Faculty Bibliography

BACKGROUND:Take-home methadone (THM) flexibility has increased since 2020, representing innovation in opioid use disorder treatment. There are no established approaches to measuring THM using insurance claims data. We proposed and validated candidate measures of THM. METHODS:Using 2020 Medicaid data from 4 states, we constructed treatment episodes for enrollees aged 18-64. Episodes started after July 1, 2020 and lasted at least 60 days. We labelled individuals as receiving THM if they received ≥6 consecutive days of THM in their 2nd month of treatment, as defined by presence of claims with a modifier code indicating THM (the "gold-standard" indicator). We defined 4 candidate indicators of THM based on intervals between in-clinic methadone administrations. We assessed performance of each candidate indicator against the gold-standard. We assessed the extent to which between-program variation explained total variation in measured THM. RESULTS:The study sample included 4836 episodes for 4801 individuals. THM was present in 14 % of episodes. Sensitivity of candidate indicators ranged from 65 to 100 %, with the most sensitive being an indicator that was true if any two adjacent in-clinic service dates had a gap of ≥7 days. Specificity ranged from 80 to 96 %, with the most specific measure being one requiring 2 consecutive intervals of ≥7 days that were of the same length. Between-program variation explained 38.6-48.3 % of variation in THM receipt. CONCLUSIONS:Two indicators of THM using Medicaid data presented excellent performance when evaluated against a gold-standard indicator. Our approach can be used to assess uptake and outcomes of THM.

BACKGROUND:Neighborhood-level overdose risk may vary over time. In Rhode Island, we developed and validated a machine learning model to identify the 20 percent of census block groups (CBGs) at the highest predicted risk of future overdose death. We updated this model periodically between November 2021 and August 2024 to generate six sets of predictions. This study aims to characterize the trajectory of each CBG's predicted overdose risk over time across these six periods. METHODS:In each prediction period, CBGs were designated as "high risk" or not designated as "high risk" based on our model's 20 percent predicted overdose risk threshold. We implemented sequence analysis to describe unique trajectories in each CBG's risk designation over each prediction period. We then calculated optimal matching distances to estimate dissimilarity between each pair of trajectories and applied agglomerative hierarchical clustering to group similar trajectories. RESULTS:The 809 CBGs included in this study followed 60 unique trajectories in predicted overdose risk designation over the six prediction periods. Clustering of trajectories favored a solution with five trajectory groups. Most CBGs (73.4 %) were rarely or never designated as "high risk", 7.9 % of CBGs were always designated as "high risk", and the remaining 18.7 % were designated as "high risk" in multiple prediction periods, represented by trajectory groups with different patterning over time. CONCLUSIONS:Given the substantial variability in which CBGs were at highest overdose risk over time, dynamic machine learning predictions may inform harm reduction resource allocation by identifying neighborhoods with emerging needs.

IMPORTANCE/UNASSIGNED:There has been a national increase in fatal and nonfatal overdoses involving stimulants, and 4.5 million US individuals meet criteria for stimulant use disorder (UD), with the highest prevalence in young adults. However, limited information exists on trends in diagnosed stimulant UD. OBJECTIVE/UNASSIGNED:To estimate trends in the proportion of adolescent and young adult Medicaid enrollees diagnosed with a stimulant UD from 2001 to 2020. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:A repeated cross-sectional study (2001-2020) was conducted using administrative health care claims data from Medicaid (public insurance program in US). Publicly insured adolescents (aged 13-17 years) and young adults (aged 18-24 or 25-29 years) from 42 US states were included. Data were analyzed from January 2025 to July 2025. MAIN OUTCOMES AND MEASURES/UNASSIGNED:Stimulant UD was defined as an inpatient or outpatient International Classification of Diseases diagnosis code in the year, with cocaine UD and noncocaine psychostimulant UD evaluated separately. The annual proportion with stimulant UD diagnoses was stratified by age group, sex, race and ethnicity, and presence of attention-deficit/hyperactivity disorder (ADHD) diagnosis or stimulant prescription in the year. Differences comparing 2001 with 2020 were summarized. Characteristics of those diagnosed with stimulant UD in 2020 were described. RESULTS/UNASSIGNED:The sample included 5.7 million (2001) to 16.1 million Medicaid enrollees (2020) per year; in 2020, 54.2% were female, and 7.1 million were adolescents. From 2001 to 2020, the proportion diagnosed with noncocaine psychostimulant UD increased from 0.09% to 0.49% (prevalence ratio [PR], 5.47 [95% CI, 5.20-5.75]) in those aged 18 to 24 years, from 0.13% to 1.63% (PR, 12.55 [95% CI, 11.83-13.31]) for ages 25 to 29 years, and from 0.10% to 0.91% among young adults aged 18 to 29 years. Among adolescents, the proportion diagnosed with noncocaine psychostimulant UD varied between 0.03% and 0.07%. The proportion diagnosed with cocaine UD was stable in young adults (range, 0.17%-0.34% [18-24 years] and 0.53%-0.79% [25-29 years]) and declined in adolescents (from 0.04% to 0.01%). Cocaine and noncocaine psychostimulant UD diagnoses were 2 to 4 times more common in patients with an ADHD diagnosis or stimulant prescription. Most patients diagnosed with a stimulant UD in 2020 were also diagnosed with a mental health disorder (68%-82%) or other substance UD (72%-78%). CONCLUSIONS AND RELEVANCE/UNASSIGNED:The prevalence of noncocaine psychostimulant UD diagnoses in young adult Medicaid patients increased over the last 2 decades, potentially associated with an increasing use of prescription and illicit stimulants along with increased clinical detection. These trends raise concerns given recent rises in stimulant-involved overdose fatalities and stress the need for evidence-based stimulant UD treatments for young people.

With the overdose crisis continuing to pose significant challenges in North America, harm reduction strategies are critical for public health systems to reduce mortality and morbidity. Despite the considerable strides in harm reduction research, high-quality evidence for decision-making is limited. This is compounded by a variation in reported outcomes, drug supply, administration changes, and policy and social impacts, which further challenge researchers and practitioners in their efforts to implement effective, nimble harm reduction interventions. Adoption of common data elements (CDEs) and common outcome measures (COMs) helps researchers standardize and enhance data collection and outcome reporting, ultimately improving the comparability and generalizability of research findings. To accelerate the pace and use of CDEs, members of the NIDA HEAL Research on Interventions for Stability and Engagement (RISE) engaged in prospective semantic harmonization and consensus on CDEs and COMs using a rigorous pragmatic Delphi community informed approach. This process resulted in a set of CDEs and COMs that standardized data collection and reporting across 10 harm reduction research projects. This paper describes this process and presents the derived CDEs and COMs, along with key considerations, challenges encountered, and lessons learned.

Between May 2020 and December 2021, there were 159,872 drug overdose deaths in the US. Higher eviction rates have been associated with higher overdose mortality. Amid the economic turmoil caused by the COVID-19 pandemic, 43 states and Washington, DC, implemented eviction moratoria of varying durations. These moratoria reduced eviction filing rates, but their impact on fatal drug overdoses remains unexplored. We evaluated the effect of these policies on county-level overdose death rates by focusing on the dates the state eviction moratoria were lifted. We obtained mortality data from NCHS and eviction moratoria dates from the COVID-19 US State Policy Database. We employed a longitudinal targeted minimum-loss-based estimation with Super Learner to flexibly estimate the average treatment effect (ATE) of never lifting the moratoria. Lifting state eviction moratoria was associated with a 0.14 per 100,000 higher rate of monthly overdose mortality (95%CI: -0.03, 0.32), although confidence intervals were wide and included zero. Eviction moratoria may not be sufficient to prevent overdose mortality during crises such as the COVID-19 pandemic.

BACKGROUND:Practical and motivational barriers can deter people from engaging in substance use disorder (SUD) treatment, even those who seek treatment. Care navigation is a psychosocial intervention that seeks to facilitate patients' timely access to care by identifying and intervening upon barriers. Few trials have tested the effectiveness of care navigation when embedding in real-world healthcare, and no trials have studied the process of implementing care navigation into clinical practice. This protocol describes a study that will evaluate whether care navigation can increase treatment engagement among patients seeking SUD treatment. METHODS:The Addressing Barriers to Care for Substance Use Disorder (ABC-SUD) study is a hybrid type I cluster-randomized effectiveness-implementation trial. It is conducted in a mental health access center of an integrated healthcare system in Washington state. Within this center, licensed mental health clinicians assess patient needs and use shared decision-making to establish SUD treatment plans for patients (usual care). This study tests whether an added care navigation intervention can improve patient engagement in SUD treatment. Care navigation begins after a treatment plan is made and provides up to 7 weeks of support focused on enhancing patient motivation to initiate and engage in treatment, problem-solving barriers (e.g., transportation logistics), and accommodating patient preferences (e.g., preferred language of care, cultural preferences). This trial uses a two period, two sequence crossover design. Clinicians are randomized to offer care navigation to patients during the first or second study period (i.e., clinicians are assigned to an initial study condition and switch conditions halfway through the trial). Care navigation is implemented with several strategies: leadership engagement, clinical workflow specifications, electronic health record (EHR) tools, training, performance improvement, and electronic learning collaborative. The primary outcome-obtained from EHRs and insurance claims-is engagement in SUD treatment, defined as ≥3 SUD treatment visits within 48 days of a treatment plan. This study uses standardized measures of implementation climate and outcomes to examine mechanisms with which the intervention strategies exert their impact on implementation and effectiveness outcomes. DISCUSSION/CONCLUSIONS:The ABC-SUD study will test whether care navigation improves SUD treatment engagement while concurrently generating information about its implementation in healthcare. TRIAL REGISTRATION/BACKGROUND:This study was prospectively registered at www. CLINICALTRIALS/RESULTS:gov (NCT06729957) on December 9, 2024.

The social determinants of health (SDOH) framework has proven useful for research and practice in addressing the social causes of many health outcomes. However, its limitations may restrict its value as the world undergoes rapid ecological and social change. We argue that SDOH does not adequately incorporate rapidly changing or "far upstream" social processes (particularly social movements), the dialectics of social conflict and creative social innovation, or bidirectional causation. Ecosocial theory addresses some of these issues, yet dialectical frameworks offer additional insights during periods of rapid social change and disruption. The implications for research methods and practice are discussed. (Am J Public Health. Published online ahead of print September 18, 2025:e1-e9. https://doi.org/10.2105/AJPH.2025.308239).

BACKGROUND/UNASSIGNED:Understanding sex differences in the effects of cannabis legalization and increasing risk for cannabis use disorder (CUD) is important. We hypothesized that from 2005 to 2019, increases in CUD prevalence due to state medical or recreational cannabis laws (MCL; RCL) would differ among male and female veterans treated at the U.S. Veterans Health Administration (VHA), with greater increases among females. METHODS/UNASSIGNED:Data obtained through the VHA Corporate Data Warehouse included veterans 18-75 years with ≥1 VHA primary care, emergency department, or mental health visit in a given year, 2005-2019. Staggered-adoption difference-in-difference analyses were used to estimate the role of MCL and RCL on trends in CUD diagnostic prevalence, fitting a linear binomial regression model with fixed effects for state and categorical year, time-varying cannabis law status, state-level sociodemographic covariates, patient-level age group (18-35, 36-64, 65-75 years), race and ethnicity. RESULTS/UNASSIGNED:CUD prevalences increased in both sexes. CUD increased more in states enacting MCL and RCL than in states that did not enact CL. However, no CUD prevalence increases attributable to the change from no-CL to MCL-only or MCL to RCL differed significantly by sex, with one exception (greater in males aged 35-64). CONCLUSIONS/UNASSIGNED:Increases in CUD prevalence following MCL or RCL enactment were greater than in states with no-CL, but generally did not show differences by sex. The increases in CUD prevalence occurring for males and females throughout the study years indicate the need for cannabis use screening by medical providers and the importance of offering evidence-based treatments for CUD.

Following federal regulatory changes during the COVID-19 pandemic, Rhode Island expanded methadone access for opioid treatment programs (OTPs) in March 2020. The policy, which permitted take-home dosing for patients, contrasted with longstanding restrictions on methadone. This study used patient-level OTP admission and discharge records to compare six-month retention before and after the policy change. We conducted a retrospective cohort study of 1,248 patients newly admitted to OTPs between March 18 and June 30 of 2019 (pre-policy) and 2020 (post-policy). We used logistic regression to estimate associations with retention before and after the policy and used a machine learning approach, the Heterogeneous Treatment Effect (HTE)-Scan, to explore heterogeneity in retention across subgroups. Overall, we found no change in retention following the policy, with an adjusted OR of 1.08 (95% CI: 0.80-1.45) and adjusted RR of 1.03 (0.90-1.18). Using HTE-Scan, we identified two subgroups with significantly increased retention above the overall cohort: (1) patients with below high school education and past-month arrest and (2) male, non-Hispanic white or Hispanic/Latino patients reporting heroin or fentanyl use with past-month arrest. We identified no subgroups with significantly decreased retention. Collectively, findings suggest that expanded methadone access may benefit vulnerable populations without harming overall retention.

PURPOSE/OBJECTIVE:In this article, we studied whether pathways in substance use disorder (SUD) treatment differ among people admitted to residential versus ambulatory settings. METHODS:We analyzed a retrospective cohort of 84,755 adults (ages ≥ 18) in Chilean SUD treatment during 2010-2019, creating a comparable sample of 11,226 pairs in ambulatory and residential treatment through cardinality matching. We used a nine-state multistate model, stratifying readmissions by baseline treatment outcome (i.e., completion vs. noncompletion) from admission to the third readmission. We estimated transition probabilities and lengths of stay in states at three-month, one-year, three-year, and five-year follow-ups. Sensitivity analyses tested different model specifications and estimated E-values. RESULTS:Patients in residential settings (vs. ambulatory) had greater treatment completion probabilities (difference at three months; 3.4% [95% CI: 2.9%, 3.9%]), and longer treatment retention (e.g., 1.6 days longer at three months, 95% CI: 0.8, 2.3). Patients in residential vs. ambulatory settings had higher first readmission probabilities regardless of baseline treatment outcome (e.g., three-month difference: 5.7% if completed baseline [95% CI: 4.4%, 7.0%] and 8.0% if did not complete baseline [95% CI: 6.7, 9.3%]). Third readmission probabilities were higher only among patients in residential settings with an incomplete baseline treatment (at least 3.7%; 95% CI: 0.2%, 7.3% at 1-year). CONCLUSION/CONCLUSIONS:Patients in residential settings at baseline were more likely to experience a second treatment and a third readmission among patients with incomplete treatments. Findings underscore the importance of completing initial SUD treatments to reduce readmissions. Residential treatments might require additional strategies to prevent readmissions.