How do youth with emotional
and substance use problems
fare in the juvenile justice
system?
Alison Evans Cuellar, PhD
Mailman School of Public Health
Columbia University
New York, New York
Pinka Chatterji, PhD
Center for Multicultural Mental Health Research
Cambridge Health Alliance – Harvard Medical School
Somerville, MA
Objective


To use a national sample of adolescents to
investigate the association between emotional
and substance use problems during adolescence
and involvement with the juvenile justice system
Focus on three types of involvement:



Arrests before age 18
Convictions in juvenile court before age 18 (among
those arrested before age 18)
Detainment before age 18 (among those convicted
before age 18)
Motivation


Ample evidence that youth with emotional and
substance use disorders are over-represented in the
juvenile justice system
Can be explained by:



Youth with emotional and substance use problems are more
likely to commit crimes or more likely to commit more serious
crimes
Youth with emotional and substance use problems are more
likely to be sanctioned conditional on committing a particular
crime
Public policy concern that the justice system is biased
against youth with these problems – but there have
been few systematic empirical tests of this claim
Background


Adults with mental disorders are more likely to be arrested and
serve longer prison sentences than adults without mental illness
(Teplin, 1984; Ditton, 1999)
High prevalence of emotional disorders (other than conduct
disorder) among youth in the juvenile justice system relative to
youth in the community

In large, random sample of arrested and detained youth in Cook
County, IL (Teplin et al, 2002):




61% of males and 70% of females met DSM diagnostic criteria for any
mental disorder other than conduct disorder
17% of males and 21% of females met criteria for ADHD
19% of males and 28% of females met criteria for any affective disorder
51% of males and 47% of females met criteria for substance use disorder
Contribution of this Study



Previous work generally is based on small and specialized samples –
typically, detained or incarcerated youth – which represent a
relatively small sub-set of all youth involved in the justice system
Past studies generally do not control for demographic characteristics
and do not have a community control group
Use of the National Longitudinal Study of Adolescent Health (Add
Health) builds on work based on more specialized and smaller
samples



Control for many potential correlates of emotional/substance use
problems and juvenile justice involvement
Longitudinal data
Capture youth with sub-threshold disorders and earlier or more limited
involvement with the juvenile justice system
Methods
J = b0 + b1 MH + b2 X + u + e
J: Juvenile justice outcome
MH: Mental health measure
X: Observed personal characteristics
u: Unobserved personal characteristics



Primarily interested in estimating b1
Standard estimation methods such as OLS can lead to biased estimates:
 if a problem of reverse causality exists (e.g. juvenile justice outcome
affects mental health) – although problem is limited by measurement of
MH at two points in time during adolescence
 if unobserved characteristics (u) exist that influence both mental health
and juvenile justice outcomes (e.g. u is correlated with J and MH)
Because of data limitations, we cannot address both of these issues directly.
We address the problem of unobserved characteristics by using a rich data
source to proxy u to the extent possible.
Outcome Measures

All youth:



Among those with 1+ arrest before age 18:


Whether or not youth was arrested prior to age 18
Number of arrests prior to age 18
Whether or not youth was convicted in juvenile court
prior to age 18
Among youth with 1+ conviction before age 18:

Whether or not youth sentenced to a detention center
(as opposed to probation or other punishment)
Data


The National Longitudinal Study of Adolescent Health (Add Health) –
nationally representative sample of adolescents designed to study
causes of health-related behaviors and young adult outcomes
Waves 1, 2 and 3:




Wave 1, In-Home Sample: 20,745 students in grades 7-12 in 1995
Wave 2: About 15,000 students from Wave 1 In-Home sample
interviewed about one year later, in 1996
Wave 3: More than 15,000 young adults aged 18-26 interviewed in
2001-2002
Main analysis sample includes students interviewed in all three
waves with information on all dependent and independent variables
of interest
Measures of Emotional and
Substance Use Problems

Elevated ADHD symptoms:


Chronic elevated depressive symptoms:


Binary indicator of whether or not youth’s score on the
Retrospective Attention Deficit Hyperactivity Scale was at the
75th percentile or higher at Wave 3 assessment
Binary indicator of whether or not youth’s CES-D score was at
the 75th percentile or higher in both Waves 1 and 2
Chronic drinking:

Binary indicator of whether youth in both Waves 1 and 2 reports
number of alcoholic drinks in the past 12 months at the 75th
percentile or higher
Other Independent Variables

Exogenous set:



Age, race/ethnicity, gender, family structure, and proxies for family
income (maternal education and welfare receipt)
All measured at Wave 1
Potentially endogenous set:

Delinquent and violent acts in past year




Grade point average and school absences with no excuse




Measured in Waves 1 and 2
Dummy variables indicating whether youth’s delinquency scale score was
medium, medium high or high relative to the sample
Dummy variables indicating violent behavior, theft, and drug sales
Measured at Wave 1
Average GPA in Math and English
Highest quartile of school absences
Level of parental involvement in adolescent’s life at Wave 1

At least one parent actively engaged in activities with youth in past four
weeks
PRELIMINARY
RESULTS
Table 1: Means
Full sample (N = 9,434)
Male
47%
Age
16.06
(1.59)
African-American
22%
Latino
15%
Asian
8%
Native American
3%
Mother is high school dropout
16%
Mother is welfare recipient
9%
GPA in Math and English
2.21
(0.84)
Table 2: Outcome Variables by Emotional/Substance
Use Problems Status
ADHD
No
ADHD
Depression
No
Depression
Arrest before
age 18
7.1%
3.3%***
5.3%
3.9%***
Number of
arrests
.20
.07***
.14
.09**
Conviction
(among those
arrested)
.47
.41
.47
.42
Detention
(among those
convicted)
.19
.24
.25
.19
Table 2 cont’
Chronic
Drinker
Not chronic
drinker
Arrest before age
18
9.9%
3.8%***
Number of arrests
.27
.09***
Conviction
(among those
arrested)
.42
.44
Detention
(among those
convicted)
.24
.21
Table 3: Effect of Emotional/Substance Use Problems
on Arrests Before Age 18
Arrested before age 18
Number of arrests
before age 18
Logistic Model
Odds Ratio
(p-value)
Poisson Model
Coefficient
(p-value)
ADHD
symptoms
1.33
(0.010)
n = 9,434
0.354
(0.000)
n = 9,497
Chronic depressive
symptoms
1.22
(0.098)
n = 9,587
0.168
(0.014)
n = 9,653
Chronic
drinking
1.30
(0.104)
n = 9,630
0.146
(0.098)
n = 9,697
Table 4: Effect of Emotional/Substance Use Problems on
Convictions and Detention Before Age 18
Among those arrested Among those convicted
before age 18,
before age 18,
convicted before age 18 sentenced to detention
before age 18
Logistic Model
Odds Ratio (p-value)
ADHD
symptoms
1.00
(0.995)
n = 413
0.910
(0.844)
n = 172
Chronic depressive
symptoms
0.998
(0.927)
n = 422
1.34
(0.582)
n = 178
Chronic
drinking
1.03
(0.934)
n = 423
0.957
(0.946)
n = 178
Summary of Results



Elevated ADHD symptoms, chronic depressive
symptoms, and chronic drinking during adolescence are
associated with statistically significant and meaningful
increases in the probability of arrest, and number of
arrests before age 18
Models control for most likely correlates of
emotional/substance use problems and juvenile justice
outcomes
No apparent effects on the probability of conviction and
a sentence of detention in juvenile court – however,
these analyses may be under-powered
Limitations

Self-reported, retrospective data



Cannot confirm juvenile justice data with
administrative information
Emotional and alcohol problem information is
not collected with a diagnostic instrument –
cutoffs are somewhat arbitrary
Possibility of unobserved factors that are
correlated with both emotional/substance
use problems and juvenile justice
involvement
Work in progress





Propensity score matching methods
Run arrest models by gender
Control for neighborhood characteristics
Test robustness of results to alternative
thresholds for ADHD, depression and
alcohol use
Consider effects of marijuana use, and
effects of youth in treatment for emotional
and substance use problems
Conclusions



Using a large sample of adolescents, preliminary findings
show that youth with elevated symptoms of ADHD,
chronic depressive symptoms and chronic drinking do
fare worse in the juvenile justice system in terms of the
probability of being arrested
Identify a group of youth whose health needs are not
being appropriately recognized and who face a greater
likelihood of being punished possibly as a result of their
disorder
Implications for outreach and education programs in the
justice system, as well as the delivery system of public
mental health services