Using decision tree models and comprehensive statewide data to predict opioid overdoses following prison release.

PURPOSE: Identifying predictors of opioid overdose following release from prison is critical for opioid overdose prevention. METHODS: We leveraged an individually linked, state-wide database from 2015-2020 to predict the risk of opioid overdose within 90 days of release from Massachusetts state prisons. We developed two decision tree modeling schemes: a model fit on all individuals with a single weight for those that experienced an opioid overdose and models stratified by race/ethnicity. We compared the performance of each model using several performance measures and identified factors that were most predictive of opioid overdose within racial/ethnic groups and across models. RESULTS: We found that out of 44,246 prison releases in Massachusetts between 2015-2020, 2237 (5.1%) resulted in opioid overdose in the 90 days following release. The performance of the two predictive models varied. The single weight model had high sensitivity (79%) and low specificity (56%) for predicting opioid overdose and was more sensitive for White non-Hispanic individuals (sensitivity = 84%) than for racial/ethnic minority individuals. CONCLUSIONS: Stratified models had better balanced performance metrics for both White non-Hispanic and racial/ethnic minority groups and identified different predictors of overdose between racial/ethnic groups. Across racial/ethnic groups and models, involuntary commitment (involuntary treatment for alcohol/substance use disorder) was an important predictor of opioid overdose.

Prison Buprenorphine Implementation and Postrelease Opioid Use Disorder Outcomes.

Importance: Agonist medications for opioid use disorder (MOUD), buprenorphine and methadone, in carceral settings might reduce the risk of postrelease opioid overdose but are uncommonly offered. In April 2019, the Massachusetts Department of Correction (MADOC), the state prison system, provided buprenorphine for incarcerated individuals in addition to previously offered injectable naltrexone. Objective: To evaluate postrelease outcomes after buprenorphine implementation. Design, Setting, and Participants: This cohort study with interrupted time-series analysis used linked data across multiple statewide data sets in the Massachusetts Public Health Data Warehouse stratified by sex due to differences in carceral systems. Eligible participants were individuals sentenced and released from a MADOC facility to the community. The study period for the male sample was January 2014 to November 2020; for the female sample, January 2015 to October 2019. Data were analyzed between February 2022 and January 2024. Exposure: April 2019 implementation of buprenorphine during incarceration. Main Outcomes and Measures: Receipt of MOUD within 4 weeks after release, opioid overdose, and all-cause mortality within 8 weeks after release, each measured as a percentage of monthly releases who experienced the outcome. Segmented linear regression analyzed changes in outcome rates after implementation. Results: A total of 15 225 individuals were included. In the male sample there were 14 582 releases among 12 688 individuals (mean [SD] age, 35.0 [10.8] years; 133 Asian and Pacific Islander [0.9%], 4079 Black [28.0%], 4208 Hispanic [28.9%], 6117 White [41.9%]), a rate of 175.7 releases per month; the female sample included 3269 releases among 2537 individuals (mean [SD] age, 34.9 [9.8] years; 328 Black [10.0%], 225 Hispanic [6.9%], 2545 White [77.9%]), a rate of 56.4 releases per month. Among male participants at 20 months postimplementation, the monthly rate of postrelease buprenorphine receipt was higher than would have been expected under baseline trends (21.2% vs 10.6% of monthly releases; 18.6 additional releases per month). Naltrexone receipt was lower than expected (1.0% vs 6.0%; 8.8 fewer releases per month). Monthly rates of methadone receipt (1.4%) and opioid overdose (1.8%) were not significantly different than expected. All-cause mortality was lower than expected (1.9% vs 2.8%; 1.5 fewer deaths per month). Among female participants at 7 months postimplementation, buprenorphine receipt was higher than expected (31.6% vs 9.5%; 12.4 additional releases per month). Naltrexone receipt was lower than expected (3.4% vs 7.2%) but not statistically significantly different. Monthly rates of methadone receipt (1.1%), opioid overdose (4.8%), and all-cause mortality (1.6%) were not significantly different than expected. Conclusions and Relevance: In this cohort study of state prison releases, postrelease buprenorphine receipt increased and naltrexone receipt decreased after buprenorphine became available during incarceration.

Rates of Opioid Overdose Among Racial and Ethnic Minority Individuals Released From Prison.

This cross-sectional study examines opioid overdose patterns by race and ethnicity among individuals released from prison in Massachusetts.

COVID-19 impact on opioid overdose after jail release in Massachusetts.

Introduction: Release from incarceration is a high-risk period for opioid overdose. Concern about COVID-19 spread in jails led to early releases; it is unknown whether pandemic era releases of persons with opioid use disorder (OUD) contributed to increases in community overdose rates. Methods: Observational data compared overdose rates three months after release among jailed persons with OUD released before (9/1/2019-3/9/2020) and during the pandemic (3/10/2020-8/10/2020) from seven jails in Massachusetts. Data on overdoses come from the Massachusetts Ambulance Trip Record Information System and Registry of Vital Records Death Certificate file. Other information came from jail administrative data. Logistic models regressed overdose on release period, controlling for MOUD received, county of release, race/ethnicity, sex, age, and prior overdose. Results: Pandemic releases with OUD had a higher risk of fatal overdose (adjusted odds ratio [aOR] 3.06; 95% CI, 1.49 to 6.26); 20 persons released with OUD (1.3%) experienced a fatal overdose within three months of release, versus 14 (0.5%) pre-pandemic. MOUD had no detectable relationship with overdose mortality. Pandemic release did not impact non-fatal overdose rates (aOR 0.84; 95% CI 0.60 to 1.18), though in-jail methadone treatment was protective (aOR 0.34; 95% CI 0.18 to 0.67). Conclusions: Persons with OUD released from jail during the pandemic experienced higher overdose mortality compared to pre-pandemic, but the number of deaths was small. They did not experience significantly different rates of non-fatal overdose. Early jail releases during the pandemic were unlikely to explain much, if any, of the observed increase in community overdoses in Massachusetts.

The descriptive epidemiology of brand-specific gun ownership in the US: results from the 2019 National Lawful Use of Guns Survey.

BACKGROUND: No previous study has identified the specific brands of guns owned by gun owners. This study aimed to: (1) ascertain and describe patterns of brand- and model-specific gun ownership among US gun owners; and (2) investigate the relationship between gun owners' brand and model preferences and their attitudes towards common firearm violence prevention policies. METHODS: Using a national, pre-recruited internet panel of US adults in 2019, we surveyed gun owners (N = 2086) to ascertain their opinions regarding firearm violence prevention policies and to assess the brands and models of guns that they owned. RESULTS: Brand-specific gun ownership was highly concentrated and was dominated by three pistol brands, two revolver brands, three rifle brands, and three shotgun brands. There was wide variation in policy attitudes among owners of different gun brands, but little variation across owners of different gun types (i.e., pistols, rifles, revolvers, shotguns). We were able to identify the specific gun models owned by 1218 (59.4%) of the gun owners. Based on the classification of these gun models into three types we categorized the gun ownership pattern of the sample as 33.4% recreational, 45.5% self-defense, and 21.1% tactical. There were marked differences in support for firearm-related policies among the three groups, with support generally highest among the Recreation group and lowest among the Tactical group. CONCLUSION: We conclude that gun brands and models are strong predictors of a gun owner's attitudes regarding firearm-related policies. This information could help public health practitioners develop segment-specific communications that will appeal to each group in order to more effectively engage gun owners in firearm violence prevention.