Faculty Bibliography

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.

PURPOSE/UNASSIGNED:Understanding the types of individuals and their position in the network may improve interventions for people who inject drugs (PWID). METHODS/UNASSIGNED:From the Transmission Reduction Intervention Project (TRIP), which enrolled PWID and their contacts in Athens, Greece, from 2013 to 2015, we extracted the largest connected component of the network (i.e., the largest group of connected individuals) and identified members who were in the top quartile of the distribution for three network centrality measures: closeness, betweenness, and eigenvector. Using logistic regression, we evaluated associations between high centrality measures and individual sociodemographic characteristics and behaviors. We also varied the definition for high centrality. RESULTS/UNASSIGNED:Among 231 individuals, 80% were male and between the ages of 25-40 years. Over half of the individuals injected at least once per day, compared to less than daily. Individuals who injected at least once per day were more likely to have high closeness (odds ratio (OR) = 3.36; 95% confidence interval (CI) = 1.57, 8.42), high betweenness (OR = 2.22; 95% CI = 1.06, 4.67), and eigenvector centrality (OR = 4.50; 95% CI = 1.89,10.68). Individuals who engaged in sex without a condom were less likely to have high closeness (OR = 0.18; 95% CI = 0.07, 0.45) or eigenvector (OR = 0.19; 95% CI = 0.07, 0.49) centrality. CONCLUSIONS/UNASSIGNED:Individual characteristics and behaviors were associated with centrality and may impact an individual's position in the network. These associations could be useful in identifying important community members to engage as part of public health initiatives.

IMPORTANCE/UNASSIGNED:Since the start of the 21st century, more than 800 000 firearm deaths and more than 2 million firearm injuries have occurred in the US. All categories of firearm violence-homicide, suicide, unintentional-result in reverberating harms to individuals, families, communities, and society. The collective responsibility of society is to safeguard the health and safety of its members, including from firearm harms. The JAMA Summit on Firearm Violence convened 60 thought leaders from a wide array of disciplines to chart an innovations roadmap that will lead to substantial reductions in firearm harms by 2040. OBSERVATIONS/UNASSIGNED:The vision for 2040 is a country where firearm violence is substantially reduced and where all people and communities report feeling safe from firearm harms. The vision centers on practical solutions with an understanding of the country's constitutional protections for firearm ownership. Achieving the 2040 vision will require expansion of proven evidence-based strategies and the development of new, innovative approaches rooted in equity, accountability, and collective responsibility. Discussions centered on projecting a safer world, community violence interventions, technologic innovations, federal and state-level oversight of firearms, ethical considerations, and primordial prevention of firearm violence. The Summit charted a roadmap of 5 essential actions in the next 5 years to achieve this vision: (1) focus on communities and change fundamental structures that lead to firearm harms, (2) harness technological strengths responsibly, (3) change the narrative around firearm harms, (4) take a whole-government and whole-society approach, and (5) spark a research revolution on preventing firearm harms. CONCLUSIONS AND RELEVANCE/UNASSIGNED:A safer world will require investing in the discovery, implementation, and scaling of solutions that reduce firearm harms and center on the people and communities most affected by firearm violence.

Methadone treatment (MT) for opioid use disorder saves lives, but the US MT system has long been dominated by punitive policies and practices that make MT inaccessible, burdensome, and traumatic for patients. After generations without changes to methadone regulations, a confluence of circumstance-including the COVID-19 pandemic and an overdose crisis that has taken over a million lives-has begun to shift the MT advocacy and political landscape. This commentary describes the building of the "Liberate Methadone" movement; a grassroots effort led by people with lived and living experience with methadone, addiction clinicians, researchers, community leaders, and people with many of these identities. The Liberate Methadone movement is dedicated to building a more accessible, equitable MT system that prioritizes patient health, promotes dignity, and is grounded in evidence. We describe the experience of planning and hosting a national conference and generating proceedings with recommendations for needed incremental and structural reforms within the US MT system. The lessons learned from this movement can motivate others across clinical, research, and policy roles to partner with and learn from patient and community-led groups, guiding needed reforms within systems of care. It is through these joint efforts and listening to those directly impacted groups who have been left out of the conversation for far too long, that we can successfully reduce overdose and suffering, toward better health, dignity, and thriving in our communities.

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.

Amid the ongoing overdose crisis, U.S. lawmakers are considering policy reforms that could significantly change availability and accessibility of methadone treatment (MT) for opioid use disorder (OUD). However, uncertainty remains about which potential changes will lead to the greatest health benefits while minimizing unintended harms. In this protocol, we describe a planned NIH-funded study (R21DA061660) to simulate alternative MT delivery scenarios currently being considered in U.S. policy discussions, and estimate their impact on population-level rates of treatment initiation and retention and opioid overdose across different sociodemographic groups. We will use an agent-based model focused on 16 counties in NY State to simulate two alternative policy scenarios compared to the current status quo of opioid-treatment program (OTP) delivered MT: 1) office-based prescribing by addiction-certified providers with pharmacy and OTP dispensing; and 2) office-based prescribing by general practitioners with pharmacy and OTP dispensing. Agents will represent individuals with OUD and we will simulate access to MT based on alternative policy scenarios (e.g., locations of existing OTPs vs. provider offices and pharmacies). Probabilities of treatment initiation, retention, and opioid overdose will be informed by estimates from the scientific literature and administrative datasets from NY State. Multiple implementation scenarios will be considered to account for potential variation in adoption of office-based methadone by patients, providers, and pharmacies. To ensure relevance to directly impacted communities and policy makers, the study involves a collaboration between academic researchers and NY State government partners and relies on input from an Expert Advisory Board of people with lived and living experience with methadone, addiction medicine, and policy experts. Findings will be disseminated via a public dashboard. This study will inform ongoing policy discussions and shed light on the potential of researcher-policy partnerships to promote evidence-based policies that can reduce overdose and improve population health.

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.

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.

INTRODUCTION/BACKGROUND:The United States is facing an opioid use disorder (OUD) epidemic, marked by unprecedented overdose death rates. In New York State, synthetic opioids significantly contribute to the increasing overdose deaths, disproportionately impacting Black and Latinx communities. There is an urgent need to address issues related to equitable access to and the quality of care provided by substance use disorder (SUD) treatment programs. In light of this, the Quality Measurement and Management Research Center (QM2-RC) brought together an academic-government partnership to develop a person-centered quality measurement system and to assess its impact on a statewide treatment system that serves approximately 180,000 individuals per year. METHODS AND ANALYSIS/METHODS:The QM2-RC encompasses three interconnected projects (Project 1, 2, and 3) aimed at developing a quality management strategy and evaluating its impact on system performance across New York State. This report specifically focuses on Project 3, which involves a stepped-wedge trial with 35 clinics receiving a quality management intervention that includes performance coaching. This intervention will be compared to a treatment-as-usual (TAU) condition for clinics not participating in the trial. Administrative data will be utilized to monitor outcomes over four years. The coaching intervention, guided by the Integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) model, emphasizes interpreting quality measures and applying insights to enhance care. Coaches will provide support on data utilization, patient-centered care, harm reduction strategies, and the use of patient monitoring tools. The trial aims to evaluate clinic staff and leadership attitudes, experiences, and behaviors through surveys, semi-structured interviews, and external facilitator notes. Primary clinic outcomes will be assessed through adverse events, decreased clinic rates of substance use related emergency department visits and hospitalizations as well as mortality among patients within the first 12 months after admission to treatment after adjusting for individual and community level characteristics. This study is being developed over a multi-year period and will be informed by a mixed-methods approach incorporating multiple data sources, qualitative interviews, patient and clinic surveys. The study is being conducted in partnership with New York State Office of Addiction Services and Supports (OASAS) and will be informed by input from patient, providers, health insurers, family members and local governing units. DISCUSSION/CONCLUSIONS:Project 3 of the QM2 study specifically targets key barriers in measuring the quality of SUD treatment, including technological limitations, unvalidated measures, workforce data literacy, and concerns about fairness in assessing clinical complexity. Through the implementation of a stepped-wedge trial involving 35 clinics, the project aims to develop new quality measures, offer performance feedback, and engage clinic leadership and staff in efforts to improve practices. The ultimate goal of Project 3 is to overcome these barriers, promote person-centered care, and improve SUD treatment practices across New York State.

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).