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.

Prior Emergency Medical Services Utilization Among People Who Had an Accidental Opioid-Involved Fatal Drug Overdose-Rhode Island, 2018-2020.

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.

Using Emergency Medical Services Data to Monitor Nonfatal Opioid Overdoses in Real Time : Development, Validation, and Use of a Case Definition, Rhode Island, 2018.

OBJECTIVE: No case definition exists that allows public health authorities to accurately identify opioid overdoses using emergency medical services (EMS) data. We developed and evaluated a case definition for suspected nonfatal opioid overdoses in EMS data. METHODS: To identify suspected opioid overdose-related EMS runs, in 2019 the Rhode Island Department of Health (RIDOH) developed a case definition using the primary impression, secondary impression, selection of naloxone in the dropdown field for medication given, indication of medication response in a dropdown field, and keyword search of the report narrative. We developed the case definition with input from EMS personnel and validated it using an iterative process of random medical record review. We used naloxone administration in consideration with other factors to avoid misclassification of opioid overdoses. RESULTS: In 2018, naloxone was administered during 2513 EMS runs in Rhode Island, of which 1501 met our case definition of a nonfatal opioid overdose. Based on a review of 400 randomly selected EMS runs in which naloxone was administered, the RIDOH case definition accurately identified 90.0% of opioid overdoses and accurately excluded 83.3% of non-opioid overdose-related EMS runs. Use of the case definition enabled analyses that identified key patterns in overdose locations, people who experienced repeat overdoses, and the creation of hotspot maps to inform outbreak detection and response. PRACTICE IMPLICATIONS: EMS data can be an effective tool for monitoring overdoses in real time and informing public health practice. To accurately identify opioid overdose-related EMS runs, the use of a comprehensive case definition is essential.