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Investigating the opioid epidemic across the United States: Associations between county-level characteristics and overdose mortality

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Abstract

The opioid crisis remains a critical public health challenge in the United States. Despite national efforts that reduced opioid prescribing by nearly 44% between 2011 and 2021, opioid overdose deaths more than tripled during the same period. This alarming trend reflects a major shift in the crisis, with illegal opioids now driving the majority of overdose deaths instead of prescription opioids. Although supply-side factors fueling this transition have been widely studied, the structural and community-level conditions that shape overdose mortality are less well understood. To help address this gap, this study has three primary objectives: (1) overcome structural gaps in national data to construct a complete nationwide county-level dataset from 2010 to 2022; (2) using data analysis, identify and investigate spatiotemporal anomalies in overdose mortality; and (3) using two machine-learning models, quantify the importance of thirteen social vulnerability variables in predicting overdose mortality. Our results identify unemployment and limited vehicle access as key county-level predictors of overdose mortality. Higher levels of these vulnerabilities are associated with elevated mortality, whereas lower levels are associated with reduced mortality. These findings highlight factors that may be relevant for public health planning and policy prioritization within the context of the opioid crisis.

Original languageEnglish
Article number101759
JournalInformatics in Medicine Unlocked
Volume63
DOIs
StatePublished - Jun 2026

Funding

This initiative is sponsored by the U.S. Department of Veterans Affairs (VA) and utilizes VA-funded resources from the Knowledge Discovery Infrastructure (KDI) at Oak Ridge National Laboratory under the Department of Energy (DOE) Office of Science. The manuscript has been authored by UT-Battelle LLC under contract DE-AC05-00OR22725 with the DOE. The US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce this manuscript or allow others to do so for US government purposes. DOE will provide public access to these results in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). The initiative aims to improve care for Veterans within the Veterans Health Administration (VHA) and involves models specifically tailored to the VHA system. The results are not generalizable outside of VHA, and the studies are formally considered non-research by the VHA. Oak Ridge National Laboratory received Institutional Review Board (IRB) approval for the secondary use of patient data, which complies with internal policies and standards. The authors acknowledge the broader partnership and express gratitude to the Veterans receiving care at the VA.

Keywords

  • Anomaly analysis
  • Autoencoder
  • Data analysis
  • Machine learning
  • Opioid crisis
  • XGBoost

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