Spatial Patterns of COVID-19 Mortality: Examining Socioeconomic Determinants in U.S. Counties Using Cluster Analysis
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Aim
This study aims to investigate the spatial patterns of COVID-19 mortality across U.S. counties and identify the socioeconomic determinants that influence these mortality trends, using spatial epidemiological methods.
Subject and Methods
We conducted a spatial analysis of COVID-19 mortality data from over 3,000 U.S. counties, applying cluster detection techniques, including SatScan, to identify areas with significant mortality trends. Spatial regression models, including spatial lag and spatial error models, were employed to examine the impact of socioeconomic variables, such as race, income inequality, and insurance rates, on COVID-19 mortality. The analysis controlled for multicollinearity and spatial autocorrelation in the data.
Results
Counties with higher proportions of Black populations and higher uninsured rates exhibited significantly lower COVID-19 trends over the study period. Spatial clustering revealed regions in the northwestern and eastern/northeastern United States with a mix of positive and negative mortality rate trends. The spatial lag model showed the strongest fit, confirming the importance of spatial dependency in explaining mortality patterns.
Conclusion
This study highlights the significant spatial disparities in COVID-19 mortality across U.S. counties. The findings emphasize the need for targeted public health interventions in vulnerable regions to address these disparities.
