adolescent outcomes of childhood conduct disorder among clinic-referred boys: predictors of...

P1: GCR

Journal of Abnormal Child Psychology pp502-jacp-374357 May 30, 2002 10:39 Style file version May 30th, 2002

Journal of Abnormal Child Psychology, Vol. 30, No. 4, August 2002, pp. 333–348 (C© 2002)

Adolescent Outcomes of Childhood Conduct DisorderAmong Clinic-Referred Boys: Predictors of Improvement

Benjamin B. Lahey,1,4 Rolf Loeber,2 Jeffrey Burke,2 and Paul J. Rathouz3

Received July 26, 2001; revision received December 19, 2001; accepted January 5, 2002

Much remains to be learned about the adolescent outcomes of clinic-referred boys whose childhoodconduct problems are serious enough to meet diagnostic criteria for conduct disorder (CD). Sixstructured diagnostic assessments were conducted over 7 years of 73 clinic-referred 7–12-year-oldboys who met criteria for CD in Wave 1. There were substantial individual differences in the adolescentoutcomes of CD, ranging from worsening to sustained recovery, with most boys showing persistent, butfluctuating levels of CD. Improvement in CD was not accounted for by treatment or incarceration, butmore positive outcomes over Waves 2–7 were predicted prospectively with substantial accuracy, usinga combination of baseline predictors: less initial severity of CD, fewer symptoms of attention-deficithyperactivity disorder, higher child verbal intelligence, greater family socioeconomic advantage, andnot having antisocial biological parents.

KEY WORDS: conduct disorder; intelligence; socioeconomic status; antisocial personality disorder; longitudinalstudy.

Children are said to exhibit conduct disorder (CD)if they engage in multiple antisocial behaviors, includingdeception, physical aggression, and violation of propertyrights. Because CD is highly impairing to the individualand harmful to victims, it has been the subject of manystudies. Although a great deal is known about the adoles-cent outcomes of childhood behavior problems in general(Farrington, Loeber, & Van Kammen, 1990; Fergusson,Horwood, & Lynskey, 1995; Nagin & Tremblay, 2001;Stattin & Magnusson, 1989; Verhulst & Van der Ende,1992), less is known about the adolescent course of con-duct problems in the subgroup of children whose problembehaviors are diverse and serious enough to meet diag-nostic criteria for CD. Because it may be misleading togeneralize findings on the longitudinal course of samplesof children who mostly exhibit minor behavior problemsto children who meet criteria for CD, it is important also

1Department of Psychiatry, University of Chicago, Chicago, Illinois.2Department of Psychiatry, University of Pittsburgh, Pittsburgh, Ohio.3Department of Health Studies, University of Chicago, Chicago, Illinois.4Address all correspondence to Benjamin B. Lahey, PhD, Depart-ment of Psychiatry (MC 3077), University of Chicago, 5841 SouthMaryland Avenue, Chicago, Illinois 60637; e-mail: [email protected].

to study the longitudinal course of the children at the ex-treme end of the behavior problem continuum who meetcriteria for CD.

Some aspects of the longitudinal course of CD havebeen studied extensively (Frick & Loney, 1999). In a num-ber of studies, children with serious conduct problemswere reassessed in adulthood, with these studies suggest-ing that (a) adults who meet criteria for antisocial per-sonality disorder (APD) almost always exhibited CD aschildren, and (b) although most children with CD show oc-cupational and social dysfunction as adults, only about onethird of children with CD later meet criteria for adult APD(Bardone, Moffitt, Caspi, & Dickson, 1996; Harrington,Fudge, Rutter, Pickles, & Hill, 1991; Robins, 1966,1978; Robins, West, & Herjanic, 1975; Storm-Mathisen &Vaglum, 1994; Zoccolillo, Pickles, Quinton, & Rutter,1992). What is not clear, however, is whether the childrenwith CD who do not meet criteria for adult APD recoverfrom CD during adolescence or continue to exhibit CDthrough adolescence, but do not meet the specific criteriafor APD during adulthood.

Three longitudinal studies of population-based sam-ples provide preliminary evidence on the adolescentcourse of conduct problems among children who met

333

0091-0627/02/0800-0333/0C© 2002 Plenum Publishing Corporation

P1: GCR


334 Lahey, Loeber, Burke, and Rathouz

diagnostic criteria for CD in childhood. In the Isle ofWight Study, 35% of 93 children who met criteria forCD at 10–11 years of age continued to meet criteria forCD at 14–15 years of age (Graham & Rutter, 1973), butin a similar German study, most (8 of 11) boys who metcriteria for CD at age 8 years met criteria for CD atage 13 years (Esser, Schmidt, & Woerner, 1990). In theOntario Child Health Study, 45% of 36 children with CDwhen they were 4–12 years of age, met criteria for CDagain at 8–16 years (Offord, Boyle, Racine, Fleming, et al.,1992). If the results of these three studies are combinedto provide a tentative “meta-analytic” estimate of the sta-bility of childhood CD into adolescence, only 56 of 140(40%) children given the diagnosis of CD were given thesame diagnosis again during adolescence. This suggeststhat slightly more than half of children with CD cease tomeet criteria for CD sometime during late childhood oradolescence.

There are at least three reasons why additional re-search is needed on the adolescent outcomes of CD. First,in a preliminary report covering the first 4 years of thepresent study (Lahey et al., 1995), we found that boyswho met diagnostic criteria for CD in the initial assess-ment showed levels of CD behaviors in subsequent wavesthat fluctuated above and below the diagnostic thresholdfor CD over time. As a result, estimating the persistenceof CD from a single follow-up assessment greatly un-derestimates the stability of CD. It is possible, therefore,that these previous studies of the adolescent outcome ofCD, which conducted only a single follow-up assessment,underestimated the persistence of CD. Second, Robins(1966) found a linear association between the number ofchildhood conduct problems and adult antisocial behavioramong clinic-referred boys, suggesting that it is importantto understand the course of CD at the level of the num-ber of symptoms over time, rather than focusing only onthe stability of the diagnosis. Third, the previous studiesof the stability of children who received diagnoses usedpopulation-based samples and used diagnostic criteria forCD that were broader than the currentDSM-IVdefinition.Although representative samples offer important advan-tages in such research, these studies did not demonstratethat the youths who received the diagnosis of CD were im-paired enough to warrant clinical diagnosis and treatment.As a result of these issues, more data are needed to improveour understanding of the adolescent outcome of childrenwith clinically-significant CD. Indeed, full knowledge ofthe adolescent outcome of childhood CD is currently the“missing link” in our understanding of the development ofserious antisocial behavior from childhood to adulthood.

There is also a pressing need to identify childhoodfactors that predict which children with CD will have more

or less favorable outcomes during adolescence. Identi-fying such early predictors will improve the prognosisof childhood CD and, if the predictors reflect modifiablecausal processes that maintain CD over time, their identifi-cation may lead to improved methods of treatment. Thereis considerable published evidence from longitudinal stud-ies of samples of children with conduct problems that werenot limited to those who met diagnostic criteria for CD thatmay point to likely baseline predictors of the outcomes ofchildhood CD (Frick & Loney, 1999). Consistent evidencesuggests that higher levels of conduct problems in child-hood predict greater stability of future conduct problems(Loeber, 1982, 1991).

In addition, six studies of the outcomes of seriouschildhood conduct problems conducted in four countriesfound that intelligence is a significant inverse predictorof antisocial outcomes over 10 years or more (Farrington,1991; Fergusson, Lynskey, & Horwood, 1996; Moffitt,1990; Robins, 1966; Schonfeld, Shaffer, O’Connor, &Portnoy, 1988; Stattin & Magnusson, 1989). Because a re-cent longitudinal study of a nonreferred community sam-ple did not find intelligence to be a significant predictorof the outcomes of aggressive children over time (Nagin& Tremblay, 2001) and because it is not yet clear whetherverbal or nonverbal aspects of intelligence are morestrongly related to conduct problems (Hogan, 1999;Lynam & Henry, 2001; Moffitt, 1993), this topic bearsfurther study. In addition, there is emerging evidence thatchildren with early onsets of CD, which tends to be morepersistent over time than CD with later ages of onset, tendto exhibit comorbid attention-deficit hyperactivity disor-der (ADHD) and oppositional defiant disorder (ODD) dur-ing childhood (Lahey et al., 1998; Lynam, 1996; Moffitt,1990). This suggests that childhood ADHD and ODD maypredict the adolescent outcome of childhood CD.

In a follow-back study (Robins, 1966) and a retro-spective study (Robins & Ratcliff, 1979), higher levelsof antisocial behavior in the parents of boys with seriousconduct problems predicted their adult antisocial behavior.Three previous longitudinal studies of nonreferred com-munity samples also found that children with high levelsof behavior problems tended to improve from childhoodinto adolescence if they were from higher socioeconomicstatus families and had mothers who did not give birth atan early age (Farrington, 1991; Nagin & Tremblay, 2001;Stattin & Trost, 2000). Robins (1966) and Robins andRatcliff (1979), however, found that socioeconomic statusaccounted for little independent variance in predicting theadult outcomes of antisocial children after the children’sinitial levels of conduct problems were controlled.

Some theorists distinguish risk factors from pro-tective factors for psychopathology (Masten, Garmezy,

P1: GCR


Adolescent Outcomes of Childhood Conduct Disorder Among Clinic-Referred Boys 335

Tellegen, Pellegrini, et al., 1988). Characteristics of chil-dren or families that are thought to be disadvantageous(e.g., low family income) are often conceptualized as riskfactors, whereas positive characteristics (e.g., higherintelligence) are sometimes construed as protective fac-tors. The designation of dichotomous predictors as riskor protective factors is arbitrary, however, and many pre-dictors that are measured on continuous scales show lin-ear associations with outcomes across their entire range.Stouthamer-Loeber et al. (1993) recommended conceptu-alizing continuous variables as protective or risk factorsonly when the theoretically adaptive or maladaptive tailof the continuous distribution is associated with better orpoorer outcomes, but variation in scores across the rest ofthe distribution is unrelated to the child’s outcome. Ad-ditional work is needed to evaluate the utility of this ap-proach to continuous variables, such as intelligence andfamily income, which may predict outcomes of child-hood CD.

Among social learning theorists, the focus has beenon the role of parenting behavior in the outcomes of CD(Kazdin, 1985; Patterson, Reid, & Dishion, 1998). Forexample, it has long been known that there is a strong, in-verse correlation between measures of parental monitor-ing and child and adolescent conduct problems in cross-sectional samples (Dishion & McMahon, 1998; Patterson& Stouthamer-Loeber, 1984). This correlation may not re-flect a causal effect of parental supervision on child con-duct problems for at least two reasons, however. First,cross-sectional studies cannot rule out the possibility of“child effects,” that is, aversive interactions with chil-dren who engage in higher levels of problem behaviormay lead some parents to “give up” and cease to mon-itor their children. Indeed, two longitudinal studies ofchildren showed that higher levels of childhood behaviorproblems predicted greater reductions in parental moni-toring over time, even when initial levels of monitoringwere controlled (Kandel & Wu, 1995; Vuchinich, Bank,& Patterson, 1992).

Second, antisocial children tend to have antiso-cial and substance abusing biological parents (Eron &Huesmann, 1990; Lahey et al., 1988; Lahey, Russo,Walker, & Piacentini, 1989; Moss, Baron, Hardie, &Vanyukov, 2001), biological mothers who first gave birthat younger ages, and biological mothers who do not con-tinue to live with the biological father (Loeber, Farrington,Stouthamer-Loeber, & Van Kammen, 1998). There is alsoevidence that parents with these characteristics are lesslikely than other parents to supervise their children closely(Loeber, Drinkwater, et al., 2000), but the causal linksbetween these parental characteristics and child behaviorproblems may not involve monitoring. That is, lax parental

monitoring could be a noncausal correlate of other parentalcharacteristics that are causally linked in other ways toconduct problems in their offspring.

Child effects and the effects of parental characteris-tics (antisocial behavior and substance abuse) can be dis-tinguished from influences of parental monitoring that aremore likely to reflect causal effects, however, using lon-gitudinal analyses that examine the association betweenlevels of parental monitoring at Time 1 and later child an-tisocial behavior, while controlling both child antisocialbehavior at Time 1 and background parental characteris-tics. One recent 2-year longitudinal study of low-incomepreschool children (Kilgore, Snyder, & Lentz, 2000) foundthat lax monitoring measured during the first assessmentpredicted child conduct problems in the second assess-ment, controlling for child conduct problems in the firstassessment. This suggests that the correlation betweenparental monitoring and child behavior problems doesnot solely reflect child effects on parental monitoring, butparental characteristics were not controlled in this studyto address the possibility that lax parental monitoring isa noncausal correlate of parental antisocial behavior, sub-stance abuse, or other parental characteristics.

Social learning theorists have also hypothesized thatparental use of physical punishment increases the like-lihood of child behavior problems by encouraging mal-adaptive coercive cycles (Patterson et al., 1998). Thereis much cross-sectional correlational data in support ofthis hypothesis, but relatively few data from studies thatallow stronger causal inferences. In support of the pun-ishment hypothesis, however, two longitudinal studies ofcommunity samples have shown that maternal reports ofgreater use of physical punishment predicted subsequentincreases in levels of late childhood and adolescent con-duct problems when initial behavior problems were con-trolled (Cohen & Brook, 1995; Kandel & Wu, 1995).

Since 1987, we have conducted a longitudinal studyof 177 clinic-referred boys who were 7–12 years of ageat the time of their first assessment (Lahey et al., 1995).Data from this sample will be used to describe the courseof CD into adolescence over the course of 6 years (fromages 7–12 years in Wave 1 through ages 13–18 years inWave 7). Because the present study involved multiple re-assessments of CD over time using contemporary crite-ria for CD, it provides needed information on the longi-tudinal course of CD among clinic-referred boys duringlate childhood and adolescence. Furthermore, the repeatedassessments of the outcomes of CD allow tests of po-tential fixed (e.g., maternal age) and time-varying (e.g.,parental monitoring) predictors of improvement in CD todetermine if factors that predict outcomes in nonclinicsamples generalize to a clinic sample of boys who meet

P1: GCR



diagnostic criteria for CD. In these tests of predictorsof outcomes, it is possible to control for the impact ofpotential confounds (such as juvenile detention or otherresidential placements that might reduce opportunities toengage in CD behaviors while detained) and to controlfor any influence of psychosocial or pharmacologic treat-ment on the course of CD. Finally, the present paper laysthe foundation for a future report of the prospective rela-tions between childhood CD and APD during adulthoodwhen data collection in this study during early adulthood iscompleted.

METHOD

Participants

The participants were 73 boys who met diagnosticcriteria for CD in Wave 1 from a longitudinal study amonga sample of 177 boys who were outpatients at one of threemental health clinics when they were 7–12-years old. Thelarger sample of 177 boys was selected to be composed ofapproximately 75% boys with disruptive behavior disor-ders and 25% boys with other disorders (Loeber, Green,Lahey, Frick, & McBurnett, 2000). Of the 73 boys withCD in Wave 1, 69 (94.5%) were reported to have exhib-ited at least one symptom before the age of 10, suggestingthat these boys had childhood-onset CD. Boys were in-eligible for participation if they were mentally retarded,psychotic, taking medication that could not be discontin-ued for 2 days prior to their initial assessment, or theirfamilies were planning to move to another city. Eligibleboys had to be living with at least one biological parent atthe time of Wave 1, which was almost always the mother.Written informed consent was obtained from the parentor legal guardian and oral assent or written consent wasobtained from the boys in each wave. Participants were re-assessed during Years 2, 3, 4, 6, and 7, with no assessmentwave during Year 5 due to budgetary limitations.

Assessment Measures

The Wechsler Intelligence Scale for Children—Revised (Wechsler, 1974) was administered duringWave 1. In each assessment wave, the boys and parentswere interviewed separately using the NIMH DiagnosticInterview Schedule for Children (DISC; Costello,Edelbrock, Kalas, & Dulcan, 1984), which queriedDSM-III andDSM-III-Rsymptoms of ADHD, CD, ODD,overanxious disorder, separation anxiety disorder, dys-thymia, and major depression occurring during the last6 months. The DISC has acceptable test-retest reliability

(Edelbrock et al., 1985), discriminates clinic-referredyouth from those without need of treatment (Costello,Edelbrock, & Costello, 1985) and correlates substantiallywith standardized parent ratings (Edelbrock & Costello,1988).

Parent and youth reports of symptoms from the DISCwere used consistent with previous findings on the relativereliability and validity of parent and youth reports of var-ious types of symptoms (Hart, Lahey, Loeber, & Hanson,1994; Jensen et al., 1999; Loeber & Lahey, 1989). Theboys’ numbers of symptoms of ADHD and ODD wereassessed by summing the number of theDSM-III-Rsymp-toms of each disorder reported by the parent. The DISCqueried both informants aboutDSM-III-R symptoms ofCD and theDSM-IVsymptom of bullying. In addition, theparent was asked about theDSM-IVsymptom of stayingout late without permission. Thus, it was possible to writean algorithm for the diagnosis ofDSM-IVCD in Wave 1,except that theDSM-III-Rsymptom of frequent lying wasused instead of the more restrictiveDSM-IVsymptom oflying to con others. The “or rule” was used to combinereports of CD behaviors, with behaviors considered tobe present if reported by either the parent or the youth(Piacentini, Cohen, & Cohen, 1992). Depression was simi-larly measured by summing the number of nonoverlappingDSM-III-Rsymptoms of major depression and dysthymiareported by the parent or the youth. Anxiety was assessedby summing the number ofDSM-III-Rsymptoms of over-anxious disorder and separation anxiety disorder reportedby either the parent or the youth.

The parent was also interviewed in Wave 1 to as-sessDSM-III-Rmental disorders of the child’s biologicalmother and father using the sections on APD and sub-stance abuse from the Schedule for Affective Disordersand Schizophrenia (SADS; Spitzer & Endicott, 1967) andthe section on mood disorders from the Structured Clini-cal Interview forDSM-III-R (SCID; Spitzer, Williams, &Gibbon, 1987). Caspi et al. (2001) found that mothers’and fathers’ reports of fathers’ antisocial behavior agreedat a high level, but that maternal reports were conserva-tive, as mothers underestimated fathers’ antisocial behav-ior somewhat. Pfiffner, McBurnett, and Rathouz (2001)similarly provided evidence supporting the validity of ma-ternal reports of the antisocial behavior of fathers usingfamily-study methods. The SADS and SCID interviewscovered varying time periods (lifetime for substance abuseand APD and past 12 months for depression). In a differentsample, Lahey et al. (1989) found that APD was uncom-mon among the biological mothers of boys with CD, buta well-validated index of antisocial personality createdby taking the mean of theK -correctedF , PsychopathicDeviate, and Mania scales of the Minnesota Multiphasic

P1: GCR



Table I. Descriptive Statistics for Potential Baseline (Wave 1) Predictors of Individual Differences in theOutcomes of CD (During Waves 2–7) Among 73 Boys Who Met Criteria for CD in Wave 1

Demographic and socioeconomic factors assessed in Wave 1Race–ethnicity 64.4% non-Hispanic White; 35.6% African AmericanBiological parents status 30.1% married or cohabiting partners in Wave 1Total family annual income Mean= 21,219;SD= 18,755Maternal education in years Mean= 12.3; SD= 2.57Maternal age at first birth Mean= 19.7; SD= 3.16

Child characteristics assessed in Wave 1WISC-R verbal intelligence Mean= 98.2; SD= 15.5WISC-R performance intelligence Mean= 97.6; SD= 16.1Number of CD symptoms Mean= 4.3; SD= 1.2 during last 6 monthsNumber of ADHD symptoms Mean= 6.4; SD= 4.0 during last 6 monthsNumber of ODD symptoms Mean= 6.7; SD= 1.8 during last 6 monthsNumber of anxiety symptoms Mean= 5.0; SD= 3.1 during last 6 monthsNumber of depression symptoms Mean= 2.5; SD= 2.3 during last 6 months

Maternal psychopathology and substance abuse assessed in Wave 1MMPI antisocial index 38.0%T-score>60Substance abuse 8.2% lifetimeMajor depression 19.2% last 12 months

Paternal psychopathology and substance abuse assessed in Wave 1Antisocial personality disorder 31.5% lifetimeSubstance abuse 48.0% lifetimeMajor depression 12.3% last 12 months

Note.WISC-R=Wechslser Intelligence Scale for Children—Revised; ADHD= attention-deficit hyperactivitydisorder; ODD= oppositional defiant disorder; MMPI= Minnesota Multiphasic Personality Inventory.

Personality Inventory (MMPI; Huesmann, Lefkowitz, &Eron, 1978) was strongly related to CD in their sons.Following standard practice for MMPIT-scores, this in-dex was dichotomized for the present analyses rather thantreating it as a continuous variable, withT scores of≥60=1 (which corresponds to 1SD above the mean of theMMPI standardization sample) andT-scores<60= 0(Hathaway & Monachesi, 1952). Descriptive statistics forthese potential baseline predictors are presented in Table I.

In each wave, the parent was asked if the child hadtaken any type of prescribed psychoactive medication forproblems of attention, emotion, or behavior for any lengthof time or had received one or more sessions of psychother-apy in the past 12 months. In order to determine if juveniledetentions or inpatient hospitalizations influenced levelsof CD behaviors due to lack of opportunity to engagein them, the parent was asked if the boys experiencedeach type of such confinement for one night or longerin each wave. Also, the parent was asked 14 questionsabout parental monitoring of the boy during the last yearin each wave, using a version of a measure that has ade-quate internal consistency and has been found to be cor-related with delinquent behavior in a community sampleof boys (Loeber et al., 1998). These items queried the ex-tent to which the boy was under direct adult supervision,the parent’s knowledge of the boy’s location and com-panions when away from home, the boy’s knowledge of

the parent’s location when away from home, the parent’ssetting and implementing of curfews for the boy, and theparent’s discussion of the boy’s plans for the coming dayand events of the current day. These items were scored 0,1, or 2 and summed, with higher scores indicating moreparental monitoring. In the full sample of the Develop-mental Trends Study (n = 169) with complete data overwaves, Cronbach’s alpha for the monitoring measure in-creased steadily fromα = .50 in Wave 1 toα = .76 inWave 7. The lower internal consistency coefficients inthe earlier waves appear to reflect a very narrow rangeof parental monitoring scores in the early waves that in-creased as the boys grew older. The parent was also askedone item in each wave about corporal punishment (slap-ping, spanking, or hitting the boy with an object), whichalso has been found to be correlated with delinquency ina community sample of boys (Loeber et al., 1998). Re-sponses were scored 0, 1, or 2 and summed, with higherscores indicating more corporal punishment.

Data Analysis

Among the 74 boys who met criteria for CD inWave 1, one boy was not interviewed in Waves 4, 6, and7 and four boys could not be interviewed in one waveeach (Wave 3 in two cases and Wave 6 in two cases). Ourdata analytic approach can estimate functions for subjects

P1: GCR



with missing data, but the one subject with missing datain all of the last three waves was dropped from all analy-ses, leaving a sample size of 73. The outcome variable inall analyses was the number of CD behaviors in each offive follow-up waves after baseline (Waves 2–7, excludingWave 5). These were treated as count variables (integersreflecting the continuous number of CD behaviors in eachwave from 0 to 11). As is typical of symptom counts, eachdistribution was highly skewed, with modal values beingat or near 0, with few boys exhibiting high numbers ofsymptoms. The proportion of boys with 0 or 1 symptomsof CD in each of Waves 2, 3, 4, 6, and 7 was 35.6, 29.6,31.5, 31.0, and 35.6%, respectively. Such data do not meetthe assumption of normality required by most classical ap-proaches to longitudinal data analysis, such as repeated-measures analysis of variance. One appropriate approachto such skewed count data is to model the mean numbersof behaviors in longitudinal log-linear regression modelsimplemented in generalized estimating equations (GEE;Zeger & Liang, 1986). GEE models the average value ofthe outcome variable for each subset of individuals whoshare the same value of the predictor variable. BecauseGEE estimates averages, and not the entire distributionof values, it is less restricted by distributional assump-tions than other approaches to longitudinal data analysis.GEE also allows the user to specify a within-person corre-lation structure to account for within-person correlationsin the outcome variable over time. In the present analy-ses, an unstructured correlation matrix was specified in allGEE analyses (except where noted for time-lagged mod-els) and the Poisson distribution was used as the workingmodel for the counts of CD behaviors. The distributionsof symptom counts were “overdispersed” relative to thePoisson distribution, as the variance exceeded the meanin each wave. However, all statistical tests in the presentGEE analyses were based on the robust (empirical) stan-dard error because it automatically adjusts for overdisper-sion and reduces concern about correct specification ofthe within-person covariance structure. Thus, GEE allowsunambiguous interpretation of analyses of counts of symp-toms when the distributions take the shape of the presentdata, which means that it is an appropriate option for manysimilar studies in developmental psychopathology.

Potential baseline predictors of CD outcomes overWaves 2–7 were measured in Wave 1 and treated as time-fixed covariates. In these GEE models, the regression co-efficients (β) have the interpretation of the log relativemean number of CD behaviors over Waves 2–7 associ-ated with a one-unit difference in the predictor. For exam-ple, in the left-hand column of Table II, the estimate ofβ = .33 for the dichotomous variable of maternal antiso-

cial personality means that the mean number of CD symp-toms is estimated to be 39%, exp(.33)= 1.39, greater dur-ing Waves 2–7 among boys with an antisocial biologicalmother than among boys without an antisocial mother.Interpretingβ as an estimate of effect size in this wayrequires a recognition that although some baseline predic-tors are dichotomous, the range of scores for other pre-dictors (waves, age, maternal age at first birth) are morecontinuous. Therefore, a smallerβ coefficient for a predic-tor with many units might indicate a stronger effect thana largerβ for a predictor with fewer units.

Similar GEE log-linear models were used to assessthe association of the time-varying covariates of treatmentand incarceration with the level of CD behaviors in thesame waves. Other log-linear models with time-varyingcovariates were used to assess the role of parenting in theoutcome of CD. Here, parenting variables in each wave(Wavet) were included as covariates in models where theresponse is the number of CD behaviors in the followingwave (Wavet + 1). In these models, the temporal asso-ciation of parenting and CD was adjusted for the numberof CD behaviors in the preceding wave by including CDat Wavet as another time-varying covariate. These mod-els were fitted using GEE with an independent correlationstructure. All tests of significance for predictor variablesin the GEE analyses were based on thez-statistic and allused 1df.

RESULTS

Longitudinal Course of Childhood CD

We first examined possible developmental trends inCD behaviors over time (see Fig. 1) to provide a frame-work for further analyses. In the first longitudinal log-linear analysis in GEE, time was treated as a continu-ous variable, with values of 1, 2, 3, 4, 6, and 7, corre-sponding to the six assessment waves conducted over the7-year period. The boys’ age at the time of the first as-sessment was included in these models to assess possibledifferences in the course of CD over time among boyswith different ages at the start of the study. In this jointmodel, the quadratic term for time (waves) was signif-icant,β = .01, z= 2.68, p < .01, as was the boys’ ageat entry into the study,β = −.07, z= −2.63, p < .01.Two subsequent longitudinal analyses were conducted tounderstand the nonlinear changes in CD over time re-flected by the significant quadratic effect for time. First,when the number of CD behaviors during only Waves 1and 2 were treated as the response variables, there was

P1: GCR



Fig. 1. The mean number of conduct disorder behaviors reported by theparent and youth (P+Y) across Waves 1–7 for boys who were 7–9 yearsof age (average of 8 years) or 10–12 years of age (average of 11 years)in Wave 1.

a marked decline in the number of CD behaviors fromWave 1 to Wave 2,β = −.43, z= −4.91, p < .0001, butthe boys’ age at the start of the study was not associ-ated with the number of CD behaviors during Waves 1and 2,β = −.03, z= −1.01, p = .31. When the age-by-time interaction was added to this model, however, it re-vealed that the decline in CD behaviors from Wave 1 toWave 2 was greater for older boys than for younger boys,β = −.14, z= −2.78, p < .01.

In the second analysis conducted to understand thenonlinear decline in CD over time, the number of CDbehaviors during Waves 2, 3, 4, 6, and 7 (the period ofthe assessment of the outcomes of CD after Wave 1 inthe present study) were treated as the response variables.In this model, there was not a significant effect for time(waves),β = .01, z= 0.37, p = .71, controlling for dif-ferences associated with the boys’ age at the start of thestudy,β = −.09, z= −2.71, p < .01. When the age-by-time interaction was added to this model, it was notsignificant,β = −.01, z= −0.81, p = .42. Therefore, asshown in Fig. 1, these initial longitudinal analyses revealthat there was a marked decline in the mean number ofCD behaviors from the start of the study to the next annualassessment (Waves 1–2), particularly among older boys.The decline in CD behaviors from Wave 1 to Wave 2 maypartly reflect regression to the mean following the youth’sreferral to a clinic during a time of peak symptoms. Dur-ing the period in which the outcomes of CD were assessedprospectively (Waves 2–7), however, there was no signif-icant change over waves in the average number of CDbehaviors.

Thus, there were two indications of possible devel-opmental trends that must be taken into consideration indescribing the adolescent outcomes of childhood CD. Thesignificant term for the boys’ age at the start of the studyin all models reflects slightly, but consistently lower meannumbers of CD behaviors across all waves among boyswho were older at the time of entry into the study. Inaddition, the decline in CD behaviors from Wave 1 toWave 2 was greater in older than younger boys. Becausethis unexplained age-by-time interaction was limited tothe change in CD behaviors from Wave 1 to Wave 2,it does not influence the evaluation of outcomes of CDafter Wave 1. On the other hand, the finding that boyswho were older in Wave 1 had lower numbers of CD be-haviors during the period in which we are assessing theoutcomes of CD (across Waves 2–7) could indicate oneof two things. First, there may be a gradual decline inthe number of CD behaviors with increasing age amongboys who are referred to clinics during elementary school(see Fig. 1). Second, selection effects may have operatedin this sample, such that boys who entered the study atearlier ages might have had more serious and persistentCD. In either case, it is necessary to control the boys’age at the start of the study in all subsequent analysesof the outcomes of CD after Wave 1 and analyses ofthe variables that predict individual differences in theseoutcomes.

It is important to note that the lack of a significanteffect of time on theaveragenumber of CD behaviors dur-ing Waves 2–7 masks a marked degree of heterogeneity inthe outcomes of individual boys during this 5-year period.The wide range of individual differences in outcomes ofCD is illustrated in Fig. 2 by plotting the number of CDbehaviors of each boy in each assessment wave. In order tofacilitate interpretation of these 73 single-subject plots, theboys who met criteria for CD in Wave 1 were arbitrarilydivided into threeillustrative groups based on their av-erage numbers of CD behaviors during Waves 4–7. The17 boys in the left panel of Fig. 2 exhibited a mean of≥4.0CD behaviors during Waves 4–7. Although they showedfluctuations in the number of symptoms over waves (aboveand below the diagnostic threshold of three symptoms forCD), no boy in this panel exhibited<1.0 CD behavior inany wave. The 47 boys in the middle panel exhibited amean of 1.0–3.9 CD behaviors during Waves 4–7. Theseboys also showed marked fluctuations in their numbersof CD behaviors over time; nearly one-third exhibitedno CD behaviors during at least one of Waves 4–7, butall continued to exhibit significant levels of CD behav-ior over time. Only the 9 boys in the right panel (meannumber of CD behaviors in Waves 4–7 of<1.0) were in

P1: GCR



Fig. 2. Individual differences in the outcomes of conduct disorder are shown by plotting the number ofconduct disorder behaviors reported by the parent and youth (P+ Y) in each wave for each boy whenboys who met criteria for conduct disorder in Wave 1 are divided into three arbitrary illustrative groupsbased on their outcomes defined in terms of their mean numbers of conduct disorder behaviors in Waves4–7 of≥4.0 (n = 17), 1.0–3.99 (n = 47), and<1.0 (n = 9).

the range that might be described as sustained recoveryfrom CD behaviors. The predictors and correlates of thebroad range of individual differences in the outcomes ofCD across Waves 2–7 illustrated in Fig. 2 are the subjectof the present paper.

Risk Versus Protective Factorsfor the Outcomes of CD

Based on the distinction proposed by Stouthamer-Loeber et al. (1993) between risk and protective factorsin terms of the shape of the function relating continuouspredictor variables to child outcomes, we conducted pre-liminary analyses of four continuous variables measuredin Wave 1 (intelligence, maternal education, total fam-ily income, and maternal age when the boy’s mother firstgave birth to a child) to determine if they should be con-ceptualized as categorical risk or protective factors for out-comes of childhood CD over the five subsequent assess-ment waves. Each of the sample distributions of potentialpredictor scores was divided into their top quartile, middlehalf, and lower quartile (Fig. 3) and separate planned com-parisons among these groups were made using longitudi-nal log-linear regression analyses for each variable, con-trolling for age at the start of the study and time. In the caseof intelligence, standardization norms were used to dividethe range of scores into three levels based on±1 SD.

Because verbal and performance intelligence maybe related differently to the persistence of CD (Lynam& Henry, 2001; Moffitt, 1993), they were analyzed sep-arately. Performance intelligence scores did not predictthe outcomes of CD over time (across Waves 2–7) whentreated as either a continuous or dichotomous variable, butboys with verbal intelligence scores≥115 (n = 14) exhib-ited fewer CD behaviors over time than boys with verbal

intelligence scores of 86–114 (n = 46), β = −.48, z=2.17, p < .03, and than boys with verbal intelligencescores<86 (n = 13), β = −.59, z= −2.34, p < .02,but boys in the middle half of the distribution of verbalintelligence scores did not exhibit significantly fewer CDbehaviors than boys with scores<86, β = −.12, z=−0.77, p = .45. Using the terminology suggested byStouthamer-Loeber et al. (1993), then, high verbal intelli-gence may be viewed as protective against persistent CD.This is because boys with verbal intelligence scores of+1SDor higher have better outcomes of CD than boys at alllower levels of verbal intelligence, but boys at the lowestlevel of verbal intelligence are no more at risk for pooroutcomes of CD than boys in the middle range of verbalintelligence.

Similarly, boys from families in the top quartile offamily income in the sample exhibited fewer CD behav-iors over time than boys in the middle half of familyincome,β = −.65, z= −3.42, p < .001, and than boysin the lower quartile of family income,β = −.52, z=−2.56, p < .02, but boys in the middle half of the familyincome did not differ from boys from the lower quartile ofincome,β = .14, z= 1.08, p = .28. Again, being from ahigh income family appears to protect against poor out-comes of CD. Similarly, having a highly educated motherappears to protect against poor CD outcomes. That is,boys in the top quartile of maternal education exhibitedfewer CD behaviors over Waves 2–7 than boys in the lowerquartile of maternal age,β = −.36, z= −2.04, p < .05,and than boys in the middle half of the distribution of ma-ternal education,β = −.36, z= −2.07, p < .04. Boyswith mothers in the lower quartile and middle half of ma-ternal education did not differ significantly on CD overwaves, however,β = −.01, z= −0.06, p = .95. Thesepreliminary findings suggest that treating high levels of

P1: GCR



Fig. 3. The mean number of conduct disorder behaviors reported by the parent and youth (P+ Y) across Waves 1–7of boys with verbal intelligence scores of<85, 85–114, or≥115 (upper left), and who were in the top quartile, middlehalf, or lower quartile of the sample of maternal education (upper right), total family income (lower left), and maternalage at the birth of her first child.

verbal intelligence, family income, and maternal educa-tion as categorical protective factors may maximize theprediction of individual differences in the outcomes ofchildhood CD.

In contrast, boys in the top quartile of maternal age(older ages at the time of the birth of her first child) exhib-ited fewer CD behaviors over time than boys in the lowerquartile of maternal age,β = −.40, z= 2.54, p < .02,but not than boys in the middle half of the distribution ofmaternal age,β = −.26, z= −1.62, p < .10, and boysin the lower quartile and middle half of maternal didnot differ significantly on CD over time,β = −.14, z=−1.11, p = .27. This suggests that maternal age wouldbe better treated as a continuous predictor of CD out-comes. Because the decision to designate some predic-tors as protective factors could capitalize on chance dif-ferences, however, the findings of the present study mustbe replicated in other studies to confirm the generalizabil-ity of these designations. On the other hand, failing todichotomize variables that are predictive only at the pro-tective end of their continuous distributions could resultin underestimating their prognostic importance.

Baseline Predictors of Individual Differencesin Adolescent Outcomes of CD

After determining which predictor variables shouldbe treated as dichotomous protective factors, a comprehen-sive evaluation was conducted of all variables measured inWave 1 that might serve as baseline predictors of the sub-sequent outcomes of CD during Waves 2–7. Controllingfor time (waves), the boys’ age at the start of the studypredicted their level of CD behaviors during Waves 2–7, β = −.09, z= −2.71, p < .01. Controlling for age atthe start of the study and time, individual differences inthe course of CD behaviors during Waves 2–7 were alsopredicted by the number of CD behaviors during Wave 1,β = .15, z= 3.15, p < .005. The tendency for boys whowere older at the start of the study to exhibit fewer CDbehaviors across Waves 2–7 did not reflect significantlyhigher levels of CD behaviors in Wave 1 among youngerboys, with the Pearson correlation between age and base-line CD levels being nonsignificant,r (73)= .13, p = .26.Age at the start of the study was not found to interact signif-icantly at thep < .05 level with either time or the number

P1: GCR



Table II. Separate Tests of Potential Baseline (Wave 1) Predictors of Individual Differences in the Outcomes of CD During Waves 2–7,Controlling for Age at the Start of the Study and Time (Waves)

Not controlling number of Also controlling number ofCD behaviors in Wave 1 CD behaviors in Wave 1

N β z p β z p

Demographic and socioeconomic factors in Wave 1Race–ethnicity (White= 0; African American= 1) 73 .09 0.71 .48 .08 0.68 .49Biological parents married/partners (no= 0; yes= 1) 73 −.17 −1.12 .26 −.10 −0.77 .36Total family income (top quartile) 70 −.60 −3.25 .001 −.47 −2.57 .01Maternal education (top quartile) 73 −.13a −1.99 .05 −.13a −2.04 .05Maternal age at first birth 73 −.02a −2.05 .05 −.02a −2.00 .05

Child characteristics in Wave 1WISC-R verbal intelligence (≥115) 73 −.51 −2.33 .01 −.40 −1.93 .05WISC-R performance intelligence 73 −.00 −0.85 .40 −.00 −0.81 .42Number of ADHD symptoms 73 .01a 1.99 .05 .01a 2.00 .05Number of ODD symptoms 73 .04 1.04 .30 .03 0.90 .37Number of anxiety symptoms 73 .02 1.05 .29 .01 0.71 .48Number of depression symptoms 73 .03 1.08 .28 .00 0.02 .98

Maternal psychopathology and substance abuse in Wave 1MMPI antisocial index (T-score>60= 1) 71 .33 2.81 .005 .28 2.53 .01Substance abuse 73 .43 2.67 .01 .19 0.94 .35Major depression 73 −.04 −0.29 .77 −.02 −0.12 .90

Paternal psychopathology and substance abuse in Wave 1APD 73 .12a 2.99 .005 .13a 2.99 .005Substance abuse 73 .10 0.79 .43 .11 0.87 .39Major depression 73 .03 0.15 .88 .04 0.30 .76

aInteraction of predictor with time (assessment waves).

of CD behaviors in Wave 1 in predicting the outcomesof CD.

Other potential baseline predictors of outcome weretested in two sets of analyses. First, each baseline variablewas tested in separate analyses as a predictor of individualdifferences in CD behaviors during Waves 2–7, control-ling for age at the start of the study and time (left side ofTable II). The number of CD behaviors in Wave 1 was alsocontrolled in the second set of analyses reported in Table II(right side) to examine baseline predictors when the initialseverity of CD was controlled. Interactions with time weretested in all cases, but the results were reported only whenthe interaction term was significant. As shown in Table II,race–ethnicity and the partner status of biological parentsin Wave 1 were not significantly related to the adolescentoutcomes of CD, but the biological mother’s education inWave 1, total family income in Wave 1, and the mother’sage at first birth inversely predicted levels of CD behav-iors across Waves 2–7 in both sets of analyses (left andright side of Table II). Boys with higher verbal intelli-gence scores exhibited fewer CD behaviors in Waves 2–7in both analyses, but performance intelligence was unre-lated to the outcome of CD. The interaction of time withthe boys’ numbers of ADHD symptoms in Wave 1 was sta-tistically significant. As shown in Fig. 4, boys with higherlevels of Wave 1 ADHD symptoms exhibited slightly, but

consistently more CD behaviors in Waves 3, 4, 6, and 7,but not in Wave 2.

The boys’ numbers of anxiety, depression, and ODDbehaviors in Wave 1 did not predict the subsequentoutcomes of CD. The latter finding may reflect the re-stricted range of the number of ODD behaviors in Wave 1among boys who met criteria for CD, however, with only

Fig. 4. The mean number of conduct disorder behaviors reported bythe parent and youth (P+ Y) across Waves 1–7 of boys whose parentsreported 0–7 or 8 or moreDSM-III-RADHD symptoms (sxs) in Wave 1.

P1: GCR



three boys with CD in Wave 1 being reported to have<4ODD behaviors. That is, ODD behaviors may not havebeen a predictor of the outcome of CD because high num-bers of ODD behaviors were ubiquitous among boys withCD in this sample.

Individual differences in the outcome of CD werealso predicted by the mother’s MMPI antisocial person-ality index in both analyses. The diagnosis of APD inthe biological father also predicted the boys’ outcome ininteraction with waves, with the sons of men with APD ex-hibiting more CD behaviors during later waves than otherboys. Maternal substance abuse predicted the outcome ofCD, but only when the number of CD behaviors in Wave 1was not controlled; paternal substance abuse was not re-lated to the boys’ CD outcomes in either model.

Joint Models of Baseline Predictors

The baseline predictors of the outcome of CD thatwere statistically significant when age at the start of thestudy, time, and the level of CD in Wave 1 were controlledwere further examined in a series of joint models. First,the three indicators of socioeconomic status and family de-mographics (family income, maternal education, and ma-ternal age at first birth) were examined together in a jointmodel to determine which variables independently pre-dicted the outcome of CD. When the top quartile of mater-nal education (its interaction with time) and the top quar-tile of family income were treated as categorical protectivefactors and maternal age (its interaction with time) wastreated as a continuous predictor in log-linear regression,high family income predicted the outcome of CD,β =−.39, z= −2.01, p < .05, but the maternal education-by-time interaction,β = −.11, z= −1.72, p = .09, andthe maternal age-by-time interaction,β = −.01, z=−1.39, p = .17, were not significant in the joint model.

It is possible that these three socioeconomic and de-mographic indicators do not independently predict the out-comes of CD because they are collinear. This was exploredin follow-up analyses. Families in the top quartile of fam-ily income were found to be more likely to also fall inthe top quartile of maternal education than other families(56.2% vs. 9.3%;OR= 12.6, 95% CI= 3.3–48.6) andthe mother’s age at the birth of her first child was found tobe older, 19.8 years vs. 18.3 years;t(68)= −2.91, p <.005, among families in the top quartile of family income.Therefore, only the top quartile of family income was usedto quantify socioeconomic status in all further analyses,as it clearly captured much of the correlated differences inmaternal age at first birth and maternal education as well.

Next, a joint model was conducted to determinewhich aspects of parental psychopathology contributed

Fig. 5. The mean number of conduct disorder behaviors reported bythe parent and youth (P+ Y) across Waves 1–7 of boys with no an-tisocial (AP) biological parents, an antisocial (AP) biological mother,an antisocial (AP) biological father, or two antisocial (AP) biologicalparents.

uniquely to the prediction of the outcomes of CD. Maternalsubstance abuse was not included in this model becauseit did not predict the outcomes of CD when the base-line severity of CD was controlled (right side of Table II).Both maternal antisocial personality,β = .28, z= 2.44,p = .02, and the paternal APD-by-time interactions,β =.13, z= 3.02, p < .005, were significant, indicating thateach baseline variable contributed independently to theprediction of CD outcomes. The three-way interaction ofwaves and the indicators of maternal and paternal per-sonality was not found to be significant in a subsequentanalysis,β = .04, z= 0.81, p = .42. The additive asso-ciations of maternal and paternal APD with their boys’CD behaviors during Waves 2–7 is shown in Fig. 5.

Third, a final joint model of baseline variables wasconducted that predicted individual differences in the out-comes of CD over Waves 2–7 using time (waves), theboys’ age in Wave 1, the boys’ number of CD behav-iors in Wave 1, the boys’ number of ADHD behaviorsin Wave 1, being in the top quartile of high family in-come, maternal antisocial personality, paternal APD, andhigh verbal intelligence of the child as the predictors. Asshown in Table III, the boys’ age and baseline numberof CD behaviors in Wave 1 each accounted for indepen-dent variance in the prediction of outcomes of CD in thefinal model. In addition, having an antisocial biologicalmother and/or an antisocial biological father significantlypredicted the subsequent outcome of CD, with the strengthof the prediction from paternal APD increasing over thefive follow-up waves. Three predictors of the outcomes

P1: GCR



Table III. Final Joint Model of the Independent Contribution of EachSignificant Baseline (Wave 1) Predictor of Individual Differences in the

Outcomes of CD Over Waves 2–7

β z p

Time (repeated assessment waves) −.14 −2.13 .03Boy’s age at start of the study −.13 −3.86 .0001Boy’s Wave 1 CD behaviors .12 2.68 .008Top quartile of family income in Wave 1 −.30 −1.80 .07Boy’s verbal intelligence≥115 −.30 −1.89 .06Mother’s MMPI antisocial index .29 2.43 .02Father’s APD-by-time .13 3.14 .002Boy’s Wave 1 ADHD behaviors-by-time .06 1.48 .14

Note.APD = antisocial personality disorder. Differences among betacoefficients for predictors reflect both differences in the scale of thepredictor and differences in the strength of association. Terms for thefather’s APD and the boy’s Wave 1 ADHD behaviors were included inthis model to provide appropriate tests of their interactions with time.

of CD fell to marginal or nonsignificant levels in the fi-nal joint model. Living in a family in the top quartile ofincome and having a verbal intelligence score≥115 onlymarginally predicted fewer CD behaviors in Waves 2–7in this final joint model and the interaction of baselineADHD-by-time was not significant. Contrary to our ear-lier report of predictors of the outcome of CD over thefirst four waves of the present study (Lahey et al., 1995),however, there was not a significant interaction of paternalAPD and the child’s verbal intelligence in predicting theoutcomes of CD across Waves 2–7.

Baseline Prognostic Index

Based on the longitudinal analyses of baseline pre-dictors of the adolescent outcomes of CD, a summarybaseline prognostic index was created by summing the fol-lowing categorical variables measured in Wave 1: (1)>3CD behaviors in Wave 1; (2) 8 or more ADHD behav-iors in Wave 1; (3) verbal intelligence<115; (4) familyincome in the lower three-quarters of the sample distri-bution; (5) biological mother with an MMPI antisocialindex meanT-score≥60; and (6) biological father whomet diagnostic criteria for APD. The percent of boys with0 (n = 1), 1 (n = 5), 2 (n = 14), 3 (n = 21), 4 (n = 13),5 (n = 14), or 6 (n = 5) baseline risk variables who ex-hibited an average of three or more CD behaviors duringWaves 4, 6, and 7 were 0, 20, 29, 33, 38, 93, and 100%,respectively. This baseline prognostic index score was tri-chotomized to facilitate interpretation of its predictive ac-curacy: A 3-point ordinal prognostic index was defined as0, 1, or 2 risk factors= 1; 3 or 4 risk factors= 2; and 5 or 6risk factors= 3. As shown in Fig. 6, log-linear regression

Fig. 6. The mean number of conduct disorder behaviors reported bythe parent and youth (P+ Y) in Waves 2–7 among boys with scoresof 0–2, 3–4, and 5–6 on a summary baseline prognostic index based onsignificant predictors of the outcomes of CD.

revealed that the 3-point baseline prognostic index sig-nificantly predicted the number of CD behaviors duringWaves 2–7,β = .36, z= 4.40, p < .0001, controlling fortime and the boys’ age in Wave 1. Furthermore, whenthe prognostic index-by-time interaction was added to themodel, it was significant,β = .07, z= 2.10, p < .05, in-dicating that the 3-point prognostic index may actuallypredict the future course of CD behaviors somewhat bet-ter in waves that are more distant in time from Wave 1.Focusing on these later waves, Table IV shows the oddsof exhibiting an average of three or more CD behaviorsduring Waves 4, 6, and 7 increased 5.8-fold (odds ratio;95% CI= 2.4–14.1) with each increase of one unit in the3-point prognostic index, controlling for the boys’ age inWave 1. The accuracy of predicting the outcome of CDdefined in this way was substantial when the baseline prog-nostic index was low (75% true negatives), but especiallywhen it was high (95% true positives).

Table IV. Prediction of an Adverse Outcome of CD, Defined as aMean of Three or More CD Behaviors During Waves 4, 6, and 7 Usingthe 3-Point Baseline Prognostic Index Based on Six Baseline Predictor

Variables

Mean of<3 CD ≥3 CD behaviorsbehaviors in last in last three

three waves waves

Baseline prognostic indexLow 15 (75% true−) 5 (25% false−)Medium 22 12High 1 (5% false+) 18 (95% true+)

P1: GCR



Time-Varying Covariates

Treatment and Incarceration.Three dichotomoustime-varying covariates of receiving psychosocial treat-ment in the past 12 months, receiving psychoactive medi-cation in the past 12 months, and being placed in juveniledetention or inpatient hospitalization for at least one nightin the last 12 months were added to the final joint modelof baseline predictors shown in Table III. The level ofCD behaviors in each assessment wave was not related tothe presence or absence in the same wave of confinementdue to either juvenile detention or inpatient hospitaliza-tion,β = −.08, z= −0.47, p = .64, or the use of outpa-tient psychotherapy,β = .07, z= 0.61, p = .54. The useof psychoactive medication in any given wave was posi-tively associated with higher levels of CD during the samewave,β = .31, z= 2.88, p < .005.

Parenting and the Outcomes of CD.We examinedthe association of parental monitoring and corporal pun-ishment of the boys in Wavet with the number of CDbehaviors in Wavet + 1, controlling for age and the num-ber of CD behaviors in Wavet . There were no statisticallysignificant associations between wave-to-wave changes inCD and the parents’ self-reported use of corporal punish-ment,β = .10, z= 1.38, p = .16, or self-reported paren-tal monitoring, β = −.01, z= −0.74, p = .46, in thepreceding waves.

When the baseline characteristics of the family thatpredicted the outcomes of CD (higher family income, ma-ternal antisocial personality, and paternal APD) were in-cluded in the model, there were still no statistically signif-icant associations between wave-to-wave changes in CDand the parents’ self-reported use of corporal punishment,β = .05, z= 0.66, p = .51, or self-reported parentalmonitoring,β = .00, z= 0.22, p = .82.

Because there is evidence that the correlation be-tween physical punishment and child behavior problemsis strongest among non-Hispanic White families in theUnited States (Deater-Deckard, Dodge, Bates, & Pettit,1996), the analyses of punishment were repeated usingonly data from the 47 White boys. When the baseline fam-ily predictors of the outcomes of CD were not in the model,lagged wave-to-wave changes in CD were significantly as-sociated with corporal punishment in the preceding wavesamong white boys,β = .23, z= 2.41, p = .02. When thefamily baseline predictors were controlled, however, cor-poral punishment was not associated with lagged wave-to-wave changes in CD,β = .14, z= 1.33, p = .18.There were no significant associations between the courseof CD and parental monitoring in either model among thewhite boys.

DISCUSSION

In the present sample of clinic-referred boys who metdiagnostic criteria for CD in Wave 1, most boys continuedto engage in significant numbers of CD behaviors fromWave 1 (ages 7–12 years) through the Wave 7 (ages 13–18 years). There were marked individual differences in thecourse of CD into adolescence, however. All boys showedsome degree of fluctuation in symptoms from year to year,but some boys exhibited increasingly more CD behaviorsacross Waves 2–7, many boys showed little or no declinein CD behaviors over Waves 2–7, and 9 boys improvedto a level that might be termed sustained recovery fromCD. This suggests that CD is persistent, if fluctuating, forthe great majority of prepubertal clinic-referred boys, withless than 15% of such boys showing enough improvementby middle to late adolescence to consider them to be re-covered. Therefore, it seems unlikely that previous studieshave found that most boys with CD did not meet criteriafor APD in adulthood because they recovered from CDduring adolescence.

The marked individual differences in the outcomesof childhood CD across Waves 2–7 were predicted bya number of characteristics of the child and family thatwere measured during baseline (Wave 1). In interpretingthe findings from the present analyses, it is important tokeep in mind that no adjustment ofp-levels was madeto protect against Type I errors. Our assumption is thatType II errors (failing to detect associations) is the greaterrisk in studies using small samples sizes, but it is possiblethat some of the multiple statistically significant findingsof the present analyses reflect chance associations that willnot be replicable.

Boys who were older at the start of the study ex-hibited somewhat fewer CD behaviors across Waves 2–7and boys exhibiting higher numbers of CD behaviors inWave 1 showed greater persistence in CD over time. Asin previous studies (Lahey et al., 1998; Lynam, 1996), thenumber of symptoms of ADHD reported by the parentin Wave 1 also predicted the mean number of symptomsof CD in the later follow-up waves to a small, but sig-nificant extent. Consistent with the view that verbal as-pects of intelligence are more strongly related to CD thannonverbal aspects (Lynam & Henry, 2001; Moffitt, 1993),having a baseline verbal intelligence score of≥115 pre-dicted a more favorable outcome of childhood CD. How-ever, verbal intelligence fell to a marginally significantlevel (p = .06) in the final joint model. This may be be-cause some of the prediction of CD outcomes reflects theassociation of verbal intelligence with the family sociode-mographic indicator also in the model.

P1: GCR



A number of correlated indicators of family advan-tage measured at baseline also contributed to the predic-tion of future levels of CD. When the child’s age at the startof the study and the initial severity of CD were controlled,boys from families with high incomes and high levels ofmaternal education showed greater improvement in CDbehaviors during adolescence, but variations in family in-come and maternal education within the middle to lowrange were unrelated to the boys’ adolescent outcomes.In contrast, variation across the entire range of maternalage at first birth was inversely related to the adolescentoutcome of childhood CD. This confirms two previouslongitudinal studies of community samples, which foundthat children with behavior problems tended to improvefrom childhood into adolescence if they were from highersocioeconomic status families and had mothers who didnot give birth at an early age (Nagin & Tremblay, 2001;Stattin & Trost, 2000). The present study extends previousfindings by showing that higher socioeconomic status andolder maternal age predict better outcomes of CD evenamong clinic-referred children who meet criteria for CDduring childhood (although these factors were not inde-pendent of one another in the present study).

Antisocial personality in the boys’ biological moth-ers (as measured by an antisocial index derived from theMMPI) and the diagnosis of APD in their biological fa-thers at baseline also combined additively to predict pooreroutcomes of CD. In the present study, maternal substanceabuse also was a significant predictor of outcome whenage at the start of the study was controlled, but not whenWave 1 CD was also controlled. This could also meanthat maternal substance abuse is related to the outcomesof CD primarily because it is linked to the initial severityof CD during childhood, which is, in turn, predictive ofthe long-term course of CD.

The fact that a number of baseline predictors con-tributed independent variance to the prediction of the out-comes of CD allowed the construction of a summary base-line prognostic index. Although this index will need to becross-validated in an independent sample to confirm itspredictive efficiency, it allowed accurate prediction of thefuture outcomes of CD behaviors in the present sample(Table III and Fig. 5). This suggests that when the boysin the present study are later assessed for APD in adult-hood that the variables in the prognostic index would begood candidates for predictors of which boys with CDduring childhood will meet criteria for APD in adulthood,but since most boys showed little consistent improve-ment in CD during adolescence, it is likely that otherfactors are involved in predicting which boys with con-tinuing CD in adolescence will meet criteria for APD inadulthood.

The present findings replicate the earlier report byDeater-Deckerd et al. (1996) that parent reports of corpo-ral punishment of the boys in each wave were associatedwith higher levels of CD in the following wave, controllingfor CD in the previous wave, only among non-Hispanicwhite boys. The association of corporal punishment withthe outcomes of CD fell to a nonsignificant level in thepresent sample when the significant family predictors ofthe outcomes of CD (higher family income and parentalantisocial personality) were added to the model, however.This could indicate that corporal punishment is typicalin antisocial families without high economic advantagein non-Hispanic white cultures, but punishment is not it-self causally related to the outcomes of CD. On the otherhand, one cannot rule out the possibility that the presentsample was too small to detect the unique impact of cor-poral punishment on the outcomes of CD when familycharacteristics were controlled. Unlike previous studies,parental reports of monitoring were not related to the out-comes of CD, even when family characteristics that predictpoor outcomes of CD were not controlled. These findingsshould be tempered by cautions in interpreting this andother studies that use self-report measures of parentingpractices. It is certainly possible that observational or othertypes of studies might index parenting in more reliable orvalid ways that could yield different findings.

Because improvement of CD over time was not posi-tively related to treatment or incarceration over waves, it isimportant to note that this study was not designed to detecttreatment effects. Participants were not randomly assignedto treatments and the treatments were the ones availablein the community rather than treatments designed to max-imize improvement. Analyses of treatment and incarcera-tion were undertaken only to estimate the extent to whichthese variables might have affected the course of CD andled to misinterpretation of findings on other predictors. Forexample, if outpatient psychotherapy had been associatedwith better outcomes and families with higher incomeswere more likely to obtain psychotherapy for their chil-dren, it would have been necessary to determine if theassociation of family income with outcomes of CD wasmediated by psychotherapy. Because there was no indica-tion of positive effects of any treatments in this sample,it seems unlikely that treatment distorted the present find-ings on the predictors of the outcomes of CD. On theother hand, studies that collect more detailed informationon treatment and/or are able to randomly assign youths totreatments may find different results.

Taken together, the present findings make good theo-retical sense: Boys who meet criteria for CD in childhoodare more likely to improve if they have less serious con-duct problems and fewer ADHD symptoms at baseline

P1: GCR



and have the advantages of high verbal intelligence andaffluent and well-educated parents who are not antisocial.Unfortunately, the present study provides little guidancefor researchers who study the prevention and treatment ofCD. Most of the predictors of the adolescent outcomes ofCD identified in this study are not modifiable (i.e., one can-not change the mother’s age at the birth of her first child).In addition, the present naturalistic study cannot tell usif intervening to change the theoretically-modifiable pre-dictors would be beneficial. For example, we cannot con-clude that helping mothers obtain additional education thatwould increase family earnings would have a beneficialimpact on the adolescent outcomes of their children. Thisis because it is possible that the family characteristics ofchildren who have poorer adolescent outcome reflect “se-lection effects” (a tendency for individuals with certaincharacteristics to select early parenthood, reduced edu-cation, and lower-paying career trajectories). The factorsunderlying such selection effects (such as they parents’own lifelong antisocial tendencies) might prove to be asdifficult to modify as their children’s antisocial behavior.

ACKNOWLEDGMENT

This study was supported by Grant R01 MH42529from the National Institute of Mental Health.

REFERENCES

Bardone, A. M., Moffitt, T., Caspi, A., & Dickson, N. (1996). Adultmental health and social outcomes of adolescent girls with depres-sion and conduct disorder.Development and Psychopathology, 8,811–829.

Caspi, A., Taylor, A., Smart, M., Jackson, J., Tagami, S., & Moffitt,T. E. (2001). Can women provide reliable information about theirchildren’s fathers? Cross-informant agreement about men’s lifetimeantisocial behavior.Journal of Child Psychology and Psychiatry,42,915–920.

Cohen, P., & Brook, J. S. (1995). The reciprocal influence of punish-ment and child behavior disorder. In J. McCord (Ed.),Coercion andpunishment in long-term perspectives(pp. 154–164). New York:Cambridge University Press.

Costello, E. J., Edelbrock, C. S., & Costello, A. J. (1985). Validity of theNIMH Diagnostic Interview Schedule for Children: A comparisonbetween psychiatric and pediatric referrals.Journal of AbnormalChild Psychology, 13,579–595.

Costello, A. J., Edelbrock, C., Kalas, R., & Dulcan, M. (1984).The NIMHDiagnostic Interview Schedule for Children (DISC): Development,reliability, and comparison between clinical and lay interviewers.Worcester, MA: University of Massachusetts Medical Center.

Deater-Deckard, K., Dodge, K. A., Bates, J. E., & Pettit, G. S. (1996).Physical discipline among African American and European Amer-ican mothers: Links to children’s externalizing behaviors.Develop-mental Psychology, 32,1065–1072.

Dishion, T. J., & McMahon, R. J. (1998). Parental monitoring and theprevention of child and adolescent problem behavior: A conceptualand empirical formulation.Clinical Child and Family PsychologyReview, 1, 61–75.

Edelbrock, C., Costello, A. J., Dulcan, M. K., Kalas, R., & Conover,N. C. (1985). Age differences in the reliability of the psychiatricinterview of the child.Child Development, 56,265–275.

Edelbrock, C., & Costello, A. J. (1988). Convergence between sta-tistically derived behavior problem syndromes and child psychi-atric diagnoses.Journal of Abnormal Child Psychology, 16, 219–231.

Eron, L. D., & Huesmann, L. R. (1990). The stability of aggressivebehavior—even unto the third generation. In M. M. S. M. Lewis(Ed.), Handbook of developmental psychopathology. Perspectivesin developmental psychology(pp. 147–156). New York: Plenum.

Esser, G., Schmidt, M. H., & Woerner, W. (1990). Epidemiology andcourse of psychiatric disorders in school-age children: Results ofa longitudinal study.Journal of Child Psychology and Psychiatryand Allied Disciplines, 31,243–263.

Farrington, D. P. (1991). Childhood aggression and adult violence: Earlyprecursors and later-life outcomes. In D. J. Pepler & K. H. Rubin(Eds.),The development and treatment of childhood aggression(pp. 5–29). Hilsdale, NJ: Erlbaum.

Farrington, D. P., Loeber, R., & Van Kammen, W. B. (1990). Long-term criminal outcomes of hyperactivity-impulsivity-attentiondeficit and conduct problems in childhood. In L. N. R. M.Robins (Ed.),Straight and devious pathways from childhood toadulthood(pp. 62–81). Cambridge, England: Cambridge UniversityPress.

Fergusson, D. M., Horwood, L. J., & Lynskey, M. T. (1995). The sta-bility of disruptive childhood behavior.Journal of Abnormal ChildPsychology, 23,379–396.

Fergusson, D. M., Lynskey, M. T., & Horwood, L. J. (1996). Factors asso-ciated with continuity and changes in disruptive behavior patternsbetween childhood and adolescence.Journal of Abnormal ChildPsychology, 24,533–553.

Frick, P. J., & Loney, B. R. (1999). Outcomes of children and adoles-cents with oppositional defiant disorder and conduct disorder. InH. C. Quay & A. E. Hogan (Eds.),Handbook of the disruptivebehavior disorders(pp. 507–524). New York: Kluwer Academic/Plenum.

Graham, P., & Rutter, M. (1973). Psychiatric disorder in the young ado-lescent.Proceedings of the Royal Society of Medicine, 66, 1226–1229.

Harrington, R., Fudge, H., Rutter, M., Pickles, A., & Hill, J. (1991). Adultoutcomes of childhood and adolescent depression: II. Links withantisocial disorders.Journal of the American Academy of Child andAdolescent Psychiatry, 30,434–439.

Hart, E. L., Lahey, B. B., Loeber, R., & Hanson, K. S. (1994). Criterionvalidity of informants in the diagnosis of disruptive behavior dis-orders in children: A preliminary study.Journal of Consulting andClinical Psychology, 62,410–414.

Hathaway, S. R., & Monachesi, E. D. (1952). The Minnesota MultiphasicPersonality Inventory in the study of juvenile delinquents.AmericanSociological Review, 17,704–710.

Hogan, A. E. (1999). Cognitive functioning in children with oppositionaland conduct disorder. In H. C. Quay & A. E. Hogan (Eds.),Hand-book of disruptive behavior disorders(pp. 317–336). New York:Kluwer Academic/Plenum.

Huesmann, L. R., Lefkowitz, M. M., & Eron, L. D. (1978). Sum of MMPIScales F, 4, and 9 as a measure of aggression.Journal of Consultingand Clinical Psychology, 46(5), 1071–1078.

Jensen, P. S., Rubio-Stipec, M., Canino, G., Bird, H. R., Dulcan, M. K.,Schwab-Stone, M. E., et al. (1999). Parent and child contributionsto diagnosis of mental disorder: Are both informants always nec-essary?Journal of the American Academy of Child and AdolescentPsychiatry, 38,1569–1579.

Kandel, D. B., & Wu, P. (1995). Disentangling mother–child effects inthe development of antisocial behavior. In J. McCord (Ed.),Coer-cion and punishment in long-term perspectives(pp. 106–123). NewYork: Cambridge University Press.

Kazdin, A. E. (1985).Treatment of antisocial behavior in children andadolescents. Homewood, IL: Dorsey Press.

P1: GCR



Kilgore, K., Snyder, J., & Lentz, C. (2000). The contribution of parentaldiscipline, parental monitoring, and school risk to early-onset con-duct problems in African American boys and girls.DevelopmentalPsychology, 36,835–845.

Lahey, B. B., Loeber, R., Hart, E. L., Frick, P. J., Applegate, B., Zhang,Q., et al. (1995). Four-year longitudinal study of conduct disorder inboys: Patterns and predictors of persistence.Journal of AbnormalPsychology, 104,83–93.

Lahey, B. B., Loeber, R., Quay, H. C., Applegate, B., Shaffer, D.,Waldman, I., et al. (1998). Validity of DSM-IV subtypes of conductdisorder based on age of onset.Journal of the American Academyof Child and Adolescent Psychiatry, 37,435–442.

Lahey, B. B., Piacentini, J. C., McBurnett, K., Stone, P. A., Hartdagen, S.,& Hynd, G. W. (1988). Psychopathology in the parents of childrenwith conduct disorder and hyperactivity.Journal of the AmericanAcademy of Child and Adolescent Psychiatry, 27,163–170.

Lahey, B. B., Russo, M. F., Walker, J. L., & Piacentini, J. C. (1989).Personality characteristics of the mothers of children with disruptivebehavior disorders.Journal of Consulting and Clinical Psychology,57,512–515.

Loeber, R. (1982). The stability of antisocial and delinquent child be-havior: A review.Child Development, 53,1431–1446.

Loeber, R. (1991). Antisocial behavior: More enduring than change-able?Journal of the American Academy of Child and AdolescentPsychiatry, 30,393–397.

Loeber, R., Drinkwater, M., Yin, Y., Anderson, S. J., Schmidt, L. C., &Crawford, A. (2000). Stability of family interaction from ages 6 to18.Journal of Abnormal Child Psychology, 28,353–369.

Loeber, R., Farrington, D. P., Stouthamer-Loeber, M., & Van Kammen,W. B. (1998).Antisocial behavior and mental health problems:Explanatory factors in childhood and adolescence. Mahwah, NJ:Erlbaum.

Loeber, R., Green, S. M., Lahey, B. B., Frick, P. J., & McBurnett, K.(2000). Findings on disruptive behavior disorders from the firstdecade of the Developmental Trends Study.Clinical Child andFamily Psychology Review, 3, 37–60.

Loeber, R., & Lahey, B. B. (1989). Optimal informants on childhooddisruptive behavior disorders.Development and Psychopathology,1, 317–337.

Lynam, D. R. (1996). Early identification of chronic offenders: Whois the fledgling psychopath?Psychological Bulletin, 120, 209–234.

Lynam, D. R., & Henry, B. (2001). The role of neuropsychologicaldeficits in conduct disorders. In J. Hill & B. Maughan (Eds.),Conduct disorders in childhood and adolescence(pp. 235–263).Cambridge, England: Cambridge University Press.

Masten, A. S., Garmezy, N., Tellegen, A., Pellegrini, D. S., Larkin, K.,& Larsen, A. (1988). Competence and stress in school children:The moderating effects of individual and family qualities.Journalof Child Psychology and Psychiatry, 29,745–764.

Moffitt, T. E. (1990). Juvenile delinquency and attention deficit disor-der: Boys’ developmental trajectories from age 3 to age 15.ChildDevelopment, 61,893–910.

Moffitt, T. E. (1993). The neuropsychology of conduct disorder.Devel-opment and Psychopathology, 5, 135–151.

Moss, H. B., Baron, D. A., Hardie, T. L., & Vanyukov, M. M. (2001).Preadolescent children of substance-dependent fathers with antiso-cial personality disorder: Psychiatric disorders and problem behav-iors.American Journal on Addictions, 10,269–278.

Nagin, D., & Tremblay, R. (2001). Parental and early childhood predic-tors of persistent physical aggression in boys from kindergarten tohigh school.Archives of General Psychiatry, 58,389–394.

Offord, D. R., Boyle, M. H., Racine, Y. A., Fleming, J. E., Cadman, D. T.,Blum, H. M., Byrne, C., Links, P. S., Lipman, E. L., MacMillan,H. L., Grant, N. I. R., Sanford, N. M., Szatmari, P., Thomas, H.,

& Woodward, C. A. (1992). Outcome, prognosis, and risk in alongitudinal follow-up study.Journal of the American Academy ofChild and Adolescent Psychiatry, 31,916–923.

Patterson, G. R., Reid, J. B., & Dishion, T. J. (1998). Antisocial boys. InJ. M. O. K. Jenkins (Ed.),Human emotions: A reader(pp. 330–336).Malden, MA: Blackwell.

Patterson, G. R., & Stouthamer-Loeber, M. (1984). The correlation offamily management practices and delinquency.Child Development,55,1299–1307.

Pfiffner, L., McBurnett, K., & Rathouz, P. J. (2001). Father absenceand familial antisocial characteristics.Journal of Abnormal ChildPsychology, 29,357–367.

Piacentini, J. C., Cohen, P., & Cohen, J. (1992). Combining discrepantdiagnostic information from multiple sources: Are complex al-gorithms better than simple ones?Journal of Abnormal ChildPsychology, 20,51–63.

Robins, L. (1966).Deviant children grown up. Baltimore: Williams andWilkins.

Robins, L. (1978). Sturdy childhood predictors of adult antisocialbehaviour: Replication from longitudinal studies.PsychologicalMedicine, 8, 611–622.

Robins, L., & Ratcliff, K. S. (1979). Risk factors in the continuation ofchildhood antisocial behavior into adulthood.International Journalof Mental Health, 7, 96–115.

Robins, L., West, P. A., & Herjanic, B. L. (1975). Arrests and delin-quency in two generations: A study of Black urban families andtheir children.Journal of Child Psychology and Psychiatry, 16,125–140.

Schonfeld, I. S., Shaffer, D., O’Connor, P., & Portnoy, S. (1988). Conductdisorder and cognitive functioning: Testing three causal hypotheses.Child Development, 59,993–1007.

Spitzer, R., & Endicott, J. (1967).Schedule for Affective Disorders andSchizophrenia. New York: New York State Psychiatric Institute.

Spitzer, R., Williams, J. B. W., & Gibbon, M. (1987).Structured Clin-ical Interview for DSM-III-R: Non-patient version. New York:New York State Psychiatric Institute.

Stattin, H., & Magnusson, D. (1989). The role of early aggressive behav-ior in the frequency, seriousness, and types of later crime.Journalof Consulting and Clinical Psychology, 57,710–718.

Stattin, H., & Trost, K. (2000). When do preschool conduct problemslink to future social adjustment problems and when do they not? InL. R. Bergman, R. R. Cairns, L.-G. Nilsson, & L. Nystedt (Eds.),Developmental science and the holistic approach(pp. 349–375).Mahwah, NJ: Erlbaum.

Storm-Mathisen, A., & Vaglum, P. (1994). Conduct disorder patients20 years later: A personal follow-up study.Acta PsychiatricaScandinavia, 89,416–420.

Stouthamer-Loeber, M., Loeber, R., Farrington, D. P., Zhang, Q., vanKammen, W., & Maguin, E. (1993). The double edge of protectiveand risk factors for delinquency: Interrelations and developmentalpatterns.Development and Psychopathology, 5, 683–701.

Verhulst, F. C., & Van der Ende, J. (1992). Six-year stability of parent-reported problem behavior in an epidemiological sample.Journalof Abnormal Child Psychology, 20,595–610.

Vuchinich, S., Bank, L., & Patterson, G. R. (1992). Parenting, peers, andthe stability of antisocial behavior in preadolescent boys.Develop-mental Psychology, 28(3), 510–521.

Wechsler, D. (1974).Wechsler Intelligence Scale for Children—Revised.New York: Psychological Corporation.

Zeger, S. L., & Liang, K.-Y. (1986). Longitudinal data analysis for dis-crete and continuous outcomes.Biometrics, 42,121–130.

Zoccolillo, M., Pickles, A., Quinton, D., & Rutter, M. (1992). The out-come of childhood conduct disorder: Implications for defining adultpersonality disorder and conduct disorder.Psychological Medicine,22,971–986.

adolescent outcomes of childhood conduct disorder among clinic-referred boys: predictors of...

Documents