Classifying healthcare facilities as predictors of COVID-19 mortality rates in US counties (2020–2021)

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ID: 318845
2026
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Abstract
Abstract Background The COVID-19 pandemic disproportionately impacted vulnerable populations, with contextual factors like healthcare accessibility influencing mortality. However, limited evidence exists on which types of healthcare facilities affect COVID-19 death rates. Methods We examined which facility types were statistically associated with, and improved prediction of, county-level COVID-19 mortality (2020–2021) using over dispersed Poisson models and healthcare facility data from the 2020 National Establishment Time Series database. Five feature selection strategies guided model construction: a theory-driven approach, three data-driven methods [Least Absolute Shrinkage and Selection Operator (LASSO), stepwise, and random forest], and a synthesized strategy integrating shared predictors. Results Based on Quasi-Akaike’s Information Criterion (QAIC), LASSO and stepwise models offered the best fit. Across methods, consistent predictors of county-level COVID-19 mortality rates included pharmacies/drug stores, hospitals and major medical centers, emergency medical transport, offices and clinics of health practitioners, and urgent care facilities. Data-driven strategies also selected chiropractors, highlighting potential confounding bias. Conclusions Our classification approach highlights facility types associated with COVID-19 mortality, offering insight into how healthcare infrastructure may influence pandemic-related health outcomes. These findings can support descriptive characterizations of local medical environments, generate hypotheses, and guide future research aimed at improving population health during public health emergencies.
Reference Key
openalex_W7166124779 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Edwin McCulley, Jana A. Hirsch, Alina Schnake‐Mahl, Brisa Sanchez, Gina S. Lovasi, Usama Bilal
Journal turkish journal of public health
Year 2026
DOI
10.1093/pubmed/fdag053
URL
https://doi.org/10.1093/pubmed/fdag053
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