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Summary
This summary is machine-generated.

Predictive modeling for overdose risk helps community organizations allocate harm reduction resources. The PROVIDENT model

Keywords:
dashboard implementationoverdose preventionpredictive modeling

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Area of Science:

  • Public Health
  • Data Science
  • Epidemiology

Background:

  • Predictive modeling aids in identifying high-risk neighborhoods for overdose deaths.
  • Machine learning models, like PROVIDENT in Rhode Island, predict overdose risk at the census block group (CBG) level.
  • This predictive capability supports harm reduction resource allocation by community organizations.

Purpose of the Study:

  • To evaluate if CBGs identified by the PROVIDENT model received increased user engagement on an online dashboard.
  • To assess the utility of a predictive overdose forecasting and resource planning dashboard for community organizations.

Main Methods:

  • Utilized modified Poisson regression to estimate prevalence ratios, adjusting for confounding CBG-level characteristics.
  • Analyzed CBG-level data from Rhode Island (N=809) between November 2021 and July 2024.
  • The exposure was whether a CBG was prioritized by the PROVIDENT model and displayed on the interactive dashboard.

Main Results:

  • Dashboard users were 1.0 to 2.4 times more likely to engage with CBGs prioritized and shown on the dashboard.
  • Adjustments were made for prior predictions, engagement, overdose counts, and demographic factors.
  • This indicates a significant association between model prioritization and user interaction with the dashboard.

Conclusions:

  • Interactive mapping tools combined with predictive modeling can effectively support harm reduction organizations.
  • These tools aid in resource allocation to neighborhoods identified as high-risk for future overdose deaths.
  • The PROVIDENT model's dashboard demonstrates potential for enhancing community-based public health interventions.