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.

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.

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.

BACKGROUND/UNASSIGNED:Rates of cannabis use disorder (CUD) have increased disproportionately among Veterans Administration (VA) patients with psychiatric disorders compared to patients with no disorder. However, VA patient samples are not representative of all U.S. adults, so results on disproportionate increases in CUD prevalence could have been biased. To address this concern, we investigated whether disproportionate increases in the prevalence of cannabis outcomes among those with psychiatric disorders would replicate in nationally representative samples of U.S. adults. METHODS/UNASSIGNED: = 36,309). Outcomes were any past-year non-medical cannabis use, frequent non-medical use (≥3 times weekly), and DSM-IV CUD. Psychiatric disorders included mood, anxiety and antisocial personality disorders. Logistic regression was used to generate predicted prevalences of the outcomes, prevalence differences calculated and additive interactions compared differences between those with and without psychiatric disorders. RESULTS/UNASSIGNED:Cannabis outcomes increased more among those with psychiatric disorders. The difference in prevalence differences included any past-year non-medical cannabis use, 2.45% (95%CI = 1.29-3.62); frequent non-medical cannabis use, 1.58% (95%CI = 0.83-2.33); CUD, 1.40% (95%CI = 0.58-2.21). For most specific disorders, prevalences increased more among those with the disorder. CONCLUSIONS/UNASSIGNED:In the U.S. general population, rates of cannabis use and CUD increased more among adults with psychiatric disorders than other adults, similar to findings from VA patient samples. Results suggest that although VA patients are not representative of all U.S. adults, findings from this important patient group can be informative. Greater clinical and policy attention to CUD is warranted for adults with psychiatric disorders.

INTRODUCTION/UNASSIGNED:Homelessness in the United States increased every year since 2016, with a 38% increase from 2023 to 2024. Much of the increase is attributable to rising home and rent costs, economic hardship caused by the recent pandemic, and the ending of protective legislation. Notably, people who experience homelessness have an increased risk of substance use disorders, HIV infection and poorer HIV outcomes than people who are stably housed. The iHouse model aims to develop feasible, effective, and cost-effective tailored approaches to improve health outcomes in this population including life expectancy, overdose, and HIV. METHODS AND ANALYSIS/UNASSIGNED:The study will employ Group Model Building methods and use insights from that process to develop an agent-based model simulating the dynamic processes contributing to HIV incidence and treatment, overdose, and life expectancy among people along the housing and homelessness continuum in Denver, CO and San Francisco, CA. The model will evaluate multiple outcomes from 4 conceptual dimensions: (1) movement along the housing continuum, (2) population health (overdose and HIV incidence and life expectancy), (3) budgetary impact, (4) economic value. ETHICS AND DISSEMINATION/UNASSIGNED:This study has been approved by the Colorado Institutional Review Board at the University of Colorado under protocol 24-0878. The data generated by this protocol, the methodologies used, and the findings will be made available in a timely manner to other researchers. iHOUSE code and parameter values will be published in Git Hub, such that all model analyses can be reproduced by independent investigators. Documentation of all parameter estimates and model results will be published for independent review and confirmation. In addition, supplemental materials and appendices for the model will be shared on a publicly available website.

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.

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.