Preventing Overdose Using Information and Data from the Environment (PROVIDENT): protocol for a randomized, population-based, community intervention trial

Brandon D L Marshall, Nicole Alexander-Scott, Jesse L Yedinak, Benjamin D Hallowell, William C Goedel, Bennett Allen, Robert C Schell, Yu Li, Maxwell S Krieger, Claire Pratty, Jennifer Ahern, Daniel B Neill, Magdalena Cerdá, Brandon D L Marshall, Nicole Alexander-Scott, Jesse L Yedinak, Benjamin D Hallowell, William C Goedel, Bennett Allen, Robert C Schell, Yu Li, Maxwell S Krieger, Claire Pratty, Jennifer Ahern, Daniel B Neill, Magdalena Cerdá

Abstract

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.

Trial registration: ClinicalTrials.gov NCT05096429.

Keywords: Machine learning; RCT; United States; overdose; overdose mortality; overdose risk; predictive analytics; predictive modelling; protocol.

Conflict of interest statement

DECLARATION OF INTERESTS

All authors have no conflicts of interest in undertaking this research.

© 2021 Society for the Study of Addiction.

Figures

FIGURE 1
FIGURE 1
Geographical distribution of unintentional drug-related overdose deaths in Rhode Island by location of injury, aggregated to the census block group, July 2016–June 2019
FIGURE 2
FIGURE 2
PROVIDENT community intervention trial logic model

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