Buprenorphine Prescribing Characteristics Following Relaxation of X-Waiver Training Requirements.

Importance: Local-level data are needed to understand whether the relaxation of X-waiver training requirements for prescribing buprenorphine in April 2021 translated to increased buprenorphine treatment. Objective: To assess whether relaxation of X-waiver training requirements was associated with changes in the number of clinicians waivered to and who prescribe buprenorphine for opioid use disorder and the number of patients receiving treatment. Design, Setting, and Participants: This serial cross-sectional study uses an interrupted time series analysis of 2020-2022 data from the HEALing Communities Study (HCS), a cluster-randomized, wait-list-controlled trial. Urban and rural communities in 4 states (Kentucky, Massachusetts, New York, and Ohio) with a high burden of opioid overdoses that had not yet received the HCS intervention were included. Exposure: Relaxation of X-waiver training requirements (ie, allowing training-exempt X-waivers) on April 28, 2021. Main Outcomes and Measures: The monthly number of X-waivered clinicians, X-waivered buprenorphine prescribers, and patients receiving buprenorphine were each summed across communities within a state. Segmented linear regression models to estimate pre- and post-policy change by state were used. Results: The number of individuals in 33 participating HCS communities included 347 863 in Massachusetts, 815 794 in Kentucky, 971 490 in New York, and 1 623 958 in Ohio. The distribution of age (18-35 years: range, 29.4%-32.4%; 35-54 years: range, 29.9%-32.5%; ≥55 years: range, 35.7%-39.3%) and sex (female: range, 51.1%-52.6%) was similar across communities. There was a temporal increase in the number of X-waivered clinicians in the pre-policy change period in all states, which further increased in the post-policy change period in each state except Ohio, ranging from 5.2% (95% CI, 3.1%-7.3%) in Massachusetts communities to 8.4% (95% CI, 6.5%-10.3%) in Kentucky communities. Only communities in Kentucky showed an increase in the number of X-waivered clinicians prescribing buprenorphine associated with the policy change (relative increase, 3.2%; 95% CI, 1.5%-4.9%), while communities in other states showed no change or a decrease. Similarly, only communities in Massachusetts experienced an increase in patients receiving buprenorphine associated with the policy change (relative increase, 1.7%; 95% CI, 0.8%-2.6%), while communities in other states showed no change. Conclusions and Relevance: In this serial cross-sectional study, relaxation of X-waiver training requirements was associated with an increase in the number of X-waivered clinicians but was not consistently associated with an increase in the number of buprenorphine prescribers or patients receiving buprenorphine. These findings suggest that training requirements may not be the primary barrier to expanding buprenorphine treatment.

Insurance Instability for Patients With Opioid Use Disorder in the Year After Diagnosis.

Importance: Transitions in insurance coverage may be associated with worse health care outcomes. Little is known about insurance stability for individuals with opioid use disorder (OUD). Objective: To examine insurance transitions among adults with newly diagnosed OUD in the 12 months after diagnosis. Design, Setting, and Participants: Longitudinal cohort study using data from the Massachusetts Public Health Data Warehouse. The cohort includes adults aged 18 to 63 years diagnosed with incident OUD between July 1, 2014, and December 31, 2014, who were enrolled in commercial insurance or Medicaid at diagnosis; individuals diagnosed after 2014 were excluded from the main analyses due to changes in the reporting of insurance claims. Data were analyzed from November 10, 2022, to May 6, 2024. Exposure: Insurance type at time of diagnosis (commercial and Medicaid). Main Outcomes and Measures: The primary outcome was the cumulative incidence of insurance transitions in the 12 months after diagnosis. Logistic regression models were used to generate estimated probabilities of insurance transitions by insurance type and diagnosis for several characteristics including age, race and ethnicity, and whether an individual started medication for OUD (MOUD) within 30 days after diagnosis. Results: There were 20 768 individuals with newly diagnosed OUD between July 1, 2014, and December 31, 2014. Most individuals with newly diagnosed OUD were covered by Medicaid (75.4%). Those with newly diagnosed OUD were primarily male (67% in commercial insurance, 61.8% in Medicaid). In the 12 months following OUD diagnosis, 30.4% of individuals experienced an insurance transition, with adjusted models demonstrating higher transition rates among those starting with Medicaid (31.3%; 95% CI, 30.5%-32.0%) compared with commercial insurance (27.9%; 95% CI, 26.6%-29.1%). The probability of insurance transitions was generally higher for younger individuals than older individuals irrespective of insurance type, although there were notable differences by race and ethnicity. Conclusions and Relevance: This study found that nearly 1 in 3 individuals experience insurance transitions in the 12 months after OUD diagnosis. Insurance transitions may represent an important yet underrecognized factor in OUD treatment outcomes.

Characterizing the Association Between Traumatic Brain Injury and Discontinuation of Medications for Opioid Use Disorder in a Commercially Insured Adult Population.

OBJECTIVE: Extending prior research that has found that people with traumatic brain injury (TBI) experience worse substance use treatment outcomes, we examined whether history of TBI was associated with discontinuation of medication to treat opioid use disorder (MOUD), an indicator of receiving evidence-based treatment. SETTING: We used MarketScan claims data to capture inpatient, outpatient, and retail pharmacy utilization from large employers in all 50 states from 2016 to 2019. PARTICIPANTS: We identified adults aged 18 to 64 initiating non-methadone MOUD (ie, buprenorphine, injectable naltrexone, and oral naltrexone) in 2016-2019. The exposure was whether an individual had a TBI diagnosis in the 2 years before initiating MOUD. During this period, there were 709 individuals with TBI who were then matched with 709 individuals without TBI. DESIGN: We created a retrospective cohort of matched individuals with and without TBI and used quasi-experimental methods to identify the association between TBI status and MOUD use. We estimated propensity scores by TBI status and created a 1:1 matched cohort of people with and without TBI who initiated MOUD. We used a Cox proportional hazards model to identify the association between TBI and MOUD discontinuation. MAIN MEASURE: The outcome was discontinuation of MOUD (ie, a gap of 14 days or more of MOUD). RESULTS: Among those initiating MOUD, the majority were under 26 years of age, male, and living in an urban setting. Nearly 60% of individuals discontinued medication by 6 months. Adults with TBI had an elevated risk of MOUD discontinuation (hazard ratio [HR] 1.13; 95% confidence interval [CI], 1.01-1.27) compared to those without TBI. Additionally, initiating oral naltrexone was associated with a higher risk of discontinuation (HR 1.63; 95% CI, 1.40-1.90). CONCLUSION: We found evidence of reduced MOUD retention among people with TBI. Differences in MOUD retention may reflect health care inequities, as there are no medical contraindications to using MOUD for people with TBI or other disabilities.

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.

Receipt of medications for opioid use disorder before and after incarceration in Massachusetts State prisons, 2014-2019.

BACKGROUND: Little is known about how use patterns of medications for opioid use disorder (MOUDs) evolve from pre-incarceration to post-incarceration among incarcerated individuals with opioid use disorder. This article describes pre- and post-incarceration MOUD receipt during a period when naltrexone was the only type of MOUD offered in a state prison system, the Massachusetts Department of Correction (MADOC). METHODS: A retrospective cohort study of individuals with opioid use disorder who had an incarceration episode in MADOC during January 2015 to March 2019. The data source was the Massachusetts Public Health Data Warehouse, a multi-sector data platform that links individual-level data from multiple statewide datasets. We described patterns of MOUD receipt during the four weeks prior to and after an incarceration episode. Multivariable logistic regression models characterized predictors of post-incarceration MOUD receipt. RESULTS: In the male sample (n=691 incarcerations), from the pre- to post-incarceration periods, receipt of buprenorphine increased (14.3 % to 18.3 %), naltrexone increased (5.0 % to 10.5 %), and methadone decreased (4.7 % to 1.7 %). Similarly, in the female sample (n=892 incarcerations), from the pre- to post-incarceration periods, receipt of buprenorphine increased (10.3 % to 12.3 %, naltrexone increased (4.5 % to 9.3 %), and methadone decreased (5.0 % to 2.9 %). Much of the post-release naltrexone receipt occurred among participants in MADOC's pre-release naltrexone program. CONCLUSIONS: MOUD receipt was low but increased slightly in the post-incarceration period. This change was driven by increases in buprenorphine and naltrexone and despite decreases in methadone.

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.

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.

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.

Out-of-pocket spending and health care utilization associated with initiation of different medications for opioid use disorder: Findings from a national commercially insured cohort.

INTRODUCTION: Buprenorphine and naltrexone are effective medications for opioid use disorder (MOUD). Naltrexone requires complete detoxification from opioids before initiation while buprenorphine does not, which leads to a differential clinical induction challenge. Few studies have evaluated economic costs associated with MOUD initiation. METHODS: We conducted a retrospective cohort analysis using the 2014-2019 Merative MarketScan database. We included individuals diagnosed with opioid use, abuse, or dependence from 2014 to 2019 who initiated one of three MOUD types: 1) buprenorphine, 2) extended-release naltrexone, or 3) oral naltrexone. We calculated total and monthly out-of-pocket spending, for overall and MOUD-specific claims, for the three months prior through three months after MOUD initiation. We also calculated utilization of detoxification, inpatient, and outpatient services monthly over this period. RESULTS: Our cohort included 27,133 individuals; 19,536, 1886, and 5711 initiated buprenorphine, extended-release naltrexone, and oral naltrexone, respectively. Individuals who initiated naltrexone had the highest out-of-pocket spending over the study period. MOUD-specific spending did not contribute substantially to total out-of-pocket spending. Difference in overall spending by MOUD type was driven by a subset of individuals who initiated naltrexone and had very high out-of-pocket spending in the month prior to MOUD initiation. In this month, mean monthly out-of-pocket spending for high-spenders (above 90th percentile within MOUD type category) was $5734 (95 % confidence interval [CI]: $5181-$6286) and $4622 (95 % CI: $4161-$5082) for those who initiated oral and extended-release naltrexone, respectively, compared with $1852 (95 % CI: $1754-$1950) for those who initiated buprenorphine. In the month prior to MOUD initiation, those who initiated naltrexone also had higher detoxification, inpatient, and outpatient episode/visit frequency. In the month prior to initiation, 28.8 % (95 % CI: 27.7 %-30.0 %) and 25.5 % (95 % CI: 23.6 %-27.5 %) of individuals who initiated oral and extended-release naltrexone had detoxification episodes, compared with 9.7 % (95 % CI: 9.3 %-10.1 %) of those who initiated buprenorphine. CONCLUSION: Findings suggest that individuals who initiated naltrexone utilized more intensive health services, including detoxification, in the period prior to MOUD initiation, resulting in significantly higher out-of-pocket spending. Out-of-pocket spending is a patient-centered outcome reflecting potential patient burden. Our results should be considered as part of the shared decision-making process between patients and providers when choosing treatment for OUD.

Telemedicine Buprenorphine Initiation and Retention in Opioid Use Disorder Treatment for Medicaid Enrollees.

Importance: Early COVID-19 mitigation strategies placed an additional burden on individuals seeking care for opioid use disorder (OUD). Telemedicine provided a way to initiate and maintain transmucosal buprenorphine treatment of OUD. Objective: To examine associations between transmucosal buprenorphine OUD treatment modality (telemedicine vs traditional) during the COVID-19 public health emergency and the health outcomes of treatment retention and opioid-related nonfatal overdose. Design, Setting, and Participants: This retrospective cohort study was conducted using Medicaid claims and enrollment data from November 1, 2019, to December 31, 2020, for individuals aged 18 to 64 years from Kentucky and Ohio. Data were collected and analyzed in June 2022, with data updated during revision in August 2023. Exposures: The primary exposure of interest was the modality of the transmucosal buprenorphine OUD treatment initiation. Relevant patient demographic and comorbidity characteristics were included in regression models. Main Outcomes and Measures: There were 2 main outcomes of interest: retention in treatment after initiation and opioid-related nonfatal overdose after initiation. For outcomes measured after initiation, a 90-day follow-up period was used. The main analysis used a new-user study design; transmucosal buprenorphine OUD treatment initiation was defined as initiation after more than a 60-day gap in buprenorphine treatment. In addition, uptake of telemedicine for buprenorphine was examined, overall and within patients initiating treatment, across quarters in 2020. Results: This study included 41 266 individuals in Kentucky (21 269 women [51.5%]; mean [SD] age, 37.9 [9.0] years) and 50 648 individuals in Ohio (26 425 women [52.2%]; mean [SD] age, 37.1 [9.3] years) who received buprenorphine in 2020, with 18 250 and 24 741 people initiating buprenorphine in Kentucky and Ohio, respectively. Telemedicine buprenorphine initiations increased sharply at the beginning of 2020. Compared with nontelemedicine initiation, telemedicine initiation was associated with better odds of 90-day retention with buprenorphine in both states (Kentucky: adjusted odds ratio, 1.13 [95% CI, 1.01-1.27]; Ohio: adjusted odds ratio, 1.19 [95% CI, 1.06-1.32]) in a regression analysis adjusting for patient demographic and comorbidity characteristics. Telemedicine initiation was not associated with opioid-related nonfatal overdose (Kentucky: adjusted odds ratio, 0.89 [95% CI, 0.56-1.40]; Ohio: adjusted odds ratio, 1.08 [95% CI, 0.83-1.41]). Conclusions and Relevance: In this cohort study of Medicaid enrollees receiving buprenorphine for OUD, telemedicine buprenorphine initiation was associated with retention in treatment early during the COVID-19 pandemic. These findings add to the literature demonstrating positive outcomes associated with the use of telemedicine for treatment of OUD.

Removal of Medicaid Prior Authorization Requirements and Buprenorphine Treatment for Opioid Use Disorder.

Importance: Buprenorphine treatment for opioid use disorder (OUD) is associated with decreased morbidity and mortality. Despite its effectiveness, buprenorphine uptake has been limited relative to the burden of OUD. Prior authorization (PA) policies may present a barrier to treatment, though research is limited, particularly in Medicaid populations. Objective: To assess whether removal of Medicaid PAs for buprenorphine to treat OUD is associated with changes in buprenorphine prescriptions for Medicaid enrollees. Design, Setting, and Participants: This state-level, serial cross-sectional study used quarterly data from 2015 through the first quarter (January-March) of 2019 to compare buprenorphine prescriptions in states that did and did not remove Medicaid PAs. Analyses were conducted between June 10, 2021, and August 15, 2023. The study included 23 states with active Medicaid PAs for buprenorphine in 2015 that required similar PA policies in fee-for-service and managed care plans and had at least 2 quarters of pre- and postperiod buprenorphine prescribing data. Exposures: Removal of Medicaid PA for at least 1 formulation of buprenorphine for OUD. Main Outcomes and Measures: The main outcome was number of quarterly buprenorphine prescriptions per 1000 Medicaid enrollees. Results: Between 2015 and the first quarter of 2019, 6 states in the sample removed Medicaid PAs for at least 1 formulation of buprenorphine and had at least 2 quarters of pre- and postpolicy change data. Seventeen states maintained buprenorphine PAs throughout the study period. At baseline, relative to states that repealed PAs, states that maintained PAs had lower buprenorphine prescribing per 1000 Medicaid enrollees (median, 6.6 [IQR, 2.6-13.9] vs 24.1 [IQR, 8.7-27.5] prescriptions) and lower Medicaid managed care penetration (median, 38.5% [IQR, 0.0%-74.1%] vs 79.5% [IQR, 78.1%-83.5%] of enrollees) but similar opioid overdose rates and X-waivered buprenorphine clinicians per 100 000 population. In fully adjusted difference-in-differences models, removal of Medicaid PAs for buprenorphine was not associated with buprenorphine prescribing (1.4% decrease; 95% CI, -31.2% to 41.4%). For states with below-median baseline buprenorphine prescribing, PA removal was associated with increased buprenorphine prescriptions per 1000 Medicaid enrollees (40.1%; 95% CI, 0.6% to 95.1%), while states with above-median prescribing showed no change (-20.7%; 95% CI, -41.0% to 6.6%). Conclusions and Relevance: In this serial cross-sectional study of Medicaid PA policies for buprenorphine for OUD, removal of PAs was not associated with overall changes in buprenorphine prescribing among Medicaid enrollees. Given the ongoing burden of opioid overdoses, continued multipronged efforts are needed to remove barriers to buprenorphine care and increase availability of this lifesaving treatment.

Applied risk mapping and spatial analysis of address-level decedent data to inform opioid overdose interventions: The Massachusetts HEALing Communities Study.

BACKGROUND: Death certificate data provide powerful and sobering records of the opioid overdose crisis. In Massachusetts, where address-level decedent data are publicly available upon request, mapping and spatial analysis of fatal overdoses can provide valuable insights to inform prevention interventions. We describe how we used this approach to support a community-level intervention to reduce opioid-involved overdose mortality. METHODS: We developed a method to clean and geocode decedent data that substituted injury locations (the likely location of fatal overdoses) for deaths recorded in hospitals. After geomasking for greater privacy protection, we created maps to visualize the spatial distribution of decedent residence addresses, alone and juxtaposed with drive and walk-time distances to opioid treatment programs (OTPs), and place of death by overdose address. We used spatial statistical analyses to identify locations with significant clusters of overdoses. RESULTS: In the 8 intervention communities, 785 individuals died from opioid-involved overdoses between 2017 and 2020. We found that 19.7% of fatal overdoses were recorded in hospitals, 50.2% occurred at the decedent's residence, and 30.1% at another location. We identified overdose hotspots in study communities. By juxtaposing decedent residence data with drive- and walk-time analyses, we highlighted actionable spatial gaps in access to OTP treatment. CONCLUSION: To better understand local fatal opioid overdose risk environments and inform the development of community-level prevention interventions, we used publicly available address-level decedent data to conduct nuanced spatial analyses. Our approach can be replicated in other jurisdictions to inform overdose prevention responses.

Validating opioid use disorder diagnoses in administrative data: a commentary on existing evidence and future directions.

BACKGROUND: A valid opioid use disorder (OUD) identification algorithm for use in administrative medical record data would enhance investigators' ability to study consequences of OUD, OUD treatment seeking and treatment outcomes. MAIN BODY: Existing studies indicate ICD-9 and ICD-10 codes for opioid abuse and dependence do not accurately measure OUD. However, critical appraisal of existing literature suggests alternative validation methods would improve the validity of OUD identification algorithms in administrative data. Chart abstraction may not be sufficient to validate OUD, and primary data collection via structured diagnostic interviews might be an ideal gold standard. CONCLUSION AND COMMENTARY: Generating valid OUD identification algorithms is critical for OUD research and quality measurement in real world health care settings.

Estimated Reductions in Opioid Overdose Deaths With Sustainment of Public Health Interventions in 4 US States.

Importance: In 2021, more than 80 000 US residents died from an opioid overdose. Public health intervention initiatives, such as the Helping to End Addiction Long-term (HEALing) Communities Study (HCS), are being launched with the goal of reducing opioid-related overdose deaths (OODs). Objective: To estimate the change in the projected number of OODs under different scenarios of the duration of sustainment of interventions, compared with the status quo. Design, Setting, and Participants: This decision analytical model simulated the opioid epidemic in the 4 states participating in the HCS (ie, Kentucky, Massachusetts, New York, and Ohio) from 2020 to 2026. Participants were a simulated population transitioning from opioid misuse to opioid use disorder (OUD), overdose, treatment, and relapse. The model was calibrated using 2015 to 2020 data from the National Survey on Drug Use and Health, the US Centers for Disease Control and Prevention, and other sources for each state. The model accounts for reduced initiation of medications for OUD (MOUDs) and increased OODs during the COVID-19 pandemic. Exposure: Increasing MOUD initiation by 2- or 5-fold, improving MOUD retention to the rates achieved in clinical trial settings, increasing naloxone distribution efforts, and furthering safe opioid prescribing. An initial 2-year duration of interventions was simulated, with potential sustainment for up to 3 additional years. Main Outcomes and Measures: Projected reduction in number of OODs under different combinations and durations of sustainment of interventions. Results: Compared with the status quo, the estimated annual reduction in OODs at the end of the second year of interventions was 13% to 17% in Kentucky, 17% to 27% in Massachusetts, 15% to 22% in New York, and 15% to 22% in Ohio. Sustaining all interventions for an additional 3 years was estimated to reduce the annual number of OODs at the end of the fifth year by 18% to 27% in Kentucky, 28% to 46% in Massachusetts, 22% to 34% in New York, and 25% to 41% in Ohio. The longer the interventions were sustained, the better the outcomes; however, these positive gains would be washed out if interventions were not sustained. Conclusions and Relevance: In this decision analytical model study of the opioid epidemic in 4 US states, sustained implementation of interventions, including increased delivery of MOUDs and naloxone supply, was found to be needed to reduce OODs and prevent deaths from increasing again.

Change in opioid and buprenorphine prescribers and prescriptions by specialty, 2016-2021.

BACKGROUND: Safer opioid analgesic prescribing and increasing use of medications for opioid use disorder, including buprenorphine, are strategies prioritized to reduce opioid overdose deaths in the United States. Specialty-specific trends in the number of prescribers and prescriptions for opioid analgesics and buprenorphine are not well characterized. METHODS: We used data from the IQVIA Longitudinal Prescription database for January 1, 2016 through December 31, 2021. We identified opioid and buprenorphine prescriptions based on NDC codes. We classified prescribers into one of 14 mutually exclusive specialty groups. We calculated the number of prescribers and number of prescriptions for opioids and buprenorphine by specialty and year. RESULTS: From 2016 to 2021, the total number of opioid analgesic prescriptions dispensed decreased by 32% to 121,693,308 and the number of unique opioid analgesic prescribers decreased 7% to 966,369. Over the same time period, the number of buprenorphine prescriptions dispensed increased 36% to 13,909,724 and unique number of buprenorphine prescribers increased 86% to 59,090. Across most specialties we identified a contraction in the number of opioid prescriptions dispensed and opioid prescribers and an expansion in the number of buprenorphine prescriptions dispensed. Among high-volume opioid prescribing specialties, the largest decrease in opioid prescribers was 32% among Pain Medicine clinicians. By 2021, Advanced Practice Practitioners overtook Primary Care clinicians as the highest volume buprenorphine prescribers. CONCLUSIONS: More work is needed to understand the impact of clinicians who stop prescribing opioids. While the trend in buprenorphine prescribing is encouraging, further expansion is warranted to meet the underlying need.

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.

Association of Early Opioid Withdrawal Treatment Strategy and Patient-Directed Discharge Among Hospitalized Patients with Opioid Use Disorder.

BACKGROUND: Medical hospitalizations for people with opioid use disorder (OUD) frequently result in patient-directed discharges (PDD), often due to untreated pain and withdrawal. OBJECTIVE: To investigate the association between early opioid withdrawal management strategies and PDD. DESIGN: Retrospective cohort study using three datasets representing 362 US hospitals. PARTICIPANTS: Adult patients hospitalized between 2009 and 2015 with OUD (as identified using ICD-9-CM codes or inpatient buprenorphine administration) and no PDD on the day of admission. INTERVENTIONS: Opioid withdrawal management strategies were classified based on day-of-admission receipt of any of the following treatments: (1) medications for OUD (MOUD) including methadone or buprenorphine, (2) other opioid analgesics, (3) adjunctive symptomatic medications without opioids (e.g., clonidine), and (4) no withdrawal treatment. MAIN MEASURES: PDD was assessed as the main outcome and hospital length of stay as a secondary outcome. KEY RESULTS: Of 6,715,286 hospitalizations, 127,158 (1.9%) patients had OUD and no PDD on the day of admission, of whom 7166 (5.6%) had a later PDD and 91,051 (71.6%) patients received some early opioid withdrawal treatment (22.3% MOUD; 43.4% opioid analgesics; 5.9% adjunctive medications). Compared to no withdrawal treatment, MOUD was associated with a lower risk of PDD (adjusted odds ratio [aOR] = 0.73, 95%CI 0.68-0.8, p < .001), adjunctive treatment alone was associated with higher risk (aOR = 1.13, 95%CI: 1.01-1.26, p = .031), and treatment with opioid analgesics alone was associated with similar risk (aOR 0.95, 95%CI: 0.89-1.02, p = .148). Among those with PDD, both MOUD (adjusted incidence rate ratio [aIRR] = 1.24, 95%CI: 1.17-1.3, p < .001) and opioid analgesic treatments (aIRR = 1.39, 95%CI: 1.34-1.45, p < .001) were associated with longer hospital stays. CONCLUSIONS: MOUD was associated with decreased risk of PDD but was utilized in < 1 in 4 patients. Efforts are needed to ensure all patients with OUD have access to effective opioid withdrawal management to improve the likelihood they receive recommended hospital care.

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.

Prior Incarceration Is Associated with Poor Mental Health at Midlife: Findings from a National Longitudinal Cohort Study.

BACKGROUND: People with mental illnesses and people living in poverty have higher rates of incarceration than others, but relatively little is known about the long-term impact that incarceration has on an individual's mental health later in life. OBJECTIVE: To evaluate prior incarceration's association with mental health at midlife. DESIGN: Retrospective cohort study PARTICIPANTS: Participants from the National Longitudinal Survey of Youth 1979 (NLSY79)-a nationally representative age cohort of individuals 15 to 22 years of age in 1979-who remained in follow-up through age 50. MAIN MEASURES: Midlife mental health outcomes were measured as part of a health module administered once participants reached 50 years of age (2008-2019): any mental health history, any depression history, past-year depression, severity of depression symptoms in the past 7 days (Center for Epidemiologic Studies Depression [CES-D] scale), and mental health-related quality of life in the past 4 weeks (SF-12 Mental Component Score [MCS]). The main exposure was any incarceration prior to age 50. KEY RESULTS: Among 7889 participants included in our sample, 577 (5.4%) experienced at least one incarceration prior to age 50. Prior incarceration was associated with a greater likelihood of having any mental health history (predicted probability 27.0% vs. 16.6%; adjusted odds ratio [aOR] 1.9 [95%CI: 1.4, 2.5]), any history of depression (22.0% vs. 13.3%; aOR 1.8 [95%CI: 1.3, 2.5]), past-year depression (16.9% vs. 8.6%; aOR 2.2 [95%CI: 1.5, 3.0]), and high CES-D score (21.1% vs. 15.4%; aOR 1.5 [95%CI: 1.1, 2.0]) and with a lower (worse) SF-12 MCS (-2.1 points [95%CI: -3.3, -0.9]; standardized mean difference -0.24 [95%CI: -0.37, -0.10]) at age 50, when adjusting for early-life demographic, socioeconomic, and behavioral factors. CONCLUSIONS: Prior incarceration was associated with worse mental health at age 50 across five measured outcomes. Incarceration is a key social-structural driver of poor mental health.

Relationships between places of residence, injury, and death: Spatial and statistical analysis of fatal opioid overdoses across Massachusetts.

Understanding the factors associated with where people who use opioids live, where their fatal overdoses occur, and where deaths are recorded can improve our knowledge of local risk environments and inform intervention planning. Through geospatial analyses of death certificate data between 2015 and 2017, we found that a majority of opioid-involved fatal overdoses in Massachusetts occurred at home. Age (adjusted odds ratio [AOR], 1.03; 95% confidence interval [CI], 1.02-1.04), living in a census tract with a higher percentage of crowded households (AOR, 1.04; 95% CI, 1.01-1.08), households without vehicles (AOR, 1.01; 95% CI, 1.00-1.02), and Hispanic ethnicity (AOR, 0.56; 95% CI, 0.42-0.74) were independently associated with fatal overdose at home. Using geographically weighted regression, we identified locations where these associations were stronger and could benefit most from home-based and culturally sensitive overdose prevention efforts, including expanded overdose education and naloxone distribution.