is advisable, therefore, to examine the effects in separate regression analyses initially (Jacobson, 1997). In the Detroit study, none of the neurobehavioral deficits detected on the Bayley Scales or on the Pagan, cross-modal transfer, or reaction time tests were related to postpartum drinking by the mother or caregiver (all ps>.15, after adjustment for the potential confounders), indicating that these effects were due to prenatal alcohol exposure and not to the socioemotional consequences of being raised by a drinking mother or caregiver (J.Jacobson et al., 1993; S.Jacobson et al., 1993; S.Jacobson, Jacobson, & Sokol, 1994). TIMING OF EXPOSURE Experimental studies by Goodlett and West and others have demonstrated the role of timing of bingelike neonatal alcohol exposure in inducing specific structural and behavioral deficits (e.g., Goodlett & Peterson, 1994; Ikonomidou, Brittigau, Ishimavu, et al., 2000; Thomas, Goodlett, & West, 1994; West, 1987), but little is known about the timing of exposure for neurobehavioral effects in humans. Larsson, Bohlin, and Tunell (1985) found that continued drinking during pregnancy resulted in worse outcomes at 2 years than reduced drinking, although speech delays were found in all exposed children. The work of Coles and her colleagues has provided evidence that Bayley Scale deficits do not appear among offspring of heavy drinkers who abstained from drinking after the first trimester (Smith et al., 1987). At 5.8 years, children whose mothers continued to drink heavily throughout pregnancy had lower sequential processing and composite IQ scores than those whose heavy-drinking mothers stopped drinking in the second trimester

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or those who did not drink during pregnancy (Coles et al., 1991). These effects persisted after controlling for postnatal drinking. The lack of Bayley Scale effects in the Pittsburgh cohort (Richardson et al., 1989; Richardson & Day, 1991) also suggests that neurobehavioral effects are more likely related to later pregnancy exposure. Although a high proportion of women in that cohort reported moderate first trimester drinking, very few reported moderate or heavy drinking in the second or third trimesters. Moderate first trimester exposure was apparently not sufficient to result in Bayley deficits,

Figure 4.2 Dose-response relations for two measures of speed of processing, adjusted for potential confounders. Group ns are shown in parentheses.

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suggesting that the potential CNS damage reflected in the Bayley had not occurred yet. Additional support comes from our Detroit study, in which we found that Bayley effects were related more strongly to later pregnancy drinking than to drinking around the time of conception (J.Jacobson et al., 1993). SENSITIVITY OF TEST Thresholds vary not only by functional domain but also within domains, depending on the sensitivity of the measures used. Even though the fixation duration and infant reaction time measures reflect the same functional domain and are correlated with each other (r=0.28-0.33, p<.01; Jacobson et al., 1992), Table 4.6 Thresholds at Which Neurobehavioral Effects Were Seen for the Seattle Cohort

Streissguth et al., 1980. bStreissguth et al., 1986. cStreissguth et al., 1989. dBarr, Streissguth, Darby, & Sampson, 1990. eStreissguth et al., 1984. fStreissguth et al., 1983. gStreissguth et al., 1990. a

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reaction time proved markedly more sensitive, detecting effects at 0.5 oz AA per day (Figure 4.2, top), compared with the 1.0 oz AA per day threshold for mean length of fixation (Figure 4.2, bottom) (S.Jacobson et al., 1993; S.Jacobson, Jacobson, & Sokol, 1994a). Similarly, the threshold for effects on IQ at 7 years (Streissguth, Barr, & Sampson, 1990) was lower than at 4 years (Streissguth et al., 1989), presumably because of the superior reliability of the test at the older age (Table 4.6). THRESHOLDS Only a few human studies on the effects of alcohol on neurobehavioral development have investigated thresholds (J.Jacobson et al., 1993; S.Jacobson et al., 1993; S.Jacobson, Jacobson, & Sokol, 1994; Streissguth, Barr, & Martin, 1983). Our review of these studies suggests that most measures appear to have thresholds ranging from 0.5 to 2.0 oz AA/day (see Tables 4.6 and 4.7). The suggestion in these data that 0.5 oz AA/day or 1 drink/day may be a lower bound threshold for neurobehavioral effects is supported by the fact that studies that have included very few mothers who drank at or above that level (e.g., Greene et al., 1991) have generally failed to detect effects on neurobehavioral development in infancy (Jacobson & Jacobson, 1994).

Table 4.7 Thresholds at Which Neurobehavioral Effects Were Seen for the Detroit Cohort

a

J.Jacobson et al., 1993. bS.Jacobson et al., 1993. cS. Jacobson, Jacobson, & Sokol, 1994.

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Figure 4.3 Number of women drinking alcohol at least 1 day per week.

Drinking Patterns Although reporting alcohol intake in terms of mean oz of AA/day permits crossstudy comparisons, this measure may obscure the importance of pattern and concentration of alcohol exposure. Animal studies have shown that alcohol is most damaging to the brain and increases the severity of behavioral deficits when consumption is concentrated into a short period (Goodlett & Peterson, 1994; Ikonomidou et al., 2000, West & Goodlett, 1990). Even though the thresholds in the analyses reported are characterized in terms of oz of AA/day, only 1 of 480 mothers actually drank every day, and only 3 drank more than 4 days/week (Figure 4.3). A mean of 0.5 oz AA/day exposure, therefore, typically represents higher doses of alcohol on those days on which drinking occurs. For the mothers who drank at least 0.5 oz AA/day, the lowest level at which deficits were consistently seen, the typical or median drinking pattern was 3.0 oz AA/drinking day or 6 standard drinks on 2.3 days of the week, a sharp contrast in pattern to the women who drank an average of less than 0.5 oz AA/day (Table 4.8). These data are consistent with other studies (e.g., Streissguth, Bookstein, Sampson, & Barr, 1994) suggesting that much of the impairment is related to relatively high levels of drinking per

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Table 4.8 Drinking Patterns during Pregnancy

occasion. On the other hand, adverse effects on neurobehavior were by no means limited to the children of alcoholics. The majority of the Detroit mothers (59.3%) who drank more than 0.5 oz AA/day were negative on the Michigan Alcoholism Screening Test (MAST), indicating that their drinking was not marked by the psychosocial sequelae of alcohol abuse. THRESHOLD EFFECTS Prospective longitudinal studies of prenatal alcohol exposure have relied primarily on multiple regression or other correlational procedures (notably, partial least squares [PLS] in the later phases of the Seattle study) to evaluate the effects of prenatal alcohol exposure. Multiple regression and PLS are appropriate for these studies because exposure levels are distributed across a broad continuum. Although appropriate for evaluating whether prenatal alcohol is related to developmental outcome, multiple regression and PLS are insensitive to threshold. By fitting a single straight-line function to the entire data set, these techniques obscure points of inflection in dose-response relationships below which neurotoxicity may not be evident. As indicated earlier, in the initial phase of the Detroit study, we investigated thresholds by means of analysis of covariance (ANCOVA) based on 5 to 6 exposure groups defined a priori. Visual inspection of bar graphs of the results of these analyses generally indicated threshold patterns (e.g., J.Jacobson et al., 1993; S.Jacobson et al., 1993). For example, in the relation of prenatal alcohol exposure to infant processing speed shown in Figure 4.2 (bottom), there appears to be little effect in infants whose mothers averaged less than 1 oz AA/day during pregnancy, with a dose-dependent effect above that level. By contrast, a lower threshold is seen for infant reaction time on the VExP test in Figure 4.2 (top). The principal limitation of this approach is that the detection of a threshold depends, in part, on there being a correspondence between the threshold value and the group cut points selected a priori.

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We have since reexamined the dose-response relations in our infant data with two different approaches: nonparametric regression and hockey stick curve fitting. For the hockey stick curve fitting, potential confounders were controlled by inclusion in the regression analyses, as described earlier. In nonparametric regression, they were controlled by residualizing the developmental outcome for its potential confounders prior to inclusion in the analysis; bottom 10th percentile cutoffs were determined in terms of the distribution of the residualized scores. Using these data analytic approaches, we have shown that, if there is a threshold below which alcohol does not have an impact on developmental outcome, multiple regression may understate the magnitude of the effect on the more heavily exposed infants, namely, those exposed above threshold (Jacobson, Jacobson, Sokol, & Ager, 1998). For the outcomes tested, nonparametric and hockey stick regression analyses both indicated essentially no relation between pregnancy drinking and developmental outcome below a median threshold of 0.5 oz AA/day, with the impact of the exposure increasing gradually above this threshold. In both the nonparametric and hockey stick regressions, as in the ANCOVAs described earlier (Figure 4.1), the threshold for the Bayley Psychomotor Index was markedly higher than for the Mental Development Index, confirming that cognitive development is more sensitive to prenatal alcohol exposure than gross motor function. FUNCTIONAL SIGNIFICANCE In addition, to date most prospective studies have focused primarily on the detection of subtle effects and have only recently attempted to evaluate the clinical or functional significance of this exposure. Although regression can be highly sensitive in detecting subtle deficits, it provides no indication of the functional significance of the effects observed. For example, we have recently used regression to examine the role of maternal age in moderating the effects of prenatal alcohol exposure (Jacobson, Jacobson, & Sokol, 1996). These analyses indicate that the effects of moderate-to-heavy pregnancy drinking are more severe in infants born to older mothers (i.e., at least 30 years of age). Additional analyses are needed, however, to evaluate the functional significance of the regression findings. Table 4.9 illustrates one approach for evaluating the functional significance of the alcohol-related deficits. We assume for purposes of these analyses that a test score in the bottom 10th percentile of the distribution indicates performance at a level likely to adversely affect day-to-day function. Exposure was dichotomized at 0.5 oz AA/day, the median threshold found in the hockey stick analyses. For the first four outcomes listed in the table, there was no increased incidence of functionally significant deficit in the infants born to

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Table 4.9 Relation of Pregnancy Drinking to Incidence of Functional Impairment in Infants Born to Younger and Older Mothers

Cell values are percentage of children scoring in the bottom 10th percentile. Each p value is based on the c2 for a 2x2 (infant outcome by pregnancy drinking) contingency table analysis. The sample consisted of 350 mothers under 30 years of age and 130 older mothers.

the younger mothers, whereas, for those born to older women, moderate-toheavy pregnancy drinking was associated with twofold to fivefold increases in functional impairment. The sole exception to this pattern was processing speed (i.e., fixation duration on the FTII and cross-modal test). Pregnancy drinking doubled the risk of functional deficit in processing speed in both age groups, an effect that was significant for the sample as a whole (p=.05). These data indicate that the multiple regression evidence of stronger effects in infants born to older moderate-to-heavy drinking women (Jacobson et al., 1998) translates into a markedly greater risk of functionally significant deficit in four of the five domains tested. The 10th percentile cutoff probably understates the prevalence of adverse effect because it ignores the exposed child with an average score who is performing more poorly than she or he would have in the absence of fetal exposure. For this child, exposure has led to “diminished potential” rather than a demonstrable deficit. Moreover, a more lenient criterion (e.g., bottom 20th percentile) would generate a larger estimate of the number of affected children. We have selected the 10th percentile based on the assumption that the day-to-day function of children performing below that cutoff is almost certain to be affected, even if their scores technically fall within the normal range. The 10th percentile, therefore, provides a conservative estimate of the degree to which the incidence of meaningful cognitive deficit has been increased by the exposure in question.

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DISCUSSION These data indicate that functionally significant developmental deficits are seen not only in children with FAS but also in infants without FAS whose mothers engage in intermittent heavy drinking during pregnancy. Although full FAS is considerably more disabling for the affected child, the less severe deficits found in this study are much more prevalent. By contrast to FAS, the less severe deficits were seen in our cohort in many infants born to women who were not alcoholic and did not appear to have alcohol-related psychosocial problems. These children meet the new Institute of Medicine criteria for alcohol-related neurodevelopmental disorder (ARND), namely, an abnormality or developmental delay in a complex pattern of behavior or cognition when the mother has engaged in substantial, regular alcohol intake or heavy episodic drinking during pregnancy. It should be emphasized that the 0.5 oz AA/day threshold represents a sample average and that more sensitive and reliable tests may detect even lower thresholds in the future. In light of individual differences in vulnerability to prenatal alcohol exposure and in the absence of information on synergistic effects with other substances, the 0.5 oz AA/day threshold should not be taken to imply that drinking below that level is “safe.” Although the specific threshold dose is difficult to determine, our data and those of other prospective studies indicate that the infant born to a mother who drinks at elevated levels (five or more drinks per drinking occasion) regularly during pregnancy is at considerably increased risk (Jacobson et al., 1998). It is usually easier to persuade a light drinker to abstain during pregnancy than to convince a heavier drinker to restrict her alcohol intake to 1 or 2 drinks per occasion. These data suggest that efforts to reduce the incidence of alcoholrelated functional impairment should specifically target both the alcoholdependent mother for whom even a single drink may prove dangerous because it is difficult for her to stop and the older non-alcohol-dependent mother who engages in intermittent heavy drinking during pregnancy, namely, a mother who drinks about 3.5 oz A A/week but does so by consuming five or more drinks per occasion once per week. ACKNOWLEDGMENTS This research was supported by grants RO1-AA06966 and P50-AA07606 from the National Institute of Alcohol Abuse and Alcoholism, with supplemental support from a Minority Access to Research Careers grant No. T34-GM08030, a Minority Biomedical Research Support grant No. SO6-RR08167 from the National Institutes of Health, and a Joseph Young, Sr. research grant from the State of Michigan. We would like to thank Robert J. Sokol, Susan Martier, Joel Ager, Melissa Kaplan-Estrin, Lisa Chiodo, Renee Berube, and Sonia Narang

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for their major contributions to this research. Portions of this paper were presented in the Children of Addiction Round Table, Biennial Meetings of the Society for Research on Child Development PreConference, April 1997. Correspondence to: Sandra W.Jacobson, Ph.D., Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, 2751 E.Jefferson, Detroit, MI 48201. REFERENCES Barr, H.M., Striessguth, A.P., Darby, B.L., & Sampson, P.D. (1990). Prenatal exposure to alcohol, caffeine, tobacco, and aspirin: Effects on fine and gross motor performance in 4-year-old children. Developmental Psychology, 26, 339–348. Bayley, N. (1969). Bayley scales of infant development. New York: Psychological Corporation. Brown, R.T., Coles, C.D., Smith, I.E., Platzman, K.A., Silverstein, J., Erickson, S., & Falek, A. (1991). Effects of prenatal alcohol exposure at school age: II. Attention and behavior. Neurotoxicology & Teratology, 13 (4), 369–376. Coles, C.D., Brown, R.T., Smith, I.E., Platzman, K.A., Erickson, S., & Falek, A. (1991). Effects of prenatal alcohol exposure at school age: I. Physical and cognitive development. Neurotoxicology & Teratology, 13 (4), 357–367. Colombo, J., & Mitchell, D.W. (1990). Individual differences in early visual attention: Fixation time and information processing, In J.Colombo (Ed.), Individual differences in infancy: Reliability, stability, prediction (pp. 193–227). Hillsdale, NJ: Lawrence Erlbaum. Colombo, J., Mitchell, D.W., & Horowitz, F.D. (1988). Infant visual attention in the paired-comparison paradigm: Test-retest and attention-performance relations. Child Development, 59, 1198–1210. Fagan, J.F., & Singer, L.T. (1983). Infant recognition memory as a measure of intelligence. In Lipsitt, L.P. (Ed.), Advances in infancy research, Vol. 2 (pp. 31–78). Norwood, NJ: Ablex. Fried, P.A., & Watkinson, B. (1988). 12- and 24-month neurobehavioural follow-up of children prenatally exposed to marihuana, cigarettes and alcohol. Neurotoxicology & Teratology, 10, 305–313. Goodlett, C.R., & Peterson, S.D. (1994). The role of duration and timing of bingelike neonatal alcohol exposure in determining the extent of alcohol-induced deficits in spatial navigation and motor learning. Alcoholism: Clinical & Experimental Research, 18, 501. Gunderson, V.M., Grant, K.S., Burbacher, T.M., Pagan, J.F.D., & Mottet, N.K. (1986). The effect of low-level prenatal methylmercury exposure on visual recognition memory in infant crab-eating macaques. Child Development, 57, 1076–1083. Haith, M.M., Hazan, C., & Goodman, G.S. (1988). Expectation and anticipation of dynamic visual events by 3.5-month-old babies. Child Development, 59, 467–479.

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Haith, M.M., & McCarty, M.E. (1990). Stability of visual expectations at 3.0 months of age. Developmental Psychology, 26, 68–74. Ikonomidou, C., Bittigau, P., Ishimaru, M.J., Wozniak, D.F., Koch, C., Genz, K., Price, M.T., Stefouska, V., Hörster, F., Tenkova, T., Dikranian, K., & Olney, J.W. (2000). Ethanol-induced apoptotic neurodegeneration and Fetal Alcohol Syndrome. Science, 287, 1056–1060. Jacobson, J.L., & Jacobson, S.W. (1994). Prenatal alcohol exposure and neurobehavioral development: Where is the threshold? Alcohol Health & Research World, 18, 30– 36. Jacobson, J.L., Jacobson, S.W., & Sokol, R.J. (1996). Increased vulnerability to alcoholrelated birth defects in the offspring of mothers over 30. Alcohol Health & Research World, 20, 359–363. Jacobson, J.L., Jacobson, S.W., Sokol, R.J., and Ager, J.W. (1998). Relation of maternal age and pattern of pregnancy drinking to functionally significant cognitive deficit in infancy. Alcohol Health & Research World, 22, 354–351. Jacobson, J.L., Jacobson, S.W., Sokol, R.J., Martier, S.S., Ager, J.W., & Kaplan-Estrin, M.G. (1993). Teratogenic effects of alcohol on infant development. Alcoholism: Clinical & Experimental Research, 17, 174–183. Jacobson, S.W. (1997). Assessing the impact of maternal drinking during and after pregnancy. Alcohol Health & Research World, 21, 199–203. Jacobson, S.W. (1998). Specificity of neurobehavioral outcomes associated with prenatal alcohol exposure. Alcoholism: Clinical & Experimental Research, 22, 313–320. Jacobson, S.W., Fein, G.G., Jacobson, J.L., Schwartz, P.M., & Dowler, J.K. (1985). The effect of intrauterine PCB exposure on visual recognition memory. Child Development, 56, 853–860. Jacobson, S.W., Jacobson, J.L., O’Neill, J.M., Padgett, R.J., Frankowski, J.J., & Bihun, J.T. (1992). Visual expectation and dimensions of infant information processing. Child Development, 63, 711–724. Jacobson, S.W., Jacobson, J.L., & Sokol, R.J. (1994). Effects of fetal alcohol exposure on infant reaction time. Alcoholism, Clinical & Experimental Research, 18, 1125– 1132. Jacobson, S.W., Jacobson, J.L., Sokol, R.J., Martier, S.S., & Ager, J.W. (1993). Prenatal alcohol exposure and infant information processing ability. Child Development, 64, 1706–1721. Jacobson, S.W., Jacobson, J.L., Sokol, R.J., Martier, S.S., Ager, J.W., & Kaplan-Estrin, M.G. (1991). Maternal recall of alcohol, cocaine, and marijuana use during pregnancy. Neurotoxicology & Teratology, 13, 535–540. Jacobson, S.W., Jacobson, J.L., Sokol, R.J., Martier, S.S., & Chiodo, L.M. (1996). New evidence for neurobehavioral effects of in utero cocaine exposure. Journal of Pediatrics, 129, 581–590. Jacobson, S.W., Ko, H.-C., Yao, B.-L., Jacobson, J.L., Chang, F.-M., & Hsu, C.-C. (1994, November). Preliminary findings confirming effect of prenatal PCB exposure on infant recognition memory. Neurotoxicology and Teratology, 16, 315.

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Kleinbaum, D.G., Kupper, L.L., & Muller, K.E. (1988). Applied regression analysis and other multivariable methods (ed. 2). Boston: PWS-Kent. Larsson, G., Bohlin, A.B., & Tunell, R. (1985). Prospective study of children exposed to variable amounts of alcohol in utero. Archives of Disabled Children, 60, 316–321. Mattson, S.N., & Riley, E.P. (1998). A review of the neurobehavioral deficits in children with fetal alcohol syndrome or prenatal exposure to alcohol. Alcoholism: Clinical and Experimental Research, 22, 279–294. Richardson, G.A., & Day, N.L. (1991, April). Prenatal exposure to alcohol, marijuana, and tobacco: Effect on infant mental and motor development. Abstract Society of Research in Child Development, 8, 421. Richardson, G.A., Day, N.L., & Taylor, P.M. (1989). The effect of prenatal alcohol, marijuana, and tobacco exposure on neonatal behavior. Infant Behavior & Development, 12, 199–209. Rose, S.A., Gottfried, A.W., & Bridger, W.H. (1978). Cross-modal transfer in infants: Relationship to prematurity and socioeconomic background. Developmental Psychology, 14, 643–652. Schlesselman, J. (1982). Case-control studies: Design, conduct, analysis. New York: Oxford University Press. Selzer, M.L. (1971). The Michigan Alcoholism Screening Test: The quest for a new diagnostic instrument. American Journal of Psychiatry, 127, 1653–1658. Shaywitz, S.E., Cohen, D.J., & Shaywitz, B.A. (1980). Behavior and learning difficulties in children of normal intelligence born to alcoholic mothers. Journal of Pediatrics, 96, 978–982. Sigman, M.D., Cohen, S.E., Beckwith, L., & Parmelee, A.H. (1985, July). Infant attention in relation to intellectual abilities in childhood. Paper presented at the International Society for the Study of Behavioral Development, Tours, France. Smith, I.E., Coles, C.D., & Falek, A. (1982). A prospective study of the effects of alcohol in utero. Paper presented at the annual meeting of the American Public Health Association, Montreal, Quebec. Stratton, K., Howe, C., & Battaglia, F. (Eds.). (1996). Fetal alcohol syndrome: Diagnosis, epidemiology, prevention, and treatment. Washington, DC: National Academy Press. Streissguth, A.P., Barr, H.M., & Martin, D.C. (1983). Maternal alcohol use and neonatal habituation assessed with the Brazelton scale. Child Development, 54, 1109–1118. Streissguth, A.P., Barr, H.M., Martin, D.C., & Herman, C.S. (1980). Effects of maternal alcohol, nicotine, and caffeine use during pregnancy on infant mental and motor development at eight months. Alcoholism: Clinical & Experimental Research, 4, 152–164. Streissguth, A.P., Barr, H.M., & Sampson, P.D. (1990). Moderate prenatal alcohol exposure: effects on child IQ and learning problems at age 7 1/2 years. Alcoholism: Clinical & Experimental Research, 14, 662–669. Streissguth, A.P., Barr, H.M., Sampson, P.D., Darby, B.L., & Martin, D.C. (1989). IQ at age 4 in relation to maternal alcohol use and smoking during pregnancy. Developmental Psychology, 25, 3–11.

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Streissguth, A.P., Barr, H.M., Sampson, P.D., Parrish-Johnson, J.C., Kirchner, G.L., & Martin, D.C. (1986). Attention, distraction and reaction time at age 7 years and prenatal alcohol exposure. Neurobehavioral Toxicology & Teratology, 8, 717–725. Streissguth, A.P., Bookstein, F.L., Sampson, P.D., & Barr, H.M. (1994). The enduring effects of prenatal alcohol exposure on child development. Ann Arbor: University of Michigan Press. Streissguth, A.P., Martin, D.C., Barr, H.M., Sandman, B.M., Kirchner, G.L., & Darby, B.L. (1984). Intrauterine alcohol and nicotine exposure: Attention and reaction time in 4-year-old children. Developmental Psychology, 20, 533–541. Thomas, J.D., Goodlett, C.R., & West, J.R. (1994). Demonstration of severe cerebellar Purkinje cell loss following two days of binge-like alcohol exposure early in the neonatal rat brain growth spurt using the stereological optical fractionator. Alcoholism: Clinical & Experimental Research, 18, 436. West, J.R. (1987). Fetal alcohol-induced brain damage and the problem of determining temporal vulnerability: A review. Alcohol & Drug Research, 7, 423–441. West, J.R., & Goodlett, C.R. (1990). Teratogenic effects of alcohol on brain development. Annuals of Medicine, 22, 319–325.

CHAPTER 5

The Teratologic Model of the Effects of Prenatal Alcohol Exposure NANCY L.DAY GALE A.RICHARDSON

An important issue in research on the long-term effects of prenatal substance exposure is the merging of disciplines that use different research models to organize hypotheses and analyze data. Initially, the effects of prenatal exposure are expressed biologically and are studied using a teratologic model. However, children exposed prenatally to substances are often raised by a substance-using parent in a household where substances are used and are exposed to the environmental influences of this use and the other characteristics that describe these households. With the aim of increasing the level of understanding between those who use a basic teratologic model and those who use developmental models, this chapter presents the basic teratologic model, some of the methodological issues associated with this research, and an example of the longterm teratologic effects of alcohol. THE MODEL The theoretical model for teratology is a biologically derived model, developed from animal research (Vorhees, 1989). The basic tenets of the teratologic model reformulated for research on human populations are as follows: The outcomes are related to dose. With increasing dose, effects will be found on the central nervous system (CNS), growth, and morphology, in that order. At lower exposures, only CNS effects will be found; at higher doses, effects will be found across domains. Within each domain, the magnitude of the effect will be related to dose. This relationship may be exhibited either as a dose-response curve or as a threshold effect. 91

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Effects will be a function of the developmental stage of the organism at the time of exposure. Time, with respect to prenatal substance exposure, has two components: the stage of pregnancy during which exposure occurs and the duration of exposure. The outcome will be an expression of the balance between the damage from the toxic exposure and the reparative properties of the organism. METHODOLOGICAL ISSUES There are several important methodological issues related to the teratologic model that must be addressed. The Shape of the Relationship One model of exposure assumes that the relationship between the teratogen and the response is a dose-response curve. Thus, theoretically, there are effects at every exposure dose. This is in distinction to the detectable level of effects, which is dependent on the ability to measure the effect, the sample size, and the resultant power of the statistical analyses. It is important to separate the scientific fact of a dose-response curve from the practical aspects of counseling; the effects at some levels of exposure may be clinically insignificant. Changes that may not be clinically significant can still be scientifically important, however, as they represent markers for the effects of a teratogen. Another potential model for the relationship between exposure and response is a threshold model. In this model, a specific level of exposure is required before the teratogen has an effect. Because of the methodological limitations of instrumentation and measurement, it is sometimes difficult to accurately describe the actual shape of the relationships. There have been reports from both the animal and the human literature that the relationship between prenatal alcohol exposure and outcomes varies with the pattern of drinking. Quantity per occasion determines the blood alcohol level attained and, therefore, the toxic exposure. Moreover, developmental processes may be differentially sensitive to different patterns of exposure. Schenker et al. (1990), for example, have reported that alcohol delivered to animals in a bolus is more damaging to the CNS than the same amount delivered over a longer period. Sampson, Streissguth, Barr, and Bookstein (1989) have reported the same in humans. In the Maternal Health Practices and Child Development (MHPCD) Project, we have found a nonlinear relationship between prenatal alcohol exposure and academic achievement, another measure of an effect on the CNS (Goldschmidt, Richardson, Stoffer, Geva, & Day, 1996). These findings have been interpreted as evidence of a threshold effect and, although this is likely to be the correct interpretation, an apparent threshold

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may also occur from methodological problems. It may, as noted earlier, reflect the inability of the research protocol to measure the level of response elicited by exposure to small doses. Further, women who binge-drink are usually heavier drinkers between binges. In the absence of also assessing the contribution of the pattern of continuous exposure, the effects cannot be attributed to the bingedrinking pattern. As we have illustrated with growth, when both maximum quantity and frequency are considered, a pattern of continuous exposure is more damaging than a pattern of high episodic exposure, after controlling for the overall amount of alcohol consumed (Day, Goldschmidt et al., 1991). It is possible to evaluate statistically whether the relationship between prenatal substance exposure and outcome is a dose-response or a threshold relationship (Goldschmidt et al., 1996). With the availability of appropriate techniques such as nonparametric smoothing, nonlinear curve fitting, and cumulative sum methods, future research needs to address this issue more fully. Time of Exposure during Pregnancy Two aspects must be considered in assessing the patterns of exposure during pregnancy: the stage of gestation when exposure occurs and the duration of exposure. Fetal development is a sequential, staged process. There are differences, for example, in the patterns of growth among length, weight, and head circumference. The peak velocity of cellular growth occurs early in gestation for fetal length, whereas growth of weight and head circumference each peak in velocity later in pregnancy (Kliegman & Hulman, 1987). There are also different mechanisms that determine growth at different times during pregnancy. For example, weight is determined both by cellular division and by fat deposition within cells. Duration of exposure also has differential effects. The outcome of offspring who were exposed throughout pregnancy differs from the outcome of offspring exposed only during early pregnancy or only at a discrete point in pregnancy. The effects also vary depending on the parameter studied. For example, the facial dysmorphologies of the fetal alcohol syndrome (FAS) occur early in gestation at a specific time in the sequence of facial development and are not repaired if the woman subsequently abstains. Growth, on the other hand, may be repaired if the toxic exposure is removed because growth occurs throughout gestation. Women who drink heavily in early pregnancy and then quit have larger children at birth than women who continue to drink throughout pregnancy. Because of these factors, it is important to assess the timing of exposure during pregnancy, particularly with respect to the stage of gestation. This requires drinking measures that are, at the least, trimester specific. Averaging exposure across the entire pregnancy obscures patterns of exposure and makes it impossible to evaluate the effects of dose or duration or the specificity of the

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exposure. In addition, our analyses have shown that the characteristics of the women who drink heavily early in pregnancy differ significantly from those of the women who continue to drink through the third trimester (Peindl, 1993). Therefore, an averaged drinking measure across the entire pregnancy will combine different populations with very different drinking profiles and significantly different risks for negative pregnancy outcomes. When evaluating the effects of a teratogen on human development, other factors that have an effect on the outcome must also be considered. It is important to move from the simple direct effects model that includes only the exposure and the outcome to incorporate additional factors. These include biologic characteristics such as genetic background, the physiological and nutritional status of the pregnant woman, and the biologic interactions between the fetus and the mother. Maternal psychological status can affect the fetus directly through physiological changes or indirectly through the use of a medication or the failure to seek appropriate care. The external environment is also critical because exposure to other substance use, infectious agents, poor diet, and poor living conditions all lead to poorer pregnancy outcomes. Therefore, the teratologic model must be extended to consider the effects of other factors: At birth, the observed effects will be the cumulative response of the child to the teratogen and the intrauterine environment. After birth, the observed effects will be the cumulative response of the child to the teratogenic exposure and the environment in which the child is raised. It is in these latter theorems that the field of teratology intersects with other fields such as developmental psychology and developmental psychopathology. These relations have been posited by Horowitz (1992) and Sameroff (1986), among others. After birth, the outcomes are molded by environmental factors. To assess these effects accurately and to separate environmental effects from the biologic effects of exposure during gestation, studies of gestational exposure must assess the environment of rearing and the effects of these environmental factors on the long-term outcome of the exposed child. The teratologic model is a direct effects model: The toxic exposure changes the fetus biologically, and this is manifested after birth as changes in the CNS, growth, or morphology. In a direct effects model, environmental factors can act independently to affect an outcome of the prenatal exposure or, alternatively, environmental factors can modify the expression of the effects of the teratogen: The relations can be additive or multiplicative, respectively. The independent or additive model is easy to conceptualize, but interaction is more difficult. It

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implies that the outcome is a heightened response due to the combined effects of the prenatal exposure and the environment. The direct effect, however, is always maintained. By contrast, developmental models are concerned with explanation of the sequence of changes in development and use a mediated model that posits that one change leads to another sequentially (Cicchetti, 1990; Dixon & Lerner, 1988; Sroufe &Rutter, 1984). DESCRIPTION OF THE MATERNAL HEALTH PRACTICES AND CHILD DEVELOPMENT STUDY The women in the MHPCD study were recruited from a prenatal clinic. Women were selected if they were in the 4th month of pregnancy and at least 18 years old. Interviewing was done in a private setting in the outpatient clinic. Women who were not interviewed at their 4th month prenatal visit were contacted again at their 5th-month visit. If the interview was not completed at this point, they were not contacted further. The refusal rate at recruitment was 15%. A total of 1,360 women was screened at the initial interview and a study sample was selected on the basis of first-trimester alcohol use (Day & Robles, 1989). All women who had an average use of three or more drinks per week and a random sample of a third of the women who drank alcohol less often or not at all were selected. In addition, a parallel cohort was selected to study the effects of marijuana use during pregnancy. In this study, all women who used marijuana during the first trimester at the rate of two or more joints per month and a random sample of women who used less than this amount were selected. This method of sample selection allowed us to include all heavier users and a representative sample of women who used smaller amounts. The two cohorts were combined for the analyses presented in this paper. The women in the MHPCD were selected from a prenatal clinic rather than from a substance use treatment center. As a result, they constituted a representative sample of lower income pregnant women. Their substance use during pregnancy was, in general, light to moderate, although subjects who represented the entire spectrum of use were included in the sample. Fortyeight percent of the women were white and the remainder were African American, reflecting the distribution of the clinic population. At first trimester, 60% of the women had completed high school, their mean age was 23 years (range 18 to 42), and their average family income was $450 per month. A majority (67%) of the women were not married, and 32% were primigravidas. At each phase of the protocol, the maternal interview consisted of a core data set and additional questions appropriate to the age of the child. The interview included an assessment of the mothers’ use of alcohol, tobacco, marijuana, and other drugs, including other illicit drugs and prescribed and over-the-counter medications. At each follow-up phase, the environment of the child was carefully

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assessed across multiple domains. Variables were selected to represent factors that are known to be risk factors in the development of the child. At each study phase, growth, morphologic abnormalities, and the developmental, cognitive, and behavioral characteristics of the offspring were assessed with age-appropriate measures by examiners who were blind to maternal substance use. All children received physical examinations; weight, height, head circumference, and palpebral fissure width were measured; and a history of illness, injury, hospitalization, vision, hearing, and developmental problems was obtained. Each of these measures was viewed as a potential outcome of the prenatal exposure to alcohol. At birth, 763 live, singleton offspring were examined from the initial combined cohort of 829 pregnancies. Attrition resulted from 18 fetal or perinatal deaths, 8 mothers who refused, 16 subjects who were missed, 21 who moved away from the city, 1 infant who was placed for adoption, and 2 sets of twins. On average, the offspring weighed 3,198 g (range 1,040 to 4,990) at birth, 10.2% were low birthweight (<2,500 g), 8.5% were premature (<37 weeks gestation), and 13.6% were small for gestational age (<10th percentile for growth). At 6 years, 668 children were examined. This represents a completion rate of 88% of the birth cohort and 94% of the eligible cohort. When the children were age 10 years, we interviewed 82% of the birth cohort, or 90% of all eligible mother-child dyads. Four and 8% of the children were not in the custody of their birth mothers at 6 and 10 years, respectively. In these cases, information on their current environment was provided by their current custodian. Measurement of Substance Use We measured alcohol use during each month of the first trimester. Women were asked to indicate on a calendar (a) when they got pregnant, (b) when they realized they were pregnant, and (c) when the pregnancy was confirmed. After the questions on alcohol consumption, the interviewer returned to the calendar and asked the woman, for the times between conception and recognition and between recognition and confirmation, whether her alcohol use was like her prepregnancy pattern or more like what she reported for her first trimester pattern. Sixty-one percent of the women reported that between conception and pregnancy recognition they were still drinking at the rate they had reported for use prior to pregnancy, and 30% were still drinking at their prepregnancy rate between recognition and confirmation. For heavier drinkers, women who drank at least a drink a day, the rates were 91% and 50%, respectively. Thus, it is important to recognize that a woman’s identification of herself as pregnant does not begin at conception but at a later cognitive time point. Substance use was assessed for each trimester of pregnancy at

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fixed time points during pregnancy. Alcohol and marijuana use were determined for each month of the first trimester. For second and third trimesters, assessment was over the entire trimester. Usual, maximum, and minimum quantity and frequency were determined for beer, wine, liquor, and wine and beer coolers and for marijuana use. The methodology for this assessment has been published elsewhere (Day & Robles, 1989). Tobacco use and illicit drug use (other than marijuana) were determined for each trimester. At 6 and 10 years, substance use was assessed over the prior year. One of the difficult problems in measuring prenatal alcohol use is that it changes over the course of the pregnancy. Thirty-eight percent of the women reported consming a drink or more per day in the year prior to pregnancy. By first trimester, this rate had decreased to 18.8%, and by the third trimester to 3.8%. Women who drank at least a drink a day (heavy drinkers) during the first trimester were more likely to be white and younger. By contrast, women who drank this amount during the third-trimester were significantly more likely to be African American, unmarried, and users of tobacco, marijuana, and other illicit drugs. In addition, 48% of the third-trimester heavier users of alcohol reported marijuana use compared with 14% of the abstainers, and 7% of the heavier drinkers reported other illicit drug use compared with < 0.1% of the abstainers. This polysubstance use is an important confounding factor in the analyses and must be accounted for statistically. Elsewhere, we have presented analyses of the misinterpretation that can occur when multiple substance exposure is not considered (Richardson & Day, 1999). Measures of the Current Environment Current environment was measured across multiple domains. Socioeconomic status was assessed by maternal education, current work status, and family income. Measures of the psychological environment included depression, anxiety and hostility, self-esteem, and the mother’s perception of how difficult the child was. A measure of household structure reflected the number and relationship of residents in the household and the number of and distance in age between siblings. The quality of the home environment was assessed. The social environment was assessed by a measure of the family environment, social support, and life events of the mother and by measures of the child’s social environment; attendance at preschool, nursery school, or day care at age 3; and school attendance at ages 6 and 10. In the analyses, we have accounted for the effects of other substances used during the prenatal period, including tobacco, marijuana, cocaine, and other illicit drugs during each trimester. Each of these substances also has a potentially teratogenic effect. Additionally, all analyses controlled for the current environment of the child and for current maternal alcohol, tobacco,

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marijuana, and other illicit drug use. Prenatal alcohol and marijuana exposure were used as continuous variables, expressed as average daily volume (ADV) and average daily joints (ADJ), respectively. Tobacco was measured in cigarettes per day. For all analyses, illicit drugs other than marijuana were grouped together and were used as a dichotomous variable. In the regression analyses, ADV and ADJ were transformed by using the natural log to reduce skewness. The outcome we have chosen to illustrate the teratologic model is growth as represented by weight, height, and head circumference at each phase. THE RELATIONSHIP BETWEEN PRENATAL ALCOHOL EXPOSURE AND GROWTH In the MHPCD study, an increased risk of having a low birthweight baby (<2,500 g) was associated with alcohol consumption in the first or second month of pregnancy. Drinking during early pregnancy was also associated with an increased risk of giving birth to an infant who was below the 10th percentile for length or head circumference (Day et al., 1989). A reanalysis of these data also showed a significant linear relationship between secondtrimester alcohol use and birthweight. Other characteristics including gender of the infant, race, maternal height and age, and tobacco, marijuana, and other illicit drug use also affected the birth size of the infants. These results from the MHPCD are mirrored by those of some, although not all (Ernhart, et al.,1985; Russell & Skinner, 1988), reports from other longitudinal studies of drinking practices. In the Seattle Longitudinal Prospective Study, approximately 500 predominantly white, middle-class women were interviewed in the 5th month of pregnancy regarding their alcohol use during the month prior to pregnancy recognition and in the 5th prenatal month. At birth, alcohol use in the 5th month was associated with reduced weight, length, and head circumference among the offspring (Streissguth, Martin, & Barr, 1981). Smith, Coles, Lancaster, Fernhoff, & Falek (1986) enrolled three groups of low socioeconomic status, single, African-American women in the Atlanta area (n=149): (1) women who did not drink during pregnancy, (2) women who quit drinking in the second trimester, and (3) women who continued to drink throughout pregnancy. At birth, there was a significant relationship between duration of use and weight, length, and head circumference. The women who continued to drink had the smallest infants, and those who never drank had the largest offspring. There was an effect of dose; those drinking more than four drinks per day had the smallest infants. In addition, there was an interaction between dose and duration; women who drank heavily and continuously throughout pregnancy had the smallest infants.

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Two other studies reported on size at birth, although because each used a measure that averaged alcohol use over the entire pregnancy in their analyses, it is not possible to estimate the effects of dose, timing, or duration. Fried and O’Connell (1987) enrolled a sample of 667 predominantly white, middleclass women. At birth, there was a significant reduction in birthweight and length among offspring of women who drank more than two drinks per day, averaged across pregnancy, compared with the remainder of the sample. Jacobson, Jacobson, Sokol, Martier, et al. (1994) interviewed a sample of 417 pregnant African-American, inner city women regarding their alcohol and drug use. Exposure to alcohol, averaged across pregnancy, was associated with decreased birthweight, length, and head circumference, although only among offspring of women over age 30. The infants of women who drank an average of more than four drinks a day were the most affected. In the MHPCD study, the relationship between prenatal alcohol exposure and growth deficits in the offspring was stronger at 8 months of age than at birth. Weight, length, and head circumference were each significantly and inversely correlated with second- and third-trimester alcohol exposure after the effects of the current environment were controlled (Day et al., 1990). In addition, difficulty of feeding was significantly correlated with alcohol exposure during the first trimester, although it was not associated with current maternal alcohol use. The pattern of growth deficits persisted at 18 months of age. Each of the growth parameters was significantly affected by prenatal exposure to alcohol during the second and third trimesters of pregnancy and by drinking continuously throughout pregnancy (Day, Goldschmidt et al., 1991). Using the 18-month data, we assessed the predictive validity of two measures of drinking: average daily volume (ADV), an averaged measure of consumption by trimester, and frequent heavy drinking (FHD), a measure of the frequency of consuming five or more drinks per occasion. Although both significantly predicted growth retardation, ADV was a better predictor of effects and, when ADV was controlled, FHD did not contribute any further explanation (Day, Goldschmidt et al., 1991). Therefore, episodic drinking was not, by itself, a significant predictor of growth deficits. This is consonant with the theoretical model, which predicts that continuous rather than episodic exposure should predict growth deficits because episodic exposure allows repair between episodes. Jacobson, Jacobson, and Sokol (1994) also found effects on growth in infancy. Prenatal alcohol exposure was significantly related to decreased weight and height at 6.5 months, although effects were detectable only at the level of more than four drinks per day. At 13 months, prenatal alcohol use was associated with decreased height only among offspring of mothers over age 30. A summary measure of alcohol use throughout pregnancy was used for these analyses. It is likely that women who drank heavily and continuously throughout pregnancy

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would score highest on this measure; therefore, these data corroborate the observations of the MHPCD project on the effects of duration. Two additional reports of predominantly white, middle-class women found no effects of prenatal exposure on infant size at 12 months (O’Connor, Brill & Sigman, 1986) or at 12 and 24 months of age (Fried & O’Connell, 1987). The Seattle study also reported that drinking in the 5th month of pregnancy had no effect on growth at 8 months, although there was an effect of drinking in the month prior to pregnancy recognition on weight and length (Barr, Streissguth, Martin, & Herman, 1984). Two trends are noticeable in the longitudinal analyses of the effects of prenatal alcohol exposure on very young children. The first is that drinking continuously throughout pregnancy and drinking in the second and third trimesters of pregnancy are each significant predictors of growth deficits in the offspring. It is difficult to separate these two patterns because the women who drank in the third trimester were, for the most part, the women who drank continuously throughout pregnancy. A second finding is that growth deficits persist in lowincome populations, but they generally are not detectable on follow-up in more advantaged populations. There is an interaction between the environment and the effects of prenatal alcohol exposure on growth. At 3 years of age, both second- and third-trimester alcohol use were significantly and inversely associated with weight (Day, Robles, 1991). The decrease in weight associated with an increase of one drink per day was 1.26 kg (2.9 lb) for the second trimester and 1.02 kg (2.2 lb) for the third trimester. Prenatal alcohol exposure during both the first and third trimesters had significant negative effects on height at 3 years after current environment was controlled. Head circumference at 3 years was negatively affected by prenatal exposure during trimesters two and three. A further analysis of these data used a general unbalanced repeatedmeasures model with a fully parameterized covariance matrix to explore the relationship between prenatal exposure and growth over time. This analysis demonstrated that prenatal alcohol exposure had a greater effect on size between birth and 8 months than during any other study follow-up interval (Geva, Day, Goldschmidt, & Stoffer, 1993). In the MHPCD study, we have continued to find a significant, inverse relationship between prenatal alcohol exposure and size at age 6 (Day, Richardson, Geva, & Robles, 1994). Weight was predicted by alcohol exposure during the first, second, and third trimesters. Height was significantly affected by first- and third-trimester maternal alcohol use (and marginally by second-trimester exposure). Second- and third-trimester exposure predicted head circumference. At age 6, the child’s appetite was significantly predicted by alcohol exposure in the first trimester. This finding was consistent with an earlier report from this cohort that showed a relationship between difficulty in feeding at 8 months and

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first-trimester alcohol exposure (Day et al., 1990). Both findings were consistent with the time of embryonic development of the appetite and satiety centers in the brain (Moore & Persaud, 1993), demonstrating specificity of timing. The effect of prenatal exposure on growth was independent of the effect of exposure on appetite, an indication that although clearly related, poor appetite did not mediate the growth deficiencies. Two additional longitudinal studies also found that head circumference was affected in school-age children. In the Atlanta study, 68 of the children were assessed at ages ranging from 5 to 8 years (Coles et al., 1991). The offspring who were exposed continuously to alcohol throughout pregnancy had smaller head circumferences than the children who were not exposed or who were exposed only early in pregnancy. Children who were exposed only through the second trimester did not differ in head circumference from the nonexposed group. The three groups were not significantly different in weight or height. In the MHPCD cohort, there was a significant relationship between prenatal alcohol exposure and the children’s weight at age 10 for each trimester of pregnancy (Day et al., 1999) . There was a 4-lb decrease in weight for a change in first-trimester alcohol exposure from 0 to an ADV of 1. The decrements in weight for a 1-drink change in the second and third trimesters were 7 and 7.1 lb., respectively. Height was predicted by each trimester, and head circumference by exposures in the first and third trimesters. There was also a significant effect of current polysubstance use in the household. Children of mothers who were polysubstance users weighed, on average, 89.9 lb, compared with 92.2 lb. among the children whose mothers did not currently use multiple substances. The effects of prenatal alcohol use were independent of the current substance use of the mother and persisted after controlling for the current substance use. The prenatal and current measures of substance use were additive in their effects. Separate analyses explored the trimesters in which exposure had the most significant effects. This was done by entering pairs of trimesters into the regression. (The tolerance was good when only two trimesters were entered, but it was not always adequate when all three were entered.) In these analyses, we found that the greatest effect on weight at 10 years was exposure during the second trimester of pregnancy; height and head circumference were predicted by first-trimester exposure. Social and environmental factors such as the number of people living in the household, quality of the household environment, presence of a male in the household, maternal social support, and psychological status were significant predictors of the children’s size at age 10. The effects of prenatal exposure were independent of the effects of the current environmental factors, and when we modeled interactions between the current environmental factors and prenatal alcohol exposure, none of the interactions was significant.

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SUMMARY AND DISCUSSION In summary, the results from the MHPCD study demonstrate that children prenatally exposed to alcohol have long-term growth deficits. We have found a statistically significant decrease in size, characterized by symmetrical growth retardation, through the age of 10. These effects model as a dose-response relationship. Similarly, reports from the Atlanta cohort have found continued deficits in head size, although not symmetrical growth retardation, through an average age of approximately 6 (Coles et al., 1991). Studies in Detroit (Jacobson, Jacobson, & Sokol, 1994) have reported growth deficits at ages 6.5 and 13 months. By contrast, several studies that reported growth deficits at birth have found that the growth deficit was not maintained on follow-up (Fried & O’Connell, 1987; O’Connor et al., 1986; Streissguth, 1992). It is notable that these studies were all of advantaged populations, in contrast with the studies listed previously that were of generally lower socioeconomic samples. We would like to restate the findings from the MHPCD study and some of the other longitudinal studies in terms of the hypotheses that were listed in the beginning section of the chapter: 1. The outcomes are related to dose. With increasing dose, effects will be found on the central nervous system (CNS), growth, and morphology, in that order. At lower exposures, only CNS effects will be found; at higher doses, effects will be found across domains. Studies such as the MHPCD and others (Day & Richardson, 1994) have shown that in populations that are exposed to lower levels of prenatal alcohol exposure, the effects are more subtle and are expressed as change in the CNS and growth. The full expression of the fetal alcohol syndrome, which includes effects across the domains of CNS, growth, and morphologic changes, is found only among the offspring of women who are very heavy drinkers or alcoholics (Day & Richardson, 1994). 2. Within each domain, the magnitude of the effect will be related to dose. This relationship may be exhibited either as a dose-response curve or as a threshold effect. The effects of alcohol exposure during pregnancy on growth have been shown in the MHPCD study to be linear or dose-response relationships. We have found a threshold effect on CNS development as reflected by school achievement (Goldschmidt et al., 1996). Similar findings have been reported by other studies (Schenker et al., 1990; Smith et al., 1986; Streissguth et al., 1981). 3. Effects will be a function of the developmental stage of the organism at the time of the exposure. Time, with respect to prenatal substance exposure, has two components: the stage of pregnancy during which exposure occurs and the duration of exposure. Weight was predicted by second-trimester alcohol exposure, and height by alcohol exposure in the first trimester. The peak velocity

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in growth of height occurs during the first trimester of pregnancy; for weight, the peak velocities of growth are toward the end of the second trimester and the early part of the third trimester (Kliegman & Hulman, 1987). This timing indicates that the effect of alcohol may be to suppress the velocity of cell division at critical times. The peak velocity for head circumference growth is also later in pregnancy. Our data demonstrate effects of both first- and third-trimester exposure, although first seems to predominate. This may mean that the decrease in head circumference is a morphologic anomaly rather than a growth deficit. This first-trimester effect parallels that found for the dysmorphic facial features associated with alcohol exposure (Day, Robles, et al., 1991). In the MHPCD findings, other outcomes are also associated with specific periods of exposure. For example, feeding difficulties and poor appetite were each related to first-trimester exposure, and neither was affected by exposure during any other time during pregnancy. This corresponds directly with the time of formation of the hunger and satiety centers within the developing CNS (Moore & Persaud, 1993). Our results and the findings of Coles et al. (1991) illustrate the effect of duration. Offspring exposed to drinking throughout pregnancy were the most affected; those whose mothers decreased their use in the second trimester did better than the continuously exposed group and less well than those who were not exposed. Thus, the decreased duration of exposure led to an attenuation of the effects of exposure and, perhaps, some repair. 4. The outcome will be an expression of the balance between the damage from the toxic exposure and the reparative properties of the organism. In the MHPCD, we have found that, for growth, if women decreased their use during pregnancy, the fetus would not be as small as a fetus that was exposed continuously through pregnancy. This represents the ability of the growth parameters to repair some of the deficits during gestation. On the other hand, other characteristics, such as the facial dysmorphology associated with FAS and fetal alcohol effect cannot be reversed. 5. At birth, the observed effects will be the cumulative response of the child to the teratogen and the intrauterine environment. Factors in addition to alcohol exposure will also affect the development of the fetus. As we noted in the analysis at birth, factors such as other maternal substance use also predicted size at birth. The study by Jacobson, Jacobson, and Sokol (1994) has demonstrated a relationship with outcome at birth and maternal age. 6. After birth, the observed effects will be the cumulative response of the child to the teratogenic exposure and the environment in which the child is raised. There were effects of prenatal alcohol exposure on the size at birth of offspring in more advantaged cohorts (Fried & O’Connell, 1987; O’Connor et al., 1986; Streissguth et al., 1981), but these effects were not detectable on follow-up. By contrast, in the low-income samples, significant growth deficits

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have been reported through school age (Coles et al., 1991) and through the age of 10 years (Day et al., in press). We have also reported in the MHPCD cohort the additional effects on growth of current polysubstance use by the mother when the child is age 10 years. What we have illustrated is a model for understanding the biologic effects of a teratogen. The example has demonstrated the direct effects of alcohol exposure during gestation on the size of the infant at birth and on the subsequent patterns of growth. In the MHPCD cohort, the children who were prenatally exposed to alcohol were smaller at birth and have remained significantly smaller through 10 years of age. Exposure at different points in gestation and for periods of varying duration will lead to different outcomes. We have illustrated this with data from the MHPCD project and from the literature. Children who were exposed throughout pregnancy were smaller than those who were not exposed to alcohol and smaller than those who were exposed for shorter periods of time. The trimester during pregnancy is also important as it determines which of the growth parameters will be affected. Additionally, the pattern of alcohol use, which determines the amount of exposure, is an important variable in determining the long-term outcome of the exposed child. There is evidence that the relationship between growth and exposure is linear. Added to these findings is the important effect of the intrauterine environment and the environment in which the child is raised. Alcohol is only one potential teratogen. The alcohol-exposed fetus has a greater risk of being exposed to other substances and to poorer maternal health than does the child of a mother who did not drink during pregnancy. After birth, the effects of the environment can heighten or ameliorate the effects of a teratogenic exposure. Higher socioeconomic status, for example, reduces the impact of prenatal alcohol exposure on growth. Polysubstance use in the household, in combination with prenatal exposure, is associated with a larger growth deficit. These conclusions await confirmation from further studies. We need more data on drinking patterns during pregnancy to enable analyses to link the quantity and frequency of alcohol use to specific elements in the biology of fetal development. In addition, we need to study much more carefully the effects of environmental factors that exacerbate or ameliorate the effects of gestational alcohol exposure. These findings have a number of important implications for prevention, counseling, and intervention. A dose-response relationship between alcohol use and outcome implies that women should be advised not to drink during pregnancy. However, women who drink should be told that quitting will increase the chances of a positive outcome, at least with respect to some outcomes. Further, optimizing the environment in which the vulnerable children are raised may offset some of the negative effects of prenatal alcohol exposure.

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REFERENCES Barr, H., Streissguth, A., Martin, D., & Herman, C. (1984). Infant size at 8 months of age: Relationship to maternal use of alcohol, nicotine, and caffeine during pregnancy. Pediatrics, 74, 336–341. Cicchetti, D. (1990). A historical perspective on the discipline of developmental psychopathology. In J.Rolf, A.Masten, D.Cicchetti, K.Nuechterlein, & S.Weintraub (Eds.), Risk and protective factors in the development of psychopathology (pp. 1– 28). Cambridge: Cambridge University Press. Coles, C., Brown, R., Smith, I., Platzman, K., Erickson, S., & Falek, A. (1991). Effects of prenatal alcohol exposure at school age: I. Physical and cognitive development. Neurotoxicology and Teratology, 13, 357–367. Day, N., Goldschmidt, L., Robles, N., Richardson, G., Cornelius, M., Taylor, P., Geva, D., & Stoffer, D. (1991). Prenatal alcohol exposure and offspring growth at eighteen months of age: The predictive validity of two measures of drinking. Alcoholism: Clinical and Experimental Research, 15, 914–918. Day, N., Jasperse, D., Richardson, G., Robles, N., Sambamoorthi, U., Taylor, P., Scher, M., Stoffer, D., & Bloom, M. (1989). Prenatal exposure to alcohol: Effect on infant growth and morphologic characteristics. Pediatrics, 84, 536–541. Day, N., & Richardson, G. (1994). Comparative teratogenicity of alcohol and other drugs. Alcohol Health and Research World, 18, 42–48. Day, N., Richardson, G., Geva, D., & Robles, N. (1994). Alcohol, marijuana and tobacco: The effects of prenatal exposure on offspring growth and morphology at age six. Alcoholism: Clinical and Experimental Research, 18, 786–794. Day, N., Richardson, G., Robles, N., Sambamoorthi, U., Taylor, P., Scher, M., Stoffer, D., Jasperse, D., & Cornelius, M. (1990). The effect of prenatal alcohol exposure on growth and morphology of the offspring at eight months of age. Pediatrics, 85, 748–752. Day, N., & Robles, N. (1989). Methodological issues in the measurement of substance use. Annals of the New York Academy of Sciences, 562, 8–13. Day, N.L., Robles, N., Richardson, G., Geva, D., Taylor, P., Scher, M., Stoffer, D., Cornelius, M., & Goldschmidt, L. (1991). The effects of prenatal alcohol use on the growth of children at three years of age. Alcoholism: Clinical and Experimental Research, 15, 67–71. Day, N.L., Zuo, Y., Richardson, G.A., Goldschmidt, L., Larkby, C.A., & Cornelius, M.D. (in press). Prenatal alcohol use and offspring size at 10 years of age. Alcoholism: Clinical and Experimental Research. Dixon, R., & Lerner, R. (1988). A history of systems in developmental psychology. In M.H.Bornstein, & M.E.Lamb (Eds.), Developmental psychology: An advanced textbook (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum. Ernhart, C., Wolf, A., Linn, P., Sokol, R., Kennard, M., & Filipovich, H. (1985). Alcoholrelated birth defects: Syndromal anomalies, intrauterine growth retardation, and neonatal behavioral assessment. Alcoholism: Clinical and Experimental Research,

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9, 447–453. Fried, P., & O’Connell, C. (1987). A comparison of effects of prenatal exposure to tobacco, alcohol, cannabis, and caffeine on birth size and subsequent growth. Neurotoxicology and Teratology, 9, 79–85. Geva, D., Day, N., Goldschmidt, L., & Staffer, D. (1993). A longitudinal analysis of the effect of prenatal alcohol exposure on growth. Alcoholism: Clinical and Experimental Research, 17, 1124–1129. Goldschmidt, L., Richardson, G., Staffer, D., Geva, D., & Day, N. (1996). Prenatal alcohol exposure and academic achievement at age six: A nonlinear fit. Alcoholism: Clinical and Experimental Research, 20, 763–770. Horowitz, F.D. (1992). The risk-environment interaction and developmental outcome: A theoretical perspective. In C.W.Greenbaum, & J.G.Auerbach (Eds.), Longitudinal studies of children at psychological risk: Cross-national perspectives (pp. 29–40). Norwood, NJ: Ablex Publishing. Jacobson, J., Jacobson, S., & Sokol, R. (1994). Effects of prenatal exposure to alcohol, smoking, and illicit drugs on postpartum somatic growth. Alcoholism: Clinical and Experimental Research, 18, 317–323. Jacobson, J., Jacobson, S., Sokol, R., Martier, S., Ager, J., & Shankaran, S. (1994). Effects of alcohol use, smoking, and illicit drug use on fetal growth in black infants. Journal of Pediatrics, 124, 757–764. Kliegman, R., & Hulman, S. (1987). Intrauterine growth retardation: Determinants of aberrant fetal growth. In A.Fanaroff & R.Martin (Eds.), Neonatal-perinatal medicine: Diseases of the fetus and infant (4th ed., pp. 69–102). St. Louis, MO: CV Mosby. Moore, K., & Persaud, T. (1993). The developing human. 5th ed., Philadelphia: WB Saunders Co. O’Connor, M., Brill, N., & Sigman, M. (1986). Alcohol use in primiparous women older than 30 years of age: Relation to infant development. Pediatrics, 78, 444–450. Peindl, K. (1993). Correlates and predictors of drinking patterns. Unpublished doctoral dissertation, University of Pittsburgh. Richardson, G.A., & Day, N.L. (1999) Studies of prenatal cocaine exposure: Assessing the influence of extraneous variables. Journal of Drug Issues, 29, 225–230. Russell, M., & Skinner, J. (1988). Early measures of maternal alcohol misuse as predictors of adverse pregnancy outcomes. Alcoholism: Clinical and Experimental Research, 12, 824–830. Sameroff, A.J. (1986). Environmental context of child development. Journal of Pediatrics, 109, 192–200. Sampson, P., Streissguth, A., Barr, H., & Bookstein, F. (1989). Neurobehavioral effects of prenatal alcohol: Part II. Partial least squares analysis. Neurotoxicology and Teratology, 11, 411–491. Schenker, S., Becker, H., Randall, C., Phillips, D., Baskin, G., & Henderson, G. (1990). Fetal alcohol syndrome: Current status of pathogenesis. Alcoholism: Clinical and Experimental Research, 14, 635–647.

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Smith, I., Coles, C., Lancaster, J., Fernhoff, P., & Falek, A. (1986). The effect of volume and duration of exposure on neonatal physical and behavioral development. Neurobehavioral Toxicology and Teratology, 8, 375–381. Sroufe, L., & Rutter, M. (1984). The domain of developmental psychopathology. Child Development, 55, 17–29. Streissguth, A. (1992). Fetal alcohol syndrome and fetal alcohol effects: A clinical perspective of later developmental consequences. In I.Zagon & T.Slotkin (Eds.), Maternal substance abuse and the developing nervous system (pp. 5–25). San Diego, CA: Academic Press. Streissguth, A., Martin, D., Martin, J., & Barr, H. (1981). The Seattle Longitudinal Prospective Study on alcohol and pregnancy. Neurobehavioral Toxicology and Teratology, 3, 223–233. Vorhees, C. (1989). Concepts in teratology and developmental toxicology derived from animal research. Annals of the New York Academy of Sciences, 562, 31–41.

CHAPTER 6

The Clinical and Social Ecology of Childhood for Children of Alcoholics Description of a Study and Implications for a Differentiated Social Policy ROBERT A.ZUCKER HIRAM E.FITZGERALD SUSAN K.REFIOR LEON I.PUTTLER DIANE M.PALLAS DEBORAH A.ELLIS

This book addresses the problems of children from drug-addicted families, with the term drug addiction used in its generic sense. In this spirit, we begin our account from this perspective, with a discussion of the epidemiology of adult drug abuse and dependence and its implications for the ecology of childhood in the families where the drug-involved adults reside. As we proceed, we describe how the more circumscribed focus of this chapter, the clinical and social ecology of children in alcoholic families, fits into this larger picture. We then describe an etiologic study, begun in the late 1970s, whose implementation required us to address a great many of these ecological issues. The bulk of the chapter provides an account of that work and uses it as a springboard to address the broader scientific and social policy questions that are the focus of this book. In our account, we use the term alcoholism as a broader umbrella, to cover both of the more specific descriptors of alcohol abuse and alcohol dependence. Although there are many damages from drug involvement that come about from the misfortune of single events, it is likely that the major effects upon children growing up in drug-addicted families are more cumulative. The child’s witness of a parent’s arrest for drunk driving or drug possession is a shaming experience that sets the child and his or her family apart from others. However, what is frequently ignored when such accounts appear in the media is that the parental event, when extreme, is more often a marker for a set of family experiences that are occurring over a substantial period of time. Possession of 109

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illegal drugs without a prior history of drug involvement is unusual. Most often it requires the development of a resource network to access such drugs, the development of an addiction history that has a progression from softer to harder drugs, preoccupation with one’s own drug involvement so that parenting is neglected, and so forth. In the same vein, the roadside survey literature indicates that the probability of being arrested with a blood alcohol concentration (BAC) in the impaired range (i.e., a BAC of 0.10% or higher) is of the order of 1 in 200 (cf. Beitel, Sharp, & Glauz, 1975). To put this in a time perspective, another study found that apprehended alcoholic drinking drivers had averaged 9.4 years in their current drinking pattern prior to the arrest that led them into classes or treatment (Fine, Steer, & Scoles, 1978). From this vantage point, the individual marker events are simply incidents that have made the family’s ongoing trouble more visible to the public. From the perspective of social policy, what is needed is a deeper understanding of the family’s sustained and ongoing impact structure. Only by such a strategy can we gain understanding of the relationship of parental drug use and addiction to their children’s development. It also is imperative that such work begin early enough in the family’s life cycle so that the full natural history of the experience can be charted. Three outcomes will accrue: First will be increased understanding of the manner in which parents’ use and abuse of drugs affects the ongoing developmental functioning of their children. Second, such data should have immediate applicability to prevention programming. Third, the familial risk structure for later outcomes can also be characterized. This is less often a concern of policy makers because such effects are less obvious and also take much longer to demonstrate. Nonetheless, given the known statistical relationship between family history and substance use disorder in offspring (Cotton, 1979; Russell, 1990), the experience of children of drug-involved parents needs simultaneously to be regarded as a potential nesting ground within which their own risk for later substance abuse and addictive disorder is being augmented. The lesser immediacy of this long-term outcome tends to make it a less visible social problem, although its potential for increasing social costs is far greater, given that the more distal negative outcome plays out over a lifetime. CHILDREN OF DRUG ADDICTION: WHAT DRUGS? Although the popular myth is that the harder drugs form the bulk of the nation’s drug problems, both the Epidemiologic Catchment Area (ECA) study (Regier et al., 1990) and the more recent National Comorbidity Study (NCS) (Kessler et al., 1994) indicate otherwise (see Table 6.1). Although the ECA data paint a similar picture, the NCS data are more recent and use a more recent version of the Diagnostic and Statistical Manual (the DSM-III-R) (American Psychiatric Association, 1987), and the age range studied, those between the ages of 15 and

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Table 6.1 Lifetime (and 12-Month) Prevalence of DSM-III-R Disorders (Estimated U.S. Population Rates)

Note. Data are fromKessler et al. (1994) and are DSM-III-R diagnoses for persons age 15 to 54 in the noninstitutionalized population.

54, more closely approximates the adult popula tion who are candidates for parenthood than does the ECA study. The NCS U.S. population estimates show that one in four individuals (26.6%) have had a substance use disorder at some point in their lives. The rate of those with an active diagnosis over the past year is one in nine. The lifetime prevalence rate for those with an “other drug” diagnosis but no alcohol diagnosis is only 1 in 32 (3.1%), and the 12-month rate is only 1 in 62. Put another way, of those who have had any substance use disorder, 88% have had an alcohol diagnosis, either with or without an otherdrug diagnosis. This pattern is evident even at the more severe diagnostic level of dependence; here 82% of those with a dependence diagnosis are either alcohol dependent or both alcohol and otherdrug dependent (Kessler et al., 1994; Kessler et al, 1997). These data were generated to characterize rates of individual disorder in the adult subpopulation. However, when they are recast from a life course perspective, they can be conceptualized as indicators of a family environment of mental disorder among that subset of individuals who are parents. Within that framework, the problem of children of addiction in the United States is primarily one where alcohol is the parental drug of abuse. A recent report utilizing data from the 1996 Substance Abuse and Mental Health Services Administration (SAMHSA) National Household Survey on Drug Abuse (Huang, Cerbone, & Gfroerer, 1998) provides an even more focused estimate of the magnitude of the problem as it impinges upon children. That report also addresses the issue of “what drug.” Projections from the SAMHSA national sample are that 6.2 million children, or approximately 8.3% of children

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in the United States under the age of 18, are living in households where one or more of the parents has been actively alcohol dependent in the past year (see Table 6.2). When the risk structure is defined as parents who are dependent on illicit drugs as well as alcohol, the figure increases to 10% (see Table 6.3). In other words, 83% (i.e., 8.3%/10%) of significantly drug involved families are dealing with alcohol dependence as either the only drug of choice or a primary drug of choice. Other drug involvement implicates only 17% of the population. Although these figures are familiar to epidemiologists, they are not common knowledge either in the popular press or among policy makers, where drug dependence and its impact on children is more often characterized as a problem of illicit drug use. Table 6.2 Estimated Number and Percentage of Children in the Household Who Had One or More Parents Dependent on Alcohol, by Children’s Ages

a

Children are defined as biologic, step, adoptive, or foster. Children age 17 and younger who were not living with one or more parents for most of the quarter of the NHSDA interview are excluded from the present analysis. According to the March 1995 Current Population Survey, this amounts to approximately 3 million or 4% of children under 18 years of age. b

Note. Alcohol dependence is determined by two responses: Alcohol was used in the past year, and the user reported meeting three of the following six DSM-IV dependence criteria: built up a tolerance for alcohol; used alcohol more often than intended; wanted to cut down or tried, but found they couldn’t; had a month or more in the past year when they spent a great deal of time getting the alcohol, using alcohol, or getting over its effects; alcohol reduced important activities; alcohol caused emotional or health problems. Note. From Office of Applied Studies, SAMHSA, National Household Survey on Drug Abuse, 1996.

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Table 6.3 Estimated Number and Percentage of Children in the Household Who Had One or More Parents Dependent on Alcohol, and/or Illicit Drugs, by Children’s Ages

a

Children are defined as biologic, step, adoptive, or foster. Children age 17 and younger who were not living with one or more parents for most of the quarter of the NHSDA interview are excluded from the present analysis. According to the March 1995 Current Population Survey, this amounts to approximately 3 million or 4% of children under 18 years of age. b

Note. Substance dependence is defined as dependence on alcohol, one or more illicit drugs, or both. It is determined by two responses: The substance was used in the past year and the user reported meeting three of the following six DSM-IV dependence criteria: built up a tolerance for the substance; used the substance more often than intended; wanted to cut down or tried but found they couldn’t; had a month or more in the past year when they spent a great deal of time getting the substance, using the substance, or getting over its effects; substance reduced important activities; substance caused emotional or health problems. Note. From Office of Applied Studies, SAMHSA, National Household Survey on Drug Abuse, 1996.

WHAT FAMILY ATTRIBUTES MARK HIGHER RISK? Other epidemiologic data allow an even tighter specification of the context structure for highest family risk. Focusing just on alcohol involvement for the moment, and using measures of heavy alcohol consumption (binge drinking and heavy frequency of use) as indicators of current psychopathology, it is apparent that the heaviest alcohol use occurs in the 18 to 30 age range (Office of Applied Studies, 1998). The data are parallel for men and women but much

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more striking among men. These figures also coincide with national estimates of higher risk for drug involvement in younger adults (Christie et al., 1988) and more recent evidence of age variability in diagnosis rates (Grant, 1997). The diagnosis of current DSM-IV alcohol dependence (i.e., 12-month prevalence) is four times more common among 18- to 24-year-old men than among men over age 35 and three times more common among 18- to 24-year-old women (Grant, 1997). The comparable rates for those age 25 to 34 are double those found among the older age groups for both men and women. These data imply that exposure to a drug-involved parent will be greater for children with younger parents. From two different directions, other literatures indirectly suggest that parenting is more impaired in these populations. (For example, Chilcoat, Breslau, and Anthony [1996] found that parent monitoring, as an indicator of both effective parenting and a protective factor against child drug involvement, was less likely when mothers had a substance abuse diagnosis.) A less obvious point derivative from this observation is that the drug-related parenting risk should map onto the poorer caregiving practices known to occur among younger parents (Belsky, 1984; Field, Widmayer, Stringer, & Ignatoff, 1980). In other words, child-rearing risk will be potentiated in families whose parents are younger, and younger parents are more likely to be drug involved. So both of these risks for impaired child development will be more likely. CHARTING THE FAMILY’S IMPACT ON THE CHILD: DESIGN ISSUES Given these data on the variability of drug involvement across subpopulations, studies being set up to understand what kind of influence the drug-addicted family has on its offspring face a number of choices about design parameters. Once made, they determine the applicability of study findings to larger issues of the family’s impact on the child in families with at least one drug-dependent parent. As we have already indicated, the three biggest choices relate to (a) selection of the drug of abuse or dependence, (b) choice of relative severity of risk structure (i.e., more vs. less dense), and (c) choice of sampling frame as this pertains to representativeness of the database. All of these issues ultimately are relevant to the generalizability of the work. But in this arena in particular, the researcher’s dilemma is frequently a choice between a general population study and a study recruited from a treatment sample. The general population study, by the nature of the sampling frame, will only sparsely study the 11 % of the population of adults who are actively drug dependent (see Table 6.1), and unless statistical power is adequate to test for interactions, descriptions of the relationships among risk factors that accrue will be heavily weighted by the nature of these associations among the less damaged majority of nonabusers and among abusers who are not drug dependent. The problem is further

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compounded in longitudinal general population studies because the clinically more distressed and socially more disadvantaged individuals in the study are more likely to drop out of school or become lost to sight in other ways. Thus, follow-up becomes more difficult, and such studies over time, unless very carefully maintained, lose their ability to claim relevance to the clinical subset of the population from which the greatest long-term payoff is to be derived. In contrast, treatment studies are selectively sampled (and hence biased) by the patients’ nonrandom routes into treatment (Goodman et al., 1997), and unless a focused effort to sample across treatment sites is carried out, the base of study generalizability will be poorly defined. Such sampling frames also overrepresent individuals of high-end severity and of greater comorbidity (Berkson, 1946), where continuity of disorder is more likely to exist (Cohen & Cohen, 1984; Kessler et al., 1994), and where heightened family risk is therefore likely to be overemphasized. We repeat these relatively straightforward design issues because they have direct relevance for the findings that accrue, and unless policy makers are able to make sense of the discrepancies across studies or, better yet, understand what has not yet been adequately studied, the field will misrepresent itself both to itself and to the larger public. SELECTING A POPULATION FOR THE PROSPECTIVE STUDY OF CHILDREN OF DRUG-INVOLVED PARENTS The data and the details of these design issues were not as well articulated in the late 1970s, when our group began to plan a prospective family study to characterize the development of risk among children of drug-involved parents. The potentially contradictory issues of population generalizability and clinical relevance were clear, yet we had a major commitment to create a design that would reconcile both sets of demands. We were also committed to the study of families in which alcohol was the primary drug of abuse, given the likelihood that such work would have the greatest policy implications in terms of the magnitude of the problem. However, as already noted via the comorbidity data in Table 6.1, substantial other-drug involvement would be expected, even given such a selection strategy. In developing a design, we were influenced by the work of Sarnoff Mednick and Thomas McNeil (1968), who had proposed the high-risk method as the design solution for prospective studies of schizophrenia and other serious psychopathology. This methodology ensured an adequate endpoint yield of clinical outcomes, even when the causes of the disorder were unknown and even though the sampling frame accessed a population where the base rate for the disorder was relatively low. Moreover, if the selection criteria for the highrisk sample were population based, then some of the generalizability problems of restricted sampling would be offset.

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FAMILIES WITH ALCOHOL INVOLVEMENT: ECOLOGICAL AND NATURAL HISTORY VARIATIONS AND RESEARCH DESIGN COMPLICATIONS With this work as background, in the late 1970s our own group began to plan and conduct pilot work for a prospective study of risk for substance abuse and dependence, involving the selection of alcoholic fathers, their partners, and their young offspring. The restriction of the initial sampling to male alcoholics was based on the fivefold difference in abuse and dependence rates known to exist between men and women at that time (Helzer & Pryzbeck, 1988). We also were aware that female alcoholism was more often of later onset (Helzer, Burnam, & McEvoy, 1991) (and thus would show up infrequently in access nets picking up adults in their early childbearing years) or occurred among women who were uncoupled. Given our interest in evaluating the socialization impact of both parents, a sampling structure that initially accessed men was judged to be the best compromise strategy. Furthermore, a substantial amount of assortment of mating is known to occur in this population (Jacob & Bremer, 1986), so the route into familial alcoholism by way of fathers would access female alcoholics as well. From the standpoint of greater diagnostic yield in adulthood, it would be most cost-effective to index the study by way of the development of male offspring because it would take a sample five times as large to be able to analyze for developmental effects and outcomes for girls with the same statistical power as for boys. Moreover, the sex ratio indicates that the magnitude of the social problem is substantially greater among males. At the same time, girls reared in alcoholic homes are known to suffer from a large number of negative outcomes at rates far greater than the general population. These outcomes include risk for depression, physical and sexual abuse, and gynecological disorders (Gomberg, 1991; Hill, 1995; Merikangas, Leckman, Prusoff, Pauls, & Weissman, 1985). On these grounds, we believed that the inclusion of girls in the families, to the extent that they were present, would be an essential design addition. Although our interest was to chart the developmental course, the issue of how early in the life cycle to begin was by no means clear. Although a large number of antecedent behavioral and family history factors had already been connected prospectively to the occurrence of alcoholism (cf. reviews by Vaillant, 1983; West & Prinz, 1987; Zucker & Gomberg, 1986), knowledge of how these connections developed was poorly understood. The extant weight of evidence pertained to adolescence, a developmental period that has increasingly been identified as a time when drinking has already begun, especially among those who are most heavily at risk for later problem outcomes (Fiett et al., 1987; Ried et al., 1987; Zucker, Fitzgerald, & Moses, 1995). In

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addition, no knowledge existed about how early etiologic variation pertaining to alcohol involvement might be detectable. The principle we followed was that a developmentally early start was desirable, provided that the presence of early risk group variations could be identified. In other words, evidence had to be available that the process of risk aggregation had begun in order to justify a study that was chronologically quite far from the first symptomatic displays of adolescence. Our initial plan to check this out was to conduct a pilot study involving high risk families in which women were pregnant and in their third trimester. If the study showed detectable differences between higher and lower risk groups, then it was worth continuing; if not, we would need to set the design for a later stage beginning. In addition, the social complications of alcoholism vary substantially with comorbidity (Babor & Dolinsky, 1988; Regier et al., 1990), and it is therefore a reasonable assumption that course and impact on offspring would be quite different as a function of this variability. Among the major psychiatric syndromes, antisocial personality disorder has the highest degree of comorbid association with the alcohol abuse-dependence syndrome. Alcoholism with antisocial comorbidity also is associated with earlier onset of alcoholic disorder, with assortative mating, with higher levels of other psychopathology, and with a denser family history of alcoholism in the pedigree. It also is a form of the disorder that is very high in the production of clinical and social complications (e.g., suicidality, difficult treatment, difficulties with the law, family violence) (Babor, 1996; Bohman, Clonniger, & Sigvardsson, 1981; Zucker, 1987; Zucker, Ellis, Bingham, & Fitzgerald, 1996; Zucker, Fitzgerald, & Noll, 1991). On all these grounds the familial risk structure for later alcoholism was likely to be substantially greater than when such comorbidity was absent. Given the awareness that this subset of the alcoholic population encompasses great lifetime clinical as well as social costs, it made good sense to home in here because etiologic findings would have a large potential for payoff and reduction of social costs. With these data as background, a feasibility study was conducted that accessed all treatment settings (inpatient, outpatient, drunk driver education programs) in a four-county area surrounding East Lansing, Michigan. The protocol stipulated that the man needed to be coupled to a woman in the second trimester of her pregnancy at time of recruitment. The decision was to index child outcomes as a function of the father’s alcoholism. Piloting over 1 year led to the discovery that treatment sites would not provide the early family access structure that we needed. That is, alcoholics did not appear in treatment at this phase of their family-of-procreation’s life cycle. This was a completely serendipitous finding, but it has interesting implications from the standpoint of risk detection for offspring. It means that treatment settings are probably a poor site from which to access populations in which early child risk may already be occurring.

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A new sampling plan was then constructed, with three stipulations in mind: (a) the plan needed to heavily access alcoholics who have variously been described as Type II (Cloninger et al., 1981) or antisocial (Hesselbrock et al., 1984; Zucker, 1987), thus assuring that the study would have access to the social and clinical complications of the disorder known to be of great social cost (e.g., abuse, legal complications, severe symptomatology; also see Zucker, Ellis, Bingham, & Fitzgerald, 1996); (b) the strategy needed to provide representativeness within the subset of the alcoholic population we hoped to generalize; and (c) pilot work would need to provide evidence to justify a start point at whatever age we settled on. That is, a familial effect needed to be already evident to warrant tracking outcomes early in the life course (Zucker, Noll et al., 1984). At that point, we generated theory-driven predictions about early differences between the risk groups, with the understanding that if no differences were found, then an early beginning would not be justified. These specifications led to the decision to utilize a drunk driver population accessed by way of a population net covering all courts in a 3.5-county area. By restricting inclusionary criteria to drunk drivers whose blood alcohol concentration at apprehension was sufficiently high to be indicative of tolerance (and hence likely of an alcohol dependence diagnosis) and by using a sample whose offense was itself indicative that the men were more likely antisocial than alcoholics who had no conviction history, we were able to effectively access the high-comorbidity population that was of special interest. We set the family stage of initial contact as the preschool years (specifically in the 3to-5-year age range), which was later than our initial prebirth target but sufficient to allow the detection of quite early patterns of behavioral regularity, should they exist. Given our interest in both parents, we also stipulated that both biologic parents had to be together in the home, at least at the time of initial contact. A community contrast group of non-substance-abusing families living in the same neighborhoods as the alcoholic families was recruited for controls. Families needed to be of nonHispanic, white heritage because census data in the four-county area we were accessing indicated that other ethnic and racial groups would represent less than 4% of the sample. Given the extensive literature documenting a substantial relationship between patterns of alcohol involvement and ethnicracial status and the fact that we could not effectively analyze for such differences with the planned study N, we opted to exclude such variation rather than have it contribute to error. This protocol was implemented and pilottested in the early 1980s, and we established that hypothesized risk group variations could be detected when children were age 3 to 5. Specifically, differences in cognitive development, early alcohol expectancy formation, and externalizing behavior were already evident (Noll & Zucker, 1983; Zucker 1987; Zucker, Baxter, Noll, Theado, & Weil, 1982; Weil, Baxter, & Noll, 1984).

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As the sampling plan was implemented, it also became clear that in some of the neighborhoods where we were recruiting controls (i.e., where the alcoholic families lived), none could be found. When families were coupled and EuropeanAmerican, they also had a history of substance abuse or dependence, although it either did not involve a drunk-driving conviction or, if it did, it was chronologically more distant from the family’s ongoing life. Alternatively, if the family involved parent(s) free of substance abuse (as required by the protocol for controls), the family was either single parent or of minority status. On the one hand, this led to the hypothesis that alcoholic families were more likely to live in disopportunitied geographic areas. On the other hand, a pilot project examining the characteristics of these families showed that the communityaccessed alcoholics were important for another reason: They were less likely to be antisocial than the court-recruited group. On these grounds, we extended the protocol to recruit these more “silent” alcoholic families into the study. Later analyses have shown that the frequency distributions of this melded 3-group design have never violated normality assumptions, indicating that the combining of the groups was statistically appropriate. By 1987, the study was adequately funded, and full-scale subject recruitment and data collection began. Unfortunately, the wisdom of review committees at the time did not include a vision that outcomes for girls in these families was of scientific interest. We had to await changes in National Institutes of Health policy in the early 1990s to allow sufficient funding to go back and add the girls in these families into the study. DESIGN AND SAMPLING FOR THE MICHIGAN STATE UNIVERSITY-UNIVERSITY OF MICHIGAN LONGITUDINAL STUDY Table 6.4 depicts the final study design and describes the basic characteristics of the assessment protocol. All sampling was carried out in a four-county area (1990 population 502,000) surrounding Michigan’s capital city (Lansing), which is itself a medium-size Midwestern city. Recruitment sites were all population based, but recruitment strategy differed as a function of the population base we were sampling. First, to access a heavily antisocial subset of alcoholics, we recruited one subset of families by using a population net of all district courts in the four-county area. The protocol captured all male convicted drunk drivers with a blood alcohol concentration (BAC) of at least 0.15% (or at least 0.12% if this was a second or more documented drinking-related legal problem), who had a biologic son between the ages of 3 and 6 currently living with them, who were of nonHispanic white heritage, and who were living with the boy’s biologic mother at time of first contact (n=159). (See Figure 6.1 for details of this recruitment.)

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Table 6.4 Design and Basic Protocol of the Michigan State UniversityUniversity of Michigan Longitudinal Study

Note. All protocols begin with coupled parents who are the biologic father and mother of a male child between the ages of 3.0 and 5.11 years, who is currently living with them in the home. Later assessments include a female sibling if 3.0 to 11.11 years of age at time of first contact. Approximately 50% of families have a daughter in this age range. All high- and moderate-risk group fathers meet Feighner diagnostic criteria at least for “probable alcoholism” and 90% meet “definite” criteria. At each wave, approximately 18 hours of data collection are completed for each family. Data are collected at both the family’s home and at the university and include questionnaires, semistructured interviews, several laboratory protocols, a developmental assessment, and extensive ratings carried out by observers who are blind to family risk status.

Second control families (n=91), of presumed lower risk for child substance abuse-dependence outcome, were recruited from the same neighborhoods where the alcoholic families resided, but neither parent made a lifetime Feighner diagnosis (Feighner et al., 1972) of either probable or definite alcoholism or drug dependence (see Figure 6.2). Feighner criteria were the research diagnoses of choice when the study was originally implemented, although later DSM III-R and IV diagnoses have been carried out on study participants and frequently thereafter have become the independent variable base of classification. Figures 6.1 and 6.2 also delineate the various stages of contact and screening involved in generating the sample. They illustrate the demanding nature of the recruitment procedure, in which records and contacts

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Figure 6.1 Recruitment flow for court alcoholic protocol (involving six district courts in four counties).

involving more than 37,000 individuals had to be reviewed, and in which only 0.5 to 1% of court and community contacts were usable. It is important to underscore that these data are not so much illustrative of the rarity of the phenomenon under study as they are of the difficulty involved in implementing a protocol that is tightly controlled for the confounds of spurious demographic, developmental, and ecological variation.

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Figure 6.2 Recruitment flow for community canvass to locate control and community alcoholic families.

There were no stipulations about antisocial symptomatology or disorder in the controls. Given the lower socioeconomic status (SES) neighborhoods in which the court alcoholic families lived, it might be anticipated that antisocial symptomatology would also appear at an elevated rate among their neighbors, the control families. Analyses presented in Table 6.5 show that it did not and that the antisocial behavior was virtually exclusively coaggregated with the alcoholism. This association is also frequently overlooked in the literature on

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Table 6.5 Social Visibility of Parental Alcoholism and Family Psychosocial Adaptation during the Early Child-Rearing Years (Children at Ages 3 to 5)

continued

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Table 6.5 (Continued)

Note. BDI=Beck Depression Inventory; HRSD-W=Hamilton Rating Scale for Depression—Worst Ever Episode; Child ASB=antisocial behavior, childhood; Adult ASB=antisocial behavior, adulthood; LAPS=Lifetime Alcohol Problem Score; Axis V=DSM-III-R Global Assessment of Functioning score. *p<.05. **p<.01. ***p<.001. a DWI>control, bDWI>community, cCommunity>control. d Contrasts are odds ratios; only contrast3 was performed.

aggression and delinquency (also see Zucker, Ellis, Fitzgerald, Bingham, & Sanford, 1996). Third, another subset of families who met alcoholism diagnostic criteria as well as other inclusionary criteria, but who were free of a drunk-driving offense (n=61), was uncovered by way of the community canvass of the neighborhoods where the court alcoholics (and control families) lived. They offered a potentially useful contrast relating to the alcoholism found in court versus noncourt settings, and they were therefore added into the design. An extensive set of comparisons of the two alcoholic groups has shown that the community-recruited group scores lower on indices of alcohol-related as well as nonalcoholic symptomatology. At the same time, on many contrasts they are lower in functioning than the nonalcoholic control families. Thus, their inclusion provided a way to sample a more intermediate level of familial risk burden. These contrasts have already been partially reported in three earlier Longitudinal Study papers (Fitzgerald & Zucker, 1995; Fitzgerald, Zucker, & Yang, 1995, Zucker et al., 1995). Table 6.5 provides an updated summary of these differences at study baseline, when children were 3 to 5 years old. As can be seen from the demographic analyses, even though living in the same neighborhoods, alcoholic families were functioning at lower levels of adaptation. At the same time, both the demographic data and the child behavior problem contrasts indicate that the greatest risk burden is found among those druginvolved families with comorbid antisocial behavior, that is, among those families

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whose alcoholism was sufficiently out of control to get them arrested (the courtrecruited families). Note also that clinical indicators of child trouble are already in about one third of the court alcoholic families but only in about 10% of the others. In parallel, father psychopathology is greatest in the court-recruited group and linearly decreases across the neighborhood-recruited alcoholic group and the control group. The parental psychopathology data make another point about the ecology of child risk. In families in which the identified patient is troubled, the nonidentified partner may have parallel difficulty, either by way of assortment of mating or as a result of the interpersonal damage inflicted from living with a troubled partner. The present data do not permit us to distinguish these two quite different influencing structures, although the later Longitudinal Study database will eventually allow us to address this issue. THE NEIGHBORHOOD STRUCTURE OF ALCOHOLISM These findings indicate that the physical location where the parent’s drug involvement surfaces is a marker of substantial differences in the clinical and social ecology of risk for the children who are connected to the adult disorder. Our group has also examined this ecological matrix in another way. Social structural explanations for drug involvement commonly involve poverty and social disopportunity as aspects of the macrostructure that moderate the prevalence of alcoholism. Although the macrolevel data supporting this hypothesis are substantial (Brenner, 1972; Curran & Zucker, 1999), few studies examining individual level variation have also explored these superordinate influences. Pallas (1992) addressed this level of process by examining the relationship between neighborhood-level indicators of poverty and social disorganization and concomitant prevalence rates for “court” and “community” alcoholism in the geographic area under study. She hypothesized that higher rates would be found in geographic areas that were more economically and socially disadvantaged. Moreover, she anticipated this effect would be stronger for alcoholism that was more socially visible (as evidenced by drunk-driving offenses) and weaker for alcoholism that was less socially visible (indicating a greater capacity to keep the psychopathology circumscribed and under control). Tract prevalence was computed as the ratio of number of alcoholic families uncovered in the tract (court or community) divided by the total number of families with like characteristics (i.e., two parent households with 3-to-5-yearolds) living in the tract. Tract-level indicators of economic and social disadvantage were compiled for each of the 67 census tracts over the four-county region from which we recruited subjects. These data were then used as the unit of analysis to explore the relationship of ecological variation in social disadvantage and disorganization to rate of appearance of alcoholism. Consistent with general trends in the

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literature, the highest rates of discovery (number of alcoholic families discovered per 1,000 population) occurred near the central part of the largest urban city in the population area. Correlations between total rate of appearance of alcoholic families and census tract level of urbanization were .34 for courtrecruited alcoholics and .42 for community-recruited alcoholics (both ps<.01). (See Table 6.6). Again at the tract level, both individual and family median incomes were inversely related to rates of appearance of alcoholic families (.52 and -.44, respectively; p<.001). As predicted, there were positive correlations between rate of presence of alcoholic families in the tract and tract measures of percentage of families living below the poverty level (.64 for the court-recruited alcoholism rate and .67 for the community-recruited alcoholism rate). Analyses also showed that elevated rates of alcoholism at the census tract level corresponded with elevated rates on other indices of family stress (separation, divorce, families on public assistance, female heads of households, renter-occupied households). However, in contrast to the analyses of individual functioning that showed substantial differences between Table 6.6 Correlations Between Census Tract Indicators of Economic Attainment and Social Disorganization and Alcoholism Discovery Rates in Those Tracts. Variations as a Function of Social Visibility of the Alcoholism.

Note. All correlations are significant at p<.001 except fora where p<.01

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court and community-recruited alcoholic families, no differences in pattern of relationship were found at the tract-neighborhood level. This may be because so much overlap exists at the level of social ecology. The community characteristics that correlate with high presence of “court alcoholism” are the same factors that relate to high presence of “silent” alcoholism in the community. Interestingly also, there was a positive correlation between tractlevel rate of appearance of drunk driver-convicted families and the individual father’s blood alcohol concentration at the time of his arrest (r=.30, p<.05). Tracts where high rates of court families were found were also places where the alcoholics were drinking at higher levels at apprehension. Table 6.6 summarizes these data. POTENTIAL RELATIONSHIP OF STUDY FINDINGS TO CLINICAL POPULATIONS AND TO RISK FOR CLINICAL OUTCOMES AMONG THE CHILDREN The Longitudinal Study’s sampling procedures have yielded a set of alcoholic families, among whom are a very substantial number characterizable as antisocial. This happens to be the comorbidity of greatest prevalence among alcoholics, and that comorbidity is also a marker for a subtype of alcoholism involving dense family history, a heavier genetic base to the disorder, significant other psychiatric comorbidity, early onset of symptomatology, and major social complications (e.g., family violence, abuse, poor occupational functioning) (Babor, 1996; Zucker, Ellis, Fitzgerald et al., 1996; Zucker et al., 1995). Virtually all of these attributes have been documented in the Longitudinal Study sample. Likewise, for those who are less antisocial, these other, correlated, characteristics should be less, and they have been shown to be so. Children of alcoholics’ risk is therefore very high for later disorder in one subset of the children, and less for another, and the study involves one of the highest risk-density, population-based samples currently reported in the literature. The fact that a population net was used to generate the recruited group has eliminated biases dependent upon selectivity of migration of this subpopulation into treatment settings. At the same time, not all drunk drivers are apprehended, and it is likely that there is some bias toward a socioeconomically less well functioning sample, given that those who pleabargained or were able to avoid the conviction entirely are not represented. However, an extensive set of analyses of this issue with the adult men in the study has shown that the aggregation of antisociality and alcoholism is itself a naturally occurring association (statistical “type”) that is much more common than uncommon and that is likewise associated with other social indicators of lower level functioning, including downward occupational mobility from own family of origin (Zucker, Ellis, Fitzgerald et al., 1996). For these reasons,

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social indicators would be expected to be lower for the court recruited (i.e., antisocial) subset, and they are (see Table 6.5). On these grounds, the issue of whether the observed recruiting group and neighborhood differences are attributable to context effects of the macrostructure must remain indeterminate. The competing hypothesis is one of assortative coupling, social selection, and migration (cf. Dohrenwend et al., 1992). Nonetheless, a variety of benchmark statistics indicate that the level of antisociality and alcoholism we have accessed approaches but does not reach that found among prison populations and is substantially greater than that found in the general U.S. population of alcohol-abusing or dependent men. Lifetime comorbidity rate for any DSM-III-R disorder among alcoholic men is 44% in the general population (Helzer et al., 1991), 52% among all alcoholic fathers in our study (but 70% among the court-recruited group), and 67% among the EGA five-site prison sample (Regier et al., 1990), indicating that the general level of psychiatric disorder is substantial (in the overall alcoholic sample) to very high (in the court subset). Lifetime comorbidity of antisocial personality disorder in alcoholic prisoners is 47%; in our court population, it is 34%; in the general U.S. male abuse and dependence population, it is 15%. In addition, analyses of the Clonninger et al.’s (1981) Type I-Type II scheme have shown that 60% of the court alcoholics were classified as Type II, 25% as Type I, and 14% were indeterminate (Zucker et al., 1994). The rates were in the opposite direction for community alcoholics. This design is well suited to study the development of alcoholism in a high risk-aggregation structure and to examine the ways that this process is similar or different when the aggregation structure is less dense. The problem with general population studies is that the variables of interest are represented with such low frequency that the ability to detect interactions is compromised. The present design is highly efficient for this function, although simultaneously, general population prevalence estimates for rates of problems and disorders cannot be established without utilization of reweighing procedures. INDICATORS OF HIGH RISK FOR BEHAVIORAL OUTCOMES AMONG THE CHILDREN Prior studies from our group have already demonstrated significant differences in externalizing behavior (Child Behavior Checklist Broad Band Score; Achenbach 1991) among children in the study based on alcoholic subtyping when the children were between the ages of 3 and 8 (Ellis et al., 1999; Puttier, Zucker, Fitzgerald, & Bingham, 1998). These studies have shown that children in antisocial alcoholic (AAL) families have higher levels of externalizing behavior than children of nonantisocial alcoholic (NAAL) or control families. Children in NAAL families were also found to be higher in externalizing

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behavior than children from control families. Early analyses carried out on Wave 2 data (6- to 8-year-olds) show that 26% of sons of AAL fathers fell within the borderline or clinical range regarding externalizing behavior problems using the average rating of both parents. This compares with 8 percent of sons of NAALs and 12% of sons of controls falling within these ranges. Comparable national sample data from the 1994 SAMHSA National Household Survey on Drug Abuse (SAMHSA, 1996) report a figure of 14.6% for 12- and 13-year-olds, the youngest group they sampled. Similarly, for 6to 8-year-old girls in the Longitudinal Study, 29% of those with an AAL father fall within the borderline or clinical range for externalizing behavior, compared with 13% of the girls from NAAL families and 15% of the girls from control families. The comparable national sample figure for 12- and 13-year-olds is 10.5%. Although these data must be regarded only as suggestive, they fit with other analyses that show that level of risk among children in AAL families is quite high. EARLY DATA ON DRINKING ONSET CHARACTERISTICS With Wave 3 data collection just completed on the study (children at ages 9.0 to 11.11) as this report is being written, we have been able to conduct very preliminary analyses of onset presence. Of these children, 10% (13% of the boys and 2% of the girls) reported their first alcohol use by this time period. Only one boy in the sample reported drunkenness at this point. We also have data on 83 youth (58 boys and 25 girls) who have reached age 12.0 through 14.11. Of these individuals, 28% (34% of the boys and 12% of the girls) have reported their first alcohol use by this age, and 11% (14% of the boys and 4% of the girls) of the youth reported being drunk for the first time. We have also examined how this behavior differs between sons of alcoholics and sons of controls (the frequency of drinking among the girls we have collected data from so far is too low to make any analyses at this point meaningful). Among the 9- to 11-year-old boys who reported their first drink by this age, 82% are children of an alcoholic father. Similarly, among the 12to 14-year-old boys who reported their first drink by this age, 84% are children of alcoholic fathers. Also, 75% of the 12- to 14-year-old boys who reported a first drunkenness were sons of alcoholics. We are aware of no large-scale, nonclinical sample databases with which to compare these findings. The Johnston et al. (1993) Monitoring the Future data are all retrospective figures generated by 10th graders, and the SAMHSA National Household Survey reports only on children ages 12 to 17, with those data in aggregated form. Nonetheless, the data indicate that onset clearly has begun in a subset of children by age 11, and the progression data indicate that familial risk structure is a salient part of the picture.

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FAMILIAL SUBTYPING AS A MARKER OF LONG-TERM RISK AGGREGATION Although the data presented in Table 6.5 documents substantial risk aggregation differences related to recruitment, physical location of recruitment is only a locational proxy for a set of individual characteristics that lead the person to that recruitment site. Given the larger study’s interest in risk aggregation, we have moved from the utilization of risk group source to an individual-level grouping variable, related to presence or absence of lifetime antisociality, as a differentiating structure and to a classification based on alcoholism with lifetime antisociality (antisocial alcoholism) versus without (nonantisocial alcoholism). In a series of papers addressing this issue (Ellis et al., 1999; Ichiyama, Zucker, Fitzgerald, & Bingham, 1996; Puttier et al., 1998; Zucker, Ellis, Bingham, & Fitzgerald, 1996) and summarized in Zucker, Fitzgerald, Ellis et al. (1996), we have noted that this set of attributes, originally established by way of the father’s antisocial behavior plus alcoholism, is a highly effective classification of assortment of coupling (high partner antisocial behavior, alcoholism, and other psychopathology), a denser positive family history for alcoholism, IQ differences among both parents and children, poorer familial social functioning, and higher scores on child core risk marker variables (especially externalizing behavior and aggression: e.g., odds ratio for clinical range externalizing behavior in children from AAL vs. control families=20.1; in children from NAAL vs. control families=1.5). In addition, no control families have parents who make an antisocial personality disorder diagnosis (i.e., as is also true in the general population, there is high assortment of ASP and alcoholism) (Helzer et al., 1991; Regier et al., 1990; Zucker, Ellis, Fitzgerald, Bingham, & Sanford, 1996). These figures show the substantial variation in risk burden that is available with this design. At the same time, these familial subtype descriptors provide only crude estimates of differences in risk aggregation, given that they are based only on father risk characteristics, and they are binary indicators rather than continuous variables. Other analyses of the sample have shown that 30% of the alcoholic families actually have two alcoholic parents. Moreover, some of the controls are anticipated to become alcoholic as the study progresses, given that their risk profiles are higher than what would be found in an unselected sample. BRIEF SYNOPSIS OF OTHER STUDY FINDINGS This project continues to have, as its long-term goal, the testing of whether early markers of risk, first among preschoolers and later in middle childhood and adolescence, will eventually be effective predictors of clinical outcome in later life. Insofar as these markers are causal, they will become important target variables for early preventive intervention programming. At present, Wave 3

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data have just been completed, we are in the middle of characterizing Wave 1 to Wave 2 variation, and the Wave 1 to Wave 3 analyses are just beginning. To give a sense of the contributions of the study to ecological issues, a very brief factual synopsis of pertinent findings is provided. Findings refer primarily to boys in the study because the girls were added later. Analyses of these results have, until quite recently, lagged behind those for the boys. Wave 1 (Boys Aged 3 to 5) Children of alcoholics (COAs) have significantly greater externalizing problems (specifically, aggression, delinquent behavior, and attention problems) and internalizing problems (specifically, depressive and social problems) than do non-COAs. These results were predicted when the study began in 1987. The externalizing behavior differences in particular are of interest as proxy indicators for later alcohol problems, abuse, and dependence, and these findings are also consistent with a just reported study of behavioral differences in 3-year-olds that predicted adult alcoholism diagnosis (Caspi, Moffit, Newman, & Silva, 1996). COAs also have lower IQ scores and are more impulsive (behavioral delay of gratification task) (Poon, Ellis, Puttier, Zucker, & Fitzgerald, 1997). Subtyping on the basis of the father’s comorbid antisocial symptomatology is a highly effective means of discriminating differences within the COA population. Offspring of antisocial alcoholic fathers have the highest levels of externalizing and internalizing behavior, the highest levels of impulsivity, the highest levels of difficult temperament, and the lowest IQs. In some areas, COAs from nonantisocial alcoholic families do not significantly differ from normal controls, indicating that much in the literature about early vulnerability, as well as about risk, may be subtype specific (Ellis et al., 1999). Subtyping on the basis of the father’s comorbid antisocial symptomatology is (a) also a strong marker for individual differences in parental risk and (b) serves an effective indicator of family-level risk aggregation as well. This aspect of subtype differences has been ignored in the extant subtyping literature, but it has major implications for the ecology of child risk. Thus, alcoholic men not only have more other psychopathology, a poorer social adaptation (lower SES), and a denser family history of alcoholism but also are downwardly socially mobile from their own parents, live in families with higher levels of family violence, and have higher rates of separation and divorce. They also to a greater degree marry women who share these characteristics (Ellis et al., 1999; Zucker, Ellis, Fitzgerald, Bingham, & Sanford, 1996). Mediational models of process at Wave 1 implicate child difficult temperament as well as spousal violence as contributors to child externalizing behavior (Ellis et al., 1999).

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Children of alcoholics have a more elaborated alcohol concept structure at age 3 to 5 than do non-COAs. They are better able to recognize alcoholic beverages from photos, they attribute more alcoholic beverage use to adults, and the extent to which they attribute alcohol use to adults (by way of picture tasks) is predicted by the same-sex parent’s own self-reported alcohol consumption (i.e., father’s reported use predicts son’s attributions of adult men’s use of alcoholic beverages; mother’s reported use predicts son’s attribution of adult women’s use). Results indicate the very early presence of an expectancy structure that is gender specific, and that is for greater consumption of alcohol by adult men and women if one is reared in an alcoholic home. (Zucker et al., 1995). It is especially noteworthy that these effects can be detected so early. They indicate that current models of child risk need to be extended substantially backward in time if the full model of risk development is to be charted. Wave 2 (Boys Aged 6 to 8) Differences in externalizing and internalizing behavior at Wave 2 parallel those observed at Wave 1, but there is some general risk dilution as the children become older (i.e., children tend to get better as development proceeds). At the same time, children from higher risk alcoholic families are most likely to sustain level of risk (i.e., sustain a “clinical range” rating of externalizing behavior) (Wong, Zucker, Puttier, & Fitzgerald, in press). The COA-non-COA IQ differences were also present at Wave 2. The COAs were three times more likely to fall in the impaired IQ (less than 80) range, and such differences were not attributable to fetal alcohol effects. In addition, impulsivity differences between children from antisocial alcoholic families and the other groups were also present and were suggestive of specific neuropsychological impairments of the prefrontal lobes. This hypothesis is being followed up (Poon et al., 1997). Other Non-Wave Specific Findings A just completed, small substudy conducted on 33 male and 12 female COAs and non-COAs differentiated on the basis of impulsive aggression at Wave 3 (age 9 to 11) showed (a) that the COAs were much higher on the impulsive aggression measure and (b) that significant differences in serotoninergic function (whole blood 5-HT) existed between the groups (high-aggression group=lower 5-HT level) (Twitchell et al., 1998). Because of later entry into the study, findings on the girls are based on summary analyses across ages 3 to 11. Girl COAs from antisocial alcoholic families had lower IQ scores than the other two groups, but in other areas behavioral differences were primarily between COAs overall and controls. Thus,

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COAs had more externalizing problems, and the younger girls showed more difficulty in functioning than the older girls (Puttier, Zucker, & Fitzgerald, 1996; Puttier et al., 1998). Parental recovery status over a 3-year interval has been related to significant differences in child functioning among the girls. (Parallel analyses have not yet been completed on the boys.) Daughters in recovering alcoholic homes were functioning at a level similar to control families, indicating that cessation of paternal alcohol problems and the associated changes in family environment are related to better child outcomes. Girls in nonrecovering homes had more total behavior problems as well as externalizing behavior, poorer spelling ability, and lower levels of intellectual functioning. These findings still need to be disaggregated for potential differences in familial risk, even precursive to recovery (Puttier et al., 1996). IMPLICATIONS FOR SOCIAL POLICY At the time our work began, it was the developmentally earliest study that was explicitly set up to track the emergence of alcohol abuse and alcoholism; to our knowledge, it still retains this distinction. Design features have allowed the study to access a wide range of variation in familial symptomatology, density of family history, and social functioning, thus providing an opportunity to address issues of heterogeneity of outcome among children of alcoholics in a manner that would not be possible with a less symptomatic set of families. The sampling plan makes it one of the densest risk samples currently being followed and makes it likely that the endpoint outcomes for offspring in adulthood will have clinical relevance. However, even before the children are old enough for clinical outcomes to become common, the nature of the study’s design has highlighted certain issues: Issues of Risk Aggregation, Developmental Variation, and Family System Change over Time 1. The exploration of risk aggregation in adults, both at individual and familial levels, has led us to the concept of “nesting structure” (Zucker, Chermack, & Curra, in press; Zucker et al., 1995), and to the hypothesis that the clustering of comorbid symptomatology, social dysfunction, alcoholism severity, and assortment of coupling will be present in a subset of adult alcoholics (what is referred to in the adult diagnostic literature as the subtype of antisocial alcoholism). A second hypothesis is that this aggregation structure changes the process model of risk cumulation because the variable network determining the effective causal structure has become sufficiently different. The characterization of family subtypes by using the father’s combined alcohol abuse-dependence

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and antisocial personality disorder as a marker has proven to be an effective marker of risk aggregation. When it is present, the evidence continues to suggest that risk cumulation will occur; when it is absent, risk does not occur at rates beyond those of the controls. These data have major implications for a differentiated policy of intervention. One set of families are candidates for early and sustained help; the others would appear to be better candidates for focused intervention at times of heightened vulnerability. 2. Although the notion of subtypes may be highly useful as a diagnostic heuristic in a clinical encounter, it does not do justice to the implicitly fluid nature of risk (Zucker et al., in press) across time. The problem is derivative from the static nature of the existing diagnostic nosology, which is established by way of the parent’s functioning either at one point in time or as a lifetime diagnosis. Thus, the ability to account for epigenetic change is missing. 3. From the perspective of the child, family risk structure will always be an imprecise marker of risk aggregation because, by its nature, it ignores the individual contribution as sustained by epigenetic processes. On these grounds, in recent work we have been moving away from a strict group characterization of subtypes in favor of experimenting with a variety of summary indicators that capture variations in risk aggregation across a variety of contributory sources (Wong, Zucker, Puttier, & Fitzgerald, 1999). 4. Families at the outset of our work were all at an early stage of family development, and their intactness assured that, from the perspective of family structure, they began at an identical relationship starting point. At the same time, marital status was never an inclusionary criterion, only coupled status involving living in the same physical household. In addition, intactness was stipulated only at the time of first family contact. As would be expected from this population, there have been significant changes in marital status over the years. At present, the parents of 30% of the families have either separated or divorced. In addition, the greatest marital dissolution has occurred in the families with the highest risk burden, based on the father’s alcoholic subtype. In families we have identified as having an antisocial alcoholic father, the separation/divorce rate is currently 50%. In families whose father’s alcoholism is not comorbid with antisocial personality, the separation-divorce rate is 29%. Both figures contrast with the control families, which currently have a separation-divorce rate of 12%. In addition, from a rearing perspective, 7% of the families have become mother-only alcoholic families because mothers have become both alcoholic and divorced as the study has proceeded. The mother-only families are primarily initially father-alcoholic families that have decoupled, but the more general point is that these characterizations are fluid and will change over time as marital dissolution occurs at increasing rates. This is more likely to be the case for the antisocial alcoholics, where marital conflict is at its highest levels (Klotz-Daugherty, Zucker, Floyd, Bingham, & Fitzgerald, 1998; Ichiyama et al., 1996). This hypothesis is still to be tested.

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The point of ensuring initially intact families with both biologic parents present was to assure that both father and mother effects could be assessed. However, given the now substantial evidence implicating a heritable component for risk for alcoholism and the still heavy preponderance of a male sex ratio for the disorder, it seemed a reasonable first-pass scientific inquiry to attempt to sample families for which father data were accessible and thus capable of being included as part of the causal structure. Moreover, requiring both biologic parents as an inclusionary criterion for involvement maximizes the opportunity for a comprehensive evaluation of risk structure in the children within the most prevalent family risk structure and also allows one to examine processes of family dissolution and their impact (or lack thereof) on child developmental process when they occur, all beginning with the same coupled status. This is not an all-encompassing design solution, but it was a reasonable first-pass choice to establish the parameters of causal variation. The “Addicted Family” as a Moving Target: Challenges in Tracking the Context Structure for Riskiness The larger point that needs to be underscored is that many family structural attributes are fluid, at whatever point one begins. As noted earlier, this is especially likely to be the case in families in which the parental drug involvement is most severe, hence, there are direct links to riskiness of child socialization structure (i.e., one-parent rearing is more common, or the marital partnership is conflicted and unstable). In documenting the impact of these changes on the children, it then becomes essential that the study is able to model those predictors that produce the dissolution, as well as look at the possible specific effect this variation may contribute to child outcome (e.g., see Loukas et al., 1997). How one deals with this issue, both in a data collection sense and in a modeling sense, then has implications for the nature of the causal structure one can impute. We give three examples from the Longitudinal Study to illustrate how this issue may be addressed. 1. In families in which the biologic parent of the target child has recoupled for 9 months or longer, that partner is deemed part of the socialization structure. Informed consent is obtained from the new spouse or partner, who becomes involved in the regular assessment protocols. 2. When a divorce occurs and one of the original parents no longer has sufficient child contact (i.e. less than twice per month), we sustain this individual’s involvement but on a more limited basis (i.e. this parent does not complete questionnaires about the participating children). 3. Particularly among the court-recruited alcoholic men, reinvolvement with the legal system over time is to be expected, and tracking and data collection with these noncustodial parents is far from a routine task. This has until now

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occurred at a fairly low level (in the past 2 years, approximately 2% of the alcoholic fathers were in prison at the time data collection was being scheduled). To access these individuals, following an initial indication of interest typically obtained via the man’s partner, we contact the appropriate research administrative personnel for each institution (protecting confidentiality of the individual by providing information only about the general nature of our study and insisting on a private interview space within the institution). In each of these cases, administrative approval of the formal contact was obtained from prison staff, informed consent was then obtained from the respondent, and data collection was thereafter completed. Data are not shared with prison staff, and they understand that this is a condition of our doing the work. The Relevance of Theory to an Understanding of the Ecology of Childhood Risk The focus on such initially young children required us to construct a base of proxy measures pertaining to later alcohol use that did not utilize alcohol involvement as a measure of risk status. We believed, at the outset, that such proxy indicators would most likely be variables that would lead to alcohol and other drug involvement, rather than be rudimentary but direct indicators of such involvement. As our work progressed (cf. Zucker, 1979), we had reason to question this presumption. This led to the development of a theoretical base that has articulated the case for the interplay between alcohol-specific and nonalcohol-specific risk factors, even at very early stages of the life cycle (Zucker et al., 1991). This theory, in turn, led to the examination of alcohol schema formation, as a rudimentary representational structure out of which later alcohol expectancies, and possibly also alcohol-related behaviors, would emerge (Zucker, Kincaid, Fitzgerald, & Bingham, 1995; Noll, Zucker, & Greenberg, 1990). Moreover, it is also evident that the rudimentary theoretical bases of earlier eras, involving notions of single gene effects or of alcohol-specific vulnerability mechanisms, are no longer appropriate for the complex developmental disorder(s) that underlie the alcohol dependence rubric (Zucker, in press). The phenomena we seek to capture involve multiple trajectories, multiple genes, and multiple context structures. The ethnic structures within which they play out also modify availability of the drug, as well as alter the trajectory of use and dependence, once onset has begun. An essential task for the next decade is to capture the variability of this structure by using complex genetic and psychosocial models of process, as well as more focused delineations of nodal points of risk. ACKNOWLEDGMENTS Preparation of this chapter was supported by a grant to the first two authors from the National Institute on Alcohol Abuse and Alcoholism (2R01 AA07065).

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CHAPTER 7

American Indian Children of Alcoholics PAUL SPICER CANDACE FLEMING

Despite widespread concern with the causes and consequences of American Indian drinking, researchers have devoted little or no attention to the predicaments of the children of these drinkers. Our goal in this chapter is to review what is known about Indian people’s experiences with alcohol in light of the theoretically and methodologically sophisticated work on children of alcoholics (COAs) that has emerged in recent years. Given the absence of comparable quantitative research in American Indian communities, our contribution is necessarily programmatic and hypothetical, but we hope it will shape and inspire future research into the experiences of American Indian COAs. As will become evident in the course of our review, there is an immense amount to be learned about the experiences of these children, and there is good reason to suspect that attention to the specificity of their situation can also lead to important insights into the predicaments of COAs more generally. AMERICAN INDIANS AND ALCOHOL Many of the problems that American Indian people have experienced as a result of alcohol use have by now been documented, and there is an emerging body of epidemiological work that suggests, at least in broad outline, some crucial dimensions of their experiences with alcohol. It has long been known, for example, that rates of alcohol-involved mortality in Indian communities are often much greater than the U.S. averages. For Indian men, deaths due to motor vehicle crashes, accidents, suicide, homicide, and alcoholism are substantially higher than U.S. averages, with the exception of suicide among the elderly. A similar pattern is also evident among Indian women, whose rates of alcoholinvolved death, although lower than Indian men’s, still exceed U.S averages for women, again with the exception of suicide among the elderly (May, 1996). 143

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Although there is reasonable scientific concern about attributing motor vehicle crashes, other accidents, homicides, and suicides solely to alcohol (Kunitz & Levy, 1994), such caveats do not apply to mortality statistics that are classified as alcohol-specific. This category includes deaths from causes such as alcohol dependence syndrome, alcoholic liver disease, alcohol overdose, alcoholic cardiomyopathy, and alcoholic gastritis. For Indian men and women of all ages, deaths from these causes are higher than U.S. averages, sometimes alarmingly so. In fact, the ratio of Indian to non-Indian deaths from these causes is higher among Indian women than it is among Indian men. As May notes, “Individual alcohol-specific death rates (which are related primarily to cirrhosis and alcohol dependence syndrome) for Indian females ages 15–24 are 31 times higher than U.S. averages, 13.3 times higher for ages 25–34, and 4.6 to 8.4 times higher for ages 35 and above” (May, 1996, pp. 244–245). Such statistics paint a grim portrait of many Indian communities, to be sure, but it is important to remember that there is an enormous amount of variation from community to community in the use of alcohol, which is obscured by such national figures. Some authors have suggested that such tribal differences in alcohol use may be a result of the degree of integration in aboriginal social structure (e.g., Levy & Kunitz, 1974; May, 1982; Stratton, Zeiner, & Paredes, 1978). According to this hypothesis, those societies that have a higher level of integration have more centralized hierarchy and control and hence lower levels of alcohol with lower levels used. In contrast, societies of integration impose fewer mandates on an individual for conformity and accordingly have higher levels of alcohol use. In addition to aboriginal social structure, however, there are also probably important connections between alcohol use and the degree of disruption that a society has endured (e.g., Dozier, 1966; May, 1982; Weisner et al., 1984). Indeed, these two classes of explanation may well be related. The disruptive influences of colonial and postcolonial life can easily result in a lower level of integration in a society, just as a lower level of integration can make a society more vulnerable to disruption as a result of contact with another society. Whatever explanatory model we choose to use in understanding tribal variations in alcohol use, it is important to remember that there is also significant variation within tribes. Surveys in Indian communities tend to document rates of abstinence that exceed U.S. averages, even among tribes with reputations for heavy drinking (May, 1996). Rates of abstinence are especially high among older segments of the population, and recent longitudinal work suggests that a good proportion of formerly heavy drinkers do find their way to abstinence without formal treatment if they live until middle age (Kunitz & Levy, 1994; Leung et al., 1993). Unfortunately, from the perspective of the children of these drinkers, these changes may come too late to prevent significant harm.

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FETAL ALCOHOL SYNDROME AND ALCOHOL-RELATED BIRTH DEFECTS Because rates of alcohol-specific mortality are so high among Indian women of child-bearing age, there has been much interest in the levels of fetal alcohol syndrome (FAS) and what are now known as alcohol-related birth defects (ARBD) in Indian communities. Until recently, though, systematic epidemiological data has been scarce. Early pioneering work by May, Hymbaugh, Aase, and Samet, (1983) was the first to systematically document levels of FAS among American Indian people. Using precise criteria for FAS, a referral system through the Indian Health Service (IHS), and a sample of diverse Indian communities from the American Southwest, these authors found prevalence rates of FAS for children (age 0–14) that ranged from 1.6/1,000 for the Navajo to 10.7/1,000 for a group of societies described as Southwestern Plains (consisting of various Apache and Ute communities). Subsequent research in Canada produced much higher prevalence rates. Asante and Nelms-Matzke (1985), working in western Canada, reported combined rates of FAS and fetal alcohol effect (FAE) of 46/1,000 in the Yukon and 25/1,000 in British Columbia. Robinson, Conry, and Conry (1987), also working in British Columbia, found a rate of FAS of 120/1,000 in one isolated native community. Whether this Canadian work is comparable to that done in non-native populations is not clear, however. This is because the methods employed in Canada were likely to result in relatively complete surveillance in areas where alcohol consumption is high, whereas the methods employed in studies of non-Indian people are probably more likely to result in undersurveillance (Bray & Anderson, 1989; May, 1991). Thus, this Canadian work should probably not be directly compared with what we know about other populations. It is also important not to generalize the Canadian findings to all Indian communities because recent work by researchers in other communities has yielded prevalence rates that are much more similar to those found by May et al. (1983). Linking data from state sources (e.g., birth and death certificates), IHS case files, and private pediatric case files, Bergeson et al. (1993) reported a 2.1/1,000 minimum prevalence rate of FAS among Alaska Natives from 1978 to 1991. Using capture-recapture analyses on the same data set, Egeland, Perham-Hester, and Hook (1995) found prevalence rates of 3.8/1,000 from 1982 to 1985 and 3.1/1,000 from 1986 to 1989. In a separate study, Duimstra et al. (1993), using surveillance data from IHS in South Dakota, found a prevalence rate of 3.9/1,000, but concerns about screening, underreporting, and unevaluated children led them to postulate a rate of 8.5 cases of FAS per 1,000 live births. The rates reported in these recent studies still exceed general U.S. averages—Abel (1995) reports 1.95/1000 in the United States—but they do suggest that rates such as those found in Canada do not represent the

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situation for the majority of American Indian and Alaska Native populations. Indeed, the picture that emerges from this epidemiological work, considered as a whole, is that FAS tends to be distributed across native communities in ways that mirror the distribution of alcohol use more generally (May, 1991). Therefore, the picture of FAS and ARBD in Indian country is by no means universally bleak. Furthermore, there is cause for optimism in recent efforts to prevent FAS. May and Hymbaugh (1989) describe preliminary results of a national prevention program conducted through the IHS. Central to this program was the training of local trainers-advocates in all areas served by the IHS. The evaluation of this program suggested that it resulted in both knowledge gain and knowledge retention in the populations served: elementary school children, secondary school children, women who were pregnant for the first time, and community members. There was also some evidence to suggest that people who had received the training were, in turn, teaching others. Whether the knowledge gains evident in this program translate into behavior change remains to be seen, but the program nevertheless constitutes an example of a coordinated national effort that successfully educated people in ways that resulted in improved understanding of FAS. May (1991) also provides an intriguing piece of evidence to suggest that macrolevel changes can have an important impact on women’s drinking and, hence, on the rate of FAS in a community. In one of the Southwestern Plains communities that was originally reported on in 1983, the rate of FAS dropped from 14.4/1,000 for those children born from 1969 to 1977 to 0/1,000 for those children born from 1978 to 1982. This dramatic decline coincided directly with a change in tribal policy. Prior to 1978, members of the tribe received per capita payments from oil, gas, and uranium royalties. In 1978, these payments were abandoned. Although May does not provide more detailed analysis of the sales of alcoholic beverages in the community, which would confirm his hypothesis, the clear implication of this dramatic drop in FAS rates is that the loss of cash income in this community had a major impact on women’s drinking. In the area of prenatal exposure to alcohol, then, there is an emerging body of epidemiological work that suggests that members of some Indian and Alaska Native communities are at increased risk for FAS and, by implication, ARBD. On the other hand, there are also tribes whose rates of the disorder appear to be as low as (or even lower than) that found in the general U.S. population (e.g., the Navajo rates reported by May et al., 1983). There have also been significant policy and program initiatives to address the concerns that Indian and Alaska Native people have about prenatal exposure to alcohol. Sadly, there has been much less attention to the problems that Indian children of alcoholics encounter once they leave their mothers’ wombs.

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THE EXPERIENCES OF AMERICAN INDIAN CHILDREN OF ALCOHOLICS In a survey of federal service providers in the southwest, Piasecki et al. (1989) found that those Indian children in treatment who were abused or neglected were significantly more likely to come from homes where there was parental alcohol abuse, divorce, a single parent, and a chaotic family. Research by Lujan, Debruyn, and colleagues, at around the same time and also in the southwest, provides even more insight into these dynamics, with evidence of alcohol abuse found in 85% of neglect cases and 63% of abuse cases (Lujan, DeBruyn, May, & Bird, 1989). Subsequent research by the same team with a control sample, however, suggests that alcohol is not necessarily the cause for child abuse and neglect because 52% of the control families’ homes were reported as alcohol abusing with no evidence of child abuse or neglect (DeBruyn, Lujan, & May, 1992). This last finding indicates that, although alcohol is certainly an important part of the problem of abuse and neglect, it is by no means the only factor. The evidence of multiple problems that emerges from the work of Piasecki et al. (1989), for example, points to a constellation of difficulties in abusive and neglectful families that are more extensive than alcohol abuse and dependence. Furthermore, DeBruyn et al. (1992) found that parents and grandparents in the target (abusing or neglectful) group of their study were significantly more likely than the control parents to have evidence of a history of abuse or neglect when they were children. Nevertheless, there can be little doubt that alcohol appears to be intimately involved in the abuse and neglect of Indian children at several points in the cycle of child maltreatment, especially when we appreciate that the abusive or neglectful parents and grandparents in the Debruyn et al. (1992) study were also significantly more likely to have been raised in an alcoholic family than were caregivers in the control group. The violence associated with alcohol abuse also probably affects Indian children indirectly, insofar as they are often witnesses to domestic violence. Although the relationship between alcohol use and domestic violence is complex, there is good reason to believe that drinking plays a significant role in many instances of domestic violence (Lee & Weinstein, 1997), and this appears to be the case in American Indian communities as well. Recent evidence from Kunitz, Levy, McCloskey, and Gabriel (1998) with a community sample of Navajo people confirms that alcohol dependence is a significant risk factor for both perpetrators and victims of domestic violence. Furthermore, recent epidemiological work in New Mexico suggests that American Indian women are significantly more likely to be victims of domestic violence homicide than are women of other ethnic groups in the state (Arbuckle et al., 1996).

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An emerging body of research literature documents many of the consequences that witnessing such domestic violence can have on children (e.g., Holden, Geffner, & Jouriles, 1998), and the effects of such witnessed violence are probably quite similar for Indian children. Among the negative outcomes attributed to being a child of a battered woman are higher rates of internalizing and externalizing behavior problems, lower self-esteem, more depression, greater attentional difficulties in school, delinquent behavior in adolescence, and violence in adulthood (Graham-Bermann, 1998; Holden, 1998). Although these consequences are certainly troubling enough in themselves, they may also be crucially implicated in the risk for subsequent alcohol problems in COAs, thereby perpetuating the cycle in the next generation. RISK AND PROTECTIVE FACTORS AMONG AMERICAN INDIAN CHILDREN OF ALCOHOLICS It is well documented that COAs are at increased risk for the development of alcohol problems of their own (e.g., Chassin, Rogosch, & Barrera, 1991; Cotton, 1979; Matthew, Wilson, Balzer, & George, 1993; Sher, Walitzer, Wood, & Brent, 1991), and several teams of researchers have pursued inquiries into the mechanisms that explain this increased risk. This work offers crucial insights into the experiences of COAs, many of which can be profitably explored in work with American Indian COAs. At the same time, though, the situation for American Indian children is often quite different from that explored in the extant research on COAs. For this reason, caution is also warranted in thinking about how to apply these models to the culturally diverse world of American Indian children. Based on his overview of the literature prior to 1990, Sher (1991) argues for a complex heuristic model, which he describes in terms of three submodels, each of which is based solidly on the findings of the research literature he reviewed. The enhanced reinforcement submodel draws on evidence that suggests that COAs are more sensitive than non-COAs to the reinforcing effects of alcohol (e.g., Newlin & Thomson, 1990). In this submodel, it is hypothesized that these individual differences in ethanol sensitivity are translated into increased expectancies of reinforcement from alcohol, which, in turn, mediate drinking behavior. The second of these submodels, the deviance-prone submodel argues that COAs are at increased risk for the development of alcoholism because of deficient socialization. Here, a family history of alcoholism has direct effects on parenting behavior, temperament and personality, and cognitive dysfunction. These factors increase the probability of school failure and, hence, involvement with substance-abusing peers, which is the proximal mediator of alcohol involvement. Finally, the negative affect submodel hypothesizes that COAs become involved with alcohol because of

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their tendency to experience negative affective states, a high level of stress, and the ineffectiveness of their coping resources. Emotional distress, moderated by coping ability and alcohol expectancies, is the proximal mediator for alcohol involvement in this submodel. Sher’s attempt to delineate the mechanisms that lead to COA involvement offers many potentially important insights that can profitably be pursued in research with American Indian COAs, especially those pathways that concern the importance of expectancies, peer involvement, and emotional distress. However, there are some elements of the more complex models he develops that raise concerns in thinking about the situation for American Indian children. Research into American Indian reactions to ethanol, which might support an enhanced reinforcement submodel of risk, has found few meaningful differences in American Indian people’s physical reactions to alcohol (May, 1994; Schaefer, 1981). Furthermore, there is some evidence that people who are the most genetically Indian (75% blood quantum and higher) are actually protected from alcohol problems (Weisner et al., 1984), which casts serious doubt on a genetically transmitted mechanism that might explain Indian problems with alcohol. The deviance-prone submodel is problematic for different reasons: It assumes that school failure is a direct result of temperament and cognitive dysfunction, and it assumes that alcohol use is deviant. In the cultural context of some Indian communities, both of these assumptions may be questionable. First, the advantages of formal education in the context of reservation life are not always clear, given the paucity of professional employment. Furthermore, educational institutions have historically been perceived as a mechanism for undermining Indian cultures, so there is a good deal of ambivalence toward school in some Indian communities. These feelings may create problems for Indian children in school that have nothing to do with their temperament or cognitive abilities. Second, we know from surveys conducted by Getting and Beauvais (e.g., 1989) that Indian adolescents are likely to start drinking at an earlier age than nonIndians and that they are more likely to drink to the point of drunkenness once they have started drinking. These results, combined with the epidemiological findings discussed earlier, suggest that, in some Indian communities at least, alcohol use and even heavy drinking may be a normative rather than a deviant part of adolescent life (e.g., O’Nell & Mitchell, 1996). These contextual dynamics make it difficult to attribute alcohol use among American Indian COAs to school failure and subsequent deviance. Nevertheless, as we shall see, there are important connections between school adjustment, peer involvement, and alcohol use in Indian communities, so the potential problems with Sher’s devianceprone submodel have less to do with these factors per se than with the attribution of school failure and subsequent alcohol involvement to the cognitive and temperamental characteristics of the child.

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There are the fewest cultural concerns with Sher’s negative affect submodel, but this model had the least empirical support at the time of Sher’s review. It has, however, been a topic of explicit focus in the research of Chassin and her colleagues, and the complex mediational model articulated by this team of researchers offers significant advances for thinking about the situation of American Indian COAs (Chassin, Curran, Hussong, & Colder, 1996; Chassin, Pillow, Curran, Molina, & Barrera, 1993; Chassin et al., 1991; Curran, Stice, & Chassin, 1997). Because this work receives detailed attention elsewhere in this book, we will not belabor the details here, but we do want to briefly note how this model can guide our thinking about the situation for American Indian COAs. In their cross-sectional test of three mediating mechanisms for adolescent COA alcohol involvement, Chassin et al. (1993) found support for the contention that recourse to alcohol among these children was mediated by father’s monitoring, peer involvement, stress, negative affect, and child’s emotionality. In a longitudinal follow-up with the same sample at two additional points in time, father’s monitoring, peer involvement, and stress remained important mediators for the growth of adolescent substance use, but negative affect, personality, and temperament variables did not (Chassin et al., 1996). In addition to its elegant design and careful testing of important hypotheses, many of the concerns raised by Sher’s (1991) enhanced reinforcement and deviance-prone submodels do not arise in this mediational model. Certainly, the construct of paternal monitoring may be less associated with adolescent substance use in the context of extended families, where grandparents, aunts, and uncles can and often do step in to perform this function. However, the idea of monitoring, in itself, echoes the concerns of many Indian people, and the other constructs in the longitudinal mediational model—peer involvement and stress—resonate with the experiences of many people in Indian country (e.g., Spicer, 1995). Moreover, especially in the emphasis on peer involvement, this work converges, in important ways, with what we know about the initiation of alcohol involvement among Indian adolescents. The work of Getting and Beauvais (Beauvais, 1992; Getting & Beauvais, 1989) on Indian adolescent substance use, although not specifically concerned with COAs, has provided important insights that can, in principle, be applied to understanding the nature of risk and protection for these children as well. Among the factors Getting and Beauvais have explored in their research are peer drug associations, family sanctions, school adjustment, family strength, and religious identification. Data on these same factors have been collected for both Indian and non-Indian youth (e.g., Beauvais, 1992; Oetting & Beauvais, 1987) and a comparison of the different path models they have derived for Indian and nonIndian youth is instructive. For both Indian and Anglo youth, the most important predictors of peer drug associations (and hence, drug use) are school adjustment and family sanctions

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against drug use. And, in both cases, family strength is argued to be the foundation of the model. The strong family, indexed by intactness and the quality of family relationships, is seen to encourage school adjustment and to discourage alcohol and drug involvement. Although these similarities are notable and provide important support for some of the linkages in Sher’s and Chassin’s models, there are also important differences between Indian and Anglo adolescents, chiefly in terms of the relative importance of peer and family factors. As Beauvais (1992) observes, the relation between peer clusters and adolescent substance use is not as strong for Indian youth as it is for Anglos, and there is also a direct link between family sanctions and adolescent substance use for Indian youth that is not present in the Anglo model. Furthermore, in contrast to the model for Anglo youth, religious involvement is much more weakly related to school adjustment among Indian youth and not at all to peer drug associations. This is probably because Indian youth are more likely to be involved in cultural activities and ceremonies that have a religious meaning, even though they do not attend church, which underscores a fundamental distinction between spirituality as a way of life and more formal church attendance (Beauvais, 1992). These findings suggest that cultural and contextual factors need to be considered in thinking about why American Indian COAs may be at increased risk for alcohol involvement— something that developmental systems theory is ideally situated to do. The theory of substance abuse articulated by Zucker, Fitzgerald, and colleagues (e.g., Fitzgerald, Davies, Zucker, & Klinger, 1994; Zucker, Fitzgerald, & Moses, 1995) is also the subject of detailed discussion elsewhere in this book. However, because it provides an especially broad perspective on the situation for COAs and is potentially quite well equipped to deal with the cultural complexities of American Indian life, we mention it here briefly. Central to this theory is the argument that the search for the causal determinants of substance abuse must consider at least five levels. These include the individual characteristics of family members, the relationships between family members, characteristics of the family (e.g., traditions, values, beliefs, and cohesiveness), the adjunctive systems that affect the family (e.g., the culture, the economy, the neighborhood), and, finally, models of how these systems change over time (Fitzgerald et al., 1994). These levels of analysis offer real potential for incorporating the social and cultural context of Indian life into our models, and preliminary results from the Michigan State University-University of Michigan (MSU-UM) Longitudinal Study offer additional insights that can be profitably pursued in future research in Indian communities. The MSU-UM Longitudinal Study began when the male children in question were 3 to 6 years old (e.g., Fitzgerald et al., 1993; Fitzgerald, Zucker, & Yang 1995; Jansen, Fitzgerald, Ham, & Zucker, 1995; Whipple, Fitzgerald, & Zucker, 1995), and the results of this research have tended to confirm the hypothesis that a child with difficult temperament reared in an

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alcoholic family is at high risk for the development of behavior problems that may antedate the development of alcoholism (e.g., Jansen et al., 1995). There is probably a strong genetic component to difficult temperament but, as Fitzgerald and Zucker (1995) note, these characteristics of the child also need to be situated in the context of the quality of the family caregiving environment, which may be compromised in an alcoholic family (e.g., Whipple et al., 1995). There has been no systematic research into the temperamental characteristics of Indian children, so the evaluation of temperamental indicators in these populations is likely to be fraught with potential political, cultural, and social problems. These difficulties raise serious questions about how validly potential pathology can be determined with existing measures, and they suggest that extreme caution is probably warranted until such time as temperament and behavior problems in Indian communities are better understood. However, with regard to the rest of the model, there can be little doubt that Indian communities are very concerned with the ways in which an alcoholic environment can compromise the quality of parenting and, hence, a child’s development (e.g., Spicer, 1995, 1997, 1998b). Other analyses of the data from the MSU-UM Longitudinal Study have provided important evidence concerning the links between parent and child variables and social class (Fitzgerald & Zucker, 1995)—findings that underscore the need to situate parental psychopathology in its larger social and cultural context. An emerging body of research suggests that certain alcohol problems (e.g., antisocial alcoholism) are strongly associated with social class (e.g., Fitzgerald & Zucker, 1995), and the MSU-UM Longitudinal Study provides additional evidence in this regard. Analyzing data from Wave 1 of their research, Fitzgerald and Zucker (1995) found that there were significant main effects for income in predicting families’ levels of antisocial behavior and alcohol problems. However, the direction of these effects is not yet clear. On the one hand, social class may “provide a climate of poverty, within which jobs are hard to come by, psychosocial stress is higher, and impulsivity and action and heavy drinking are highly valued for their escape and masking functions” (Fitzgerald & Zucker, 1995, p. 137). On the other hand, variation in the level of alcohol involvement may result in social class variation. “If one is frequently drunk, job stability is impossible to sustain and downward occupational drift is one outcome” (Fitzgerald & Zucker, 1995, pp. 137–138). Regardless of the direction of effect, the findings of Fitzgerald and Zucker (1995) remind us about the importance of attending to the adjunctive systems that have an impact on the family system, and these systems include not only social class but also social, historical, and cultural factors. Given the epidemiology of alcohol use in Indian communities and the ubiquity of heavy drinking among adolescents (e.g., Oetting & Beauvais, 1989), it is quite likely that Indian parents have learned to drink in a context where alcohol is readily

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available and its use is an integral part of people’s lives—a contention that gains support from existing ethnographic studies. THE CULTURAL CONTEXT OF AMERICAN INDIAN DRINKING Anthropologists have devoted a significant amount of attention to the ways in which Indian drinking is culturally patterned. The classic argument in this regard can be found in the work of Levy and Kunitz (1974), who contend that Navajo drinking is better understood as a product of Navajo culture rather than as a product of the disruptions that the Navajo have experienced as a result of contact with Americans of European ancestry. In particular, as noted previously, Levy and Kunitz suggest that heavy public binge drinking is normal among Navajo men, owing to the lower level of integration in Navajo society, which places relatively fewer restrictions on an individual’s conduct. In an elaboration of this view, Topper (1980) has argued that heavy drinking is especially pronounced among Navajo male adolescents because of the stresses that inhere in their role, both traditionally and under the influence of EuropeanAmerican cultural forms. Cultural values also gain an emphasis in Waddell’s work among urban Papago drinkers in Tucson (e.g., 1973, 1975, 1980). According to his analysis, drinking among Papago men is fundamentally shaped by the egalitarian ethos of Papago society, with an emphasis on sharing, relatedness, and belonging. Drink serves as an oral token of relatedness, analogous to food, and sharing drink provides Papago men with a way of validating their membership in Papago society. In this context, Waddell argues that refusing to drink is tantamount to denying connection to one’s fellows—something that few Papago men are willing or able to do—and he documents the strength of the social pressures that compel Papago men to drink, often against the very strong wishes of their wives. Finally, the importance of supernatural power among American Indian people has also been linked to their use of alcohol. As Levy and Kunitz (1974) put it, “In a tribe with a positive value placed on individual prowess and magical power, the mind altering effects of alcohol will neither be rejected nor denied” (p. 181). This argument is even more strongly articulated by Mohatt (1972), who provides an intriguing analysis of the Lakota man’s quest for personal power through drinking. According to Mohatt (1972), Lakota men had developed a way of life that placed a premium on their individual prowess in war and hunting. Reservation life and the encroachment of European-American culture seriously compromised their ability to establish their masculinity in these ways at the same time that alcohol became readily available. The resultant heavy drinking and violence among Lakota men, Mohatt argues, are entirely consistent with the aboriginal culture’s emphasis on male prowess in that drinking provides a man with a way of feeling powerful.

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These analyses provide important documentation of the role of culture in the American Indian experience with alcohol, and few people would argue with the fundamental insights provided by these authors. However, these works generally neglect the other side of the American Indian experience with alcohol—the ways in which alcohol is destructive of much that is of value to the drinker (Spicer, 1997). And, by emphasizing how drinking is culturally continuous in many Indian communities, these analysts sometimes fail to document the myriad ways in which Indian people have attempted to resist alcohol (e.g., Mancall, 1995; Wallace, 1972). When we turn our attention to the accounts of American Indian adult children of alcoholics (ACOAs), however, these aspects of the American Indian experience with alcohol emerge with vivid clarity. THE EXPERIENCES OF AMERICAN INDIAN ADULT CHILDREN OF ALCOHOLICS Research on ACOAs can be potentially quite useful in identifying risk and protective factors that can then be applied in longitudinal research with COAs, and ethnographic research by Spicer (1995, 1997, 1998a, 1998b) on American Indian ACOAs provides us with insight into some possible dimensions for future research. In a study of drinking in an urban Indian community, Spicer (1995, 1998b) noted that talk about one’s own drinking was usually rooted in a discussion of how one’s own childhood was disrupted by parents’ and other family members’ drinking. Fully 34 of the 50 men and women interviewed in this research reported one or both parents who were often drunk. And, because of the open-ended nature of the interviews, it is likely that the number of people whose parents drank heavily was even higher than this: Only 4 people stated explicitly that their parents did not drink, and the 12 other accounts could not be evaluated in this regard because the individuals simply did not mention their parent’s drinking. The experience of being raised by drinking parents was not universally condemned—some people said they also remembered the laughter and levity of drinking parties—but the positive memories people had were often marred by the violent conflicts that developed as the evening wore on. Furthermore, drinking parents often left their children to take care of themselves. Undoubtedly, the expectation that an older sibling would take care of his or her younger brothers and sisters is, to some extent, consistent with Indian traditions of child care (e.g., Ishisaka, 1978). However, the drinking of parents sometimes meant that the roles of parent and child were reversed, with children literally taking care of their parents. Furthermore, the idea that children would be left to raise themselves was, in the eyes of at least some grandparents, an affront to the love of children that they felt was at the heart of their culture (Spicer, 1998b).

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There can be little doubt that being raised in such drunken environments caused serious consequences in the lives of these children. In addition to the fear and anger that many of them experienced as a result of their exposure to violence (which is consonant with the negative affect linkages in Sher’s and Chassin’s models), these children were put in a situation where they were forced to take on adult responsibilities at a very early age, which many of them have represented as part of the more general atmosphere of neglect in their parents’ homes. This latter dynamic has, surprisingly, received relatively little attention in the research literature on COAs, although it has been a dominant concern in what Sher (1991) refers to as the clinical literature. Although the construct of parental monitoring used by Chassin and colleagues taps some of the dimensions of this experience, what American Indian ACOAs describe is more general than this, having to do with the child’s developing sense of self in relation to others. As such, their observations bring us back to two of the most influential psychodynamic theories of the addictions. One of the longstanding observations in the cross-cultural work on alcohol use is that drinking tends to be heaviest in those societies where childrearing practices promote dependency conflict (e.g., Bacon, 1974, 1976; Bacon, Barry, & Child, 1965). In a cross-cultural study of 139 societies using the Human Relations Area Files (HRAF), Bacon, et al. (1965) found considerable support for the hypothesis that a conflict over dependency needs would result in more drinking as a culturally sanctioned way of indulging one’s dependency needs. There were strong negative correlations between measures of general alcohol consumption and an indulgence of dependency needs in childhood or adulthood, and there were strong positive correlations between pressures toward achievement and the use of alcohol. These findings were subsequently confirmed in a regression analysis for 38 of the original societies for which complete information on all of the variables was available. Taken together, these factors explained 46% of the variance in the frequency of drunkenness among these societies (Bacon, 1974). This formulation was soon criticized by McClelland, Davis, Kalin, and Wanner (1972), who argued that a need for power was much more evident than dependency in the folklore of heavy-drinking societies and in projective tests with heavy-drinking men. However, as Bacon (1976) observes, a need for power is entirely consistent with those aspects of dependency conflict theory that emphasize responsible, independent, and achieving behavior. In an intriguing synthesis of these two views, Levin (1991) has argued that these two perspectives are probably best seen as complementary perspectives on the same dynamic. Although Levin’s analysis does not explicitly discuss the implications of being raised by alcoholic caregivers, his use of Kohut’s self psychology

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(e.g., Kohut, 1971) can be read to suggest that being raised by drunken and inattenlive caregivers can seriously jeopardize the mirroring experiences upon which a healthy sense of self depends, engendering both a need for power and a conflict over dependency needs. And, as the analysis of dependency conflict cited previously suggests, there are some very powerful associations between this state of affairs and recourse to alcohol, both at a societal level and an individual level. Spicer’s ethnographic work also suggests that the most common intervention in the lives of American Indian COAs—foster care—may actually aggravate rather than ameliorate these problems (Spicer, 1998b). One quarter (13) of the people he interviewed had spent substantial amounts of their childhoods in foster homes headed by nonrelatives. Although people did not universally condemn the experience—at least two men reported that they appreciated the structure and discipline provided by their foster parents—most described a situation in which they felt that their foster parents did not really care for them—a feeling that was compounded by the fact that children were often rapidly cycled in and out of a series of foster homes. When this occurred, it inevitably exacerbated the feelings of neglect that these children had experienced in their birth families, adding to the difficulties that they experienced in their adolescent and adult years. In a comparison of those children raised outside their biologic parents’ homes, Spicer (1998b) noted that the parenting outcomes for those who were raised by members of their extended families (e.g., grandparents, aunts, and uncles) were generally better than those for people raised in foster care by nonrelatives; that is, they were less likely to lose custody of their own children. In large part, the difficulties encountered by former foster children had to do with the level of their own adult drinking, lending further support to the hypothesis developed previously that recourse to alcohol by COAs is mediated in crucial ways by their sense of self and their experiences with caregiving during childhood. POLICY IMPLICATIONS A number of interesting policy implications emerge from this review. First, it is abundantly clear that there is a need for basic research into the lives of American Indian and Alaska Native COAs. For instance, although the epidemiology of FAS and ARBD may be emerging, we know next to nothing about the experiences of these children—how they are regarded in Indian and Native communities and the kinds of services that may be most needed or appropriate. Furthermore, we know very little about what life is like for those children who are not born with these disabilities but whose development is nevertheless compromised by the child-rearing practices and

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violence associated with the postnatal drinking of their parents. As is abundantly clear in our review of the literature, there are no studies in Indian and Alaska Native communities that can compare to the high-quality work on COAs more generally that has emerged in recent years. Clearly, concrete policy recommendations must await more detailed knowledge about these children and their development. This research must build on the extant work reviewed in this chapter, but it must also be cognizant of the very different cultural contexts in which Indian children live. This will require detailed consideration of the validity of established measures and quite probably the development of new measures to capture key dimensions of the American Indian experience, such as the importance of spirituality and spiritual practices as protective factors (e.g., Beauvais, 1992). Yet, despite the paucity of data on the specific situation for American Indian COAs, we know a fair amount about what should be avoided. Centuries of federal Indian policy have been devoted toward eradicating Indian culture and assimilating Indian people into European-American society. Many of these efforts have specifically targeted the family by attempting to get Indian children out of their natal communities (e.g., Unger, 1974), and there is a danger that interventions in the lives of American Indian COAs may reproduce these traumas of the past. Although boarding schools have long been held up as the prototype of this kind of intervention, Westermeyer (1979a, 1979b) observes that foster care placement out of Indian communities may actually be worse for Indian children. In boarding schools, Indian children were at least surrounded by Indian peers, even if the goal was to keep them from being involved in their cultures. In contrast, many children raised in foster care are deprived of even these peer interactions. As a consequence, they find themselves even more alienated from their cultures and the sources of strength therein (Westermeyer 1979a, 1979b). It seems clear that whatever is done to address the issues for American Indian COAs, it must be done in a way that is respectful of Indian cultures and preserves their considerable strengths when it comes to raising children. There can be little doubt that some Indian communities have been severely impacted by alcohol abuse, but the epidemiological evidence reviewed in this chapter makes it clear that the consequences of alcohol have not been uniformly distributed throughout these communities. Service providers should use care when deciding how to intervene in the life of a family that has been affected by alcohol in order to ensure that children will be able to maintain contact with members of their extended family who are not drinking in this way. In this way, children can be isolated from the ravages of an alcoholic home at the same time that they are able to maintain contact with the sources of strength in their own traditions.

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THE EMERGENCE OF THE NATIONAL ASSOCIATION FOR NATIVE AMERICAN CHILDREN OF ALCOHOLICS Until the 1980s, advocates for Indian and Alaska Native COAs were scattered throughout various Indian communities without the benefit of an organized network, and there were very few opportunities for healing the harm done by a parent or guardian’s drinking. However, several recent developments in Indian alcohol treatment have generated greater attention to the needs of the alcoholic’s family members, which, in turn, have given rise to a powerful movement for Native American children of alcoholics. First, Indian-specific treatment centers began slowly incorporating the family in the assessment and treatment phases, which revealed the extent to which they were also affected by the alcoholic’s drinking. Second, Indian clients and their counselors began recognizing the generational pattern of alcohol abuse and dependence in many Indian families. Because their clients were so often COAs, attention to their childhoods began to emerge as a central issue in their recovery. Third, a growing number of sober Indian adults began to celebrate their abstinence publicly through the organization of sobriety dinners, fun runs, and campouts, which included family members who were able to share their own experiences of living with an alcoholic. These events have also had the effect of removing the stigma attached to being an alcoholic and making it easier for others to seek treatment. Finally, there has been an increasing awareness that cultural devastation and loss, like alcoholism, have often been passed across the generations. These problems have been aggressively acknowledged with treatment programming that honors and utilizes the strength and wisdom of Indian beliefs and practices in healing from the historical oppression of Indian people. One significant early project that addressed the needs of American Indian COAs was sponsored by the Seattle Indian Health Board and funded by the Office of Minority Health. This project conducted training for community members from several Indian communities in and around the Puget Sound. A written curriculum was developed that contained information from the clinical literature for COAs. The project also commissioned material that addressed unique cultural issues, including chapters that discussed the concepts of cultural oppression, shame, the cycle of grief, spirituality, sexual orientations, warrior roles, and resiliency from Indian and Alaska Native points of view. Through meetings that were a mixture of lectures and self-help group sessions, a cadre of trainers was equipped with information that honored the many different ways Indian and Native cultures addressed healing and self-care. In 1988, the National Association for Children of Alcoholics (NACOA) invited a number of Indian people to attend NACOA’s annual conference and consider if that organization could be a venue to galvanize the energies of Indian

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communities. The delegates decided that a separate organization would be most appropriate, and the National Association for Native American Children of Alcoholics (NANACOA) was founded. The vision articulated by the founding members was of healing the suffering among Native American people caused by generations of substance abuse and chemical dependency. Beginning as an all-volunteer working board, NANACOA has grown to a staff of five with an international board of directors. NANACOA’s present work centers on a unique model of intensive training that helps individuals take the next step in their personal healing journey while building a community of safety and support with others. As an integral part of the healing movement in native communities today, NANACOA provides opportunities to focus on self- and other-care, resiliency, sharing, prayer, and song, as well as information on families and communities with alcoholic members, trauma, unresolved grieving, cultural oppression, and paths to healing. NANACOA has sponsored many intensive training programs in Indian communities around the United States and Canada, supported annual conferences, produced several publications and videos, actively collaborated with other national and international substance abuse prevention programs, and cosponsored a national summit with other national Indian organizations. Recently, there have been efforts made to inform and work with local, regional, and national policy makers about the needs of Native American COAs. In fulfilling its mission, NANACOA articulates messages of hope that American Indians and Alaska Natives can produce changes leading to healthy individuals, families, and communities. CONCLUSIONS The emergence of NANACOA underscores the seriousness with which COA issues are regarded in Indian and Alaska Native communities and the important steps that they have already taken to deal with some of these problems. Our purpose in this chapter has been to highlight some of areas where research is likely to be particularly productive and useful to Indian and Native communities as they further develop their work on these issues. Our hope is that researchers, working in partnership with American Indian and Alaska Native communities, will be able to pursue inquiries in this area that will help us better understand the experiences of native COAs and that this research can then be directly applied to ameliorate the harm that is of such concern to all who work in Indian country. REFERENCES Abel, E. (1995). An update on incidence of FAS: FAS is not an equal opportunity birth defect. Neurotoxicology and Teratology, 17, 437–443.

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Arbuckle, J., Olson, L., Howard, M., Brillman, J., Anctil, C., & Sklar, D. (1996). Safe at home? Domestic violence and other homicides among women in New Mexico . Annals of Emergency Medicine, 27, 210–215. Asante, K., & Nelms-Matzke, J. (1985). Survey of children with chronic handicaps and fetal alcohol syndrome in the Yukon and Northwest B.C. Ottawa: National Native Advisory Council on Alcohol and Drug Abuse, Health and Welfare Canada. Bacon, M. (1974). The dependency-conflict hypothesis and the frequency of drunkenness: Further evidence from a cross-cultural study. Quarterly Journal of Studies on Alcohol, 55, 836–875. Bacon, M. (1976). Alcohol used in tribal societies. In B.Kissin & H.Begleiter (Eds.), Social Aspects of Alcoholism, Volume 4 (pp. 1–36). New York: Plenum Press. Bacon, M., Barry, H., & Child, I. (1965). A cross-cultural study of drinking: II. Relations to other features of culture. Quarterly Journal of Studies on Alcohol, Volume 26, Supplement 3, 29–48. Beauvais, F. (1992) Characteristics of Indian youth and drug use. American Indian and Alaska Native Mental Health Research, 5, 51–67. Bergeson, M., Ingle, D., McKenzie, E., Pearson, K., Zangri, A., Middaugh, J.P., Perry, S., Cassidy, S., & Clarren, S. (1993). Linking multiple data sources in fetal alcohol syndrome surveillance—Alaska. Morbidity and Mortality Weekly Report, 42, 312– 314. Bray, D.L., & Anderson, P.D. (1989). Appraisal of the epidemiology of fetal alcohol syndrome among Canadian native peoples. Canadian Journal of Public Health, 80, 42–45. Chassin, L., Curran, P.J., Hussong, A.M., & Colder, C.R. (1996). The relation of parent alcoholism to adolescent substance use: A longitudinal follow-up study. Journal of Abnormal Psychology, 105, 70–80. Chassin, L., Pillow, D.R., Curran, P.J., Molina, B.S.J., & Barrera, M. (1993). Relation of parental alcoholism to early substance use: A test of three mediating mechanisms. Journal of Abnormal Psychology, 102, 3–19. Chassin, L., Rogosch, F., & Barrera, M. (1991). Substance use and symptomatology among adolescent children of alcoholics. Journal of Abnormal Psychology, 100, 449–463. Cotton, N.S. (1979). The familial incidence of alcoholism: A review. Journal of Studies on Alcohol, 40, 89–116. Curran, P.J., Stice, E., & Chassin, L. (1997). The relation between adolescent alcohol use and peer alcohol use: A longitudinal random coefficients model. Journal of Consulting and Clinical Psychology, 65, 130–140. DeBruyn, L.M., Lujan, C.C., & May, P.A. (1992). A comparative study of abused and neglected American Indian children in the southwest. Social Science and Medicine, 35, 305–315. Dozier, E.P. (1966). Problem drinking among American Indians: The role of sociocultural deprivation. Quarterly Journal of Studies on Alcohol, 27, 72–87.

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Duimstra, C., Johnson, D., Klutsch, C., Wang, B., Zetner, M., Kellerman, S., & Welty, T. (1993). A fetal alcohol syndrome surveillance pilot project in American Indian communities in the northern plains. Public Health Reports, 108, 225–229. Egeland, G.M., Perham-Hester, K.A., & Hook, E.B. (1995). Use of capture-recapture analyses in fetal alcohol syndrome surveillance in Alaska. American Journal of Epidemiology, 141, 335–341. Fitzgerald, H.E., Davies, W.H., Zucker, R.A., & Klinger, M. (1994). Developmental systems theory and substance abuse: A conceptual and methodological framework for analyzing patterns of variation in families. In L.L’Abate (Ed.), Handbook of developmental family psychology and psychopathology (pp. 350–372). New York: John Wiley and Sons. Fitzgerald, H.E., Sullivan, L.A., Ham, H.P., Zucker, R.A., Bruckel, S., Schneider, A. M., & Noll, R.B. (1993). Predictors of behavior problems in three-year-old sons of alcoholics: Early evidence for the onset of risk. Child Development, 64, 110–123. Fitzgerald, H.E., & Zucker, R.A. (1995). Socioeconomic status and alcoholism: The contextual structure of developmental pathways to addiction. In H.E.Fitzgerald, B.M.Lester, & R.A.Zucker (Eds.), Children of poverty: Research, health, and policy issues (pp. 125–145). New York: Garland Publishers. Fitzgerald, H.E., Zucker, R.A., & Yang, H.Y. (1995). Developmental systems theory and alcoholism: Analyzing patterns of variation in high risk families. Psychology of Addictive Behaviors, 9, 8–22. Graham-Bermann, S.A. (1998). The impact of woman abuse on children’s social development: Research and theoretical perspectives. In G.W.Holden, R. Geffner, & E.N.Jouriles (Eds.), Children exposed to marital violence: Theory, research, and applied issues (pp. 21–54). Washington, DC: American Psychological Association. Holden, G.W. (1998). Introduction: The development of research into another consequence of family violence. In G.W.Holden, R.Geffner, & E.N.Jouriles (Eds.), Children exposed to marital violence: Theory, research, and applied issues (pp. 1–18 ). Washington, DC: American Psychological Association. Holden, G.W., Geffner, R., & Jouriles, E.N. (1998). Children exposed to marital violence: Theory, research, and applied is sues. Washington, DC: American Psychological Association. Ishisaka, H. (1978). American Indians and foster care: Cultural factors and separation. Child Welfare, 57, 299–308. Jansen, R.E., Fitzgerald, H.E., Ham, H.P., & Zucker, R.A. (1995). Pathways into risk: Temperament and behavior problems in three- to five-year old sons of alcoholics. Alcoholism: Clinical and Experimental Research, 19, 501–509. Kohut, H. (1971). The analysis of the self: A systematic approach to the psychoanalytic treatment of narcissistic personality disorders. New York: International Universities Press. Kunitz, S.J., & Levy, J.E. (1994). Drinking careers: A twenty-five-year study of three Navajo populations. New Haven, CT: Yale University Press.

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Kunitz, S.J., Levy, J.E., McCloskey, J., & Gabriel, K.R. (1998). Alcohol dependence and domestic violence as sequelae of abuse and conduct disorder in childhood. Child Abuse and Neglect, 22, 1079–1091. Lee, W.V., & Weinstein, S.P. (1997). How far have we come? A critical review of the research on men who batter . In M.Gallanter (Ed.), Recent developments in alcoholism; Vol. 13, Alcohol and violence (pp. 337–356). New York: Plenum Press. Leung, P.K., Kinzie, J.D., Boehnlein, J.K., & Shore, J.H. (1993). A prospective study of the natural course of alcoholism in a Native village. Journal of Studies on Alcohol, 54, 733–738. Levin, J.D. (1991). Treatment of alcoholism and other addictions: A self-psychology approach. Northvale, NJ: Jason Aronson. Levy, J., & Kunitz, S.J. (1974). Indian drinking: Navajo practices and Anglo-American theories. New York: John Wiley and Sons. Lujan, C., DeBruyn, L.M., May, P.A., & Bird, M.E. (1989). Profile of abused and neglected children in the southwest. Child Abuse and Neglect, 13, 449–461. Mancall, P.C. (1995). Deadly medicine: Indians and alcohol in early America. Ithaca, NY: Cornell University Press. Matthew, R.J., Wilson, W.H., Balzer, D.G., & George, L.K. (1993). Psychiatric disorders in adult children of alcoholics: Data from the epidemiologic catchment area project. American Journal of Psychiatry, 150, 793–800. May, P.A. (1982). Substance abuse and American Indians: Prevalence and susceptibility. International Journal of the Addictions, 17, 1185–1209. May, P.A. (1991). Fetal alcohol effects among North American Indians: Evidence and implications for society. Alcohol Health and Research World, 15, 239–248. May, P.A. (1994). The epidemiology of alcohol abuse among American Indians: The mythical and real properties. American Indian Culture and Research Journal, 18, 121–143. May, P.A. (1996). Overview of alcohol abuse epidemiology for American Indian populations. In G.D.Sandfur, R.R.Rundfuss, and B.Cohen (Eds.), Changing numbers, changing needs: American Indian demography and public health (pp. 235–261). Washington, DC: National Academy Press. May, P.A., & Hymbaugh, K.J. (1989). A macro-level fetal alcohol syndrome prevention program for Native Americans and Alaska Natives: Description and evaluation. Journal of Studies on Alcohol, 50, 508–518 May, P.A., Hymbaugh, K.J., Aase, J.M., & Samet, J.M. (1983). Epidemiology of fetal alcohol syndrome among American Indians of the southwest. Social Biology, 30, 374–387. McClelland, D.C. (1972). Examining the research basis for alternative explanations of alcoholism. In D.C.McClelland, W.N.Davis, R.Kalin, & E.Wanner (Eds.), The drinking man (pp. 276–315). New York: Free Press. McClelland, D.C., Davis, W.N., Kalin, R., & Wanner, E. (Eds.), (1972). The drinking man. New York: Free Press.

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Mohatt, G. (1972). The sacred water: The quest for personal power through drinking among the Teton Sioux. In D.C.McClelland, W.N.Davis, R.Kalin, & E.Wanner (Eds.), The drinking man (pp. 261–275). New York: Free Press. Newlin, D.B., & Thomson, J.B. (1990). Alcohol challenge with sons of alcoholics: A critical review and analysis. Psychological Bulletin, 108, 383–402. Oetting, E.R., & Beauvais, F. (1987). Peer cluster theory, socialization characteristics, and adolescent drug use: A path analysis. Journal of Counseling Psychology, 34, 205–213. Oetting, E.R., & Beauvais, F. (1989). Epidemiology and correlates of alcohol use among Indian adolescents living on reservations. In D.Spiegler, D.Tate, S. Atkin, & C.Christian (Eds.), Alcohol use among U.S. ethnic minorities: Proceedings of a conference on the epidemiology of alcohol use and abuse among ethnic minority groups, September 1985 (pp. 239–267). Washington, DC: U.S. Government Printing Office. O’Nell, T.D., & Mitchell, C.M. (1996). Alcohol use among American Indian adolescents: The role of culture in pathological drinking. Social Science and Medicine, 42, 565– 578. Piasecki, J.M., Manson, S.M., Biernoff, M.P., Hiatt, A.B., Taylor, S.S., & Bechtold, D.W. (1989). Abuse and neglect of American Indian children: Findings from a survey of federal providers. American Indian and Alaska Native Mental Health Research, 3, 43–62. Robinson, G.C., Conry, J.L., & Corny, R.F. (1987). Clinical profile and prevalence of fetal alcohol syndrome in an isolated community in British Columbia. Canadian Medical Association Journal, 137, 203–207. Schaefer, J.M. (1981). Firewater myths revisited: Review of findings and some new directions. Journal of Studies on Alcohol (Supplement 9), 99–117. Sher, K.J. (1991). Children of alcoholics: A critical appraisal of theory and research. Chicago: University of Chicago Press. Sher, K.J., Walitzer, K.S., Wood, P.K., & Brent, E.E. (1991). Characteristics of children of alcoholics: Putative risk factors, substance use and abuse, and psychopathology. Journal of Abnormal Psychology, 100, 427–448. Spicer, P. (1995). Suffering selves: The contradictions of drinking in an American Indian community. Unpublished doctoral dissertation, University of Minnesota. Spicer, P. (1997). Toward a (dys)functional anthropology of drinking: Ambivalence and the American Indian experience with alcohol. Medical Anthropology Quarterly, 11, 306–323. Spicer, P. (1998a). Narrativity and the representation of experience in American Indian discourses about drinking. Culture, Medicine and Psychiatry, 22, 139–169. Spicer, P. (1998b). Drinking, foster care, and the intergenerational continuity of parenting in an urban Indian community. American Indian Culture and Research Journal, 22, 335–360. Stratton, R., Zeiner, A., & Paredes, A. (1978). Tribal affiliation and prevalence of alcohol problems. Journal of Studies on Alcohol, 39, 1166–1177.

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Topper, M.D. (1980). Drinking as an expression of status: Navajo male adolescents. In J.O.Waddell and M.W.Everett (Eds.), Drinking behavior among southwestern Indians (pp. 103–147). Tucson; University of Arizona Press. Unger, S. (1974). The destruction of American Indian families. New York: Association on American Indian Affairs. Waddell, J.O. (1973). “Drink, friend!” Social contexts of convivial drinking and drunkenness among Papago Indians in an urban setting. In M.Chafetz (Ed.), Proceedings of the first annual alcoholism conference of the National Institute on Alcohol Abuse and Alcoholism: Clinical problems and special populations (pp. 237– 251). Washington, DC: U.S. Government Printing Office. Waddell, J.O. (1975). For individual power and social credit: The use of alcohol among Tucson Papagos. Human Organization, 34, 9–15. Waddell, J.O. (1980). Drinking as a means of articulating social and cultural values: Papagos in an urban setting. In J.O.Waddell & M.W.Everett (Eds.), Drinking behavior among southwestern Indians (pp. 37–82). Tucson: University of Arizona Press. Wallace, A.F.C. (1972). The death and rebirth of the Seneca. New York: Vintage Books. Weisner, T., Weibel-Orlando, J., & Long, J. (1984). “Serious drinking,” “Whiteman’s drinking,” and “Teetotaling”: Drinking levels and styles in an urban American Indian population. Journal of Studies on Alcohol 45, 237–250. Westermeyer, J. (1973). Indian powerlessness in Minnesota. Society, 10, 45–52. Westermeyer, J. (1979a). Ethnic identity problems among ten Indian psychiatric patients. International Journal of Social Psychiatry, 25, 188–197. Westermeyer, J. (1979b). The apple syndrome in Minnesota: A complication of racialethnic discontinuity. Journal of Operational Psychiatry, 16, 18–23. Whipple, E.E., Fitzgerald, H.E., & Zucker, R.A. (1995). Parent-child interactions in alcoholic and nonalcoholic families. American Journal of Orthopsychiatry, 65, 153– 159. Zucker, R.A., Fitzgerald, H.E., & Moses, H.D. (1995). Emergence of alcohol problems and the several alcoholisms: A developmental perspective on etiologic theory and life course trajectory. In D.Chicchetti & D.J.Cohen (Eds.), Developmental psychopathology; Vol. 2: Risk, disorder, and adaptation (pp. 677–711). New York: John Wiley and Sons.

CHAPTER 8

Alcohol and Drug Use among African-American Youth H.ELAINE RODNEY

As the nation grapples with the problem of health care, it is imperative to understand the nature, scope, and role of alcohol and drug abuse. In 1994, the Substance Abuse and Mental Health Services Administration (SAMHSA) reported about 519,000 drug-related episodes in hospital emergency departments nationwide. In the first half of 1995, there were about 279,000 episodes, representing an increase of 10% from the first half of 1994 (252,6000) (Byrne, 1996). Blacks are overrepresented in health statistics for life-threatening illnesses, alcoholism being the most prevalent. They are three times more likely than whites to be treated for this disease. Cirrhosis of the liver is twice as common among black males than among whites. Heart disease is one of the leading causes of death in the United States, accounting for nearly 50% of all deaths— roughly three times the number caused by cancer. Once it was thought that these diseases were relatively fixed in the population. It is now well known that there are dramatic differences in the rates of heart attacks in different populations. Further, these differences have been clearly linked with a variety of “risk factors.” The most significant risk factors are cigarette smoking, hypertension, and hypercholesterolemia (Committee on the Future of Alcohol and Other Drug Use Prevention, 1989). In African-American communities, alcohol and drugs have been shown to be the primary cause of violent behavior, and according to some studies, account for as much as 75% of violent episodes (Marshall, 1989). As the victims of violence crowd our hospital emergency wards with younger and younger patients, the need for a solution becomes urgent. The identical factors that seem to result in a high level of violence in black America also produce high rates of drug and alcohol abuse, low income, physical deterioration of the neighborhood, welfare dependency, disrupted families, lack of social support, low levels of education and vocational skill, and high unemployment (Copeland, 1992). 165

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Thompson and Cooper (1988) report that since 1979, the use and abuse of alcohol has increased by more than 86%, with 65% of black youth engaging in regular weekly use. They also found a rise in the number of black females using alcohol and other drugs. According to a survey conducted by the inspector general (U.S. Department of Health and Human Services, 1991), 68% of all students drank alcohol at least once, and 51% had at least one drink within the past year. The average student in the survey was 16 years old. Furthermore, it has been reported that more than half of all students in the 12th grade have used alcohol, and one third became intoxicated every 2 weeks (Johnston, O’Malley, & Bachman, 1993a). It has also been reported that high school students drink an estimated 35% of all wine coolers sold in the United States and more than 1 billion cans of beer each year. The health toll exacted by this unlawful behavior is clear. Almost 5 million teenagers have a drinking problem. Alcohol-related accidents are a leading cause of death among young people 15 to 24, and about half of all youthful deaths in drowning, fires, and homicide are alcohol related (U.S. Department of Justice, 1988). A substantial number of suicides are known to occur in association with alcohol and other drug use (Murphy, 1988). Therefore, adolescent substance abuse extracts a high cost from society in health care, drug and alcohol treatment, and juvenile crime (Hawkins, Catalano, & Miller, 1992). This makes drug abuse among young people one of the greatest challenges in American society. Almost daily, we are besieged by media reports of drug-related tragedy, of shootings in our schools, gang warfare, and overdoserelated deaths (Shedler & Block, 1990). Experimentation in early adolescence with alcohol, cigarettes, and illicit drugs is an important predictor of future persistent drug use (Kandel, Kessler, & Marquiles, 1978; Newcomb, Maddahian, Sakger, & Bentler, 1987), and certain risk factors have predicted early drug use with considerable accuracy in a limited number of regional and national studies (Smith & Fogg, 1978; Newcomb, Maddahian, & Bentler, 1986; Bry, McKeon, & Pandina, 1982; Gorsuch & Butler, 1976). Jessor and Jessor (1977) claim that the period of adolescence is commonly viewed as a time when significant alcohol involvement starts. Unfortunately for many, the initial use leads to prolonged, problem-ridden abuse. Alcohol has received the highest acceptance in our society and remains the drug most persistently used by adolescents. The period of adolescence is a time of profound and rapid change, both biologically and psychosocially, as part of growing up. In their search to establish their own identities, adolescents tend to rebel against parental and other adult authority, values, and even morals. This rebellion sometimes includes substance use and other risk-taking behaviors. In fact, intense rebellion is a definite sign of serious trouble, and too often it leads to chemical dependency and death in alcohol-related crashes. Alcohol and other drug use is often linked to a wide range of negative behaviors, from truancy and school failure to criminal behavior

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and suicide (Kumpfer, 1987). Even infrequent use of alcohol or other drugs can lead to a tragic outcome, for example, an overdose or automobile crash after drinking. I conducted a series of studies on African-American high school and college students, including an investigation of the prevalence of alcohol and drug use. These studies were aimed at examining different conditions of the youths’ lives, such as the impact of their family structure on their behavior, their involvement in alcohol and drug use, the impact of alcohol on their school performance, and the impact of parental alcoholism on their behaviors. The investigation led to very clear conclusions that there is a major concern with the use of drugs among youth that mirrors the broader concern in society. Although alcohol and tobacco are not illegal drugs for those over age 21, they constitute major health problems for those who use and abuse them. They are gateway drugs for illicit drug use. For adolescents, all drugs are illegal. Use of any drug could prevent them from fulfilling their potential in life, including their potential to make constructive contributions to the lives of others. Therefore, this chapter is designed to encourage and stimulate drug-effect research and to encourage additional investigation in the field of prevention. In reporting these studies, the chapter has been organized into two sections. The first reports on three studies conducted on African-American youth in general, with the study sample consisting of community participants (Rodney & Mupier, in press-a; Rodney, Mupier, & O’Neal, 1997; Rodney, Rodney, Mupier, & Crafter, in press). The studies report the negative consequences or problems associated with alcohol or other substance abuse. The second section reports briefly on seven studies that were conducted on a subpopulation of children of alcoholics (COAs) (Rodney, 1994, 1995; Rodney & Mupier, 1997, in press-c; Rodney, Mupier, & Crafter, 1996; Rodney & Rodney, 1996). They also demonstrate the impact of the negative behaviors of parents on their children. For the purposes of the chapter, the definition of youth extends to young people in college. STUDIES OF COMMUNITY-BASED SAMPLES OF AFRICAN-AMERICAN YOUTH Behavioral Difference between African-American Male Adolescents with and without Biologic Fathers at Home Currently in the United States, approximately 24 million (28%) children do not live with their biologic fathers, a figure representing an increase of almost 18% from 36 years ago (Shapiro, Schrof, Sharp, & Friedman, 1995). Forty percent of all children of divorced parents have not seen their fathers in the past year (Cooper, Koszmovszky, Gutfeld, Guthrie, & Hobica, 1995). In this

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regard, Brown-Cheatham (1993) has asserted that 50% of today’s children will spend at least part of their childhood in a single-parent female-headed household. As of 1993, only 51% of all children still live with both biologic parents, and 90% of black children will spend part of their childhood in a singleparent female-headed household. Many social scientists believe that the increase in female-headed families is devastating and constitutes a major erosion of the traditional American family. Additional analyses of family structure indicate that father absence is linked to delinquency, serious criminal behaviors, alcoholism, and occupational underachievement (McCord, 1991). However, intergenerationally transmitted values can be a protective shield to reduce the chances of children, especially boys, becoming involved in negative behaviors. Among factors that may protect are the absence of marital discord (Farran & Cooper, 1986), family cohesion (Felner, Abner, Primavera, & Cauve, 1985), established family rules and regulations in the home (Werner, Bierman, & French, 1971), parental respect for children’s individuality, family stability and cohesion (Werner & Smith, 1977), and children’s positive relationship with at least one parent (Campbell, 1987). These protective factors, whether in two-parent or single-parent families, seem to enhance and facilitate children’s ability to resist negative behaviors. Most studies dealing with father absence in the home have concentrated on poor urban black families and have been inconclusive in establishing a direct link between father absence and children’s behavior problems. To fill that void, I investigated father absence by using a sample drawn from the many socioeconomic classes represented in the African-American community in a Midwestern region. Specifically, I investigated the difference between father absence and father presence among male adolescents and the degree of their involvement with alcohol and conduct disorder. The results of the study (Rodney & Mupier, in press-a) revealed that 74% of the adolescents did not live with their fathers. The most frequent reason offered for father absence was the failure of parents to marry. The finding provides some support for Blankenhorn’s (1995) assertion that America has become a fatherless society, and it is consistent with other data indicating 74% father absence among African Americans, 54% among Hispanics, and 39% among whites, for an overall rate of 65% of the three ethnic groups (Brooks-Gunn, Klebanov, Liaw, & Duncan, 1995). No significant difference was found between father-absent and fatherpresent adolescents with respect to their alcohol involvement, but father-absent adolescents experienced significantly more behavior problems than fatherpresent adolescents, notably in terms of running away from home, skipping or cutting classes, being suspended from school, and getting in trouble with the law. For example, more than 36% of father-absent versus 23% of father-

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present adolescents skipped school or cut classes more than three times. Seventy-four percent of father-absent versus 50% of father-present adolescents were suspended from school more than three times. Forty-two percent of fatherabsent versus 29% of father-present adolescents had trouble with the law. Five percent of father-absent versus 0.9% of father-present adolescents ran away from home twice or once and never returned. All of these behavioral problems constitute conduct disorder problems that may potentially lead to later problems if they go unattended. Loeber (1990) has observed that adolescents who engage in these behaviors may be at increased risk for drug abuse, juvenile delinquency, violence, school dropout, and some teen pregnancy. Alcohol Use among Youth Retained and Suspended in School Concern that alcohol remains the most commonly abused substance among adolescents is foremost in the minds of health providers and others. According to the National Adolescent Student Health Survey, 76% of 8th-grade and 87% of l0th-grade students have used alcohol at least once (Windle, 1990). Of greater significance is the widespread occurrence of heavy drinking (defined as consuming five or more drinks in a row) (National Institute on Alcohol Abuse and Alcoholism, 1991). Consumption of five or more drinks on one occasion is one indicator of drunkenness. Media messages on television, in magazines, and on billboards associate alcohol with glamour, sophistication, and maturity. With the use of very attractive models, advertisements create the illusion that liquor brings success, strength, wealth, and sexual conquest. Other images that significantly affect the way black youth see themselves include the unemployed gathered on the street corner, the abundance of liquor stores in black neighborhoods, and the “drunk” at the bus stop. Drug and alcohol abuse in black lower income communities are ways of coping with unemployment, poverty, and all the accompanying social and economic derivatives. “Hanging out on the street corners” and drinking or abusing drugs give black youth something to do (Copeland, 1992). Academic failure is one of the largest and most consistent predictors of later drug and alcohol use, delinquent behavior, teenage pregnancy, and school dropout (Gottfredson, 1987; Grissom & Shepard, 1989). Not only can alcohol and other drug use result in poor performance and dropout but also early school failure can be a precursor for later alcohol and drug use (Holmberg, 1985; Jessor & Jessor, 1977; Smith & Fogg, 1978). Delinquency and illicit drug use have a similarly reciprocal relationship (Hawkins, Lishner, Jenson, & Catalano, 1987). Just as early delinquency is associated with later alcohol and drug use (Kandel, Simcha-Fagan, & Davis, 1986), early use of illicit drugs

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enhances involvement in delinquent behavior (Brunswick & Boyle, 1979). The Jessors (Jessor, 1987; Jessor & Jessor, 1977) postulate intercorrelations among a series of specific problem behavior areas (e.g., drug use, sexual intercourse, and other deviant behaviors) and indicators of conventional behaviors (e.g., academic achievement, church attendance) such that higher levels of problem behavior and lower levels of conventional behavior are associated with drinking. In one study (Rodney, Rodney, Mupier, & Crafter, in press), we examined the variables associated with grade retention among 243 African-American adolescent males. Fourteen percent of the sample met the criteria for alcohol abuse. Alcohol abuse was rigorously defined in the sense that to be qualified as an alcohol abuser the adolescent not only had to have drunk six times or more but also must have exhibited symptoms such as getting involved in physical fights, quitting school or a job, or experiencing withdrawal symptoms. Moreover, 85% of those who abused alcohol reported having been suspended from school, and 33% reported having been held back. Alcohol abusers made up more than 20% of those held back and 17% of those suspended from school. The findings from this study show a connection between alcohol abuse and suspension, which leads to dropping out and subsequent academic failure. If adolescents use any drug more than a few times, they are likely to continue using it through a significant part of their lives (Kandel, 1982). Most Americans are familiar with this process for drug use and abuse. Therefore, any effort to reduce the prevalence of alcohol abuse among African-American youth should help reduce the dropout rate. Alcohol use cannot be seen as a transient and casual behavior, a sort of rite of passage into adolescence. Among the factors that contribute to students giving up on school and subsequently dropping out is substance abuse. Suspensions and expulsions tend to speed up the dropping-out process, and many students leave partly because of their illicit drug use. In particular, students who have been suspended or have had trouble with the police are much more likely to dropout. They are described as “pushouts” in that at-risk children are continually receiving signals from their schools that they are neither able nor worthy to continue to graduation, and they are frequently encouraged to leave (DeRidder, 1991). If good health is associated with education, as studies suggest, it is imperative to focus attention on academic education as well as on awareness about the illicit drug usage among African-American adolescents. AfricanAmerican males not only are suspended and expelled from school at a higher rate than any other ethnic group (Bickel & Quails, 1980; Costenbader & Markson, 1994; Moody, 1978; Streitmatter, 1986) but also drop out of school at disproportionately higher rates. In fact, the African-American student dropout rate, in some cases, is almost 70% higher than the rate for EuropeanAmerican students (Richardson & Gerlack, 1980).

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Alcohol and Drug Use among Youth in Public Housing Even though the effects of alcohol and drug use are experienced keenly in inner city African-American communities, the psychosocial correlates have not received enough attention. The family environment provides one context for studying the psychosocial determinants of drug use; other contexts involve the school, the peer group, and the neighborhood. These domains can generate risk factors that lead to adolescent onset of alcohol and drug use. Risk of alcohol and other drug use can be seen as falling into five broad categories: genetic factors, family factors, peer factors, psychological factors, and community factors. As Hawkins, Lishner, Jenson, and Catalano (1987) indicate, these categories are not mutually exclusive because risk factors in one area often interact with risk factors in another area. Community characteristics play a major role in the etiology of alcohol and other drug use. Children living in extreme poverty, who experience high levels of mobility and density, are more likely to become entangled in delinquent and drug-using behaviors (Farrington, 1985). High levels of alcohol and drug problems in communities contribute to social breakdown and disorganization, consequently restricting opportunities for success. Such communities are less likely to provide the necessary support and opportunities for all their members. Farrell (1993) examined the relationship between risk factors and drug use in a sample of 1,375 middle school students in an urban school system and found that the total number of risk factors to which the adolescents were exposed was significantly related to subsequent use of beer, wine, hard liquor, cigarettes, and marijuana. A comparison of several path models relating risk factors to the frequency of drug use between the seventh and eighth grades indicated that a reciprocal model provided the best fit to the data. Within this model, drug use was predicted by previous levels of risk factors, and the risk factor index was predicted by previous levels of risk factors and drug use. These findings provide strong support for using a risk factor approach to identify variables associated with drug use among adolescents. Using this approach of risk factors, researchers have found that children who live in deteriorating and crime-ridden neighborhoods characterized by extreme poverty are more likely to develop problems with delinquency, teen pregnancy, school dropout, and violence. Children who live in these areas and have behavior and adjustment problems early in life are also more likely to have problems with drugs later on (Copeland, 1992; Developmental Research and Programs, 1993; Loeber, 1990). Others have also found indicators of socioeconomic disadvantage such as poverty, overcrowding, and poor housing to be associated with an increased risk for childhood conduct problems. However, the apparent link to negative socioeconomic status and delinquency has not been found for adolescent drug use. Only when poverty is extreme and occurs

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in conjunction with childhood behavior problems has it been shown to increase the risk for later alcoholism and drug problems (Hawkins, Catalano, & Miller, 1992). Likewise, the attitudes of the parents and their behavior toward drugs, crime, and violence influence the attitudes and behavior of their children. If children are raised in a family with a history of addiction to alcohol or other drugs, the risk of their having alcohol and other drug problems themselves increases. Poor family management has been shown to increase the risk for drug abuse, delinquency, teen pregnancy, school dropout, and violence. Parents who fail to monitor their children, who do not provide clear expectations for child behavior, and who use excessively severe or inconsistent punishment have children with a heightened risk of alcohol and other drug use. Finally, academic failure in school can also increase the risk of drug abuse, delinquency, violence, pregnancy, and school dropout. This is particularly troubling because, in many school districts, African-American, Native American, and Hispanic students have disproportionately higher rates of academic failure than white students. In many communities of color, education is seen as a way out, similar to the way early immigrants viewed education. Other groups in the same community may view education and school as a form of negative acculturation. Young people who adopt this view are likely to be at higher risk for developing health and behavior problems (Developmental Research and Programs, 1993). Fortunately, however, some adolescents who are exposed to multiple risks do not become substance abusers, juvenile delinquents, school dropouts, or teen parents. Because none of the previous studies cited specifically examined drug use among African-American youth in public housing, and given the generally increased perception that public housing constitutes a “berceau” for illicit drug use, the study by Rodney, Mupier, and O’Neal (1997) focused on alcohol and drug use among African-American youth in those communities. Of 123 teenagers (13 to 17) investigated, 12% were classified as abusing alcohol. Among those who abused alcohol, eight were male (53%) and seven were female (47%). None of the adolescents reported using cocaine, speed, opiates, or hallucinogens. One person reported using downers, and 33 reported using other drugs (which included glues, inhalants, and drugs not given by a doctor), which represented 15% of the adolescents in the sample. Among those who abused drugs, 10 were males (53%) and 9 were females (47%). In other words, the prevalence of alcohol and drug abuse among male and female adolescents was about the same, which is consistent with the existing literature. Other researchers have reported that annual and 30-day prevalence rates for the use of alcohol and a broad range of illicit drugs tended to be higher for males than for females, but in most cases the differences were minimal (Farrell, Danish & Howard, 1992; Johnston, O’Malley, & Bachman, 1993a, 1993b). According to Archambault (1992), most adolescents

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in treatment had begun using drugs other than alcohol before the seventh grade. National survey data show that the peak age for use of drugs is 18 to 25, both for illegal drugs such as marijuana and cocaine and for legal drugs such as alcohol and tobacco. According to Hansen (1993), there is significant evidence that alcohol is the first drug used by most youth and that alcohol serves as a gateway to the use of other drugs. The use of tobacco, marijuana, and inhalants has been found to follow alcohol use in that order and seems to precede experimentation with cocaine, heroin, hallucinogens, amphetamines, and other “hard” drugs (Ellickson, Hays, & Bell, 1991; Graham, Collins, Wugalter, Chung, & Hansen, 1991; Kandel, Yamaguchi, & Chen, 1992). The correlation between alcohol and drug use is strong. Rodney, Mupier, and O’Neal (1997) found that inhalants were identified as being used among the “other” drugs. Rodney confirmed the prevalence of drug abuse and unacceptable levels of alcohol consumption, however, there was no evidence that consumption levels approached those reported in the literature. The findings are more consistent with reports that black adolescents have low levels of use for all substances (alcohol, cocaine, marijuana, and cigarettes) (Vega, Zimmerman, Warheit, Apospori, & Gil, 1993). These contradictions point to the need for more studies so that facts, not myths, define the problem of alcohol and drug abuse among children in public housing. If our nation is to remain competitive in the 21st century, public policy must be developed to minimize the health care problems associated with alcohol and drug abuse. An understanding of the problem and a subsequent reduction of alcohol and drug abuse are essential in meeting this goal. The literature and many of these findings point to the importance of the family and the environment in which children live for generating programs to reduce delinquency and to enhance academic performance. The next section of this chapter looks more closely at one of those special family environments—the alcoholic family—to determine whether being an offspring of an alcoholic parent puts a child at higher risk for substance abuse than non-COAs. STUDIES OF CHILDREN OF ALCOHOLICS The Alcoholic Family The familial nature of alcoholism is well established, and a variety of approaches have been used to examine different possible modes of parent-offspring transmission. Environmental, behavioral, and genetic factors have been suggested as possible contributors to both the risk and the transmission of the disorder (Porjesz & Begleiter, 1985; Schuckit, 1985).

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Children being raised in a household with an alcoholic parent are exposed to high levels of stress. Parental alcoholism can be thought of as a form of psychological maltreatment of children that constitutes an unhealthy pattern of parent-child and family relations, negatively affects children’s development, and leaves them at risk for psychological and behavioral disorders in childhood, adolescence, and adulthood (Tharinger & Koranek, 1988). It is estimated that 7 million U.S. children, or about 10% of the population under the age of 18, live in families with one or more alcoholic parents (Russell, Henderson, & Blume, 1985). Tharinger and Koranek (1988) refer to Black’s (1981) study, in which he stated that 15 million school-age children were affected by family alcoholism. Using the Family Environment Scale, Brinson (1991) found evidence to support the view that alcohol affects the perception of the family environment among black male and female users. Females tend to perceive some aspects of family functioning more favorably than males. One of the explanations Brinson gives to this difference in perception is the “rite of passage.” Accordingly, many male adolescents consider using alcohol use symbolic of their transformation into manhood, whereas female adolescents are less likely to consider consuming alcohol as a rite of passage. Adolescent rites of passage may include such activities as beer-drinking contests and smoking cigarettes. Also, many young black males may associate heavy drinking with having a good time. The problem may be even more complicated, considering the fact that drug and alcohol abuse in black lower income communities is a method of coping with unemployment, poverty, and all the accompanying social and economic derivatives. My own work has focused on differences between children of alcoholics (COAs) and non-COAs (that is, those below 18 years of age), and between adult children of alcoholics (ACOAs) and the non-ACOAs (that is, those above 18 years of age). In one study (Rodney, et al, 1997), involving a first-time sample of 595 African-American male adolescents 13 to 17 years old, 24% were classified COAs as measured by the Children of Alcoholics Screening Test (CAST) (Jones, 1981). The COAs reported that one or both parents were alcoholics. In another study conducted on a sample of 554 students on a black college campus (Rodney, 1994), 19% of the subjects were classified as ACOAs, and in another study of 500 students on the same college campus (Rodney, & Rodney, 1996), 27% reported to be ACOAs. In all three cases pertaining to African-Americans, the rate of COAs is much higher than the reported national rate (12.5%) (Ackerman, 1987). Although the two populations do not fall in the same age group, there seem to be relatively more COAs in the African-American population than there are in the general population of the United States. Studies by others have found rates ranging from 15% (Perkins & Berkowitz, 1991) to 18% (Roosa, Sandier, & Beals, 1988) to 20% (Stratton & Penney, 1992), and to

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30% (Roosa, Beals, Sandier, & Pillow, 1990; Roosa, Gensheimer, Ayers, & Short, 1990). The question may not be so much the rate of COAs, but rather the problems or outcomes facing them. Because much has been written about the problems associated with being COAs, it was necessary to investigate if the same problems found in the general population were prevalent among AfricanAmerican adolescents and young adults. Of particular interest in most of the studies was the quest to find the proportion of youth in general and that of COAs in particular who drank or abused alcohol. Rodney and Mupier (1997) divided the total sample of adolescents according to whether they had ever abused alcohol over their lifetimes. Alcohol abusers were those who drank alcohol more than six times over their lifetimes, and in addition, exhibited at least three times particular symptoms as a consequence of drinking: (a) drinking draws criticism and leads to physical fights, loss of friends, depression, and isolation; (b) drinking while knowing it is a danger to self and to school performance; (c) drinking to the point of quitting school and other activities; (d) increased alcohol tolerance by at least 50%; (e) inability to quit drinking; (f) drinking more than initially intended; (g) drinking to relieve or avoid withdrawal symptoms; (h) drinking while experiencing withdrawal (shakes and at least one other symptom); and (i) making drinking a preoccupation. Using these criteria, 117 adolescents (20% of the sample) were classified as alcohol abusers. The age distribution was as follows: 2% were 13 years of age, 12% were 14 years old, 24% were 15 years old, 25% were 16 years of age and 37% were 17 years of age. Older adolescents abused alcohol more than younger ones. Thirty-two percent of the abusers came from the COA population. On the other hand, 17% of the non-COA population abused alcohol. In another study, only 26% of the ACOAs were in the group considered not to have a drinking problem, compared with 46% of the non-ACOAs (Rodney, 1994). Means and standard deviations of respondents’ scores for ACOAs and non-ACOAs showed that the ACOAs had higher scores than those of non-ACOAs (8.06 versus 4.48). Also, in comparing respondents’ MAST scores across gender lines, men scored higher than women in all comparisons. These data suggest that the men reported themselves as having significantly more drinking-related problems than the women. However, there is no indication that men non-COAs have more drinking problems than female COAs. Overall, the ACOAs were abusing alcohol more than the non-ACOAs, which is in keeping with the popular literature that ACOAs have a greater chance of abusing alcohol than the non-ACOAs. Collegiate ACOAs have reported more alcohol problems that non-ACOAs (Andrasi, 1987; Perkins & Berkowitz, 1991; Rodney, 1994). Parents who drink to the point of being classified as alcoholics indirectly cause their offspring to abuse alcohol to the

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extent that they report not only emotional distress but also some physical (somatic) impairments, such as loss of concentration, poor hearing, and headaches (Rodney, et al, 1997). In another study conducted on a predominantly black college campus, 6% of the ACOAs indicated that their mother was the alcoholic parent, 6% said both parents were alcoholic, and 8% reported that the father was alcoholic (Rodney, 1993). In comparing African-American ACOAs and nonACOAs on the college campus with respect to the level of social support, Rodney (1995) found that only in the case of the “mother support” was there a significant difference in scores between the two groups of students, whereas no significant difference was found for “father support.” Males reported receiving a greater level of information and emotional support from their mothers than did females. The ACOAs who reported having a healthier or more functional family reported having significantly fewer drinking problems, as did the ACOAs who reported receiving stronger emotional and informational support from their mothers. In alcoholic families, even nonalcoholic parents can be adversely affected in ways that make them less attentive to the emotional needs of their children. As Brown (1988) explains, with the onset of alcoholism and the need to deny it, the partner begins to adjust his or her life in response to the partner’s addiction. The behavior of the alcoholic becomes an obsession for the codependent, who begins to be preoccupied with the welfare of the alcoholic. Because of the obsession with alcohol, both parents may be irresponsible, chronically or periodically. This could be a key explanation for ACOAs reporting less information and support from their mothers than did the nonACOAs; the mother might have become codependent and unable to function appropriately within the household (Rodney, 1994). Along the same line of ACOAs reporting more drinking problems than non-COAs, several other researchers have documented that ACOAs are at greater risk for developing drinking problems. Adult children with parents who are heavy drinkers have a higher percentage of dependent problem drinking than those without heavydrinking parents. Specifically, college students who report having a parent or grandparent diagnosed or treated for alcoholism also report a higher incidence of alcohol-related problems and a significantly greater level of problem drinking than students who are not ACOAs (Hunt, 1989; Parker & Harford, 1987; Perkins & Berkowitz, 1991; Rodney, 1994). Most of the studies reported on COAs or ACOAs tend to corroborate the fact that alcoholism has an intergenerational effect and that the offspring of alcoholics are at more risk for developing drinking problems and other types of disorders than the rest of the population. Moreover, if the problem of alcoholism runs in a black family, it tends to develop quickly, given the presence of other risk factors such as unemployment, poverty, drug exposure, and crowded housing.

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Children of Alcoholics and Involvement in Conduct Disorder The assertion that COAs are different from non-COAs was also investigated on a different ground—their involvement in conduct disorder. Conduct disorder was assessed with the child version of the C-SAGA (Children-Structured Assessment for the Genetics of Alcoholism), which was developed from the Diagnostic Interview for Children and Adolescents (DICA) in compliance with the criteria set in the DSM-III-R manual. Accordingly, conduct disorder is defined as involvement in frequent, persistent, and patterned delinquent behaviors, whether against property (e.g., burglary, fire-setting), people (e.g., fighting, violent behavior, rape), or self (e.g., truancy, substance abuse), as well as other problem behavior (e.g., lying, stealing, hurting animals) (American Psychiatric Association [APA], 1987; Loeber, 1990; Mrazek & Haggerty, 1994). In addition to physical aggression and covert stealing, other associated features include low self-esteem, anxiety, depression, low academic achievement, impulsiveness, and hyperactivity. The onset is usually prepubertal and ranges from mild forms frequently showing improvement over time to severe forms tending to be chronic (APA, 1987). Other researchers have investigated these conduct problems. Osgood’s (1995) extensive review of existing research on drugs, alcohol, and violence concluded that violent behavior is first seen at substantially younger ages than substance use and that the most serious violent acts decline at earlier ages and more sharply than does the rate of substance use. An example of this trend is the Schubiner, Scott, and Tzelepis (1993) study that determined age to be significantly related to violence. Comparison of three age groups (14–16, 17–19, and 20–24) revealed that the youngest group was significantly more likely to have seen and been involved in a physical fight. According to Osgood (1995), African-American adolescents have substantially higher rates of violent behavior than whites, but the difference is small for rates of substance use. Thus, rates of substance use do not appear to be of value for explaining the demographic patterns of adolescent violence. For instance, the especially high rate of violence among today’s African-American males certainly is not explained by their relatively low rate of substance use. If aggregate rates of substance use contribute to rates of adolescent violence, the contribution must be through indirect and complex avenues, such as having a different effect for different types of people, or the aggregate pattern of violence is dwarfed by other causal factors. Similar results from Vega et al. (1993) also determined that blacks had the lowest levels of use for all substances (alcohol, marijuana, cocaine, and cigarettes). Their prevalence levels were 20% to 30% lower than those in other racial and ethnic subsamples. However, the strongest association between risk factors and alcohol use occurred with blacks; their proportion was almost four times greater than those with no risk factors. Risk

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factors were found to be consistently related to alcohol and illicit drug use among the sixth- and seventh-grade boys in their sample, affirming the value of risk factors for predicting substance use among adolescents. Hawkins, Lishner, Jenson, and Catalano (1987) refer to four general related family risk factors: family history of alcoholism, family history of antisocial behavior, inadequate parental direction and discipline, and parental alcohol and other drug use and attitudes favorable to such use. Risk factors as cited in Newcomb, Maddahian, and Bentler (1986) included the following: parental drug use, perceived adult drug use, poor grades in school, poor relationship with parents, low self-esteem, depression, psychological distress, unconventionality and tolerance for deviance, sensation seeking and the desire for novel and unusual experiences, low sense of social responsibility, a lack of religious commitment, a lack of purpose in life, disruptive life events, and early use of alcohol. In addition to those influences, school transitions represent a time of risk for increased symptoms of negative behaviors. Difficulty in achievementrelated activity among adolescents also has proved important, with studies documenting the following problems among those who later become alcoholic: poor school performance, less productivity in high school, greater truancy and greater incidence of dropping out. Aggressive behavior and the combination of aggressive behavior and shyness in the first grade have been found to predict heavy alcohol use at ages 16 and 17 (Mrazek & Haggerty, 1994). By the late elementary grades, children at risk are made still more visible by evidence of school failure, and by adolescence the low commitment to school and associated academic failure are evident (Jessor & Jessor, 1977; Smith & Fogg, 1978). Rodney et al. (1977) analyzed 13 examples of conduct disorders: (a) stealing without confrontation, (b) running away, (c) lying often, (d) deliberately setting fires, (e) skipping school or cutting class, (f) breaking into property, (g) deliberately destroying property, (h) deliberately being cruel to animals, (i) forced sexual activity, (j) starting physical fights, (k) using a weapon in a fight, (1) stealing with confrontation, and (m) being physically cruel to other people. In this sample of 595 adolescents, 423 (73)% reported involvement in some type of conduct disorder. Unlike alcohol abuse, which increases with age, conduct disorder involvement is nearly equally distributed across age. Further analysis gives the breakdown of involvement in conduct disorder between COAs and non-ACOAs. One hundred and nine COAs (26%) versus 314 non-COAs (74%) were involved in conduct disorder. However, this figure of 109 COAs represents 78% of the COA population, whereas the figure of 314 non-COAs represents 69% of the non-COA population. Therefore, given an equal number of COAs and non-COAs, there will be more COAs involved in conduct disorder than non-COAs. Accordingly, there seems to be a strong

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association between alcohol involvement and conduct disorder, in that 90% of those who were abusing alcohol were also involved in conduct disorder. For those who were not abusing alcohol, the figure was about 60%. Research on alcohol use among adolescents tends to link alcohol-related problems, such as violence and interpersonal difficulties, with the amount of alcohol consumed (Hansen, 1993). To illustrate the strong relationship between alcohol, tobacco, marijuana, and other illegal drug use and delinquency, Watts and Wright (1990) conducted a study of 348 high school males (154 whites, 172 Mexican-American, and 22 blacks) and 89 adjudicated delinquent males confined to a maximum-security facility for violent and repeat offenders (37 whites, 25 Mexican- Americans, and 27 blacks) that used self-administered questionnaires. The analysis revealed that the use of legal and illegal substances accounted for 40 to 47% of the variance in minor delinquency and 59% of the variance in violent delinquency among blacks. The best predictors of violent delinquency were the frequent use of illegal drugs, followed by marijuana. Similar implications can be drawn from a study in which drinking was the strongest single predictor of criminal offenses, both minor and serious, among blacks (Dawkins & Dawkins, 1989). So far, however, the controversy concerning the factors that lead to juvenile delinquency does not seem to be settled. Furthermore, as Cernkovich and Giordano (1992) noted, there is a neglect of blacks in delinquency research. Additionally, the literature on the correlates of delinquency among AfricanAmerican adolescent males has not been explicitly presented within the conceptual framework of their specific behaviors and home environment. This therefore led us to a study of African-American adolescent males in juvenile detention, who were compared with an equal number of those not in juvenile detention (Rodney & Mupier, in press-c). The sample was 106 adolescents, age 13 to 17 in each group, for a total of 212. The comparison was done on alcohol abuse, involvement in conduct disorder, and other characteristics. With respect to alcohol abuse, a relatively larger proportion of those in juvenile detention than the others (a) drank heavily or got into fights after drinking (45% vs. 15%); (b) continued to drink while knowing it was dangerous (46% vs. 9%); (c) drank to the point of developing tolerance for alcohol (23% vs. 9%); (d) wanted to drink less but couldn’t (26% vs. 6%); (e) drank more than they intended (27% vs. 6%), and (f) spent a lot of time trying to get alcohol (19% vs. 1%). The most recent comprehensive study released by the U.S. Department of Justice (1987) was conducted to determine the extent to which use of alcohol is associated with youth criminal activity. It reported that 32% of youth under age 18 in long-term, state-operated juvenile institutions in 1987 were under the influence of alcohol at the time of the offense. In addition, 55% admitted that they drank one or more times per week in the year before their incarceration.

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The overall picture that emerged from the Rodney and Mupier study (in press-c) was that adolescents in juvenile detention are those who are experiencing significantly more problem behavior than those not in juvenile detention. This seems to be in keeping with Annis and Watson (1975), who report that the adolescent substance user may drop out of school and become identifiable in other populations and settings. Children of Alcoholics and Self-Esteem In 1987, Wilson noted that constant disappointment created by lack of job opportunities over a period of 2 or 3 years causes black youth to develop an unemployment lifestyle. They become accustomed to not working and, after some months or years, stop looking for work. According to Hare and Hare (1986), drinking among these adolescents is often associated with low selfesteem, a sense of powerlessness, poor interpersonal and social skills, poor academic or vocational performance, negative peer pressure, and poor family relationships. Young, Werch, and Backema (1989) assert that adolescents who refrain from drinking alcohol have higher self-esteem than do adolescents who drink. Among ACOAs, there is a risk of developing drinking and alcohol-related problems, as well as difficulties with emotional and interpersonal relationships and self-esteem. Findings in early research indicated that low self-esteem is a risk factor for the initiation of alcohol and other drug use by African-American youth (Center for Substance Abuse Prevention, 1990, 1992). Research also identifies self-dissatisfaction as a consequence of substance abuse (Holcomb, Sarvela, Sliepcevich, & Jellen, 1990). It was not surprising, therefore, that, using self-esteem as a dependent measure and the level of health in the family of origin and the level of drinking as predictors, Rodney (1994) found that in ACOAs and non-ACOAs 35% of the variance in self-esteem could be accounted for by both the perceived level of health in the family of origin and the current level of the participants’ drinking problems. Other researchers have described ACOAs as possessing low self-esteem (Cermak, 1989; Gravitz & Bowden, 1985), guilt, and shame and as struggling with issues of control, hypervigilance, excessive feelings of responsibility, and difficulties in identifying and expressing feelings (Cermak, 1982). Another study of college ACOAs found that they reported greater self-depreciation among female ACOAs, which may reflect greater familial identification and greater sensitivity to the destructive aspects of parental alcoholism (Berkowitz & Perkins, 1988). DeSimone, Murray, and Lester (1994) examined the association among alcohol use, self-esteem, depression, and suicidality in high school students. They found in a multiple regression analysis that frequency of use was associated positively with both depression and self-esteem, but not

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significantly with age and gender. By the same token, they found alcohol misuse to be positively associated with both depression and self-esteem, indicating that those who drank more often and misused alcohol had higher self-esteem Scores. Their interpretation was that the students who drank more and misused alcohol were “faking good” on self-esteem. I examined the effect of self-esteem on alcohol drinking by using the Rosenberg Self-Esteem Scale (1979). The effect of being COAs or nonCOAs on self-esteem was the same across gender. However, the main effects of status of COA or non-COA and of gender were statistically significant. The COAs had lower self-esteem scores than the non-COAs. Similarly, females had lower self-esteem scores than males. The combined effect of gender and the status of COA versus non-COA provided no indication that female COAs differed from male COAs on self-esteem. However, female and male drinkers in general differed significantly on their level of self-esteem, with females showing lower self-esteem than males. Rodney and Rodney (1996) found no significant gender differences between groups of African-American male ACOAs and non-COAs or between the female ACOAs and non-COAs with respect to their level of self-esteem or self-concept (Rodney, 1996). But the COAs showed lower self-esteem than non-COAs overall. However, many empirical studies have failed to find significant differences between ACOAs and non-ACOAs on measures of self-esteem, or, at least, findings in this area are mixed (Carroll, 1991; Churchill, Broida, & Nicholson, 1990; Clair & Genest, 1987; Duprez, 1987; Gravitz & Bowden, 1985; Rodney, 1995; Tweed & Ryff, 1991). Age, self-esteem, and the status of COA versus non-COA were found to be the predictors of alcohol involvement. Our study has also found that adolescents with lower self-esteem appeared to drink more than those with higher self-esteem. This seems contrary to the findings by DeSimone et al. (1994), who report alcohol misuse to be positively associated with both depression and self-esteem. Furthermore, Rodney and Rodney (1996) found the level of drinking to be significantly and positively correlated with selfesteem among ACOAs. In other words, the increase in the level of drinking as measured by the MAST was associated with high scores on self-esteem. Higher scores on self-esteem in this case mean lower self-esteem. Many circumstances can lead to low self-esteem, including the perception of less worthiness, failure in shool or in other activities, poor upbringing, or the fact of being a COA, as pointed out earlier. These factors may cause one to resolve to drink as a way of overcoming inadequacies. Fortunately, however, although a portion of this population may prove to be at risk for alcoholism as well as other emotional and behavioral problems, such as self-esteem, a percentage of children, despite all odds, develop “healthy” and “positive” behaviors. This kind of resiliency has also become the focus of national attention in clinical investigation and social research. Studies of

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high-risk populations show that the roots of resiliency lie in protective factors such as the personality characteristics of the individual, supportive family milieu, and available environmental controls (Children of Alcoholics Foundations, 1992). Children of Alcoholics and Social Support There is in the literature a significant difference between ACOAs and nonACOAs on perception of family support, with ACOAs perceiving less family support than non-ACOAs (Rodney, 1994). The ACOAs have described their families as more dysfunctional and reported that they received less guidance from others. The scores of ACOAs on the Family Environment Scale (FES) were lower than the scores of a comparison group on cohesion and intellectcultural orientation and higher on the conflict subscale. The results of the Dimensions of Social Support Scale showed that the informational support reported by ACOAs during adolescence was significantly less than that reported by comparison subjects (Clair & Genest, 1987; Purvis, 1992). Rodney (1994) observed that scores on the four social support scales for ACOAs were slightly lower than scores for non-ACOAs on three of the four scales (mother, father, and friend support). Analysis of variance revealed, however, that only in the case of the mother support scale was there a statistically significant difference in scores between these two groups. More specifically, ACOAs reported receiving less informational and emotional support from their mothers than did non-ACOAs. What this study revealed is that the ACOAs reported a less healthy environment in their families of origin but hardly any difference in social support from that received by the non-ACOAs, except in the case of the mother, who might have become codependent, thus taking on some of the behaviors of the alcoholic. It was originally thought that those ACOAs who grew up in a healthier family environment and who also had stronger social support networks would report fewer drinking problems because both emotional and informational support are related to adjustment in alcoholic families (Clair & Genest, 1987). However, in this study of ACOAs, I found that although health in the family of origin and the availability and use of mother support were related to fewer drinking problems, there was no significant relationship to health in the family of origin or social support, even though the ACOAs reported less informational and emotional support from their mothers. Children of Alcoholics and Developmental Tasks Little research has been done on ACOAs by using an instrument to measure their mastery of developmental tasks. One study (Rodney, 1994) examined the

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differences between black ACOAs and non-ACOAs attending a black university relative to mastery of three development tasks (autonomy, mature interpersonal relationship, and purpose) (Winston, Miller & Prince, 1979). Five predictor variables were identified: (a) family of origin, (b) mother, (c) father, (d) friend, and (e) significant other. When each of the predictor variables was examined individually with each of the three developmental task scales, there was no significant relationship observed between the level of support received from mother, father, friend, or significant other adult and mastery of any of the three developmental tasks. Therefore, the greater the emotional and informational support they received from a close friend, the more likely ACOAs were to succeed in developing a sense of purpose. When the five predictor variables were examined simultaneously with a stepwise multiple regression analysis for each of the developmental tasks, no significant relationship was observed. For females, no significant relationship was observed between the five predictor variables and any of the three respective developmental tasks. Two predictor variables—significant other adult support and mother support—yielded a regression model that accounted for 27% of the variance in the criterion variable, developing purpose. The significant other variable was the only variable of the two that made a significant unique contribution in predicting developing purpose. The variable accounted for approximately 16% of the explained variance in the criterion variable, developing purpose. The fact that ACOAs did not differ significantly from the non-ACOAs in regard to their mastery of developmental tasks (mature interpersonal relationships, autonomy, and sense of purpose) despite the distress they experienced in their family of origin is consistent with earlier studies that failed to find significant differences between ACOAs and non-ACOAs (Duprez, 1987; Hunt, 1989; Seefeldt & Lyon, 1992). Contrary to the finding of Berkowitz and Perkins (1988) that male ACOAs reported significantly greater independence and autonomy, the present study found no gender differences in regard to that issue. Overall, these findings lend further support to the notion of Seefeldt and Lyon (1992) that ACOAs are not a homogeneous group and do not necessarily conform to the categorical symptomatology reported in the popular literature. It also lends some credence to the suggestion of Kashubeck and Christensen (1992) that college ACOAs may be more resilient than noncollege populations. It would seem that those ACOAs who grew up in a healthier family environment and who had stronger social support networks would report fewer drinking problems and improved mastery of developmental tasks. For example, Clair and Genest (1987) found that both emotional and informational support were related to adjustment in alcoholic families. Yet although health in the family of origin and the availability and use of mother support were related to fewer drinking problems, no relationship was found between health in the family of

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origin or social support and the mastery of developmental tasks by ACOAs. This was especially true for female ACOAs. In yet another study (Rodney, 1995), I reported that ACOAs had experienced a significantly less healthy or less functional family of origin that nonACOAs. I found a significant correlation between the social support the participants received from their mothers during adolescence, the participants’ level of selfesteem, and the perceived level of health in the participants’s family of origin. For this sample of ACOAs, it appears that as health in the family of origin improves and the level of social support from the mother increases, so does the ACOAs level of self-esteem. These findings are not surprising. Andrasi (1987) found that family functioning as well as emotional and informational support were related to the adjustment outcomes of ACOAs. It is important to note that only social support from the mother (not from the father, friend, or significant other) was related to self-esteem. According to the 1990 census, household compositions involving single adults (mother or grandmother) represented the home environment of some 56% of African-American children (Sineath, 1993). The literature about COAs tends to see them as unique and having many problems. In a recent investigation of the possibility that mental health professionals held negative stereotypes toward COAs, 80 mental health workers were asked to watch videotapes of adolescents who were described as having a positive or negative family history of alcoholism and as having either a high degree of social success (school leader) or social behavior problems. The adolescents labeled as COAs were judged as more pathological than those labeled non-COAs in terms of current and predicted psychological health and psychopathology, regardless of the teenagers’ current behavior. The authors of the study concluded that “seeing deviance for COAs where there was none reported could have serious consequences for COAs, including inappropriate placement in treatment programs” (Burk & Sher, 1990, p. 162). SUMMARY AND CONCLUSION This brief description of each of the foregoing studies provides mixed results regarding the use of alcohol and other drugs among African-American adolescents. Nevertheless, the disturbing deterioration of the health and death of the nation’s youth and the abundant previous studies have found adjustment outcomes and problems with African-American youth who use and abuse alcohol and other drugs. It behooves us, therefore, as professionals to garner our efforts in helping to set the nation’s agenda in prevention programs, policies, and research. Positive goals must be established. They must be aimed directly at reducing alcohol and other drug abuse among youth. A quick word of caution, however, is that of not labeling some youth “at risk,” thus subjecting them to problems that would not have occurred in the absence of the label. Some youth

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who are at risk will develop problems, others will not, and care must be taken to avoid the self-fulfilling prophecy. Notwithstanding, actions must be taken that are in the best interest of the youth and the society at large. This is a complex responsibility for parents, community, and society because most youth who are at high risk are relatively unconcerned about the delayed negative health consequences of their behavior. REFERENCES Ackerman, R. (1987). Children of alcoholics: A guide for parents, educators, and therapists. New York: Simon & Schuster. American Psychiatric Association (1987). Diagnostic and statistical manual of mental disorders (3rd ed, Rev.). Washington, DC: Author. Andrasi, P. (1987). An examination of the relationship between self-esteem and the ability of the family of origin to promote autonomy, expression of feelings between and trust development in adult children of alcoholics. Dissertation Abstracts International, 47 (11), 3977A Annis, H.M., & Watson, C. (1975). Drug use and school drop-outs: A longitudinal study. Canadian Counseling, 6 (34), 155–162. Archambault, D. (1992). Adolescence: A physiological, cultural, and psychological no man’s land. Adolescent Substance Abuse: Etiology, Treatment, and Prevention, 6, 11–28. Berkowitz, A., & Perkins, H.W. (1988). Personality characteristics of children of alcoholics. Journal of Consulting and Clinical Psychology, 56 (2), 206–209. Bickel, F., & Quails, R. (1980). The impact of school climate on suspension rates in Jefferson County public schools. Urban Review, 12, 79–86. Black, C. (1981). It will never happen to me. Denver, CO: Mac Publishing. Blankenhorn, D. (1995). Fatherless America: Confronting our most urgent social problem. Basic Books. Brinson, J. (1991). A comparison of the family environments of black male and female adolescent alcohol users. Adolescence, 26 (104), 879–884. Brooks-Gunn, J., Klebanox, P., Liaw, F., & Duncan, G. (1995). Toward an understanding of the effects of poverty upon children. In H.Fitzgerald, B.Lester, & B. Zuckerman (Eds.), Children of poverty: Research, health, and policy issues, New York: Garland Publishing. Brown, S. (1988). Treating adult children of alcoholics. New York: Wiley & Sons. Brown-Cheatham, M. (1993). The Rorschach mutuality of autonomy scale in the assessment of black father-absent male children. Journal of Personality Assessment, 61, 524–530. Brunswick, A.F., & Boyle, J.M. (1979). Patterns of drug involvement: Developmental and secular influences on age at initiation. Youth and Society, 2, 139–162.

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Bry, B.H., McKeon, P., & Padina, R.J. (1982). Extent of drug use as a function of the number of risk factors. Journal of Abnormal Psychology, 91, 273–279. Burk, J., & Sher, K. (1990). Labeling the child of an alcoholic: Negative stereotyping by mental health professionals and peers. Journal of Studies on Alcohol, 51, 156–163. Byrne, C. (1996). Fact sheet: Drug data summary. Drug and crime data. Rockville, MD: Office of National Drug Control Policy Drug & Crime Clearinghouse. Campbell, A. (1987). Self-report delinquency and home life: Evidence from a sample of junior high girls. Journal of Youth and Adolescence, 16, 167–177. Carroll, S. (1991). Personality characteristics of adult children of alcoholics. Dissertation Abstracts International, 51 (7), 3556-B. Center for Substance Abuse Prevention (1990). Ecology of alcohol and other drug use: Helping black high-risk youth. Rockville, MD: DHHS Publication No. [ADM] 920– 1672. Center for Substance Abuse Prevention (1992). Working with youth in high risk environments: Experiences in prevention. Rockville, MD: DHHS Publication No. [ADM] 92–1815. Cermak, T. (1982). Interactional group therapy with adult children of alcoholics. International Journal of Group Psychotherapy, 32, 375–389. Cermak, T. (1989). A time to heal: The road to recovery for adult children of alcoholics. New York: Avon. Cernkovich, S.A., & Giordano, P.C. (1992). School bonding, race, and delinquency. Criminology, 30 (2), 261–292. Children of Alcoholics Foundation, Inc. (1992). Report of the Forum on Protective Factors, Resiliency, and Vulnerable Children. Grand Central Station, New York: Author. Churchill, J., Broida, J., & Nicholson, N. (1990). Locus of control and self-esteem of adult children of alcoholics. Journal of Studies on Alcohol, 51, 373–376. Clair, D., & Geanest, M. (1987). Variables associated with the adjustment of offspring of alcoholic fathers. Journal of Studies on Alcohol, 48, 345–355. Cooper, A., Koszmovszky, K., Gutfeld, M., & Hobica, G. (1995). Why fathers count. Men’s Health, p 6a. Copelaand, P. (1992). Prevention of alcoholism in black youth. In G.Lawson & A.W. Lawson (Eds.), Adolescent substance abuse: Etiology, treatment, and prevention. (pp. 507–515). Gaithersburg, MD: Aspen. Costenbader, V., & Markson, S. (1994, October). School suspension: A survey of current policies and practices. NASSP Bulletin, 78, 103–107. Dawkins, R., & Dawkins, M.P. (1989). Alcohol use and delinquency among black, white, Hispanic adolescent offenders. Adolescence, 18, 799–809. DeRidder, L.M. (1991). How suspension and expulsion contribute to dropping-out. Educational Horizons, 68, 153–157. DeSimone, A., Murray, P., & Lester, D. (1994). Alcohol use, self-esteem, depression, and suicidality in high school students. Adolescence, 29 (116), 939–942.

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Developmental Research and Programs, Inc. (1993). Risk-focused prevention using the social development strategy: An approach to reducing adolescent problem behaviors. Communities That Care, 6, 1–23. Duprez, C. (1987). Levels of depression and self-esteem in adult children of alcoholics and adult children of nonalcoholics. Dissertation Abstracts International, 48, 1800B. Ellickson, P.L., Hays, R.D., & Bell, R.M. (1991). Stepping through the drug use sequence: Longitudinal scalogram analysis of initiation and regular use. Journal of Abnormal Psychology, 101, 441–450. Farrell, A.D. (1993). Risk factors for drug-use by urban adolescents: Three wave longitudinal study. Journal of Drug Issues, 23 (3), 443–462. Farrell, A.D., Danish, S.J., & Howard, C.W. (1992). Risk factors for drug use in urban adolescents: Identification and cross-validation. American Journal of Community Psychology, 20, 263–286. Felner, R.D., Abner, M.S., Primavera, J., & Cauve, A.M. (1985). Adaptation and vulnerability in high-risk adolescents: An examination of environmental mediators. American Journal of Community Psychology, 13, 365–379. Gonzales, G.M. (1983). Time and place of first drinking experience and parental knowledge as predictors of alcohol use and misuse in college. Journal of Alcohol and Drug Education, 27(11), 1–13. Gorsuch, R.L., & Butler, M.C. (1976). Initial drug abuse: A review of predisposing social psychological factors. Psychological Bulletin 83, 120–137. Gottfredson, G.D. (1987). American education—American delinquency. Today’s Delinquent, 6, 5–70. Graham, J.W., Collins, L.M., Wugalter, S.E., Chung, N.K., & Hansen, W.B. (1991). Modeling transitions in later stage-sequential processes: A substance abuse prevention example. Journal of Consulting and Clinical Psychology 59, 1–11. Gravitz, H., & Bowden, J. (1985). Recovery: A guide for adult children of alcoholics. New York: Simon & Schuster. Grissom, J.B., & Shepard, L.A. (1989). Repeating and dropping out of school. In L.A.Shepard & M.L.Smith (Eds.), Flunking grades: Research and policies on retention (pp. 34–63). New York: Palmer. Hansen, W. (1993). School-based alcohol prevention programs. Alcohol Health and Research World, 17(1), 54–60. Hare, N., & Hare, J. (1986). The endangered black family. San Francisco: Black Think Tank. Hawkins, J.D., Catalano, R.F., & Miller, J.Y. (1992). Risk and protective factors for alcohol and other drug problems in adolescence and early adulthood: Implications for substance abuse prevention. Psychological Bulletin 112, 105. Hawkins, J.D., Lishner, D.M., Jenson, J.M., & Catalano, R.F. (1987). Delinquents and drugs: What evidence suggests about prevention and treatment programming. In B.S.Brown & A.R.Mills (Eds.), Youth at risk for substance abuse (pp. 81–131). Rockville, MD: National Institute on Drug Abuse.

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Holcomb, D., Sarvela, P., Sliepcevich, E., & Jellen, H. (1990). Self-reported negative consequences of drug use among rural adolescents . Health Education, 21, 36–40. Holmberg, M.B. (1985). Longitudinal studies of drug abuse in a fifteen year old population. ACTA Psychiatria Scandinavian, 16, 129–136. Hunt, P. (1989). Adult children of alcoholic parents: A three-group study. Dissertation Abstracts International, 50, 1110B. Jessor, R. (1987). Problem-behavior theory, psychosocial development and adolescent problem drinking: A longitudinal study of youth. New York: Academic Press. Jessor, R., & Jessor, S.L. (1977). Problem behavior and psychosocial development: A longitudinal study of youth. New York: Academic Press. Johnston, L.D., O’Malley, P.M., & Bachman, J.G. (1993a). National survey results on drug use from the Monitoring the Future study, 1975–1992, 1. Secondary school students. Rockville, MD: National Institute On Drug Abuse. Johnston, L.D., O’Malley, P.M., & Bachman, J.G. (1993b). National survey results on drug use from the Monitoring the Future study, 1975–1992, 2. College students and young adults. Rockville, MD: National Institute on Drug Abuse. Jones, J.W. (1981). The children of alcoholics screening test (CAST). Chicago: Family Recovery Press. Kandel, D.B. (1982). Epidemiological and psychosocial perspectives on adolescent drug use . Journal of American Academic Clinical Psychiatry, 21, 328–347. Kandel, D.B., Kessler, R.C., & Marguiles, R.Z. (1978). Antecedents of adolescent initiation into stages of drug use: A developmental analysis. Journal of Youth Adolescence, 7, 13–40. Kandel, D.B., Simcha-Fagan, O., & Davis, M. (1986). Risk factors for delinquency and illicit drug use for adolescence to young adulthood. Journal of Drug Issues, 60, 67– 90. Kandel, D.B., Yamaguchi, K., & Chen, K. (1992). Stages of progression in drug involvement from adolescence to adulthood: Further evidence for the gateway theory. Journal of Studies on Alcohol, 53, 447–457. Kashubeck, S., & Christensen, S. (1992). Differences in distress among adult children of alcoholics. Journal of Counseling Psychology, 39, 356–362. Kumpfer, K.L. (1987). Special populations: Etiology and prevention of vulnerability to chemical dependency in children of substance abusers. In B.S.Brown, & A. R.Mills (Eds.), Youth at high risk for substance abuse (pp. 1–72). Rockville, MD: National Institute on Drug Abuse. Loeber, R. (1990). Development and risk factors of juvenile antisocial behavior and delinquency. Clinical Psychology Review 10, 1–41. Marshall, O.A. (1989). Conference on chemical dependency and the black community. Paper presented at the conference of Morehouse Cork Institute on Black Alcohol and Drug Abuse, Atlanta, Georgia. McCord, J. (1991). Family relationship, juvenile delinquency, and adult criminality. Criminology, 29, 397–417.

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Moody, C.D. (1978). Student rights and discipline: Policies, programs and procedures. (ERIC Document Reproduction Service No. ED 160 926.) Mrazek, P., & Haggerty, R. (Eds.). (1994). Risk and protective factors for the onset of mental disorders: Alcohol abuse and dependence. In Reducing risks for mental disorder, (pp. 154–163). Washington, DC: National Academy Press. Murphy, G.D. (1988). Suicide and substance use. Archives of General Psychiatry 45, 593–594. National Institute on Alcohol Abuse and Alcoholism (1991). Alcohol and youth. Alcohol Health and Research World. Washington, DC: USDHHS Newcomb, M.D., Maddahian, E., & Bentler, P.M. (1986). Risk factors for drug use among adolescents: Concurrent and longitudinal analyses. American Journal of Public Health. 76, 525–531. Newcomb, M.D., Maddahian, E., Sakger, R., & Bentler, P.M. (1987). Substance abuse and psychosocial risk factors among teenagers: Associations with sex, age, ethnicity, and type of school. American Journal of Drug Alcohol Abuse, 13, 413–433. Osgood, W. (1995). Drugs, alcohol and adolescent violence. Center for the Study and Prevention of Violence. Boulder, CO: University of Colorado Press. Parker, D., & Harford, T. (1987). Alcohol-related problems of children of heavy-drinking parents. Journal of Studies on Alcohol, 48, 265–268. Perkins, H.W., & Berkowitz, A.D. (1991). Collegiate COAs and alcohol abuse: Problem drinking in relation to assessments of parent and grandparent alcoholism. Journal of Counseling and Development, 69, 237–240. Projesz, B., & Begleiter, H. (1985). Human brain electrophysiology and alcoholism. In R.E.Tarter & D.H.Thiel (Eds.), Alcohol and the brain: Chronic effects, (pp. 138– 182). New York: Plenum Press. Purvis, M.A. (1992). Comparison of adult children of alcoholics and nonadult children of alcoholics with respect to perceptions of family support and interpersonal behaviors. Dissertation Abstracts International, 53, 1103 A. Richardson, R., & Gerlack, C. (1980). Black drop-outs: A study of significant factors contributing to black student’s decisions. Urban Education, 14, 489–494. Rodney, H.E. (1993). A study of the impact of alcohol on adult children of alcoholics. Unpublished manuscript. Central State University, Wilberforce, OH. Rodney, H.E. (1994). What differentiates ACOAs and nonACOAs on a black college campus . Journal of American College Health, 43, 57–62. Rodney, H.E. (1995). A profile of college black adult children of alcoholics. Journal of College Students Development, 36, 228–235. Rodney, H.E., & Mupier, R. (1997). Alcohol abuse and conduct disorder among COAs and nonCOAs. Journal of Child and Adolescent Substance Abuse, 7, 37–51. Rodney, H.E., & Mupier, R. (in press a). Behavioral differences between African-American male adolescents with biological fathers and those without biological fathers in the home. Journal of Black Studies, 30 (1), 45–61. Rodney, H.E., & Mupier, R. (1999 b). The impact of parental alcoholism on self-esteem

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and depression among African-American adolescents. Journal of Child and Adolescent Substance Abuse, 8 (3), 55–71. Rodney, H.E., Mupier, R. (in press c). Comparison of the behaviors and social environments of offending and non-offending African-American adolescents. Journal of Offender Rehabilitation. Rodney, H.E., Mupier, R., & Crafter, B. (1996). Predictors of alcohol drinking among African-American adolescents: Implications for violence prevention. Journal of Negro Education, 65, 434–444. Rodney, H.E., Mupier, R., & O’Neal, S. (1997). African-American youth in public housing showing low alcohol and drug use. Journal of Child and Adolescent Substance Abuse, 6, 55–73. Rodney, H.E., & Rodney, L.W. (1996). An exploratory study of African American collegiate adult children of alcoholics. Journal of American College Health, 44 (6), 267–272. Rodney, L.W., Rodney, H.E., Mupier, R., & Crafter, B. (in press). Variables contributing to grade retention among African-American adolescent males. Journal of Educational Research, 92 (3), 185–190 . Roosa, M.W., Beals, J., Sandier, I.N., & Pillow, D.R. (1990). The role of risk and protective factors in predicting symptomatology in adolescent self-identified children of alcoholic parents. American Journal of Community Psychology, 18, 725–741. Roosa, M.W., Gensheimer, L.K.Ayers, T.S., & Short, J.L. (1990). Development of a school-based prevention program for children in alcoholic families. Journal of Primary Prevention, 22 (2), 119–141. Roosa, M.W., Sandier, I.N., & Beals, J. (1988). Risk status of adolescent children of problem-drinking parents. American Journal of Community Psychology, 16, 225– 239. Rosenberg, M. (1979). Rosenberg self-esteem. In Conceiving the self (pp. 291–295). New York: Basic Books. Russell, M., Henderson, C., & Blume, S.B. (1985). Children of alcoholics: A review of the literature. New York: Children of Alcoholics Foundation. Schubiner, H., Scott, R., & Tzelepis, A. (1993). Exposure to violence among innercity youth. Journal of Adolescent Health, 14, 214–219. Schuckit, M.A. (1985). Ethanol induced changes in body sway in men at high alcoholism risk. General Psychiatry, 42, 375–379. Seefeldt, R., & Lyon, M. (1992). Personality characteristics of adult children of alcoholics. Journal of Counseling and Development, 70, 588–593. Shapiro, J., Schrof, J., Sharp, M., & Friedman, D. (1995). Honor thy children. U.S. News & World Report, 39–49. Shedler, J., & Block, J. (1990). Adolescent drug use and psychological health: A longitudinal inquiry. American Psychologist, 45, 612–630. Sineath, N. (1993). The relationship among coping resources for stress, adult children of alcoholics and family functionality. Dissertation Abstracts International, 53, 4215 A. Smith, G.M., & Fogg, C.P. (1978). Psychological predictors of early use, late use, and

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nonuse of marijuana among teenage students. In A.B.Kandel (Ed.), Longitudinal research on drug use: Empirical findings and methodological issues (pp. 101–113). Washington, DC: Hemisphere. Stratton, A., & Penney, A. (1992). High school and college student children of alcoholics: A pilot educational program and assessment of readiness for assistance. Journal of Alcohol Drug Education, 38 (1), 100–112. Streitmatter, J.L. (1986). Ethnic/racial and gender equity in school suspensions. The High School Journal, 69, 139–143. Tharinger, D., & Koranek, M. (1988). Children of alcoholics at risk and unserved: A review of research and service roles for school psychologists. School Psychologist Review, 17, 166–191. Thompson, T., & Cooper, C.S. (1988). Chemical dependency treatment and black adolescents. Journal of Drug Issues, 18, 21–24. Tweed, S., & Ryff, C. (1991). Adult children of alcoholics: Profiles of wellness amidst distress. Journal of Studies on Alcohol, 52, 133–141. U.S. Department of Health and Human Services. (1991). Youth and alcohol: Selected reports to the Surgeon General. Washington, DC: U.S. Department of Education. U.S. Department of Justice. (1988). Survey of youth in custody, 1987. In Bureau of Justice Statistics Special Report (p. 6). Ann Arbor, MI: Office of Justice Programs, Bureau of Justice Statistics. Vega, W., Zimmerman, R., Warheit, G., Apospori, E., & Gil, A. (1993). Risk factors for early adolescent drug use in four ethnic and racial groups. American Journal of Public Health, 83, 185–189. Watts, D., & Wright, L. (1990). The relationship of alcohol, tobacco, marijuana, and other illegal drug use to delinquency among Mexican-American, black and white adolescent males. Adolescence, 25, 171–181. Werner, E.E., Bierman, J.M., & French, F.E. (1971). Children of Kauai. Honolulu: University of Hawaii Press. Werner, E.E., & Smith, R.S. (1977). Kauai’s children come of age. Honolulu: University of Hawaii Press. Windle, M. (1990). Longitudinal study of antisocial behaviors in early adolescence as predictors of late adolescent substance use: Gender and ethnic group differences. Journal of Abnormal Psychology, 99, 86–91. Winston, R.B., Miller, T., & Prince, J.S. (1979). Student developmental task and lifestyle manual. Athens, GA: Student Development Association. Young, M., Werch, E.E., & Backema, S. (1989). Area specific self-esteem scales and substance abuse among elementary and middle-school children. Journal of School Health, 59, 251–254.

CHAPTER 9

Substance Use and Abuse Outcomes in Children of Alcoholics From Adolescence to Young Adulthood LAURIE CHASSIN AARON BELZ

Parental alcoholism is a risk factor of great public health significance because it has the potential to affect a large number of children and adolescents. Although prevalence estimates for alcoholism vary with the operational definition of the disorder, Russell, Henderson, and Blume (1985) used epidemiological data to estimate that there were approximately 6.6 million children of alcoholic parents (COAs) under age 18, as well as 22 million “adult” COAs (i.e., aged 18 or older). Because such a large segment of the population is exposed to parental alcoholism, any adverse effects that it conveys have the potential to produce widespread negative health outcomes. In addition to its public health significance, parental alcoholism is of particular theoretical importance to researchers who are interested in the etiology of substance use and abuse. It has long been recognized that alcoholism “runs in families” (Cotton, 1979). Because COAs are at high risk for adult alcohol abuse and dependence (see, e.g., McGue, 1994; Russell et al., 1985), researchers hope that studies of COAs will identify the factors and processes that are responsible for producing substance use and abuse. This chapter focuses on understanding substance use and abuse outcomes among COAs. In particular, we focus on substance use outcomes during the adolescent and young adult years. In terms of substance use and abuse, these are developmental periods of great importance because they represent periods of substance use initiation and escalation. Developmentally, most substance use begins in adolescence, reaches a peak in the mid-20s, and then declines, in part perhaps because of the assumption of adult social roles (Bachman et al., 1997; Yamaguchi & Kandel, 1985). Adolescence and young adulthood then are developmental periods of great importance from the perspective of primary prevention of substance abuse and dependence. If we can identify factors responsible for the initiation and escalation of substance use during these years, prevention programs can be designed to combat these processes. 193

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To achieve an understanding of substance use and abuse outcomes among adolescent COAs requires a rather ambitious research agenda addressing multiple questions. First, what is the magnitude of parent alcoholism risk for substance use and abuse? Is the risk substantial enough to have clinical and policy implications? Second, what is the specificity of parent alcoholism risk? Given that parent alcoholism covaries with other forms of parent psychopathology (particularly affective disorders and antisocial personality disorder), is the adolescent’s risk for substance use related specifically to parent alcoholism or to parental psychopathology more broadly? Third, what are the mediating mechanisms underlying these parent alcoholism effects? That is, why are COAs more likely to develop substance use and abuse than are their non-COA peers? Fourth, what are the protective factors that may buffer parent alcoholism risk? Identifying potentially modifiable mediators and protective factors is an important task for designing preventive interventions to reduce substance abuse outcomes. The extent of our existing knowledge varies widely across these different questions, and a comprehensive review is beyond the scope of this chapter (for recent reviews, see Sher, 1991; West & Prinz, 1987). Instead, for each research question, this chapter briefly summarizes the state of existing knowledge and highlights the results of our ongoing longitudinal study designed to address these questions (the Adult and Family Development Project at Arizona State University). For each research question, we also attempt to identify the limits of existing knowledge in order to suggest directions for future research, as well as implications for policy. THE ADULT AND FAMILY DEVELOPMENT PROJECT. In 1987, we began a longitudinal study of COAs and demographically matched controls in early to middle adolescence (age 11–15). Methodologically, we attempted to improve on earlier studies by (a) recruiting a large sample of community-dwelling alcoholics (rather than relying on a clinical sample, which limits generalizability), (b) including both maternal and paternal alcoholism, (c) directly ascertaining parental alcoholism and associated parental psychopathology (rather than relying on offspring report), and (d) including data from multiple reporters rather than relying on a single source. A detailed report of recruitment procedures and participant characteristics can be found elsewhere (Chassin, Barrera, Bech, & Kossak-Fuller, 1992). Briefly, alcoholic families (n=246) were recruited through court records of arrests for driving under the influence, community telephone screenings, and wellness questionnaires from newly enrolling members of a health maintenance organization. After screening, direct interviews had to confirm that a biologic and custodial parent met Diagnostic and Statistical Manual, edition 3 (DSM-

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III) criteria for lifetime alcohol abuse or dependence, using the Diagnostic Interview Schedule (DIS) interview (or family history research diagnostic criteria if the alcoholic parent could not be directly interviewed). Control families (n=208) were recruited by telephone surveys, and the target adolescent in the family was demographically matched to a COA (in age, ethnicity, gender, family structure, and property value code for the family residence). Neither biologic nor custodial parents could meet diagnostic criteria for alcohol abuse or dependence, but families were not excluded from either the control or COA groups on the basis of other parental psychopathology. Data were collected via three annual computer-assisted interviews with the adolescent and his or her parents. A young adult follow-up was conducted 5 to 7 years later with the original target (now age 18–25, mean age=20), her or his young adult full biologic siblings (mean age 22), and their parents. Data were also collected from peer collaterals and from school records. Subject retention was high, and we retained 98% of the targets during the three adolescent waves. At the young adult follow-up, 90% of the original adolescents were reinterviewed, 88% of mothers and 80% of fathers who participated in at least one of the adolescent assessments were reinterviewed, and 87% of eligible siblings were interviewed. Are Children of Alcoholics at Elevated Risk for Substance Use and Abuse Outcomes? There is clear consensus in the empirical literature that COAs are at higher risk for alcohol abuse and dependence in adulthood. Even adopted-away offspring of alcoholic parents are at higher risk for alcoholism than are their non-COA peers, suggesting that some of the risk is genetically mediated (McGue, 1994). However, the magnitude of risk associated with parental alcoholism varies substantially across samples (with highest risk among offspring of severely alcoholic fathers with early-onset criminality; see McGue, 1994; Russell et al., 1985, for reviews of these findings). The findings from our young adults also confirm this elevated risk (Chassin, Todd, & DeLucia, 1997). The COAs had higher rates of lifetime alcohol abuse or dependence (53%) than did controls (25%, odds ratio=3.3). The COAs had higher rates of moderate to severe alcohol dependence (29%) than did controls (11%, odds ratio=3.4), and COAs had higher rates of lifetime drug abuse or dependence (21%) than did controls (9%, odds ratio=2.8). The magnitude of the odds ratios suggests moderate to strong effects of parent alcoholism, and both the odds ratios and prevalence rates demonstrate that these effects are clinically meaningful as well as statistically significant. There is somewhat less consensus about whether COAs show elevated levels of alcohol and drug use in the adolescent years. Pandina and Johnson (1989)

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studied a community sample and found that family history effects on alcohol use emerged only for their oldest cohort (age 18–21). However, this study considered adolescents to have a positive family history when a second-degree relative (not necessarily a parent) was alcoholic. Johnson, Leonard, and Jacob (1989) found COAs to be elevated in drug use but not alcohol use compared with their non-COA peers. However, that study required that fathers have no co-occurring psychopathology and that mothers have no alcohol abuse or dependence or major psychopathology. Moreover, there was a wide age range of subjects (10–18 years) within a small sample size, which may have limited their ability to detect effects. Data from our baseline assessment (Chassin, Rogosch, Barrera, & Rogosch, 1991) showed significant elevations in both alcohol and drug use among COAs at age 11 to 15, as well as significant elevations in negative consequences associated with alcohol and drug use. Moreover, paternal alcoholism was associated with steeper escalation of substance use over time during the 3 years of our adolescent study (Chassin & Barrera, 1993; Chassin, Curran, Hussong, & Colder, 1996). Thus, our data confirm that the elevated risk for substance use among COAs can be detected even in early and middle adolescence. Although our own data and previous literature show that parental alcoholism conveys appreciable risk for substance use and abuse, there is also substantial heterogeneity in the extent of this risk. At the outset, it is important to note that most COAs do not develop negative outcomes and that there is danger in overpathologizing this group. Such overpathologizing can lead to negative labeling effects that themselves create risk for self-fulfilling prophecies and potential harm for COAs (Burk & Sher, 1990). In fact, the heterogeneity of outcomes among COAs is not well understood and represents an important direction for future research. From a methodological standpoint, it is important to remember that parental alcoholism itself is a heterogeneous category. Effects of parental alcoholism might vary with the severity of the alcoholism, with the gender of the alcoholic parent, with the timing of the child’s exposure to the parent alcoholism (including prenatal exposure), with the recency and duration of the parent alcoholism, with the extent of co-occurring parental psychopathology (especially parental antisociality; cf. Zucker, Ellis, Bingham, & Fitzgerald, 1996), and with the density of alcoholism in the family (e.g., multigenerational alcoholism; cf. Pihl, Peterson, & Finn, 1990). These sources of heterogeneity create a formidable methodological challenge for research studies to simultaneously consider (or control for) these many different dimensions while maintaining a feasible sample size.

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SPECIFICITY OF PARENT ALCOHOLISM RISK: ARE HIGHER RATES OF ALCOHOL AND DRUG USE AMONG CHILDREN OF ALCOHOLICS DUE TO PARENT ALCOHOLISM OR TO ASSOCIATED PARENT PSYCHOPATHOLOGY? Parental alcoholism frequently occurs in the context of other kinds of parental psychopathology (Robins & Regier, 1991), particularly antisocial personality disorder and affective disorders. Thus, in attempting to understand substance use and abuse outcomes among COAs, it is important to know whether elevations in these outcomes are due to the parental alcoholism itself, to associated psychopathology, or to both. Indeed, it is possible that both parental alcoholism and associated psychopathology influence alcohol and drug outcomes but do so in different ways. For example, Cadoret, Yates, Troughton, Woodworth, and Stewart (1995a) in a study of adult outcomes among adoptees, found that parental alcoholism had a direct effect on substance use outcomes, whereas parent antisocial personality had an indirect effect on substance use outcomes by increasing offspring aggression and antisociality. Analyses of our baseline data showed that, above and beyond parental antisocial personality and depression, father’s alcoholism had a unique effect on adolescent alcohol use, mother’s alcoholism had a unique effect on adolescent drug use, and both mother’s and father’s alcoholism had unique effects on the number of negative consequences associated with the adolescent’s substance use (Chassin et al., 1991). Analyses of our most recent (young adult) data extend these findings into both another developmental period and to a more serious clinical outcome (alcohol and drug abuse and dependence). These analyses show that (above and beyond parental antisocial personality and depression) both maternal and paternal alcoholism are associated with higher rates of young adult DSM-III-R alcohol and drug abuse or dependence (Chassin et al., 1997). Thus, although associated parental psychopathology (particularly parent antisocial personality disorder) also influences adolescent and young adult substance use and abuse, our data suggest that parent alcoholism does have a unique effect on substance use and abuse outcomes. RECENCY OF PARENTAL ALCOHOLISM: DOES PARENTAL RECOVERY INFLUENCE ADOLESCENT SUBSTANCE USE? Another important dimension of parental alcoholism that has been understudied is its recency. Some studies consider any history of parental alcoholism as the operative risk factor, and other studies consider only current parental alcoholism. However, very few studies have compared outcomes for children whose parents have “recovered” from alcoholism (i.e., discontinued heavy drinking and ceased

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to suffer alcohol-related consequences or dependency symptoms) with outcomes for children whose parents are actively alcoholic. The major exception is the work of Moos and Billings (1982), who found that children of relapsed alcoholics had more symptoms of emotional disturbance (including anxiety and depression) than did control children but that children of recovered alcoholics were functioning as well as the control children. The three groups did not significantly differ on ratings of alcohol, drug, and cigarette use. Such improved outcomes for children of recovered alcoholic parents may be due, in part, to recovery in the family environment. Moos and Billings found that parents in families with relapsed alcoholics rated their family environment less favorably than did control parents on the Family Environment Scale measures of cohesion, expressiveness, independence, achievement, and intellectual-cultural and active-recreational orientation. In contrast, family environment ratings by parents in families of recovered alcoholics did not significantly differ from ratings in control families. Similarly, Callan and Jackson (1986), in studying alcoholic families recruited through Alateen (a self-help group for adolescent COAs), found that children of recovered alcoholics and controls rated their families as happier, more likely to have members show interest in each other, and more likely to laugh and talk together than did children with actively alcoholic fathers. Children of recovered alcoholics also judged their families as more likely to work on something together than either control or actively alcoholic families. Children of active alcoholics rated their families as more tense, moody, miserable, unreliable, and strict; and less happy, affectionate, loving, trusting, secure, warm, and understanding. Findings by Moos and Billings (1982) and Callan and Jackson (1986) suggest that with parental recovery comes recovery of the child-rearing environment and recovery of psychological symptomatology for COAs. However, because the studies did not use a longitudinal design, it is impossible to know whether the family environments for recovered alcoholics actually improved with their recovery or were simply better to begin with than were the family environments of active alcoholics. These findings may also reflect differences in subtype or severity of parent alcoholism, with the most severe alcoholism both persisting over time and also associated with childhood symptomatology (Ozkaragoz, Satz, & Noble, 1997). Moreover, Moos and Billings did not find differences among controls, children of recovered alcoholics, and children of active alcoholics in their alcohol and drug use (although the sample sizes were small and the age-range was heterogeneous). Theoretically, alcohol and drug use outcomes might be determined quite differently than are anxiety and depression. Despite parental “recovery” in terms of modeling substance use, and despite potential improvement in the family environment, adolescents’ alcohol and drug use might become selfsustaining, maintained either by a substance-using peer environment, by the reinforcing properties of substance use itself, or by both.

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In our data, we have examined this question by comparing baseline levels of use among children of controls, “recovered” alcoholics (who had experienced no DSM-III symptom of alcohol abuse or dependence in the past 3 years), and “current” alcoholics (who reported symptoms within that time period; Chassin et al., 1991). In terms of their own current use, children of recovered alcoholics constituted an intermediate group. Prevalences for past year alcohol use were 23% for controls, 38% for children of recovered alcoholics, and 45% for children of current alcoholics. Prevalences for past-year drug use were 6% for controls, 10% for children of recovered alcoholics, and 17% for children of current alcoholics. Comparing children of recovered alcoholics with controls on these outcomes showed odds ratios of 1.9 for alcohol use (p<.10) and 1.8 for drug use (n.s.). There were no significant differences between children of current and recovered alcoholics (odds ratios of 1.4 and 1.8, respectively, both n.s.). Thus, our data suggest that COAs’ alcohol and drug use are somewhat less likely to “recover” than are anxiety and depression, as reported by Moos and Billings (1982). The impact of parental recovery on substance use outcomes for children of alcoholics is important at both theoretical and applied levels. Theoretically, if substance use outcomes for COAs change as a function of parental recovery, this suggests possible underlying mechanisms of effect. For example, improvements in child outcome as a function of parental recovery might be due to “recovery” in the family environment, as suggested by Moos and Billings, or due to improvements in parenting behavior. Pelham and Lang (1993) found that the quality of parenting is influenced by episodes of parental alcohol consumption. They assessed parents’ behavior in structured interactions with child confederates who were trained to behave in a normal or a deviant manner, under conditions where parents received a controlled dose of alcohol or no alcohol. Findings showed that when parents received alcohol they made less negative ratings of deviant children, they showed less appropriate attending to child behaviors, and they showed increases in commands, indulgence, talking, and physical contact. If the quality of parenting is undermined during episodes of alcohol consumption, then parental recovery could result in improved parenting behaviors and consequent improvement in child outcomes. If parental recovery creates a positive impact on the family environment and on child outcome, there are important implications for interventions. That is, treatment of parental alcoholism itself could function as an intervention for COAs, and adult alcohol treatment programs could incorporate attempts to change parenting behaviors. The stigma of the COA label and the risk of labeling effects makes it difficult (and potentially risky) to identify and target COAs in many settings. However, within alcohol treatment settings, where the parent rather than the child is the identified patient, interventions focusing on parenting and family environment might improve COA outcomes.

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MEDIATORS OF CHILDREN OF ALCOHOLICS’ RISK FOR SUBSTANCE USE AND ABUSE The substantial variation in substance use outcomes for COAs also has implications for studying the underlying mediating mechanisms that account for COAs’ elevations in substance use. Variation in outcomes among COAs may reflect varying degrees of exposure to the mediating variables that are responsible for substance use and abuse outcomes. For example, perhaps only a subset of COAs are temperamentally predisposed to behavioral underregulation, or only a subset of COAs receive poor parenting, and these are the COAs who develop substance abuse or dependence. If so, then temperamental characteristics or parenting environments might be the mediators that are responsible for elevations in substance use and abuse. The study of mediating mechanisms that account for COA risk is an area of great importance. Theoretically, identification of the mediating mechanisms is, of course, the central goal of etiological research to understand the processes that produce substance use and abuse. On an applied level, identification of the mediating mechanisms is a key step in designing preventive interventions. When the processes underlying the development of substance abuse and dependence can be identified, then intervention programs can be targeted at these factors (Chassin, Presson, & Sherman, 1985; Sandier & Chassin, 1993). Because of the importance of understanding the mediating mechanisms underlying parent alcoholism effects, a great deal of theoretical attention and empirical efforts have been devoted to identifying potential mediators. Unfortunately, however, little work has actually applied formal tests of mediational models to adolescent COAs. Thus, our knowledge of the processes that are responsible for elevations in COA substance use and abuse is still quite limited. An integrative guiding theoretical framework for investigating these mediational mechanisms has been provided by Sher (1991). Embedded within this larger biopsychosocial model are three interrelated and interacting submodels that Sher labeled (a) the enhanced reinforcement model, (b) the deviance-proneness model, and (c) the negative affect model. The enhanced reinforcement model hypothesizes that COAs derive greater subjective benefit from the pharmacological effects of alcohol (or drug) use because they have greater sensitivity to the reinforcing effects, sensitivity to the punishing effects of substances, or both. Although a review of this literature is beyond the scope of this chapter, laboratory studies of adult participants have demonstrated significant differences between COAs and non-COAs in terms of alcohol effects (see Newlin & Thomson, 1990; Sher, 1991, for reviews). Moreover, although few studies have attempted to test whether these individual differences in alcohol effects mediate family history risk for alcoholism, Schuckit and

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Smith (1996, 1997) have reported both that COAs have lowered sensitivity to alcohol consumption (less body sway and lowered ratings of intoxication) and that lowered sensitivity prospectively predicts future alcoholism. This is consistent with the notion that individual differences in sensitivity to the pharmacological effects of substances is one risk pathway underlying parent alcoholism risk. Of course, because of ethical concerns, laboratory studies of alcohol effects have been confined to adult populations. Sher’s negative affect model depicts parental alcoholism as increasing children’s exposure to stressful life events, which, in turn, produce emotional distress (anxiety and depression). Moreover, emotional distress among COAs may also be elevated because of temperamental predispositions to negative emotionality. Emotional distress is then hypothesized to lead to involvement with alcohol or drugs. Alcohol or drug use can serve directly to regulate negative affect, or there may be additional mediation through a deviant peer group. That is, children with high levels of emotional distress may turn to a deviant peer group for acceptance as a way of increasing self-esteem (as described in Kaplan’s [1980] “self-derogation” theory). This drug-using peer network then increases risk for alcohol and drug use. As noted earlier, the empirical literature contains very few formal tests of these mediational models. However, data do suggest that COAs are exposed to more life stress than are their non-COA peers (Chassin, Pillow, Curran, Molina, & Barrera, 1993; Roosa, Tein, Groppenbacher, Michaels, & Dumka, 1993). The data are more mixed with regard to temperamental negative emotionality, with some studies (e.g., Sher, Walitzer, Wood, & Brent, 1991) finding elevations in neuroticism and others (e.g., Schuckit, 1983) finding no significant relation, leading Sher (in press) to call into question whether there was a reliable relation between neuroticism and parental alcoholism. Our own data found some evidence for a link between maternal alcoholism and negative emotionality (Chassin et al., 1993). To the extent that studies sample paternal alcoholism (which is more common) rather than maternal alcoholism, this may account for conflicting findings. Also controversial in the adolescent literature is the notion that negative emotionality is a unique predictor of adolescent substance use, with some researchers finding such prediction (Paton, Kessler, & Kandel, 1977) and others suggesting that negative affect may be more a result than an antecedent of adolescent substance use (Hansell & White, 1991). In the larger theoretical literature, negative affect regulation models of substance abuse and dependence have been thought to apply more to late-onset adult forms of substance use disorders than to the adolescent years (Zucker, 1994). In Sher’s deviance-proneness model, ineffective parenting is hypothesized to interact with COAs’ difficult temperamental characteristics and cognitive dysfunctions to set off a process of school failure and association with deviant peers. This process increases the likelihood of antisocial outcomes for COAs,

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including substance use and delinquency. As noted previously, there are few empirical studies to formally test this mediational model. However, many of the individual relations within the larger mediational model have been empirically supported. COAs have been shown to have more difficult temperaments than do non-COAs (see Tarter, Alterman, & Edwards, 1985; Windle, 1990, for reviews), including poorer self-regulation and elevations in behavioral undercontrol (Pihl, Peterson, & Finn, 1990; Sher et al., 1991). According to Sher (1997), characteristics of sensation seeking, aggressiveness, impulsivity, and psychoticism have repeatedly been found to be elevated in COAs. These characteristics, in turn, are associated with risk for adolescent substance use (Hawkins, Catalano, & Miller, 1992). In terms of cognitive deficits, male offspring of antisocial alcoholics and current alcoholics have been reported to have poorer visual-spatial and attentional performance (Ozkaragoz et al., 1997). COAs have also been shown to have poorer academic achievement, which is, in part, mediated by poorer self-regulation (McGrath, Watson, & Chassin, in press). Poor academic achievement is associated with membership in drug-using peer groups and with adolescent substance use (Hawkins et al., 1992). Finally, parenting and family environments for COAs have been shown to be characterized by higher levels of conflict (Claire & Genest, 1987; Moos & Billings, 1982), higher risk for violence (Ellis, Zucker, & Fitzgerald, 1997; Miller, Maguin, & Downs, 1997; Rivara et al., 1997), impaired monitoring of child behavior (Dishion, Patterson, & Reid, 1988) and lower levels of support and nurturance (Moos & Billings, 1982; see Jacob & Leonard, 1994, for a review). These family factors are associated with higher levels of child and adolescent substance use (Curran & Chassin, 1996; Hawkins et al., 1992; Hussong & Chassin, 1997; Stice & Barrera, 1995; see Jacob & Leonard, 1994, for a review). Thus, despite the absence of formal model tests, empirical evidence supports individual linkages within the deviance-proneness model of COA risk for substance use. Given the lack of formal tests of mediational models of COA risk for substance use, one goal of our research program has been to test such mediational links within an integrative model (Chassin et al., 1993, 1996). Figure 9.1 shows the results of this model as it predicted escalation in substance use over the course of the study (i.e., the slope of the growth curve was the outcome variable of most interest). As shown in Figure 9.1, our findings were consistent with both Sher’s negative affect and deviance-proneness submodels. Consistent with stress and negative affect models, COAs showed higher levels of environmental stress and negative emotionality, which were associated with more negative affect. Negative affect, in turn, increased risk for associating with drug-using peers, and drug-using peer groups predicted steeper substance use growth over time. It should be noted, however, that the data did not support simple negative affect regulation models of substance use (because there was

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Figure 9.1 Growth model predicting adolescent’s initial substance use (intercept) and growth over time in substance use (slope). Standardized path coefficients are shown. For solid lines, p<.05; for dashed lines, p<.10; chi squared (62, n=316)=88.6, p<.01, Tucker-Lewis fit index=.95, comparative fit index=.98.

no direct effect of negative affect on substance use growth over time). Rather, the results were more consistent with Kaplan’s (1980) self-derogation model, in which negative affect increases risk for substance use growth because distressed adolescents are more likely to affiliate with drug-using peers, and this peer network is a proximal influence on their drug use. Consistent with Sher’s deviance-proneness model, COAs received less paternal monitoring of their behavior, which, in turn, was associated with affiliations with drugusing peers and steeper substance use growth. Finally, however, an important result of these analyses was that inclusion of these mediators did not fully explain the effect of paternal alcoholism on growth over time in adolescent substance use. In fact, the magnitude of the paternal alcoholism effect was largely unchanged by the inclusion of the mediators. This suggests that important mediators of paternal alcoholism were still unmeasured by our study. For example, any enhanced reinforcement or lowered sensitivity to drug effects that COAs might experience would not be captured by these mediating variables. Thus, theoretical speculations, the general lack of formal mediational tests in the empirical literature, and our own mediational modeling suggest that multiple mediating mechanisms are necessary to explain COA risk for substance use and abuse. Identifying these mediating mechanisms is an important direction for future research.

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Moderators of Parent Alcoholism Effects on Adolescent Substance Use ond Abuse: Identifying Protective Factors As noted earlier, there is substantial variation in substance use outcomes among COAs. To this point, we have discussed this variation as resulting from the heterogeneity within parent alcoholism and from differential exposure to the mediating variables underlying substance use and abuse outcomes. An alternative (although not mutually exclusive) explanation is that variation in substance use outcomes for COAs is a function of differential exposure to moderator variables that either magnify or weaken the effects of parent alcoholism. Factors that weaken or buffer the negative effects of parental alcoholism are termed protective factors. From the point of view of prevention, there is particular interest in protective factors because they might be incorporated into preventive interventions. For example, although COAs may receive poor parenting, some COAs may benefit from a special relationship with a grandparent or teacher that serves to buffer these parenting effects. Preventive interventions might work to develop such a positive relationship with another adult in the family network. As with mediating variables, the search for moderating variables is important both on a theoretical level to construct theories of substance use etiology and on a practical level for the design of preventive intervention, particularly where the risk mediators are not modifiable. In these cases, interventions can be designed to provide or strengthen some protective factor that blocks the mediator from operating. Sher’s (1991) review identified the following as possible moderators of parental alcoholism effects on substance use outcomes: social class, preservation of family rituals, mother’s esteem for the alcoholic father, amount of attention from primary caregivers, family conflict during infancy, birth of another sibling within the first 2 years of life, social support, personality, self-awareness, cognitive-intellectual functioning, and coping. However (even more than for mediating variables), the level of empirical evidence lags far behind theorizing in identifying such protective factors. Few studies actually provide formal (or statistically appropriate) tests of moderation. To do so requires an interaction between parent alcoholism and some protective factor. However, the claims of empirical studies have often rested on demonstrating some predictor of outcomes among COAs without examining a control or comparison group. Although these findings are important in identifying predictors of adjustment for COAs, they do not provide evidence of special “protection.” We do not know, for example, how well the factor would predict adjustment in non-COA samples or how the “well-adjusted” COAs would compare with a control group. In an 18-year longitudinal study of 49 COAs from the island of Kauai, Hawaii, Werner (1986) identified several factors that differentiated “resilient” children (those who had not developed serious problems by age 18) from children who had developed problems. Resilient COAs were characterized as having fewer

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stressful events in the first 2 years of life, a “cuddly and affectionate” temperament early in life, an internal locus of control, a more positive selfconcept, and more competent communication skills than COAs who developed problems by age 18. This study identifies important predictors of COA adjustment, but without a comparison group the interpretation of these findings as evidence of moderation effects is limited, and the study did not focus specifically on substance use outcomes for COAs. Wolin and Bennett’s studies on maintenance of family rituals is another wellknown research program on protective factors for COAs. Wolin, Bennett, Noonan, and Teitelbaum, (1980) examined the relationship between consistency of family rituals (dinner time, vacations, holidays) and risk of developing alcohol problems in COAs in a sample of 25 alcoholic families. Families were classified as “subsumptive” (the alcoholic’s drinking altered family rituals), “intermediate” (drinking affected some but not all rituals), or “distinctive” (rituals were not affected by drinking). Wolin et al. (1980) found that children from distinctive families were significantly less likely to develop alcohol problems than children from subsumptive families. In a related study, Bennett, Wolin, Reiss, and Teitelbaum (1987) found that COAs who demonstrated a high degree of deliberateness and planning in choosing and establishing family rituals in their own marriages were less likely to develop alcohol problems themselves. This study also replicated their earlier finding that COAs from distinctive families are at decreased risk for developing alcohol problems. Despite the potential importance of this work, the conclusions are limited by the lack of a nonalcoholic control group, the cross-sectional design, and the small sample size. Just a few studies of substance use among COAs have provided true tests of moderation. Ohannessian and Hesselbrock (1993) examined the potential moderating effects of perceived social support (ISEL and PSS-Family and PSS-Friends) in a sample of 85 adult COAs and 68 adult control subjects. The pattern of means suggested that COAs with low friend support and low overall support had higher MAST scores, drank more, and expressed more concern about their drinking than did other subjects. For the most part, COAs with high friends’ social support closely resembled controls. Family support did not show moderation effects. Ohannessian, Hesselbrock, Tennen, and Affleck (1994) examined individual characteristics (pessimism-optimism) and environmental characteristics (daily hassles and uplifts) as potential moderators. Results indicated that daily hassles moderated the relationship between family history of alcoholism and alcohol consumed and that pessimism moderated the relationship between family history and alcohol problems, as indicated by the MAST. We have found evidence of moderation effects in our own research on adolescent COAs in considering their mental health outcomes. A compelling basic finding was that COAs were more vulnerable to the effects of stress than

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were children without alcoholic parents (Barrera, Li, & Chassin, 1993, 1995). This effect was found in cross-sectional analyses of two annual assessments and in prospective analyses of adolescents’ internalizing and externalizing symptomatology. There was additional but limited evidence that ethnicity and family conflict moderated the effects of parental alcoholism (Barrera et al., 1993, 1995). Compared to whites, Hispanics were more resilient to the effects of parental alcoholism during the first annual assessment, but this effect was not replicated in subsequent assessments. Also, family conflict (Barrera et al., 1995), parent-adolescent conflict, and parental social support (Barrera et al., 1993) moderated the influences of parental alcoholism. In all of these cases, COAs who had parental support or who lacked conflict resembled our control subjects in terms of symptomatology. On the other hand, COAs who lacked parental support or who reported conflict in family relations showed elevated levels of psychological distress. We have also found some evidence of protective factors that buffer the effects of parent alcoholism on substance use initiation among adolescent COAs. Longitudinal data considering adolescents who abstained from substance use at the beginning of our study found that COAs were more likely to later initiate substance use than were non-COAs. However, high levels of perceived control, of self-esteem, and of cognitive coping buffered the risk that was associated with parent alcoholism (Hussong & Chassin, 1997; McGrath & Chassin, 1995). Regarding the evidence in support of moderators suggested by Ohannessian, Hesselbrock, and colleagues and by our own data, it should be emphasized that these interactions have been tested only rarely in empirical studies and, when found, tend to be small in magnitude, inconsistently observed across criteria, and rarely observed in prospective analyses. Thus, as with tests of mediational models, the study of moderators of parent alcoholism effects remains a significant gap in the empirical literature. Children of Alcoholics’ Beliefs about Substance Use: A Proximal and Potentially Modifiable Mediator Many models of adolescent substance use view adolescents’ cognitions about substance use as an important proximal mediator of their substance use decisions (Beyth-Marom & Fischoff, 1997; Sher, 1991). Multiple influencesincluding socialization provided by peers and parents, media images, and individual differences in the pharmacological effects of consuming a substance—may all act to shape adolescents’ beliefs about the effects of engaging in substance use. These beliefs, in turn, affect the likelihood of adolescents’ using alcohol or drugs (Christiansen, Smith, Roehling, & Goldman, 1989; Sher et al., 1991). Thus, many powerful biobehavioral influences may be filtered through adolescents’ subjective evaluations of the

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costs and benefits of engaging in alcohol and drug use. Moreover, because adolescents’ beliefs about substance use are potentially modifiable, these beliefs are important targets for preventive intervention. In terms of knowledge about alcohol, studies have shown that COAs have greater knowledge than do their non-COA peers. Even in the preschool years, parental levels of alcohol consumption have been shown to predict children’s ability to identify alcoholic beverages by smell (Noll, Zucker, & Greenberg, 1990). Moreover, even among preschoolers, COAs are better able to identify specific alcoholic beverages than are their non-COA peers (Zucker, Kincaid, Fitzgerald, & Bingham, 1995). The young age of these subjects suggests that their own drinking experience is unlikely to account for differences in knowledge about alcohol, and the more accurate recognition by smell suggests that media influence alone is unlikely to be responsible (Noll et al., 1990). Thus, the authors conclude that COAs are exposed to a different socialization about alcohol use than are non-COAs. Whether because of parental socialization, biologically based individual differences in sensitivity to the pharmacological properties of alcohol, or both, adolescent and young adult COAs have also been shown to have more positive beliefs about alcohol effects than do their non-COA peers. (Brown, Creamer, & Stetson, 1987; Mann, Chassin, & Sher, 1987; Sher et al., 1991). Formal tests of mediation have produced mixed results, with one study finding that alcohol expectancies partially account for the relation between family history of alcoholism and alcohol use in college students (although with small magnitudes of effect, Sher, Wood, Wood, & Raskin, 1996) and our data with younger adolescents showing no significant mediation of parental alcoholism effects on growth curves of alcohol consumption over a 3-year period (Colder, Chassin, Stice, & Curran, 1997). Most studies of parental alcoholism and beliefs about alcohol effects have focused on adolescents’ positive expectancies, that is, their beliefs that alcohol consumption produces positive benefits. However, adolescents also form beliefs about the negative effects of drinking and hold reasons for limiting their drinking (or for abstaining altogether). Some researchers (Harburg, Davis, & Caplan, 1982) have speculated that, as a result of witnessing their parents’ negative experiences with alcohol, a subgroup of COAs may come to hold extremely negative views of alcohol and rigidly avoid any alcohol consumption. Harburg et al. (1982) suggest that COAs may develop polarized outcomes with respect to alcohol and drug use: overrepresented (compared with their non-COA peers) in heavy consumption and problem drinking, overrepresented in abstinence, and underrepresented in moderate social drinking. Although our data with adolescents have not shown such bimodal drinking patterns among COAs, we have found some evidence that negative beliefs about drinking may be related to lowered consumption levels in a subgroup

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of COAs (even more strongly than in controls). In these analyses, we divided adolescents into controls, COAs whose fathers were “recovered” (i.e., showed no alcohol-related consequences over the 3 years of our study), and COAs whose fathers continued to experience alcohol-related consequences during the study (see Chassin & Barrera, 1993, for a more detailed report). The offspring of recovered alcoholic fathers showed the strongest correlation between self-control reasons for limiting drinking and their own consumption; that is, among adolescents whose fathers’ alcoholism had remitted, there was the strongest association between lower consumption levels and limiting drinking because of liking to feel in control and believing that drinking is a sign of personal weakness. Moreover, adolescents whose fathers had recovered were the only group to show a significant correlation between lowered consumption levels and limiting their drinking because of “seeing the negative effect of alcohol on someone else.” It might be surprising that witnessing the negative effects of alcohol on someone else had significant relation to drinking in the “recovered” group rather than the “actively alcoholic” group, for whom the negative effects of paternal alcoholism might be expected to be the most vivid and salient. Perhaps because recovered fathers had succeeded in restraining their own drinking, they were able to serve as more effective socialization agents and models for restraint in alcohol use. In these families, there might be less discrepancy between parents’ verbal messages about restraint and parents’ actual behavior. Taken together, the fact that COAs show different knowledge about alcohol from very early ages, their stronger endorsement of beliefs about positive alcohol effects, and their stronger association of cognitive restraint with lower levels of consumption (among children of recovered alcoholics) suggest that COAs receive different parental socialization about alcohol use than do their non-COA peers. Currently, little is known about the socialization about alcohol or drug use that is provided by alcoholic parents and their spouses. In fact, even in the general population, there has been relatively little attention paid to parents’ attempts to socialize their children about alcohol and drug use (perhaps because substance use prevention programs have been directed primarily at combating peer influences). Recent models of parent socialization (e.g., Darling & Steinberg, 1993; Grusec & Goodnow, 1994) suggest that researchers need to consider both parents’ general parenting behaviors and styles and their approaches to specific issues such as substance use. Research must consider parents’ explicit messages about alcohol and drug use as well as the more subtle messages that may be communicated by the parents’ own substance use behavior and the negative consequences that result from that behavior. From the point of view of preventive intervention, it is important to study whether successfully recovered alcoholic parents can be effective deterrents to their adolescents escalating substance use.

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SUMMARY AND IMPLICATIONS FOR PREVENTION The goals of this chapter and of our longitudinal study are to examine parent alcoholism risk for substance use and abuse outcomes in adolescence and young adulthood, to evaluate the magnitude and specificity of that risk, and to identify mediators and moderators of that risk. This research agenda contributes to the generative database on which to build preventive interventions. Our own data and those of other research projects suggest several conclusions. First, COAs are indeed at greater risk for alcohol and drug use and for alcohol- and drugrelated problems than are their non-COA peers. Second, the magnitude of the risk suggests that these effects are of clinical importance, not merely statistical significance. Third, the risk is specifically associated with parent alcoholism above and beyond other parental psychopathology. Fourth, there is substantial variation in substance use outcomes among COAs, and it would be a distortion to conclude that negative outcomes are inevitable. Fifth, this variation in outcomes reflects substantial heterogeneity within parent alcoholism itself. COA outcomes may differ as a function of the gender of the alcoholic parent, timing of parent alcoholism (including prenatal exposure), recency of parental alcoholism (and recovery), density of alcoholism within the family pedigree, and other parental psychopathology (particularly antisociality). Sixth, this variation in outcome for COAs likely reflects differential exposure to risk mediators and moderators. Potential mediating mechanisms have included differential sensitivity to the pharmacological effects of alcohol and drugs, deviance proneness and externalizing pathways (hypothesized to be due to temperamental behavioral undercontrol, poor parenting and family environments, cognitive deficits, and school failure), and negative affect pathways (hypothesized to be due to temperamental negative emotionality and high levels of life stress and negative affect). Proximal mediators of these pathways may include adolescents’ expectancies about substance use effects and their associations with drug-using peers. Although some evidence relates these variables to either parent alcoholism or to substance use outcomes, formal tests of mediational models (particularly those using longitudinal data) are quite rare. Potential moderating variables that have been empirically supported include social support, self-esteem, cognitive coping, and perceived control, although, again, formal tests of moderation are quite rare. Given this slim foundation of generative data, considerable caution is warranted in the development of preventive interventions, and to date there is little evidence of successful preventive intervention targeted at COAs. Programs that have been attempted have too rarely reported rigorous empirical evaluation (Williams, 1990). There is also reason for concern because of the very real possibility of stigma and negative labeling effects that can occur for COAs (Burk & Sher, 1990); indeed, it has been reported that children are reluctant to join COA groups in school-based prevention programs (DiCicco, Davis, Travis,

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& Ornstein, 1984). In terms of substance use outcomes, perhaps prevention efforts for COAs within school settings can be usefully integrated into the universal drug-prevention programs that currently exist. These programs often include attempts to change expectancies about alcohol and drug effects and attempts to teach ways of coping with life stress (Hawkins et al., 1992), both of which might benefit COAs. Unfortunately, given that many of the mediators and moderators of COA risk focus on parenting and family environment factors, school-based programs may not be sufficiently powerful to affect outcomes in many cases. One alternative to school-based programs is preventive interventions for COAs within alcohol treatment facilities. This approach has the potential advantage of involving parents and thus, being able to target parenting and family environment factors without publicly labeling children as COAs within their school environments. Potentially useful targets for intervention include reducing family conflict, increasing parental support and monitoring of child behavior, and increasing consistency of discipline. Moreover, given that parental recovery itself is associated with some improvement in COA outcomes, treatment might function as a preventive intervention for the children. Unfortunately, preventive interventions in alcohol treatment settings can reach only a minority of COAs because only a minority of alcoholics receive treatment. Moreover, substance use outcomes for COAs may be less likely to “recover” than other mental health outcomes. Given the limitations associated with both school-based and treatment-based approaches, future directions might consider other settings that include families at risk, such as prenatal clinics and early childhood programs, and using family-based approaches to reduce risk for early externalizing behavior (Maguin, Zucker, & Fitzgerald, 1994). FUTURE RESEARCH DIRECTIONS Clearly, there are significant gaps in our empirical understanding of substance use and abuse outcomes for COAs. The most basic gap is our lack of knowledge of the mediating mechanisms that underlie parent alcoholism effects on substance use outcomes and our lack of knowledge of moderating variables that either buffer or magnify these effects. Formal tests of mediational and moderational models, particularly ones using longitudinal data, are lacking, and future studies are needed in these areas. Identification of mediating and moderating variables is important because (to the extent that they are modifiable) preventive interventions can be aimed at changing these mediators and moderators. Also, more needs to be known about parental socialization regarding alcohol and drugs for COAs and the effects that this socialization might have on COAs’ substance use outcomes, and more needs to be known about the effect of parental recovery on these outcomes. However, producing better answers to these

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questions requires closer integration across the diverse perspectives that are relevant to COA risk. Research would benefit from a better developmental perspective, more closely integrating our knowledge of normal development with our knowledge of clinical outcomes. For example, knowledge of the development of stress reactivity and self-regulation should illuminate ways in which stress-related pathways and deviance-proneness pathways interact to produce substance use among COAs. As illustrated in Sher’s (1991) heuristic model, future studies must continue to integrate diverse perspectives into biopsychosocial models so that, rather than simply labeling risk as “genetic” or “environmental,” we can move toward an understanding of how multiple risk factors interrelate and interact to produce outcomes. For example, recent integrations of adoptee designs and family interaction assessments suggest that genetically influenced antisocial behavior both evokes poor parenting behaviors and is influenced by poor parenting (Ge et al., 1996) and that parenting behaviors can act as buffers to reduce the risk associated with these heritable predispositions (Cadoret, Yates, Troughton, Woodworth, & Stewart, 1995b). Gene-environment interactions have also been reported to predict cognitive functioning among COAs (Berman & Noble, 1997). Such integrative approaches are likely to similarly illuminate the processes underlying substance use outcomes for COAs. Finally, of course, future research must surmount formidable methodological challenges, including issues of sampling and representativeness, consideration of the heterogeneity of parent alcoholism, and appropriate statistical analyses of complex models in longitudinal designs. AUTHOR NOTE This chapter is based on a presentation at the Children of Addiction Round Table sponsored by the Irving B.Harris Foundation and the Society for Research in Child Development, April 2, 1997, Washington, D.C. Support for this project was provided by the National Institute on Drug Abuse Grant DA05227 to Laurie Chassin and Manuel Barrera, Jr. REFERENCES Bachman, J.D., Wadsworth, K.N., O’Malley, P.M., Schulenberg, J., & Johnston, L.D. (1997). Marriage, divorce, and parenthood during the transition to young adulthood: Impacts on drug use and abuse. In Schulenberg, J.Maggs, J.L., & Hurrelmann, K. (Eds.), Health risks and developmental transitions during adolescence, pp. 246– 279. New York: Cambridge University Press. Barrera, M., Jr., Li, S.A., & Chassin, L. (1993). Ethnic group differences in vulnerability to parental alcoholism and life stress: A study of Hispanic and non-Hispanic Caucasian adolescents. American Journal of Community Psychology, 21, 15–35.

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Author Index

Aase, J.M., 145, 146 Abel, E., 145, 159 Aboagye, K., 2, 3, 5, 8, 9 Achenbach, T., 128 Ackerman R., 174, 185 Affleck, G., 199 Ager, J.W., 3, 7, 71, 72, 73, 77, 78, 80, 81, 83, 84, 85, 86, 99 Albert, V., 60, 62 Alessandri, S.M., 17, 19, 20, 22 Alexander, G.J., 183 Alterman, A., 181, 202 Altieri, I., 10 Amaro, H., 2, 3, 8, 9 Ambre, J., 9, 22 American Psychiatric Association, vii, 51, 62, 110, 177, 185 Amoruso, L.P., 21 Anctil, C., 147 Anderson, P.D., 145 Andrasi, P., 175, 184, 185 Angellil, M.L., 13, 16, 20 Annis, H.M., 180, 185 Anthony, E.J., 37, 41 Anthony, J.C., 111, 114 Anton, S.F., 50 Apospori, E., 173 Arbuckle, J., 147 Archambault, D., 172, 185 Arendt, R., 59

Arnold, H.M., 46 Asante, K., 145 Ashley, M.J., 4, 22 Augustyn, M., 2 Ayers, T.S., 175 Azuma, S.D., 51, 62 Babor, T.F., 117, 118, 127 Bachman, J.D., 193, 211 Bachman, J.G., 129, 166, 172 Backema, S., 180 Bacon, M., 155 Bacon, S., 3, 7 Bailey, D.N., 21, 22 Baker, D., 13, 16, 20 Balzer, D.G., 148 Barnett, M., 5, 8 Barocas, R., 51 Barr, H.L., 6, 81, 92, 100, 102, 103 Barr, H.M., 3, 70, 71, 76, 81, 82, 87 Barrera, M., 148, 150, 196, 199, 201, 202, 211 Barry, H., 155 Barth, R.P., 60, 62 Bartholow, B.D., 202 Baskin, G., 102 Battaglia, F., 69 Bauchner, H., 2, 3, 8, 9 Bauer, C.R., 33, 41 Bauman, P.S., 48, 62 217

218 Baumgartner, A.M., 10, 22 Baxter, J.A., 118 Bayley, N., 70, 87 Beals, J., 174 Beauvais, F., 149, 150, 151, 152, 157 Bech, K., 194, 197 Bechtold, D.W., 147 Becker, H., 102 Beckwith, L., 48, 53, 54, 55, 59, 61, 76 Beeghly, M., 59, 62 Begleiter, H., 173 Behnke, M., 10, 13, 14, 16, 19, 20, 22 Beitel, G., 110 Bell, R.M., 173 Belsky, J., 114 Bendersky, M., 17, 18, 19, 20, 22, 45 Bennett, L.A., 205 Bentler, P.M., 166, 178 Bergeson, M., 145 Berkowitz, A.D., 174, 175, 176, 180, 183, 185 Berkson, J., 115 Berman, S., 211 Bernstein, V.J., 47, 49, 50, 51, 53, 59, 61, 62 Bernstein, V.J., 50, 55, 56 Berrick, J., 60, 62 Bertholf, R.L., 10, 22 Besharov, D.J., 45, 62 Betancourt, L., 19, 20 Beyth-Marom, R., 206 Bickel, F., 170, 185 Bierman, J.M., 36, 168 Biernoff, M.P., 147 Bihun, J.T., 76 Billings, A., 198, 199, 202 Bingham, C.R., 14, 117, 124, 127, 128, 130, 131, 134, 135, 136, 196, 207 Bird, M.E., 147 Bittigau, P., 78, 82 Black, C., 174, 185 Black, T.C., 10, 12 Black, M., 49 Blank, D.L., 10 Blankenhorn, D., 168, 185 Block, J., 166

Author Index Blume, S.B., 174, 193 Boehnlein, J.K., 144 Bohlin, A.B., 78 Bookstein, F.L., 3, 82, 92 Bornstein, M.H., 33, 41, 59 Bowden, J., 180, 181 Boyd, T.A., 71, 72, 81 Boyd, J.H., 114 Boyle, C., 21 Boyle, J., 169, 185 Bradley, R., 49 Brady, M.J., 2, 8 Braitman, L.E., 19, 20 Bray, D.L., 145 Brazelton, T.B., 13, 22 Bremer, D., 116 Brenner, M.H., 125 Brent, E.E., 148, 206, 207 Bridger, W.H., 76 Brill, N., 100, 102, 103 Brillman, J., 147 Brinson, J., 174, 185 Brodsky, N., 19, 20 Broiada, J., 181 Brooks-Gunn, J., 51 Brown-Cheatham, M., 168, 185 Brown, R.T., 76, 79, 87, 101, 102, 103, 104 Brown, S.A., 176, 185, 207 Brown, V.B., 58 Bruckel, S., 151 Brunner, S., 30, 31 Brunswick, A.F., 169, 185 Brust, J.C.M., 15, 20, 21 Bry, B.H., 166, 185 Budde, D., 50 Burbacher, T.M., 77, 87 Burk, J.P., 184, 185, 196, 209 Burkett, G., 13, 22 Burnam, A., 116, 128, 130 Burns, K.A., 3 Burns, W.M., 47 Butler, M.C., 166 Byrne, C., 165 Creamer, V.A., 207

Author Index Cabral, H., 2, 3, 8, 9, 13, 14, 17, 19, 20, 21 Cacciola, J., 6 Cadoret, R.J., 197, 211 Cahalan, D., 4, 7, 22 Caldwell, B., 49 Callahan, C.M., 10, 22 Callan, V.J., 198 Calsyn, D.A., 58 Campbell, A., 168 Cantor, S.B., 8 Caplan, R., 207 Carroll, S., 181 Carroll, K., 50 Casanova, O.Q., 10, 22 Caspi, A., 131 Cassidy, S., 145 Catalano, R.F., 166, 169, 171, 172, 178, 202, 210 Center for Substance Abuse Prevention, 180 Cerbone, F.G., 111 Cermak, T., 120 Cernkovich, S.A., 179 Chandler, J.J., 34, 75 Chandler, L.S., 45 Chang, F.-M., 77, 78 Chariot, C., 118 Chasnoff, I.J., 3, 5, 8, 47, 51, 61, 62 Chassin, L., 19, 148, 150, 194, 195, 196, 197, 199, 200, 201, 202, 206, 207, 211 Chawarska, K., 59 Chen, K., 173 Chermack, S.T., 133, 134 Chilcoat, H.D., 114 Child, I., 155 Children of Alcoholics Foundation, Inc., 182 Chiodo, L.M., 18, 19, 20, 33, 77 Chiriboga, C.A., 15, 20, 21 Christensen, S., 183 Christiansen, B.A., 206 Christie, K.A., 114 Chung, N.K., 173 Churchill, J., 114

219 Cicchetti, D., 95 Cisin, H., 4, 7, 22 Clair, D., 181, 182, 183, 202 Clark, G., 10, 22 Clarren, S., 145 Clayton, R.R., 149 Cohen, D.J., 76 Cohen, J., 33, 41, 115 Cohen, P., 115 Cohen, S.E., 76 Colder, C.R., 150, 196, 207 Cole, R.E., 37, 41 Coles, C.D., 70, 71, 76, 78, 79, 87, 98, 101, 102, 103, 104 Collins, L.M., 173 Colombo, J., 76, 87 Colten, M.E., 61 Committee on the Future of Alcohol and Other Drug Use Prevention, 165 Conger, R., 211 Conlon, M., 10, 13, 14, 16, 19, 20, 22 Corny, J.L., 145 Corny, R.F., 145 Cook, E.H., 132 Cooper, A., 167 Cooper, C.S., 166 Cooper, J.D., 168 Copeland, P., 166, 169, 171 Cornelius, M., 93, 99, 100, 101, 103 Cornelius, M.D., 101, 104 Costenbader, V., 170 Cotton, N.S., 110, 148, 193 Covington, C., 13, 16, 20 Coyne, J.C., 59 Crafter, B., 167, 170 Crayton, J.W., 10 Crum, R.M., 111 Gumming, W., 13, 19, 22 Curran, G.M., 125, 133, 134 Curran, P., 150, 207 Curran, P.J., 150, 196, 201, 202 Daniel, P., 5 Daniels, C.R., 1 Danish, S.J., 172

220 Darby, B.L., 76, 87 Darling, N., 208 Davies,W.H., vii, x, 151 Davis, D., 207 Davis, M., 169 Davis, R., 210 Davis, W.N., 155 Dawkins, M.P., 179 Dawkins, R., 179 Dawson, D.A., 114 Day, N., 92, 93, 95, 97, 98, 99, 100, 101, 102 Day, N.L., 2, 13, 16, 20, 21, 71, 79, 97, 100, 101, 103 DeBruyn, L.M., 147 Decoufle, P., 21 Delaney-Black, V., 13, 16, 20 DeLucia, C., 195 Derewlany, L.O., 5, 21 DeRidder, L.M., 170 DeSimone, A., 180, 181 Developmental Research and Programs, Inc., 171 DiCicco, L., 210 DiPietro, J., 45, 50 Dishion, T., 202 Disney, E.R., 45, 59 Dixon, R., 95 Doakes, S.S., 61 Dohrenwend, B.P., 128 Dolinsky, 117 Dougherty, F.E., 48, 62 Dowler, J.K., 77 Downey, G., 59 Downs, W.R., 14, 21, 50, 202 Dozier, E.P., 144 Drake, R.E., 58 Drakow, J.B., 37 Draznin, T.H., 118 Drechsler, M., 61 Dreher, M.C., 22 Drikes, K., 3 Dulcan, M., 115 Dumka, L., 201 Duncan, G., 165, 185 Dunn, J., 45

Author Index Duprez, C., 181, 183 Eaves, L., 45 Edlund, M.J., 50, 57 Edwards, K., 181, 202 Ehrlich, S.M., 53 Einarson, T.R., 32 Ekwo, E., 59 Ellickson, P.L., 173 Ellis, D.A., 117, 124, 127, 128, 130, 131, 132, 196, 202 Erickson, S., 76, 79, 87, 101, 102, 103, 104 Ernhart, C.B., 3, 71, 72, 81, 98 Eshleman, S., 110, 111, 115 Espinosa, M., 53, 54, 59, 61 Evans, F., 6 Ewing, J.A., 7 Eyler, F.D., 10, 13, 14, 16, 19, 20 Fagan, J.F., 76, 77, 87 Falek, A., 70, 71, 76, 78, 87, 98, 101, 102, 103, 104 Famularo, R., 59 Farkas, K., 59 Farmer, M.E., 110, 117, 129, 130 Farran, D.C., 168 Farrell, A.D., 171, 172 Farrington, D., 171 Feighner, J.P., 120 Fein, G.G., 77 Fenton, T., 59 Fernhoff, P., 98, 102 Ferrence, R., 4, 22 Field, T., 114 Fielding, B., 115 Fiks, K.B., 51, 55 Filipovich, H., 98 Fine, E.W., 110 Finkelstein, N., 50, 58 Finn, P., 196, 202 Finnegan, L.P., 48, 50, 53 First, M.B., 6, 8 Fischman, M.W., 5 Fischoff, B., 206 Fisher, L., 37, 41

Author Index Fitzgerald, H.E., vii, ix, x, 14, 48, 116, 117, 124, 127, 128, 130, 132, 133, 134, 135, 136, 151, 152, 196, 202, 207, 209, 210 Florey, C., 1 Floyd, F.J., 134 Fogg, C.P., 166, 169, 178 Foltin, R.W., 5 Forrest, F., 1 Fox, W.W., 5 Franchina, J.J., 46 Frank, D.A., 2, 3, 8, 9, 13, 14, 17, 19, 20, 21 Frank, R.G., 50, 57 Frankowski, J.J., 76 Freedland, R.L., 59 Freier, K., 30, 59 French, F.E., 36, 168 Fried, L., 2 Fried, P.A., 71, 87, 99, 100, 102, 103 Friedman, D., 167 Gabriel, K.R., 147 Garcia, D.C., 10 Garcia, G.C., 10 Gardner, J.M., 59 Garmezy, N., 37, 41 Gause, S., 2, 8 Gawin, F., 50 Ge, X., 211 Geanest, M., 181, 182, 183, 202 Geffner, R., 148 Gelfand, D.M., 59 Gensheimer, L.K., 175 Genz, J.W., 78, 82 George, L.K., 148 Gerlack, C., 170 Geva, D., 93, 99, 100, 102, 103 Gfroerer, J.C., 111 Giannetta, J., 19, 20 Gibbon, M., 6, 8 Gil, A., 173 Giordano, P.C., 179 Glaser, F.B., 50 Glassman, M.B., 51 Glassroth, J., 47

221 Glauz, W., 110 Godolphin, G.W., 9 Goldberger, B.A., 10 Goldman, M.S., 206 Goldschmidt, L., 2, 13, 16, 20, 21, 93, 99, 100, 101, 103 Gomberg, E.S.L., 116 Gondoli, D.M., 50 Goodlett, C.R., 78, 82, 87 Goodman, G.S., 76, 87 Goodman, S.H., 115 Goodnow, J., 208 Goodwin, F.K., 110, 117, 129, 130 Goodwin, G.A., 46 Gordon, M., 36 Gorsuch, R.L., 166 Gottfredson, G.D., 169 Gottfried, A.W., 76 Graham, K., 10, 32 Graham, J.W., 173 Graham-Bermann, S.A., 148 Granger, R.H., 33, 41, 59 Grant, B.F., 114 Grant, K.S., 77, 87 Grant, T.M., 10, 22 Grattan, M.P., vii, x, 47 Gravitz, H., 180, 181 Greenberg, G.S., 139, 207, 118 Greene, T., 71, 72, 81 Greenspan, S., 51 Greenwald, M., 10 Greif, G.L., 61 Griffin, M.L., 50 Griffith, D.R., 3 Griffith, J., 6 Grissom, J.B., 169 Groppenbacher, N., 201 Grusec, J., 208 Gunderson, V.M., 77, 87 Gutfeld, M., 167 Guze, S., 120 Hack, C., 13, 16, 20 Haggerty, R., 177, 178 Haith, M.M., 76, 87 Halstead, A.C., 9

222 Ham, H.P., 151, 152 Hamel, S.C., 2, 13, 16, 20, 21, 22, 54 Haney, M., 5 Hanna, G.L., 132 Hans, S.L., vii, x, 47, 49, 50, 51, 53, 55, 56, 59, 62 Hansell, S., 201 Hansen,W.B., 173, 179 Harburg, E., 207 Hare, J., 180 Hare, N., 180 Harford, T., 176 Harkey, M.R., 10 Harris, T.R., 50 Hatcher, R., 47 Hauser, W.A., 15, 20, 21 Hawkins, J.D., 166, 169, 171, 172, 178, 202, 210 Hawley, T.L., 45, 59 Hays, R.D., 173 Hazan, C., 76, 87 Helzer, J.E., 116, 128, 130 Henderson, C.R., 22, 174, 193 Henderson, G.L., 10, 102 Henson, L.G., 50, 55, 56, 59 Herman, C.S., 70, 71, 100 Hesselbrock, M.N., 50, 118 Hesselbrock, V.M., 118, 199 Heyser, C.J., 46 Hiatt, A.B., 147 Hien, D., 50 Hill, S.Y., 116 Hinderliter, S.A., 22, 54 Hingson, R., 3, 8, 9 Hobica, G., 167 Hofkosh, D., 22, 54 Holcomb, D., 180 Holden, G.W., 148 Hollingshead, A., B. 51 Holmberg, M.B., 169 Holmes, L.B., 5 Holzer, C.E., 8 Hormel, J., 45 Horowitz, F.D., 76, 87 Householder, J., 48 Howard, B.J., 53, 61

Author Index Howard, C.W., 172 Howard, J., 48, 53, 54, 55, 59, 61 Howard, M., 147 Howe, C., 69 Hsu, C.-C., 77, 78 Huang, L.X., 59, 111 Huba, G.J., 58 Hudgins, R., 22 Hughs, M., 110, 111, 115 Hulman, S., 87, 93 Hunt, P., 176, 183 Huntington, K.S., 5 Hurt, H., 19, 20 Hussong, A.M., 150, 196, 202, 206 Hutchins, E., 45, 50 Hwang, J., 9, 177 Hymbaugh, K.J., 145, 146 Ibrahim, H.M., 15 Ichiyama, M.A., 130, 134 Ignatoff, E., 114 Ikonomidou, C., 78, 82 Ingle, D., 145 Ishimaru, M.J., 78, 82 Ishisaka, H., 78 Jackson, D., 198 Jacob, P., 10 Jacob, T., 60, 116, 196, 202 Jacobson, J., 5 Jacobson, S.W., 3, 4, 5, 6, 18, 19, 20, 21, 33, 45, 73, 76, 77, 78, 80, 81, 83, 84, 85, 86, 99, 102, 103 Jacobson, J.L., 3, 4, 6, 18, 19, 20, 21, 33, 45, 73, 76, 77, 78, 80, 81, 83, 84, 85, 86, 99, 102, 103 Jamison, S., 46 Janicki, P.I., 60 Jansen, R.E., 151, 152 Jasperse, D., 101 Jaudes, P.K., 59 Jellen, H., 180 Jensen, A.R., 40 Jenson, J.M., 169, 171, 178 Jeremy, R.J., 47, 50, 53, 59, 62 Jessor, S.L., 166, 169, 178

Author Index Jessor, R., 166, 169, 178 Johnson, H.L., 49, 51, 53, 55 Johnston, L.D., 129, 166, 172, 193, 211 Johnson, S., 196 Johnson, V., 195 Jones, J.W., 174 Jones, P.F., 10, 22 Jones, R.T., 10 Jouriles, E.N., 148 Judd, L.L., 110, 117, 129, 130 Kalin, R., 155 Kaltenbach, K., 48 Kandel, D.B., 166, 169, 170, 173, 193, 201 Kaplan, H.B., 201, 203 Kaplan-Estrin, M.G., 3, 73, 78, 80, 81, 83 Karlix, J., 10 Karmel, B.Z., 59 Kasari, C., 48 Kashubeck, S., 183 Katikaneni, L.P., 15 Kayne, H., 2, 3, 8, 9 Keener, J.J., 50 Keil, A., 50 Keith, S.J., 110, 117, 129, 130 Kellermann, A., 202 Kendler, K.S., 110, 111, 115 Kennard, M., 98 Kessler, R.C., 50, 57, 110, 111, 115, 166, 201 Kidwell, D.A., 10 Kight, C., 62 Kincaid, S.B., 14, 136, 207 Kinscherff, R., 59 Kinzie, J.D., 144 Kirchner, G.L., 76, 81 Klassen, A.D., 50 Klebanox, P., 165, 185 Klein, D., 4 Klein, J., 2, 5, 8, 10, 21 Kleinbaum, D.G., 74 Kliegman, R., 87, 93 Klinger, M., vii, x, 151 Klotz-Daughtery, M., 134

223 Knie, B., 5, 21 Knobloch, H., 36 Ko, H.-C., 77, 78 Koch, C., 78, 82 Kohut, H., 155 Kokes, R.F., 37, 41 Kolody, B., 9, 177 Koop, C.B., 37 Koranek, M., 174 Koren, G., 5, 10, 21, 32 Kossak-Fuller, J., 194, 197 Koszmovszky, K., 167 Kumpfer, K.L., 167 Kunitz, S.J., 144, 147, 153 Kupper, L.L., 74 LaGasse, L., 30, 31, 38 Lahey, B.B., 115 Lancaster, J., 98, 102 Landress, H., 5, 8 Lane, S.J., 45 Lang, A.R., 199 Lange, U., 50 Larkby, C.A., 101, 104 Larsson, G., 78 Lavoie, L., 13, 22 Lawson, M., 55 Leaf, P.J., 50, 57 Leckman, J.R., 116 Lee, W.V., 147 Leiken, J.B., 9, 21 Leonard, K., 196, 202 Lerner, R., 95 Lester, B.M., x, 30, 31, 33, 35, 38, 45 Lester, D., 180, 181 Leung, P.K., 144 Levav, I., 128 Levenson, S.M., 2, 3, 8, 9 Leventhal, J.M., 55, 59 Levin, J.D., 155 Levin, B., 2, 5, 8, 10 Levy, J.E., 144, 147, 153 Lewis, D.E., 9, 21 Lewis, M., 17, 18, 19, 20, 22, 45 Lewis, D., 5, 21 Li, S.A., 206, 211

224 Liaw, F., 51, 165, 185 Lieberman, E., 5 Lieberman, K.W., 183 Lilienfeld, A.M., 36 Link, E.G., 128 Linn, P., 98 Lishner, D.M., 169, 171, 178 Little, K.Y., 132 Locke, B.Z., 110, 114, 117, 129, 130 Lockitch, G., 9 Loeber, R., 169, 171, 177 Lombardero, N., 10, 22 Long, J., 13, 16, 20, 144 Loukas, A., 135 Luborsky, L., 6 Lujan, C.C., 147 Luthar, S., 61 Lutiger, B., 32 Lyon, M., 183 Maarkson, S., 170 McArthur, P.D., 15 McCarten, K., 19, 21 McCarty, M.E., 76 McCloskey, J., 147 McCord, J., 168 McEvoy, L.T., 116, 128, 130 McGonagle, K.A., 50, 57, 110, 111, 115 McGrath, C., 202, 206 MacGregor, S., 3 McGue, M., 195 McKenzie, E., 145 McKeon, P., 166, 185 McKinzie, D.L., 46 McLellan, A.T., 6, 58 McNeil, T.F., 115 Maddahian, E., 166, 178 Maguin, E., 14, 21, 202, 210 Malmud, E., 19, 20 Mancall, P.C., 154 Mann, L.M., 207 Manson, S.M., 147 Marcus, J., 50, 53, 62 Marguiles, R.Z., 166 Marler, M.R., 71, 72, 81 Marshall, O.A., 165

Author Index Martier, S.S., 3, 5, 7, 18, 19, 20, 33, 71, 72, 73, 77, 78, 80, 81, 83, 99 Martin, D.C., 70, 71, 76, 81, 100, 102, 103 Martin, J., 81, 102, 103 Martinez, M., 13, 22 Martison, D.B., 116 Masten, A., 37 Matheny, A.P., Jr., 55 Mathews, D., 46 Matthew, R.J., 148 Mattson, S.N., 73 Maughan, B., 45 May, P.A., 143, 144, 145, 146, 147, 149 Mayes, L.C., 33, 41, 48, 59 Maynard, E.C., 21 Mednick, S.A., 115 Melchior, L.A., 58 Mendoz, C., 202 Merikangas, K.R., 116 Mever, R.E., 118 Meyer, R.E., 50, 58 Meyer, J., 45 Michaels, M., 201 Midanik, L.T., 4 Middaugh, J.P., 145 Miller, B.A., 14, 21, 50, 60, 202 Miller, D., 3 Miller, J.Y., 166, 172, 202, 210 Miller, B., 50 Minnes, S., 59 Mirin, S.M., 50 Mirochnick, M., 13, 14, 17, 19, 20, 21 Mitchell, C.M., 149 Mitchell, D.W., 76, 87 Mitchell, J., 3 Moffit, T.E., 131 Mohatt, G., 153 Molina, B.S.J., 150, 201, 202 Molina,V.A., 46 Moody, C.A., 46 Moody, C.D., 170 Moore, C.M., 9, 21 Moore, K., 101, 103 Moos, R., 198, 199, 202 Morelock, S., 3

Author Index Morrow-Tlucak, M., 3 Moses, H.D., vii, x, 116, 124, 127, 132, 133, 151 Mottet, N.K., 77, 87 Mrazek, P., 177, 178 Mudar, P., 5, 60 Mueller, B., 202 Mulford, H., 3 Muller, K.E., 74 Mulvey, K.P., 3 Mun, E-Y., vii, ix, x Munoz, R., 120, 167, 170, 172, 173, 182 Mupier, R., 167, 168, 174, 175, 178, 179 Murphy, L.B., 37 Murphy, G.D., 166 Murray, P., 180, 181 Nair, P., 49, 62 Narrow, W., 115 National Institute on Alcohol Abuse and Alcoholism (NIAAA), 169 National Institute on Drug Abuse (NIDA), 39, 50 Naud, S., 128, 130, 131 Naveh, B., 128 Neiderhiser, J., 211 Nelms-Matzke, J., 145 Nelson, C.B., 50, 57, 110, 111, 115 Nesbitt, L., 15 Ness, J.W., 46 Neuspiel, D.R., 1 Newcomb, M.D., 166, 178 Newlin, D.B., 148, 200 Newman, D.L., 131 Nicholson, N., 181 Niswander, K.R., 36 Noble, A., 9, 177 Noble, E., 198, 202, 211 Noll, R.B., 117, 118, 136, 151, 207 Noonan, D.L., 205 Nordstrom-Klee, B., 13, 16, 20 Novack, A.H., 10, 22 Nugent, K., 22 O’Brian, C.P., 6 O’Connell, C., 99, 100, 102, 103

225 O’Connor, F.L., 10 O’Connor, M.J., 48, 100, 102, 103 O’Donnel, J.S., 149 O’Donnell, K.J., 53, 61 Oehlberg, S.M., 50, 53 Getting, E.R., 149, 150, 152 Office of Applied Studies, Substance Abuse and Mental Health Services Administration, 113 Oh, W., 21 Ohannessian, C.M., 199 Olds, D.L., 22 Olson, L., 147 O’Malley, P.M., 129, 166, 172, 193, 211 O’Neal, S., 167, 172, 173 O’Neill, J.M., 76 O’Nell, T.D., 149 Ornstein, A., 210 Osgood, W., 177 Osher, F.C., 58 Oskamp, M., 5, 21 Ostrea, E., Jr., 13, 16, 20 Ostrea, E.M., 2, 8, 10 Ozkaragoz, T., 198, 202 Pacifici, R., 10 Padgett, R.J., 76 Pallas, D.M., 125 Palow, D., 13, 22 Pandina, R.J., 166, 185, 195 Papageorgiou, A., 5, 21 Paredes, A., 144 Park, H., 19, 21 Parker, D., 176 Parker, S., 3, 8, 9 Parkhurst, E., 36 Parmelee, A.H., 76 Parrish-Johnson, J.C., 76, 81 Pasamanick, K.R., 36 Passa, A., 10 Paton, S., 201 Patterson, G.R., 202 Pauls, D.L., 116 Pearson, K., 145 Pelham, W.E., 199 Penney, A., 174

226 Percansky, C., 49, 61, 62 Perez-Reyes, M., 6 Perkins, H.W., 174, 175, 176, 180, 183, 185 Perkins, P.M., 37, 41 Perry, S., 145 Persaud, T., 101, 103 Peterson, J., 196, 202 Peterson, C.M., 5 Peterson, K.P., 5 Peterson, S.D., 78, 82, 87 Phillips, D., 102 Phipps, P., 10, 22 Piasecki, J.M., 147 Pichinci, S., 10 Pickles, A., 45 Pihl, R.O., 196, 202 Pillow, D.R., 150, 174, 201, 202 Plasse, B.R., 61 Platzman, K.A., 76, 79, 87, 101, 102, 103, 104 Plomin, R., 45 Plough, A., 3 Polin, R.A., 5 Poon, E., 131, 132 Potter, S., 5, 21 Presson, C.C., 200 Pringle, J.L., 22, 54 Prinz, R.J., 116, 194 Projesz, B., 5, 21 Prusoff, B.A., 50, 116 Pryzbeck, T.R., 116 Purvis, M.A., 182 Puttier, L.I., vii, ix, x, 128, 130, 131, 132, 133, 134 Qualls, R., 170, 185 Rae, D.S., 110, 114, 117, 129, 130 Rajachandran, L., 46 Randall, C., 102 Rankin, J., 4, 22 Raskin White, H., 201 Raskin, G., 207, 201, Raymundo, A.L., 2, 8 Redlich, F.C., 51

Author Index Regan, D.O., 50, 53 Regier, D.A., 110, 114, 115, 117, 129, 130, 197 Reid, R.W., 10 Reid, J., 202 Reid, L.D., 116 Reider, E.E., 118 Reiss, D., 205 Richardson, G., 93, 99, 100, 102, 103 Richardson, G.A., 2, 13, 16, 20, 21, 71, 79, 97, 101 Richardson, R., 170 Riley, E.P., 73 Risys, V.A., 10, 22 Rivara, F. 202 Roberts, D.E., 1, 3 Robins, E., 120 Robins, L., 197 Robinson, G.C., 145 Robles, N., 93, 95, 97, 98, 99, 100, 102, 103 Robson, C.D., 19, 21 Roby, P., 62 Rock, 49 Rodney, H.E., 167, 168, 170, 172, 173, 174, 175, 176, 178, 179, 180, 181, 184 Rodning, C., 48, 55 Roehling, P.V., 206 Rogosch, F., 148, 150, 196, 199 Romero, A., 13, 16, 20 Room, R., 4, 22 Roosa, M.W., 174, 175, 201 Rosa, M., 10 Rose, S.A., 76 Rosen, T.S., 49, 51, 53, 55 Rosenberg, M., 181 Rosenthal, R.L., 38 Rosnow, R.L., 38 Ross, H.E., 50 Rounsaville, B.J., 50 Rudrauff, M.E., 50, 53 Rusforth, N., 202 Russell, M., 5, 7, 98, 110, 174, 193 Rutter, M., 45 Ryan, L., 5

Author Index Saitz, R., 3 Sakger, R., 166, 178 Sallee, F.R., 15 Sambamoorthi, U., 101 Sameroff, A.J., 34, 37, 51, 75 Samet, J.H., 3 Samet, J.M., 145, 146 Sampson, P.D., 3, 76, 81, 82, 87, 92 Sandler, I.N., 174, 200 Sandman, B.M., 76 Sanford, K.P., 124, 127, 130, 131 Sarvela, P., 180 Satz, P., 198, 202 Saxon, A.J., 58 Schaefer, J.M., 149 Scheier, J., 50 Schenker, S., 102 Scher, M., 101, 103 Schittini, M., 2, 5, 8, 10 Schlesselman, J., 74 Schneider, A.M., 151 Schneiderman, J., 10 Schnoll, S.H., 47 Schrof, J., 167 Schubiner, H., 177 Schuckit, M.A., 173, 200, 201 Schulenberg, J., 111, 193, 211 Schuler, M., 49, 62 Schwartz, P.M., 77 Schwartz, S., 128 Scoles, P.E., 110 Scott, R., 177 Sechzer, J.A., 183 Seefeldt, R., 183 Segal, S., 9 Seifer, R., 37, 38, 51 Seilhamer, R.A., 60 Seltzer, M.L., 72 Sethuraman, G., 15 Shankaran, S., 99 Shapiro, J., 167 Sharp, M., 110, 167 Shaywitz, B.A., 76 Shaywitz, S.E., 76 Shedler, J., 166 Sher, K.J., 60, 148, 150, 155, 184, 185,

227 194, 196, 200, 201, 202, 204, 206, 207, 209, 211 Sherman, S.J., 200 Sherpard, L.A., 169 Shore, J.H., 144 Short, J.L., 175 Shroud, P.E., 128 Sigman, M.D., 48, 76, 100, 102, 103 Silva, P.A., 131 Silverstein, J., 76, 87 Silvestre, M.A., 13, 16, 20 Simcha-Fagan, O., 169 Siminoff, E., 45 Simms, D., 60 Simone, C., 5, 21 Sineath, N., 184 Singer, L.T., 59, 76, 87 Single, E., 4, 22 Skinner, J., 98 Sklar, D., 147 Skodol, A.E., 128 Sliepcevich, E., 180 Smith, G.M., 166, 169, 178 Smith, G.T., 206 Smith, I.E., 70, 71, 76, 78, 79, 87, 98, 101, 102, 103, 104 Smith, R.S., 168 Smith, T.L., 200, 201 Smyth, N., 60 Sokol, R.J., 3, 5, 7, 18, 19, 20, 33, 71, 72, 73, 77, 78, 80, 81, 83, 84, 85, 86, 99, 102, 103 Somes, G., 202 Sorenson, J.R., 3 Spear, L.P., 46 Spicer, P., 150, 152, 154, 156 Spitzer, R.L., 6, 8 Sroufe, L., 95 Stabenau, J.R., 118 Stack, D.M., 5, 21 Steer, R.A., 110 Steinbauer, J.R., 8 Steinberg, L., 208 Stetson, B.A., 207 Stevens, M., 2, 8 Stewart, M.A., 197, 211

228 Stiasny, S., 50 Stice, E., 150, 202, 207 Staffer, D., 93, 99, 100, 101, 103 Stokes, P.E., 183 Stoler, J.M., 5 Stott,W., 5, 21 Stratton, A., 174 Stratton, K., 69 Stratton, R., 144 Strauss, R., 3, 7 Straussner, S.L.A., 61 Streissguth, A.P., 10, 22, 70, 71, 76, 81, 82, 87, 92, 102, 103 Streitmatter, J.L., 170 Stringer, S., 114 Stueve, A., 128 Substance Abuse and Mental Health Service Administration (SAMHSA), 111, 129 Sullivan, L.A., 151 Susser, M., 2, 5, 8, 10 Swital, J., 22, 54 Tagel, M.T., 13, 16, 20 Tarter, R., 181, 202 Tatelbaum, R., 22 Taylor, P.M., 71, 79, 93, 99, 100, 101, 103 Taylor, S.S., 147 Tebbett, I., 10 Tein, J., 201 Teitelbaum, M.A., 205 Tellegen, A., 37 Tennen, H., 199 Teti, D.M., 59 Tharinger, D., 174 Theado, D.P., 118 Thomas, J.D., 78 Thompson, T., 166 Thomson, J.B., 148, 200 Timperi, R., 3, 8, 9 Todd, M., 195 Topper, M.D., 153 Travis, J., 210 Tronick, E.Z., 13, 17, 20, 21, 33, 35, 59, 62

Author Index Troughton, E., 197, 211 Trull, T.J., 202 Tunell, R., 78 Turner, A., 14, 21 Twitchell, G.R., 132 Tyler, R., 53, 54, 59, 61 Tzelepis, A., 177 U.S. General Accounting Office (GAO), 39, 166 U.S. National Center for Education Statistics, 39 Valiante, G., 5, 21 Vallient, G.E., 116 VanBremen, J.R., 61 Vega, W.A., 9, 173, 177 Vieth, A., 202 Vinci, R., 3, 8, 9 Vogeltanz, N.D., 50 Volk, R.J., 8 Voorhis, J.V., 59 Vorhees, C.V., 46, 91 Waarheit, G., 173 Wachtel, R., 62 Wadsworth, K.N., 193, 211 Wakschlag, L.S., 51 Wald, H.L., 22, 54 Walitzer, K.S., 148, 206, 207 Wallace, A.F.C., 154 Walsh, K.G., 61 Wanner, E., 155 Ward, A.S., 5 Warner, L.A., 111 Wasserman, D.R., 55, 59 Watkinson, B., 71, 87 Watson, A., 202 Watson, C., 180, 185 Watts, D., 179 Weaver, L.C., 116 Wechsler, N., 61, 62 Weibel-Orlando, J., 144 Weidenman, M., 118 Weidman, D., 183 Weil, C.M., 118

Author Index Weinstein, S.P., 147 Weisner, T., 144 Weiss, R.D., 50 Weissman, M.M., 116 Werch, E.E., 180 Werner, E.E., 36, 37, 168, 204 West, J.R., 78, 82 West, M.O., 116, 194 Westermeyer, J., 157 Whipple, E.E., 209 Widmayer, S.M., 114 Williams, J.B., 6, 8 Williams, C., 209 Wilsnack, S.C., 50 Wilson, W.H., 148 Wilson, G.S., 55 Windle, M., 169, 202 Winecker, R.E., 10 Winokur, G., 120 Wittchen, H.U., 110, 111, 115 Wobie, K., 10, 13, 14, 16, 19, 20, 22 Wolf, A., 98 Wolin, S.J., 205 Wong, M.M., 132, 134 Wood, M.D., 201, 207 Wood, P.K., 148, 163, 201, 206, 207 Woodruff, R.A., 120 Woods, N.S., 13, 14, 16, 19, 20, 22 Woodworm, G., 197, 211 Wozniak, D.F., 78, 82

229 Wright, L., 179 Wugalter, S.E., 173 Wulczyn, F., 62 Wynee, L.C., 37, 41 Yamaguchi, K., 173 Yamaguchi, L., 193 Yamashita, T., 59 Yang, H-Y, 124, 151 Yao, B.-L., 77, 78 Yasin, S., 13, 22 Yates, W.R., 197, 211 Young, M., 180 Zahn, E.G., 4 Zangri, A., 145 Zax, M., 51 Zeiner, A., 144 Zelazo, P.R., 5, 21 Zhao, S., 110, 111, 115 Zhou, C., 10 Zimmerman, R., 173 Zuccaro, P., 10 Zucker, R.A., vii, ix, x, 14, 116, 117, 118, 124, 125, 127, 128, 130, 131, 132, 133, 134, 135, 136, 151, 152, 196, 201, 202, 207, 209, 210 Zuckerman, B. x, 2, 3, 8, 9, 14, 17, 19, 20, 21, 33, 41 Zuo, Y., 101

Subject Index

adolescent drinking and rites of passage, 174 African American (black) adolescent violence rates, 177 families, 1, 56, 72–73, 98, 99, 165, 170 health risks, 165 and increase in alcohol use among youth, 166 and low levels of substance use, 177 and single parent households, 168 aggressive behavior, 178 Alaska natives, 145 alcohol consumption average daily volume, 99 developmental trajectories, 193 frequent heavy drinking, 99, 169 alcohol expectancies, 118, 132, 206–207 alcoholism and antisocial behavior, 117, 119, 124, 127, 152, 197 and attention, 76 and cognitive functioning, 169–170, 202 comorbidity, 117, 124, 127, 128, 131, 196 definition of, 109 dependence, 114, 176 and dependency needs, 155 dose dependence, 74

and family stress, 174 information processing speed, 76–78 intergenerational effect, 176, 196 maternal age, 84 and marital status, 134 onset, 129 and parent monitoring, 114, 155 predictors of, 181 prevalence rates, 111, 114, 123, 145, 175, 195 and postpartum drinking, 78 and poverty, 171 and power needs, 155 and recovery status, 133, 198–199, 208 and self-esteem, 180–181 social visibility, 122–124, 126 and threshold effects, 81, 82–84, 91 alcoholism development deviance-prone model, 148–149, 200–201 enhanced reinforcement model, 148–149, 200–201 negative affect model, 148–149, 200–201 risk cumulation model, 109–116 systems models, 151, 152 alcoholism research mediators and moderators, 200–205 methodological issues, 114, 194

231

232 alcoholism research (cont.) prospective approaches, 115 and treatment studies, 115 alcohol subtypes, 128–129, 131 American Indians and child abuse, 147 and domestic violence, 147–148 and foster care, 156 heterogeneity among tribes, 144 Navajo, 146, 147, 153 Papago, 153 and paternal monitoring, 150 and rates of alcohol induced mortality, 143 and temperament research, 152 amniotic fluid, 10 assessing substance use Addiction Severity Index, 6 AUDIT, 8 CAGE, 5, 7 Children of Alcoholics Screening Test, 174 Diagnostic Interview Schedule, 195 Drug CAGE, 5, 7 Quantity-frequency (Q.F Index), 3, 7 Quantity-frequency Variability Index, 7 SCID and SCID-NP, 8 T-ACE, 7 TWEAK, 5, 7 Volume variability-index, 4, 7 assortative mating, 117, 128 Bayley Scales of Infant Development, 20, 54, 70, 75, 78, 84 behavior problems difficult temperament, 131, 151–152 externalizing behavior, 128, 131, 177 internalizing behavior, 132 and neuropsychological impairment, 132 behavioral teratology, 46 binge drinking, 113 biologic assays, 2, 8, 20 chromatographic techniques, 9

Subject Index fetal hair, 11 immunoassay techniques, 9 maternal hair, 10 meconium assay, 11, 12 urine, 9, 11, 31 advantages of, 9 limitations of, 9, 10 child abuse and neglect, 59–60 child rearing risk and parental age, 114 classification cocaine exposure cranial ultrasound outcomes, 19 and environmental variables, 45 and IQ, 39–40 and language development, 40 methodological issues, 2 prenatal, 1 quantifying effects, 21 social use, 1 cocaine research national data base, 30 comorbidity, 117 antisocial personality disorder, 117 conduct disorder and alcohol abuse, 179 definition of, 177 examples of, 178 crack babies, 1 divorce, 134, 167–168 drinking patterns, 82–83 dose effects infancy and early childhood, 17–19 neonatal exposure, 15–17 and outcomes, 21, 102 prenatal exposure, 6, 12–15 response curves, 91 drug addiction, 109 drug exposed infants biological assays, 2, 7 identification of, 2 maternal interview, 2 drug exposure cocaine, 12 fetal effects, 36 prenatal, 1, 93–95

Subject Index timing of, 78–80, 102 drug use and children’s daily lives, 58 confounding factors medical and health status, 32–33 polydrug use, 31–32, 173 social demographics, 32 intervention effects, 33 neurobehavioral outcome, 34 and parenting, 48–50, 53, 172 sample size limitations, 33 effect size, 33 Epidemiologic Catchment Area Study, 110 Fagan Test of Infant Intelligence, 20, 76 Family Environment Scale, 174 familial risk structure, 117, 134, 168 father absence, 168–169 fetal alcohol effects, 145 fetal alcohol syndrome and alcohol related birth defects, 145 in American Indians, 146 and facial dysmorphology, 93 and functional deficits, 86 and levels of exposure, 70 foster care, 60–61 Haith Visual Expectancy Paradigm, 76 high risk children, 37 Hispanics, 206 illicit drug use immunoassay, 8, 9 intraventricular hemorrhage, 19 IQ effects and alcoholism, 132 and cocaine exposure, 39–41 and timing of exposure, 81, 101, 102 language development expressive language, 40 receptive language, 40 maternal alcoholism and infant cognitive processing , 77

233 Maternal Health Practices and Child Development Project, 92, 95–96 maternal psychopathology, 59 maternal self-report, 5, 6, 31 advantages of, 5 and scientific research, 1, 39 limitations of, 3 Michigan Alcoholism Screening Test, 72 Monitoring the Future Study, 129 mortality rates among American Indians, 144 mother-infant interaction, 48, 59 MSU-UM Longitudinal Study, 119–125, 151, 152 National Association for Children of Alcoholics, 158 National Association for Native American Children of Alcholics, 159 National Perinatal Collaborative Study, 36 National Pregnancy and Health Study, 39 neurobehavioral measures, 35 Neonatal Behavioral Assessment Scale, 13 Neonatal Intensive Care Unit Network Neurobehavioral Scale, 34 Neurobehavioral Assessment Scale, 34 Parent Child Observation Guides for Program Planning , 49 Parent Health and Child Development Project, 46–48, 54–55 parenting continuity in, 55 intervention, 61 by non-maternal caregivers, 60 of older children, 60 peer drug associations predictors of, 150–151 polydrug exposure, 50, 73, 97, 101 prenatal exposure level of, 19–20 stage of gestation, 93–95 and duration, 93–95

234 and growth deficits, 99–101, 103–104 and head circumference, 101 preterm infants heteogeneity of, 36 prospective studies, 115–116 high-risk method, 115 protective factors, 168, 204–205 psychosocial risk components of, 51–53, 57 and parenting problems, 53 policy implications and alcoholism, 110, 133–136, 156–157 for future research, 58–61, 209–211 of prenatal cocaine exposure, 38–40 resilience, 37 risk aggregation, 117, 130, 131, 133, 134 and process model, 133 and child socialization structure, 135 risk factors, 50 among African Americans, 165–166, 178 alcohol specific, 136, 144

Subject Index alcohol non-specific, 136 and fetal alcohol syndrome, 69 among American Indians, 154 poverty, 171 school academic failure, 172 dropout, 170 social support parental, 176, 182–184, 205 Substance Abuse and Mental Health Services Administration and rates of alcohol dependence , 111–113, 129 teenage rebellion against authority, 166 temperament, 201, 205 teratology, 91 definition of, 91 as direct effects, 94 and dose-response curve, 92 and threshold model, 92 Wechsler Preschool and Primary Scale of Intelligence-Revised, 20