The Influence of Mental Health Disorders on Severity of Reoffending in Juveniles

Abstract

The authors conducted secondary data analyses on mental health assessment and offense history data for 700 juveniles referred to juvenile justice agencies in Alabama (probation and detention). Multiple regression analysis was applied to predict subsequent offense severity by disorder profile, adjusting for prior offense severity and background variables. Juveniles with a substance use disorder with or without co-occurring disorders were at greater risk for escalations in offense seriousness over time. Early in juvenile justice system contact, juveniles should get effective treatment for substance use to prevent offending escalation.

Keywords

psychiatric disorder substance use recidivism juvenile

Youths in juvenile justice settings are more likely to have mental health problems than their peers in the general population (Colins et al., 2010; Vermeiren, Jespers, & Moffitt, 2006; Wasserman et al., 2003). Prevalence figures for mental health problems differ across types of juvenile justice setting (Wasserman, McReynolds, Schwalbe, Keating, & Jones, 2010), increasing with further penetration of the justice system. Earlier, in a multisite study with almost 10,000 youth in a range of juvenile justice settings, we found that those in pretrial detention and in secure care were significantly more likely to have a psychiatric disorder than were youths at system intake, such as court or probation settings (Wasserman et al., 2010). Almost 65% of incarcerated juveniles and 60% of detained juveniles met criteria for one or another disorder, whereas 35% of those in system intake settings received provisional diagnoses (Wasserman et al., 2010). These rates are substantially higher than those in the general population, where adolescent mental health prevalence rates have been shown to be approximately 15% (e.g., Roberts, Attkisson, & Rosenblatt, 1998).

Identifying which youths will reoffend is a primary goal for juvenile justice agencies as they aim to protect public safety. Mental health status may improve that identification since there is evidence that mental health disorder increases the likelihood of recidivism. For example, a meta-analysis revealed that substance abuse, conduct problems, and anxiety and stress were all associated with juvenile reoffending (Cottle, Lee, & Heilbrun, 2001). However, most existing studies (including 12 of 13 considered in the Cottle et al., 2001, meta-analysis) of the mental health–recidivism link have relied on unstandardized assessment measures, often derived from chart review of prior unsystematic clinical interview, making comparisons difficult (Niarhos & Routh, 1992), and resulting in a variety of nonspecific associations between mental health disorders and recidivism.

Studies making use of structured mental health assessments, such as the DISC or the CAS,¹ have consistently revealed that youths with mental health disorders are more likely to recidivate and have offered further clarification of the types of mental health concerns most likely to raise recidivism risk. However, even when standardized mental health assessments are used, small sample sizes may still lead to inconsistent results. For example, in one small study of incarcerated juveniles (N = 100), most recidivists (more than 90%) had at least one disorder on the CAS, compared to only half among nonrecidivists (Vermeiren, Schwab-Stone, Ruchkin, De Clippele, & Deboutte, 2002). Compared to those without the disorder, those with conduct disorder were more likely, and those with major depression less likely, to reoffend. In another small study of incarcerated male youths (N = 232), substance use disorder and co-occurrence of two or more disorders on the DISC-IV-predicted substance-related recidivism, compared to those without a disorder (Colins et al., 2011). In contrast, Wierson and Forehand (1995), using an earlier version of the DISC (DISC-II), found that incarcerated males with substance use disorder were less likely to reoffend than nondisordered males (N = 75).

Two recent studies of youths with justice system contact, relying on both large sample sizes as well as standardized mental health assessments, found more consistent results, highlighting the recidivism risk associated with substance use disorder with or without other mental health problems. Incarcerated juvenile offenders with a substance use disorder (whether or not they indicated additional mental health disorders) were more likely to reoffend compared to nondisordered youth, even when controlling for criminogenic factors (i.e., peer influence and antisocial history; Schubert, Mulvey, & Glasheen, 2011). The “mental health” construct on which these authors relied combined data from diagnostic tools and symptom scales and aggregated conditions across a wide spectrum of internalizing (e.g., mood and anxiety) and externalizing (e.g., ADHD) disorders, so that the degree to which specific disorder profiles have differing associations with recidivism remains unclear. In an earlier study, when we examined profiles for internalizing and externalizing disorders separately (McReynolds, Schwalbe, & Wasserman, 2010), youths with comorbid substance use and disruptive behavior disorders at probation intake had an increased risk of recidivism, whereas when substance use disorder occurred along with either affective or anxiety disorder, there was no increase in the odds of recidivism, relative to nondisordered youth. In a third large study that also employed standardized mental health assessments (Copeland, Miller-Johnson, Keeler, Angold, & Costello, 2007), somewhat different results were found: Children with comorbid internalizing and disruptive behavior disorder, rather than substance disorder, were more likely to offend in young adulthood, although the differences in both sample and time frame limit comparisons.

Even when sample size is adequate, and measures of mental health are sound, investigations have not offered a full picture of the range of reoffending activities. Earlier studies have considered dichotomous (e.g., McReynolds et al., 2010) or frequency-based (e.g., Schubert et al., 2011) measures, although escalation in offense seriousness more particularly reflects a worsening of impairment and might be an especially critical indicator of an individual’s need for secondary prevention services. Although continued offending at the same level of severity is surely worrisome, escalation in seriousness of criminal activity reflects an increased threat to public safety.

In sum, several studies have found that youth with mental health concerns are more likely to reoffend, but findings with regard to reoffense severity are lacking. Particularly for those early in their contact with the juvenile justice system, such as at probation intake or in pretrial detention, it is important to know to what extent mental health problems affect reoffending and to what extent they may be at risk of committing more severe offenses in the future to prevent their further impairment.

To examine the contributions of different profiles of mental health concerns, here we consider the rank-ordered severity of future offenses as predicted by combinations of psychiatric and substance use disorders. We analyze associations between disorder profile and severity of reoffenses, adjusting for prior offense severity and background variables, using offense history data through 18 years of age. We consider the degree to which substance use and internalizing and disruptive behavior disorders, alone and in comorbid patterns, predict reoffense severity in a sample of youths at relatively early points in the juvenile justice system. We compare the results to those for reoffense frequency and recidivism.

Method

Design

This longitudinal investigation included secondary analyses on mental health assessment and offense history data on Alabama juveniles in five counties. At baseline, youths completed a psychiatric assessment (Voice Diagnostic Interview Schedule for Children; Wasserman, McReynolds, Lucas, Fisher, & Santos, 2002). At the close of data collection, mental health records were linked to state records of each youth’s full juvenile offense history, both before and after baseline.

Participants

Youths in Dallas (n = 238), Jefferson (n = 491), Mobile (n = 174), Montgomery (n = 119), and Morgan (n = 145) Counties participated in a collaboration with the Center for the Promotion of Mental Health in Juvenile Justice (CPMHJJ) between 2002 and 2006. Following either a systematic universal or randomized (by day of the week) sampling protocol, depending on county, 1,167 youths referred to juvenile justice agencies (874 at probation intake and 293 in detention) reported on mental health status. For 700 youths, data were also available on full juvenile offense history, through December 13, 2007 (see below). A second report (in preparation) considers reoffending based on adult criminal court data. Data from youths from four counties were previously included in the National Archive of Mental Health in Juvenile Justice, contributing to reports of the prevalence of psychiatric disorder across a range of juvenile justice settings (Wasserman et al., 2010; Wasserman & McReynolds, 2011); these were supplemented by the additional sample from Morgan County.

Procedure

At baseline, youths completed an audio computer-assisted diagnostic self-interview soon after intake into either their county’s probation or detention system, after which assessment data were sent to CPMHJJ. A little over a year after the close of data collection (M = 14 months), the Alabama Administrative Office of the Courts (AAOC) attempted to match individual youths’ assessment data to their cumulative juvenile justice records, relying on agency case number, date of birth, gender, race, county, and admit date at baseline. For each matched youth, AAOC provided the date and type of all charges, beginning with the first complaint through December 13, 2007. After AAOC returned the offense data set to CPMHJJ, data were matched to baseline assessment results and again deidentified.

Measures

Demographic characteristics

Background information (date of birth, assessment date, gender, race, and justice setting type) was recorded by local staff at baseline.

Psychiatric disorder

At baseline, youths self-assessed mental health status on the Voice Diagnostic Interview Schedule for Children (V-DISC). The V-DISC measures 20 disorders in four clusters (substance use [SUD], disruptive behavior [DBD], anxiety [ANX], and affective [AFF] disorder) based on past-month symptoms according to the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders (American Psychiatric Association, 1994) and utilizes an audio computer-assisted self-interview format; it has been widely used in juvenile justice settings (e.g., Wasserman et al., 2004; Wasserman et al., 2009; Wasserman, McReynolds, Ko, Katz, & Carpenter, 2005).

To examine the unique contributions of disorder profiles to offending, we first created non-comorbid measures to denote youths who had mental health concerns only in a single domain: internalizing (anxiety or affective: INT alone), DBD alone, SUD alone. In addition to the non-comorbid measures, indications of mental health concerns in multiple domains were created: internalizing and disruptive behavior disorder (INT + DBD), substance use and internalizing disorder (SUD + INT), substance use and disruptive behavior disorder (SUD + DBD), and substance use, internalizing, and disruptive behavior (SUD + INT + DBD).

Offense characteristics

Two types of offense data were available. Prior offenses included charges before the baseline mental health assessment (including the current charge). Subsequent offenses were charges after baseline up to age 18. We also present descriptive information separately on youths’ current offense, although analyses aggregated current offenses into prior offenses (see below). For each youth, we calculated the number of months after the baseline mental health assessment for which offense records were reviewed, through December 13, 2007 (the “censor date”) or age 18 years, whichever came first. Although our primary outcome was offense seriousness, we also examined a categorical measure of postbaseline offending as well as the frequency of postbaseline offending to increase comparability with earlier reports.

Offense seriousness for each offense was coded, based on the severity ranking system developed by the National Center of Juvenile Justice (Butts, 1997; National Juvenile Court Data Archive, 2007). Each offense was coded (between 1 and 77), with smaller numbers reflecting increased severity. As an example, the most serious charges (e.g., capital murder) received a code of 1, and the least serious charges (e.g., violation of the seatbelt law) received a code of 77. Participants who did not recidivate received a code of 78. We defined prior offense severity as the most serious offense committed before baseline and subsequent offense severity as the most serious postbaseline offense. Because no determination could be made of their seriousness, charges that referenced administrative actions (e.g., miscellaneous filing or dispositional hearing) were not examined.

We also calculated the number of days on which offenses occurred prior to baseline and the number of days of subsequent, postbaseline offenses (offense days). AAOC records sometimes noted multiple offenses on the same date that were part of the same event. This could happen if a youth was charged with both trespassing and a residential burglary for the same event or if he or she drove a stolen car through two counties. To simplify the tallying of offenses, then, for the frequency constructs we aggregated all offenses charged on a single date. The 700 participants were charged with offenses committed on 2,095 dates, 1,629 of which were prior offenses and 466 of which were subsequent offenses.

Data Analysis

We compared the demographic (age, gender, race, county, juvenile justice setting) and mental health characteristics (any disorder and any anxiety, any affective, any substance use, and any disruptive behavior disorder) of those with and those without offense history data. Youths from Morgan were significantly less often matched, χ²(1) = 20.7, p < .001; this likely was a result of Morgan County using a different system to track youth ID, which then had to be further reconciled with state codes. Aside from this system difference, of nine further comparisons, only one was significant, a rate consistent with chance results. Disruptive behavior disorder was somewhat more prevalent in unmatched youths (21.6%) than in those matched (16.6%), χ²(1) = 4.7, p < .05. There were no other significant differences between matched and unmatched participants, indicating that selective attrition had only a limited effect on our results.

Our primary models predicted severity of subsequent offenses from disorder profile via multiple regression analysis. The first model considered only demographic and offense features, including prior offense severity, number of months postbaseline of available offense data, baseline age (in years), gender, race, and justice setting type (detention versus probation). In predicting subsequent offense severity from a model that included prior offense severity, analyses were essentially predicting increases in severity, or worsening course. The second model examined further contributions of non-comorbid disorder profiles, INT alone, DBD alone, and SUD alone, and excluding youth who endorsed multiple disorder clusters. The final model considered the added contribution of cross-cluster comorbidity, comparing comorbid youths (INT + DBD, SUD + INT, SUD + DBD, and SUD + INT + DBD) to those with no disorder. For all analyses, nondisordered youths were the reference group for psychiatric disorder profile, female was the reference group for gender, and probation was the reference group for justice setting. A single participant was neither African American nor White; comparisons by race considered White as the reference group and included this youth among non-White participants. The final model was also applied to examine frequency of offense days and subsequent postbaseline offending (scored dichotomously).

Although analyses controlled for the length of follow-up (months reviewed), incarceration might have further limited opportunity for offending during follow-up, so that incarcerated and nonincarcerated youths would have varied in their opportunity to reoffend. We examined regression results without youths who experienced a time in secure care after baseline. Only 7% of the total sample had been incarcerated during follow-up (n = 46). Results, though attenuated by reduced power, were essentially unchanged (results are available on request).

Results

Table 1 presents sample characteristics. Baseline age ranged between 8 and 18 years. The average youth was 15 years old at baseline; most (69%) were non-White. Almost half were from Jefferson County (n = 319) and about a fifth were from Dallas County (n = 137), with smaller proportions from Mobile (n = 105), Montgomery (n = 78), and Morgan (n = 63) Counties.

Table 1:

Sample Characteristics for the Total Sample, and by Recidivism Status

	Total Sample		Nonrecidivists		Recidivists
	(N = 700)		(n = 459)		(n = 241)
	n	%	n	%	n	%
Age^a***	15.2	1.6	15.5	1.6	14.8	1.4
Male	458	65.4	294	64.1	164	68.0
Non-White***	485	69.3	291	63.4	194	80.5
Detention***	181	25.9	91	19.8	90	37.3
Number of prior offenses^a **	2.3	2.2	2.1	2.1	2.7	2.3
Number of subsequent offenses^a ***	0.7	1.2	0	0	1.9	1.3
Current offense severity^a,c†	38.6	17.4	39.6	17.6	36.9	16.9
Prior offense severity^a,c**	34.1	17.2	35.3	17.6	31.7	16.0
Subsequent offense severity^a,c***	63.5	22.6	—	—	36.0	18.2
At least one disorder**	308	44.0	184	40.1	124	51.5
Any internalizing disorder (INT)^b*	212	30.3	128	27.9	84	34.9
Any disruptive behavior disorder (DBD)^b**	116	16.6	64	13.9	52	21.6
Any substance use disorder (SUD)^b*	112	16.0	67	14.6	45	18.7
Two or more clusters^b*	109	15.6	64	14.0	45	18.7
Disorder profile^b
INT alone	125	17.9	77	16.8	48	19.9
DBD alone*	27	3.9	14	3.1	13	5.4
SUD alone	47	6.7	29	6.3	18	7.5
INT and DBD	44	6.3	26	5.7	18	7.5
SUD and INT	20	2.9	14	3.1	6	2.5
SUD and DBD	22	3.1	13	2.8	9	3.7
SUD, INT and DBD*	23	3.3	11	2.4	12	5.0

Note. DBD = disruptive behavior disorder; INT = internalizing disorder; SUD = substance use disorder.

M, SD.

The reference category is the nondisordered group.

Smaller numbers reflect increased severity.

†

p = .05. *p < .05. **p < .01. ***p < .001.

Approximately half the sample reported one or another psychiatric disorder, and 15% reported disorders in two or more clusters. Internalizing disorders were most frequently identified (about a third met criteria for any INT), followed by DBD and SUD. As expected, cross-cluster comorbidity was considerably less common, so that each comorbid group included fewer than 10% of participants. Recidivists were on average younger at baseline, t(698) = 5.5, p < .001, and were more often non-White, χ²(1) = 21.7, p < .001, and in pretrial detention, χ²(1) = 25.3, p < .001 (see Table 1). They were significantly more likely to have committed more serious prior offenses, t(698) = 2.7, p < .01, and to have a psychiatric disorder, χ²(1) = 8.3, p < .01, than were nonrecidivists. For example, half had at least one disorder, compared to 40% of nonrecidivists. Because results of bivariate analyses likely reflect several biases (e.g., associations between baseline age and recidivism are likely biased by the length of follow-up), our main focus is on the multivariate models.

Across all cumulative juvenile offenses, youths were most often charged with a person-related offense (e.g., assault), followed by property, substance, and weapon offenses (see Table 2). About a third could not be classified into any of these offense categories (e.g., traffic offenses). Considering current offenses only, youths were most often charged with disorderly conduct (13.3%, severity rank = 57), assault in the third degree (10.4%, severity rank = 24), and possession of marijuana in the second degree (8.0%, severity rank = 49). About 35% of the matched sample committed a further offense after baseline.

Table 2:

Cumulative Offenses by Offense Type

	Total		Prior		Subsequent
	(N = 2,231)		(n = 1,727)		(n = 504)
	n	%	n	%	n	%
Person related	664	29.7	519	30.0	145	28.8
Property	526	23.6	393	22.8	133	26.4
Weapon	64	2.9	51	3.0	13	2.6
Substance	190	8.5	153	8.9	37	7.3
Other	787	35.3	611	35.4	176	34.9

Note. “Prior” includes current offenses. “Other” includes charges not classified into another category.

Table 3 presents results of analyses predicting offense severity. The first model, considering demographic and offense characteristics only, significantly predicted subsequent offense severity, F(5, 694) = 20.3, p < .001; R² = .15. Youths who had committed more severe offenses prior to baseline were more likely to commit even more severe subsequent offenses (β = .09, p < .01). As expected, youths whose records were reviewed for longer periods postbaseline were more likely to commit additional offenses that were more severe than youths whose records were reviewed for shorter periods (β = −.30, p < .001). Males (β = −.12, p < .001), non-Whites (β = −.13, p < .001), and detainees (β = −.09, p < .05) were more likely to commit more serious subsequent offenses, resulting in a severity increase of 6 units for males (B = −5.78) and for non-Whites (B = −6.39) and almost 5 units for detainees (B = −4.60), where negative coefficients reflect worsening severity.

Table 3:

Predicting Severity of Reoffending From Mental Health Disorder Profiles

	Model 1			Model 2			Model 3
	B	SE B	β	B	SE B	β	B	SE B	β
Covariates
Prior offense severity	0.12	0.05	.09*	0.10	0.05	.08*	0.11	0.05	.08*
Months reviewed	−0.72	0.13	−.30***	−0.71	0.14	−.31***	−0.74	0.13	−.31***
Age	−0.44	0.76	−.03	−0.63	0.83	−.04	−0.46	0.77	−.03
Male	−5.78	1.69	−.12**	−6.18	1.85	−.13**	−5.90	1.70	−.12**
Non-White	−6.39	1.74	−.13***	−6.36	1.93	−.13**	−7.18	1.76	−.15***
Detention (vs. probation)	−4.60	2.08	−.09*	−4.73	2.39	−.09*	−3.28	2.11	−.06***
Disorder profiles^a
INT alone	—	—	—	−4.17	2.18	−.08	−3.97	2.19	−.07
DBD alone	—	—	—	−7.50	4.17	−.07	−7.68	4.18	−.07
SUD alone	—	—	—	−8.87	3.25	−.11**	−9.34	3.25	−.10**
INT + DBD	—	—	—	—	—	—	−4.25	3.34	−.05
SUD + INT	—	—	—	—	—	—	−0.14	4.81	.00
SUD + DBD	—	—	—	—	—	—	−9.15	4.69	−.07^†
SUD + INT + DBD	—	—	—	—	—	—	−11.09	4.52	−.09*

Note. B = unstandardized coefficient; β = standardized coefficient; DBD = disruptive behavior disorder; INT = internalizing disorder; SE = standard error; SUD = substance use disorder.

The reference category is the nondisordered group.

†

p = .05. *p < .05. **p < .01. ***p < .001.

Considering non-comorbid disorder profiles, the second model significantly predicted subsequent offense severity, F(8, 585) = 12.8, p < .001; R² = .17, with contributions of demographic and offense characteristics essentially unchanged, and a small improvement in explained variance. Compared to nondisordered youth, those with SUD alone were more likely to commit more serious subsequent offenses (β = −.11, p < .01), resulting in an increase, relative to baseline, of almost 9 units (B = −8.87).

In the third model, comorbid profiles made additional contributions to subsequent offense severity, F(12, 687) = 11.0, p < .001; R² = .17, explaining the same proportion of the variance as found for Model 2. Compared to nondisordered youth, those with SUD alone (β = −.10, p < .01) or in combination with DBD (β = −.07, p = .05), and those with DBD and INT (β = −.09, p < .05) were more likely to commit more serious subsequent offenses. Those with SUD with or without DBD increased in severity by approximately 9 units, whereas those meeting criteria for SUD, INT, and DBD increased by 11 units. The partial r values of SUD only, SUD + DBD, and SUD + INT + DBD were about .10, indicating small effect sizes (Cohen, 1988). Comparable results were found for the models that predicted subsequent offense frequency and recidivism (dichotomous measure), where significant effects were found for SUD with or without comorbid INT and DBD (results are available on request).

Comparing types of subsequent offenses between those with and without substance use disorder, post hoc analyses revealed that subsequent offenses committed by youths with substance use disorder (with or without co-occurring disorders) were significantly more likely to be property offenses (36% of reoffenses among substance abusers were property offenses) than was the case for offenses committed by counterparts without a substance use disorder (for whom 24% of reoffenses were property offenses), χ²(1) = 5.6, p < .05. Rates for person-related, substance, and weapon offenses were relatively similar for those with a substance use disorder relative to their peers, and other offenses (e.g., traffic offenses) were less common among those with a substance use disorder, χ²(1) = 4.7, p < .05.

Discussion

We examined the degree to which mental health disorder profiles predicted increased severity of subsequent juvenile offending, adjusting for prior offense severity and demographics. In contrast to earlier studies, we focused on increasing severity, or worsening course, rather than using a dichotomous or frequency-based measure of offending, as earlier studies have done. By using this new measure we found that youths with substance use disorder with or without co-occurring disorders committed not only more reoffenses but also more severe reoffenses. A further strength of this investigation is its longitudinal design; the data incorporated full juvenile offending histories, allowing us to adjust for severity for all offenses prior to baseline and to predict reoffending up to age 18 (on average, 17.7 months after baseline). Because this study considers a relatively early point in juvenile justice processing, results underscore that interventions at this juncture, by preventing a worsening offending course, are likely to affect future public safety.

Consistent with previous research that focused on other measures of offending and other types of juvenile samples, we found that substance-disordered youth (regardless of other comorbid conditions) are at particular risk for an escalating pattern of future juvenile offenses, particularly those involving property offenses. Furthermore, detainees and males showed significant escalation in future offending relative to either youth in probations or females.

An example can illustrate the increases in severity of reoffending over time. Although the magnitude of current effect sizes was small, even small effect sizes resulted in substantial escalation in offense seriousness. The severity ranking scale employed here does not measure offense severity on an interval scale, so that changes may not reflect the same degree of severity across all levels of severity spectrum. With this in mind, however, in youths with comorbid substance use and internalizing and disruptive behavior disorders, offending severity worsened by approximately 11 units across follow-up. This might correspond to a change from 12 on the severity scale (robbery) to 1 (capital murder) or from 71 (no driver’s license) to 59 (making a terrorist threat), so that even a small effect may have important implications for public safety.

Substance Use Disorder and its Association with Reoffending

Prior studies with different methods and different samples, but relying on standardized and universal mental health assessment methods, have found that substance use disorder with or without co-occurring disorders increased the likelihood of juvenile recidivism (McReynolds et al., 2010; Schubert et al., 2011). These earlier investigations, however, have relied on dichotomous (recidivism vs. desistence) or frequency-based (number of reoffenses) measures, rather than offending severity. The present focus on escalation in offense seriousness reflects a worsening of youth impairment over time.

Several explanations have been suggested for the substance abuse–offending link (Bennett, Holloway, & Farrington, 2008). Evidence provides most support for the proposition that substance use causes illegal activity (Bennett et al., 2008), as users of illicit substances are more likely to commit property crimes to obtain those substances (Goldstein, 1985). Supporting this theory, we found that youths with substance use disorder were more likely (a third more likely) to commit further property crimes, compared to counterparts without substance use disorder.

Associations of Detention with Reoffending

We found an association with justice setting type: Detainees were more likely to commit more severe reoffenses than were youths at probation intake. Earlier, we reported that increased rates of externalizing and internalizing disorders and comorbid conditions covaried with deeper penetration of the juvenile justice system (Wasserman et al., 2010). In that study, for example, although 15% of the youth at system intake (i.e., entering probation) met criteria for a disruptive behavior disorder, twice as many of those in detention did so.

Given that our analyses adjusted for prior offense severity, the contribution of setting to offense severity cannot be explained by its standing as a proxy for increased current offense severity. Rather, the contributions of other characteristics either of the detention setting or of the detainee himself or herself must be considered. Although studies on the effects of juvenile detention are limited, several have shown that both detention (pretrial) and incarceration (postadjudication) are linked to increased recidivism (for an overview, see Holman & Ziedenberg, 2006; Lipsey & Cullen, 2007). For example, in one study, juvenile detainees were 3 times more likely to be later committed to a juvenile facility than were youth who were not detained, adjusting for other factors such as offense seriousness (Frazier & Cochran, 1986). Studies also found increased recidivism in those who were incarcerated, relative to those convicted but not imprisoned (e.g., Nieuwbeerta, Nagin, & Blokland, 2009; Smith, 2006). Housing delinquent youth together can worsen their behavior over time (Cecile & Born, 2009; Dishion, McCord, & Poulin, 1999), perhaps explaining why meta-analysis demonstrates that interventions carried out in youths’ living environments are generally more effective at reducing reoffending than are those occurring in detention facilities (Lipsey & Wilson, 1998).

Although we did not have access to measures such as IQ or family support, which might also explain the higher level of reoffending severity in detainees, a recent study (Gatti, Tremblay, & Vitaro, 2009) showed that youths who were impulsive, poorly supervised at home, or from families of more limited social circumstances were more likely to undergo juvenile justice interventions, regardless of their level of antisocial behavior; juvenile justice intervention history, in turn, increased the risk of adult offending. Residential juvenile justice interventions had the most negative impact (Gatti et al., 2009), showing that characteristics of both the detention setting as well as of the detainee himself or herself contributed to reoffending.

Limitations

We lacked power to examine the degree to which the impact of psychiatric disorder on reoffense severity may be moderated by gender. Earlier, we found that affective disorder predicted recidivism in co-occurrence with disruptive behavior or substance use disorder in a juvenile probation intake sample (McReynolds et al., 2010). In that study, females with comorbid affective and substance use disorder were more likely to reoffend than were males with the same disorder profile or nondisordered males. Given the overrepresentation of males in the juvenile justice system, the proportion of females in the current sample was small, as expected (n = 242, 35%), and their particular disorder profiles were too infrequent (e.g., only four females reported comorbid substance use and disruptive behavior disorder) for us to consider gender by disorder profile interactions. Further research, with larger samples, should investigate the extent to which psychiatric disorder relates to future offense severity in both genders.

As another limitation, we were unable to find a measure for offense severity with strong psychometric properties. Several offending severity measures exist, but most have been developed to rank self-reported delinquency (e.g., Bird et al., 2005) rather than the official records considered here. Others focus more globally on the type of offense (e.g., violent offenses are always ranked as more severe than property offenses; Harris, Lockwood, Mengers, & Stoodley, 2001), which could mean that minor interpersonal offenses (e.g., affray) would be classified as more serious than some more substantive property offenses (e.g., burglary). For this investigation, we used the system developed by the National Center of Juvenile Justice (Butts, 1997; National Juvenile Court Data Archive, 2007), which is a national severity ranking system but has not been validated across states. More work on the validation and reliability of severity scales for the ranking of a broad range of offenses is needed. Finally, the study lacked information on other important measures that could explain the detention–recidivism link, such as IQ and family support.

Clinical Implications

Not all mental health conditions bear the same association to increased severity in reoffending. Juvenile offenders with substance use disorder, with or without other co-occurring disorders, are at risk for offense escalation and are more likely to commit more severe reoffenses than are juveniles without disorder. In an earlier randomized controlled trial (Cuellar, McReynolds, & Wasserman, 2006), mental health or substance use diversion services effectively prevented or delayed recidivism in juvenile probationers. Treatments exist for these conditions, although interventions vary by type of disorder profile, and it is important that justice agencies effectively match youths to programs to address their mental and behavioral health needs.

Standards for needs assessment in juvenile justice settings underscore the importance of mental health evaluation for identifying youths with substance use disorder and mental health problems (Skowyra & Cocozza, 2006; Wasserman et al., 2003). Present findings further document that information from scientifically sound instruments can contribute to the identification of those juveniles most likely to engage in a future escalating course of offending. Identifying youths with substance use disorder with or without co-occurring disorders and referring them to specialized and effective interventions are likely to decrease their risk for an escalating pattern of future juvenile offenses.

Footnotes

This work was supported by the Carmel Hill Fund and by a Marie Curie grant of the European Union awarded to Machteld Hoeve (FP7-PEOPLE-2010-IOF, Project 274337).

Notes

Machteld Hoeve, PhD, is a Marie Curie fellow at the Division of Child and Adolescent Psychiatry of Columbia University (2011–2012) and assistant professor of forensic child and youth care sciences at the Research Institute Child Development and Education of the University of Amsterdam, Netherlands. Her research interests include the development of delinquency, specifically, environmental influences such as family processes, psychopathology, and gender differences in relation to development of criminal behavior.

Larkin S. McReynolds, PhD, is an assistant professor at Columbia University’s Departments of Epidemiology and Mental Health Services and Policy Research. As research director for the Center for the Promotion of Mental Health in Juvenile Justice, her work has focused on implementing and evaluating screening and assessment protocols across a range of juvenile justice settings.

Gail A. Wasserman, PhD is a professor of clinical psychology in child psychiatry at Columbia University. She directs Columbia’s Center for the Promotion of Mental Health in Juvenile Justice (), a research and policy center offering guidance to juvenile justice agencies on protocols for efficient identification and service referral for those with mental health service needs. She has been conducting research on developmental psychopathology for 35 years. She has authored more than 100 articles for academic and practitioner audiences.

Cary McMillan is the Information Technology Project Manager in the Legal Division of the Administrative Office of Courts in Alabama. She has been working in information technology with the courts for over 24 years, specializing in the juvenile systems. She continues to be the technology consultant for all the juvenile data sharing and statistical initiatives for aggregate and case specific data. The Juvenile Technology Project, under Mrs. McMillan’s leadership, strives to provide better information to court personnel charged with the responsibility of making appropriate decisions for Alabama’s children and families.

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