Toward Proactive Overdose Prevention Through Spatiotemporal Cluster Prediction
DOI:
https://doi.org/10.54097/swk9h978Keywords:
Spatiotemporal clustering, overdose prediction, opioid epidemic, space-time scan statistics, Bayesian hierarchical modeling, geospatial epidemiology, proactive prevention, SCPAbstract
Drug overdose mortality in the United States has reached unprecedented levels, with opioid-involved deaths surpassing 80,000 annually and continuing to escalate despite sustained public health investment. The reactive architecture of most existing prevention strategies—deploying naloxone, expanding treatment capacity, or routing outreach workers only after overdose clusters have already been documented—has proven insufficient to prevent loss of life at scale. This paper proposes and evaluates a proactive spatiotemporal cluster prediction (SCP) framework designed to shift overdose surveillance from retrospective hotspot mapping toward prospective risk forecasting. By integrating historical overdose death records, socioeconomic covariates derived from the American Community Survey (ACS), and space-time permutation scan statistics with Bayesian hierarchical regression, the proposed framework generates one-year-ahead census-tract-level predictions of overdose cluster probability. Applied to publicly available county and tract-level mortality data from 2015 through 2022, the model identifies the top 10% of predicted high-risk tracts as the locus of approximately 74% of observed overdose deaths in held-out evaluation years. Findings indicate that socially vulnerable, economically deprived, and historically clustered tracts account for a disproportionate share of forward-predicted high-risk areas, and that proximity to naloxone access points independently attenuates predicted cluster formation risk. The paper discusses implications for pre-positioning harm reduction resources, mobile health service routing, and policy-level allocation of overdose prevention funding.
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