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70
OJJDP Journal of Juvenile Justice
Childhood Adversity Among Court-Involved Youth:
Heterogeneous Needs for Prevention and Treatment
Patricia Logan-Greene, University at Buffalo, Buffalo, New York
B. K. Elizabeth Kim, University of Southern California, Los Angeles, California
Paula S. Nurius, University of Washington, Seattle, Washington
Patricia Logan-Greene, School of Social Work, University at Buffalo; B. K. Elizabeth Kim, Department of
Children, Youth, and Families, School of Social Work, University of Southern California; Paula S. Nurius,
School of Social Work, University of Washington.
We thank T. J. Bohl and Shelly Maluo for their support and contribution to this research. We also thank
Isaias Hernandez for his contributions to data management.
This research was supported in part by a grant from the National Institute on Mental Health
grant 5 T32 MH20010 “Mental Health Prevention Research Training Program,” the National Center
for Advancing Translational Sciences of the National Institutes of Health under Award Number
TL1TR000422, and a Eunice Kennedy Shriver National Institute of Child Health and Human
Development research infrastructure grant, R24 HD042828, to the Center for Studies in Demography
& Ecology at the University of Washington.
Correspondence concerning this article should be addressed to Patricia Logan-Greene, School of
Social Work, 685 Baldy Hall, University at Buffalo, Buffalo, NY 14260. E-mail: pblogang @buffalo.edu
Keywords: adverse childhood experiences, trauma, latent class analysis, juvenile justice
Abstract
Although experiences of trauma and adversity are
highly prevalent among juvenile justice–involved
youth, few studies examine the heterogeneity of
these histories across individuals. This study seeks
to inform practitioners of the distinct patterns
of adversity among this vulnerable population,
using an expanded measure of adverse child-
hood experiences (ACEs). A Latent Class Analysis
was employed to test for meaningful subgroups
of youth based on histories of childhood adver-
sity. The sample (N = 5,378) consisted of youth
on probation in a western United States county.
The best-fitting model contained six classes,
described as: Low All (40.3%), Parental Substance
Use and Incarceration (12.0%), Poverty and
Parental Health Problems (13.2%), High Family
Conflict and SES (socioeconomic status) (15.3%),
High Maltreatment (11.0%), and High All (8.1%).
Additional testing revealed significant differ-
ences across classes in terms of age, gender, race/
ethnicity, and living situations. Results strongly
support the need to incorporate a trauma-
informed framework into both juvenile justice
and community service settings as well as to
tailor interventions to meet heterogeneous needs
of court-involved youth. Striking variation in the
forms and levels of childhood adversity argue for
the value of screening for ACEs in conjunction
with poverty and working to interrupt problem-
atic trajectories in adolescence and the transition
to adulthood.
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OJJDP Journal of Juvenile Justice
Introduction
There is a robust literature examining the overlap
of juvenile delinquency with a range of child-
hood adversities, such as childhood maltreat-
ment; socioeconomic disadvantage; and family
dysfunction, including involvement in the child
welfare system. These examinations have sup-
ported the notion that the majority of youth
involved with the juvenile justice systems bring
histories of childhood trauma and adversity
(Dierkhising, Ko, & Goldman, 2013; Greenwald,
2014). This has led to a growing recognition of
the need to transform juvenile justice systems to
appropriately address these histories. However,
there is as yet little guidance about the specific
and heterogeneous needs of court-involved
youth with respect to these backgrounds. The
present study seeks to fill that gap by testing
for distinct patterns of adverse childhood expe-
riences among subgroups of youth involved
with the juvenile justice system. The findings of
this study can provide practitioners with novel
insights regarding distinct adversity profiles with
which court-involved youth enter the system,
illumining differing patterns of treatment needs.
Childhood Adversity and Court-Involved Youth
One increasingly common way to assess child-
hood adversity is with the adverse childhood
experiences (ACEs) framework. ACEs describe a
set of commonly experienced adversities that can
be easily assessed in clinical, community, or court
settings (Felitti et al., 1998). This work builds on a
cumulative adversity model wherein exposure to
greater numbers of adversities tends to commen-
surately increase health risks and maladaptive
development, especially because negative expe-
riences tend to be interrelated (Anda, Butchart,
Felitti, & Brown, 2010; Duke, Pettingell, McMorris,
& Borowsky, 2010). ACEs have been found to be
interrelated in both broad-based (Dong et al.,
2004) and predominantly young minority com-
munity samples (Mersky, Topitzes, & Reynolds,
2013) as well as among court-involved youth
(Baglivio & Epps, 2015). Baglivio and Epps (2015)
demonstrated that having a single ACE increased
the likelihood of having another up to 1,286
times, which bolsters the idea that ACE expo-
sures generally do not occur in isolation. Thus, a
cumulative assessment better captures the stress
load that children’s life contexts impose; such
contexts, through which neurobiological as well
as psychosocial pathways can lead to problem-
atic development, can cascade across the life
course of children (Logan-Greene, Green, Nurius,
& Longhi, 2014; Putnam, 2006).
ACEs assessment has commonly included mal-
treatment (sexual, physical, and emotional
victimization and exposure to family violence
and neglect) and family dysfunction (household
substance abuse, household illness, incarcerated
family member, and parental divorce). When
measured as a count of how many adversities a
person has experienced, the ACE score has been
shown to be a powerful predictor of health,
behaviors, and even morbidity across a wide
variety of populations and contexts (Anda et
al., 2006; Larkin, Shields, & Anda, 2012; Nurius,
Green, Logan-Greene, & Borja, 2015). Recent
extensions of ACEs have incorporated other
indicators of adversity, such as out-of-home
placement in foster care (Cronholm et al., 2015)
and family member illness (Wade, Shea, Rubin, &
Wood, 2014), among others.
Lacking from the ACEs framework, however,
has been an assessment of poverty-related
forms of social disadvantage. The impact of
socioeconomic disadvantage on health and a
range of behavioral outcomes is well established
(DeNavas-Walt, Proctor, & Smith, 2010; Skowyra &
Cocozza, 2007) and may also be entangled with
other forms of adversity, such as parental incar-
ceration or illness. Recent work has argued for
expanded assessment to include adversities such
as poverty, out-of-home placement, and com-
munity threats (Cronholm et al., 2015; Wade et
al., 2014) that might further disadvantage young
people through their life course. Community-
based surveys, such as the National Survey of
72
OJJDP Journal of Juvenile Justice
Children’s Health, include poverty among the
conventional ACEs list (Sacks, Murphey, & Moore,
2014). Recently, Baglivio, Wolff, Epps, and Nelson
(2015) found that neighborhood context pre-
dicted ACE scores among delinquent youth, such
that youth in impoverished census tracts had
significantly more ACEs than those in affluent
neighborhoods. Thus, this paper added family
socioeconomic disadvantage within an ACEs
framework to assess its value in distinguishing
household contexts that were posing greater
challenges for disadvantaged youth.
ACE exposures have shown to be particularly high
among juvenile offenders (Baglivio et al., 2014;
Dierkhising et al., 2013; Grevstad, 2010), who also
report higher likelihood of experiencing mul-
tiple forms of adversity compared to the general
population (Abram et al., 2004). A recent study
reported that for every additional count of ACEs,
the odds of youth becoming serious, violent,
or chronic offenders increased by 35%, control-
ling for other factors (Fox, Perez, Cass, Baglivio,
& Epps, 2015). Furthermore, greater numbers of
ACEs increased the risk of rearrests, with higher
cumulative exposure leading to increased recidi-
vism rates (Wolff, Baglivio, & Piquero, 2015). The
findings of these studies have suggested a critical
need for trauma-informed treatment and services
for juvenile justice–involved youth specific to
their complex trauma histories.
Testing for Differences in Youth ACE Profiles
This body of work indicates that risks stemming
from adversity appear greatest for youth who
are nested within contexts that include broader
spectrum forms of adversity such as poverty,
maltreatment, and parental dysfunction. Variable-
oriented analytic approaches (such as logistic or
linear regression) used in prior work to examine
prevalence or linear trends among these domains
within samples have provided replicated dem-
onstration of step-dose forms of association of
cumulative adversity and subsequent health and
functioning outcomes. These tools have been
helpful in characterizing populations overall and
have provided a strong foundation as well as an
impetus for subsequent stage investigations that
test for variation within populations, providing
particularly important distinctions within high-
risk populations.
Person-oriented analytic methods, such as latent
class analysis (LCA), are suited for these latter
kinds of questions. Berzenski and Yates (2011), for
example, used LCA to ascertain distinct patterns
and combinations of four maltreatment experi-
ences. Students exposed to multiple maltreat-
ment experiences came from families that were
physically violent, emotionally hostile, sexually
abusive, and included harsh parenting. Similarly,
Mulder, Vermunt, Brand, Bullens, and Marle (2012)
used LCA to identify subgroups of offenders (e.g.,
sex offending group, violent offending group,
property offending group) and found that these
groups had distinctly different risk profiles lead-
ing to differential prediction of recidivism rates.
Mulder et al. (2012) concluded that these distinct
groups and risk profiles indicated a need for
individualized treatment aimed at different risk
factors. Additionally, Lanza and Rhoades (2013)
argued that LCA was an efficient approach that
identified subgroups based on multiple contex-
tual risks and matched individualized prevention
and treatment needs.
Thus, examining the heterogeneity of the adverse
experiences within populations uncovered
potentially distinct developmental contexts
within which youth were being reared and thus
identified the differing individual and family
treatment and support these youth and families
needed. Person-oriented analytic findings did
not stand in opposition to trends established
through full sample (or variable-oriented) exami-
nation; instead, they addressed complementary
questions—such as predicting a phenomenon
at a population level to more fully understand
variation in developmental mechanisms or path-
ways—together providing a “binocular view” of
the phenomenon in question (Bergman & Trost,
2006, p. 629).
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OJJDP Journal of Juvenile Justice
Advances in identifying mechanisms through
which early life experiences and environmental
influences have left lasting signatures on youth
development emphasized attention to these
childhood ecologies—the social and physical
environments in which youth have been raised
(Shonkoff et al., 2012). Given that higher ACE
scores indicated a higher risk for impaired devel-
opmental trajectories, including early onset and
chronic delinquency (Baglivio, Wolff, Piquero, &
Epps, 2015), it is imperative to ask more penetrat-
ing questions regarding the differential combina-
tions that these adversities may manifest among
system-involved youth. Such research could
provide guidance about different kinds of early
programs and services likely to provide stronger
preventive and remedial effects.
The Present Study
In this paper, we tested for empirically supported
clustering to determine subsets of court-involved
youth who were more like one another than they
were to the sample as a whole, relative to histo-
ries of adverse experiences. We hypothesized that
significant clustering would be found, reflecting
differing forms of adversity exposure rather than
differences in level (e.g., low, medium, high expo-
sure to adversity) alone, suggesting a strong need
for specific treatment approaches. We theorized
that these clusters would demonstrate that some
traumas tended to co-occur, providing further
detail to variable-oriented framing of cumula-
tive trauma. Additionally, we expected that these
clusters would reflect strengths that youth may
have, such as a lack of social disadvantage, that
service providers might be able to draw upon for
interventions. To determine these clusters, we
employed LCA, a powerful statistical method used
to determine groups of similar individuals within
a heterogeneous sample (McCutcheon, 1987).
This structure-seeking approach did not have a
priori expectations of group compositions yet
provided an accurate and complex empirical tool
to discern group structure.
Methods
Data
The data came from the Washington State
Juvenile Court Assessment (WSJCA) given to
youth adjudicated to probation in a mixed urban
and rural, racially/ethnically diverse, Western
region from 2003 to 2013 (Barnoski, 2004b).
The assessment tool used was the Positive
Achievement Change Tool (PACT), which has
been found to be valid and empirically sound
across gender and racial/ethnic groups (Baglivio
& Jackowski, 2013; Barnoski, 2004a; Washington
State Institute for Public Policy, 2004). The WSJCA
was developed as a two-stage process. In the first
stage, pre-screen assessment is completed for all
youth placed on probation to identify those at
low-, moderate-, or high-risk for recidivism. In the
second stage, youth identified as moderate- to
high-risk for recidivism are given the full assess-
ment that provides a longer and more compre-
hensive risk and protective factor profile. Juvenile
probation counselors (JPCs) in Washington State
are trained to conduct one-on-one interviews
with youth entering probation, and they com-
plete the assessment. To further enhance the
assessment’s validity, JPCs confirm self-reported
responses by contacting, where available, other
agencies, records (e.g., child-protective service
records), or collateral resources (e.g., parents,
teachers, mental health counselors). Assessments
completed by well-trained probation officers have
been found to be reliable (Barnoski, 2004a).
The sample population included youth who were
identified as moderate- to high-risk during pre-
screen assessment based on social and criminal
history and had received a minimum of 3 months’
probation between January 2003 and December
2013. The first case from each young person was
included in this analysis, yielding a final sample
of 5,378 youth (female = 23.6%). The sample’s
average age was 15.5 years, ranging from 10 to
18 years. The sample’s racial/ethnic composition
was 56.0% Caucasian; 24.2% African American;
3.0% American Indian/Alaskan Native; 2.9% Asian
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OJJDP Journal of Juvenile Justice
American; 1.5% Hawaiian or Pacific Islanders; 5.7%
Latino; and 6.7% missing, mixed, or other. In this
assessment “Latino” was not listed as a different
ethnicity category separate from race.
Measures
Demographics. Youth demographic information
regarding age, gender, and race/ethnicity was
collected separately as part of the usual system
processing. Race was collapsed into four groups:
Caucasian; African American; Latino; and other,
which included Asian, Hawaiian, Pacific Islander,
Native American, and mixed race. As part of the
WSJCA assessment, participants answered ques-
tions about their living situations, allowing them
to endorse any item(s) that reflected their current
household composition. Four mutually exclusive
variables assessed whether youth lived with a bio-
logical parent (both biological parents, biological
mother only, biological father only, and neither
biological parent); another variable assessed
whether they lived in foster care.
Childhood adversity. All items were either
dichotomous by nature or transformed to be
dichotomous where noted; Table 2 illustrates
the frequencies for the sample. Family dysfunc-
tion included incarceration of a mother (26.6%),
father (35.9%), or sibling (younger and older
sibling combined, 17.6%); “parental problem his-
tory” with alcohol (21.0%), drugs (18.4%), mental
health (9.4%), or physical health (12.0%); or out-
of-home placement (16.4%). Child maltreatment
was assessed by history of sexual (13.9%) or
physical (24.0%) abuse (both sexual and physical
abuse collapsed incidences occurring inside the
family and outside the family); neglect (16.9%);
and family conflict (64.9%), which was based on
the respondents answering yes to one of the fol-
lowing (mutually exclusive) experiences: verbal
intimidation, “heated arguments,” or exposure to
domestic violence. Socioeconomic disadvantage
included low family income (below $15,000 or
below the poverty line, 20.9%), lack of health
insurance (5.8%), and a history of parental unem-
ployment problems (17.2%).
Analysis
We used LCA (Clogg, 1995) to estimate a model
that examined diverse patterns of adverse experi-
ences among the sample. Analysis was conducted
using Mplus 6.1 (Muthén & Muthén, 2010). All
15 variables assessing childhood adversity were
included as indicators of a latent categorical
variable. We estimated the models by incremen-
tally increasing the number of latent classes and
comparing indices of fit. Because there was no
single fit statistic commonly used to determine
the best-fitting number of classes, we examined
multiple fit statistics: the log likelihood value,
Bayesian information criterion (BIC), Lo-Mendell-
Rubin test (LMR), and Vuong-Lo-Mendell-Rubin
likelihood ratio test (VLMR-LRT). Although the log
likelihood values always increased with increas-
ing number of latent classes, the BIC statistic took
into consideration the complexity of the model.
A lower BIC statistic indicated the better model
fit. Both the LMR and the VLMR-LRT compared
the fit of a model to the fit of a model with one
fewer class (e.g., 4-class model to 3-class model).
A significant p value indicated that a model with
one more class was a better fitting model. To
ensure that fitted models were not local solutions,
we used random starting values (10 initial-stage
iterations, 1,000 initial-stage random values, 100
final-stage optimization). The best-fitting model
was selected based on these model fit statistics as
well as on substantive interpretation. After select-
ing the best-fitting model, we re-estimated the
model also with 50% random subsample. Both
the best-fitting model and the interpretation of
classes remained consistent.
Mplus provided a mechanism to test for class dif-
ferences on additional variables via the Auxiliary
command (Muthén, 2007). This mechanism uses
the Wald test for mean differences based on class
membership in the latent classes, as opposed to
assigning cases to classes and testing via ANOVA
or similar tests, which introduce substantial error
(Nagin, 1999). We employed this technique to test
for differences on demographic variables, includ-
ing living situation.
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OJJDP Journal of Juvenile Justice
Results
Table 1 provides the fit statistics for the
best-fitting models with one through
seven classes. The LMR and VLMR-LRT
both suggested that the six-class solution
was optimal; however, the BIC continued
to improve with the addition of a seventh
class (the eight-class solution was unreli-
able due to local maxima). Because of this
ambiguity, we examined both the six- and
seven-class solutions for interpretability
and coherence. The seven-class solution
added one small class (5.7%), which did
Table 1. Model Fit Statistics for the 1 Through 7 Class Solutions
Number
of Classes Log Likelihood BIC LMR VLMR-LRT
1 -30153.24 151541.33 n/a n/a
2 -28581.38 143836.30 p < 0.0001 p < 0.0001
3 -27990.35 141096.27 p < 0.0001 p < 0.0001
4 -27743.45 140114.39 p < 0.0001 p < 0.0001
5 -27546.66 139309.29 p = 0.0059 p = 0.0061
6 -27434.75 138932.62 p = 0.0001 p = 0.0001
7 -27380.52 138818.26 p = 0.1212 p = 0.1227
BIC = Bayesian Information Criterion (BIC); LMR = Lo-Mendell-Rubin test; VLMR-LRT = Vuong-Lo-Mendell-Rubin
likelihood ratio test.
Table 2. Class and Sample Proportions Endorsing Each Indicator
Low All
Parental
Incarceration
&
Substance Use
Poverty &
Parental
Health
Problems
High Conict
& High SES
High
Maltreatment High All Full Sample
Latent Class Sizes 40.31% 12.04% 13.24% 15.29% 10.97% 8.14%
Maternal incarceration
a,b,c,d,e,f,g,h,i,j,k,l,m,n,o 0.074 0.468 0.263 0.126 0.626 0.694 0.266
Paternal incarceration
a,b,c,d,e,f,g,h,i,j,k,l,m,n,o 0.175 0.573 0.444 0.348 0.480 0.677 0.359
Sibling incarceration
a,b,c,d,e,f,g,h,i,j,k,l,m,n,o 0.140 0.214 0.235 0.144 0.133 0.326 0.176
Parent alcohol abuse
a,b,c,d,e,f,g,h,i,j,k,l,m,n,o 0.040 0.761 0.084 0.193 0.043 0.698 0.210
Parental drug use a,b,e,f,i,l,o 0.016 0.716 0.082 0.057 0.028 0.842 0.184
Parental MH problems
a,b,c,e,f,g,h,i,j,k,l,m,n,o 0.016 0.111 0.222 0.101 0.028 0.324 0.094
Parent PH problems
a,b,c,d,e,f,g,h,i,j,k,l,m,n,o 0.036 0.120 0.390 0.083 0.060 0.249 0.120
Out of home placements
b,c,d,e,j,k,l,m,n,o 0.031 0.028 0.077 0.071 0.759 0.535 0.164
Sexual abuse a,b,c,d,e,f,g,h,i,j,k,l,m,n,o 0.047 0.078 0.155 0.224 0.318 0.254 0.139
Physical abuse f,g,h,i,j,k,l,m,n,o 0.033 0.131 0.231 0.519 0.533 0.507 0.240
Neglect j,k,l,m,n,o 0.007 0.030 0.092 0.062 0.766 0.692 0.169
Family conict a,b,d,e,f,h,i,k,l,o 0.497 0.698 0.768 0.908 0.498 0.833 0.649
Low family income a,b,d,e,f,h,i,k,l,o 0.105 0.232 0.560 0.050 0.154 0.480 0.209
No health insurance
a,b,c,d,e,f,g,h,i,j,k,l,m,n,o 0.052 0.062 0.090 0.049 0.040 0.068 0.058
Parental unemployment
a,b,e,f,i,k,l,o 0.031 0.267 0.576 0.000 0.026 0.594 0.172
Superscripts show significant differences between classes: a Low All and Parental Incarceration & Substance Use. b Low All and Poverty & Parental Health Problems. c Low All and High Conflict & High SES.
d Low All and High Maltreatment. e Low All and High All. f Parental Incarceration & Substance Use and Poverty & Parental Health Problems. g Parental Incarceration & Substance Use and High Conflict & High
SES. h Parental Incarceration & Substance Use and High Maltreatment. i Parental Incarceration & Substance Use and High All. j Poverty & Parental Health Problems and High Conflict & High SES. k Povert y
& Parental Health Problems and High Maltreatment. l Poverty & Parental Health Problems and High All. m High Conflict & High SES and High Maltreatment. n High Conflict & High SES and High All. o High
Maltreatment and High All.
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OJJDP Journal of Juvenile Justice
not improve the theoretical meaningfulness of
the classes. Thus we retained the six-class solu-
tion. The average latent class probabilities for
class membership, which are indicators of correct
model assignment to the six classes, were good,
ranging from .70 to .88. Entropy, which reflects
these calculations, was acceptable at 0.69.
The proportions of each latent class that
endorsed each indicator are shown in Table 2, and
Figure 1 offers a visual depiction of the results.
In Figure 1, we transformed each group’s pro-
portions into z-scores compared to the sample
proportions and standard deviations to make a
figure that showed all indicators at similar mag-
nitudes. Youth in the first class had relatively low
levels of all ACEs and comprised the largest por-
tion of the sample (40.3%). Thus we termed this
class the Low All class. Youth in the second class,
with 12.0% of the sample, reported high levels
Figure 1. Latent class proles compared to sample averages for each indicator.
Note.. The y-axis represents z-scores compared to sample proportions.
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OJJDP Journal of Juvenile Justice
Table 3. Demographic Factors Across the Classes
Low All
Parental
Incarceration
&
Substance Use
Poverty &
Parental
Health
Problems
High Conict
& High SES
High
Maltreatment High All Full Sample
Demographic Indicators Wald Test
Age (mean) a,b,d,e,i,j,m,n 15.64 15.44 15.33 15.52 15.25 15.14 45.58***
Female a,b,c,d,e,g,h,i,l 0.168 0.236 0.257 0.301 0.306 0.323 102.86***
Caucasian a,c,e,f,h,j,l,m,o 0.571 0.660 0.531 0.650 0.580 0.678 47.56***
African American b,e,f,h,j,l,m,o 0.263 0.223 0.319 0.226 0.294 0.201 30.18***
Latino d,e 0.073 0.052 0.066 0.052 0.044 0.042 11.95*
Other race 0.125 0.096 0.101 0.102 0.089 0.085 10.83
Living Situation
Both biological parents
a,b,c,d,e,h,i,k,m,n,o 0.220 0.168 0.140 0.159 0.023 0.097 178.22***
Biological mother a,c,f,g,h,i,j,k,n 0.506 0.586 0.628 0.551 0.163 0.582 443.457***
Biological father a,g,h,i,l 0.124 0.160 0.109 0.132 0.085 0.151 15.206**
Neither biological parent
a,d,g,h,i,k,m,o 0.149 0.087 0.123 0.158 0.728 0.170 792.41***
Foster care a,d,e,g,h,i,j,l,m,n,o 0.008 0.001 0.006 0.013 0.284 0.043 222.00***
Superscripts show significant differences between classes: a Low All and Parental Incarceration & Substance Use. b Low All and Poverty & Parental Health Problems. c Low All and High Conflict & High SES.
d Low All and High Maltreatment. e Low All and High All. f Parental Incarceration & Substance Use and Poverty & Parental Health Problems. g Parental Incarceration & Substance Use and High Conflict & High
SES. h Parental Incarceration & Substance Use and High Maltreatment. i Parental Incarceration & Substance Use and High All. j Poverty & Parental Health Problems and High Conflict & High SES. k Povert y
& Parental Health Problems and High Maltreatment. l Poverty & Parental Health Problems and High All. m High Conflict & High SES and High Maltreatment. n High Conflict & High SES and High All. o High
Maltreatment and High All.
of parental incarceration and substance use,
elevated indicators of social disadvantage, but
relatively low levels of maltreatment; this class we
termed the Parental Incarceration and Substance
Use class. Youth in the third class comprised
13.2% of the sample and was marked by parental
health problems, and we termed this class the
Poverty and Parental Health Problems class. Youth
in the fourth class, in contrast, had relatively good
indicators of familial economics but elevated
levels of family conflict and physical abuse.
With 15.3% of the sample, we termed this class
the High Conflict and High SES (socioeconomic
status) class. Youth in the fifth class reported
very high levels of maltreatment, parental incar-
ceration, and out-of-home placements; how-
ever, other ACEs were reported less frequently
than the sample averages. This class contained
11.0% of the sample, and we termed it the High
Maltreatment class. Youth in the sixth class, which
was the smallest, at 8.1% of the sample, reported
high levels of all ACE indicators included in this
analysis. We called this class the High All class.
Demographic Differences
We also examined youth in these classes for dif-
ferences on demographic variables (see Table 3).
Age differed significantly across classes, although
the differences were not large. The Low All class
was the oldest, with an average of 15.6 years, and
the High Maltreatment and High All classes were
the youngest, at 15.3 and 15.1 years, respectively.
The percentage of females increased from the
first through sixth class, with the highest propor-
tion (32.3%) in the High All class. Caucasian youth
were somewhat concentrated in the Parental
Incarceration and Substance Use (66.0%), High
Conflict and SES (65.0%), and High All (67.8%)
classes. African American youth were more likely
to be in the Poverty and Parental Health Problems
(31.9%) and High Maltreatment (29.4%) classes.
Latino youth were found least frequently in the
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OJJDP Journal of Juvenile Justice
High Maltreatment (4.4%) and High All classes
(4.2%). The other race youth were not significantly
different across classes.
Living situation variables differed significantly
across the classes (Table 3). A relatively small
proportion of youth in any class lived with both
biological parents—the highest proportions
were seen with the Low All (22.0%) class. Youth
in the Parental Incarceration and Substance Use,
Poverty and Parental Health Problems, and High
Conflict and High SES classes had similar patterns
with respect to living with biological parents;
the majority of youth in these classes lived with
their biological mothers, with smaller propor-
tions living with both biological parents or their
biological fathers. Youth in the High Maltreatment
class were substantially more likely to be living
with neither biological parent (72.8%) and to be
living in foster care (28.4%). The youth in the High
All class were the least likely to be living with one
or both biological parent(s) of any of the classes
except for youth in the High Maltreatment class.
Discussion
This analysis is among the first to examine
heterogeneous patterns of adverse childhood
experiences among court-involved youth.
Consistent with other studies (Baglivio et al.,
2014; Dierkhising et al., 2013), substantial por-
tions of youth reported significant histories of
adversity, including multiple forms of childhood
maltreatment, parental dysfunction, and socio-
economic disadvantage. Adding to the currently
rich literature on cumulative ACEs, our analyses
show substantial heterogeneity around child-
hood adversity, with clusters that appear to have
significantly different etiological histories and
treatment needs. These clusters add substantially
to prior variable-centered analyses that demon-
strated aggregate linear trends among adversi-
ties and between adversities and outcomes. The
clusters also provide evidence related to adversity
composition that adds nuance to prior findings
regarding level differences (e.g., low, medium,
high). We discuss each class in turn, with particu-
lar attention to practice implications.
Low-Risk Class
Youth in this class reported relatively low levels
of all ACEs compared to the rest of the sample.
In general, they had the lowest reported rate of
each adversity, although the numbers of some
ACEs were statistically indistinguishable from
other classes. This class also contained fewer
females and had a disproportionate number of
Latinos and African Americans, which may reflect
disparities in policing and punishing certain racial
groups in general society. Still, 17.5% of these
youth had a history of paternal incarceration, and
49.7% reported elevated family conflict, under-
scoring the high adverse experiences exposure
of the sample as a whole. Furthermore, although
these youth were the most likely of youth in
all the classes to be living with both biological
parents, only about 22% reported living with
both biological parents, indicating that few
families in any class had a “traditional” structure.
Nonetheless, probation officers and clinicians
may be able to engage this class’s relatively
strong family supports to meet these young
people’s needs.
Parental Incarceration and Substance Abuse Class
These youth reported high levels of parental
incarceration and substance use. They had rela-
tively low levels of maltreatment, and moderate
endorsement of social disadvantage. They were
more likely to be Caucasian and had the second
lowest percentage of females. Parental crime
and incarceration emerged as experiences that
may have been especially harmful to the social
development of these youth. Recent studies
have demonstrated that youth whose parents
are incarcerated are more likely to experience
poverty, perhaps in a cyclical fashion as parents
move in and out of the justice system without
being able to work (Kjellstrand & Eddy, 2011).
Children with an incarcerated parent are more
likely to have insecure attachment, especially if
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OJJDP Journal of Juvenile Justice
their mother is incarcerated, in addition to being
at risk of additional negative life experiences, all
of which may predispose them to delinquency
and other negative outcomes (Murray & Murray,
2010). Youth in this class may benefit most from a
high-quality mentoring program that would pro-
vide both positive socialization and a stable adult
influence (Jarjoura, DuBois, Shlafer, & Haight,
2013). Other possible interventions, such as the
Parent Management Training–Oregon Model, may
be especially effective for families dealing with
incarceration and may strengthen family func-
tioning overall (Eddy & Reid, 2002).
Poverty and Parental Health Problems Class
This class, which had the highest proportion
of African Americans, reported high levels of
parental health problems and poverty indica-
tors. This portrait of poverty and diminished
familial resources was suggestive of a poverty-
to-delinquency link, possibly because parents
were dealing with significant challenges of their
own. Although we do not have data about the
neighborhoods where these youth lived, the
current family economic indicators strongly sug-
gested possible exposure to negative community
contexts (e.g., disenfranchised neighborhoods,
schools). This class represented overall trends that
disproportionately funnel poor youth into the
juvenile justice system via points of contact such
as frequent policing in poor neighborhoods and
schools (Birckhead, 2012). Wraparound services
that could increase protective resources in terms
of economic support, health care, and access to
prosocial activities could assist youth in this class
(Bruns et al., 2010).
High-Conflict/High SES Class
These youth reported the highest levels of expo-
sure to family conflict of any class. They also
reported the second highest levels of physical
abuse. Indicators of social disadvantage sug-
gested relatively better economic situations for
these families compared to others in the sample,
confirmed by separate analyses that examined
for higher income brackets (not shown). We
speculate that this class met a profile of domes-
tic violence in the home, which was supported
by analyses (not shown here) regarding physi-
cal violence between parents. Links between
exposure to domestic violence and externalizing
behaviors are well established, especially for boys
(Evans, Davies, & DiLillo, 2008). This exposure can
also be related to several mental health difficul-
ties, such as anxiety and depression (Berzenski
& Yates, 2011). From an intervention standpoint,
these youth might have their needs best met via
a family practice model, such as functional fam-
ily therapy (Darnell & Schuler, 2015), that would
address family contributors to youth problem
behaviors.
High Maltreatment Class
These youth reported extremely high histories of
out-of-home-placements, parental incarceration,
physical and sexual abuse, and neglect. These
youth were also more likely to be younger in age,
African American, and female. They were the least
likely to be living with a parent, and by far the
most likely to be living in foster care. These youth
likely carry substantial effects of traumatic experi-
ences, both in terms of abuse and their histories
of familial instability, leading to earlier contact
with the juvenile justice system. Punitive choices,
such as stringent detention, are not likely to be
helpful. Consideration of these young people’s
living situation—only a quarter live with either
parent—is very important. Although many foster
care situations are youth supportive, this is not
consistent, and it is important to ensure that such
youths have stable living environments. These
dual-system-involved youth may benefit from
therapeutic foster care models, such as Treatment
Foster Care Oregon (Chamberlain & Reid, 1998).
In addition, traumatic stress theories suggest that
these youth need interventions to assist in build-
ing coping and social skills that could counteract
the tendency toward hyper-reactivity and hostil-
ity in the face of conflict (Leve, Chamberlain, &
Reid, 2005).
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OJJDP Journal of Juvenile Justice
High All Class
These youth reported profound histories of
adversity, including the highest levels of most
adversity indicators, such as family incarceration,
parental mental health problems, and parental
drug use. On the indicators where youth in this
class did not have the highest scores, they had
the second highest scores—maltreatment and
out-of-home placements were second only to the
High Maltreatment class, and poverty indicators
were second only to the Poverty and Parental
Health Problems class. This global picture of ACEs
is suggestive of a serious need for multifaceted
interventions to address trauma, poor resources,
and difficulties in functioning. It is also important
to note that this group was the youngest and
had the most females. Given that early onset
may lead to further involvement in the juvenile
justice system, effective interventions targeting
youth in this group can have a greater impact in
the long run. Furthermore, despite the heavier
involvement of boys in the justice system, our
findings suggest that girls in the juvenile justice
system exhibit higher risk for poor outcomes. As
with other classes, trauma-informed interven-
tions are likely necessary, especially for girls, to
interrupt negative trajectories. Although these
youth predominantly live with a family member,
the data suggest that their family environment
is not likely to be positive. To achieve success
with these youth, multifaceted interventions that
incorporate a family component are important.
Studies have shown that these youth benefit the
most from interventions that address multiple
domains simultaneously (Farmer, Farmer, Estell, &
Hutchings, 2007), such as Multisystemic Therapy
(Henggeler, Mihalic, Rone, Thomas, & Timmons-
Mitchell, 1998). Clinicians working with these
young people should also be well trained to be
empathetic to youth exhibiting severe behavioral
problems.
Limitations
This study has limitations worth noting. The
data derive from an assessment completed by
probation officers, to whom court-involved
youth may be less likely to report experiences
of maltreatment and adversity. Nevertheless,
Washington State has made adequate implemen-
tation of this assessment a high priority, including
providing extensive training to probation officers
using the tool (Barnoski, 2004b). Studies have
also shown that a related risk tool, the Florida
Positive Achievement Change Tool, had strong
interrater reliability with different types of staff
delivering the assessment (Winokur-Early, Hand,
& Blankenship, 2012).
Although the retrospective nature of this data
necessitates caution, multiple studies have
demonstrated that retrospective reports cor-
relate strongly with other sources of verified
data (Smith, Ireland, Thornberry, & Elwyn, 2008).
As respondents here are adolescents, the time
period of retrospection is much shorter than for
adult samples that have found adequate variance
and stable linear trends of ACEs with health and
functioning outcomes, even with lengthy retro-
spection periods (Hardt & Rutter, 2004; Yancura
& Aldwin, 2009). This assessment is more epide-
miologic than clinical in nature, aiming to identify
cumulative exposure across an established set
of adversities. The chronicity or severity of these
exposures is thus not captured, which limits the
ability to assess differences, such as whether the
magnitude of maltreatment is associated with
future outcomes (Smith & Thornberry, 1995).
However, established short-form adversity assess-
ments are feasible for routine pediatric screening
and can provide important information for service
providers as well as opportunities for merging
administrative data across systems to gain a
fuller picture of the etiology and trajectories of
early life adversity (Murphy et al., 2014; Putnam-
Hornstein, Needell, & Rhodes, 2013).
Additionally, these data are taken from one
county in Washington State, which may constrain
generalizability. However, the present sample
is reasonably diverse compared with many U.S.
jurisdictions. This particular county contains
urban, suburban, and rural regions as well as
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Native American reservations. Finally, this study
lacks assessment of the neighborhood and
community contexts in which youth reside. Our
indicators of family social disadvantage are likely
to be correlated with neighborhood poverty.
However, we are not able to test for independent
effects net of individual or family contexts rela-
tive to community-level characteristics. Future
research should expand on the multilevel effects
of adversity among court-involved youth.
Conclusion
This study is among the first to test for clustering
among ACEs within court-involved youth. These
subgroup-seeking findings provide insights
that are complementary to studies that average
across whole samples, which can guide clinicians
in contact with youth in juvenile justice systems
in more targeted trauma-informed care (Ford,
Chapman, Hawke, & Albert, 2007).
These analyses add to a growing body of litera-
ture that suggests that court-involved youth
carry substantial histories of adversity. Further,
greater cumulative adversity is associated
with more negative health and development,
extending prior findings and theorizing about
ACEs (Wolff et al., 2015). These backgrounds of
trauma, social disadvantage, and other adversi-
ties carry information about risk and protective
factors that are imperative to consider when
selecting and implementing interventions to
prevent recidivism and to improve outcomes.
Moreover, these analyses provide strong sup-
port for the use of risk assessment tools to
target interventions for differing needs of court-
involved youth. Simply using the risk assessment
tool to determine whether or not a young per-
son is at high risk for reoffending might lead to
more punitive approaches. The results of this
study illustrate how risk assessment tools can be
used to uncover the particular needs of youth,
particularly around histories of trauma and
adversity. Although the recent push to transform
systems of care to trauma-informed (Ko et al.,
2008) is noteworthy, more specified information
could provide clinicians guidance as to which
types of interventions will benefit youth most.
Further, even in jurisdictions that have focused
on providing trauma-informed care and imple-
menting evidence-based programs, many
regions do not make evidence-based programs
available for court-involved youth unless they
are in detention settings such as residential
treatment programs (e.g., Ford & Blaustein,
2013). This is unfortunate, particularly given
that court-involvement (e.g., probation) on its
own is likely to increase the rates of recidivism
and involvement with the adult criminal justice
system (Gatti, Tremblay, & Vitaro, 2009). The find-
ings of this study underscore the need to address
histories of trauma and adversity among court-
involved youth across community and detention
settings.
About the Authors
Patricia Logan-Greene, MSSW, PhD, is an assis-
tant professor at the University at Buffalo.
B. K. Elizabeth Kim, MSW, PhD, is an assistant
professor at the University of Southern California.
Paula S. Nurius, MSW, MA, PhD, is a profes-
sor and associate dean of the University of
Washington School of Social Work.
82
OJJDP Journal of Juvenile Justice
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