Faculty Bibliography

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.

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.

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.

BACKGROUND:Between 2022 and 2023, overdose mortality decreased among non-Hispanic (NH) white people but stayed the same or increased among people of colour in the USA. County racialised economic segregation may contribute to overdose mortality. METHODS:measures, one for higher-income NH white and lower-income black residents and another for higher-income NH white and lower-income Hispanic residents. Models included random effects for county, year and county-year interaction, and fixed effects for proportion male, proportion aged 25-44, land area, state and year. We estimated relative risk (RR) by quintile (least vs most privileged) and the difference in overdose mortality per 100 000 (RD) had all counties shifted to the risk of the most advantaged counties (Q5). RESULTS:Counties with the highest proportion of lower-income racially minoritised residents (Q1) had an increased RR of overdose deaths compared with Q5 counties, both overall (aRRs 1.64 (1.51-1.78); 1.40 (1.29-1.52)), and among subgroups. Had all counties experienced the risk of Q5 counties, we estimated an average reduction in overdose deaths overall (RDs per 100 000: -7.20 (-8.25 to -6.10); -6.37 (-7.38 to -5.25)) and among subgroups. CONCLUSION/CONCLUSIONS:County racialised economic segregation was associated with overdose mortality risk in 2018-2022. Investment in evidence-based strategies to reduce overdose risk in places experiencing harms related to racialised economic segregation is critical.

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.

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.

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.