An Overdose Forecasting Dashboard for Local Harm-Reduction Response

Abstract

As the United States grapples with an ongoing overdose crisis, states and jurisdictions are adopting novel approaches to reduce overdose mortality. In one novel approach, public health researchers and leaders in Rhode Island leveraged the state’s robust surveillance data and collaborations between government, academic, and community-based organizations (CBOs) to launch the PROVIDENT (PReventing OVerdose using Information and Data from the EnvironmeNT) project, a population-based randomized controlled research trial (NCT05096429) in December 2019. The PROVIDENT trial utilizes machine learning (ML) methods to identify neighborhoods at risk of future overdose deaths at the census-block-group level to inform community-level overdose prevention resource distribution. To disseminate the ML model predictions, our research team developed an interactive, online mapping dashboard in close collaboration with three statewide CBOs. We measured whether these organizations utilized the PROVIDENT dashboard to allocate harm-reduction services based on ML model predictions and collected information about their data-driven decision-making processes. This case study describes how we assembled and piloted this overdose forecasting dashboard for use by CBOs between November 2021 and August 2024. By measuring dashboard logins, completed surveys, and engagement with ongoing training, we illustrate how organizations utilized ML and surveillance data to inform their outreach efforts and generate valuable insights at a neighborhood level.