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 releasees 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.

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.

Small Area Forecasting of Opioid-Related Mortality: Bayesian Spatiotemporal Dynamic Modeling Approach.

BACKGROUND: Opioid-related overdose mortality has remained at crisis levels across the United States, increasing 5-fold and worsened during the COVID-19 pandemic. The ability to provide forecasts of opioid-related mortality at granular geographical and temporal scales may help guide preemptive public health responses. Current forecasting models focus on prediction on a large geographical scale, such as states or counties, lacking the spatial granularity that local public health officials desire to guide policy decisions and resource allocation. OBJECTIVE: The overarching objective of our study was to develop Bayesian spatiotemporal dynamic models to predict opioid-related mortality counts and rates at temporally and geographically granular scales (ie, ZIP Code Tabulation Areas [ZCTAs]) for Massachusetts. METHODS: We obtained decedent data from the Massachusetts Registry of Vital Records and Statistics for 2005 through 2019. We developed Bayesian spatiotemporal dynamic models to predict opioid-related mortality across Massachusetts' 537 ZCTAs. We evaluated the prediction performance of our models using the one-year ahead approach. We investigated the potential improvement of prediction accuracy by incorporating ZCTA-level demographic and socioeconomic determinants. We identified ZCTAs with the highest predicted opioid-related mortality in terms of rates and counts and stratified them by rural and urban areas. RESULTS: Bayesian dynamic models with the full spatial and temporal dependency performed best. Inclusion of the ZCTA-level demographic and socioeconomic variables as predictors improved the prediction accuracy, but only in the model that did not account for the neighborhood-level spatial dependency of the ZCTAs. Predictions were better for urban areas than for rural areas, which were more sparsely populated. Using the best performing model and the Massachusetts opioid-related mortality data from 2005 through 2019, our models suggested a stabilizing pattern in opioid-related overdose mortality in 2020 and 2021 if there were no disruptive changes to the trends observed for 2005-2019. CONCLUSIONS: Our Bayesian spatiotemporal models focused on opioid-related overdose mortality data facilitated prediction approaches that can inform preemptive public health decision-making and resource allocation. While sparse data from rural and less populated locales typically pose special challenges in small area predictions, our dynamic Bayesian models, which maximized information borrowing across geographic areas and time points, were used to provide more accurate predictions for small areas. Such approaches can be replicated in other jurisdictions and at varying temporal and geographical levels. We encourage the formation of a modeling consortium for fatal opioid-related overdose predictions, where different modeling techniques could be ensembled to inform public health policy.

Massachusetts Justice Community Opioid Innovation Network (MassJCOIN).

A major driver of the U.S. opioid crisis is limited access to effective medications for opioid use disorder (MOUD) that reduce overdose risks. Traditionally, jails and prisons in the U.S. have not initiated or maintained MOUD for incarcerated individuals with OUD prior to their return to the community, which places them at high risk for fatal overdose. A 2018 law (Chapter 208) made Massachusetts (MA) the first state to mandate that five county jails deliver all FDA-approved MOUDs (naltrexone [NTX], buprenorphine [BUP], and methadone). Chapter 208 established a 4-year pilot program to expand access to all FDA-approved forms of MOUD at five jails, with two more MA jails voluntarily joining this initiative. The law stipulates that MOUD be continued for individuals receiving it prior to detention and be initiated prior to release among sentenced individuals where appropriate. The jails must also facilitate continuation of MOUD in the community on release. The Massachusetts Justice Community Opioid Innovation Network (MassJCOIN) partnered with these seven diverse jails, the MA Department of Public Health, and community treatment providers to conduct a Type 1 hybrid effectiveness-implementation study of Chapter 208. We will: (1) Perform a longitudinal treatment outcome study among incarcerated individuals with OUD who receive NTX, BUP, methadone, or no MOUD in jail to examine postrelease MOUD initiation, engagement, and retention, as well as fatal and nonfatal opioid overdose and recidivism; (2) Conduct an implementation study to understand systemic and contextual factors that facilitate and impede delivery of MOUDs in jail and community care coordination, and strategies that optimize MOUD delivery in jail and for coordinating care with community partners; (3) Calculate the cost to the correctional system of implementing MOUD in jail, and conduct an economic evaluation from state policy-maker and societal perspectives to compare the value of MOUD prior to release from jail to no MOUD among matched controls. MassJCOIN made significant progress during its first six months until the COVID-19 pandemic began in March 2020. Participating jail sites restricted access for nonessential personnel, established other COVID-19 mitigation policies, and modified MOUD programming. MassJCOIN adapted research activities to this new reality in an effort to document and account for the impacts of COVID-19 in relation to each aim. The goal remains to produce findings with direct implications for policy and practice for OUD in criminal justice settings.