Faculty Bibliography

INTRODUCTION/BACKGROUND:A 2021 federal rule permits opioid treatment programs (OTPs) to provide methadone through mobile medication units (MMUs), creating an opportunity to provide medication for people in residential care facilities. We used simulations to quantify the potential of MMUs to expand methadone access to people residing in residential substance use treatment facilities (RTF), skilled nursing facilities (SNF), and nursing facilities (NF) in New York State under different scenarios. METHODS:For each facility (RTF, SNF, and NF), a need score was created using three items: facility opioid use disorder (OUD) population, driving distance to nearest OTP, and county overdose mortality rate. We then demonstrated potential patient reach following the launch of 50 hypothetical MMUs making one stop per day to the highest need facilities. In refinements, we examined three additional scenarios involving more daily stops and prioritizing rural areas. RESULTS:Our sample included 3214 people with OUD estimated to be housed in 1052 facilities in New York, with the majority in RTFs (51.5%). The demonstrated percentage of OUD population served ranged from 23.5% to 35.8%, and the percentage of facilities served ranged from 23.8% to 37.4%. Each scenario reached a large percentage of rural facilities (73-76%). Prioritizing rural facilities decreased the proportion of OUD population served (10% reduction) but did not substantially increase driving time. Allowing multiple stops increased the proportion of OUD population served (32-36% vs. 24-26%). CONCLUSIONS:Using methods based on location information and spatial relationships, state officials can develop priorities and assess tradeoffs of MMU deployment and distribution strategies.

BACKGROUND:Overdose rates in the U.S. rose dramatically during the COVID-19 pandemic. Well-documented racial and sociodemographic inequities in the impact of the pandemic suggest the potential for similar inequities for overdose. Our objective was to identify subgroups of New York State Medicaid enrollees who experienced the greatest increases in non-fatal opioid overdose risk following onset of the COVID-19 pandemic. METHODS:Data are from a retrospective cohort of 1,021,889 people enrolled in New York State Medicaid from 2019-2020. To identify subgroups with the greatest increased risk of non-fatal overdose following onset of the COVID-19 pandemic, we used Heterogeneous Treatment Effect (HTE)-Scan, a novel machine learning method developed for accurate and computationally efficient discovery of heterogeneous treatment effects in complex data. RESULTS:In the total sample, risk of non-fatal opioid overdose increased 22% after onset of the pandemic. We also identified two subgroups with elevated risk relative to the total sample: subgroup 1 (Black and Hispanic males aged 45-64 years old with no baseline documentation of opioid use disorder (OUD); N = 53,065) and subgroup 2 (people aged 45-64 years old with documented aged/blind/disabled status and no baseline documentation of OUD; N = 73,694). These subgroups experienced a 54% and 57% increase in non-fatal overdose risk, respectively. CONCLUSIONS:We estimated heterogeneous effects of onset of the COVID-19 pandemic on non-fatal overdose, with elevated risks estimated for older working-aged, structurally disadvantaged adults without documented OUD. These findings illustrate the importance of structural factors in driving heterogeneous risk of overdose following complex social events.

INTRODUCTION/BACKGROUND:The criminal legal system (CLS) is the most common treatment referral source for adolescent cannabis-related problems. Understanding trends in racial/ethnic disparities of CLS-referred treatment could help identify groups in need of additional supports. METHODS:This observational study used repeated cross-sectional data from the Treatment Episode Dataset-Admissions 2010-2022 and Census population denominators to calculate annual specialty CLS-referred cannabis-related treatment admission rates per 10,000 adolescents ages 12-17 by race and ethnicity. Joinpoint models examined trends in CLS-referred treatment rates by race and ethnicity, and group differences in the percent of cannabis admissions referred through the CLS were described. RESULTS:Adolescent cannabis CLS-referred treatment rates decreased overall by 81.6% from 2010 (16.93/10,000) to 2022 (3.11/10,000). Joinpoint models identified different turning points when significant reductions started by race and ethnicity. Compared to non-Hispanic White adolescents across all years, CLS-referred treatment admission rates were higher for American Indian/Alaska Native non-Hispanic adolescents (rate difference [RD]=19.53 95% CI=7.91-31.15) and Black non-Hispanic adolescents (RD=10.14, 95% CI=0.19, 20.08), but not other groups. American Indian/Alaska Native and Black adolescents had higher CLS-referred treatment than white adolescents in 2010 in both absolute and relative terms; differences with wider confidence intervals in 2022 were no longer statistically significant. CONCLUSIONS:CLS-referred cannabis-related treatment admissions decreased substantially across groups over time. High CLS-referral rates among American Indian/Alaska Native and Black non-Hispanic adolescents highlight the importance of distinguishing heterogeneous racial/ethnic groups. Targeted structural interventions are needed to increase non-CLS-related referrals and address cannabis-related treatment gaps in a changing cannabis policy environment.

BACKGROUND:Individuals with opioid use disorder (OUD) may experience fewer barriers to treatment following incarceration if offered in-jail medications for OUD (MOUD). We aimed to identify care trajectories of community OUD treatment after incarceration and examine the association between receiving in-jail MOUD and experiencing specific community treatment trajectories. METHODS:This retrospective cohort study using matched New York City (NYC) health care administrative data included adults with OUD incarcerated on or after May 2011 and discharged during 2014-2017. We defined states of community OUD treatment at the weekly level over one year following index jail discharge and performed state sequence analysis (SSA) to identify trajectories of treatment after jail and assessed the influence of receiving in-jail MOUD on treatment trajectories. RESULTS:Of 14,923 eligible individuals, 26.2% received in-jail MOUD. SSA identified eight clusters of community care trajectories: continuous methadone treatment (9.7%), methadone treatment discontinuation (3.7%), methadone treatment and reincarceration (6.7%), methadone treatment initiation (4.8%), continuous reincarceration (3.5%), short reincarceration with little community treatment (20.3%), long reincarceration with little community treatment (7.0%), and no community OUD treatment or reincarceration (44.5%). Receiving in-jail MOUD was associated with belonging to the continuous methadone treatment cluster compared to the no community OUD treatment or reincarceration cluster (adjusted OR: 12.5, 95% CI: 9.9-15.7). CONCLUSION/CONCLUSIONS:We identified eight unique patterns of community OUD treatment after jail release. Receipt of in-jail MOUD was associated with belonging to the continuous methadone treatment cluster. These findings suggest that provision of in-jail MOUD could improve methadone uptake in the community.

BACKGROUND:Methadone is a highly effective, strictly regulated medication to treat opioid use disorder. COVID-19 flexibilities allowed for up to 28 days of take-homes versus daily travel to clinics for observed dosing, but receiving take-homes differed widely across clinics and individuals. We examined the relationship between state take-home policies and social vulnerability on take-home methadone receipt and days' supply. METHODS:Data were from the VOICES study, a telephone survey conducted between 1/2023-8/2024 of people who use drugs from Wisconsin, Michigan, New Mexico, and New Jersey. We estimated average marginal effects of state methadone policy (flexibility-adoption vs non-adoption) on methadone take-home receipt and days' supply. Models were fully adjusted for individual sociodemographic characteristics. RESULTS:Most participants were recruited from flexibility-adoption states (n = 285/428, 67%). Over half received take-home methadone (65%; average 3.1 days' supply, SD 6.2); 19% of take-home recipients (n = 54) received ≥3 days' supply. Take-home receipt was higher for participants in flexibility-adoption states (AME 0.52, p < 0.0001). Receiving ≥3 days' supply was lower in people reporting unemployment (vs. employment, AME -0.23, p = 0.0032) and past 30-day drug use (vs. no drug use, AME -0.23, p = 0.0014). CONCLUSIONS:State take-home policy was most strongly associated with take-home methadone receipt. Receiving longer days of take-home supplies remains rare. Take-home eligibility guidelines should be established and consider potential social vulnerability factors to daily on-site dosing.

OBJECTIVE:The CDC recommends initiating opioids for pain treatment at the lowest effective dose and duration. We examine how interactions between dose, duration, and other medication factors (e.g., drug type) influence opioid use disorder (OUD) risk-a gap not considered by CDC guidelines. SUBJECTS/METHODS:Using Medicaid claims data (2016-2019) from 25 states, we analyzed opioid-naïve adults, newly diagnosed with musculoskeletal pain who initiated opioids within three months of diagnosis. A 6-month washout confirmed no prior opioid exposure or musculoskeletal diagnosis. METHODS:Initial opioids were categorized by "dose-days supplied" (low [>0-20 mg MME] to very high [>90 mg MME] dose, and short [1-7 days] to moderate [>7-30 days] supply), and by opioid type; physical therapy (PT) sessions were also recorded. Using Poisson regression models, we estimated the OUD risk associated with dose-days categories, adjusting for baseline demographics, clinical characteristics, and medications. We separately examined opioid dose-days and PT, and assessed PT's moderating effect on dose-days' impact. RESULTS:Among 30,536 patients, half initiated opioids at 20-50 MME for 1-7 days, and 20% received PT. OUD risk was 2-3 times higher for opioids initiated for >7-30 days compared to 1-7 days across doses, and 5.5 times higher for opioids initiated for >7-30 days at > 90 MME versus 1-7 days at < 20 MME. PT alone, neither affected OUD risk nor mitigated the increased risk from longer or higher-dose opioids. CONCLUSIONS:Our findings support the need for careful opioid prescribing and alternative pain management strategies, as the observed associations between initial prescription characteristics and OUD were not mitigated by adjunctive PT. PERSPECTIVE/CONCLUSIONS:This study demonstrated that initial opioid prescriptions of 7-30 days, especially above 90 MME/day, increased OUD risk in opioid-naïve patients with musculoskeletal pain; physical therapy did not mitigate the risk. Different opioids posed varied risks, even at the same dose and duration. Careful prescribing and alternative pain management are essential.

INTRODUCTION/UNASSIGNED:The COVID-19 pandemic had a devastating impact on people who use drugs (PWUD). Reductions in access to harm reduction tools and treatment services elevated rates of fatal overdose for many. We explore the mechanism through which these factors influenced the rise in overdose mortality during COVID-19 from the perspective of people with overdose encounters. METHODS/UNASSIGNED:We conducted in-depth, semi-structured, 60-minute telephone-based interviews with 43 overdose survivors and witnesses between January and May 2022. Participants were from nine states (ME, MI, MD, NJ, NY, NM, PA, TN, WV) and Washington, DC. Data were analyzed thematically following the Continuum of Overdose Risk. RESULTS/UNASSIGNED:Most reported worsening mental health and increasing substance use during COVID-19. Isolation due to quarantining measures, coping behaviors, despair, and traumatic grief contributed to resumptions in drug use and risky behaviors. Some discussed how these stressors, combined with the rapid availability of financial resources led to increased use. Participants also attributed increased overdose risk to the increasingly toxic drug supply and stifled harm reduction access. Accounts of methadone treatment varied, however several expressed inconsistent access to take-home methadone, potentially contributing to resumed use. CONCLUSIONS/UNASSIGNED:Numerous micro- and macro-social factors, as well as the drug supply and treatment disruptions contributed to the acceleration in overdose risk. Increased funding and policy reform are needed to reduce overdose mortality in future public health emergencies, including improving harm reduction and treatment service adaptations to fit the needs of clients, as well as upholding and expanding novel methadone treatment delivery models.

Despite robust evidence that medications for opioid use disorder (MOUD) reduce overdose and mortality, substantial care gaps remain following opioid-related hospital encounters. The opioid use disorder (OUD) Cascade of Care framework conceptualizes progression from identification to treatment initiation and retention, yet limited research has examined how real-world OUD treatment trajectories unfold, particularly across treatment episodes and multiple care settings. This paper describes an NIH-funded study protocol (1R01DA061367-01A1) to conduct a longitudinal observational study using linked administrative data across New York City to characterize OUD treatment trajectories following opioid-related hospital encounters. Using the OUD Cascade of Care framework, we will apply state sequence analysis to identify common patterns of OUD treatment engagement in the year following hospitalization, including transitions between treatment modalities and periods in and out of care. We will examine how care trajectories vary by individual and neighborhood characteristics, and assess associations between trajectories and key outcomes, including rehospitalization, overdose, and mortality. By applying novel data-driven longitudinal methods, this study will advance understanding of the complex, non-linear nature of OUD treatment engagement. Findings will inform health system and policy efforts to identify populations at elevated risk, hospital-based interventions, and opportunities to address gaps in care to reduce overdose-related harms.