Faculty Bibliography

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.

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.

Policy Points There have been significant advancements in expanding care for opioid use disorder and suicide in general medical settings in the first quarter of the 21st century. Incessant barriers in the US health system continue to hinder progress in sufficiently scaling up evidence-based behavioral health interventions and getting them to those at highest risk. State policymakers have multiple levers available to make significant improvements to address ongoing challenges and improve access to evidence-based behavioral health services in emergency and primary care settings.

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.

IMPORTANCE/UNASSIGNED:As the primary facilities authorized to dispense methadone, opioid treatment programs (OTPs) are a critical access point for medications for opioid use disorder (MOUD). However, research is limited on the extent to which OTPs offer a broad range of MOUD and on the characteristics of programs that provide more comprehensive medication offerings. OBJECTIVE/UNASSIGNED:To assess the percentage of US OTPs offering all 3 forms of MOUD (methadone, buprenorphine, and naltrexone) and compare organizational and county characteristics of OTPs with different MOUD service offerings. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:This longitudinal cross-sectional study used data on a panel of OTPs listed in the annual National Directory of Drug and Alcohol Use Treatment Facilities from 2017 to 2023. MAIN OUTCOMES AND MEASURES/UNASSIGNED:Measures included the percentage of OTPs offering buprenorphine, extended-release naltrexone, or all 3 MOUD from 2017 to 2023 (assuming all OTPs offered methadone). Descriptive statistics on organizational and county characteristics of OTPs by MOUD offerings were collected. Three longitudinal logistic regression models were used to estimate the odds of different MOUD offerings within OTPs, adjusting for organizational and county-level characteristics. RESULTS/UNASSIGNED:This analysis included 10 298 facility-year observations, ranging from 1211 in 2017 to 1421 in 2023. From 2017 to 2023, the percentage of OTPs offering MOUD beyond methadone increased (buprenorphine: 811 [67.0%] in 2017 to 1209 [85.1%] in 2023; naltrexone: 463 [38.2%] in 2017 to 749 [52.7%] in 2023; all 3 MOUD: 402 [33.2%] in 2017 to 639 [45.0%] in 2023). OTPs offering all 3 MOUD (3985 [38.7%]) had significantly higher odds of accepting Medicare (adjusted odds ratio [AOR], 2.14; 95% CI, 1.67-2.74); offering peer services (AOR, 1.63; 95% CI, 1.25-2.12), mental health services (AOR, 2.07; 95% CI, 1.53-2.80), and telemedicine services (AOR, 1.53; 95% CI, 1.22-1.92); and being private nonprofit (AOR, 7.45; 95% CI, 4.67-11.87) or government operated (AOR, 41.83; 95% CI, 19.71-88.75) compared with private for profit. CONCLUSIONS/UNASSIGNED:In this cross-sectional study of OTPs, although the availability of MOUD beyond methadone increased over time, most OTPs still did not offer all 3 forms of MOUD as of 2023. Specific organizational characteristics, such as being government operated and accepting Medicare, were associated with more comprehensive MOUD offerings. Future research should evaluate why OTPs vary in their MOUD offerings.