Search

Objective: Examine differences in neighborhood characteristics and services between overdose hotspot and non-hotspot neighborhoods and identify neighborhood-level population factors associated with increased overdose incidence., Methods: We conducted a population-based retrospective analysis of Rhode Island, USA residents who had a fatal or non-fatal overdose from 2016 to 2020 using an environmental scan and data from Rhode Island emergency medical services, State Unintentional Drug Overdose Reporting System, and the American Community Survey. We conducted a spatial scan via SaTScan to identify non-fatal and fatal overdose hotspots and compared the characteristics of hotspot and non-hotspot neighborhoods. We identified associations between census block group-level characteristics using a Besag-York-Mollié model specification with a conditional autoregressive spatial random effect., Results: We identified 7 non-fatal and 3 fatal overdose hotspots in Rhode Island during the study period. Hotspot neighborhoods had higher proportions of Black and Latino/a residents, renter-occupied housing, vacant housing, unemployment, and cost-burdened households. A higher proportion of hotspot neighborhoods had a religious organization, a health center, or a police station. Non-fatal overdose risk increased in a dose responsive manner with increasing proportions of residents living in poverty. There was increased relative risk of non-fatal and fatal overdoses in neighborhoods with crowded housing above the mean (RR 1.19 [95 % CI 1.05, 1.34]; RR 1.21 [95 % CI 1.18, 1.38], respectively)., Conclusion: Neighborhoods with increased prevalence of housing instability and poverty are at highest risk of overdose. The high availability of social services in overdose hotspots presents an opportunity to work with established organizations to prevent overdose deaths., Competing Interests: Declaration of competing interest None., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

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., Competing Interests: The authors report no conflicts of interest., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

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., (©Jesse Yedinak, Maxwell S Krieger, Raynald Joseph, Stacey Levin, Sarah Edwards, Dennis A Bailer, Jonathan Goyer, Colleen Daley Ndoye, Cathy Schultz, Jennifer Koziol, Rachael Elmaleh, Benjamin D Hallowell, Todd Hampson, Ellen Duong, Abdullah Shihipar, William C Goedel, Brandon DL Marshall. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 12.02.2024.)

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., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

The association between recent release from incarceration and dramatically increased risk of fatal overdose is well-established at the individual level. Fatal overdose and. arrest/release are spatially clustered, suggesting that this association may persist at the neighborhood level. We analyzed multicomponent data from Rhode Island, 2016-2020, and observed a modest association at the census tract level between rates of release per 1000 population and fatal overdose per 100,000 person-years, adjusting for spatial autocorrelation in both the exposure and outcome. Our results suggest that for each additional person released to a given census tract per 1000 population, there is a corresponding increase in the rate of fatal overdose by 2 per 100,000 person years. This association is more pronounced in suburban tracts, where each additional release awaiting trial is associated with an increase in the rate of fatal overdose of 4 per 100,000 person-years and 6 per 100,000 person-years for each additional release following sentence expiration. This association is not modified by the presence or absence of a licensed medication for opioid use disorder (MOUD) treatment provider in the same or surrounding tracts. Our results suggest that neighborhood-level release rates are moderately informative as to tract-level rates of fatal overdose and underscore the importance of expanding pre-release MOUD access in correctional settings. Future research should explore risk and resource environments particularly in suburban and rural areas and their impacts on overdose risk among individuals returning to the community., Competing Interests: Declaration of Competing Interest The authors report no conflicts of interest., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Objectives: To compare the characteristics of individual overdose decedents in Rhode Island, 2016-2020 to the neighborhoods where fatal overdoses occurred over the same time period., Methods: We conducted a retrospective analysis of fatal overdoses occurring between January 1, 2016 and June 30, 2020. Using individual- and neighborhood-level data, we conducted descriptive analyses to explore the characteristics of individuals and neighborhoods most affected by overdose., Results: Most overdose decedents during the study period were non-Hispanic White. Across increasingly more White and non-Hispanic neighborhoods, rates of fatal overdose per 100,000 person-years decreased. An opposite pattern was observed across quintiles of average neighborhood poverty., Conclusions: Rates of fatal overdose were higher in less White, more Hispanic, and poorer neighborhoods, suggesting modest divergence between the characteristics of individuals and the neighborhoods most severely affected. These impacts may not be uniform across space and may accrue differentially to more disadvantaged and racially/ethnically diverse neighborhoods.

Background and Aims: In light of the accelerating drug overdose epidemic in North America, new strategies are needed to identify communities most at risk to prioritize geographically the existing public health resources (e.g. street outreach, naloxone distribution efforts). We aimed to develop PROVIDENT (Preventing Overdose using Information and Data from the Environment), a machine learning-based forecasting tool to predict future overdose deaths at the census block group (i.e. neighbourhood) level., Design: Randomized, population-based, community intervention trial., Setting: Rhode Island, USA., Participants: All people who reside in Rhode Island during the study period may contribute data to either the model or the trial outcomes., Intervention: Each of the state's 39 municipalities will be randomized to the intervention (PROVIDENT) or comparator condition. An interactive, web-based tool will be developed to visualize the PROVIDENT model predictions. Municipalities assigned to the treatment arm will receive neighbourhood risk predictions from the PROVIDENT model, and state agencies and community-based organizations will direct resources to neighbourhoods identified as high risk. Municipalities assigned to the control arm will continue to receive surveillance information and overdose prevention resources, but they will not receive neighbourhood risk predictions., Measurements: The primary outcome is the municipal-level rate of fatal and non-fatal drug overdoses. Fatal overdoses will be defined as unintentional drug-related death; non-fatal overdoses will be defined as an emergency department visit for a suspected overdose reported through the state's syndromic surveillance system. Intervention efficacy will be assessed using Poisson or negative binomial regression to estimate incidence rate ratios comparing fatal and non-fatal overdose rates in treatment vs. control municipalities., Comments: The findings will inform the utility of predictive modelling as a tool to improve public health decision-making and inform resource allocation to communities that should be prioritized for prevention, treatment, recovery and overdose rescue services., (© 2021 Society for the Study of Addiction.)

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., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Overdose deaths across the country have spiked since the onset of the COVID-19 pandemic. It is crucial now, more than ever, to address the continuing and worsening, complex and dynamic opioid and overdose epidemics. In 2018, The Center of Biomedical Research Excellence (COBRE) on Opioids and Overdose, based at Rhode Island Hospital, launched with three major goals: 1) establish a center of scientific excellence on opioids and overdose; 2) train the next generation of scientists to become independent investigators and address the opioid and overdose crises; and 3) contribute to the scientific progress and solutions to combat these epidemics. To date, we have made substantial progress. While the opioid and overdose crises continue to evolve, the COBRE on Opioid and Overdose and its team of investigators are well poised to address the daunting task of understanding and meaningfully addressing these deadly epidemics, with the ultimate goal of saving lives.

Introduction: Effective approaches to increase engagement in treatment for opioid use disorder (OUD) and reduce the risk of recurrent overdose and death following emergency department (ED) presentation for opioid overdose remain unknown. As such, we aim to compare the effectiveness of behavioural interventions delivered in the ED by certified peer recovery support specialists relative to those delivered by licensed clinical social workers (LCSWs) in promoting OUD treatment uptake and reducing recurrent ED visits for opioid overdose., Methods and Analysis: Adult ED patients who are at high risk for opioid overdose (ie, are being treated for an opioid overdose or identified by the treating physician as having OUD) (n=650) will be recruited from two EDs in a single healthcare system in Providence, Rhode Island into a two-arm randomised trial with 18 months of follow-up postrandomisation. Eligible participants will be randomly assigned (1:1) in the ED to receive a behavioural intervention from a certified peer recovery support specialist or a behavioural intervention from an LCSW. The primary outcomes are engagement in formal OUD treatment within 30 days of the initial ED visit and recurrent ED visits for opioid overdose within 18 months of the initial ED visit, as measured through statewide administrative records., Ethics and Dissemination: This protocol was approved by the Rhode Island Hospital institutional review board (Approval Number: 212418). Data will be presented at national and international conferences and published in peer-reviewed journals., Trial Registration Number: NCT03684681., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: The United States is in the midst of an opioid overdose crisis. Little is known about the role of environmental factors in increasing risk of fatal opioid overdose., Methods: We conducted a case-crossover analysis of 3,275 opioid overdose deaths recorded in Connecticut and Rhode Island in 2014-2017. We compared the mean ambient temperature on the day of death, as well as average temperature up to 14 days before death, to referent periods matched on year, month, and day of week., Results: Low average temperatures over the 3-7 days before death were associated with higher odds of fatal opioid overdose. Relative to 11°C, an average temperature of 0°C over the 7 days before death was associated with a 30% higher odds of death (odds ratio: 1.3; 95% confidence interval, 1.1, 1.5)., Conclusions: Low average temperature may be associated with higher risk of death due to opioid overdose.

Background: The timing of social service benefit issuance is thought to be associated with increased drug overdose fatalities. However, the extent to which this excess mortality is concentrated in communities with higher levels of benefit receipt has not been studied. We sought to examine if benefit receipt at the neighborhood level was associated with spatiotemporal patterns of overdose fatalities., Methods: We conducted a retrospective review of all accidental overdose deaths recorded in Rhode Island from 2014 to 2016 (n = 838). Overdose incident locations were geocoded to the census block group level. Clusters of census block groups with excess overdose mortality at the beginning of a month were identified using spatial scan methods. Logistic regression models were fit to identify characteristics associated with the inclusion of a census block group within a cluster., Results: Increased rates of overdose fatalities at the beginning of a month were observed relative to the end of a preceding month (Ratio: 1.17; 95% CI: 1.04, 1.38). The proportions of residents receiving cash public assistance or Supplemental Security Income were not associated with excess mortality at the beginning of a month; however, the proportion of residents living in unaffordable housing was (OR: 1.42; 95% CI: 1.05, 1.91)., Conclusion: Despite previous research on benefit check issuance and overdose, welfare receipt was not associated with excess overdose mortality at the beginning of a month at the neighborhood level. Future research on housing cost burden and its influence on overdose death risk at the individual level is needed., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Background: The overdose epidemic has been exacerbated by a dramatic increase in deaths involving illicitly manufactured fentanyl (IMF). Drug checking is a novel strategy to identify IMF in illicit drugs. We examined the uptake and acceptability of rapid fentanyl test strips among young adults., Methods: From May to September 2017, we recruited 93 young adults in Rhode Island who reported injecting drugs or using heroin, cocaine, or illicitly obtained prescription pills in the past 30 days. Participants were asked to test either their urine after drug use (post-consumption) or a drug sample prior to use (pre-consumption) using rapid fentanyl test strips. After a questionnaire and a brief training, participants received ten strips for their personal use and were asked to return for a one-month follow-up visit, which assessed the uptake and acceptability of the rapid strips tests and the behavioral outcomes associated with receipt of a positive test., Results: Of the 81 (87%) participants who returned for follow-up and who had complete data, the mean age was 27, 45 (56%) were male, and 37 (46%) were non-white. A total of 62 participants (77%) reported using at least one test strip. Of these, 31 (50%) received at least one positive result. A positive result was associated with older age, homelessness, heroin use, injection drug use, ever witnessing an overdose, and concern about overdose or drugs being laced with fentanyl (all p < 0.05). Receiving a positive result was significantly associated with reporting a positive change in overdose risk behavior between baseline and follow-up (p ≤  0.01). Among all participants, 79 (98%) reported confidence in their ability to use the test strips and 77 (95%) wanted to use them in the future., Conclusions: Young adults reported high uptake and acceptability of fentanyl test strips to detect IMF in illicit drugs., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Background: In light of the accelerating and rapidly evolving overdose crisis in the United States (US), new strategies are needed to address the epidemic and to efficiently engage and retain individuals in care for opioid use disorder (OUD). Moreover, there is an increasing need for novel approaches to using health data to identify gaps in the cascade of care for persons with OUD.Methods and Findings: Between June 2018 and May 2019, we engaged a diverse stakeholder group (including directors of statewide health and social service agencies) to develop a statewide, patient-centered cascade of care for OUD for Rhode Island, a small state in New England, a region highly impacted by the opioid crisis. Through an iterative process, we modified the cascade of care defined by Williams et al. for use in Rhode Island using key national survey data and statewide health claims datasets to create a cross-sectional summary of 5 stages in the cascade. Approximately 47,000 Rhode Islanders (5.2%) were estimated to be at risk for OUD (stage 0) in 2016. At the same time, 26,000 Rhode Islanders had a medical claim related to an OUD diagnosis, accounting for 55% of the population at risk (stage 1); 27% of the stage 0 population, 12,700 people, showed evidence of initiation of medication for OUD (MOUD, stage 2), and 18%, or 8,300 people, had evidence of retention on MOUD (stage 3). Imputation from a national survey estimated that 4,200 Rhode Islanders were in recovery from OUD as of 2016, representing 9% of the total population at risk. Limitations included use of self-report data to arrive at estimates of the number of individuals at risk for OUD and using a national estimate to identify the number of individuals in recovery due to a lack of available state data sources.Conclusions: Our findings indicate that cross-sectional summaries of the cascade of care for OUD can be used as a health policy tool to identify gaps in care, inform data-driven policy decisions, set benchmarks for quality, and improve health outcomes for persons with OUD. There exists a significant opportunity to increase engagement prior to the initiation of OUD treatment (i.e., identification of OUD symptoms via routine screening or acute presentation) and improve retention and remission from OUD symptoms through improved community-supported processes of recovery. To do this more precisely, states should work to systematically collect data to populate their own cascade of care as a health policy tool to enhance system-level interventions and maximize engagement in care. [ABSTRACT FROM AUTHOR]