Research ArticleOpen Access, Volume 4 Issue 1

Predictors of depression among justice involved youth

Richard Dembo, PhD*; Alexis Swezey; James Schmeidler, PhD

Agency for Community Treatment Services, University of South Florida, Mt. Sinai Medical School, USA.

*Corresponding author: Richard Dembo

Agency for Community Treatment Services, University of South Florida, Mt. Sinai Medical School, USA.

Email: rdembo@usf.edu

Received : Jan 29, 2026       Accepted : Feb 23, 2026       Published : Mar 02, 2026

Epidemiology & Public Health - www.jpublichealth.org

Copyright: Dembo R © All rights are reserved

Citation: Dembo R, Swezey A, Schmeidler J. Predictors of depression among justice involved youth. Epidemiol Public Health. 2026; 4(1): 1092.

Abstract

Depressed mood remains a significant mental health problem among justice involved youth. There is a well established correlation between depressed mood and conduct problems (e.g., conduct disorder and oppositional defiant disorder) during childhood and adolescence, which tends to become more prevalent during adolescence. Studies suggest early problems with both depression and delinquency can lead to difficulties with school success, substance use, continued depression and criminal offending in adulthood. Teplin and Associates [35] found among Cook County, Illinois detained youth that 15 years after detention, 52.3% of males and 30.9% of females still had a psychiatric disorder. Informed by the research literature, we conducted a study of the demographic, background and psychosocial predictors of depression among newly arrested male and female youth admitted to a centralized juvenile assessment center in a southern U.S. city. Multi group Bayesian regression analysis found male and female youth whose parents had been in jail or prison had higher depression scores, than youth whose parents had not been incarcerated. In addition, female youth who experienced sexual assault, had significantly higher levels of depression than female youth without such experiences. Implications of these findings are discussed.

Introduction

Depression remains a significant mental health issue experi enced by justice involved youth, a condition which is often relat ed to other co-occurring mental health problems, such as PTSD, trauma and substance misuse. Depression and its often-asso ciated negative outcomes [18,19,30] have been found to have long term, adverse mental health outcomes. In particular, Teplin and her associates [35] found among the Cook County, Illinois detained they studied those 15 years after detention, 52.3% of males and 30.9% of females still had a psychiatric disorder, and disorder prevalence rates that were higher than in the general population. Males were more likely to persist with a psychiatric disorder than females; and untreated traumas and other disor ders made it harder to finish school, get a job, and stay out of jail. The study also found that males with depressive disorder at baseline were more likely than those without this disorder to have mood, anxiety, and alcohol use disorders 15 years later. Females with depressive disorders at baseline were more likely than those without to have nearly all disorders.

Depressed mood is also associated with several problem behaviors. A well-established correlation exists between de pressed mood and conduct problems (e.g., conduct disorder and oppositional defiant disorder) during childhood and adoles cence [2,5], which tends to become more prevalent during ado lescence (e.g., Cohen P et al. 1993). Studies of youths involved in delinquency who enter the justice system have found high prev alence rates of depression and other mood disorders [1,33]. For example, Teplin, Abram, McClelland, et al. [32] found two-thirds of detained males and almost three-quarters of females met di agnostic criteria for one or more psychiatric disorders. Half of the males and females had a substance use disorder. Affective disorders were also prevalent, especially among females, with 20% meeting diagnostic criteria for a major depressive episode [32]. Other studies have identified similar prevalence of depres sive symptoms and substance use disorders [12,26]. Related research on justice-involved youths has identified an associa tion between depression and sexual risk behavior [33], sexual assault victimization and depression [9] and a comorbidity in marijuana use and depression [1,31]. Moreover, longitudinal studies suggest early problems with both depression and de linquency can lead to difficulties with school success, substance use, continued depression and criminal offending in adulthood [5,11,20,21]. Johnson, Esposito-Smythers, Miranda, et al. [17] note that while research has improved our knowledge of “dis ruptive behavior disorders among incarcerated youths, far less is known about the factors associated with depression and oth er internalizing symptoms in this population” (p. 1096).

Sex effects

It is also important to consider sex differences in the multiple problems often experienced by justice-involved youth. Females experience higher rates of psychiatric disorders, Sexually Trans mitted Infections (STIs) and the consequential health effects of sexual assaults, than males [6-8,13-15,32,33]. On the other hand, males are more likely to be involved in substance use, and peer delinquency, than females [28]. The effectiveness of behavioral health services could be improved by greater insight into these gender specific relationships and the prevalence of subgroups of male and female youth reflecting different combi nations or patterns of trauma and health risk behaviors.

Informed by the above noted research literature, the pres ent study involving justice involved youth, addressed three re search questions:

1. Does a similar factor structure exist for an eight-item depression construct among male and female youth?

2. What demographic, background and psychosocial factors relate to depression among the male and female youth?

3. How similar are the male and female predictive factors for depression?

Methods

Project setting: Juvenile Assessment Center (JAC) health coach services

Data were collected in an innovative, comprehensive health coach service for youths entering a Juvenile Assessment Cen ter (JAC), a centralized intake facility located in a southeastern U.S. city. The data collection period ranged from 3/3/2024 to 8/30/2025. The service, which involves a collaboration with the county health department, has four major goals [7].

1. The service offers HIV and STD evidence-based risk reduction information and education to youths using gender appropriate and developmentally appropriate curricula.

2. The service performs analysis of youth provided urine specimens to identify recent drug use (via the Enzyme Multiplied Immunoassay Technique [EMIT] procedure), STDs (Chlamydia and Gonorrhea via the Aptima testing procedure), and swab testing for HIV (only one HIV positive case was found, and that child was placed in treatment).

3. The service seeks to follow-up with STD and HIV positive youth, and promptly link them with appropriate treatment. Youth who screen high on a depression inventory [22,27] are also linked to follow-up services.

Health Coaches are trained to follow a detailed data collection and service delivery protocol, including Department of Health’s STD and HIV pretest and posttest counseling. Data collection and entry are routinely monitored for integrity and quality by the program manager (a coauthor of this article). Data were collected in accordance with the requirements of the Institutional Review Board.

Participants

Participation in the health coach service for the present data collection period occurred from 3/3/2024 to 8/30/2025. Following informed consent, 206 girls and 489 boys received health coach services. Eighty-one (11.6%) of the 695 youths entered the JAC more than once (most often a second time), during which occasion(s) they received additional health coach services. Since a relatively small number and percent of youth received heath coach services more than once during the data collection time period, for youth with multiple entries, only health coach data collected during their first entry were used in the present study.

Participation in this service was voluntary. Florida public health law does not require youths 12 years and over to obtain parental consent for STD or HIV testing or treatment. No rou tinely available data were collected on youth who declined to participate [4] in health coach services.

Measures of depression

We used the eight-item version of the widely used 20-item Center for Epidemiological Studies Depression Scale [27], de rived from the psychometric work of Melchior, Huba, Brown, et al. [4], to measure depression. The measure included the fol lowing 8 items:

(1) I felt I could not shake off the blues even with the help from my family and friends; (2) I felt sad, (3) I felt depressed, (4) I thought my life had been a failure, (5) I felt fearful, (6) My sleep was restless, (7) I felt lonely, and (8) I had crying spells. The time frame used for these experiences was the past week. Each item was scored as follows: 0 -- less than one day, 1-1 to 2 days, 2-3 to 4 days, and 3-5 to 7 days.

Covariate measures

Sociodemographic characteristics.

Birth gender: male=0, female=1; Age (in number of years, 11 to 17); Race: 0=Non-Black, 1=Black; Ethnicity: 0=non-Hispanic, 1=Hispanic.

Background experiences

Health coaches collected the following background informa tion from youth receiving their services: Parents ever separated or divorced: 0=no, 1=yes; household member placed in jail or prison: 0=no, 1=yes.

Psychosocial factors

Each youth was asked if they had ever been sexually assault ed. Responses were coded as 0=no, 1=yes.

As noted above, youth provided urine samples which were tested for STDs and drug use. The STD tests probed for the presence of Gonorrhea or Chlamydia. Each variable was cod ed: 0=negative, 1=positive. Since only 10 youth (1.4%) tested positive for Gonorrhea, this variable was not included in our analyses. The Chlamydia test results were coded as 0=negative, 1=positive.

The youths’ recent drug use was probed by EMIT urine testing for the following drugs:

Amphetamine, Buprenorphine, Benzodiazepine, Cocaine, Al cohol, Fentanyl, Methylenedioxyamphetamine, Methamphet amine, Opiates/Morphine, Methadone, Oxycodone, and Mari juana. Each test result was coded as: 0=negative, 1=positive.

Less than 2.8% of the youth tested positive for any drug oth er than marijuana (66.6%). Hence, only marijuana was included in our analyses.

Strategy of analysis

Descriptive data were analyzed using SPSS version 29.0.2.0 (IBM, 2023). Factor invariance and regression analyses were conducted using Mplus 8.11 [25]. The analyses proceeded in two major steps. First, we provide a descriptive summary of the variables involved in our analyses. Second, we conducted a male-female, multigroup Bayesian [10] regression analysis, in volving scalar invariance [3] to assess the relationship between the sociodemographic, background experience, and psychoso cial variables and youth depression.

Results

Descriptive statistics

Descriptive statistics for male and female youth demograph ic, background and psychosocial characteristics are shown in (Table 1). There were more boys (70.4% to 29.6%) than girls in the study. The majority of the boys and girls self-identified as African-American (70.8% and 67.5%, respectively). The boys and girls in this study averaged 15.4 and 15.0 years of age, re spectively. A minority of both males and females were Hispanic (8.6% and 10.2% respectively.

In regard to background experiences, a large majority of both gender groups reported their parents had separated or di vorced. Further, nearly 1 in 5 male and female youth reported a parent had spent time in jail or prison.

Psychosocial factors reflected gender group differences as well. Male youth had higher marijuana test positive rates, than females. On the other hand, females reported higher rates of being sexually assaulted and tested positive for Chlamydia at a higher rate than the males.

Table 1: Demographic characteristics and description of major variables.
Male (n=485-489) Female (n=193-206)
Age (Mean,SD) 15.45 (1.30) 15.09(1.44) **
Black 70.80% 67.50%
Hispanic 8.60% 10.20%
Parents separated/Divorced 18.60% 19.20%
Sexually assaulted 1.30% 16.80%**
Marijuana positive 71.10% 59.90%**
Chlamydia 8.40% 14.10%*
Depression Items
Could not shake off blues 0.16(0.56) 0.31(0.77)***
Felt sad 0.38(0.79) 0.76(1.07)***
Felt depressed 0.25(0.72) 0.63(1.08)***
Thought my life a failure 0.21(0.65) 0.43(0.89)***
Felt fearful 0.12(0.46) 0.37(0.83)**
Sleep was restless 0.51(1.00) 0.64(1.04)
Felt lonely 0.29(0.76) 0.57(1.07)***
Had cryingspells 0.17(0.54) 0.63(1.00) ***

Two-Tailed p-values: *p<.05; **p<..01; ***p< .001

Females reported significantly higher levels of depression on seven of the eight depression items, than the males. Important ly, 24.3% of female youth, compared to 10.2% of the males, had a depression score of 7 or higher, a designated threshold score indicative of potentially needing clinical intervention [4,29].

Male-female multigroup analysis

Measurement invariance analysis sought to identify whether the depression measure had the same meaning and statisti cal structure across the male and female youth groups [3,23]. Hence, we conducted a male-female multigroup regression analysis involving scalar invariance for the depression measure, and a regression of the depression measure on our sociodemo graphic, background experience, and psychosocial predictor variables. The results are shown in (Table 2).

Table 2: Multigroup model fit information.
Number of FreeParameters 49
Bayesian Posterior Predictive Checking using Chi-Square -35.565
95%Confidence Interval for the Difference Between the Observed and the ReplicatedChi-Square Values 81.726
Posterior Predictive P-Value 0.220
Potential Scale Reduction (PSR): 1.061
Posterior Predictive P-Value (Confidence Limits) From Each Group
Group 1 (0) 0.427 (-36.027, 43.907)
Group 2 (1) 0.197 (-24.215, 63.666)
FinalClass Counts andProportions for The Latent ClassesBased on The Estimated Model
Latent Classes
1 447 0.70173
2 190 0.29827
MODEL RESULTS – MALES
Variable Estimate Posterior S.D. One-Tailed P-Value 95% C.I. Significance
Lower 2.5% Upper 2.5%
TOTDEPR by
DEP1 1.000 0.000 0.000 1.000 1.000
DEP2 1.263 0.192 0.000 0.932 1.714 *
DEP3 1.218 0.213 0.000 0.878 1.701 *
DEP4 0.981 0.158 0.000 0.705 1.326 *
DEP5 0.860 0.138 0.000 0.624 1.170 *
DEP6 0.533 0.079 0.000 0.391 0.696 *
DEP7 0.914 0.146 0.000 0.665 1.238 *
DEP8 0.768 0.111 0.000 0.574 1.010 *
TOTDEPR on
ETHNIC -0.097 0.355 0.390 -0.803 0.598
RBLACK -0.663 0.232 0.001 -1.150 -0.242 *
RAGE -0.021 0.073 0.384 -0.167 0.121
THC -0.116 0.205 0.282 -0.527 0.285
CHLAMYDIA -0.452 0.372 0.102 -1.224 0.247
SEXASSLT 0.617 0.725 0.190 -0.804 2.065
SEPARATE/DIVORCE -0.039 0.207 0.423 -0.449 0.371
PARENT JAIL/PRISON 0.864 0.248 0.000 0.424 1.404 *
Intercepts
TOTDEPR -1.577 1.215 0.090 -4.070 0.719
Thresholds
DEP1$1 0.143 0.343 0.356 -0.542 0.750
DEP1$2 0.706 0.346 0.015 0.050 1.342 *
DEP1$3 1.524 0.364 0.000 0.871 2.229 *
DEP2$1 -1.237 0.435 0.000 2.096 -0.498 *
DEP2$2 -0.203 0.428 0.337 -1.021 0.554
DEP2$3 0.758 0.430 0.026 -0.004 1.556
DEP3$1 -0.490 0.411 0.143 -1.222 0.265
DEP3$2 0.023 0.409 0.482 -0.699 0.784
DEP3$3 0.768 0.411 0.016 0.045 1.541 *
DEP4$1 -0.056 0.346 0.448 -0.708 0.536
DEP4$2 0.495 0.343 0.091 -0.134 1.089
DEP4$3 1.183 0.344 0.000 0.569 1.810 *
DEP5$1 0.262 0.297 0.209 -0.276 0.811
DEP5$2 0.931 0.296 0.000 0.400 1.490 *
DEP5$3 1.528 0.312 0.000 0.970 2.130 *
DEP6$1 -0.244 0.192 0.109 -0.610 0.108
DEP6$2 0.128 0.190 0.271 -0.229 0.476
DEP6$3 0.590 0.192 0.000 0.233 0.943 *
DEP7$1 -0.202 0.319 0.284 -0.813 0.341
DEP7$2 0.178 0.315 0.317 -0.412 0.731
DEP7$3 0.795 0.315 0.003 0.188 1.352 *
DEP8$1 -0.110 0.259 0.351 -0.595 0.365
DEP8$2 0.551 0.261 0.009 0.078 1.039 *
DEP8$3 1.180 0.274 0.000 0.706 1.712 *
Residual Variances
TOTDEPR 2.221 0.594 0.000 1.429 3.721 *
MODEL RESULTS – FEMALES
Variable Estimate Posterior S.D. One-Tailed P-Value 95% C.I. Significance
Lower 2.5% Upper 2.5%
TOTDEPR by
DEP1 1.000 0.000 0.000 1.000 1.000
DEP2 1.263 0.192 0.000 0.932 1.714 *
DEP3 1.218 0.213 0.000 0.878 1.701 *
DEP4 0.981 0.158 0.000 0.705 1.326 *
DEP5 0.860 0.138 0.000 0.624 1.170 *
DEP6 0.533 0.079 0.000 0.391 0.696 *
DEP7 0.914 0.146 0.000 0.665 1.238 *
DEP8 0.768 0.111 0.000 0.574 1.010 *
TOTDEPR on
ETHNIC 0.089 0.479 0.426 -0.856 1.035
RBLACK -0.082 0.317 0.396 -0.707 0.545
RAGE -0.137 0.037 0.000 -0.222 -0.075 *
THC 0.172 0.271 0.258 -0.355 0.713
CHLAMYDIA 0.114 0.377 0.378 -0.624 0.861
SEXASSLT 1.143 0.359 0.000 0.495 1.912 *
SEPARATE/DIVORCE 0.386 0.311 0.096 -0.193 1.032
PARENT JAIL/PRISON 0.705 0.333 0.012 0.091 1.396 *
Intercepts
TOTDEPR 0.000 0.000 1.000 0.000 0.000
Thresholds
DEP1$1 0.143 0.343 0.356 -0.542 0.750
DEP1$2 0.706 0.346 0.015 0.050 1.342 *
DEP1$3 1.524 0.364 0.000 0.871 2.229 *
DEP2$1 -1.237 0.435 0.000 -2.096 -0.498 *
DEP2$2 -0.203 0.428 0.337 -1.021 0.554
DEP2$3 0.758 0.430 0.026 -0.004 1.566
DEP3$1 -0.490 0.411 0.143 -1.222 0.265
DEP3$2 0.023 0.409 0.482 -0.699 0.784
DEP3$3 0.768 0.411 0.016 0.045 1.541 *
DEP4$1 -0.056 0.346 0.448 -0.708 0.536
DEP4$2 0.495 0.343 0.091 -0.134 1.089
DEP4$3 1.183 0.344 0.000 0.569 1.810 *
DEP5$1 0.262 0.297 0.209 -0.276 0.811
DEP5$2 0.931 0.296 0.000 0.400 1.490 *
DEP5$3 1.528 0.312 0.000 0.970 2.130 *
DEP6$1 -0.244 0.192 0.109 -0.610 0.108
DEP6$2 0.128 0.190 0.271 -0.229 0.476
DEP6$3 0.590 0.192 0.000 0.233 0.943 *
DEP7$1 -0.202 0.319 0.284 -0.813 0.341
DEP7$2 0.178 0.315 0.317 -0.412 0.731
DEP7$3 0.795 0.315 0.003 0.188 1.352 *
DEP8$1 -0.110 0.259 0.351 -0.595 0.365
DEP8$2 0.551 0.261 0.009 0.078 1.039 *
DEP8$3 1.180 0.274 0.000 0.706 1.712 *
Residual Variances
TOTDEPR 2.221 0.594 0.000 1.429 3.721 *

As (Table 2) shows, the depression model fit was excellent for both gender groups. The specified Bayesian regression mod els were run with an initial 40,000 iterations, followed by 80,000 iterations to confirm model convergence stability reflected in its Potential Scale Reduction (PSR) value. Excellent PSR values of 1.061 were found in the 40,000-iteration run and 1.041 in the 80,000-iteration run, respectively, reflecting an expected reduc tion in PSR value. Additional convergence assessment involved a review of Markov Chain Monte Carlo trace plots, tracking stability in the movement of the chain across the sampling al gorithm [10]. The Bayesian measure of model fit, the Posterior Predictive P-value (PPP), was good for each regression model with PPP values of 0.220 and 0.231 for the 40K and 80K iteration runs, respectively.

Regression results indicate Black male youth reported signifi cantly lower depression than nonBlack (mainly White) youth. On the other hand, male youth who reported a parent spent time in jail or prison reported significantly more depression, than male youth whose parents were not reported to have in carceration experience. For females, and similar to males, youth with parents who had incarceration experience reported sig nificantly more depression than youth who did not report any parent incarceration. In addition, older age youth reported sig nificantly lower depression than younger aged youth. Further, females who experienced being sexually assaulted reported a significantly higher level of depression, than those without such an experience.

Discussion/conclusion

In addition to confirming the psychometric soundness of the measure of depression we used in this study; regression analy sis highlighted some significant predictors of depression that add to the literature on this topic. The results provide answers to the research questions informing this study.

Research Question #1: Does a similar factor structure exist for the eight-item depression construct among male and fe male youth? Multigroup analysis provided statistical evidence supporting the view that the depression measure we used was psychometrically sound and scalar invariant (same number of factors, same factor loadings and same threshold values across the male and female youth in the study).

Research Question #2: What demographic, background and psychosocial factors relate to depression among the male and female youth. As reported earlier, for both males and females, only two demographic factors related to the youths’ depression scores. For youth in both gender groups, neither THC (marijua na) use nor STD test results for Chlamydia were significantly re lated to depression. On the other hand, for both gender groups, background factors (for males: parent jail or prison; females: being sexually assaulted, parent spending time in jail or prison) were significant predictors of higher depression scores.

Research Question #3: How similar are the male and female predictive factors for depression? The pattern of results points to the importance of considering background experiences in understanding justice involved youth depression, particularly at the point of entry into the justice system.

There were several significant gender effects in the corre lates of depression we identified.

Specifically, for both males and females, youth whose par ents experienced jail or prison had higher depression scores, than youth whose parent(s) did not have such experience. Fur ther, females who reported being sexually assaulted reported significantly higher depression, than females not reporting this experience. Meta-analytic studies have identified family issues, such as incarceration, as being consistently related to youth antisocial behavior [24]. However, little is known about the dy namics or paths of causal influence of parental incarceration on their children’s mental health. This represents an important area for further research.

The significant relationship between parent incarceration history and youth depression underscores the importance of further study of this relationship. Murray, Farrington, and Sekols’ [24] meta-analytic review of 40 studies led them to recommend more rigorous studies on this important topic. In particular, criminal justice system reform may be needed to prevent the negative consequences of parental incarceration on their children. Such efforts are especially needed in the U.S., which is among the nations with the highest rates of incarcera tion in the world.

The lack of association between depression, marijuana use and STDs is particularly interesting. Importantly, as (Table 1) shows, males have a significantly higher positive rate for mari juana, than females; and females have a significantly higher Chlamydia positive rate than males. However, when we control for gender in our regression models, these relationships disap pear. These findings underscore the importance of assessing gender specific effects in research studies involving depression among justice involved youth.

There are a number of strong points in the research reported in this paper. Among them are the use of biological tests of re cent drug use and the existence of an STD (Chlamydia and/or Gonorrhea). These test results prevent the self-report bias of ten encountered in studies involving these issues among justice involved youth.

At the same time, there are several limitations to the re search reported in this paper. First, the multigroup, multivari ate analyses were conducted on cross-sectional data. Hence, no causal interpretations of our findings are possible. Second, the results of the study may not generalize to male and female youths arrested in other jurisdictions, reflecting different so ciodemographic and contextual circumstances. Future research should conduct similar studies in other jurisdictions.

Our research underscores the importance of obtaining routine, comprehensive background information on justice involved male and female youth entering the justice system, which can provide a better understanding of their common and gender specific service needs. Such understanding can inform the development of more effective delivery of mental health and related services to the many troubled youth who enter the system.

Declarations

Acknowledgement: We are grateful for County Department of Health support for the STD and HIV testing material, laboratory testing, and ongoing training of Health Coaches.

Data availability: Data were collected in accordance with the requirements the Institutional Review Board, and deidentified for this study. These public health data are restricted to analysis by the identified senior author-researcher, reflected in a Memorandum of Agreement (MOA).

Conflicts of interest: The authors of this manuscript have no conflicts of interest to report.

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