Unveiling Spatial Associations Between COVID-19 Severe Health Index, Racial/Ethnic Composition, And Community Factors

AbstractLimited efforts have been made to incorporate various predisposing factors, including racial/ethnic composition, into prediction models exploring the spatial distribution of COVID-19 Severe Health Risk Index (SHRI). This study examines county-level data from 3,107 US counties, utilizing publicly available datasets. Spatial and non-spatial regression models were constructed, adjusting for rurality, socio-demographic factors, physical health, smoking, sleep, health insurance, healthcare providers, hospitalizations, and environmental risks. Findings reveal spatial models effectively explain geospatial disparities of COVID-19 SHRI. White, Hispanic, and other racial/ethnic majority counties exhibit lower burdens compared to majority Black counties. Older population, lower income, smoking, insufficient sleep, and preventable hospitalizations are associated with higher burdens. Counties with better health access and internet coverage experience lower burdens. This study provides insights into at-risk populations, guiding resource allocation. Racial/ethnic inequalities play a significant role in driving disparities. Addressing these factors reduces health outcome disparities. This work establishes a baseline typology for exploring social, health, economic, and political factors contributing to different health outcomes.