Purpose
This study aims to understand how spatial structures, the interconnections between counties, matter in understanding the coronavirus disease 2019 (COVID-19) period prevalence across the United States.
Methods
We assemble a county-level data set that contains COVID-19–confirmed cases through June 28, 2020, and various sociodemographic measures from multiple sources. In addition to an aspatial regression model, we conduct spatial lag, spatial error, and spatial autoregressive combined models to systematically examine the role of spatial structure in shaping geographical disparities in the COVID-19 period prevalence.
Results
The aspatial ordinary least squares regression model tends to overestimate the COVID-19 period prevalence among counties with low observed rates, but this issue can be effectively addressed by spatial modeling. Spatial models can better estimate the period prevalence for counties, especially along the Atlantic coasts and through the Black Belt. Overall, the model fit among counties along both coasts is generally good with little variability evident, but in the Plain states, the model fit is conspicuous in its heterogeneity across counties.
Conclusions
Spatial models can help partially explain the geographic disparities in the COVID-19 period prevalence. These models reveal spatial variability in the model fit including identifying regions of the country where the fit is heterogeneous and worth closer attention in the immediate short term.
Objective: To investigate how racial/ethnic density and residential segregation shape the uneven burden of COVID-19 in US counties and whether (if yes, how) residential segregation moderates the association between racial/ethnic density and infections. Design: We first merge various risk factors from federal agencies (e.g. Census Bureau and Centers for Disease Control and Prevention) with COVID-19 cases as of June 13th in contiguous US counties (N = 3,042). We then apply negative binomial regression to the county-level dataset to test three interrelated research hypotheses and the moderating role of residential segregation is presented with a figure. Results: Several key results are obtained. (1) Counties with high racial/ethnic density of minority groups experience more confirmed cases than those with low levels of density. (2) High levels of residential segregation between whites and non-whites increase the number of COVID-19 infections in a county, net of other risk factors. (3) The relationship between racial/ethnic density and COVID-19 infections is enhanced with the increase in residential segregation between whites and non-whites in a county. Conclusions: The pre-existing social structure like residential segregation may facilitate the spread of COVID-19 and aggravate racial/ethnic health disparities in infections. Minorities are disproportionately affected by the novel coronavirus and focusing on pre-existing social structures and discrimination in housing market may narrow the uneven burden across racial/ethnic groups.
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