# Reducing Drug-Related Mortality Using Predictive Analytics: A Randomized, Statewide, Community Intervention Trial

> **NIH NIH R01** · BROWN UNIVERSITY · 2020 · $654,124

## Abstract

PROJECT SUMMARY
Overdose deaths have skyrocketed in the United States since 1999. The epidemic has prompted widespread
federal and state actions, yet the number of people who die of an overdose continues to increase. In light of
the accelerating and rapidly evolving overdose epidemic, new strategies are needed to identify communities
most at risk, and to utilize resources more effectively to curb overdose deaths. To address these public health
priorities, we will develop a forecasting tool to predict overdose deaths before they occur, and then conduct a
randomized, statewide, community-level intervention to evaluate resource targeting based on these
predictions. The study will take place in Rhode Island, a state with the 10th highest rate of overdose fatality in
2016. The study has two phases. First, we will develop a predictive analytics model that forecasts future
overdose mortality at the neighborhood-level, using publicly available information and data from a
multicomponent overdose surveillance system. This tool, called PROVIDENT (Preventing Overdose using
Information and Data from the Environment) will be used to predict the likelihood of magnitude of future
overdose deaths in every neighborhood across Rhode Island. Next, we will conduct a randomized policy
experiment to evaluate whether targeting overdose prevention interventions to neighborhoods at highest risk
reduces overdose morbidity and mortality. The state's department of health will receive PROVIDENT model
predictions for half of the 39 cities/towns in Rhode Island. Within these cities/town, the health department will
work with stakeholders to target overdose prevention interventions to neighborhoods with the highest
probability of future overdose deaths. Interventions include efforts to: (1) prevent high-risk prescribing
(through academic detailing and other educational efforts); (2) expand access to opioid agonist therapy,
including buprenorphine and methadone; (3) increase naloxone distribution (through community and
pharmacy-based efforts); and (4) expand street-based peer recovery coaching and referrals. Control
cities/town will continue to receive these interventions, but without targeting to specific neighborhoods. Fatal
and non-fatal opioid overdose rates in the control cities/towns will be compared to those that received the
PROVIDENT model predictions. To achieve these aims, we will leverage a unique partnership between an
academic institution and a state's health department, which allows for unprecedented access to and sharing
of population-based overdose surveillance data. Our results will improve public health decision-making and
inform resource allocation to communities that should be prioritized for evidence-based prevention, treatment,
recovery, and overdose rescue services. If found to be effective, the PROVIDENT forecasting model will be
disseminated to other states, which could adapt the tool to guide resource allocation and maximize public
health impact. In sum,...

## Key facts

- **NIH application ID:** 10026087
- **Project number:** 5R01DA046620-02
- **Recipient organization:** BROWN UNIVERSITY
- **Principal Investigator:** Magdalena Cerda
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $654,124
- **Award type:** 5
- **Project period:** 2019-09-30 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10026087

## Citation

> US National Institutes of Health, RePORTER application 10026087, Reducing Drug-Related Mortality Using Predictive Analytics: A Randomized, Statewide, Community Intervention Trial (5R01DA046620-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10026087. Licensed CC0.

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