Determinants of COVID-19 Case Fatality Rate in the United States: Spatial Analysis Over One Year of the Pandemic

• Main Authors: Niranjan J. Kathe, Rajvi J. Wani
• Format: Article
• Language: English
• Published: Columbia Data Analytics, LLC 2021-05-01
• Series: Journal of Health Economics and Outcomes Research
• Online Access: https://jheor.scholasticahq.com/article/22978-determinants-of-covid-19-case-fatality-rate-in-the-united-states-spatial-analysis-over-one-year-of-the-pandemic.pdf

**Background:** The United States continues to account for the highest proportion of the global Coronavirus Disease-2019 (COVID-19) cases and deaths. Currently, it is important to contextualize COVID-19 fatality to guide mitigation efforts. **Objectives:** The objective of this study was to assess the ecological factors (policy, health behaviors, socio-economic, physical environment, and clinical care) associated with COVID-19 case fatality rate (CFR) in the United States. **Methods:** Data from the New York Times’ COVID-19 repository and the Centers for Disease Control and Prevention Data (01/21/2020 - 02/27/2021) were used. County-level CFR was modeled using the Spatial Durbin model (SDM). The SDM estimates were decomposed into direct and indirect impacts. **Results:** The study found percent positive for COVID-19 (0.057% point), stringency index (0.014% point), percent diabetic (0.011% point), long-term care beds (log) (0.010% point), premature age-adjusted mortality (log) (0.702 % point), income inequality ratio (0.078% point), social association rate (log) (0.014% point), percent 65 years old and over (0.055% point), and percent African Americans (0.016% point) in a given county were positively associated with its COVID-19 CFR. The study also found food insecurity, long-term beds (log), mental health-care provider (log), workforce in construction, social association rate (log), and percent diabetic of a given county as well as neighboring county were associated with given county’s COVID-19 CFR, indicating significant externalities. **Conclusion:** The spatial models identified percent positive for COVID-19, stringency index, elderly, college education, race/ethnicity, residential segregation, premature mortality, income inequality, workforce composition, and rurality as important ecological determinants of the geographic disparities in COVID-19 CFR.