Joint exposure to urban-rural status and medically underserved area residence and risk of severe COVID-19 outcomes in 2020.

Introduction: The purpose of this study is to estimate the risk of severe COVID-19 among individuals residing in rural, medically underserved counties compared to those living in other counties.

Methods: Individual-level COVID-19 hospitalization and death data and demographic variables were downloaded from the Centers for Disease Control and Prevention. The 2013 National Center for Health Statistics Urban-Rural Classification Scheme was used to classify urban and rural counties. Health Resources and Services Administration's medically underserved area (MUA) designation was used to identify underserved counties. County-level data were drawn from the 2015-2019 American Community Survey 5-year estimates. Analytic samples included data from Minnesota and Montana in 2020. Urban-rural/MUA joint exposure categories were created: rural/MUA, rural/non-MUA, urban/MUA, urban/non-MUA. Hierarchical logistic regression models estimated associations (odds ratios and 95% confidence intervals (CI)) between rurality, MUA status, joint urban-rural/MUA status, and severe COVID-19, overall and stratified by age and state. Models were adjusted for individual- and county-level demographics.

Results: The odds of severe outcomes among those living in rural counties were 13% lower (95%CI: 0.83-0.91) than those in urban counties. The odds of severe outcomes among those living in MUA counties were 24% higher (95%CI: 1.18-1.30) than those in non-MUA counties. For joint exposure analyses, the odds of severe outcomes were highest among those living in urban/MUA counties compared to those in rural/non-MUA counties (adjusted odds ratio: 1.36, 95%CI: 1.27-1.44).

Conclusion: In 2020, the risk of severe COVID-19 was more pronounced in urban counties and underserved areas. Results highlight the need for locality-based public health recommendations that account for rural and underserved areas and may inform future pandemic preparedness by identifying counties most in need of resources and education at various stages of the pandemic.