MENTAL HEALTH AND JUVENILE ARRESTS: CRIMINALITY, CRIMINALIZATION, OR COMPASSION

CRIMINOLOGY VOLUME 44 NUMBER 3 2006 593

MENTAL HEALTH AND JUVENILE ARRESTS: CRIMINALITY, CRIMINALIZATION, OR COMPASSION?*

PAUL HIRSCHFIELD Rutgers University

TINA MASCHI Monmouth University

HELENE RASKIN WHITE Rutgers University

LEAH GOLDMAN TRAUB Rutgers University

ROLF LOEBER University of Pittsburgh and Free University, Amsterdam

KEYWORDS: juvenile justice, mental health, arrests, child psycho-pathology, criminalization, adolescent males

Juveniles in secure confinement allegedly suffer from more mental

health problems than their peers. This may reflect background and behavioral characteristics commonly found in clients of both mental health and juvenile justice systems. Another explanation is that mental disorders increase the risk of arrest. These interpretations were tested on a sample of Pittsburgh boys (n = 736). Findings indicate that arrested

* This research was supported, in part, by grants from the National Institute of

Mental Health (MH 66170; MH 050778), the National Institute on Drug Abuse (DA 17552; DA 411018), and the Office of Juvenile Justice and Delinquency Prevention (96-MU-FX-0012). Points of view in this document are those of the authors and do not necessarily represent the official position or policies of the U.S. Department of Justice. Jeffrey Draine, William Fisher, Linda Teplin, Nancy Wolff, and three anonymous reviewers provided valuable comments and suggestions. We also acknowledge Rebecca Stallings, Lisa Metzger, and Sabrina Koester for their assistance in preparing the data files and the manuscript. An earlier version of this paper was presented at the American Society of Criminology annual meeting, November 2004, Nashville, TN. Please direct correspondence to Paul J. Hirschfield; Department of Sociology; Rutgers University; 54 Joyce Kilmer Avenue; Piscataway, NJ 08854; e-mail: [email protected].

594 HIRSCHFIELD ET AL.

youth exhibit more attention deficit hyperactivity (ADH) problems, oppositional defiant (OD) problems, and nondelinquent externalizing symptoms prior to their first arrests compared to their never-arrested peers. However, arrested and nonarrested youth score similarly on prior affective and anxiety problems and internalizing symptoms. Net of delinquency, substance use, and other selection factors, internalizing problems lower the risk of subsequent arrest, whereas OD problems and nondelinquent externalizing symptoms increase it. ADH problems have no effect on arrest net of delinquency and substance use. These findings lend only partial support to the criminalization hypothesis. Whereas some mental health symptoms increase the risk of arrest, others elicit more cautious or compassionate official responses.

More than two decades of research consistently reports a link between juvenile justice involvement and mental disorder (Domalanta et al., 2003; Otto et al., 1992; Teplin et al., 2002; Vermeiren, 2003; Wasserman, Ko, and McReynolds, 2004). A recent review of rates of psychopathology at various points in juvenile justice processing finds that externalizing disorders, such as attention deficit hyperactivity disorder (ADHD), range from 20 to 72 percent for incarcerated adolescents. Internalizing disorders, such as depression, vary from 11 to 33 percent (Vermeiren, 2003). The largest study in this field (n = 1,829) estimates that anxiety disorders, affective disorders, ADHD, and oppositional defiant disorder (ODD) each range between 15 and 21 percent among males recently confined to a juvenile detention center (Teplin et al., 2002).

Such prevalence estimates foster the common perception that mentally disordered youth (MDY) comprise an increasingly disproportionate share of juvenile justice clientele (Boesky, 2002). They also trouble juvenile justice scholars and practitioners, because secure facilities are unlikely to screen for mental disorders, much less to provide appropriate and sufficient treatments (Cocozza and Skowyra, 2000; Teplin et al., 2002). Among 303 Chicago juvenile detainees with a major mental disorder, only 16 percent received any treatment within 6 months or prior to case disposition (Teplin et al., 2005). Most psychiatric conditions worsen if left untreated, especially in juvenile justice facilities, whose conditions may foster isolation, fear, resentment, and victimization (Ginsburg and Demeranville, 2001; Thompson, 2004).

To some observers, this situation suggests a failure on the part of family support, educational, child welfare, and mental health systems to address the needs of troubled youth in the community. These failures are evident in recent reports that schools increasingly summon the police to control emotionally disturbed youth (Rice, 2003), that thousands of nondelinquent MDY are warehoused in juvenile detention centers while awaiting mental

MENTAL HEALTH AND JUVENILE ARRESTS 595

health placements (U.S. Congress, 2004), and that thousands of parents relinquish custody of their children to the state so that their children can receive mental health services (GAO, 2003), often in facilities that also house serious and chronic offenders (Ginsburg and Demeranville, 2001).

Although agreement appears to be widespread that MDY are overrepresented and underserved in the juvenile justice system, the reasons for these high rates are not well understood. For instance, research has not investigated the extent of correspondence between disproportionate rates of confinement among MDYs and disproportionate rates of arrest. Youth at earlier stages of juvenile justice processing, such as detention and probation, show lower psychopathology prevalence rates than incarcerated juveniles (Vermeiren, 2003). However, it is impossible to determine whether the elevated levels of mental health problems observed in particular studies on incarcerated youth reflect higher rates of arrest among MDYs or elevated risks of confinement of MDYs following arrest. In addition, assuming that MDYs show elevated rates of arrest, research has not examined whether this is due primarily to differential background characteristics, illegal or risky behavior, or system response. This study therefore examines the extent to which arrested and nonarrested youth vary in their levels of prior psychological symptomatology and tests several possible explanations for these differences.

FOUR INTERPRETATIONS OF THE MENTAL HEALTH–ARREST RELATIONSHIP

The literature suggests four explanations for the overrepresentation of MDYs among arrested youth, none of which has been adequately tested. These explanations, respectively, view a relationship between mental health and juvenile arrests as consequences of the effects of social background, of delinquency and substance use, or of extralegal behavioral factors on mental health problems and juvenile arrest, or of the impact of mental health problems on arrest.

The first explanation, derived from a social causation perspective (Miech et al., 1999), is that mental health is related to arrest, because both mental health and arrest are caused by social background factors such as socioeconomic status (SES), neighborhood poverty, and family criminality. Three background variables—age, disadvantaged minority status, and low SES—consistently predict elevated police contact or arrest (Beckett et al., 2005; Brownfield, Sorenson, and Thompson, 2000; Werthman and Piliavin, 1967; Wolfgang, Thornberry, and Figlio, 1987), and often, though not consistently, are associated with mental health (Conger, Conger, and Elder, 1997; Costello et al., 1998; Hammack, 2003;


McCloyd, 1997; Wong, Eccles, and Sameroff, 2003). It bears mention that associations between race and mental health are a source of controversy owing to charges of cultural bias in mental health assessment (Cuellar and Paniagua, 2000; Davison and Ford, 2001).

Although strongly correlated with race and individual SES, neighborhood conditions may also exert independent effects on mental health problems and arrest. Evidence suggests that high rates of community violence and neighborhood poverty promote some child mental health problems (Leventhal and Brooks-Gunn, 2003; Rosenthal, 2000). In addition, the increased emphases on drug enforcement and quality-of-life policing during the 1990s (Parenti, 2000) along with evidence of an “ecological contamination” effect on police discretion (Smith, 1986) suggest a more aggressive police response in disadvantaged neighborhoods, though others maintain that police respond more leniently to minor offenses in high-crime neighborhoods (Klinger, 1997).

Another background factor that has received little attention in the literature on the determinants of adolescent mental health and arrests is family criminality. Classic field studies suggest that the reputation of a youth’s family can influence the harshness of the police response to that individual (Werthman and Piliavin, 1967). In addition, parental criminality and incarceration may impair children’s mental health (Parke and Clarke-Stewart, 2003).

The second and most common interpretation of elevated juvenile justice contact among MDYs is that there is greater prevalence, frequency, or seriousness of delinquency and substance use among MDYs. Much research has documented the frequent co-occurrence of mental disorders and illegal drug use among youth (Molina and Pelham, 2003; White et al., 2002), as well as the comorbidity between conduct disorders and other mental health disorders besides substance abuse (Loeber, Stouthamer-Loeber, and White, 1999). No studies were identified that examine the relationship between mental health and offense seriousness (across offense categories) among juveniles. However, research has documented the co-occurrence of adolescent mental disorders and serious violent crime (Elliott, Huizinga, and Menard, 1989; Johnson et al., 2000). One large-scale study comparing offense seriousness between mentally disordered and other juvenile and adult suspects reports inconsistent results (Engel and Silver, 2001).

The third explanation is that both mental health and arrest are a function of extralegal behavioral patterns, such as interactions at school and with peers. Stressful life events—such as school problems and peer conflict—can cause anger, depression, and anxiety (Aseltine, Gore, and Gordon, 2000). In addition, isolation and lack of peer support may lead to depression and remove social buffers against adverse psychological


consequences of stressful events (Elgar and Arlett, 2002; Windle, 1992). School and peer dynamics, in turn, may influence the likelihood of police encounters. Truant or suspended students may attract extra police attention through spending more time on the street, and because truancy is a status offense (Parenti, 2000). Time spent with peers, though positively predicting social support, may also increase exposure to the police, because groups have higher visibility (Brownfield, Sorenson, and Thompson, 2000). Peer conflict may not independently influence arrest risk, but one possible source of peer conflict—peer delinquency—may also invite greater police scrutiny (Morash, 1984).

This explanation is aligned with a social causation interpretation of the link between mental health and arrest insofar as it attributes mental health problems to social factors rather than vice versa. However, a finding that extralegal behavioral variables account for the relationship between mental health problems and arrest is equally consistent with a rival social selection explanation (Miech et al., 1999). That is, mental health problems may select youth for problems in school and peer relationships, which lead to arrest. In fact, ADHD (which has an early age of onset) and ODD are much more plausibly a cause of school problems (including specialized classroom placement) and association with deviant peers. Likewise, anxiety disorders and depression may lead to truancy through increasing anxiety and apathy about school, respectively (Brandibas et al., 2004). Problems in forming or sustaining friendships have been reported for youth who suffer from depression (Armsden et al., 1990). Depressed youth, therefore, should be less apt to be spotted by police loitering or carousing with their peers in public places. ODD, by contrast, through damaging one’s relationships with authority figures, should increase time spent with peers.

If, in fact, school and peer factors mediate the relationship between mental health problems and arrest, this complicates efforts to explain this relationship. To the extent that mental health problems elevate school problems, peer delinquency, and time with peers, controlling for these factors attenuates the estimated overall impact of these factors on arrest. On the other hand, under these circumstances, information on the impact of mental health problems, net of these mediators, is still useful. Controlling for school problems and peer interactions that elevate the risk of police encounters helps to partially disentangle the impact of mental health on arrest from its impact on police encounters. Measuring the direct impact of mental health problems on arrest may be especially useful in the event that the impact of a mental health problem on police encounters and arrest decisions diverge.

The fourth explanation is that mental health problems exert a direct influence on the various stages of the arrest process. Mental health


problems may elevate the risk of arrest by increasing the odds of offense detection, calls to the police, and police decisions to arrest. Youth suffering from serious depression may care less about being caught or even, as an act of self-sabotage or as a cry for help, deliberately allow detection and apprehension. Certain mental disorders may also increase the chances that an offense will result in a call to the police. School personnel, often reluctant to discipline students diagnosed with emotional and behavioral disorders, increasingly summon the police (Rice, 2003). In addition, family members—disproportionately the victims of the crimes of mentally troubled suspects (Engel and Silver, 2001)—may request police intervention and arrest as a last ditch effort to obtain help.

In addition to increasing detection and calls to the police, the mental health status of suspects can directly affect police arrest decisions. The nature of this impact may depend on the specific mental health symptoms and on the orientation and training of the police officer. The criminalization hypothesis (Teplin, 1984) suggests that the mentally ill face an increased risk of arrest because police officers may perceive their erratic behavior as threatening, and few alternatives to arrest are available. This theory is most applicable to encounters with suspects suffering from psychotic disorders, who often behave erratically. It is important to note that psychotic disorders are very rare among juveniles. Vermeiren’s (2003) review of the literature reports prevalence estimates of schizophrenia among adjudicated and incarcerated adolescents of between 2 and 4 percent, whereas Teplin and her colleagues (2002) detected psychotic disorders in only 1 percent of juvenile detainees.

Evidence suggests, moreover, that even psychotic symptoms may not aggravate the risk of arrest. The criminalization hypothesis first finds support in observations of Chicago police (Teplin, 1984), but meets resistance in the work of Kalinich and Senese (1987) and Engel and Silver (2001).

Engel and Silver find that observable mental disorders do not increase the risk of an arrest, net of legalistic, demographic, and situational controls. In fact, they observe that mentally disordered offenders face a significantly smaller risk of arrest. However, their models control for suspects’ homelessness, intoxication, and demeanor, all of which could be direct consequences of mental illness. They provide a very tenuous basis on which to predict the effects of mental disorders on juvenile arrests. Fewer than 10 percent of the suspects identified as mentally disordered in their study were juveniles, thereby diminishing the applicability of their findings to encounters with juvenile suspects. On the other hand, their findings accord with observational and survey evidence that police are often reluctant to arrest mentally disordered persons, especially when alternative responses (for example, crisis intervention services) are


available and the police are trained to use them (Borum et al., 1998; Green, 1997). Building on the work of Engel and Silver, one may also speculate that certain types of mental disorders are more likely to solicit a lenient or treatment-oriented response than others. Specifically, the sullen or self-blaming demeanor of a depressed youth or the fearful behavior of an anxious youth may elicit sympathy or mercy from police.

Although Engel and Silver (2001) find that psychotic symptoms do not independently aggravate the risk of arrest, many mentally disordered suspects may have eluded classification in their study. Their designation of suspects as mentally ill was based on the observations of field researchers or the police, neither of whom are trained in psychiatric assessment. Furthermore, the mental disorders of juvenile suspects may be mistaken for other characteristics and behaviors that antagonize or threaten police officers, and thereby increase the risk for arrest. For example, MDYs may appear extremely intoxicated when they use alcohol and drugs because of interactions of substance use with their mental disorder symptoms. Likewise, what officers view as a hostile demeanor may be the noncompliant reaction of a youth suffering from ODD. Youth with ADHD may be more likely to act impulsively or to fail to readily and completely follow police commands. Thus, existing observational studies of arrest decisions shed little light on the independent influence of mental disorders on juvenile arrest decisions, and much less on whether mental health exerts an influence at earlier stages in the arrest process.

PRESENT STUDY

In this study we examine how mental health disorders affect the odds of arrest among juveniles. Expansive measurement of mental health combined with a longitudinal survey design permits us to address several important research questions. First, we examine whether and which types of mental problems are more common in arrested populations than in nonarrested populations. Based on the extant literature, we hypothesize that all types of mental problems are more prevalent among males who will soon come into contact with the juvenile justice system than among those of similar ages who will not. Second, we evaluate the four explanations of this relationship that we considered earlier. Statistical models test, through the step-wise addition of explanatory variables, whether the relationship between mental health problems is explained by background risk factors alone, by delinquency and substance use (plus background risk factors), by peer and school factors (plus the other risk factors), or whether mental health problems exert independent effects on arrest, net of observed selection and mediating factors. In keeping with the criminalization hypothesis, we predict that a positive, direct effect of


externalizing problems, including symptoms associated with ODD and ADHD, though diminished in strength with each successive model, will remain significant even with controls for background factors, delinquency, substance use, and other risk factors. By contrast, we hypothesize that internalizing problems, including depression and anxiety, lower the risk of arrest net of controls.

This study addresses limitations in earlier research on the relationship between mental health and arrest by including multiple types of mental health problems using both empirical and rational approaches to measurement; controlling for prior delinquent behavior, substance use, and other antecedent factors not discernible in observational studies of police encounters; using data collected prospectively from multiple sources including adolescents, their primary caretakers, and court records; and comparing a community sample of youth who will eventually be arrested to their peers who are not arrested as juveniles, with respect to prior mental health and other expected arrest predictors.

METHODS

SURVEY DESIGN AND SAMPLING POOL

Data were collected as part of the Pittsburgh Youth Study (PYS). The PYS is a prospective longitudinal study of the development of delin-quency, substance use, and mental health problems (Loeber et al., 1998a). In 1987 and 1988, random samples of first and seventh grade boys enrolled in the City of Pittsburgh public schools were selected. Approximately 850 boys in each grade (85 percent of the target sample) were screened. The 15 percent nonparticipation rate did not result in sample selection bias (Loeber et al., 1998a). As part of the screening assessment, information was collected on antisocial behavior from mothers, teachers, and the boys themselves. Boys who ranked in the top 30 percent in antisocial behavior, as well as a roughly equal number of boys randomly selected from the remainder, were selected for longitudinal follow-up resulting in 506 boys in the older cohort and 503 in the younger cohort.

During the first 3 years of the study, the boys were followed up at 6-month intervals. Annual assessments were then conducted. In our analysis, this affects variable measurement among the majority of cases in the older cohort but none of the younger cohort. For affected cases, these 6-month assessments are combined into an annual measure by summing the count variables (for example delinquency), averaging the scaled measures, and using the maximum value (0 or 1) for dichotomous measures. The younger sample was followed until the approximate age of 19 or 20 and the older sample to 24 or 25.


The sample retention rate has averaged more than 90 percent during 14 years of data collection. The sampling pool is 57.5 percent African American, with the remainder almost all non-Hispanic white (less than 2 percent are Hispanic or Asian). In addition, 36.2 percent of the boys’ families received public assistance or food stamps. For greater detail on participant selection and sample characteristics see Loeber and colleagues (1998a). The current study combines subsamples of both cohorts at ages 13 through 17.

SAMPLE SELECTION AND MEASURES

The measures are based on self-reports from the boys, their primary caretakers, and the interviewers, as well as official records. The age of first official arrest is the principal subsample selection criteria as described immediately below. Table 1 presents a description of the sample as a whole with respect to the key measures used in this paper.

FIRST OFFICIAL ARREST

Whether a juvenile is first arrested during adolescence (between 13 and 17) is our outcome of interest. We exclude arrests at earlier ages, because parent- and child-reported mental health measures during the year before arrest are required, and the majority of cases (n = 588) lack these measures before age 12. Data on the age of first official arrest are based on a compilation of participants’ court records collected from Allegheny Juvenile Court and the Commonwealth of Pennsylvania. Official arrests are used instead of self-reported police contact (that is, being “picked up or arrested by the police”) because the latter captures mostly incidents that do not result in a referral to the juvenile court. In addition, we avoid measurement bias resulting from using the same source for the dependent and independent variables. First official arrest is used to ensure that the observed effects of mental health on arrest are not bound up with the effects of prior official arrests and ongoing juvenile justice contact.

Based on the presence and timing of first arrests, the participants are divided into two groups. The arrested group consists of the 362 youth who experience their first official arrest at Time 2 (T2) between ages 13 and 17. The mental health and the selection factors are measured at Time 1 (T1), the survey year before the first arrest (ages 12 through 16). For example, for those arrested at age 15 (T2), we measure their mental health and


Tab

le 1

. Sa

mpl

e C

hara

cter

isti

cs a

t Tim

e 1

Var

iabl

e D

escr

ipti

on

N

Mea

n or

%SD

M

in

Max

A

rres

t (T

ime

2)

1 if

1st o

ffic

ially

arr

este

d bt

w 1

3 &

17;

0 e

lse

736

45%

0.00

1.

00B

lack

1

if bl

ack,

His

pani

c, o

r ot

her;

0 if

Asi

an o

r w

hite

73

6 55

%

0.

00

1.00

Old

er c

ohor

t 1

if ol

der

coho

rt; 0

if y

oung

er c

ohor

t 73

6 50

%

0.

00

1.00

Age

A

ge a

t T1

736

15.0

7 1.

31

13.0

0 17

.00

OD

pro

blem

s Su

m o

f 5 Y

SR/C

BC

L it

ems

688

3.09

1.

84

0.00

9.

50A

DH

pro

blem

s Su

m o

f 11

YSR

/CB

CL

item

s 68

8 5.

53

4.05

0.

00

19.0

0A

ffec

tive

pro

blem

s Su

m o

f 13

YSR

/CB

CL

item

s 68

8 2.

19

1.87

0.

00

11.0

0A

nxie

ty p

robl

ems

Sum

of 6

YSR

/CB

CL

item

s 68

8 1.

57

1.34

0.

00

9.50

Ext

erna

lizin

g Su

m o

f 20

YSR

/CB

CL

item

s 68

8 9.

82

5.16

0.

00

25.5

0In

tern

aliz

ing

Sum

of 3

2 Y

SR/C

BC

L it

ems

688

8.09

5.

24

0.00

32

.86

Min

or d

elin

quen

cy

Log

of

num

ber

of m

inor

del

inqu

ent a

cts

68

3 0.

42

1.02

0.

00

5.90

Seri

ous

delin

quen

cy

Log

of

num

ber

of s

erio

us d

elin

quen

t act

s 68

3 0.

55

1.35

0.

00

6.99

Alc

ohol

use

L

og o

f nu

mbe

r of

tim

es u

sed

alco

hol

691

0.65

1.

26

0.00

6.

64M

ariju

ana

use

Log

of

num

ber

of ti

mes

use

d m

ariju

ana

691

0.42

1.

25

0.00

5.

90F

amily

SE

S H

ollin

gshe

ad in

dex

of s

ocia

l sta

tus

662

38.1

3 12

.39

6.00

66

.00

Nei

ghbo

rhoo

d

Sum

of 1

7 ne

gati

ve c

hara

cter

isti

cs

633

24.9

8 9.

21

17.0

0 51

.00

Fam

ily c

rim

inal

ity

Num

ber

of p

olic

e co

ntac

ts w

ith

rela

tive

s 65

8 1.

67

2.93

0.

00

23.0

0P

eer

delin

quen

cy

Sum

of 1

1 or

dina

l mea

sure

s 63

0 6.

54

6.59

0.

00

37.0

0T

ime

wit

h fr

iend

s 1

if 0–

5 hr

s/w

k; 2

if 6

–10;

3 if

11+

63

9 1.

88

0.72

1.

00

3.00

Scho

ol p

robl

ems

1 if

trua

nt o

r su

spen

ded;

0 e

lse

692

59%

0.00

1.

00

Not

e: A

ll ob

serv

atio

ns a

re w

eigh

ted

usin

g PY

S w

eigh

ts t

o m

atch

the

Pit

tsbu

rgh

publ

ic s

choo

l pop

ulat

ion

from

w

hich

the

sam

ple

was

dra

wn.

Bor

derl

ine-

clin

ical

leve

ls ta

ken

from

Ach

enba

ch a

nd R

esco

rla

(200

1).


selection factors at age 14 (T1). Fifty-eight (16 percent) of this group are first arrested at age 13, seventy-seven (21 percent) at age 14, seventy (19 percent) at age 15, ninety-eight (27 percent) at age 16, and fifty-nine (16 percent) at age 17.

The comparison group consists of 374 youth who are not officially arrested before they turn 18. To ensure that each nonarrested case is included only once and to maintain comparable age composition between the arrested and not arrested groups, each nonarrested youth is randomly assigned an age to provide T1 data in proportion to the age divisions for the arrested group. In other words, 16 percent of the nonarrested group provides T1 data at age 12, 21 percent at age 13, 19 percent at age 14, 27 percent at age 15, and 16 percent at age 16. Owing to the design of the PYS, the nonarrested group also contains a substantial number of high-risk youth; 26 percent of the youth in the comparison group are arrested after age 17, and 15 percent report committing a relatively serious offense (for example, carrying a weapon or attacking someone) at T1.1 Likewise, 34 percent of the nonarrested group, compared to 63 percent of the arrested group, were classified as high risk by the PYS at their initial assessment, indicating that they were at risk of future behavioral problems.

The 736 youth that comprise our sample represent only 73 percent of the sampling pool. The remaining 273 youth were excluded for a variety of reasons. First, twenty-four who meet our sampling criteria (three arrested and twenty-one not arrested) were, due to attrition from the study, missing all mental health, delinquency, and other nondemographic variables employed in the analyses. This represents an attrition rate of only 3 percent. Second, sixty-four excluded youth were officially arrested before age 13. Third, we exclude 185 youth who have no official arrest but do have a self-reported police contact before they are 18. The first police contact occurred between ages 13 and 17 for 123 of these cases, and before age 13 for sixty-two of them. It is impossible to determine the share of these self-reported police contacts that involved being picked up and released without charges and how many involved an actual arrest not recorded or detected in the official data. We exclude these cases to offer clean bivariate comparisons between arrested and nonarrested youth and to minimize measurement error on the outcome variable. The danger in excluding these 185 cases is that it inflates the disparities in mental health between arrested and nonarrested youth, because the 123 excluded youth with only self-reported police contact between 13 and 17 may be less mentally troubled than official arrestees. Likewise, the sixty-two excluded

1. The Federal Bureau of Investigation provided information on adult arrests for use by the PYS.


youth with no police contact of any kind between 13 and 17, but who self-report police contact before age 13 may be more troubled than the other nonarrested youth. Sensitivity analyses, described later, assess the robustness of our results to changes in sampling.

MENTAL HEALTH PROBLEMS

The primary explanatory variable of interest is mental health. The most effective strategy for measuring child mental health problems is a point of considerable contention. Deductively derived diagnostic measures of mental disorder, such as the DSM IV (American Psychiatric Association, 1994) and the Diagnostic Interview Schedule for Children (DISC) (Costello et al., 1998) predominate in studies that assess the mental health of juvenile justice samples (Teplin et al., 2002; Wasserman, Ko, and McReynolds, 2004). The alternative, empirically based approach involves the factor analysis of a comprehensive checklist of behavioral, cognitive, and emotional symptoms to discern clusters of symptoms, which are designated syndromes (Achenbach, Dumneci, and Rescorla, 2003). The most widely used empirically based assessment tool for child psychopathology is the child behavior checklist (CBCL) (Achenbach, 1991), a 112-item questionnaire covering a wide range of child behavior problems such as anxiety, depression, compulsions, oppositional behaviors, hyperactivity, and delinquency. The choice to use either rationally or empirically derived measures of child psychopathology entails particular trade-offs that have been discussed at length elsewhere (Achenbach, Dumneci, and Rescorla, 2003; Ferdinand et al., 2004; Krueger and Piasecki, 2002; Lengua et al., 2001). Given the varying contributions and limitations of both approaches, researchers have recently argued that combining empirical and rational approaches provides the most complete view of psychopathology (Achenbach, Bernstein, and Dumenci, 2005; Ferdinand et al., 2004).

Adopting this multitaxonomic approach, we use both empirically and deductively derived measures of child mental health problems. Because proximate clinical diagnostic assessments are not available on the PYS, we use four DSM-oriented (rationally derived) subscales that Achenbach and colleagues (2003) developed from the CBCL: affective problems (α = 0.76), anxiety problems, (α = 0.71), oppositional defiant (OD) problems (α = 0.81), and attention deficit/hyperactivity (ADH) problems (α = 88).2 These measures seem to combine strengths of both approaches. They conform to prevailing “clinical conceptualizations of symptom-

2. We exclude the conduct problems scale because it is virtually indistinguishable from delinquency and the somatic problems scale because it is not expected to influence arrest.


atology” (Lengua et al., 2001) and measure variation in the severity and number of symptoms. These subscales also exhibit high internal consistency, high test-retest reliability, strong parent-parent agreement, moderate parent-youth agreement, and strong associations (criterion-related validity) with counterpart DSM-IV diagnoses (Achenbach, Dumneci, and Rescorla, 2003).

Following Achenbach and Rescorla (2001), the DSM-oriented subscales are computed through combining scores on the CBCL from the caretaker and the youth self-report (YSR) (both covering the previous year).3 Appendix A lists all the items that comprise each of the DSM-oriented subscales. The response set for each symptom includes 0 (“not true”), 1 (“somewhat or sometimes true”), and 2 (“very true or often true”). The corresponding item scores on each instrument are averaged into a single-item score.4 To obtain the DSM-oriented subscale scores, the sum of the nonmissing item scores comprising each scale is divided by the number of nonmissing items and then multiplied by the total number of items in each scale. In our sample, the DSM-oriented subscales exhibit levels of internal consistency (average α = .78) approximating levels reported by Achenbach and his colleagues (2003). As evident from the correlation matrix presented in appendix B, sampled youth with symptoms of one disorder are more likely to exhibit symptoms of another.

In addition to the DSM-oriented subscales, we compute the widely used, empirically derived, broadband syndrome scales from the CBCL and YSR, internalizing and externalizing problems (Achenbach and Rescorla, 2001). The internalizing problems scale is an aggregate of three subscales (anxious-depressed, depressed-withdrawn, and somatic) and contains thirty-one items (α = .90). Externalizing problems is a 20-item scale that combines the dimensions of rule-breaking behavior and aggressive behavior (α = .90). We eliminated twelve items from the externalizing problem scale that measure individual or peer delinquency or substance use. The computational procedures for the internalizing and externalizing problem scales and the DSM-oriented subscales are identical. The individual items for the former are also presented in appendix A.

3. Our measure does not incorporate the teacher report form (TRF), because Achenbach and colleagues (2003) report modest parent-teacher (r = .29) and teacher-youth (r = .21) agreement, and Hartman et al. (1999) find poor fit indices of the CBCL-TRF cross-informant syndromes.

4. The ADH subscale includes six items that are measured only on the CBCL. For each of the scales, if an item is missing from one of the instruments, only the nonmissing item scores are included.


BACKGROUND VARIABLES

All control variables are selected for this analysis on the basis of their predicted or observed relationships to mental health and arrest. Age is measured on the survey wave corresponding to T1 as an interval-level variable ranging from 12 through 16. Because the sample is about 97 percent African American or non-Hispanic white, race is measured dichotomously by the variable, black. A handful of mixed race, Hispanic, and Native American participants receive a 1 along with the African American participants, whereas Asians, a socioeconomically advantaged minority, are joined with non-Hispanic whites in the reference category. To measure family SES, we employ the Hollingshead (1975) index of social status as reported by the primary caretaker. It equals the highest score between the two caretakers or the score of the single caretaker. Our models of arrest also uniquely include family criminality, which is the sum of police contacts with one’s immediate and extended family.5 Next, we measure neighborhood problems as the sum of the primary caretaker’s perceptions of seventeen neighborhood problems, which include unemployment, gang activity, burglaries, assaults, and abandoned houses (Loeber et al., 1998a). The response set for each item is 0 (“not a problem”), 1 (“somewhat of a problem”), and 2 (“big problem”) (α = .95). Finally, we also include in all models a dichotomous measure of cohort membership (1 = older, 0 = younger) to control for the influence of any secular trends in arrest risk and in its unmeasured covariates.6

DELINQUENCY, SUBSTANCE USE, AND OTHER BEHAVIORAL FACTORS

We use measures that tap both the seriousness and frequency of delinquency. These measures have proven to be valid and reliable (Loeber et al., 1998a, 1998b). Minor and serious delinquency are derived from the self-reported delinquency (SRD) scale. Scale items are detailed in appendix A. The frequency of minor delinquency is the sum of eight minor delinquency items, where each item is the number of times the youth reported engaging in that behavior in the past year as measured at T1. A scale measuring more serious delinquency is the sum of seventeen

5. Family members include brothers, sisters, parents (biological and acting), grandparents, aunts, uncles, and cousins. This variable, unlike the other control variables, was assessed only once in 1989 when the younger cohort was approximately 8 and the older cohort approximately 14. Thus, family criminal history is more proximately measured for the older cohort.

6. The older cohort, on average, reached prime juvenile arrest age (16) in about 1991 or 1992, when juvenile arrest rates were nearly at their peak, whereas the younger cohort did not reach this age until about 1997 or 1998, subsequent to substantial declines in overall juvenile arrest rates (Snyder, 2005).


items. Both constructs are converted into their natural logs because of skewness.

We also measure the frequency of both marijuana and alcohol use. Marijuana use is measured as the log of the number of times a youth self-reported using marijuana during the last year at T1. Alcohol use is computed as the log of the number of times youth self-reported using beer, wine, and hard liquor during the last year at T1.

School problems combines the interrelated phenomena of truancy and suspensions. It is coded 1 if a youth, according to the YSR or the CBCL, was truant or suspended at T1 and 0 if not. Time with friends is a trichotomized measure collected from caretakers (coded 1 if 0 to 5 hours per week, 2 if 6 to 10 hours per week, and 3 if 11 or more hours per week). Finally, self-reported peer delinquency is the sum of eleven 5-point Likert scales indicating how many of the boys’ friends (none, few, half, most, or all) participated in delinquent acts such as skipping school, lying, stealing, joyriding, attacking someone, and carrying a weapon (α = .83).

STATISTICAL PROCEDURES

Missing data are infrequent or absent with respect to all the key variables.7 Although only sixty cases are missing more than three independent variables, 196 cases (27 percent) are missing at least one. To avoid the attrition bias and sample selection bias that could arise from the list-wise deletion of these cases, multiple imputation procedures were performed with the statistical software program, SAS, using procedures Schafer (1997) developed and Allison (2000) validated. Missing observations were imputed ten times for sixteen variables at T1. The imputation model included a total of thirty-nine variables, including the prior wave’s (T1-1) measures of all imputed variables as well as other highly correlated variables. Each of the logistic regression models reported is estimated using each imputed data set, and the results reported below are a composite of these models.8 Durbin-Watson tests and tolerance tests

7. Three variables, time with friends, peer delinquency, and neighborhood problems, were missing for 13 to 14 percent of the observations. Age, race, and cohort were known for all participants. All of the remaining independent variables were absent in about 5 percent of the cases. Visual examination of the patterns of missing data revealed no clear patterns in the distribution of missing data that violate the “missing at random” assumption. In addition, we attempt to include the possible causes for the missing data in the imputation model, reducing the potential bias of our imputed estimated values. Not controlling for variables that are correlated with the reasons for the missing data in the multiple imputation procedure can violate the “missing at random” assumption.

8. These results are not an artifact of the multiple imputation procedures. When the full models are estimated with only the 540 youth with no missing data


of OLS models involving the predictor variables for most models reported here reveal no evidence of autocorrelation or multicollinearity, respectively. The only exceptions, discussed later, are models incorporating at least three mental health measures.

To make the sample representative of same-aged boys in the Pittsburgh public schools (compensating for the oversampling by risk status), we weight each observation according to its probability of being in the original screening sample. Because the weights are normalized to the size of our sample, their addition does not change the standard errors of our estimates or the implications of our test statistics.

RESULTS

BIVARIATE COMPARISONS

First we examine whether the officially arrested sample had higher levels of mental health problems relative to the nonarrested sample. Table 2 compares arrested and nonarrested youth on all of the measures. The odds ratio tests assess whether a unit change in each variable significantly alters the odds of membership in the arrested group. These tests verify that the arrested youth have higher levels of ADH, OD, and nondelinquent externalizing problems than nonarrested youth (p < .01). However, differences between the two groups in affective, anxiety, and internalizing problems, though in the expected direction, are small and not statistically significant. In addition, arrested youth report significantly (p < .01) higher rates of minor and serious delinquency, school problems, alcohol use, and marijuana use than their counterparts who were not arrested. The remaining control variables, except age and older cohort membership, distinguish arrested and nonarrested youth in a decisive and expected manner.9

MULTIVARIATE ANALYSES

Next, we estimate multivariate logistic regression models corresponding to the four alternative explanations of the posited relationships between adolescent mental health problems and arrest. Initial models consider each mental problem separately owing to the high intercorrelations among disorders (see appendix B).

(nonimputed sample) the key results remain the same. 9. The parity between the two groups with respect to age reflects the fact that the risk

of first arrest, in contrast to any arrest, is fairly evenly distributed across ages 13 to 16.


Table 2. Comparisons of Arrested and Nonarrested Youth (Model I)

Variable MeanArrested

Mean Nonarrested

Odds Ratio a

95 Percent Confidence Interval b

Age 15.10 15.03 1.042 0.932 1.166 Black 66% 47% 2.231 1.653 3.012 Older cohort 53% 48% 1.201 0.898 1.606 OD problems 3.58 2.73 1.303 1.193 1.424 ADH problems 6.19 5.06 1.073 1.033 1.115 Affective problems 2.22 2.16 1.018 0.938 1.104 Anxiety problems 1.56 1.57 0.995 0.888 1.116 Externalizing problems 11.42 8.69 1.116 1.080 1.152 Internalizing problems 8.19 8.01 1.007 0.978 1.037 Minor delinquency 0.60 0.25 1.474 1.249 1.741 Serious delinquency 0.97 0.22 1.705 1.453 2.001 Alcohol use 1.07 0.35 1.675 1.446 1.941 Marijuana use 0.77 0.16 1.583 1.348 1.859 Family SES 33.93 41.08 0.950 0.936 0.963 Neighborhood problems 26.73 23.72 1.037 1.019 1.056 Family criminality 2.26 1.21 1.145 1.077 1.218 Peer delinquency 8.73 4.96 1.101 1.071 1.133 Time with friends 2.01 1.78 1.593 1.271 1.998 School problems 0.84 0.41 7.758 5.337 11.279 n 362 374 Notes: All observations are weighted using PYS weights to match the Pittsburgh public school population. For continuous variables, the odds ratios represent the increase in the odds of being arrested due to a unit increase in the explanatory variable. a The odds ratios correspond to the effects of explanatory variables in base models of arrest (model I) that include only the explanatory variable and the intercept. b If the range of a variable’s confidence interval includes 1, then it is less than 95 percent certain that the variable predicts arrest in the direction indicated by the odds ratio.

Logistic regression allows one to estimate the effects of multiple

variables on arrest, a binary dependent variable (Greene, 2002). The base models (model I), shown as odds ratios in table 2, include only the mental health measures and the intercept. The odds ratio corresponding to the effect of OD problems (1.30) suggests that a unit increase in OD problems is associated with a 30 percent increase in the risk of arrest. Similarly, a unit increase in ADH and externalizing problems increases the risk of arrest by 7 percent and 12 percent, respectively.


The second set of models (II) tests whether social causation processes account for the relationship between each mental health problem and arrest. These models add six control variables to the base models presented in table 2. These models, shown in table 3, indicate that the positive effects of OD, ADH, and externalizing problems on arrest remain robust after taking into account the effects of race, family SES, neighborhood problems, family criminality, age, and cohort. Affective, anxiety, and internalizing problems have no significant effect on the odds of arrest. The effects of the remaining predictors in each model, except older cohort membership, are in the expected direction, though not always significant. Black racial identification, lower SES, and higher family criminality consistently and significantly increase the odds of arrest. The effect of being black on arrest is particularly strong in all the models reported in this paper.10

The third set of logit models (III), presented in table 4, tests whether the impact of mental health on arrest is explained by involvement in delinquency and substance use. These models add the logs of minor delinquency, serious delinquency, alcohol use, and marijuana use to the models reported in table 3. Results reveal that OD and externalizing problems still significantly increase arrest risk (p < .01), even after the associations of these variables with illegal behaviors are taken into account. By contrast, the effect of ADH problems is no longer significant at the 5 percent level (p = .09) owing to the inclusion of serious delinquency and alcohol use. The null effects of anxiety, affective, and internalizing problems persist.

The next set of logistic regression models (IV) conservatively assesses whether mental health exerts an independent effect on arrest, by including controls for extralegal behavioral factors that may mediate and, therefore, erode the estimated impact of mental health problems. The results of these full models are presented in table 5. Including school problems, peer delinquency, and time with friends reduces the positive effects of OD and externalizing problems to the point of nonsignificance (p = .08 and .06, respectively). School problems is the major mediator of these effects.

10. Because black and poor youth with mental health problems have less access to treatment through the private mental health system (Isaacs, 1992; Sheppard and Benjamin-Coleman, 2001), we predicted that mental health problems are more likely to increase (and less likely to decrease) juvenile arrests among such youth. For each model reported in this section, we separately added product terms representing interactions between mental health and race as well as between mental health and family SES (not shown). None of these interactions are significant.


Table 3. Social Causation Models (II) of the Impact of Six Mental Health Problems on Arrest (n = 736)

Odds Ratios for Model II OD ADH Affective Anxiety External Internal Intercept 0.06** 0.14 0.52 0.49 0.03** 0.46 OD problems 1.41*** — — — — — ADH problems — 1.09*** — — — — Affective problems — — 1.03 — — — Anxiety problems — — — 1.04 — — Externalizing — — — — 1.15*** — Internalizing — — — — — 1.01 Age 1.22** 1.20** 1.12 1.13 1.26** 1.13 Black 1.47*** 1.35*** 1.35** 1.35*** 1.46*** 1.35** Older cohort 0.83* 0.89 0.93 0.93 0.80* 0.92 Family SES 0.95*** 0.95*** 0.95*** 0.95*** 0.95*** 0.95*** Neighborhood 1.01 1.02 1.02 1.02 1.01 1.02 Family criminality 1.11** 1.13** 1.14*** 1.14*** 1.10** 1.14*** -2 Log likelihood 845.60 881.35 896.85 897.06 834.32 896.92 Difference Χ2 statistic 51.49*** 39.25*** 36.83*** 36.78*** 50.92*** 36.73*** Pseudo R2 27.15% 21.85% 19.46% 19.43% 28.78% 19.45%

Notes: All observations are weighted using PYS weights to match the Pittsburgh public school population; OD = oppositional defiant; ADH = attention deficit-hyperactivity. The difference Χ2 statistic is the difference in the -2 Log Likelihood between model I (not shown in table 2) and model II. ***p < .001 **p < .01 *p < .05

Controlling for these three additional behavioral factors divergently

impacts the estimated effects of internalized symptoms. Significant, negative effects of internalizing, anxiety, and affective problems on arrest become evident in this model. Once the positive relationships between internalizing symptoms and school problems and delinquency are taken into account, these symptoms appear to mitigate further risk of arrest.11

As expected, time with friends, in all the full models, increases the odds of arrest, but peer delinquency—surprisingly—does not. The latter result is

11. Separate estimations of the full model using the specific syndromes comprising the internalizing scale confirm that these effects are driven by the anxious-depressed syndrome (OR = .82, p < .01) and the depressed-withdrawn syndrome (OR = .83, p < .01) rather than the somatic syndrome (OR = .92, p > .05). In addition, we examine the effects of another widely used measure of depression, the Recent Mood and Feelings Questionnaire (Costello and Angold, 1988). In the same fashion as the previous measures, depressed mood negatively predicts arrest risk in the full model (OR = .87, p < .01).


Tab

le 4

. M

odel

s (I

II)

Ass

essi

ng D

elin

quen

cyan

d Su

bsta

nce

Use

as

Med

iato

rs o

f the

Im

pact

of M

enta

l Hea

lth

Pro

blem

s on

Arr

est (

n =

736)

Odd

s R

atio

s fo

r M

odel

III

OD

A

DH

A

ffec

tive

Anx

iety

E

xter

nal

Inte

rnal

In

terc

ept

0.32

0.

69

1.74

1.

95

0.21

2.

27

OD

pro

blem

s 1.

25**

* —

—

—

—

—

A

DH

pro

blem

s —

1.

04

—

—

—

—

Aff

ecti

ve p

robl

ems

—

—

0.92

—

—

Anx

iety

pro

blem

s —

—

—

0.

92

—

—

Ext

erna

lizin

g —

—

—

—

1.

09**

* —

In

tern

aliz

ing

—

—

—

—

—

0.97

A

ge

1.08

1.

06

1.02

1.

00

1.11

1.

00

Bla

ck

1.65

***

1.57

***

1.56

***

1.57

***

1.63

***

1.58

***

Old

er c

ohor

t 0.

86

0.92

0.

97

0.97

0.

85

0.98

F

amily

SE

S 0.

95**

* 0.

95**

* 0.

95**

* 0.

95**

* 0.

95**

* 0.

95**

* N

eigh

borh

ood

1.01

1.

01

1.01

1.

01

1.01

1.

01

Fam

ily c

rim

inal

ity

1.11

**

1.12

**

1.13

**

1.12

**

1.10

**

1.13

**

Min

or d

elin

quen

cy

1.03

1.

08

1.14

1.

12

1.01

1.

13

Seri

ous

delin

quen

cy

1.35

**

1.37

**

1.38

**

1.39

**

1.33

**

1.39

***

Alc

ohol

use

1.

45**

* 1.

44**

* 1.

45**

* 1.

44**

* 1.

43**

* 1.

44**

* M

ariju

ana

use

1.11

1.

13

1.14

1.

14

1.12

1.

14

-2 L

og li

kelih

ood

754.

62

768.

98

769.

26

770.

40

752.

25

769.

65

Dif

fere

nce Χ

2 sta

tist

ic

90.9

8***

112.

37**

* 12

7.59

***

126.

66**

* 82

.07**

* 12

7.27

***

Pse

udo

R2

39.5

6%

37.7

0%

37.6

6%

37.5

1%

39.8

6%

37.6

1%

Not

e: A

ll ob

serv

atio

ns a

re w

eigh

ted

usin

g PY

S w

eigh

ts t

o m

atch

the

Pit

tsbu

rgh

publ

ic s

choo

l pop

ulat

ion;

O

D =

opp

osit

iona

l def

iant

; A

DH

= a

tten

tion

def

icit

-hyp

erac

tivit

y. T

he d

iffe

renc

e Χ

2 sta

tist

ic is

the

diff

eren

ce

in th

e -2

Log

like

lihoo

d be

twee

n m

odel

II

and

mod

el I

II.

*** p

< .0

01

**p

< .0

1

* p <

.05


most likely due to the high correlation (r = .52) between peer delinquency and serious delinquency, which has a strong effect on arrest. The remaining predictors maintain a persistent pattern of effect direction and size. Table 5. Full Models (IV) of the Impact of Six Mental Health Problems on Arrest

(n = 736) Odds Ratios for Model IV OD ADH Affective Anxiety External Internal Intercept 0.18 0.35 0.51 0.82 0.16 0.87 OD problems 1.11 — — — — — ADH problems — 1.00 — — — — Affective problems — — 0.88* — — — Anxiety problems — — — 0.84* — — Externalizing — — — — 1.04 — Internalizing — — — — — 0.96* Age 1.07 1.04 1.02 0.99 1.08 0.99 Black 1.68*** 1.63*** 1.60*** 1.61*** 1.67*** 1.62*** Older cohort 0.85 0.87 0.92 0.93 0.84 0.94 Family SES 0.95*** 0.95*** 0.95*** 0.95*** 0.95*** 0.95*** Neighborhood 1.01 1.01 1.01 1.01 1.01 1.01 Family criminality 1.10* 1.11** 1.11** 1.11** 1.10* 1.11** Minor delinquency 1.00 1.03 1.06 1.04 0.99 1.05 Serious delinquency 1.34** 1.35** 1.35** 1.36** 1.34** 1.36** Alcohol use 1.32* 1.30* 1.30* 1.28* 1.31* 1.29* Marijuana use 1.13 1.14 1.15 1.16 1.14 1.15 School problems 2.00*** 2.11*** 2.14*** 2.17*** 2.00*** 2.15*** Time with friends 1.63*** 1.64*** 1.65*** 1.65*** 1.61** 1.63*** Peer delinquency 1.00 1.01 1.01 1.01 1.00 1.01 -2 Log likelihood 692.01 695.02 689.45 698.68 691.76 690.20 Difference Χ2 statistic 62.61*** 73.96*** 79.81*** 71.72*** 60.49*** 79.45*** Pseudo R2 47.24% 46.89% 47.54% 47.52% 47.27% 47.45% Note: All observations are weighted using PYS weights to match the Pittsburgh public school population; OD = oppositional defiant; ADH = attention deficit-hyperactivity. The difference Χ2 statistic is the difference in the -2 Log likelihood between model III and model IV. ***p < .001 **p < .01 *p < .05

As mentioned earlier, our pursuit of clean bivariate comparisons between arrested and nonarrested youth may have imposed artificial dissimilarity between these groups. To examine whether our exclusion criteria inflated the association between mental health problems and arrest status, models I through IV were reestimated adding 118 youth (five were lost to attrition) with self-reported police contacts between ages 13 to 17 to the arrested group and sixty-two with earlier self-reported police contacts to the comparison group (n = 916). With a few minor exceptions,


the pattern of estimated mental health effects on arrest is insensitive to changes in the sampling design.12

We also estimate models of arrest that include multiple mental health measures. These models are necessary to rule out the possibility that the observed effects of some of the individual mental health problems are artifacts of their strong intercorrelations with other mental health problems. In addition, simultaneously estimating the effects of both internalizing and externalizing types of symptoms measures the extent to which the positive relationship between them suppresses their opposing effects on arrest.

The first model of the independent effects of the DSM-oriented measures on arrest includes not only all the control and mediator variables used previously but also all four DSM-oriented measures. This model is presented as model 1 in table 6. The effect of OD problems, which was not significant in the full model, regains its significance (p < .01) and doubles in size. Affective and anxiety problems still approach statistical significance (p = .074 and .106 respectively).

The results of model 1, rather than representing a more valid estimation of the impact of the various mental health problems, may overstate or understate the significance of their effects owing to multicollinearity. The variance inflation factors for OD, anxiety, and affective problems increase to 2.26, 2.01, and 1.94, respectively in these models.13 In response, we estimate models with different combinations of mental health measures that may be less problematic. For each DSM-oriented mental health problem, we control for the nonoverlapping disorder with which it most correlated (for example, OD with ADH problems) as well as the nonoverlapping broadband syndrome scale (for example, OD with internalizing problems). In these models, models 2 and 3 in table 6, the broadband syndrome scales substitute for the two DSM-oriented scales with which they are closely identified. Incidentally, these models conservatively control for the two nonoverlapping mental health subscales with which each DSM-oriented measure is most highly correlated. Model 2

12. The more inclusive sampling design would have slightly reduced the estimated effects of the three externalizing mental health problems in models II and III (primarily due to the addition of youth with pre-adolescent police contact to the comparison group), although any significant effects would remain statistically significant at the .01 or .001 levels. This also would have altered the significance of internalizing effects in two models. In model III, the negative effects of affective and internalizing problems would have been significant (p < .05). In model IV, the effects of anxiety problems would no longer be significant (OR = .89; p = .085).

13. An additional indication that this model yields problematic multicollinearity is that the overall adjusted pseudo R2 from this estimated model is smaller than the adjusted R2s from models of each of the mental health measures regressed on all explanatory variables (Greene, 2002).


Table 6. Full Models (IV) of Arrest with Multiple Mental Health Measures (n = 736)

Odds Ratios for Model IV 1 2 3 4 Intercept 0.32 0.50 0.20 0.35 OD problems 1.24** 1.26** — — ADH problems 1.00 0.99 — — Affective problems 0.88 — 0.84* — Anxiety problems 0.84 — 0.80* — Externalizing problems — — 1.12*** 1.13*** Internalizing problems — 0.92** — 0.89*** Age 1.03 1.01 1.06 1.03 Black 1.68*** 1.72*** 1.69*** 1.74*** Older cohort 0.92 0.94 0.91 0.95 Family SES 0.95*** 0.95*** 0.95*** 0.95*** Neighborhood problems 1.01 1.01 1.01 1.01 Family criminality 1.11** 1.11** 1.10* 1.10* Minor delinquency 1.02 1.01 1.01 1.00 Serious delinquency 1.33** 1.35** 1.31* 1.33** Alcohol use 1.31* 1.31* 1.31* 1.31* Marijuana use 1.14 1.13 1.16 1.15 School problems 1.99*** 1.96*** 1.94*** 1.91*** Time with friends 1.66*** 1.62** 1.61** 1.56** Peer delinquency 1.01 1.01 1.00 1.00 -2 Log likelihood 678.17 679.76 671.68 673.34 Pseudo R2 48.86% 48.67% 49.60% 49.41%

Note: All observations are weighted using standardized PYS representative weights; OD = oppositional defiant; ADH = attention deficit-hyperactivity ***p < .001 **p < .01 *p < .05

examines the effects of OD problems and ADH problems, net of internalizing problems. The positive impact of OD problems on arrest grows (p < .01) after controlling for ADH problems and internalizing problems. Likewise, the estimated negative impact of internalizing problems appears to strengthen (compared to table 5) when the two externalizing problems are included. This suggests that controlling for both internalizing and OD problems allows their partially suppressed, opposing effects to emerge. A similar release of mutually suppressed effects is evident in model 3, which examines the effects of affective and anxiety problems on arrest, net of the broadband externalizing problems scale. The presence of externalizing problems in the model strengthens the negative effects of both of these mental health problems on arrest. Similar augmented effect sizes are observed when only two opposing mental health problems (for example, OD and anxiety problems) are included in the models. When the effects of internalizing and externalizing problems,


which are highly correlated (r = .67), are modeled simultaneously (see model 4), their effects become more pronounced relative to when they were individually estimated (table 5). Thus, the independent effects on juvenile arrest of these two commonly used mental health measures are robust and in opposition.

DISCUSSION AND IMPLICATIONS

This study achieved its two major objectives. First, we learn whether male minors involved with the juvenile justice system suffer from more mental health problems than boys who are not involved. Second, we discern whether differences between arrested and nonarrested boys in the prevalence of various mental problems reflect a direct effect of mental health on arrest or whether they can be explained by social causation, delinquency and drug use, or extralegal behavioral correlates of mental health and arrest.

With respect to our first goal, we uncover an intriguing inconsistency. Arrested youth, before their first arrest, score predictably and substantially higher in terms of oppositional defiant, attention deficit-hyperactivity, and general nondelinquent externalizing problems than their nonarrested peers assessed in corresponding years. However, arrested youth exhibit levels of anxiety, affective, and overall internalizing problems that are similar to those of their peers who have not been arrested.

Because the high-risk PYS sample was weighted back to the assessment population, the results should be generalizable to young men in Pittsburgh (and cities like it). Further, differences between arrested and nonarrested youth with respect to most other predictors including race, delinquency, and substance use are concordant with prior research, adding validity to our findings. Hence, the absence of differences with respect to internalized mental health problems appears valid and in opposition to the common assumption that youth enter the juvenile justice system more depressed and anxious than their counterparts in the community (Wasserman, Ko, and McReynolds, 2004).

With respect to our second goal, our regression results help adjudicate among the four alternate explanations of the relationships between mental health problems and arrest. We can largely rule out the social causation interpretation. The positive effects of OD, ADH, and externalizing problems on arrest are not diminished even after taking into account the strong positive effects on arrest of black racial status, low SES, and family criminality.

The second interpretation, that MDYs participate more in delinquency and substance use, finds some support in our data. Involvement in serious


delinquency and alcohol use fully explain the impact of ADH on arrest. Involvement in these illegal behaviors also helps explain the impact of OD and externalizing problems, though the effects of the latter scales on arrest remain robust. Thus, our findings suggest that police, in the normal course of responding to adolescent deviance and drug use, would likely arrest a disproportionate share of youth who suffer from ADH problems and other externalizing symptoms.

The third explanation, that extralegal behavioral factors explain the relationship between mental health problems and arrests, appears to hold true with respect to the residual effects of OD problems and nondelinquent externalizing symptoms. Including school problems (truancy and suspensions) and time spent with friends in the model attenuates the effect of OD problems to the point of nonsignificance. However, this result is not surprising given that these behaviors may be direct consequences of oppositional defiance.

With respect to the fourth explanation, we find evidence that both OD problems and the symptoms of internalizing disorders exert independent effects on arrest. Substantial positive effects of OD problems on arrest are evident both when potential mediators are not included in the model, and when the co-occurrence of internalizing problems is taken into account. Research on arrest decision making consistently demonstrates that a hostile attitude garners a harsh response (for example, Worden, Shepard, and Mastrofski, 1996). Some may question whether the symptoms that comprise ODD (as well as ADHD) are, in fact, reflective of underlying psychopathology, as these patterns of individual behavior cannot be divorced from particular social situations and sociocultural contexts in which they occur (Davison and Ford, 2001). On the other hand, if ODD is a legitimate psychological disorder, our findings raise the troubling prospect that punitive agents who weigh demeanor in their decision making may, effectively, punish many children for a mental health problem.

By contrast, we find that affective and anxiety problems and the broadband internalizing syndrome scale significantly lower the risk of arrest, net of a range of factors known to jointly predict mental health and arrest. These negative effects did not become statistically significant until the significant positive effects on arrest of school problems and time with peers were taken into account. Similarly, our findings from table 6 suggest that the negative effects of internalizing problems on arrest were suppressed by the positive association of these problems with externalizing mental heath problems. The counteracting effects of various symptoms on police arrest decisions may help explain why Engel and Silver (2001) find no positive effects of mental disorder on arrest in two data sets.


The size of these arrest-reduction effects is not trivial, even when suppressor variables are excluded. With other variables in the full model held to their means, a standard deviation increase from the mean in anxiety problems lowers the predicted probability of arrest by 10.2 percent. Standard deviation increases in affective and internalizing problems lower arrest probabilities by similar amounts. By comparison, a standard deviation increase in serious delinquency increases the predicted probability of arrest by an average of 19.1 percent across these three models.

Assuming its internal validity, the surprising negative relationship between internalizing problems and arrest can be interpreted in multiple ways. The first plausible interpretation is that police or other potential complainants take pity on depressed and anxious youth. Of course, sympathy and mercy have their limits. The compassionate police, school, or family responses that may underlay the observed negative effects of internalizing problems on first arrest, may not apply to youthful recidivists. As mentioned, some desperate families may request police intervention as a means to obtain surrogate mental health care for chronically troubled youth.

Compassion and mercy are not the only interpretations of a reduced risk of arrest among youth with internalized symptoms. Officers’ perceptions of the effectiveness of various responses may also drive police decisions concerning particular juveniles (for example arrest, referral to services, or release). Officers may view youth with symptoms of major depression as more amenable to mental health treatment or as less deterrable through arrest, because their delinquent behavior falls outside of a rational choice rubric. On the other hand, officers may attribute the behaviors of youth with OD problems as freely chosen and thus as amendable through arrest.

LIMITATIONS

The comparability of our findings to those reported in earlier studies, as well as their applicability to other settings are limited. First, our sample is restricted to males living in Pittsburgh and is predominantly white or black. Second, whereas most research in this area uses clinical diagnostic instruments to measure the presence or absence of a wide array of mental disorders, the measures employed in this study are not intended to assign specific diagnoses. Rather, they indicate the frequency and severity of purported symptoms of two specific disorders, two general classes of disorders, and two broadband syndrome scales. Although the subscales of OD, ADH, affective, and anxiety problems are reportedly highly correlated with their counterpart diagnoses on the DSM-IV (Achenbach,


Dumenci, and Rescorla, 2003), we cannot verify that this holds true for the present sample. Third, the prevalence of mental health problems in our arrested sample, which is measured during the year before their first arrests, even if comparably measured, would likely be lower than that observed in prior studies. Most studies measure the contemporaneous prevalence of mental disorders in institutionalized populations, many of whom may be jailed while awaiting mental health placement or psychologically harmed by confinement (Ulzen and Hamilton, 1998). A recent review reports that studies of incarcerated youth typically show higher rates of, for example, ADHD and anxiety disorders, than studies of nonincarcerated delinquent youth (Vermeiren, 2003). Finally, we lacked data on the young men’s behavior immediately preceding and during the police encounters and, therefore, could only speculate as to the role of participants’ behavior and reactions to it.

RECOMMENDATIONS FOR POLICY AND RESEARCH

Our findings may be useful to policy makers and practitioners who seek to reduce the number of MDYs in the custody of the juvenile justice system. We provide grounds for speculation that, to the extent that youth with symptoms indicative of depression and anxiety disorders are overrepresented in secure facilities, it is not because they are more likely to be arrested than youth without such conditions. Elevated arrest risk likely figures into the disproportionate confinement of youth with ODD and ADHD, however. Interventions with such youth that aim to reduce their involvement in serious delinquency and alcohol use, as well as reduce their school problems and time with peers should lower their arrest risk. Our findings also suggest that police responses to MDYs may be a promising target of intervention. Although available police guidebooks for responding to youth with mental health problems describe ODD and encourage a compassionate approach to all mentally troubled youth (Healy and Hirschhorn, 2001), perhaps a greater emphasis on ODD-afflicted youth is warranted. On the other hand, our results suggest that, with respect to internalizing mental health problems, the police guidebooks and other training are being put into practice.

We recommend that future survey and field research directly examine the effects of various mental health problems on arrest using clinical diagnostic measures of mental disorders. Furthermore, research should directly examine the relationship between juvenile mental health problems and offense detection, calls to the police, and arrest decision making. Observational research on police encounters with juveniles that incorporates interviews with the police and assessments of their knowledge about adolescent mental health problems could be particularly


useful in explaining the intriguing inconsistencies in mental health effects on arrest reported in the present study.

Future research should also examine the impact of mental health on arrests using a more representative sample that includes females and youth with prior records. The challenge in including youth with prior records is obtaining data that permit the temporal ordering of prior arrests and prior mental health, as well as measures of ongoing court involvement. This study, which demonstrates effects of mental health on arrest, while stringently limiting threats to internal validity, will support causal inferences made when these stringent conditions are relaxed.

This research only partly and cautiously supports the concept of criminalization with respect to juveniles with mental disorders. Echoing prior research on adults (Engel and Silver, 2001), we find no compelling evidence that symptoms of a number of common child mental health problems—such as ADHD, depression, and anxiety—are independent aggravating factors in juvenile arrests. Moreover, the criminalization of the mentally ill is a problematic notion because it treats mental illness as a monolithic entity. Our research suggests that some mental health symptoms may aggravate the risk of arrest, whereas others may mitigate it. The fate of mentally disordered youth within the juvenile justice system after they are arrested, especially after several arrests, however, may be an entirely different story.

REFERENCES

Achenbach, Thomas M. 1991. Integrative Guide to the 1991 CBCL/4-18, YSR, and TRF Profiles. Burlington: University of Vermont, Department of Psychology.

Achenbach, Thomas M., and Leslie A. Rescorla. 2001. Manual for the ASEBA School-Age Forms and Profiles. Burlington: University of Vermont, Research Center for Children, Youth, and Families.

Achenbach, Thomas M., Amit Bernstein, and Levent Dumenci. 2005. DSM-oriented scales and statistically based syndromes for ages 18 to 59: Linking taxonomic paradigms to facilitate multitaxonomic approaches. Journal of Personality Assessment 84:49–63.

Achenbach, Thomas M., Levent L. Dumenci, and Leslie Rescorla. 2003. DSM-oriented and empirically based approaches to constructing from the same item pools. Journal of Clinical and Adolescent Psychology 32:328–40.

Allison, Paul D. 2000. Multiple imputation for missing data: A cautionary tale. Sociological Methods and Research 28:301–09.


American Psychiatric Association. 1994. Diagnostic and Statistical Manual of Mental Disorders, 4th ed. Washington, DC: American Psychiatric Association.

Armsden, Gay, Elizabeth McCauley, Mark Greenberg, Patrick Burke, and Jeffrey Mitchell. 1990. Parent and peer attachment in early adolescent depression. Journal of Abnormal Child Psychology 18:683–97.

Aseltine, Robert, Susan Gore, and Jennifer Gordon. 2000. Life stress, anger and anxiety, and delinquency: An empirical test of general strain theory. Journal of Health and Social Behavior 41:256–75.

Beckett, Katherine, Kris Nyrop, Lori Pfingst, and Melissa Bowen. 2005. Drug use, drug arrests, and the question of race: Lessons from Seattle. Social Problems 52(3): 419–41.

Boesky, Lisa M. 2002. Juvenile Offenders with Mental Disorders: Who Are They and What Do We Do with Them? Lanham, MD: American Correctional Association.

Borum, Randy, Martha Williams Deane, Henry J. Steadman, and Joseph Morrissey. 1998. Police perspectives on responding to mentally ill people in crisis: Perceptions of program effectiveness. Behavioral Sciences and the Law 16:393–405.

Brandibas, Gilles, Benoìt Jeunier, Claude Clanet, and Raymond Fourasté. 2004. Truancy, school refusal and anxiety. School Psychology International 25:117–26.

Brownfield, David, Ann Marie Sorenson, and Kevin M. Thompson. 2000. Gang membership, race, and social class: A test of the group hazard and master status hypotheses. Deviant Behavior 22:73–89.

Cocozza, Joseph J., and Kathleen R. Skowyra. 2000. Youth with mental health disorders: Issues and emerging responses. Office of Juvenile Justice and Delinquency Prevention Journal 7:3–31.

Conger, Rand D., Jewsbury K. Conger, and Glen H. Elder, Jr. 1997. Family economic hardship and adolescent adjustment: Mediating and moderating processes. In Consequences of Growing Up Poor, eds. Greg J. Duncan and Jeanne Brooks-Gunn. New York: Russell Sage Foundation.

Costello, E. Jane, and Adrian Angold. 1988. Scales to assess childhood and adolescent depression. Journal of American Academy of Child and Adolescent Psychiatry 27:726–37.

Costello, E. Jane, Stephen C. Messer, Hector R. Bird, Patricia Cohen, and Helen Z. Reinherz. 1998. The prevalence of serious emotional disturbance: A re-analysis of community studies. Journal of Child and Family Studies 7(4): 411–23.


Cuellar, Israel, and Freddy A. Paniagua, eds. 2000. Handbook of Multicultural Mental Health: Assessment and Treatment of Diverse Populations. New York: Academic Press.

Davison, Judy C., and Donna Y. Ford. 2001. Perceptions of attention deficit hyperactivity disorder in one African American community. The Journal of Negro Education 70:264–74.

Domalanta, Dina, William L. Risser, Robert E. Roberts, and Jan Mary Hale Risser. 2003. Prevalence of depression and other psychiatric disorders among incarcerated youths. Journal of the American Academy of Child and Adolescent Psychiatry 42:477–84.

Elgar, Frank J., and Christine Arlett. 2002. Perceived social inadequacy and depressed mood in adolescents. Journal of Adolescence 25(3): 301–05.

Elliott, Delbert S., David Huizinga, and Scott Menard. 1989. Multiple Problem Youth: Delinquency, Substance Use, and Mental Health Problems. New York: Springer-Verlag Publishing.

Engel, Robin S., and Eric Silver. 2001. Policing mentally disordered suspects: A reexamination of the criminalization hypothesis. Criminology 39:225–52.

Ferdinand, Robert F., Jeroen Heijmens Visser, Kirsten N. Hoogerheide, Jan Van der Ende, Marianne C. Kasius, Hans M. Koot, and Frank C. Verhulst. 2004. Improving estimation of the prognosis of childhood psychopathology: Combination of DSM-III-R/DISC diagnoses and CBCL scores. Journal of Child Psychology and Psychiatry and Allied Disciplines 45:599–699.

Ginsburg, Carl, and Helen Demeranville. 2001. Sticks and stones: The jailing of mentally ill kids. In Zero Tolerance: Resisting the Drive for Punishment in our Schools: A Handbook for Parents, Students, Educators, and Citizens, eds. William Ayers, Bernardine Dohn, and Rick Ayers. New York: New Press.

Green, Thomas M. 1997. Police as frontline mental health workers: The decision to arrest or refer to mental health agencies. International Journal of Law and Psychiatry 20:469–86.

Greene, William H. 2002. Econometric Analysis, 5th ed. Upper Saddle River, NJ: Prentice Hall.

Hammack, Phillip L. 2003. Towards a unified theory of depression among urban African American youth: Integrating socioecologic, cognitive, family stress, and biopsychosocial perspectives. Journal of Black Psychology 29:187–209.


Hartman, Catherine A., Joop Hox, Judith Auerbach, Nese Erol, Antonio C. Fonseca, Gideon Mellenbergh, Torunn S. Nøvik, Jaap Oosterlaan, Alexandra C. Roussos, Ruth S. Shalev, Nelly Zilber, and Joseph A. Sergeant. 1999. Syndrome dimensions of the Child Behavior Checklist and the Teacher Report Form: A critical empirical evaluation. Journal of Child Psychology and Psychiatry 40:1095–116.

Healy, Gwen, and Janet Hirschhorn. 2001. Police Pocket Guide: Responding to Youths with Mental Health Needs. Boston, MA: Parent/Professional Advocacy League. http://www.ppal.net.

Hollingshead, August B. 1975. Four Factor Index of Social Status. New Haven, CT: Yale University, Department of Sociology.

Isaacs, Mareasa. 1992. Assessing the mental health needs of children and adolescents of color in the juvenile justice system: Overcoming institutionalized perceptions and barriers. In Responding to the Mental Health Needs of Youth in the Juvenile Justice System, ed. Joseph Cocozza. Seattle, WA: National Coalition for the Mentally Ill in the Criminal Justice System.

Johnson, Jeffrey G., Patricia Cohen, Elizabeth Smailes, Stephanie Kasen, John M. Oldham, Andrew E. Skodol, and Judith S. Brook. 2000. Adolescent personality disorders associated with violence and criminal behavior during adolescence and early adulthood. American Journal of Psychiatry 157:1406–412.

Kalinich, David B., and Jeffrey D. Senese. 1987. Police discretion and the mentally disordered in Chicago: A reconsideration. Police Studies 10:185–91.

Klinger, David A. 1997. Negotiating order in patrol work: An ecological theory of police response to deviance. Criminology 35:277–306.

Krueger, Robert F., and Thomas M. Piasecki. 2002. Toward a dimensional and psychometrically-informed approach to conceptualizing psychopathology behaviour research and therapy. Behavioral Research and Therapy 40(5): 485–99.

Lengua, Liliana J., Christine A. Sadowski, William N. Friedrich, and Jennifer Fisher. 2001. Rationally and empirically derived dimensions of children’s symptomatology: Expert ratings and confirmatory factor analyses of the CBCL. Journal of Consulting and Clinical Psychology 69:683–98.

Leventhal, Tama, and Jeanne Brooks-Gunn. 2003. Moving to Opportunity: An experimental study of neighborhood effects on mental health. American Journal of Public Health 93:1576–582.


Loeber, Rolf, Magda Stouthamer-Loeber, and Helene Raskin White. 1999. Developmental aspects of delinquency and internalizing problems and their association with persistent juvenile substance use between ages 7 and 18. Journal of Clinical Child Psychology 28:322–32.

Loeber, Rolf, David P. Farrington, Magda Stouthamer-Loeber, and Welmoet B. Van Kammen. 1998a. Antisocial Behavior and Mental Health Problems: Explanatory Factors in Childhood and Adolescence. Mahwah, NJ: Lawrence Erlbaum Associates.

Loeber, Rolf, David P. Farrington, Magda Stouthamer-Loeber, and Welmoet B. Van Kammen. 1998b. Multiple risk factors for multiproblem boys: Co-occurrence of delinquency, substance use, attention deficit, conduct problems, physical aggression, covert behavior, depressed mood, and shy/withdrawn behavior. In New Perspectives on Adolescent Risk Behavior, ed. Richard Jessor. Cambridge: Cambridge University Press.

McCloyd, Vonnie C. 1997. The impact of poverty and low socioeconomic status on the socioemotional functioning of African-American children and adolescents: Mediating effects. In Social and Emotional Adjustment and Family Relations in Ethnic Minority Families, eds. Ronald D. Taylor and Margaret C. Wang. Mahwah, NJ: Lawrence Erlbaum Associates.

Miech, Richard A., Avshalom Caspi, Terrie E. Moffitt, Bradley R. Entner Wright, and Phil Silva. 1999. Low socio-economic status and mental illnesses: A longitudinal study of selection and causation during young adulthood. American Journal of Sociology 104:112–47.

Molina, Brooke S.G., and William E. Pelham, Jr. 2003. Childhood predictors of adolescent substance use in a longitudinal study. Journal of Abnormal Psychology 112:497–507.

Morash, Merry. 1984. Establishment of a juvenile police record: The influence of individual and peer group characteristics. Criminology 22(1): 97–111.

Otto, Randy K., Jonathan J. Greenstein, Michael K. Johnson, and Robert M. Friedman. 1992. Prevalence of mental disorders among youth in the juvenile justice system. In Responding to the Mental Health Needs of Youth in the Juvenile Justice System, ed. Joseph J. Cocozza. Seattle, WA: The National Coalition for the Mentally Ill in the Criminal Justice System.

Parenti, Christian. 2000. Lockdown America: Police and Prisons in the Age of Crisis. London: Verso.


Parke, Ross D., and Alison Clarke-Stewart. 2003. Effects of parental incarceration on children: Perspectives, promises, and policies. In From Prisoners Once Removed: The Impact of Incarceration and Reentry on Children, Families, and Communities, eds. Jeremy Travis and Michelle Waul. Washington, DC: Urban Institute Press.

Rice, Harvey. 2003. Schools accused of criminalizing disability: Discipline leading to lawsuits. Houston Chronicle, December 29, 13.

Rosenthal, Beth S. 2000. Exposure to community violence in adolescence: Trauma symptoms. Adolescence 35:271–84.

Schafer, Joe L. 1997. Analysis of Incomplete Multivariate Data. London: Chapman and Hall.

Sheppard, Vanessa B., and Richardean Benjamin-Coleman. 2001. Determinants of service placements for youth with serious emotional and behavioral disturbances. Community Mental Health Journal 37:53–65.

Smith, Douglas A. 1986. The neighborhood context of police behavior. In Communities and Crime, eds. Albert J. Reiss and Michael Tonry. Chicago: University of Chicago Press.

Snyder, Howard. 2005. Juvenile Arrests 2003. Washington, DC: Office of Juvenile Justice and Delinquency Prevention.

Teplin, Linda A. 1984. Criminalizing mental disorder: The comparative arrest rate of the mentally ill. American Psychologist 39:794–803.

Teplin, Linda A., Karen M. Abram, Gary M. McClelland, Mina K. Dulcan, and Amy A. Mericle. 2002. Psychiatric disorders in youth in juvenile detention. Archives of General Psychiatry 59:1133–143.

Teplin, Linda A., Karen M. Abram, Gary M. McClelland, Jason J. Washburn, and Ann K. Pikus. 2005. Detecting mental disorder in juvenile detainees: Who receives services? American Journal of Public Health 95(10): 1773–780.

Thompson, Don. 2004. CYA inmates caged, drugged, study says. Ventura County Star [California]. January 29, 1.

Ulzen, Thaddeus P.M., and Hayley Hamilton. 1998. The nature and characteristics of psychiatric comorbidity in incarcerated adolescents. Canadian Journal of Psychiatry 43:57–63.

U.S. Congress. House Committee on Government Reform—Minority Staff. 2004. Special Investigations Division. “Incarceration of Youth Who Are Waiting for Community Mental Health Services in the United States.” Washington, DC: Government Printing Office. http://democrats.reform.house.gov/Documents/20040817121901-25170.pdf.


U.S. General Accounting Office (GAO). 2003. Child Welfare and Juvenile Justice. GAO-03-397. Washington, DC: Government Printing Office.

Vermeiren, Robert. 2003. Psychopathology and delinquency in adolescents: A descriptive and developmental perspective. Clinical Psychology Review 23:277–31.

Wasserman, Gail, Susan J. Ko, and Larkin S. McReynolds. 2004. Assessing the Mental Health Status of Youth in Juvenile Justice Settings. Washington, DC: U.S. Department of Justice, Office of Juvenile Justice and Delinquency Prevention.

Werthman, Carl, and Irving Pilavin. 1967. Gang members and the police. In The Police: Six Sociological Essays, ed. David J. Bordua. New York: John Wiley & Sons.

White, Helene Raskin, Minge Xie, Wes Thompson, Rolf Loeber, and Magda Stouthamer-Loeber. 2001. Psychopathology as a predictor of adolescent drug use trajectories. Psychology of Addictive Behaviors 15:210–18.

Windle, Michael. 1992. Temperament and social support in adolescence: Interrelations with depressive symptoms and delinquent behaviors. Journal of Youth and Adolescence 21(1): 1–21.

Wolfgang, Marvin E., Terence P. Thornberry, and Robert M. Figlio. 1987. From Boy to Man, from Delinquency to Crime. Chicago: University of Chicago Press.

Wong, Carol A., Jacquelynne S. Eccles, and Arnold Sameroff. 2003. The influence of ethnic discrimination and ethnic identification on African American adolescents’ school and socioemotional adjustment. Journal of Personality 71:1197–232.

Worden, Robert E., Robin L. Shepard, and Stephen D. Mastrofski. 1996. On the meaning and measurement of suspects’ demeanor toward the police: A comment on “Demeanor and Arrest.” Journal of Research in Crime and Delinquency 33:324–32. Paul Hirschfield is an assistant professor in the Department of

Sociology and the Program in Criminal Justice at Rutgers University, New Brunswick. He studies the relationships between the justice system and other institutions of social control, as well as the effects and effectiveness of justice system interventions.


Tina Maschi is an assistant professor at Monmouth University, Social Work Department. Dr. Maschi’s research interests include mental health issues in juvenile and adult offenders, trauma, child welfare, and juvenile delinquency.

Helene Raskin White is a professor at the Center of Alcohol Studies and Sociology Department at Rutgers University. Her research focuses on the antecedents, consequences and comorbidity of substance use, and crime and mental health problems in community and high-risk samples.

Leah Goldman Traub is a graduate student in the Department of Economics at Rutgers University, New Brunswick.

Rolf Loeber is a distinguished professor of psychiatry, and professor of Psychology and Epidemiology, University of Pittsburgh and a professor of Juvenile Delinquency and Social Development, Free University, Amsterdam, Netherlands. Dr. Loeber’s interests are in how and why young people develop serious problems, including delinquency, mental problems, and drug problems, as well as the emergence and persistence of risk and protective factors and how this knowledge informs intervention. He is the principal investigator for the Pittsburgh Youth Study.


Appendix A. Description of Mental Health and Delinquency Measures

Mental health measures Each item is the average of scores on the CBCL and YSR (0 = not true; 1 = somewhat or sometimes true; 2 = very true or often true). Subscales are sums of scores on the items.

Affective problems Cries Harms self Doesn’t eat well Worthless

Feels too guilty Overtired Sleeps less Sleeps more

Talks suicide Sleep problems Underactive Sad

Anxiety problems Dependent Fears Fears school

Nervous Fearful Worries or cries

ADH problems Fails to finish* Can’t concentrate Can’t sit still Loud *CBCL only

Difficulty with directions* Disturbs others* Impulsive

Talks out of turn* Inattentive* Talks too much Fails to carry out tasks*

OD problems Argues Stubborn Disobedient at home Disobedient at school Temper

Internalizing problems Anxious-depressed Cries Fears a lot Fears school Fears doing bad Must be perfect Feels unloved Feels worthless Fearful Nervous Feels too guilty Self-conscious Talks suicide Worries

Withdrawn-depressed Rather be alone Won’t talk Secretive Shy-timid Underactive Sad Withdrawn

Somatic problems Nightmares Constipated Feels dizzy Overtired Aches Headaches Nausea Eye problems Skin problems Stomach problems Vomiting

Externalizing problems Rule-breaking behaviorLacks guilt Lies or cheats Prefers older kids Sex problems Thinks about sex too much Swearing

Aggressive behavior Argues Mean Demands attentions Destroys own things Disobedient at home Disobedient at schoolScreams a lot Stubborn-sullen

Mood changes Sulks Suspicious Teases a lot Temper Loud


Appendix A. Description of Mental Health and Delinquency Measures

(continued)

Delinquency measures Each measure is the logged sum of the frequencies with which the youth committed each of the listed behaviors during the year preceding T1.

Minor delinquency Set fires Minor vandalism Avoid paying

Stealing < $5 Shoplifting Credit card fraud

Cheating someone Check fraud

Serious delinquency Major vandalism Major fraud Stealing bike, skateboard, or something > $5

Joyriding Pickpocketing Stealing from a car Holding stolen goods Carrying weapon Gang fighting

Drug dealing Breaking & entering Auto theft Strong arming Attacking someone Rape


App

endi

x B

. B

ivar

iate

Cor

rela

tion

Mat

rix1

1

2 3

4 5

6 7

8 9

10

11

12

13

14

15

1617

181.

OD

pro

blem

s 1

2.

AD

H p

robl

ems

.68

1

3.

Aff

ecti

ve p

robl

ems

.49

.51

1

4. A

nxie

ty p

robl

ems

.47

.56

.63

1

5.

Ext

erna

lizin

g pr

oble

ms

a a

.59

.57

1

6. I

nter

naliz

ing

prob

lem

s .5

7.6

0a

a .6

71

7. M

inor

del

inqu

ency

.3

5.2

8.2

6.1

6.4

0.2

21

8.

Ser

ious

del

inqu

ency

.2

7.1

7.1

4.0

7.3

1.1

0.4

31

9. A

lcoh

ol u

se

.18

.08

.13

-.01

.21

.03

.40

.50

1

10. M

ariju

ana

use

.13

.03

.06

-.01

.13

-.02

.24

.45

.54

1

11

. Fam

ily S

ES

-.09

-.11

-.08

-.06

-.13

-.08

-.01

-.09

.01

.01

1

12. N

eigh

borh

ood

qual

ity

.03

.04

.02

.06

.08

.08

.02

.03

.00

.01

-.19

1

13. F

amily

cri

min

alit

y .1

6.1

2.1

0.0

9.1

9.1

4.0

9.0

9.0

5-.

04 -

.08

.10

1

14. P

eer

delin

quen

cy

.30

.21

.20

.12

.37

.14

.43

.52

.46

.38

-.11

.04

.04

1

15

. Tim

e w

ith

frie

nds

.11

.05

.03

.03

.14

-.01

.10

.09

.11

.06

-.04

-.0

7.0

1.1

21

16

. Age

-.

13-.

24-.

06-.

25-.

17-.

23-.

03.1

3.2

4.1

7 .0

7 -.

02-.

01.1

0-.

071

17. B

lack

-.

06.0

4-.

03.0

1-.

01.0

6-.

05-.

06-.

09.0

3 -.

14 .

31.1

1.1

0-.

15.0

31

18

. Old

er c

ohor

t .2

0.1

2.2

2.2

5.2

2.3

1.0

6.0

5-.

01-.

22 -

.13

.02

.15

-.04

-.02

.04

.02

1 19

. Sch

ool p

robl

ems

.38

.25

.16

.16

.40

.18

.25

.27

.29

.20

-.20

.13

.09

.35

.17

.08

.13

.12

1 C

orre

lati

ons

betw

een

cont

inuo

us v

aria

bles

are

com

pute

d as

Pea

rson

’s r

, bet

wee

n di

chot

omou

s an

d co

ntin

uous

as

Spea

rman

’s r

and

bet

wee

n di

chot

omou

s va

riab

les

as P

hi c

oeff

icie

nts.

a

Coe

ffic

ient

s no

t rep

orte

d du

e to

ove

rlap

ping

item

s on

thes

e m

enta

l hea

lth

scal

es.

V

alue

s >

.08

are

sign

ific

ant a

t 5 p

erce

nt le

vel.

OD

= o

ppos

itio

nal d

efia

nt, A

DH

= a

tten

tion

def

icit

-hyp

erac

tivi

ty

MENTAL HEALTH AND JUVENILE ARRESTS: CRIMINALITY, CRIMINALIZATION, OR COMPASSION

Documents

Transcript of MENTAL HEALTH AND JUVENILE ARRESTS: CRIMINALITY, CRIMINALIZATION, OR COMPASSION