Determinants of COVID-19 Incidence and Mortality in the US: Spatial Analysis
Abstract
OBJECTIVES
The US continues to account for the highest proportion of the global Coronavirus Disease-2019 (COVID-19) cases and deaths. Currently, it is important to contextualize the spread and success of mitigation efforts. The objective of this study was to assess the ecological determinants (policy, health behaviors, socio-economic, physical environment, and clinical care) of COVID-19 incidence and mortality in the US.
METHODS
Data from the New York Times’ COVID-19 repository (01/21/2020-10/27/2020), 2020 County Health Rankings, 2016 County Presidential Election Returns, and 2018-2019 Area Health Resource File were used. County-level logged incidence and mortality rate/million were modeled using the Spatial Autoregressive Combined model and spatial lag model.
RESULTS
Counties with higher proportions of racial minorities (African American β= 0.007, Native Americans β= 0.008, Hispanics β= 0.015), non-English speakers (β= 0.010), population density ([logged] β= 0.028), and air pollution (β= 0.062) were significantly associated with high COVID-19 incidence rates. Subsequently, counties with higher Republican voters (β= 0.017), excessive drinkers (β= 0.107), children in single-parent households (β= 0.018), uninsured adults (β= 0.038), racial minorities (African American β= 0.032, Native Americans β= 0.034, Hispanics β= 0.037), females (β= 0.101), and population density ([logged] β= 0.270), air pollution (β= 0.130), and non-Whites/Whites’ residential segregation (β= 0.014) were significantly associated with high COVID-19 mortality rates. Additionally, longer state-level restrictions were associated with lower COVID-19 incidence and mortality rates.
CONCLUSIONS
The spatial models identified longer state-level restrictions, population density, air pollution, uninsured rate, and race/ethnicity as important determinants of the geographic disparities in COVID-19 incidence and mortality.
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