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Classifying healthcare facilities as predictors of COVID-19 mortality rates in US counties (2020-2021)
Journal article   Open access   Peer reviewed

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

Edwin M McCulley, Jana A Hirsch, Alina Schnake-Mahl, Brisa Sanchez, Gina S Lovasi and Usama Bilal
Journal of public health (Oxford, England), Forthcoming
26 Jun 2026
DOI: https://doi.org/10.1093/pubmed/fdag053
PMID: 42361302
url
https://doi.org/10.1093/pubmed/fdag053View
Published, Version of Record (VoR) Open

Abstract

COVID-19 mortality prediction classification healthcare facilities healthcare accessibility
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. 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. 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. 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.

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