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Justice involvement patterns, overdose experiences, and naloxone knowledge among men and women in criminal justice diversion addiction treatment.

ABSTRACT: BACKGROUND:Persons in addiction treatment are likely to experience and/or witness drug overdoses following treatment and thus could benefit from overdose education and naloxone distribution (OEND) programs. Diverting individuals from the criminal justice system to addiction treatment represents one treatment engagement pathway, yet OEND needs among these individuals have not been fully described. METHODS:We characterized justice involvement patterns among 514 people who use opioids (PWUO) participating in a criminal justice diversion addiction treatment program during 2014-2016 using a gender-stratified latent class analysis. We described prevalence and correlates of naloxone knowledge using quasi-Poisson regression models with robust standard errors. RESULTS:Only 56% of participants correctly identified naloxone as an opioid overdose treatment despite that 68% had experienced an overdose and 79% had witnessed another person overdose. We identified two latent justice involvement classes: low involvement (20.3% of men, 46.5% of women), characterized by older age at first arrest, more past-year arrests, and less time incarcerated; and high involvement (79.7% of men, 53.5% of women), characterized by younger age at first arrest and more lifetime arrests and time incarcerated. Justice involvement was not associated with naloxone knowledge. Male participants who had personally overdosed more commonly identified naloxone as an overdose treatment after adjustment for age, race, education level, housing status, heroin use, and injection drug use (prevalence ratio [95% confidence interval]: men 1.5 [1.1-2.0]). CONCLUSIONS:All PWUO in criminal justice diversion programs could benefit from OEND given the high propensity to experience and witness overdoses and low naloxone knowledge across justice involvement backgrounds and genders.

SUBMITTER: Gicquelais RE

PROVIDER: S-EPMC6636104 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Justice involvement patterns, overdose experiences, and naloxone knowledge among men and women in criminal justice diversion addiction treatment.

Gicquelais Rachel E RE Mezuk Briana B Foxman Betsy B Thomas Laura L Bohnert Amy S B ASB

Harm reduction journal 20190716 1

<h4>Background</h4>Persons in addiction treatment are likely to experience and/or witness drug overdoses following treatment and thus could benefit from overdose education and naloxone distribution (OEND) programs. Diverting individuals from the criminal justice system to addiction treatment represents one treatment engagement pathway, yet OEND needs among these individuals have not been fully described.<h4>Methods</h4>We characterized justice involvement patterns among 514 people who use opioid ...[more]

PMID: 31311572

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Project description:ImportanceResponding to the opioid crisis requires tools to identify individuals at risk of overdose. Given the expansion of illicit opioid deaths, it is essential to consider risk factors across multiple service systems.ObjectiveTo develop a predictive risk model to identify opioid overdose using linked clinical and criminal justice data.Design, setting, and participantsA cross-sectional sample was created using 2015 data from 4 Maryland databases: all-payer hospital discharges, the prescription drug monitoring program (PDMP), public-sector specialty behavioral treatment, and criminal justice records for property or drug-associated offenses. Maryland adults aged 18 to 80 years with records in any of 4 databases were included, excluding individuals who died in 2015 or had a non-Maryland zip code. Logistic regression models were estimated separately for risk of fatal and nonfatal opioid overdose in 2016. Model performance was assessed using bootstrapping. Data analysis took place from February 2018 to November 2019.ExposuresControlled substance prescription fills and hospital, specialty behavioral health, or criminal justice encounters.Main outcomes and measuresFatal opioid overdose defined by the state medical examiner and 1 or more nonfatal overdoses treated in Maryland hospitals during 2016.ResultsThere were 2 294 707 total individuals in the sample, of whom 42.3% were male (n = 970 019) and 53.0% were younger than 50 years (647 083 [28.2%] aged 18-34 years and 568 160 [24.8%] aged 35-49 years). In 2016, 1204 individuals (0.05%) in the sample experienced fatal opioid overdose and 8430 (0.37%) experienced nonfatal opioid overdose. In adjusted analysis, the factors mostly strongly associated with fatal overdose were male sex (odds ratio [OR], 2.40 [95% CI, 2.08-2.76]), diagnosis of opioid use disorder in a hospital (OR, 2.93 [95% CI, 2.17-3.80]), release from prison in 2015 (OR, 4.23 [95% CI, 2.10-7.11]), and receiving opioid addiction treatment with medication (OR, 2.81 [95% CI, 2.20-3.86]). Similar associations were found for nonfatal overdose. The area under the curve for fatal overdose was 0.82 for a model with hospital variables, 0.86 for a model with both PDMP and hospital variables, and 0.89 for a model that further added behavioral health and criminal justice variables. For nonfatal overdose, the area under the curve using all variables was 0.85.Conclusions and relevanceIn this analysis, fatal and nonfatal opioid overdose could be accurately predicted with linked administrative databases. Hospital encounter data had higher predictive utility than PDMP data. Model performance was meaningfully improved by adding PDMP records. Predictive models using linked databases can be used to target large-scale public health programs.

Project description:Importance:Health disparities between African Americans and Whites have persisted in the United States. Researchers have recently hypothesized that the relatively poor health of African Americans may be caused, in part, by African American overrepresentation in the criminal justice system. Objectives:To test the hypothesis that criminal justice system involvement is associated with poor health and greater health risk when controlling for unobserved family factors through a discordant sibling design. Methods:Subjects were drawn from the Carolina African American Twin Study of Aging (CAATSA). Criminal conviction records were extracted from North Carolina's Department of Public Safety. Six measures of health and one measure of health risk were analyzed. The health of convicted respondents was compared to that of unrelated non-convicted respondents matched on childhood and demographic factors ("matched sample"). Convicted respondents were also compared to non-convicted siblings ("discordant sibling sample"). Results:The matched sample included 134 CAATSA respondents. On average, convicted CAATSA respondents, compared to matched non-convicted respondents, were in worse health. Convicted respondents had worse mean self-reported health, worse lung function, more depressive symptoms, and smoked more. The discordant sibling sample included 74 respondents. Convicted siblings and non-convicted siblings had similar self-reported health, depressive symptoms, and smoking. In general, non-convicted siblings were in worse health than non-convicted respondents from the matched sample, implying that poor health runs in families. Conclusions:This study provided preliminary evidence that some of the association between a criminal record and poor health is confounded by family factors. Though more research is needed to support these results, the study suggests that criminal involvement may not be associated with the surfeit of health problems observed among African Americans. The criminal justice system, nonetheless, could be used to decrease the health disparity.

Project description:Background and aimsDespite the high prevalence of substance use among people in the US criminal justice system, little is known about the incidence of overdose mortality by use patterns, drug convictions and supervision setting. We examined the associations between these characteristics and overdose mortality.DesignRetrospective cohort study.Setting and participantsIndividuals sentenced to prison, jail, probation or jail plus probation for a felony conviction in Michigan, USA from 2003 to 2006.MeasurementsUsing the National Death Index, we assessed overdose mortality to December 2012. We calculated overdose mortality rates by pre-sentence opioid use, drug convictions and supervision setting. Multivariable analyses were conducted using competing risks regression with time-varying covariates.FindingsAmong 140 266 individuals followed over a mean of 7.84 years [standard deviation (SD) = 1.52], 14.9% of the 1131 deaths were due to overdose (102.8 per 100 000 person-years). Over the follow-up, more than half of overdose deaths occurred in the community (57.7%), nearly a third (28.8%) on probation and 12.8% on parole. The adjusted risk of overdose death was lower on probation [hazard ratio (HR) = 0.71, 95% confidence interval (CI) = 0.60, 0.85] than in the community without probation or parole (HR = 1.00) but not significantly different on parole (HR = 1.13, 95% CI = 0.87, 1.47). Pre-sentence daily opioid use (HR = 3.54, 95% CI = 3.24, 3.87) was associated with an increased risk. Drug possession (HR = 1.11, 95% CI = 0.93, 1.31) and delivery convictions (HR = 0.92, 95% CI = 0.77, 1.09) were not significantly associated with overdose mortality.ConclusionsBased on the absolute or relative risk, parole, probation and community settings are appropriate settings for enhanced overdose prevention interventions. Ensuring that individuals with pre-sentence opioid use have access to harm reduction and drug treatment services may help to prevent overdose among people involved with the criminal justice system.

Project description:AIMS:To compare long-term criminal justice outcomes among opioid-dependent individuals randomized to receive buprenorphine or methadone. DESIGN, SETTING AND PARTICIPANTS:A 5-year follow-up was conducted in 2011-14 of 303 opioid-dependent participants entering three opioid treatment programs in California, USA in 2006-09 and randomized to receive either buprenorphine/naloxone or methadone. INTERVENTION AND COMPARATOR:Participants received buprenorphine/naloxone (BUP; n = 179) or methadone (MET; n = 124) for 24 weeks and then were tapered off their treatment over ? 8 weeks or referred for ongoing clinical treatment. Midway through the study, the randomization scheme was switched from 1 : 1 BUP : MET to 2 : 1 because of higher dropout in the BUP arm. MEASUREMENTS:Study outcomes included arrests and self-reported incarceration. Predictors included randomization condition (buprenorphine versus methadone), age, gender, race/ethnicity, use of cocaine, drug injection in the 30 days prior to baseline and study site. Treatment status (buprenorphine, methadone, none) during follow-up was included as a time-varying covariate. FINDINGS:There was no significant difference by randomization condition in the proportion arrested (buprenorphine: 55.3%, methadone: 54.0%) or incarcerated (40.9%, 47.3%) during follow-up. Among methadone-randomized individuals, arrest was less likely with methadone treatment (0.50, 0.35-0.72) during follow-up (relative to no treatment) and switching to buprenorphine had a lower likelihood of arrest than those receiving no treatment (0.39, 0.18-0.87). Among buprenorphine-randomized individuals, arrest was less likely with receipt of buprenorphine (0.49, 0.33-0.75) during follow-up and switching to methadone had a similar likelihood of arrest as methadone-randomized individuals receiving no treatment. Likelihood of arrest was also negatively associated with older age (0.98, 0.96-1.00); it was positively associated with Hispanic ethnicity (1.63, 1.04-2.56), cocaine use (2.00, 1.33-3.03), injection drug use (2.19, 1.26-3.83), and study site. CONCLUSIONS:In a US sample of people treated for opioid use disorder, continued treatment with either buprenorphine or methadone was associated with a reduction in arrests relative to no treatment. Cocaine use, injection drug use, Hispanic ethnicity and younger age were associated with higher likelihood of arrest.

Project description:Health system data incompletely capture the social risk factors for drug overdose. This study aimed to improve the accuracy of a machine-learning algorithm to predict opioid overdose risk by integrating human services and criminal justice data with health claims data to capture the social determinants of overdose risk. This prognostic study included Medicaid beneficiaries (n = 237,259) in Allegheny County, Pennsylvania enrolled between 2015 and 2018, randomly divided into training, testing, and validation samples. We measured 290 potential predictors (239 derived from Medicaid claims data) in 30-day periods, beginning with the first observed Medicaid enrollment date during the study period. Using a gradient boosting machine, we predicted a composite outcome (i.e., fatal or nonfatal opioid overdose constructed using medical examiner and claims data) in the subsequent month. We compared prediction performance between a Medicaid claims only model to one integrating human services and criminal justice data with Medicaid claims (i.e., integrated model) using several metrics (e.g., C-statistic, number needed to evaluate [NNE] to identify one overdose). Beneficiaries were stratified into risk-score decile subgroups. The samples (training = 79,087, testing = 79,086, validation = 79,086) had similar characteristics (age = 38±18 years, female = 56%, white = 48%, having at least one overdose = 1.7% during study period). Using the validation sample, the integrated model slightly improved on the Medicaid claims only model (C-statistic = 0.885; 95%CI = 0.877-0.892 vs. C-statistic = 0.871; 95%CI = 0.863-0.878), with small corresponding improvements in the NNE and positive predictive value. Nine of the top 30 most important predictors in the integrated model were human services and criminal justice variables. Using the integrated model, approximately 70% of individuals with overdoses were members of the top risk decile (overdose rates in the subsequent month = 47/10,000 beneficiaries). Few individuals in the bottom 9 deciles had overdose episodes (0-12/10,000). Machine-learning algorithms integrating claims and social service and criminal justice data modestly improved opioid overdose prediction among Medicaid beneficiaries for a large U.S. county heavily affected by the opioid crisis.

Dataset Information

Justice involvement patterns, overdose experiences, and naloxone knowledge among men and women in criminal justice diversion addiction treatment.

Publications

Justice involvement patterns, overdose experiences, and naloxone knowledge among men and women in criminal justice diversion addiction treatment.

Similar Datasets

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