influence of mental health and substance use problems and criminogenic risk on outcomes in serious...

NEW RESEARCH

Influence of Mental Health and SubstanceUse Problems and Criminogenic Risk onOutcomes in Serious Juvenile OffendersCarol A. Schubert, M.P.H., Edward P. Mulvey, Ph.D., Cristie Glasheen, Ph.D.

Objective: To investigate the relations among certain mental health problems (MHPs;affective, anxiety, attention-deficit/hyperactivity disorder [ADHD], and substance use disor-ders), criminogenic risk, and outcomes in a sample of serious adolescent offend-ers. Method: Using data from a longitudinal study of serious adolescent offenders (N � 949;mean age � 16 years, SD � 1.10 years; 84% male; 78% minority), we evaluated the associationof MHPs with three distinct outcomes (rearrest, self-reported antisocial activity, and gainfulactivity), tested whether having an MHP contributed any unique explanatory power regardingthese outcomes over and above criminogenic risk markers, and examined whether MHPsmoderated the relationship between risk markers and outcomes. Negative binomial andordinal regressions were used. Data for the study were derived primarily from youthself-report over a 7-year period, with parent collaterals reporting on ADHD, and officialrecords as the source for rearrest information. Results: Of the sample, 57.5% met the criteriafor at least one of the assessed MPHs. The presence of a substance use disorder showedconsistent associations with the outcomes. After controlling for risk markers and demographiccharacteristics, MHPs were not associated with most outcomes. The co-occurrence of asubstance use disorder and an MHP moderated the relations between several risk markers andoutcomes. Conclusions: Current juvenile justice policies that focus treatment efforts on bothcriminogenic and mental health factors (with particular emphasis on treating substance usedisorders) appear to be well founded. It is unlikely that focusing solely on treating MHPs inserious offenders will have a distinct impact on later outcomes. J. Am. Acad. Child Adolesc.Psychiatry, 2011;50(9):925–937. Key Words: mental health, outcomes, juvenile offenders,risk factors, substance use

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O ver the past two decades, there has beena heightened awareness of the highprevalence of mental health problems in

the juvenile justice population. Researchers haveestablished that juvenile offender populationshave disproportionately high rates of mentalhealth problems compared with the general pop-ulation of adolescents. Estimates suggest thatbetween 50% and 70% of juvenile offenders havea diagnosable psychiatric disorder1-5 comparedwith 9% to 21% in the general adolescent popu-lation,6-8 with a majority of the diagnosableyouth in the juvenile system also having a co-occurring substance use disorder.9,10 Conductdisorder is the most common diagnosis amongjuvenile offenders,1 but there is also a substantialprevalence of other mental health disorders in
these adolescents (60% of males in juvenile de-
JOURNAL OF THE AMERICAN ACADEMY OF CHILD & ADOLESCENT PSYCHIATRY

VOLUME 50 NUMBER 9 SEPTEMBER 2011

tention with diagnoses other than conduct disor-der).3 These rates appear to differ somewhat bygender. In a large, comprehensive study, Teplinet al. found that nearly two-thirds of boys andthree-quarters of girls (1,829 detained youth inChicago) met diagnostic criteria for at least onedisorder, and about 57% of females and 46% ofmales met criteria for multiple disorders.9 Al-hough prevalence estimates differ across locales,y the sample examined, and by the method ofiagnosis, significantly higher rates of disordersre found uniformly, even in studies using na-ionally representative samples.11-13

This awareness has led to more rigorousscreening for mental health problems, more fo-cused provision of mental health services, andexpansion of mental health diversion programs
in the juvenile system. Although important for
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ensuring appropriate services for at-risk youth,the potential of these innovations for reducingcriminal involvement by juvenile offenders witha mental health problem remains an open ques-tion. There are few data on whether and howmental health problems relate to later offendingor to the positive adjustment of adolescentoffenders.

Outcomes for Youth With Mental Health ProblemsThere is evidence that youth with mental healthproblems, compared with youth without theseproblems, are more likely to have court involve-ment and other troublesome life outcomes.Vander Stoep et al.14,15 have demonstrated thatadolescents with a psychiatric disorder have ele-vated arrest rates compared with nondiagnosedadolescents in the general population. Other re-searchers have also shown that the presenceof co-occurring disorders increases the chancesof criminal involvement in emerging adult-hood,16,17 and that both the presence and num-ber of co-morbid disorders within a sample ofsubstance-abusing and delinquent adolescentspredicted subsequent negative outcomes, includ-ing arrest.18 Substance use disorders and exter-nalizing disorders appear to be particularly prob-lematic for a range of outcomes, including highschool dropout, family cohesion issues, and gen-eral delinquency.19-25 Gender appears to moder-ate many of these effects.18,26

High prevalence rates of mental health prob-lems among youth in the juvenile system anddemonstrations that youth with mental healthproblems have an increased risk for criminalinvolvement indicate only an association be-tween the presence of mental health problemsand increased risk for criminal involvement (i.e.,criminogenic risk). They do not provide informa-tion about the mechanisms by which mental disor-ders elevate the risk of court involvement. Domental health disorders place an adolescent atincreased risk for other life stresses that also pro-mote offending? Or is the relationship between thetwo spurious, with a root cause producing bothmental disorder and criminal activity?

There are several theories on how the presenceof certain mental health problems or specificsymptoms might be related to offending. Ryanand Redding,27 for instance, note that youth withmajor depression or dysthymia often experiencehopelessness, fail to consider future conse-
quences for their actions, and show impaired p
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ocial functioning, leading them to “distort infor-ation processing in ways which can make themore vulnerable to engaging in delinquent be-

avior”(p. 1399). Similarly, one could posit thatania can lead to risk-taking and sensation-

eeking behaviors, and substance use intoxica-ion may reduce inhibition and impair judg-

ent, thus increasing a youth’s willingness toommit crimes.12 Co-occurring disorders may

intensify the relationships between mentalhealth problems and antisocial behavior, withmultiple diagnoses, such as attention-deficit/hyperactivity disorder (ADHD), conduct disor-der, or bipolar disorder in combination increas-ing the likelihood of aggression, impulsivity,and subsequent offending.28 However, empiri-cal evidence supporting these relationships isgenerally lacking.

Disentangling Shared RiskSorting out the relationships between mentalhealth problems and offending in adolescence isa complicated task. Although prior work showsthat mental health problems and poor outcomesoften go hand in hand,29 this overlap is notclearly causal, as the risk factors for the develop-ment of mental disorders and offending behavioroverlap.30,31 For both the development of mental

ealth problems and offending behavior, riskactors can cross domains (e.g., youth may be att risk because of neglectful parenting and/orssociation with antisocial peers),32-34 aggregate

regularly (it is rare for just one risk factor to bepresent),33,35 and are rarely unidirectional (riskfactors influence the behavior, whereas behav-ioral outcomes dampen or exacerbate the riskfactor).36,37 In addition, the risk factors can be

ynamic, changing with age and context,20 andhe presence of certain risk factors does notuarantee a uniform response since individualactors may alter their impact.38,39 Finally, youth

in the juvenile system often have a constellationof problems, of which a mental health problemmay be only one.32,34,40 Thus, sorting out the role

f mental health problems in this complex set ofnfluences is a task that requires more focused

ork than has been done to date.

he Need for Policy-Relevant Researchven though extant literature has not fully sortedut the relationships between mental health
roblems and offending, it has still formed the
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CRIME RISK, MENTAL HEALTH, AND OUTCOMES

basis for policy and practice initiatives aimed atproviding more mental health services for juve-nile offenders with mental health problems. Un-fortunately, many of these initiatives have beenimplemented with an implication that mentalhealth problems are a criminogenic risk factorsuch that, if addressed, will decrease youths’ riskfor recidivism and increase their involvement inprosocial activities. For example, a report fromthe National Conference of State Legislatures41

states that “comprehensive responses to court-involved juveniles with mental health needs canhelp . . . . to produce healthier young people whoare less likely to act out and commit crimes.” Asimilar assertion is found in a statement by theBazelon Center, suggesting that youth wouldstay out of jail if given the mental health treat-ment they need.42 Although reasonable asser-tions can be made that mental health problemsare often the untreated cause for a sizable pro-portion of juvenile offending, there is little re-search that directly addresses this point.

The Current StudyThis study examined part of the relationshipbetween mental health problems and outcomesfor youth in the juvenile system. Using a sampleof adolescents found guilty of a serious charge,we assessed the relationship of certain mentalhealth problems (exclusive of conduct disorder)on outcomes over a six-year period and testedwhether the presence of a mental health problemaffected the relationship of several criminogenicrisk markers (markers for increased risk of crim-inal recidivism) to outcomes in these adolescents.Specifically, this study 1) evaluated the associa-tion of mental health problems on three distinctoutcomes, 2) tested whether having a mentalhealth problem contributed any unique explana-tory power regarding these outcomes over andabove criminogenic risk markers, and 3) exam-ined whether mental health problems moderatedthe relationship between criminogenic risk mark-ers and outcomes.

The current study did not assess for the pres-ence of conduct disorder and thus cannot ad-dress the relationships between this specificdisorder and the risk markers or outcomesexamined. It is expected, however, that the ma-jority of the diagnosed youth in this samplewould meet the criteria for conduct disorder.Multiple studies have demonstrated that conduct
disorder is often either a precursor to or coexists


ith criminal behavior43,44 with prevalence ratesanging from 40% to near 90%3,45 among juvenile

offenders. In addition, conduct disorder oftenco-occurs with mood disorders, often found to bethe primary diagnosis in juvenile offender sam-ples.46 Finally, the current study did prospec-tively assess antisocial behavior and found thatthis sample continued these behaviors over time.Although these behaviors are not synonymouswith a diagnosis of conduct disorder, they over-lap considerably with the diagnosis.

METHODParticipants and ProceduresData for these analyses came from the Pathways toDesistance Study, a multi-site, longitudinal study ofserious juvenile offenders making the transition intoearly adulthood. Beginning in 2000, a total of 1,354participants were recruited based on a court recordreview in Philadelphia, PA (N � 654) and Phoenix, AZ(N � 700). Participants included adolescents aged 14hrough 17 years who were adjudicated delinquent oround guilty of a serious (overwhelmingly felony-evel) offense at their current court appearance. Theumber of males enrolled for a drug offense wasapped at 15% to avoid an over-representation of thisffense type. All females who met enrollment criteriaere recruited for enrollment, as were all youth trans-

erred to the adult system. Except for some times whenotential enrollee flow exceeded staff capacity, theample reflected consecutive eligible adolescent of-enders appearing before the court.

Baseline interviews were conducted shortly afterhe participant’s adjudication hearing in juvenileourt or the preliminary hearing in the adult system.ollow-up interviews occurred every 6 months afterhe baseline for the first 3 years and annually thereafterhrough 7 years. Collateral interviews were conducted

ith a parent/guardian at baseline. Follow-up datahrough the 72-month (6-year) assessment were usedn these analyses.

All participants provided informed consent and theesearch activities were approved by the institutionaleview boards at all participating universities. Sampleetention for the Pathways project was high at eachollow-up, ranging from 84% to 94% (mean � 90%) of

the full sample. Additional details regarding the theoret-ical background for the study as well as the recruitmentand study procedures can be found elsewhere.47,48

Of the 1,354 participants, 949 were included in theseanalyses (797 males and 152 females). Participantswere excluded if their case was transferred to adultcourt (n � 244); if they had an incomplete diagnosticinterview (n � 48); if they were missing data for morethan 1.5 years of follow-up period (n � 112); or if they
did not spend at least 3 weeks of 1 month during the
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follow-up period in the community (n � 1). Thesample was primarily male (84 %), minority (31%Hispanic, 43% black, 22% white, and 4% other) andslightly more likely to be from Philadelphia thanPhoenix (56% versus 44%). The mean age at baselinewas 16 years (SD � 1.10 years). On average, theseindividuals had three (SD � 2.10) petitions to courtbefore enrollment and were 15 years old (SD � 1.60) atthe time of their first petition. The study index offensewas a violent crime against person (e.g., robbery orassault) for 37% of the sample, 28% were propertycrimes (e.g., burglary), 18% were drug-related of-fenses, 9% were weapons offenses, 4% were sexcrimes, and 4% were other types of crimes (e.g.,conspiracy, intimidation of a witness). The sample hadan average of 50 (SD � 19.63) “eligible months” duringwhich the outcomes were measured. An “eligiblemonth” was defined as a month in the outcome periodduring which the youth was in the community (not ina facility) for at least 3 weeks of the month.

MeasuresCriminogenic Risk Markers. The Pathways study in-cluded measures that reflected constructs with empir-ical support for their relationship to criminal offending(referred to as criminogenic risk markers) and salienceto the transition into adulthood. From these baselinemeasures, we selected 121 possible risk markers relatedto continued offending, including demographic, familyhistory, peer, legal, psychological, psychosocial maturity,and adjustment measures. More information regardingthe measures included in the Pathways study can befound at www.pathwaysstudy.pitt.edu, and informationregarding the subset of 121 risk markers chosen initiallyis available from the first author.

A principal components analysis (PCA)49 usingorthogonal rotation was conducted to reduce the 121baseline variables to a set of uncorrelated factorsreflecting dimensions of risk for future offending. ThePCA included all variables correlated at 0.2 or greaterwith the outcome measures and excluded all highlycorrelated variables (0.6 or greater). The PCA did notspecify a preset number of solutions. Seven compo-nents emerged with eigenvalues greater than 1.0, butonly six were used in further analyses. The seventhcomponent was not readily interpretable and ex-plained only a small additional amount of the variance(3.5%). The six components, reflecting generally recog-nized risk markers for continued offending and ex-plaining 56% of the variance among the measures,were labeled as follows: negative peer influence, anti-social attitudes, antisocial history, psychosocial matu-rity, perceived severity of court sanctions, and parentcriminality/parent substance use. Each case was as-signed a risk marker score for each of the six dimen-sions. More information regarding these risk markers
and the PCA solution is available from the first author. i
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Mental Health Problems. Several assessment toolswere used to indicate the presence of mental healthproblems. Eight modules from the Composite Inter-national Diagnostic Interview (CIDI)50 were used tossess the lifetime presence of major depression, dys-hymia, mania, alcohol abuse and dependence, drugbuse and dependence, and post-traumatic stressisorder (PTSD). The CIDI is a comprehensive, fullytructured clinical assessment of psychiatric disordersbased on DSM IIIR criteria). The Revised Children’s

anifest Anxiety Scale (RCMAS)51 was used to iden-tify youth with high anxiety symptoms. A total anxietyscore is computed based on 28 items across threesubscales (physiological anxiety, worry/oversensitivity,and social concerns/concentration). Scores at or above1 SD of the sample mean were taken as indicators of“high anxiety” for the purpose of these analyses.Although not a diagnostic tool, the RCMAS has em-pirical support for its validity as a measure of anxietyin youth of the same age and ethnic composition as thePathways sample.52-54 Attention and hyperactivity

roblems (ADHD) were assessed at baseline using theisruptive Behavior Disorders scale.55 This scale com-rises the DSM-III-R diagnostic criteria for disruptiveehavior disorders. Unlike the previous assessments,hich relied on youth self-report, the assessment ofDHD symptoms was based on the parent/guardian

eport, a more valid indicator in this context.56

Mental health problems were categorized in severalways. First, a single dichotomous variable indicatedthe presence/absence of any assessed mental healthproblem. Second, four dichotomous variables indi-cated the presence or absence of each of four groups ofproblems: affective disorders (depression, dysthymia,or mania; n � 97), anxiety disorders (PTSD or highnxiety; n � 212), ADHD (n � 77), and substance usealcohol or drug abuse/dependence; n � 413). Eachase was also assigned a categorical indicator ofhether the individual had a co-occurring substancese diagnosis and a mental health problem.

Outcome Measures. Three adjustment outcomes wereconsidered, two negative and one positive.

Rearrest. Indicators of arrest before the age of 18years were based on petitions to juvenile court re-corded in each county. Arrests after age 18 were basedon a combination of the county court record informa-tion and Federal Bureau of Investigation (FBI) arrestrecords. Probation violations were not counted asrearrests, as they do not necessarily represent a newcriminal act. A rate of rearrest was calculated bydividing the number of petitions/arrests by the num-ber of eligible months.

Antisocial Activity. We used a modified version ofhe Self-Report of Offending (SRO)57,58 scale at eachnterview to measure the adolescent’s involvement in2 antisocial and illegal activities (the most serious actsommonly found on self-report scales). The participant
ndicated whether he/she was involved in each activ-


http://www.pathwaysstudy.pitt.edu


ity and the month the activity occurred. The propor-tion of months during which the youth reportedantisocial acts was used as an indicator of level ofinvolvement in antisocial activity (calculated as thenumber of months with two or more antisocial activ-ities reported divided by the number of eligiblemonths).

Gainful Activity. Participants were considered“gainfully active” for a month if they attended schoolor worked during that time. Attending school in amonth was defined as being enrolled in school (of anytype) and not absent more than 5 days during themonth. Youth were considered employed in a month ifthey worked at least 21 hours per week for at least 2weeks during the month. A proportion score repre-sented the number of months the youth attendedschool or was employed divided by the number ofeligible months. The proportion scores were convertedinto tertiles: low, medium, and high proportion of timegainfully active.

Each of these outcomes was examined separatelybecause it represents a qualitatively different process.Rate of rearrest is associated with detected crime andpolicing practices, whereas reported antisocial activityreflects involvement in often-undetected actions thatmight or might not get a person arrested.59 The out-come of gainful activity, meanwhile, is a process ofcommunity adjustment that may be only marginallyrelated to either self-reported offending or rearrest.Many adolescents continue to offend, hold jobs,and/or go to school, and these patterns have beenobserved in prior analyses of these and other datasets.60,61 In this sample, these outcomes are not highlycorrelated. Proportion of months with two or moreantisocial behaviors and rate of rearrest are correlatedat r � 0.313. The proportion of months with two ormore antisocial behaviors and proportion of monthsgainfully active are correlated at r � �0.298, and therate of rearrest and proportion of months gainfullyactive are correlated at r � �0.333.

Analytic ApproachAnalyses followed three steps. First, we examined theuncontrolled (bivariate) association between the men-tal health problems and the three outcomes. Second,we examined whether the presence of a mental healthproblem (across all disorders or for each particulardisorder) increased or decreased an individual’s riskfor each outcome after controlling for the identifiedrisk markers. Finally, we tested whether the presenceof a mental health problem moderated the effect of therisk markers on each outcome (i.e., whether a mentalhealth problem altered the strength or direction of arelationship between the risk and outcome).62 Moder-ation (as opposed to mediation) analyses were chosenbecause our goal was to see if the relationship betweenthe risk marker and the outcome operates in the same
way when mental health problems are considered,


rather than establishing a casual model. Moderationtesting was done only with risk markers that demon-strated a significant bivariate relationship with theoutcome being tested. Each set of analyses were doneseparately by gender, given previously observedgender-based differences.3,9,13,63

The characteristics of each of the outcomes requireddifferent analytic techniques. Rates of rearrest andself-reported antisocial activities were based on countvariables, following an overdispersed Poisson distri-bution. Negative binomial regression (using STATA)64

was used to assess the effects of risk markers andmental health problem indicators on these outcomes,65

with the number of eligible months specified as theexposure time. Ordinal regression (in SPSS)66 wasused to analyze the ordinal (low, medium, high)gainful activity outcome. All regression models werechecked for distributional assumptions, collinearity,and in the case of ordinal regression, the assumption ofparallel lines. Model diagnostics indicated these mod-els were appropriate for the data.

Finally, for all analyses testing the effect of having aparticular disorder, a “clean” reference sample wasused, e.g., those meeting criteria for an affective disor-der were compared with those who had no indicationof a mental health problem. As a result, differentregression analyses may have used different samplesizes, with some analyses in females being underpow-ered, a factor that must be considered when interpret-ing the results. The reference group for the moderationanalyses differed slightly from those used in the firstset of regressions. Two moderation models weretested. The first model included those with a mentalhealth disorder (limited to affective, anxiety, or ADHDdisorders) and compared them with those without amental health disorder. Individuals with a substanceuse disorder were excluded from this set of analysis.The second model included those with a substance usedisorder compared with those with no substance usedisorder; those with only a non–substance-use mentaldisorder (only an affective, anxiety, and/or ADHDproblem) were excluded in these analyses.

RESULTSBivariate Relation of MentalHealth Problems and OutcomesMore than half of the participants (57.5%) met thecriteria for at least one of the assessed mentalhealth problems. Of participants with at least onemental health problem, the most common was asubstance use disorder (76%), followed by highanxiety (33%), ADHD (14%), depression (12%),PTSD (12%), mania (7%), and dysthymia (�1%).Of these youth, 39% met the threshold for more
than one mental health problem.
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Table 1 shows the results of the uncontrolledbivariate regressions for mental health problemsand the three outcomes. The relationship be-tween mental health problems and antisocialactivity was more consistent than those betweenmental health problems and rearrest or level ofgainful activity. In addition, having any mentalhealth problem other than a substance use disor-der (Table 1, first row) showed no significantassociation with any of the three outcomes. Con-versely, the presence of a substance use disordershowed consistent associations with the out-comes across genders.

A few gender comparisons are noteworthy.Anxiety problems were associated with pooreroutcomes across genders, doubling the likeli-hood of rearrest for females (incidence rate ratio(IRR) � 2.24, p � .039, CI � 1.04–4.81), andraising it to more than 60% for males (IRR � 1.64,p � .001, CI � 1.28–2.1). Individual genderdifferences were difficult to interpret regarding

TABLE 1 Bivariate Associations Between Mental Health P

Type of Mental Health Problem (MHP)

Rate of R

IRR

Any MHP other than substance use disorderFemales 1.25Males .96

Affective disorderFemales 1.45Males 1.16

ADHDFemales 1.02Males 1.92

Anxiety disorderFemales 2.24Males 1.64

Substance use disordersSubstance use disorder only

Females 2.22Males 1.58

Substance use and another MHP.Females 2.41Males 1.86

Note: Each line represents a separate regression model; reference groupcomparing the incidence rate among the exposed proportion of the pinterpreted like a relative risk, so an IRR of 1.52 means that thoseattention-deficit/hyperactivity disorder.

particular associations, however, because of the

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ow sample sizes for females. For example, thereas a statistically significant association betweenDHD and rearrest and antisocial activity forales (IRR � 1.92, p � .001, CI � 1.38–2.66 and

RR�1.73, p � .039, CI � 1.03–2.9, respectively)ut the large IRR in females did not reach statis-ical significance (IRR � 4.02, p � .169, CI �55–29.15).

elation of Risk Markers to MPHsseries of logistic regressions were run with the

ndicator for each disorder group (affective,DHD, anxiety, and substance use disorder) as

he dependent variable and the risk markers andemographic variables (age, gender, and ethnic-

ty) as predictor variables. These models wereignificant (each at p � .001) but not stronglyredictive. Nagelkerke R2 values for the models

were fairly low (affective � .010, ADHD � 0.13,anxiety � 0.26), except for the substance use

ems and Three Outcomes

st

Proportion ofMonths With

Antisocial ActivityProportion of MonthsWith Gainful Activity

alue IRR p Value � p Value

88 1.9 .315 0.151 .72859 1.06 .814 0.09 .674

49 3.43 .071 0.344 .48772 1.72 .023 �0.320 .175

74 4.02 .169 �0.487 .44401 1.73 .039 0.121 .628

39 3.92 .002 �0.388 .31101 1.65 .007 �0.373 .039

78 1.70 .272 0.151 .73001 1.70 .001 �0.322 .063

43 4.69 .001 �0.368 .37401 1.94 �.001 �0.457 .017

l comparisons is no diagnosis. An incidence rate ratio (IRR) is a measureion to the incidence rate in the unexposed portion of the population. It isthe exposure are at 1.5 times greater risk for the outcome. ADHD �

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the fewest risk markers (only antisocial historyand parent criminality/substance use), whereasall six of the risk markers were associated withaffective disorders, and five of the six wereassociated with substance use disorder and anx-iety problems (psychosocial maturity and parentcriminality/substance use were not predictivefor substance use disorder or anxiety). Tests ofcollinearity (i.e., the variance inflation factor)examining risk markers and indicators of mentalhealth problems as predictors were fewer thantwo and within guidelines for smaller samplelogistic regression models.67

Relation of MHPs With OutcomesAfter Controlling for Risk MarkersSeparate models were run for each disordergroup (i.e., affective disorder versus no disorder,substance use versus no disorder, ADHD versusno disorder, and anxiety disorder versus nodisorder). The models included demographics(age, race/ethnicity), the six risk marker scores,and the specific mental health problem indicatorbeing tested. A backward removal strategy wasused to determine the most parsimonious model.Covariates that were significant at the p � .1 levelin bivariate relationships were included in thesemodels.

After controlling for the demographic vari-

TABLE 2 Significant Effects from Multivariate Negative BDemographic Variables and Risk Markers

Variable

Females

IRR 95%

Age — —Ethnicity (white � reference)

Black 0.42 0.21–Hispanic 0.38 0.17–Other 1.24 0.35–

Negative peer influence — —Antisocial attitudes — —Antisocial history 1.56 1.12–Psychosocial maturity 0.75 0.57–Perceived severity of court sanctions 1.62 1.19–Parent substance use/criminality — —

Note: Dashes indicate that the variable did not significantly contribute to th(IRR) is a measure comparing the incidence rate among the exposed ppopulation. It is interpreted like a relative risk, so an IRR of 1.52 meaCI � confidence interval.

ables and risk markers, none of the mental p



ealth indicators was associated with the neg-tive outcomes (rate of rearrest or antisocialctivities) in either gender. Several demo-raphic variables and risk marker variables,owever, did show significant associationsith these outcomes across the regressions. As

n indication of the patterns seen in thesenalyses, Tables 2 and 3 show the results ofender-stratified multivariate regressions forhe rearrest and antisocial outcome using the

hole sample and the demographic variablesnd risk markers.

There are a few notable consistencies acrosshese two tables. Ethnicity was a significant pre-ictor for both of these outcomes for females, butot for males. For females, individuals of whitethnicity had higher rates of rearrest and antiso-ial activity. Antisocial history was a significantredictor of each outcome in the analyses of eachender group.

Gainful activity was the only outcome thatas associated with a disorder; the presence of

n affective disorders was related to this out-ome. Table 4 shows the model for the significantesults from the regression testing the effect ofaving an affective disorder. For males, thereas no significant effect of having an affectiveisorder once background characteristics andisk markers were controlled. For females, the

ial Regression Model Results for Rate of Rearrest Using

152) Males (n � 797)

p Value IRR 95% CI p Value

— 0.92 0.85–0.99 .034

.014 — — —

.014

.793— 1.18 1.09–1.28 �.001— 1.09 1.0–1.19 .046

.008 1.051 1.38–1.64 �.001

.043 — — —

.002 1.39 1.28–1.51 �.001— 1.11 1.02–1.21 .021

el (-2 log likelihood p �.05) and was eliminated. An incidence rate ratioon of the population to the incidence rate in the unexposed portion of thet those with the exposure are at 1.5 times greater risk for the outcome.

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SCHUBERT et al.

with higher levels of gainful activity (� � 2.044,p � .006, CI � 0.572–3.516).

Mental Health and Substance UseProblems as Moderators of the RelationshipBetween Risk Markers and OutcomesThe previous analyses showed that indicators ofmental health problems were not associated withmost outcomes once demographic characteristicsand general criminogenic risk markers were takeninto account. However, this does not mean thatmental health problems might not moderate howrisk markers affect outcomes. Certain risk factors

TABLE 3 Significant Effects from Multivariate Negative BUsing Demographic Variables and Risk Markers

Variable

Females

IRR 95%

Ethnicity (white � reference)Black 0.51 0.23–Hispanic 0.49 0.21–Other 1.36 0.34–

Negative peer influence 1.84 1.30–Antisocial attitudes 1.74 1.32–Antisocial history 1.53 1.05–Psychosocial maturity 0.72 0.52–Perceived severity of court sanctions 1.83 1.24–Parental substance use/criminality — —

Note: Dashes indicate that the variable did not significantly contribute to tbut did not significantly contribute to either model. An incidence rate ratiof the population to the incidence rate in the unexposed portion of the pwith the exposure are at 1.5 times greater risk for the outcome. CI �

TABLE 4 Significant Effects from Multivariate Ordinal RePresence of an Affective Disorder

Variable

Fema

B

Affective disorder (yes) 2.04 0.57Ethnicity (whites � reference) —

BlackHispanicOther

Negative peer influence �0.60 �1.26Antisocial attitudes —Antisocial history �0.56 �1.09Psychosocial maturity 0.748 0.23Perceived severity of prior court sanctions �0.35 �0.91

Note: Dashes indicate that the variable did not significantly contribute to the
score were also tested but did not significantly contribute to models for eithe
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(e.g., antisocial history) might operate differently inadolescents with mental health problems than inadolescents without mental health problems.

We followed the procedures outlined byKenny62 to test for the moderation of the risk

arkers by our indicators of mental health prob-ems for each of the three outcomes. A series ofegressions were conducted for each outcome.hese included each risk marker score, the binary

ndicator for the presence of a mental healthroblem (having any of the mental health prob-

ems), and the interaction of the risk marker scoreith the mental health problem indicator. The

ial Regression Model Results for Antisocial Activities

152) Males (n � 797)

p Value IRR 95% CI p Value

.091 — — —

.100

.665

.001 1.52 1.36–1.70 �.001�.001 1.48 1.31–1.67 �.001

.028 1.48 1.32–1.66 �.001

.044 1.18 1.06–1.32 .004

.002 — — —— 1.22 1.09–1.37 .001

del (-2 log likelihood p � .05) and was eliminated. Age was also testedis a measure comparing the incidence rate among the exposed proportiontion. It is interpreted like a relative risk, so an IRR of 1.52 means that thoseence interval.

ion Model Results for Gainful Activities and the

� 62) Males (n � 393)

CI p Value B 95% CI p Value

51 .006 — — —

�1.14 �1.51 to �0.76 �.001�0.37 �0.75 to 0.02 .065

0.12 �0.68 to 0.91 .77607 .078 �0.276 �0.41 to �0.14 �.001

0.17 0.03 to 0.30 .0170.03 .039 �0.34 �0.48 to �0.20 �.00127 .005 0.23 0.09 to 0.37 .00221 .224 �0.47 �0.61 to �0.33 �.001

l (-2 log likelihood p � .05) and was eliminated. Age and parental issues

inom

(n �

CI

1.111.155.442.612.302.220.992.69

he moo (IRR)opula

gress

les (n

95%

to 3.—

to 0.—

to �

to 1.to 0.

mode
r gender. CI � confidence interval.


Fdmancmwiwt

uahr.Toormrpdhmo


reference group included participants with nomental health problem. Significant interactionterms indicated that the risk marker operateddifferently in the groups with and without themental health problem.

Interaction of Risk Markers and a BinaryIndicator of a Mental Health ProblemRegressions showed no significant interactionsbetween any of the risk markers and the binaryindicator of a mental health problem in predict-ing negative or positive outcomes. This findingwas consistent for males and females. Thus, therewas no indication that the presence of a mentalhealth problem affected how risk markers af-fected any of the outcomes.

Interactions of Risk Markersand Substance Use DisorderFurther analyses were performed to examine thepotential moderating role of substance use disor-ders in particular. Two potential moderatingrelationships were tested: 1) the effect of a sub-stance use disorder alone (with no accompanyingmental health problem), and 2) the effect of aco-occurring substance use disorder and mentalhealth problem. These tests looked at adolescentswith these diagnostic profiles compared withindividuals without any identified mental healthor substance use problem.

Substance use disorder s (either alone or witha co-occurring mental health problem) did notaffect the direction or strength of the relation-ships between any of the risk markers and theself-reported antisocial activity outcome for ei-ther gender. None of the interaction analysesreached statistical significance.

The presence of a substance use disorder ap-peared to moderate the relationship betweencertain risk markers and rearrest and gainfulactivity, but with different effects by gender. Thepresence of a substance use disorder alone al-tered the relationship between the risk marker“negative peer influence” and rate of rearrest formales (IRR � 1.35, p � .025) with this risk markerhaving a significant relation to rearrest in thegroup with a substance use disorder alone andvirtually no relation to rearrest in the groupwithout a substance use disorder. For females,there were no statistically significant interactionsbetween any of the risk markers and the presenceof a substance use disorder alone for these two
outcomes.


The co-occurrence of a substance use disorderand a mental health problem altered the relationsbetween several risk markers and outcomes formales and females. For males, the presence ofco-occurring problems moderated the effect ofthe risk markers “antisocial history” and “per-ceived severity of court sanctions” on proportionof time gainfully active (estimate � 0.575, p �.008, and estimate � 0.417, p � .046, respectively).

or males with a co-occurring substance useisorder and mental health problem, the riskarker “antisocial history” had no effect on the

mount of time spent in gainful activity, butegatively influenced this outcome for adoles-ents without a co-occurring disorder. The riskarker “perceived severity of court sanctions”as associated with a reduction in gainful activ-

ty in both the co-occurring group and the groupith no diagnosis, but the effect was stronger in

he group with no diagnosis.In addition, the co-occurrence of a substance

se disorder and mental health problem moder-ted the effects of the risk markers “antisocialistory” and “negative peer influence” on theate of rearrest for males (estimate � 0.749, p �04 and estimate � 1.35, p � .025 respectively).he risk marker “antisocial history” had no effectn the rate of rearrest in the group with co-ccurring problems, but heightened the risk ofearrest in the group with no diagnosis. The riskarker “negative peer influence” increased the

ate of rearrest in the group with co-occurringroblems, but had no effect in the group with noiagnosis. Finally, this co-occurrence of mentalealth problems moderated the effect of the riskarker “perceived severity of court sanctions”

n rate of rearrest for females (effect � 2.74; p �.028). In the females with co-occurring problems,this risk marker was related to higher rate ofrearrest.

DISCUSSIONThere is considerable evidence indicating that theprevalence of mental health problems amongadolescent offenders may be four to six timesgreater than the general adolescent population,and there is also evidence that youth with mentalhealth problems are at increased risk for negativeoutcomes, including justice system involvement.To the extent that a mental health problem in-creases the likelihood of continued offending or
adjustment problems for offenders, then target-
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SCHUBERT et al.

ing these issues for intervention is justified onutilitarian as well as humanistic grounds. Todate, however, there has been little empiricalevidence to indicate that the presence of a mentalhealth problem per se (as opposed to other riskmarkers that are often present along with mentalhealth problems) increases the risk of futureoffending or affects other outcomes for theseadolescents.

This study examines the influence of the pres-ence of a mental health problem on later adjust-ment in a sample of serious adolescent offenders.The approach taken here has several strengths.First, it focuses on a sample of serious juvenileoffenders, a clinically challenging and policy-relevant but largely understudied, group. Sec-ond, the analyses consider a broader range ofbackground characteristics than possible in manyprior studies. Finally, the study evaluates multi-ple outcomes over a long period of time, allow-ing a look at differential relationships for posit-ive and negative outcomes over enough time tosee the emergence of meaningful behavioralpatterns.

In general, the results indicate that the pres-ence of a mental health problem contributes onlymarginally and selectively to the rate of rearrestand level of gainful activity over 6 years in thissample. Instead, the presence of a mental healthproblem appears to be related to higher levels ofcriminogenic risk and the future challenge liesin disentangling this relationship (similar tothe situation in studies of adult mental healthproblems and violent crime).68,69 Our resultsshow that when criminogenic risk is consid-ered, most mental health problems tested(ADHD, affective, and anxiety disorders) havelittle independent contribution to the observedoutcomes. However, when substance use dis-orders are part of the clinical picture, there aresignificant relations with some outcomes. Theeffects of mental health problems on gainfulactivity (a positive outcome) follow differentpatterns from those seen with rearrest andantisocial activity (negative outcomes).

Substance use disorders emerge as particu-larly important. For male offenders, the presenceof a substance use disorder appears to have aninfluence on both positive and negative out-comes. Substance use disorders, distinct fromother mental health problems, contribute to out-comes for serious offenders beyond the influence
of a range of criminogenic risk markers, and alter t
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he effect of certain risk markers (most notably,ntisocial history). Substance use disorders, par-icularly when they co-occur with other mentalealth problems, appear to moderate the influ-nce of certain risk markers on two of the threeutcomes examined and for both genders. Al-hough the patterns of moderation are nototally consistent, the majority of the analysesndicate that the co-occurrence of a substancese disorder and a mental health problemeduces the relative impact of certain riskarkers. These findings are consistent with

ther studies with adult offenders that showubstance use disorders are the primary factorelated to later arrest and adjustment prob-ems.70-72 In addition, the potential complexityf these findings support calls for work toxpand our understanding of the complicatingole of substance use disorders for juvenileffenders,18,25,26 independent from and in ad-

dition to the effects of other mental healthproblems.

This study has several limitations that couldinfluence the findings. First, the study assess-ment does not measure all possible disorders(e.g., conduct disorder), nor does it measure all ofthe mental health problems with equal rigor. TheCIDI50 is a validated method for determining

iagnosis, but not all the modules were admin-stered. Although the most common mentalealth problems found in juvenile offender pop-lations were measured at the time of the base-

ine interview,73 results might differ slightly if allpossible diagnoses had been included or if thesame instrument had been applied across allmental health problems assessed (e.g., assess-ment of ADHD relied on parent report). Mea-surement of conduct disorder, for example,might have uncovered other effects or interac-tions with substance use or other disorders thatcould not be tested in the current design.Whether such an examination would produceany unique information that would alter thecurrent general conclusions seems questionable,however, given the expected high prevalence ofthis disorder in the sample and the demonstratedoverlap of conduct disorder with the measureddisorders.46 Second, the assessment of mental

ealth problems was done at baseline, with noccounting for disorders emerging after thatoint. It is possible that some adolescents in theno disorder” group later developed disorders or
hat some adolescents with single identified dis-



orders later developed co-occurring disorders.This bias could attenuate the group differencesbut the influence would have to be substantial toalter the overall patterns of findings. Third, thesample is composed of convicted serious adoles-cent offenders, a more homogenous and high-risk group than commonly found in the juvenilesystem. As a result, while these findings areimportant because they relate to a largely under-studied and highly policy-relevant group, theymight not generalize to adolescents involved inless serious offenses or identified earlier in thejustice process. Finally, there is no control forinterventions that could affect outcomes. Adoles-cents with mental health problems might receiveor be excluded from interventions differentlythan other juvenile offenders, although otherresearch on this sample indicates that this isunlikely.61,74 Nonetheless, the effects of treat-ment involvement, and differential treatment in-volvement, were not addressed and are an im-portant next analytic step.

These findings do not imply that the recentpolicy and practical focus on identifying adoles-cent offenders with mental health problems andproviding evidence-based treatments are fornaught. Indeed, a strong case can be made forsecuring mental health care for these adolescents;these are adolescents in the care of the state andwhatever would be done for one’s own childrenshould be done for them by the state. The highprevalence of mental health problems in samplesof juvenile offenders obviously points to address-able impediments to the future health and adjust-ment of these adolescents.

A legitimate argument can be made, however,that the juvenile justice system is not chargedwith optimizing development for youth in itscare, nor is it equipped to do so.75 As a result, thestrongest argument for ensuring mental healthcare for serious juvenile offender would comefrom a clear demonstration that the presence of amental health problem greatly increases the riskof future offending and that treatment wouldsignificantly reduce that risk. These findings,however, do not support such a direct argument,as some mental health problems (i.e., substance
use disorders) are related to recidivism and oth-
2. Fazel S, Doll H, Langstrom N. Mental disorders among adoles-cents in juvenile detention and correctional facilities: a systematic



ers (e.g., depression, PTSD) are not. Instead, ourfindings suggest that public safety is a thin reedupon which to base an argument for the juvenilejustice system being the main provider of mentalhealth services to young offenders (these servicesneed to continue beyond the purview of the juve-nile system) and that expectations that such treat-ment will necessarily reduce later offending shouldbe tempered (see Skeem et al.76 for a similar con-clusion regarding adult probationers). Althoughthere is room for careful consideration of the qual-ity of care to juvenile offenders and reasonableexpectations for improved approaches for adequatemental health treatment in the juvenile system, thecurrent policies that focus attention on both crimi-nogenic and mental health factors (with particularemphasis on treating substance use disorders) ap-pear to be well founded. &

Accepted June 6, 2011.

Ms. Schubert and Dr. Mulvey are with Western Psychiatric Institute andClinic, University of Pittsburgh School of Medicine. Dr. Glasheen iswith Research Triangle Institute (RTI) International.

The project described was supported by funds from the following:Office of Juvenile Justice and Delinquency Prevention (2007-MU-FX-0002), National Institute of Justice (2008-IJ-CX-0023), John D. andCatherine T. MacArthur Foundation, William T. Grant Foundation,Robert Wood Johnson Foundation, William Penn Foundation, Cen-ter for Disease Control, National Institute on Drug Abuse(R01DA019697), Pennsylvania Commission on Crime and Delin-quency, and the Arizona Governor’s Justice Commission.

The content of this paper is solely the responsibility of the authors anddoes not necessarily represent the official views of the fundingagencies. We are grateful for their support.

Disclosure: Ms. Schubert’s salary is supported by the National Instituteof Justice (NIJ), the National Institute on Drug Abuse (NIDA), the Officeof Juvenile Justice and Delinquency Prevention, and the John D. andCatherine T. MacArthur Foundation. Dr. Mulvey serves on the scienceadvisory board for the Office of Justice Programs, US Department ofJustice. He serves on the Committee on Assessing Juvenile JusticeReform for the National Academy of Sciences. He participates in theModels for Change Project for the John D. and Catherine T. MacArthurFoundation. He is a member of the Pennsylvania Commission onCrime and Delinquency. He is a member of the Board of Fellows of theNational Center for Juvenile Justice. He has received research fundingand salary support from NIDA, the Pennsylvania Commission on Crimeand Delinquency, NIJ, the Office of Juvenile Justice and DelinquencyPrevention, and the John D. and Catherine T. MacArthur Foundation.Dr. Glasheen reports no biomedical financial interests or potentialconflicts of interest.

Correspondence to Ms. Carol A. Schubert, 3811 O’Hara Street,Pittsburgh, PA 15213; e-mail: [email protected]

0890-8567/$36.00/©2011 American Academy of Child andAdolescent Psychiatry

DOI: 10.1016/j.jaac.2011.06.006

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