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Context: Data dashboards have emerged as critical tools for surveillance and informing resource allocation. Despite their utility and popularity during COVID-19, there is a growing need to understand what tools and training are tailored to nonprofit community-based organizations that may partner with public health officials., Program: In June 2021, the Rhode Island Department of Health and Brown University partnered to create Project SIGNAL (Spatiotemporal Insights to Guide Nuanced Actions Locally), which utilizes spatiotemporal analytics to identify Rhode Island's largest disparities in COVID-19-related outcomes (eg, testing, diagnosis, vaccinations) at the neighborhood level. Results were hosted in an interactive online dashboard (signal-ri.org) designed using principles of the CDC Clear Communication Index. The target audience included a network of 15 geographic areas called Health Equity Zones, funded by the health department to provide critical grassroots public health programs to address social, health, and economic outcomes in their communities., Implementation: To disseminate the dashboard, a 6-hour virtual workshop series was created to train leaders to use the dashboard and increase their confidence in understanding common public health data terminology and concepts and better prepare attendees for rapid decision making during future public health emergencies., Evaluation: The Project SIGNAL dashboard was launched in August 2022 and has been accessed over 7500 times. A total of 84 community leaders were trained to use this dashboard, increasing their confidence in applying common public health metrics to make decisions about their COVID-19-related activities., Discussion: While several studies have outlined best practices for data dashboards, this is among the first to examine incorporating these practices into a spatiotemporal decision tool designed specifically for community organizations. Project SIGNAL demonstrates that by incorporating design best practices and pairing data dashboards with hands-on training, we can empower community leaders to utilize advanced spatiotemporal methods to identify health disparities and take localized action., Competing Interests: The authors have indicated that they have no potential conflicts of interest to disclose ., (Copyright © 2024 Wolters Kluwer Health, Inc. 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.)

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.)

Objective: Accurate naloxone distribution data are critical for planning and prevention purposes, yet sources of naloxone dispensing data vary by location, and completeness of local datasets is unknown. We sought to compare available datasets in Massachusetts, Rhode Island, and New York City (NYC) to a commercially available pharmacy national claims dataset (Symphony Health Solutions)., Data Sources and Study Setting: We utilized retail pharmacy naloxone dispensing data from NYC (2018-2019), Rhode Island (2013-2019), and Massachusetts (2014-2018), and pharmaceutical claims data from Symphony Health Solutions (2013-2019)., Study Design: We conducted a descriptive, retrospective, and secondary analysis comparing naloxone dispensing events (NDEs) captured via Symphony to NDEs captured by local datasets from the three jurisdictions between 2013 and 2019, when data were available from both sources, using descriptive statistics, regressions, and heat maps., Data Collection/extraction Methods: We defined an NDE as a dispensing event documented by the pharmacy and assumed that each dispensing event represented one naloxone kit (i.e., two doses). We extracted NDEs from local datasets and the Symphony claims dataset. The unit of analysis was the ZIP Code annual quarter., Principal Findings: NDEs captured by Symphony exceeded those in local datasets for each time period and location, except in RI following legislation requiring NDEs to be reported to the PDMP. In regression analysis, absolute differences in NDEs between datasets increased substantially over time, except in RI before the PDMP. Heat maps of NDEs by ZIP code quarter showed important variations reflecting where pharmacies may not be reporting NDEs to Symphony or local datasets., Conclusions: Policymakers must be able to monitor the quantity and location of NDEs in order to combat the opioid crisis. In regions where NDEs are not required to be reported to PDMPs, proprietary pharmaceutical claims datasets may be useful alternatives, with a need for local expertise to assess dataset-specific variability., (© 2023 Health Research and Educational Trust.)

Background: The North American overdose crisis has continued at unprecedented rates with more than 100,000 overdose deaths estimated to have occurred in the United States in 2022. Regional variations in overdose rates signify differences in local drug supplies. State-level drug supply surveillance systems have been limited in their ability to document and communicate the rapidly changing drug supplies which can hinder harm reduction efforts at the community level. We sought to address by piloting a two-year, community-engaged local drug supply surveillance program in Rhode Island (RI)., Methods: The first set of samples (n = 125) were collected from May 2022 to January 2023 across RI and included used paraphernalia (e.g., cookers), refuse (e.g., baggies), and product. Samples were tested using comprehensive toxicology testing approaches via liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS). Results were disseminated to participants and the broader public across platforms., Results: Fentanyl was detected in 67.2% of all samples tested. 39.2% (n = 49) of samples were expected to be fentanyl. Xylazine was detected in 41.6% of all samples-always in combination with fentanyl-and no samples were expected to contain xylazine. In expected stimulant samples (n = 39), 10% contained fentanyl and/or analogues as major substances and 30.8% contained trace amounts of fentanyl and/or analogues. In expected stimulant samples, 15.4% contained xylazine with fentanyl. No opioids or benzodiazepines were detected in expected hallucinogen or dissociative samples (n = 7). In expected benzodiazepine samples (n = 8), no opioids were detected., Conclusions: Our results describe part of the local drug supply in Rhode Island, including a presence of NPS and adulterants (e.g., designer benzodiazepines, xylazine). Importantly, our findings underscore the feasibility of developing a community-driven drug supply surveillance database. Expanding drug supply surveillance initiatives is imperative for improving the health and safety of people who use drugs and informing public health approaches to addressing the overdose crisis., Competing Interests: Declarations of Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

Background: Buprenorphine is a unique μ-opioid receptor partial agonist with avid receptor binding, nominal euphoric reward, and a ceiling effect on sedation and respiratory depression. Despite a pharmacologic profile that enhances safety, cases of fatal opioid overdose with buprenorphine on postmortem toxicology are reported, but details of these cases in the literature are limited., Methods: A retrospective review of opioid-involved drug overdose fatalities in Rhode Island (RI) from 2016 to 2018 using the RI Department of Health State Unintentional Drug Overdose Reporting System (SUDORS) database. Deaths with buprenorphine on toxicology testing versus opioid-involved overdose deaths without buprenorphine were compared to assess the type and number of co-exposures., Results: Of 534 opioid-involved deaths, 29 (5.4%) included buprenorphine and/or norbuprenorphine on toxicology. Most frequent co-exposures are as follows: fentanyl (75.9%), norfentanyl (72.4%), cocaine (41.4%), benzoylecgonine (41.4%), cannabinoids (31.0%), ethanol (31.0%), levamisole (31.0%), and free morphine (31.0%). An average number of co-exposures for fatalities with buprenorphine were 9.24 versus 6.68 in those without buprenorphine. In one case buprenorphine was the only drug listed to cause death; all other fatalities with buprenorphine on toxicology reported additional drugs contributing to death., Conclusion: Decedents with buprenorphine detected on toxicology testing commonly had documented polysubstance use. Although data are limited, buprenorphine may provide some risk mitigation against full agonist opioid overdose including fentanyl. Further work should explore the use of postmortem concentrations of buprenorphine, norbuprenorphine, and other opioid metabolites to determine the role of buprenorphine in fatal overdose pharmacology.