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Objective: To help understand whether decreased emergency medical services (EMS) utilization due to the COVID-19 pandemic contributed to increased accidental fatal drug overdoses, we characterized recent EMS utilization history among people who had an accidental opioid-involved fatal drug overdose in Rhode Island. Methods: We identified accidental opioid-involved fatal drug overdoses among Rhode Island residents that occurred from January 1, 2018, through December 31, 2020. We linked decedents by name and date of birth to the Rhode Island EMS Information System to obtain EMS utilization history. Results: Among 763 people who had an accidental opioid-involved fatal overdose, 51% had any EMS run and 16% had any opioid overdose–related EMS run in the 2 years before death. Non-Hispanic White decedents were significantly more likely than decedents of other races and ethnicities to have any EMS run (P <.001) and any opioid overdose–related EMS run (P =.05) in the 2 years before death. Despite a 31% increase in fatal overdoses from 2019 through 2020, corresponding with the onset of the COVID-19 pandemic, EMS utilization in the prior 2 years, prior 180 days, or prior 90 days did not vary by time frame of death. Conclusion: In Rhode Island, decreased EMS utilization because of the COVID-19 pandemic was not a driving force behind the increase in overdose fatalities observed in 2020. However, with half of people who had an accidental opioid-involved fatal drug overdose having an EMS run in the 2 years before death, emergency care is a potential opportunity to link people to health care and social services. [ABSTRACT FROM AUTHOR]

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 R 2 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. [ABSTRACT FROM AUTHOR]

The article presents a study of accidental drug overdose deaths in Rhode Island from January 1, 2016 to July 31, 2020, with an aim to develop informed and targeted interventions to prevent deaths in the future. Also cited are the strategies adopted by the Rhode Island Department of Health (RIDOH) to prevent drug-related harms, and the class of drugs that caused deaths like opioids, cocaine, and alcohol.

The article focuses on accidental drug overdose deaths increased by over 30% in the U.S., with over 93,000 predicted deaths, while accidental drug overdose deaths have declined from 2016 to 2019 in Rhode Island. Topics include Rhode Island mirrored national trends and the number of accidental drug overdose deaths increased by 25%, and the provisional demographics data shows that drug overdose deaths in 2020 increased across all age and race/ethnicity groups when compared to 2019.