Faculty Bibliography

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.

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.

BACKGROUND/UNASSIGNED:We investigated whether the associations of state medical and recreational cannabis legalization (MCL, RCL enactment) with increasing prevalence of Cannabis Use Disorder (CUD) differed among patients in the United States (US) Veterans Health Administration (VHA) who did or did not have common psychiatric disorders. METHODS/UNASSIGNED:Electronic medical record data (2005-2022) were analyzed on patients aged 18-75 with ≥1 VHA primary care, emergency department, or mental health visit and no hospice/palliative care within a given year (sample sizes ranging from 3,234,382 in 2005 to 4,436,883 in 2022). Patients were predominantly male (>80%) and non-Hispanic White (>60%). Utilizing all 18 years of data, CUD prevalence increases attributable to MCL or RCL enactment were estimated among patients with affective, anxiety, psychotic-spectrum disorders, and Any Psychiatric Disorder (APD) using staggered difference-in-difference (DiD) models and 99% Confidence Intervals (CIs), testing differences between patient groups with and without psychiatric disorders via non-overlap in the 99% CIs of their DiD estimates. FINDINGS/UNASSIGNED:Among APD-negative patients, CUD prevalence was <1.0% in all years, while among APD-positive patients, CUD prevalence increased from 3.26% in 2005 to 5.68% in 2022 in no-CL states, from 3.51% to 6.35% in MCL-only states, and from 3.41% to 6.35% in MCL/RCL states. Among the APD group, DiD estimates of MCL-only and MCL/RCL effects were modest-sized, but the lower bound of the 99% CI for the DiD estimate for MCL-only and MCL/RCL effects was larger than the upper bound of the 99% CI among the no-APD group, indicating significantly stronger MCL-only and MCL/RCL effects among patients with APD. Results were similar for MCL-only and MCL/RCL effects among disorder-specific groups (depression, post-traumatic stress disorder [PTSD], anxiety or bipolar disorders) and for MCL/RCL effects among patients with psychotic-spectrum disorders. INTERPRETATION/UNASSIGNED:Cannabis legalization contributed to greater CUD prevalence increases among patients with psychiatric disorders. However, modest-sized DiD estimates suggested operation of other factors, e.g., commercialization, changing attitudes, expectancies. As cannabis legalization widens, recognizing and treating CUD in patients with psychiatric disorders becomes increasingly important. FUNDING/UNASSIGNED:This study was supported by National Institute on Drug Abuse grant R01DA048860, the New York State Psychiatric Institute, and the VA Centers of Excellence in Substance Addiction Treatment and Education.

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.

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.

IMPORTANCE/UNASSIGNED:In the context of the US opioid crisis, factors associated with the prevalence of opioid use disorder (OUD) must be identified to aid prevention and treatment. State medical cannabis laws (MCL) and recreational cannabis laws (RCL) are potential factors associated with OUD prevalence. OBJECTIVE/UNASSIGNED:To examine changes in OUD prevalence associated with MCL and RCL enactment among veterans treated at the Veterans Health Administration (VHA) and whether associations differed by age or chronic pain. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:Using VHA electronic health records from January 2005 to December 2022, adjusted yearly prevalences of OUD were calculated, controlling for sociodemographic characteristics, receipt of prescription opioids, other substance use disorders, and time-varying state covariates. Staggered-adoption difference-in-difference analyses were used for estimates and 95% CIs for the relationship between MCL and RCL enactment and OUD prevalence. The study included VHA patients aged 18 to 75 years. The data were analyzed in December 2023. MAIN OUTCOME AND MEASURES/UNASSIGNED:International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) or International Statistical Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) OUD diagnoses. RESULTS/UNASSIGNED:From 2005 to 2022, most patients were male (86.7.%-95.0%) and non-Hispanic White (70.3%-78.7%); the yearly mean age was 61.9 to 63.6 years (approximately 3.2 to 4.5 million patients per year). During the study period, OUD decreased from 1.12% to 1.06% in states without cannabis laws, increased from 1.13% to 1.19% in states that enacted MCL, and remained stable in states that also enacted RCL. OUD prevalence increased significantly by 0.06% (95% CI, 0.05%-0.06%) following MCL enactment and 0.07% (95% CI, 0.06%-0.08%) after RCL enactment. In patients aged 35 to 64 years and 65 to 75 years, MCL and RCL enactment was associated with increased OUD, with the greatest increase after RCL enactment among older adults (0.12%; 95% CI, 0.11%-0.13%). Patients with chronic pain had even larger increases in OUD following MCL (0.08%; 95% CI, 0.07%-0.09%) and RCL enactment (0.13%; 95% CI, 0.12%-0.15%). Consistent with overall findings, the largest increases in OUD occurred among patients with chronic pain aged 35 to 64 years following the enactment of MCL and RCL (0.09%; 95% CI, 0.07%-0.11%) and adults aged 65 to 75 years following RCL enactment (0.23%; 95% CI, 0.21%-0.25%). CONCLUSIONS AND RELEVANCE/UNASSIGNED:The results of this cohort study suggest that MCL and RCL enactment was associated with greater OUD prevalence in VHA patients over time, with the greatest increases among middle-aged and older patients and those with chronic pain. The findings did not support state cannabis legalization as a means of reducing the burden of OUD during the ongoing opioid epidemic.

Policy Points We can leverage data science and artificial intelligence to inform state and local resource allocation for overdose prevention. Data science and artificial intelligence can help us answer four questions: (1) What is the impact of laws on access to interventions and overdose risk? (2) Where should interventions be targeted? (3) Which types of demographic subgroups benefit the most and the least from interventions? and (4) Which types of interventions should they invest in for each setting and population? Advances in data science and artificial intelligence can accelerate the pace at which we can answer these critical questions and help inform an effective overdose prevention response.