Mapping the Ecological Terrain of Stroke Prehospital Delay: A Nationwide Registry Study.

BACKGROUND: Delays in hospital presentation limit access to acute stroke treatments. While prior research has focused on patient-level factors, broader ecological and social determinants have not been well studied. We aimed to create a geospatial map of prehospital delay and examine the role of community-level social vulnerability. METHODS: We studied patients with ischemic stroke who arrived by emergency medical services in 2015 to 2017 from the American Heart Association Get With The Guidelines-Stroke registry. The primary outcome was time to hospital arrival after stroke (in minutes), beginning at last known well in most cases. Using Geographic Information System mapping, we displayed the geography of delay. We then used Cox proportional hazard models to study the relationship between community-level factors and arrival time (adjusted hazard ratios [aHR] <1.0 indicate delay). The primary exposure was the social vulnerability index (SVI), a metric of social vulnerability for every ZIP Code Tabulation Area ranging from 0.0 to 1.0. RESULTS: Of 750 336 patients, 149 145 met inclusion criteria. The mean age was 73 years, and 51% were female. The median time to hospital arrival was 140 minutes (Q1: 60 minutes, Q3: 458 minutes). The geospatial map revealed that many zones of delay overlapped with socially vulnerable areas (https://harvard-cga.maps.arcgis.com/apps/webappviewer/index.html?id=08f6e885c71b457f83cefc71013bcaa7). Cox models (aHR, 95% CI) confirmed that higher SVI, including quartiles 3 (aHR, 0.96 [95% CI, 0.93-0.98]) and 4 (aHR, 0.93 [95% CI, 0.91-0.95]), was associated with delay. Patients from SVI quartile 4 neighborhoods arrived 15.6 minutes [15-16.2] slower than patients from SVI quartile 1. Specific SVI themes associated with delay were a community's socioeconomic status (aHR, 0.80 [95% CI, 0.74-0.85]) and housing type and transportation (aHR, 0.89 [95% CI, 0.84-0.94]). CONCLUSIONS: This map of acute stroke presentation times shows areas with a high incidence of delay. Increased social vulnerability characterizes these areas. Such places should be systematically targeted to improve population-level stroke presentation times.

Lessons Learned from Public Health and State Prison Collaborations during COVID-19 Pandemic and Multifacility Tuberculosis Outbreak, Washington, USA.

The large COVID-19 outbreaks in prisons in the Washington (USA) State Department of Corrections (WADOC) system during 2020 highlighted the need for a new public health approach to prevent and control COVID-19 transmission in the system's 12 facilities. WADOC and the Washington State Department of Health (WADOH) responded by strengthening partnerships through dedicated corrections-focused public health staff, improving cross-agency outbreak response coordination, implementing and developing corrections-specific public health guidance, and establishing collaborative data systems. The preexisting partnerships and trust between WADOC and WADOH, strengthened during the COVID-19 response, laid the foundation for a collaborative response during late 2021 to the largest tuberculosis outbreak in Washington State in the past 20 years. We describe challenges of a multiagency collaboration during 2 outbreak responses, as well as approaches to address those challenges, and share lessons learned for future communicable disease outbreak responses in correctional settings.

PROVIDENT: Development and Validation of a Machine Learning Model to Predict Neighborhood-level Overdose Risk in Rhode Island.

BACKGROUND: Drug overdose persists as a leading cause of death in the United States, but resources to address it remain limited. As a result, health authorities must consider where to allocate scarce resources within their jurisdictions. Machine learning offers a strategy to identify areas with increased future overdose risk to proactively allocate overdose prevention resources. This modeling study is embedded in a randomized trial to measure the effect of proactive resource allocation on statewide overdose rates in Rhode Island (RI). METHODS: We used statewide data from RI from 2016 to 2020 to develop an ensemble machine learning model predicting neighborhood-level fatal overdose risk. Our ensemble model integrated gradient boosting machine and super learner base models in a moving window framework to make predictions in 6-month intervals. Our performance target, developed a priori with the RI Department of Health, was to identify the 20% of RI neighborhoods containing at least 40% of statewide overdose deaths, including at least one neighborhood per municipality. The model was validated after trial launch. RESULTS: Our model selected priority neighborhoods capturing 40.2% of statewide overdose deaths during the test periods and 44.1% of statewide overdose deaths during validation periods. Our ensemble outperformed the base models during the test periods and performed comparably to the best-performing base model during the validation periods. CONCLUSIONS: We demonstrated the capacity for machine learning models to predict neighborhood-level fatal overdose risk to a degree of accuracy suitable for practitioners. Jurisdictions may consider predictive modeling as a tool to guide allocation of scarce resources.

Association of Opioid Use Disorder Diagnosis with Management of Acute Low Back Pain: A Medicare Retrospective Cohort Analysis.

BACKGROUND: Practice guidelines recommend nonpharmacologic and nonopioid therapies as first-line pain treatment for acute pain. However, little is known about their utilization generally and among individuals with opioid use disorder (OUD) for whom opioid and other pharmacologic therapies carry greater risk of harm. OBJECTIVE: To determine the association between a pre-existing OUD diagnosis and treatment of acute low back pain (aLBP). DESIGN: Retrospective cohort study using 2016-2019 Medicare data. PARTICIPANTS: Fee-for-service Medicare beneficiaries with a new episode of aLBP. MAIN MEASURES: The main independent variable was OUD diagnosis measured prior to the first LBP claim (i.e., index date). Using multivariable logistic regressions, we assessed the following outcomes measured within 30 days of the index date: (1) nonpharmacologic therapies (physical therapy and/or chiropractic care), and (2) prescription opioids. Among opioid recipients, we further assessed opioid dose and co-prescription of gabapentin. Analyses were conducted overall and stratified by receipt of physical therapy, chiropractic care, opioid fills, or gabapentin fills during the 6 months before the index date. KEY RESULTS: We identified 1,263,188 beneficiaries with aLBP, of whom 3.0% had OUD. Two-thirds (65.8%) did not receive pain treatments of interest at baseline. Overall, nonpharmacologic therapy receipt was less prevalent and opioid and nonopioid pharmacologic therapies were more common among beneficiaries with OUD than those without OUD. Beneficiaries with OUD had lower odds of receiving nonpharmacologic therapies (aOR = 0.62, 99%CI = 0.58-0.65) and higher odds of prescription opioid receipt (aOR = 2.24, 99%CI = 2.17-2.32). OUD also was significantly associated with increased odds of opioid doses ≥ 90 morphine milligram equivalents/day (aOR = 2.43, 99%CI = 2.30-2.56) and co-prescription of gabapentin (aOR = 1.15, 99%CI = 1.09-1.22). Similar associations were observed in stratified groups though magnitudes differed. CONCLUSIONS: Medicare beneficiaries with aLBP and OUD underutilized nonpharmacologic pain therapies and commonly received opioids at high doses and with gabapentin. Complementing the promulgation of practice guidelines with implementation science could improve the uptake of evidence-based nonpharmacologic therapies for aLBP.

Impact of 30-day prescribed opioid dose trajectory on fatal overdose risk: A population-based, statewide cohort study.

BACKGROUND: Both increases and decreases in patients' prescribed daily opioid dose have been linked to increased overdose risk, but associations between 30-day dose trajectories and subsequent overdose risk have not been systematically examined. OBJECTIVE: To examine the associations between 30-day prescribed opioid dose trajectories and fatal opioid overdose risk during the subsequent 15 days. DESIGN: Statewide cohort study using linked prescription drug monitoring program and death certificate data. We constructed a multivariable Cox proportional hazards model that accounted for time-varying prescription-, prescriber-, and pharmacy-level factors. PARTICIPANTS: All patients prescribed an opioid analgesic in California from March to December, 2013 (5,326,392 patients). MAIN MEASURES: Dependent variable: fatal drug overdose involving opioids. Primary independent variable: a 16-level variable denoting all possible opioid dose trajectories using the following categories for current and 30-day previously prescribed daily dose: 0-29, 30-59, 60-89, or ≥90 milligram morphine equivalents (MME). KEY RESULTS: Relative to patients prescribed a stable daily dose of 0-29 MME, large (≥2 categories) dose increases and having a previous or current dose ≥60 MME per day were associated with significantly greater 15-day overdose risk. Patients whose dose decreased from ≥90 to 0-29 MME per day had significantly greater overdose risk compared to both patients prescribed a stable daily dose of ≥90 MME (aHR 3.56, 95%CI 2.24-5.67) and to patients prescribed a stable daily dose of 0-29 MME (aHR 7.87, 95%CI 5.49-11.28). Patients prescribed benzodiazepines also had significantly greater overdose risk; being prescribed Z-drugs, carisoprodol, or psychostimulants was not associated with overdose risk. CONCLUSIONS: Large (≥2 categories) 30-day dose increases and decreases were both associated with increased risk of fatal opioid overdose, particularly for patients taking ≥90 MME whose opioids were abruptly stopped. Results align with 2022 CDC guidelines that urge caution when reducing opioid doses for patients taking long-term opioid for chronic pain.

Public Health Dashboards in Overdose Prevention: The Rhode Island Approach to Public Health Data Literacy, Partnerships, and Action.

As the field of public health rises to the demands of real-time surveillance and rapid data-sharing needs in a postpandemic world, it is time to examine our approaches to the dissemination and accessibility of such data. Distinct challenges exist when working to develop a shared public health language and narratives based on data. It requires that we assess our understanding of public health data literacy, revisit our approach to communication and engagement, and continuously evaluate our impact and relevance. Key stakeholders and cocreators are critical to this process and include people with lived experience, community organizations, governmental partners, and research institutions. In this viewpoint paper, we offer an instructive approach to the tools we used, assessed, and adapted across 3 unique overdose data dashboard projects in Rhode Island, United States. We are calling this model the "Rhode Island Approach to Public Health Data Literacy, Partnerships, and Action." This approach reflects the iterative lessons learned about the improvement of data dashboards through collaboration and strong partnerships across community members, state agencies, and an academic research team. We will highlight key tools and approaches that are accessible and engaging and allow developers and stakeholders to self-assess their goals for their data dashboards and evaluate engagement with these tools by their desired audiences and users.

A simple heuristic for allocating opioid settlement funding to reduce overdose mortality in the United States.

As resolution for opioid-related claims and litigation against pharmaceutical manufacturers and other stakeholders, state and local governments are newly eligible for millions of dollars of settlement funding to address the overdose crisis in the United States. To inform effective use of opioid settlement funds, we propose a simple framework that highlights the principal determinants of overdose mortality: the number of people at risk of overdose each year, the average annual number of overdoses per person at risk, and the average probability of death per overdose event. We assert that the annual number of overdose deaths is a function of these three determinants, all of which can be modified through public health intervention. Our proposed heuristic depicts how each of these drivers of drug-related mortality - and the corresponding interventions designed to address each term - operate both in isolation and in conjunction. We intend for this framework to be used by policymakers as a tool for identifying and evaluating public health interventions and funding priorities that will most effectively address the structural forces shaping the overdose crisis and reduce overdose deaths.

Missouri's overdose field report: descriptive analysis, survival trends, and naloxone dosing patterns from a community-based survey tool, 2018-2022.

Background: Missouri's Overdose Field Report (ODFR) is a community-based reporting system which intends to capture overdoses which may not be otherwise recorded.Objectives: Describe the factors related to non-fatal overdoses reported to Missouri's ODFR.Methods: This study used a descriptive epidemiological approach to examine the demographics and circumstances of overdoses reported to the ODFR. We used binary logistic regression to evaluate factors associated with survival and ordinal logistic regression to evaluate factors associated with number of doses used. Factors were chosen based on their relevance to overdose education and survival, and naloxone distribution.Results: Between 2018 and 2022, 12,225 overdoses (67% male; 78% White) were reported through the ODFR, with a 96% (n = 11,225) survival rate. Overdose survival (ps < .02) was associated with younger age (OR = .58), no opioid and stimulant co-involvement (OR = .61), and private location (OR = .48). Intramuscular naloxone in particular was associated with a significantly higher odds of survival compared to nasal naloxone (OR = 2.11). An average of 1.6 doses of naloxone per incident were administered. Additional doses were associated (ps < .02) with being older (OR = .45), female (OR = .90), nasal naloxone (versus intravenous) (OR = .65), and the belief fentanyl was present (OR = 1.49).Conclusion: Our reporting form provides a comprehensive picture of the events surrounding reported overdoses, including factors associated with survival, how much naloxone was used, and the effects of respondents believing fentanyl was involved. Missouri's report can provide support for current naloxone dosing, contextualize refusing post-overdose transport, and can be used to improve overdose response by community and first responders.

"Make yourself un-NIMBY-able": stakeholder perspectives on strategies to mobilize public and political support for overdose prevention centers in the United States of America.

BACKGROUND: Overdose prevention centers (OPCs), also known as supervised injection facilities and safe consumption sites, are evidenced-based interventions for preventing overdose deaths and drug-related morbidities. The pathways to legalizing OPCs in the USA have confronted multiple social, political, and legal obstacles. We conducted a multi-site, qualitative study to explore heterogeneities in these pathways in four jurisdictions, as well as to understand stakeholder perspectives on valuable strategies for galvanizing political and public support for OPCs. METHODS: From July 2022 to February 2023, we conducted 17 semi-structured, in-depth interviews with OPC policymakers, service providers, advocates, and researchers from California, New York City, Philadelphia, and Rhode Island, where efforts have been undertaken to authorize OPCs. Using inductive thematic analysis, we identified and compared contextually relevant, salient approaches for increasing support for OPCs. RESULTS: Participants described several strategies clustering around five distinct domains: (1) embedding OPC advocacy into broader overdose prevention coalitions to shape policy dialogs; (2) building rapport with a plurality of powerbrokers (e.g., lawmakers, health departments, law enforcement) who could amplify the impact of OPC advocacy; (3) emphasizing specific benefits of OPCs to different audiences in different contexts; (4) leveraging relationships with frontline workers (e.g., emergency medicine and substance use treatment providers) to challenge OPC opposition, including 'NIMBY-ism,' and misinformation; and (5) prioritizing transparency in OPC decision-making to foster public trust. CONCLUSION: While tailored to the specific socio-political context of each locality, multiple OPC advocacy strategies have been deployed to cultivate support for OPCs in the USA. Advocacy strategies that are multi-pronged, leverage partnerships with stakeholders at multiple levels, and tailor communications to different audiences and settings could yield the greatest impact in increasing support for, and diffusing opposition to, future OPC implementation.

Association between willingness to use an overdose prevention center and probation or parole status among people who use drugs in Rhode Island.

BACKGROUND: Overdose prevention centers (OPCs) are being implemented in the United States as a strategy to reduce drug-related mortality and morbidity. Previous studies have suggested that people who use drugs (PWUD) with a history of criminal legal system (CLS) involvement (e.g. current probation/parole) are at greater risk of overdose but may also encounter significant barriers to OPC use. The objective of this study was to explore the association between willingness to use an OPC and probation/parole status in a sample of PWUD in Rhode Island. METHODS: This study utilized data from the Rhode Island Prescription and Illicit Drug Study, which enrolled adult PWUD from August 2020 to February 2023. We used Pearson's chi-square and Wilcoxon rank-sum tests to assess bivariate associations between willingness to use an OPC and probation/parole status (current/previous/never), as well as other sociodemographic and behavioral characteristics. In multivariable Poisson analyses, we examined the association between willingness to use an OPC and probation/parole status, adjusting for key sociodemographic and behavioral characteristics. RESULTS: Among 482 study participants, 67% were male, 56% identified as white, 20% identified as Hispanic/Latine, and the median age was 43 (IQR 35-53). Nearly a quarter (24%) had never been on probation/parole, 44% were not currently on probation/parole but had a lifetime history of probation and parole, and 32% were currently on probation/parole. Most participants (71%) reported willingness to use an OPC, and in both bivariate and multivariable analyses, willingness to use an OPC did not vary by probation/parole status. Crack cocaine use and lifetime non-fatal overdose were associated with greater willingness to use an OPC (all p < 0.05). CONCLUSIONS: These data demonstrate high willingness to use OPC among PWUD in Rhode Island regardless of CLS-involvement. As OPCs begin to be implemented in Rhode Island, it will be imperative to engage people with CLS-involvement and to ensure access to the OPC and protection against re-incarceration due to potential barriers, such as police surveillance of OPCs.

The association between naloxone claims and proportion of independent vs. chain pharmacies: A longitudinal analysis of naloxone claims in the US.

BACKGROUND: Expanding access to naloxone through pharmacies is an important policy goal. Our objective was to characterize national county-level naloxone dispensing of chain versus independent pharmacies. METHODS: The primary exposure in our longitudinal analysis was the proportion of chain pharmacies in a county, identified through the US Department of Homeland Security 2010 Infrastructure Foundation-Level Data. We defined counties as having "higher proportion" of chain pharmacies if at least 50% of pharmacies were large national chains. The primary outcome was quarter-year (2016Q1-2019Q2) rate of pharmacy naloxone claims per 100,000 persons from Symphony Health at the county-level. We compared the naloxone dispensing rate between county types using two-sample t-tests. We estimated the association between county-level chain pharmacy proportion and rate of naloxone claims using a linear model with year-quarter fixed effects. RESULTS: Nearly one third of counties (n=946) were higher proportion. Higher proportion counties had a significantly higher rate of naloxone claims across the study period, in 4 of 6 urban-rural classifications, and in counties with and without naloxone access laws. The linear model confirmed that higher proportion counties had a significantly higher rate of naloxone claims, adjusting for urban/rural designation, income, population characteristics, opioid mortality rate, co-prescribing laws and naloxone access laws. CONCLUSION: In this national study, we found an association between naloxone dispensing rates and the county-level proportion of chain (versus independent) pharmacies. Incentivizing naloxone dispensing through educational, regulatory, or legal efforts may improve naloxone availability and dispensing rates - particularly in counties with proportionately high numbers of independent pharmacies.

Invited Commentary: On the Mathematization of Epidemiology as a Socially Engaged Quantitative Science.

Ensuring that patients with opioid use disorder (OUD) have access to optimal medication therapies is a critical challenge in substance use epidemiology. Rudolph et al. (Am J Epidemiol. 2023;XXX(X):XXXX-XXXX) demonstrated that sophisticated data-adaptive statistical techniques can be used to learn optimal, individualized treatment rules that can aid providers in choosing a medication treatment modality for a particular patient with OUD. This important work also highlights the effects of the mathematization of epidemiologic research. Here, we define mathematization and demonstrate how it operates in the context of effectiveness research on medications for OUD using the paper by Rudolph et al. as a springboard. In particular, we address the normative dimension of mathematization and how it tends to resolve a fundamental tension in epidemiologic practice between technical sophistication and public health considerations in favor of more technical solutions. The process of mathematization is a fundamental part of epidemiology; we argue not for eliminating it but for balancing mathematization and technical demands equally with practical and community-centric public health needs.

Translating Predictive Analytics for Public Health Practice: A Case Study of Overdose Prevention in Rhode Island.

Prior applications of machine learning to population health have relied on conventional model assessment criteria, limiting the utility of models as decision support tools for public health practitioners. To facilitate practitioners' use of machine learning as a decision support tool for area-level intervention, we developed and applied 4 practice-based predictive model evaluation criteria (implementation capacity, preventive potential, health equity, and jurisdictional practicalities). We used a case study of overdose prevention in Rhode Island to illustrate how these criteria could inform public health practice and health equity promotion. We used Rhode Island overdose mortality records from January 2016-June 2020 (n = 1,408) and neighborhood-level US Census data. We employed 2 disparate machine learning models, Gaussian process and random forest, to illustrate the comparative utility of our criteria to guide interventions. Our models predicted 7.5%-36.4% of overdose deaths during the test period, illustrating the preventive potential of overdose interventions assuming 5%-20% statewide implementation capacities for neighborhood-level resource deployment. We describe the health equity implications of use of predictive modeling to guide interventions along the lines of urbanicity, racial/ethnic composition, and poverty. We then discuss considerations to complement predictive model evaluation criteria and inform the prevention and mitigation of spatially dynamic public health problems across the breadth of practice. This article is part of a Special Collection on Mental Health.

Multi-level influences on increased overdose risk behaviors during the COVID-19 pandemic among people who use drugs in Rhode Island: a qualitative investigation.

BACKGROUND: The ongoing COVID-19 pandemic has disproportionately affected structurally vulnerable populations including people who use drugs (PWUD). Increased overdose risk behaviors among PWUD during the pandemic have been documented, with research underscoring the role of influencing factors such as isolation and job loss in these behaviors. Here, we use qualitative methods to examine the impact of the COVID-19 pandemic and pandemic-related response measures on drug use behaviors in a sample of PWUD in Rhode Island. Using a social-ecological framework, we highlight the nested, interactive levels of the pandemic's influence on increased overdose risk behaviors. METHODS: From July to October 2021, semi-structured interviews were conducted with 18 PWUD who self-reported any increase in behaviors associated with overdose risk (e.g., increased use, change in drug type and/or more solitary drug use) relative to before the pandemic. Thematic analysis was conducted using a codebook with salient themes identified from interview guides and those that emerged through close reading of transcribed interviews. Guided by a social-ecological framework, themes were grouped into individual, network, institutional, and policy-level influences of the pandemic on drug use behaviors. RESULTS: Individual-level influences on increased overdose risk behaviors included self-reported anxiety and depression, isolation and loneliness, and boredom. Network-level influences included changes in local drug supply and changes in social network composition specific to housing. At the institutional level, drug use patterns were influenced by reduced access to harm reduction or treatment services. At the policy level, increased overdose risk behaviors were related to financial changes, job loss, and business closures. All participants identified factors influencing overdose risk behaviors that corresponded to several nested social-ecological levels. CONCLUSIONS: Participants identified multi-level influences of the COVID-19 pandemic and pandemic-related response measures on their drug use behavior patterns and overdose risk. These findings suggest that effective harm reduction during large-scale crises, such as the COVID-19 pandemic, must address several levels of influence concurrently.

Racial/ethnic differences in receipt of naloxone distributed by opioid overdose prevention programs in New York City.

INTRODUCTION: We evaluated racial/ethnic differences in the receipt of naloxone distributed by opioid overdose prevention programs (OOPPs) in New York City (NYC). METHODS: We used naloxone recipient racial/ethnic data collected by OOPPs from April 2018 to March 2019. We aggregated quarterly neighborhood-specific rates of naloxone receipt and other covariates to 42 NYC neighborhoods. We used a multilevel negative binomial regression model to assess the relationship between neighborhood-specific naloxone receipt rates and race/ethnicity. Race/ethnicity was stratified into four mutually exclusive groups: Latino, non-Latino Black, non-Latino White, and non-Latino Other. We also conducted racial/ethnic-specific geospatial analyses to assess whether there was within-group geographic variation in naloxone receipt rates for each racial/ethnic group. RESULTS: Non-Latino Black residents had the highest median quarterly naloxone receipt rate of 41.8 per 100,000 residents, followed by Latino residents (22.0 per 100,000), non-Latino White (13.6 per 100,000) and non-Latino Other residents (13.3 per 100,000). In our multivariable analysis, compared with non-Latino White residents, non-Latino Black residents had a significantly higher receipt rate, and non-Latino Other residents had a significantly lower receipt rate. In the geospatial analyses, both Latino and non-Latino Black residents had the most within-group geographic variation in naloxone receipt rates compared to non-Latino White and Other residents. CONCLUSIONS: This study found significant racial/ethnic differences in naloxone receipt from NYC OOPPs. We observed substantial variation in naloxone receipt for non-Latino Black and Latino residents across neighborhoods, indicating relatively poorer access in some neighborhoods and opportunities for new approaches to address geographic and structural barriers in these locations.

Receipt of Prescription Psychostimulants and Stimulant-Involved Fatal Overdose: A Population-Based Case-Control Study.

Background: Rates of psychostimulant use, misuse, and hospitalization have increased markedly over the past decade. The objective of this study was to estimate the association between receipt of a psychostimulant prescription in the past year and fatal, unintentional psychostimulant-involved overdose. Methods: We conducted a population-based case-control study using linked, state-level databases from the Rhode Island Department of Health. Cases were defined as Rhode Island residents who experienced a fatal, unintentional drug overdose involving a psychostimulant, and controls included non-psychostimulant involved fatal overdoses occurring between May 1, 2017 and May 31, 2020 The primary exposure of interest was receipt of a psychostimulant prescription within 12 months prior to death, ascertained through linkage to the state's prescription drug monitoring program. Conditional logistic regression was used to estimate unadjusted and adjusted odds ratios. Results: Of 894 eligible overdose fatalities, the majority were white/non-Hispanic (72%), mean age was 43 years, and most resided in Providence County (69%). A total of 39 (4%) involved a psychostimulant. After adjusting for year of death and matching by sex, age, and county of residence, cases had 4.1 (95% confidence interval: 1.6, 10.6) times the odds of receiving a prescription stimulant in the past year compared to controls. Conclusions: Our findings suggest that there is a strong, positive association between prescription psychostimulant receipt and psychostimulant-involved fatal overdose. In response to an evolving polysubstance use landscape, current harm reductions measures, including naloxone training, fentanyl test strip distribution, and overdose education, should be expanded to include patients who receive psychostimulant prescriptions.

Racial/Ethnic and Geographic Trends in Combined Stimulant/Opioid Overdoses, 2007-2019.

In the United States, combined stimulant/opioid overdose mortality has risen dramatically over the last decade. These increases may particularly affect non-Hispanic Black and Hispanic populations. We used death certificate data from the US National Center for Health Statistics (2007-2019) to compare state-level trends in overdose mortality due to opioids in combination with 1) cocaine and 2) methamphetamine and other stimulants (MOS) across racial/ethnic groups (non-Hispanic White, non-Hispanic Black, Hispanic, and non-Hispanic Asian American/Pacific Islander). To avoid unstable estimates from small samples, we employed principles of small area estimation and a Bayesian hierarchical model, enabling information-sharing across groups. Black Americans experienced severe and worsening mortality due to opioids in combination with both cocaine and MOS, particularly in eastern states. Cocaine/opioid mortality increased 575% among Black people versus 184% in White people (Black, 0.60 to 4.05 per 100,000; White, 0.49 to 1.39 per 100,000). MOS/opioid mortality rose 16,200% in Black people versus 3,200% in White people (Black, 0.01 to 1.63 per 100,000; White, 0.09 to 2.97 per 100,000). Cocaine/opioid overdose mortality rose sharply among Hispanic and Asian Americans. State-group heterogeneity highlighted the importance of data disaggregation and methods to address small sample sizes. Research to understand the drivers of these trends and expanded efforts to address them are needed, particularly in minoritized groups.

Identifying Predictors of Opioid Overdose Death at a Neighborhood Level With Machine Learning.

Predictors of opioid overdose death in neighborhoods are important to identify, both to understand characteristics of high-risk areas and to prioritize limited prevention and intervention resources. Machine learning methods could serve as a valuable tool for identifying neighborhood-level predictors. We examined statewide data on opioid overdose death from Rhode Island (log-transformed rates for 2016-2019) and 203 covariates from the American Community Survey for 742 US Census block groups. The analysis included a least absolute shrinkage and selection operator (LASSO) algorithm followed by variable importance rankings from a random forest algorithm. We employed double cross-validation, with 10 folds in the inner loop to train the model and 4 outer folds to assess predictive performance. The ranked variables included a range of dimensions of socioeconomic status, including education, income and wealth, residential stability, race/ethnicity, social isolation, and occupational status. The R2 value of the model on testing data was 0.17. While many predictors of overdose death were in established domains (education, income, occupation), we also identified novel domains (residential stability, racial/ethnic distribution, and social isolation). Predictive modeling with machine learning can identify new neighborhood-level predictors of overdose in the continually evolving opioid epidemic and anticipate the neighborhoods at high risk of overdose mortality.

Longitudinal analysis of the prevalence and correlates of heavy episodic drinking and self-reported opioid use among a national cohort of patients with HIV.

BACKGROUND: Heavy episodic drinking (HED) is a risk factor for opioid-related overdose and negatively impacts HIV disease progression. Among a national cohort of patients with HIV (PWH), we examined sociodemographic and clinical correlates of concomitant HED and self-reported opioid use. METHODS: We used data collected from 2002 through 2018 from the Veterans Aging Cohort Study, a prospective cohort including PWH in care at eight US Veterans Health Administration sites. HED was defined as consuming six or more drinks at least once in the year prior to survey collection. We examined the relationship between HED and self-reported opioid use and created a 4-level composite variable of HED and opioid use. We used multinomial logistic regression to estimate odds of reporting concomitant HED and self-reported opioid use. RESULTS: Among 3702 PWH, 1458 (39.4%) reported HED during the study period and 350 (9.5%) reported opioid use. In the multinomial model, compared to reporting neither HED nor opioid use, lifetime housing instability (adjusted odds ratio [aOR] 1.54, 95% confidence interval [CI] 1.01 to 2.35), Veterans Aging Cohort Study Index 2.0 (a measure of disease severity; aOR 1.14, 95% CI 1.02 to 1.28), depressive symptoms (aOR 2.27, 95% CI 1.42 to 3.62), past-year cigarette smoking (aOR 3.06, 95% CI 1.53 to 6.14), cannabis use (aOR 1.69, 95% CI 1.09 to 2.62), and cocaine/stimulant use (aOR 11.54, 95% CI 7.40 to 17.99) were independently associated with greater odds of concomitant HED and self-reported opioid use. Compared to having attended no college, having some college or more (aOR 0.39, 95% CI 0.26 to 0.59) was associated with lower odds of concomitant HED and self-reported opioid use. CONCLUSIONS: Among PWH, concomitant HED and self-reported opioid use are more common among individuals with depressive symptoms and substance use, structural vulnerabilities, and greater illness severity. Efforts to minimize opioid-related risk should address high-risk drinking as a modifiable risk factor for harm among these groups.

Periods of Homelessness Linked to Higher VACS Index Among HIV-Positive People Who Use Drugs.

We sought to evaluate the impact of homelessness on HIV disease progression among people who use unregulated drugs (PWUD) living with HIV and test if this association was mediated by adherence to antiretroviral therapy (ART). We applied general linear mixed-effects modeling to estimate the longitudinal relationship between homelessness and the Veterans Aging Cohort Study (VACS) Index, a validated measure of HIV disease progression that predicts all-cause mortality, among a prospective cohort of PWUD. In a longitudinal model adjusted for ART adherence, homelessness was significantly associated with increased VACS Index scores and 16% of the association was mediated by ART adherence. These findings indicate that homelessness was a significant risk factor for HIV disease progression and this association was marginally mediated by ART adherence. Future studies are needed to quantify the other mechanisms (e.g., food insecurity, mental health) by which homelessness increases mortality risk among PWUD living with HIV.