Faculty Bibliography

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:Wider availability of cannabis through medical and recreational legalization (MCL alone and RCL+MCL) has been hypothesized to contribute to reductions in opioid use, misuse, and related harms. We examined whether state adoption of cannabis laws was associated with changes in opioid outcomes overall and stratified by cannabis use. METHODS:Using National Survey on Drug Use and Health (NSDUH) data from 2015 to 2019, we estimated cannabis law associations with opioid (prescription opioid misuse and/or heroin use) misuse and use disorder. All logistic regression models (overall models and models stratified by cannabis use), included year and state fixed effects, individual level covariates, and opioid-related state policies. Stratified analyses were restricted to individuals who reported lifetime cannabis use prior to law adoption to reduce potential for collider bias. Estimates accounted for multiple comparisons using false discovery rate (FDR) corrections and sensitivity to unmeasured confounding using e-values. RESULTS:Overall, MCL and RCL adoption were not associated with changes in the odds of any opioid outcome. After restricting to respondents reporting past-year cannabis use, we observed decreased odds of past year opioid misuse (adjusted odds ratio [AOR]: 0.57 [95 % confidence interval [CI]: 0.38, 0.85]; FDR p-value: 0.07), among individuals in states with MCL compared to those in states without cannabis laws. RCLs were not associated with changes in the odds of any opioid outcome beyond MCL adoption. CONCLUSION/CONCLUSIONS:Comparing individuals in MCL alone states to those in states without such laws, we found an inconsistent pattern of decreased odds of opioid outcomes, which were more pronounced among people reporting cannabis use. The pattern did not hold for individuals in RCL states. In line with a substitution-oriented perspective, findings suggests that MCLs may be associated with reductions in opioid use among people using cannabis but additional work to replicate and expand on these findings is needed.

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.

INTRODUCTION/BACKGROUND:People who use drugs (PWUD) are at risk of HIV infection, but the frequency and distribution of transmission-associated behaviors within rural communities is not well understood. Further, while interventions designed to more explicitly affirm individuals' sexual orientation and behaviors may be more effective, descriptions of behavior variability by orientation are lacking. We sought to describe how disease transmission behaviors and overdose risk vary by sexual orientation and activity among rural PWUD. METHODS:From 01/2018-03/2020, rural PWUD participating in the Rural Opioid Initiative were surveyed across 8 sites. Collected data included: demographics; experiences with drug use, overdose, and healthcare; stigma; gender identity; and sexual orientation and partners. Participants were categorized as: monosexual by orientation and behavior (Mono-only), monosexual by orientation but behaviorally bisexual (Mono/Bi), and bisexual by orientation (Bi+). Analyses included descriptive summaries, bivariate examination (chi-square), and logistic regression (relative risk [RR] and 95 % confidence interval [CI]). RESULTS:The 1455 participants were 84.8 % Mono-only, 3.2 % Mono/Bi, and 12.0 % Bi+. Compared to Mono-only men, Mono/Bi and Bi+ men had greater risk of transactional sex (RR = 9.71, CI = 6.66-14.2 and RR = 5.09, CI = 2.79-9.27, respectively) and sharing syringes for injection (RR = 1.58, CI = 1.06-2.35 and RR = 1.85, CI = 1.38-2.47). Compared to Mono-only women, Mono-Bi and Bi+ women had greater risk of transactional sex (RR = 4.47, CI = 2.68-7.47 and RR = 2.63, CI = 1.81-3.81); and Bi+ women had greater risk of sharing syringes for injection (RR = 1.49, CI = 1.23-1.81), sharing syringes to mix drugs (RR = 1.44, CI = 1.23-1.69), and experiencing an overdose (RR = 1.32, CI = 1.12-1.56). Bi+ men and women both more frequently reported selling sex as a source of income (versus Mono-only, both p < 0.050) and measures of perceived stigma (all p < 0.050). CONCLUSIONS:Rural PWUD who are bisexual by orientation or behavior are significantly more likely to engage in behaviors associated with infectious disease transmission and to experience stigma and drug overdose. Given the growing recognition of bisexuality as a distinct orientation that warrants individualized consideration, interventions that are specifically acknowledging and affirming to the circumstances of this group are needed.

BACKGROUND AND AIMS/OBJECTIVE:The opioid crisis continues to exert a tremendous toll in North America, with existing interventions often falling short of addressing ongoing needs. Psychedelics are emerging as a possible alternative therapy for mental health and substance use disorders. This study aimed to gather insights on how people use or are considering using psychedelics to manage opioid use disorder (OUD), how these experiences are perceived to impact opioid use and what these lessons imply for future research and practice. METHODS:We conducted a qualitative study using the Reddit online community platform. We extracted posts that contained key psychedelic terms from the two most subscribed-to subreddits dedicated to discussions of OUD treatment (r/OpiatesRecovery and r/Methadone) from 2018 to 2021. We thematically analyzed content from 151 relevant posts and their respective comments. RESULTS:Two prominent themes identified in discussions were perspectives on the effectiveness of psychedelics in treating OUD, and mechanisms through which psychedelics were thought to impact use and desire to use opioids. For many, psychedelics were deemed to have a strong impact on opioid use via multiple mechanisms, including alleviating physical symptoms of dependence, shifting motivations around desire to use opioids and addressing underlying mental health problems and reasons for use. Others saw the potential promise around psychedelics as exaggerated, acknowledging many people eventually return to use, or even considered psychedelics dangerous. CONCLUSIONS:There appear to be diverse perspectives on the effects of using psychedelics to treat opioid use disorder and an urgent need for controlled studies to better understand the impact of different psychedelics on opioid use, how they may be used in the context of existing treatments and what strategies they must be combined with to ensure safety and effectiveness. Integrating the experiences of people who use drugs will help guide psychedelics research toward effective person-centered interventions to enhance health and wellness.