The Impact of Dual Diagnosis on Drug Court Failure

Impulsivity, trauma history, and interoceptive awareness contribute to completion of a criminal diversion substance use treatment program for women

Introduction

In the US, women are one of the fastest-growing segments of the prison population and more than a quarter of women in state prison are incarcerated for drug offenses. Substance use criminal diversion programs can be effective. It may be beneficial to identify individuals who are most likely to complete the program versus terminate early as this can provide information regarding who may need additional or unique programming to improve the likelihood of successful program completion. Prior research investigating prediction of success in these programs has primarily focused on demographic factors in male samples.

Methods

The current study used machine learning (ML) to examine other non-demographic factors related to the likelihood of completing a substance use criminal diversion program for women. A total of 179 women who were enrolled in a criminal diversion program consented and completed neuropsychological, self-report symptom measures, criminal history and demographic surveys at baseline. Model one entered 145 variables into a machine learning (ML) ensemble model, using repeated, nested cross-validation, predicting subsequent graduation versus termination from the program. An identical ML analysis was conducted for model two, in which 34 variables were entered, including the Women's Risk/Needs Assessment (WRNA).

Results

ML models were unable to predict graduation at an individual level better than chance (AUC = 0.59 [SE = 0.08] and 0.54 [SE = 0.13]). Post-hoc analyses indicated measures of impulsivity, trauma history, interoceptive awareness, employment/financial risk, housing safety, antisocial friends, anger/hostility, and WRNA total score and risk scores exhibited medium to large effect sizes in predicting treatment completion (p < 0.05; ds = 0.29 to 0.81).

Discussion

Results point towards the complexity involved in attempting to predict treatment completion at the individual level but also provide potential targets to inform future research aiming to reduce recidivism.

Digital Health Interventions for Mental Health, Substance Use, and Co-occurring Disorders in the Criminal Justice Population: A Scoping Review

BACKGROUND

Substance use disorder (SUD), mental health disorders (MHD), and co-occurring mental health and substance use disorders are common among criminal justice populations. Digital health interventions (DHI) represent an opportunity to expand co-occurring disorder treatment for justice involved populations, but efficacy data are lacking.

OBJECTIVES

The current scoping review aims to address this gap via following objectives: (1) Describe trends involving DHIs for MHD, SUD, or co-occurring disorders studied in criminal justice settings; and (2) review available evidence for the impact of DHIs on criminal justice-, substance-, and mental health-related outcomes.

METHODS

PubMed was searched for relevant articles that met the follow inclusion criteria: (1) focus on criminal justice-involved individuals; (2) description of an intervention focused on SUD, MHD, or co-occurring disorders; and (3) use of DHI. Articles were assessed using standardized data abstraction and quality assessment tools.

RESULTS

Four-hundred unique articles were identified on initial search, and 19 were included in the final review. The most common focus of the intervention was SUDs. The most common modalities were telehealth and computer assisted interventions, with most utilized as an adjunct to treatment as usual. No DHIs used wearable devices, and one included justice involved youth. Feasibility and acceptability were high, and the studies that measured substance and mental health-related outcomes reported equivocal or positive results. No studies focused on long-term justice-related outcomes.

CONCLUSIONS

Literature on DHIs for criminal justice involved populations diagnosed with SUD, MHD and co-occurring disorders is limited, and largely focuses on telehealth or eHealth, with less data on mHealth approaches. Future research should focus on the inclusion of diverse populations and include objective monitoring tools.

A co-occurring disorders intervention for drug treatment court: 12-month pilot study outcomes

Purpose

Many individuals in drug treatment courts (DTCs) have co-occurring mental health and substance use disorders (COD), which can negatively impact treatment engagement, behavioral health and criminal justice outcomes. This paper aims to report results of DTC participants with a COD, who received a 12-month wraparound treatment intervention called MISSION-Criminal Justice (MISSION-CJ) alongside DTC to improve treatment engagement and behavioral health outcomes and reduce reincarcerations.

Design/methodology/approach

In this pre-post, single-group pilot, 48 clients enrolled and 81% completed 12-month follow-up assessments (N = 39) and weekly MISSION-CJ fidelity for type and intensity of services delivered. Generalized linear mixed models (GLMMs) were computed with a fixed term for fidelity (e.g. high or low MISSION-CJ), time and a fidelity x time interaction term.

Findings

Among participants, at 12 months, 81% of the participants remained engaged in treatment at study completion, and 89% had high MISSON-CJ fidelity. Clients demonstrated significant reductions from baseline to 12 months in average nights in jail (B = −0.1849511, p < 0.0344), mental health symptoms via the Behavior and Symptom Identification Scale (BASIS) total and subscale scores (B = −0.121613,p< 0.0186) and trauma symptoms on the PTSD Checklist-5 (PCL-5) (B = −0.928791, p < 0.0138). High MISSION-CJ fidelity further improved criminal justice, and behavioral health outcomes.

Originality/value

This was the first reported 12-month MISSION-CJ trial. While feasible to implement, given the design limitations, future research should include a large randomized controlled trial.