Faculty Bibliography

CONTEXT/BACKGROUND:Predictive modeling can identify neighborhoods at elevated risk of future overdose death and may assist community organizations' decisions about harm reduction resource allocation. In Rhode Island, PROVIDENT is a research initiative and randomized community intervention trial that developed and validated a machine learning model that predicts future overdose at a census block group (CBG) level. The PROVIDENT model prioritizes the top 20th percentile of CBGs at highest risk of future overdose death over the subsequent 6-month period. In CBGs assigned to the trial intervention arm, these predictions are then displayed for partnering community organizations via an interactive mapping dashboard. OBJECTIVE:To evaluate whether CBGs prioritized by the PROVIDENT model were associated with increased user engagement via an online dashboard for fatal overdose forecasting and resource planning. DESIGN/METHODS:We estimated prevalence ratios using modified Poisson regression models, adjusted for CBG-level characteristics that may confound the relationship between model predictions and dashboard engagement. SETTING/METHODS:We used CBG-level data in Rhode Island (N = 809) from November 2021 to July 2024. INTERVENTION/METHODS:Our exposure of interest was whether each CBG was prioritized by the PROVIDENT model and shown as prioritized on the interactive mapping dashboard. MAIN OUTCOME MEASURE/METHODS:Our primary outcome was whether a dashboard user from any partnering community organization engaged (eg, clicked, interacted with dashboard elements, or completed assessment or planning surveys) with each CBG on the interactive mapping dashboard. RESULTS:After adjusting for previous model predictions and dashboard engagement, nonfatal overdose counts, and distribution of race and ethnicity, poverty, unemployment, and rent burden, dashboard users were 1.0 to 2.4 times as likely to engage with CBGs prioritized by the PROVIDENT model that were shown as prioritized on the dashboard as compared to CBGs that were prioritized by the PROVIDENT model that were blinded on the dashboard. CONCLUSIONS:Interactive mapping tools with predictive modeling may be useful to support community-based harm reduction organizations in the allocation of resources to neighborhoods predicted to be at high risk of future overdose death.

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.

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.

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.

UNLABELLED:Policy Points State "must-query" prescription drug monitoring programs (PDMPs) were associated with increased overdose deaths, suggesting these policies may have unintended consequences. Black and Hispanic populations experienced disproportionately higher increases in overdose deaths following must-query PDMP adoption, highlighting that these policies may contribute to health disparities. Addressing systemic inequities in health care access and substance use treatment may help supplement the effective components of PDMPs, ensuring that these policies reduce rather than exacerbate overdose deaths. CONTEXT/BACKGROUND:Despite recent declines in national overdose deaths, these reductions have not been equitably experienced. Black and Hispanic communities continue to face rising rates of opioid-related mortality, even as overdose death rates among White individuals have begun to decline. One of the most widely implemented policy responses to the overdose crisis has been the adoption of prescription drug monitoring programs (PDMPs), particularly "must-query" mandates requiring prescribers to consult the PDMP before issuing controlled substances. However, limited research has examined whether the impact of these mandates varies by race and ethnicity. METHODS:We used restricted-use National Vital Statistics System data from 2013 to 2020 to estimate county-level overdose mortality stratified by drug type and race and ethnicity. We categorized deaths as follows: (1) all drug overdoses, (2) all opioid overdoses, and (3) natural/semisynthetic opioid overdoses. Exposure to must-query mandates was modeled as the proportion of the prior year during which mandates were in effect. Using Bayesian spatiotemporal models with county random effects and spatial autocorrelation, we estimated relative rates (RRs) for each outcome overall and by race and ethnicity, adjusting for state policies and sociodemographic characteristics. FINDINGS/RESULTS:Must-query mandates were associated with increases in overdose deaths across all groups, with the largest relative increases among Hispanic (RR = 1.32, 95% credible interval [CrI]: 1.21-1.44) and Black individuals (RR = 1.23, 95% CrI: 1.14-1.33) compared with White individuals (RR = 1.14, 95% CrI: 1.10-1.19). These increases were also observed among Black and Hispanic individuals for natural/semisynthetic opioid overdoses. CONCLUSIONS:PDMP must-query mandates are not uniformly protective across racial and ethnic groups. Increases in overdose mortality following adoption, particularly among Black and Hispanic populations, underscore the need to evaluate drug policies through an equity lens and consider broader structural determinants of health that shape their effectiveness.

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.

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.