Medication for Opioid Use Disorder After Serious Injection-Related Infections in Massachusetts.

Importance: Serious injection-related infections (SIRIs) cause significant morbidity and mortality. Medication for opioid use disorder (MOUD) improves outcomes but is underused. Understanding MOUD treatment after SIRIs could inform interventions to close this gap. Objectives: To examine rehospitalization, death rates, and MOUD receipt for individuals with SIRIs and to assess characteristics associated with MOUD receipt. Design, Setting, and Participants: This retrospective cohort study used the Massachusetts Public Health Data Warehouse, which included all individuals with a claim in the All-Payer Claims Database and is linked to individual-level data from multiple government agencies, to assess individuals aged 18 to 64 years with opioid use disorder and hospitalization for endocarditis, osteomyelitis, epidural abscess, septic arthritis, or bloodstream infection (ie, SIRI) between July 1, 2014, and December 31, 2019. Data analysis was performed from November 2021 to May 2023. Exposure: Demographic and clinical factors potentially associated with posthospitalization MOUD receipt. Main Outcomes and Measures: The main outcome was MOUD receipt measured weekly in the 12 months after hospitalization. We used zero-inflated negative binomial regression to examine characteristics associated with any MOUD receipt and rates of treatment in the 12 months after hospitalization. Secondary outcomes were receipt of any buprenorphine formulation, methadone, and extended-release naltrexone examined individually. Results: Among 8769 individuals (mean [SD] age, 43.2 [12.0] years; 5066 [57.8%] male) who survived a SIRI hospitalization, 4305 (49.1%) received MOUD, 5919 (67.5%) were rehospitalized, and 973 (11.1%) died within 12 months. Of those treated with MOUD in the 12 months after hospitalization, the mean (SD) number of MOUD initiations during follow-up was 3.0 (1.7), with 956 of 4305 individuals (22.2%) receiving treatment at least 80% of the time. MOUD treatment after SIRI hospitalization was significantly associated with MOUD in the prior 6 months (buprenorphine: adjusted odds ratio [AOR], 16.51; 95% CI, 13.81-19.74; methadone: AOR, 28.46; 95% CI, 22.41-36.14; or naltrexone: AOR, 2.05; 95% CI, 1.56-2.69). Prior buprenorphine (incident rate ratio [IRR], 1.17; 95% CI, 1.11-1.24) or methadone (IRR, 1.89; 95% CI, 1.79-2.01) use was associated with higher treatment rates after hospitalization, and prior naltrexone use (IRR, 0.86; 95% CI, 0.77-0.95) was associated with lower rates. Conclusions and Relevance: This study found that in the year after a SIRI hospitalization in Massachusetts, mortality and rehospitalization were common, and only half of patients received MOUD. Treatment with MOUD before a SIRI was associated with posthospitalization MOUD initiation and time receiving MOUD. Efforts are needed to initiate MOUD treatment during SIRI hospitalizations and subsequently retain patients in treatment.

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.

Target trial emulation for comparative effectiveness research with observational data: Promise and challenges for studying medications for opioid use disorder.

Medications for opioid use disorder (MOUD) increase retention in care and decrease mortality during active treatment; however, information about the comparative effectiveness of different forms of MOUD is sparse. Observational comparative effectiveness studies are subject to many types of bias; a robust framework to minimize bias would improve the quality of comparative effectiveness evidence. This paper discusses the use of target trial emulation as a framework to conduct comparative effectiveness studies of MOUD with administrative data. Using examples from our planned research project comparing buprenorphine-naloxone and extended-release naltrexone with respect to the rates of MOUD discontinuation, we provide a primer on the challenges and approaches to employing target trial emulation in the study of MOUD.

Intersectional inequities and longitudinal prevalence estimates of opioid use disorder in Massachusetts 2014-2020: a multi-sample capture-recapture analysis.

Background: As overdoses continue to increase worldwide, accurate estimates are needed to understand the size of the population at risk and address health disparities. Capture-recapture methods may be used in place of direct estimation at nearly any geographic level (e.g., city, state, country) to estimate the size of the population with opioid use disorder (OUD). We performed a multi-sample capture-recapture analysis with persons aged 18-64 years to estimate the prevalence of OUD in Massachusetts from 2014 to 2020, stratified by sex and race/ethnicity. Methods: We used seven statewide administrative data sources linked at the individual level. We developed log-linear models to estimate the unknown OUD-affected population. Uncertainty was characterized using 95% confidence intervals (95% CI) on the total counts and prevalence estimates. Findings: The estimated OUD prevalence increased from 5.47% (95% CI = 4.89%, 5.98%) in 2014 to 5.79% (95% CI = 5.34%, 6.19%) in 2020. Prevalence among Hispanic females doubled (2.46% in 2014 to 4.23% in 2020) and prevalence rose to nearly 10% among Black non-Hispanic males and Hispanic males from 2014 through 2019. Estimates for Black non-Hispanic females more than doubled from 2014 through 2019 (3.39% to 7.09%), and then decreased to 5.69% in 2020. Interpretation: This study is the first to provide OUD prevalence trend estimates by binary sex and race/ethnicity at a state level using capture-recapture methods. Using these methods as the international overdose crisis worsens can allow jurisdictions to appropriately allocate resources and targeted interventions to marginalised populations. Funding: NIDA.

Trends in Fatal Opioid-Related Overdose in American Indian and Alaska Native Communities, 1999-2021.

INTRODUCTION: Opioid-related overdose mortality rates have increased sharply in the U.S. over the past two decades, and inequities across racial and ethnic groups have been documented. Opioid-related overdose trends among American Indian and Alaska Natives require further quantification and assessment. METHODS: Observational, U.S. population-based registry data on opioid-related overdose mortality between 1999 and 2021 were extracted in 2023 using ICD-10 codes from the U.S. Centers for Disease Control and Prevention's Wide-Ranging Online Data for Epidemiologic Research multiple cause of death file by race, Hispanic ethnicity, sex, and age. Segmented time series analyses were conducted to estimate opioid-related overdose mortality growth rates among the American Indian and Alaska Native population between 1999 and 2021. Analyses were performed in 2023. RESULTS: Two distinct time segments revealed significantly different opioid-related overdose mortality growth rates within the overall American Indian and Alaska Native population, from 0.36 per 100,000 (95% CI=0.32, 0.41) between 1999 and 2019 to 6.5 (95% CI=5.7, 7.31) between 2019 and 2021, with the most pronounced increase among those aged 24-44 years. Similar patterns were observed within the American Indian and Alaska Native population with Hispanic ethnicity, but the estimated growth rates were generally steeper across most age groups than across the overall American Indian and Alaska Native population. Patterns of opioid-related overdose mortality growth rates were similar between American Indian and Alaska Native females and males between 2019 and 2021. CONCLUSIONS: Sharp increases in opioid-related overdose mortality rates among American Indian and Alaska Native communities are evident by age and Hispanic ethnicity, highlighting the need for culturally sensitive fatal opioid-related overdose prevention, opioid use disorder treatment, and harm-reduction efforts. Future research should aim to understand the underlying factors contributing to these high mortality rates and employ interventions that leverage the strengths of American Indian and Alaska Native culture, including the strong sense of community.

Massachusetts Prevalence of Opioid Use Disorder Estimation Revisited: Comparing a Bayesian Approach to Standard Capture-Recapture Methods.

BACKGROUND: The National Survey on Drug Use and Health (NSDUH) estimated the prevalence of opioid use disorder (OUD) among the civilian, noninstitutionalized people aged 12 years or older in Massachusetts as 1.2% between 2015 and 2017. Accurate estimation of the prevalence of OUD is critical to the success of treatment and resource planning. Various indirect estimation approaches have been used but are subject to data availability and infrastructure-related issues. METHODS: We used 2015 data from the Massachusetts Public Health Data Warehouse (PHD) to compare the results of two approaches to estimating OUD prevalence in the Massachusetts population. First, we used a seven-dataset capture-recapture analysis under log-linear model parameterization, controlling for the source dependence and effects of age, sex, and county through stratification. Second, we applied a benchmark-multiplier method in a Bayesian framework by linking health care claims data to death certificate data assuming an extrapolation of death rates from observed untreated OUD to unobserved OUD. RESULTS: Our estimates for OUD prevalence among Massachusetts residents (aged 18-64 years) were 4.62% (95% CI = 4.59%, 4.64%) in the capture-recapture approach and 4.29% (95% CrI = 3.49%, 5.32%) in the Bayesian model. Both estimates were approximately four times higher than NSDUH estimates. CONCLUSION: The synthesis of our findings suggests that the disease surveillance system misses a large portion of the population with OUD. Our study also suggests that concurrent use of multiple methods improves the justification and facilitates the triangulation and interpretation of the resulting estimates. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04111939.

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.

Opioid-related mortality: Dynamic temporal and spatial trends by drug type and demographic subpopulations, Massachusetts, 2005-2021.

BACKGROUND: Fatal opioid-related overdoses (OOD) present significant public health challenges. Intuitive and replicable analytical approaches are needed to inform targeted public health responses. METHODS: We obtained fatal OOD data for 2005-2021 from the Massachusetts Registry of Vital Records and Statistics. We conducted heatmap analyses to assess trends in fatal OOD rates per 100,000 residents, visualizing rates by death year and decedent age at one-year intervals, stratifying by race/ethnicity, sex, rurality, and involved substances. We calculated Getis-Ord Gi* statistics to identify spatial clusters of OOD rates. RESULTS: Among 20,774 fatal OODs, rates were higher among males, and highly variable by race/ethnicity, age group, and rurality. While fatal OOD rates increased in urban before rural communities, rates were higher in rural communities by 2018-2019. Stimulant-related fatal OODs were elevated in 2020 and 2021. Fatal OOD rates involving fentanyl and stimulants increased precipitously and simultaneously in the non-Hispanic Black population in 2020 and 2021, with a bimodal age distribution peaking among those in their 40s and 60s. Elevated rates among 30-to-60 year old Hispanic residents were largely tied to synthetic opioids from 2015 to 2021. Spatial clusters were detected for prescription opioids, heroin, and stimulants in western Massachusetts. For synthetic opioids, hotspots became more ubiquitous across the state from 2016 to 2021, intensifying in southeastern Massachusetts. CONCLUSION: Our novel approach uncovered new time varying and spatial patterns in fatal OOD rates not previously reported. Identified shifts in fatal OOD rates by sex, age, and race/ethnicity can inform location-specific field actions targeting subpopulations at disproportionally high risk.

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.

Redefining and categorizing emergency medical service opioid-related incidents in Massachusetts.

AIMS: To create a novel emergency medical service (EMS) opioid-related incident (ORI) tiering framework to describe more accurately the opioid epidemic in Massachusetts. By classifying the data, we could more accurately detail differing trends among the new categories. DESIGN: Free-text fields of Massachusetts EMS reports, from 2013 through 2020, were analyzed to identify ORIs and then categorized into a five-tier severity cascade based on symptom presentation: 'dead on arrival,' 'acute overdose,' 'intoxication,' 'withdrawal' and 'other ORI.' As a validation of the new classification, an emergency medical technician, paramedic and emergency medical physician reviewed clinical reports and assigned a severity category to 100 randomly selected cases. The algorithm then assessed the same 100 cases to determine if it could accurately identify the severity category for each case. FINDINGS: Validation of the algorithm by clinical review indicated a substantial level of agreement between the algorithm and the reviewers. Over half of all ORIs were acute overdose (55%), 21% were intoxication, 20% were other ORI, 3% were withdrawal, and 1% were dead on arrival. Overall ORIs decreased in 2020, but the number of 'dead on arrival' increased 32% from 2019. Administration of naloxone also differed between the categories, with 95% of acute overdose and 29% of intoxication receiving naloxone. CONCLUSIONS: This novel categorization of emergency medical service opioid-related incidents in Massachusetts, United States, reveals new trend details and strains on the emergency medical service system. Using these categories also improves dataset linkage within the state and interstate rate comparisons.

Assisted Reproductive Technology and Perinatal Mortality: Selected States (2006-2011).

OBJECTIVE: This study aimed to compare trends and characteristics of assisted reproductive technology (ART) and non-ART perinatal deaths and to evaluate the association of perinatal mortality and method of conception (ART vs. non-ART) among ART and non-ART deliveries in Florida, Massachusetts, and Michigan from 2006 to 2011. STUDY DESIGN: Retrospective cohort study using linked ART surveillance and vital records data from Florida, Massachusetts, and Michigan. RESULTS: During 2006 to 2011, a total of 570 ART-conceived perinatal deaths and 25,158 non-ART conceived perinatal deaths were identified from the participating states. Overall, ART perinatal mortality rates were lower than non-ART perinatal mortality rates for both singletons (7.0/1,000 births vs. 10.2/1,000 births) and multiples (22.8/1,000 births vs. 41.2/1,000 births). At <28 weeks of gestation, the risk of perinatal death among ART singletons was significantly lower than non-ART singletons (adjusted risk ratio [aRR] = 0.46, 95% confidence interval [CI]: 0.26-0.85). Similar results were observed among multiples at <28 weeks of gestation (aRR = 0.64, 95% CI: 0.45-0.89). CONCLUSION: Our findings suggest that ART use is associated with a decreased risk of perinatal deaths prior to 28 weeks of gestation, which may be explained by earlier detection and management of fetal and maternal conditions among ART-conceived pregnancies. These findings provide valuable information for health care providers, including infertility specialists, obstetricians, and pediatricians when counseling ART users on risk of treatment. KEY POINTS: · ART use is associated with a decreased risk of perinatal deaths prior to 28 weeks of gestation.. · ART perinatal mortality rates were lower than that for non-ART perinatal mortality.. · This study used linked data to examine associations between use of ART and perinatal deaths..

Stimulant-related incident surveillance using emergency medical service records in Massachusetts, 2013-2020.

BACKGROUND: As stimulant use increases across the United States, emergency medical services (EMS) are crucial touchpoints in the health care system. To better measure the prevalence of stimulant use, misuse, and EMS incidents related to stimulant intoxication, definitions for stimulant-related incidents (SRIs) are needed. METHODS: We used the Massachusetts Ambulance Trip Record Information System (MATRIS) from 2013 to 2020 to develop definitions of stimulant-related incidents. EMS runs reported to MATRIS were categorized based on stimulant-related words and symptoms. The three tiers were "any stimulant use" (class 1), "problematic stimulant use" (class 2), and "acute stimulant-related incidents" (class 3). A group of four reviewers studied over 650 cases in eight rounds to refine the search terms, achieving definitions with a correct characterization of over 80% of cases that the code selected. RESULTS: SRI definitions were applied against all EMS runs within Massachusetts between 2013 and 2020 (n = 6,584,836 runs). Of these, 43,538 (0.7%) met the class 1 definition, 38,669 (0.6%) met the class 2 definition, and 19,157 (0.3%) met the class 3 definition. Incidents at all tiers of severity increased over time and were more likely to occur among younger adults and males. Race and ethnicity data indicated that Hispanic/Latinx and Black non-Hispanic/non-Latinx residents formed a disproportionately large percentage of SRIs relative to their total percentage of EMS runs. CONCLUSIONS: The prevalence of all three tiers of SRIs are increasing in Massachusetts, and this protocol provides a source of administrative data on stimulant use that complements sources such as hospital, treatment-based, and/or prescribing records.

Factors associated with opioid-involved overdose among previously incarcerated people in the U.S.: A community engaged narrative review.

BACKGROUND: People with a history of incarceration are at high risk for opioid overdose. A variety of factors contribute to this elevated risk though our understanding of these factors is deficient. Research to identify risk and protective factors for overdose is often conducted using administrative data or researcher-derived surveys and without explicit input from people with lived experience. We aimed to understand the scope of U.S. research on factors associated with opioid overdose among previously incarcerated people. We did this by conducting a narrative review of the literature and convening expert panels of people with lived experience. We then categorized these factors using a social determinants of health framework to help contextualize our findings. METHODS: We first conducted a narrative review of the published literature. A search was performed using PubMed and APA PsycInfo. We then convened two expert panels consisting of people with lived experience and people who work with people who were previously incarcerated. Experts were asked to evaluate the literature derived factors for completeness and add factors that were not identified. Finally, we categorized factors as either intermediary or structural according to the World Health Organization's Social Determinants of Health (SDOH) Framework. RESULTS: We identified 13 papers that met our inclusion criteria for the narrative review. Within these 13 papers, we identified 22 relevant factors for their role in the relationship between overdose and people with a history of incarceration, 16 were risk factors and six were protective factors. Five of these were structural factors (three risk and two protective) and 17 were intermediary factors (13 risk and four protective). The expert panels identified 21 additional factors, 10 of which were structural (six risk and four protective) and 11 of which were intermediary (eight risk and three protective). CONCLUSION: This narrative review along with expert panels demonstrates a gap in the published literature regarding factors associated with overdose among people who were previously incarcerated. Additionally, this review highlights a substantial gap with regard to the types of factors that are typically identified. Incorporating voices of people with lived experience is crucial to our understanding of overdose in this at-risk population.

One year mortality of patients treated with naloxone for opioid overdose by emergency medical services.

Study objective: Prehospital use of naloxone for presumed opioid overdose has increased markedly in recent years because of the current opioid overdose epidemic. In this study, we determine the 1-year mortality of suspected opioid overdose patients who were treated with naloxone by EMS and initially survived. Methods: This was a retrospective observational study of patients using three linked statewide datasets in Massachusetts: emergency medical services (EMS), a master demographics file, and death records. We included all suspected opioid overdose patients who were treated with naloxone by EMS. The primary outcome measures were death within 3 days of treatment and between 4 days and 1 year of treatment. Results: Between July 1, 2013 and December 31, 2015, there were 9734 individuals who met inclusion criteria and were included for analysis. Of these, 807 (8.3% (95% confidence interval (CI) 7.7-8.8%)) died in the first 3 days, 668 (6.9% (95% CI 6.4-7.4%)) died between 4 days and 1 year, and 8259 (84.8% (95% CI 84.1-85.6%)) were still alive at 1 year. Excluding those who died within 3 days, 668 of the remaining 8927 individuals (7.5% (95% CI 6.9-8.0%)) died within 1 year. Conclusion: The 1-year mortality of those who are treated with naloxone for opioid overdose by EMS is high. Communities should focus both on primary prevention and interventions for this patient population, including strengthening regional treatment centers and expanding access to medication for opioid use disorder.

Opioid-related incident severity and emergency medical service naloxone administration by sex in Massachusetts, 2013-2019.

Background: A Cross-sectional study of all emergency ambulance runs reported by licensed Emergency Medical Services (EMS) providers between 2013 and 2019 was undertaken to determine if the sex of a patient experiencing opioid-related symptoms had an impact on their odds of receiving naloxone from EMS. Methods: All runs within Massachusetts for individuals 11 years and older with a reported sex between 2013 and 2019 (n = 5,533,704 runs) were included. Covariates modeled were patient age, year of the incident, and county of the incident. Runs were separated into those that were opioid-related versus not; opioid-related runs were further subdivided into five severity categories including dead on arrival, acute opioid overdose, opioid intoxicated, opioid withdrawal, and other opioid-related incident. Results: Among opioid-related runs, women had 24% lower odds (95% CI 0.68-0.86) of appearing in the dead on arrival category and 20% lower odds (95% CI 0.78-0.82) of appearing in the acute opioid overdose category than men. Among acute opioid overdoses, runs where patient symptoms met Massachusetts EMS guidelines for naloxone administration, women had 18% lower odds (95% CI 0.76-0.89) of receiving naloxone than men. Conclusions: Sex-related differences persist in the odds of naloxone administration by EMS providers when controlling for symptom presentation.

A Novel Imputation Approach for Sharing Protected Public Health Data.

Objectives. To develop an imputation method to produce estimates for suppressed values within a shared government administrative data set to facilitate accurate data sharing and statistical and spatial analyses. Methods. We developed an imputation approach that incorporated known features of suppressed Massachusetts surveillance data from 2011 to 2017 to predict missing values more precisely. Our methods for 35 de-identified opioid prescription data sets combined modified previous or next substitution followed by mean imputation and a count adjustment to estimate suppressed values before sharing. We modeled 4 methods and compared the results to baseline mean imputation. Results. We assessed performance by comparing root mean squared error (RMSE), mean absolute error (MAE), and proportional variance between imputed and suppressed values. Our method outperformed mean imputation; we retained 46% of the suppressed value's proportional variance with better precision (22% lower RMSE and 26% lower MAE) than simple mean imputation. Conclusions. Our easy-to-implement imputation technique largely overcomes the adverse effects of low count value suppression with superior results to simple mean imputation. This novel method is generalizable to researchers sharing protected public health surveillance data. (Am J Public Health. 2021; 111(10):1830-1838. https://doi.org/10.2105/AJPH.2021.306432).

Methadone and buprenorphine discontinuation among postpartum women with opioid use disorder.

BACKGROUND: The postpartum year is a vulnerable period for women with opioid use disorder, with increased rates of fatal and nonfatal overdose; however, data on the continuation of medications for opioid use disorder on a population level are limited. OBJECTIVE: This study aimed to examine the effect of discontinuing methadone and buprenorphine in women with opioid use disorder in the year following delivery and determine the extent to which maternal and infant characteristics are associated with time to discontinuation of medications for opioid use disorder. STUDY DESIGN: This population-based retrospective cohort study used linked administrative data of 211,096 deliveries in Massachusetts between 2011 and 2014 to examine the adherence to medications for opioid use disorder. Individuals receiving medications for opioid use disorder after delivery were included in the study. Here, demographic, psychosocial, prenatal, and delivery characteristics are described. Kaplan-Meier survival analysis and Cox regression modeling were used to examine factors associated with medication discontinuation. RESULTS: A total of 2314 women who received medications for opioid use disorder at delivery were included in our study. Overall, 1484 women (64.1%) continued receiving medications for opioid use disorder for a full 12 months following delivery. The rate of continued medication use varied from 34% if women started on medications for opioid use disorder the month before delivery to 80% if the medications were used throughout pregnancy. Kaplan-Meier survival curves differed by maternal race and ethnicity (the 12-month continuation probability was .65 for White non-Hispanic women and .51 for non-White women; P<.001) and duration of use of prenatal medications for opioid use disorder (12-month continuation probability was .78 for women with full prenatal engagement and .60 and .44 for those receiving medications for opioid use disorder ≥5 months [but not throughout pregnancy] and ≤4 months prenatally, respectively; P<.001). In all multivariable models, duration of receipt of prenatal medications for opioid use disorder (≤4 months vs throughout pregnancy: adjusted hazard ratio, 3.26; 95% confidence interval, 2.72-3.91) and incarceration (incarceration during pregnancy or after delivery vs none: adjusted hazard ratio, 1.79; 95% confidence interval, 1.52-2.12) were most strongly associated with the discontinuation of medications for opioid use disorder. CONCLUSION: Almost two-thirds of women with opioid use disorder continued using medications for opioid use disorder for a full year after delivery; however, the rates of medication continuation varied significantly by race and ethnicity, degree of use of prenatal medications for opioid use disorder, and incarceration status. Prioritizing medication continuation across the perinatal continuum, enhancing sex-specific and family-friendly recovery supports, and expanding access to medications for opioid use disorder despite being incarcerated can help improve postpartum medication adherence.

Homelessness and Veteran Status in Relation to Nonfatal and Fatal Opioid Overdose in Massachusetts.

BACKGROUND: Compared with non-Veterans, Veterans are at higher risk of experiencing homelessness, which is associated with opioid overdose. OBJECTIVE: To understand how homelessness and Veteran status are related to risks of nonfatal and fatal opioid overdose in Massachusetts. DESIGN: A cross-sectional study. PARTICIPANTS: All residents aged 18 years and older during 2011-2015 in the Massachusetts Department of Public Health's Data Warehouse (Veterans: n=144,263; non-Veterans: n=6,112,340). A total of 40,036 individuals had a record of homelessness, including 1307 Veterans and 38,729 non-Veterans. MAIN MEASURES: The main independent variables were homelessness and Veteran status. Outcomes included nonfatal and fatal opioid overdose. RESULTS: A higher proportion of Veterans with a record of homelessness were older than 45 years (77% vs. 48%), male (80% vs. 62%), or receiving high-dose opioid therapy (23% vs. 15%) compared with non-Veterans. The rates of nonfatal and fatal opioid overdose in Massachusetts were 85 and 16 per 100,000 residents, respectively. Among individuals with a record of homelessness, these rates increased 31-fold to 2609 and 19-fold to 300 per 100,000 residents. Homelessness and Veteran status were independently associated with higher odds of nonfatal and fatal opioid overdose. There was a significant interaction between homelessness and Veteran status in their effects on risk of fatal overdose. CONCLUSIONS: Both homelessness and Veteran status were associated with a higher risk of fatal opioid overdoses. An understanding of health care utilization patterns can help identify treatment access points to improve patient safety among vulnerable individuals both in the Veteran population and among those experiencing homelessness.

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.

Operationalizing and selecting outcome measures for the HEALing Communities Study.

BACKGROUND: The Helping to End Addiction Long-termSM (HEALing) Communities Study (HCS) is a multisite, parallel-group, cluster randomized wait-list controlled trial evaluating the impact of the Communities That HEAL intervention to reduce opioid overdose deaths and associated adverse outcomes. This paper presents the approach used to define and align administrative data across the four research sites to measure key study outcomes. METHODS: Priority was given to using administrative data and established data collection infrastructure to ensure reliable, timely, and sustainable measures and to harmonize study outcomes across the HCS sites. RESULTS: The research teams established multiple data use agreements and developed technical specifications for more than 80 study measures. The primary outcome, number of opioid overdose deaths, will be measured from death certificate data. Three secondary outcome measures will support hypothesis testing for specific evidence-based practices known to decrease opioid overdose deaths: (1) number of naloxone units distributed in HCS communities; (2) number of unique HCS residents receiving Food and Drug Administration-approved buprenorphine products for treatment of opioid use disorder; and (3) number of HCS residents with new incidents of high-risk opioid prescribing. CONCLUSIONS: The HCS has already made an impact on existing data capacity in the four states. In addition to providing data needed to measure study outcomes, the HCS will provide methodology and tools to facilitate data-driven responses to the opioid epidemic, and establish a central repository for community-level longitudinal data to help researchers and public health practitioners study and understand different aspects of the Communities That HEAL framework.