Faculty Bibliography

BACKGROUND:U.S. state electronic prescription drug monitoring programs (PDMPs) are associated with reduced opioid dispensing among people with chronic pain and may impact use of other chronic pain treatments. In states with medical cannabis laws (MCLs), patients can use cannabis for chronic pain management, reducing their need for chronic-pain related treatment visits and moderating effects of PDMP laws. OBJECTIVE:Given high rates of chronic pain among Medicaid enrollees, we examined associations between PDMP enactment in the presence or absence of MCL on chronic pain-related outpatient and emergency department (ED) visits. DESIGN/METHODS:We created annual cohorts of Medicaid enrollees with chronic pain diagnoses using national Medicaid claims data from 2002-2013 and 2016. Negative binomial hurdle models produced adjusted odds ratios (aOR) for the likelihood of any chronic pain-related outpatient or ED visit and incident rate ratios (IRR) for the rate of visits among patients with ≥ 1 visit. PARTICIPANTS/METHODS:Medicaid enrollees aged 18-64 years with chronic pain (N = 4,878,462). MAIN MEASURES/METHODS:A 3-level state-year variable with the following categories: 1) no PDMP, 2) PDMP enactment in the absence of MCL, or 3) PDMP enactment in the presence of MCL. Healthcare codes for chronic pain-related outpatient and ED visits each year. KEY RESULTS/RESULTS:The sample was primarily female (67.2%), non-Hispanic White (51.2%), and ages 40-55 years (37.2%). Compared to no-PDMP states, PDMP enactment in the absence of MCL was not associated with chronic pain-related outpatient visits but PDMP enactment in the presence of MCL was associated with lower odds of chronic pain-related outpatient visits (aOR = 0.81, 95% CI:0.71-0.92). PDMP enactment was not associated with ED visits, irrespective of MCL. CONCLUSIONS:During a period of PDMP and MCL expansion, our findings suggest treatment shifts for persons with chronic pain away from outpatient settings, potentially related to increased use of cannabis for chronic pain management.

BACKGROUND AND AIMS/OBJECTIVE:Evidence from high-income countries has linked duration and compliance with treatment for substance use disorders (SUDs) with reductions in substance use and improvements in mental health. Generalizing these findings to other regions like South America, where opioid and injection drug use is uncommon, is not straightforward. We examined if length of time in treatment and compliance with treatment reduced subsequent substance use and presence of psychiatric comorbidities. DESIGN/METHODS:Prospective cohort analysis (3 assessments, 18 months) using inverse probability weighting to account for confounding and loss to follow-up. SETTINGS/METHODS:Outpatient/inpatient programs in Región Metropolitana, Chile. PARTICIPANTS/METHODS:Individuals initiating publicly funded treatment (n = 399). MEASUREMENTS/METHODS:Exposures included length of time in (0-3, 4-7, 8 + months, currently in) and compliance with treatment (not completed, completed, currently in) measured in the intermediate assessment (12 months). Primary outcomes were past-month use of primary substance (most problematic) and current psychiatric comorbidities (major depressive episode, panic, anxiety or post-traumatic stress disorders) measured 6 months later (18 months). Secondary outcomes included past month use of alcohol, cannabis, cocaine powder and cocaine paste. FINDINGS/RESULTS:18.3% [95% confidence interval (CI) = 14.7%-22.6%] of individuals participated for 3 or fewer months in treatment and 50.1% (95% CI = 45.2%-55.1%) did not complete their treatment plan at 12 months. Participating for 8 + months in treatment was associated with lower risk of past month use of primary substance at 18 months [vs. 0-3 months, risk ratio (RR) = 0.62, 95% CI = 0.38-1.00] and completion of treatment (vs. not completed, RR = 0.49, 95% CI = 0.30-0.80). Neither participating 8 + months (vs. 0-3 months, RR = 0.83, 95% CI = 0.57-1.22) nor treatment completion (vs. not completed, RR = 1.02, 95% CI = 0.72-1.46) were associated with lower risk of psychiatric comorbidity at 18 months. CONCLUSIONS:Longer time in treatment and compliance with treatment for substance use disorders in Chile appears to be associated with lower risk of substance use but not current comorbid psychiatric conditions 18 months after treatment initiation.

BACKGROUND:Despite proliferation of acute-care interventions to initiate medications for opioid use disorder (MOUD), significant challenges remain to supporting care continuity following discharge. Research is needed to inform effective hospital strategies to support patient transitions to ongoing MOUD in the community. OBJECTIVE:To inform a taxonomy of care transition strategies to support MOUD continuity from hospital to community-based settings and assess their perceived impact and feasibility among experts in the field. DESIGN/METHODS:A modified Delphi consensus process through three rounds of electronic surveys. PARTICIPANTS/METHODS:Experts in hospital-based opioid use disorder (OUD) treatment, care transitions, and hospital-based addiction treatment. MAIN MEASURES/METHODS:Delphi participants rated the impact and feasibility of 14 OUD care transition strategies derived from a review of the scientific literature on a scale from 1 to 9 over three survey rounds. Panelists were invited to suggest additional care transition strategies. Agreement level was calculated based on proportion of ratings within three points of the median. KEY RESULTS/RESULTS:Forty-five of 71 invited panelists participated in the survey. Agreement on impact was strong for 12 items and moderate for 10. Agreement on feasibility was strong for 11 items, moderate for 7, and poor for 4. Strategies with highest ratings on impact and feasibility included initiation of MOUD in-hospital and provision of buprenorphine prescriptions or medications before discharge. All original 14 strategies and 8 additional strategies proposed by panelists were considered medium- or high-impact and were incorporated into a final taxonomy of 22 OUD care transition strategies. CONCLUSIONS:Our study established expert consensus on impactful and feasible hospital strategies to support OUD care transitions from the hospital to community-based MOUD treatment, an area with little empirical research thus far. It is the hope that this taxonomy serves as a stepping-stone for future evaluations and clinical practice implementation toward improved MOUD continuity and health outcomes.

OBJECTIVES/OBJECTIVE:To evaluate the predictive performance of different data sources to forecast fatal overdose in Rhode Island neighborhoods, with the goal of providing a template for other jurisdictions interested in predictive analytics to direct overdose prevention resources. METHODS:We evaluated seven combinations of data from six administrative data sources (American Community Survey (ACS) five-year estimates, built environment, emergency medical services non-fatal overdose response, prescription drug monitoring program, carceral release, and historical fatal overdose data). Fatal overdoses in Rhode Island census block groups (CBGs) were predicted using two machine learning approaches: linear regressions and random forests embedded in a nested cross-validation design. We evaluated performance using mean squared error and the percentage of statewide overdoses captured by CBGs forecast to be in top percentiles from 2019 to 2021. RESULTS:Linear models trained on ACS data combined with one other data source performed well, and comparably to models trained on all available data. Those including emergency medical service, prescription drug monitoring program, or carceral release data with ACS data achieved a priori goals for percentage of statewide overdoses captured by CBGs prioritized by models on average. CONCLUSIONS:Prioritizing neighborhoods for overdose prevention with forecasting is feasible using a simple-to-implement model trained on publicly available ACS data combined with only one other administrative data source in Rhode Island, offering a starting point for other jurisdictions.

OBJECTIVES/OBJECTIVE:The escalating overdose crisis in the United States points to the urgent need for new and novel data tools. Overdose data tools are growing in popularity but still face timely delays in surveillance data availability, lack of completeness, and wide variability in quality by region. As such, we need innovative tools to identify and prioritize emerging and high-need areas. Forecasting offers one such solution. Machine learning methods leverage numerous datasets that could be used to predict future vulnerability to overdose at the regional, town, and even neighborhood levels. This study aimed to understand the multi-level factors affecting the early stages of implementation for an overdose forecasting dashboard. This dashboard was developed with and for statewide harm reduction providers to increase data-driven response and resource distribution at the neighborhood level. METHODS:As part of PROVIDENT (Preventing OVerdose using Information and Data from the EnvironmeNT), a randomized, statewide community trial, we conducted an implementation study where we facilitated three focus groups with harm reduction organizations enrolled in the larger trial. Focus group participants held titles such as peer outreach workers, case managers, and program coordinators/managers. We employed the Exploration, Preparation, Implementation, Sustainment (EPIS) Framework to guide our analysis. This framework offers a multi-level, four-phase analysis unique to implementation within a human services environment to assess the exploration and preparation phases that influenced the early launch of the intervention. RESULTS:Multiple themes centering on organizational culture and resources emerged, including limited staff capacity for new interventions and repeated exposure to stress and trauma, which could limit intervention uptake. Community-level themes included the burden of data collection for program funding and statewide efforts to build stronger networks for data collection and dashboarding and data-driven resource allocation. DISCUSSION/CONCLUSIONS:Using an implementation framework within the larger study allowed us to identify multi-level and contextual factors affecting the early implementation of a forecasting dashboard within the PROVIDENT community trial. Additional investments to build organizational and community capacity may be required to create the optimal implementation setting and integration of forecasting tools.

Mandatory prescription drug monitoring programs and cannabis legalization have been hypothesized to reduce overdose deaths. We examined associations between prescription monitoring programs with access mandates ("must-query PDMPs"), legalization of medical and recreational cannabis supply, and opioid overdose deaths in United States counties in 2013-2020. Using data on overdose deaths from the National Vital Statistics System, we fit Bayesian spatiotemporal models to estimate risk differences and 95% credible intervals (CrI) in county-level opioid overdose deaths associated with enactment of these state policies. Must-query PDMPs were independently associated with on average 0.8 (95% CrI: 0.5, 1.0) additional opioid-involved overdose deaths per 100,000 person-years. Legal cannabis supply was not independently associated with opioid overdose deaths in this time period. Must-query PDMPs enacted in the presence of legal (medical or recreational) cannabis supply were associated with 0.7 (95% CrI: 0.4, 0.9) more opioid-involved deaths, relative to must-query PDMPs without any legal cannabis supply. In a time when overdoses are driven mostly by non-prescribed opioids, stricter opioid prescribing policies and more expansive cannabis legalization were not associated with reduced overdose death rates.

BACKGROUND:Medications for opioid use disorder are associated with a lower risk of drug overdoses at the individual level. However, little is known about whether these effects translate to population-level reductions. We investigated whether county-level efforts to increase access to medication for opioid use disorder in 2012-2014 were associated with opioid overdose deaths in New York State during the first years of the synthetic opioid crisis. METHODS:We performed an ecologic county-level study including data from 60 counties (2010-2018). We calculated rates of people receiving medication for opioid use disorder among the population misusing opioids in 2012-2014 and categorized counties into quartiles of this exposure. We modeled synthetic and nonsynthetic opioid overdose death rates using Bayesian hierarchical models. RESULTS:Counties with higher rates of receiving medications for opioid use disorder in 2012-2014 had lower synthetic opioid overdose deaths in 2016 (highest vs. lowest quartile: rate ratio [RR] = 0.33, 95% credible interval [CrI] = 0.12, 0.98; and second-highest vs. lowest: RR = 0.20, 95% CrI = 0.07, 0.59) and 2017 (quartile second-highest vs. lowest: RR = 0.22, 95% CrI = 0.06, 0.83), but not 2018. There were no differences in nonsynthetic opioid overdose death rates comparing higher quartiles versus lowest quartile of exposure. CONCLUSIONS:A spatio-temporal modeling approach incorporating counts of the population misusing opioids provided information about trends and interventions in the target population. Higher rates of receiving medications for opioid use disorder in 2012-2014 were associated with lower rates of synthetic opioid overdose deaths early in the crisis.

BACKGROUND:Identifying the most effective state laws and provisions to reduce opioid overdose deaths remains critical. METHODS:Using expert ratings of opioid laws, we developed annual state scores for three domains: opioid prescribing restrictions, harm reduction, and Medicaid treatment coverage. We modeled associations of state opioid policy domain scores with opioid-involved overdose death counts in 3133 counties, and among racial/ethnic subgroups in 1485 counties (2013-2020). We modeled a second set of domain scores based solely on experts' highest 20 ranked provisions to compare with the all-provisions model. RESULTS:From 2013 to 2020, moving from non- to full enactment of harm reduction domain laws (i.e., 0 to 1 in domain score) was associated with reduced county-level relative risk (RR) of opioid overdose death in the subsequent year (adjusted RR = 0.84, 95 % credible interval (CrI): 0.77, 0.92). Moving from non- to full enactment of opioid prescribing restrictions and Medicaid treatment coverage domains was associated with higher overdose in 2013-2016 (aRR 1.69 (1.35, 2.11) and aRR 1.20 (1.11, 1.29) respectively); both shifted to the null in 2017-2020. Effect sizes and direction were similar across racial/ethnic groups. Results for experts' highest 20 ranked provisions did not differ from the all-provision model. CONCLUSIONS:More robust state harm reduction policy scores were associated with reduced overdose risk, adjusting for other policy domains. Harmful associations with opioid prescribing restrictions in 2013-2016 may reflect early unintended consequences of these laws. Medicaid coverage domain findings did not align with experts' perceptions, though data limitations precluded inclusion of several highly ranked Medicaid policies.

BACKGROUND:Recreational cannabis use is increasing in Mexico, where legalization is a possibility. The current area-level socioeconomic context of cannabis use has not been studied in the country, limiting our understanding and public health response. We aimed to analyze the association between the municipal socioeconomic environment and recreational cannabis use in Mexico. METHODS:We used data from the National Survey of Drug, Alcohol and Tobacco Consumption 2016-17, the National Health and Nutrition Survey 2023, the 2015 intercensal survey and the 2020 census to study the association of municipal income and municipal education with past-year recreational cannabis use. We fitted Poisson models with robust variance to obtain prevalence ratios and assessed for effect modification by individual-level sex and age, and household-level education. RESULTS:For every unit increase in municipal education, we observed a 1.5 % increase in the prevalence of recreational cannabis use in 2016-17, and a 2.9 % increase in 2023. For each unit increase in municipal income, we observed a 1.5 % increase in the prevalence of recreational cannabis use in 2016-17, and a 1.8 % increase in 2023. We found no effect modification except for a single age group (20- to 29-year-olds vs to 12- to 19-year-olds). CONCLUSION/CONCLUSIONS:Recreational cannabis use in Mexico is currently higher in more socioeconomically advantaged municipalities. Recreational cannabis use through socioeconomic areas should be monitored closely. Further research of the modifiable causes of this association could help inform current and future public health policies.