Abstract
Aim It is well known that social determinants of health (SDoH) have affected COVID-19 outcomes, but these determinants are broad and complex. Identifying essential determinants is a prerequisite to address widening health disparities during the evolving COVID-19 pandemic. Methods County-specific COVID-19 fatality data from California, Illinois, and New York, three US states with the highest county-cevel COVID-19 fatalities as of June 15, 2020, were analyzed. Twenty-three county-level SDoH, collected from County Health Rankings & Roadmaps (CHRR), were considered. A median split on the population-adjusted COVID-19 fatality rate created an indicator for high or low fatality. The decision tree method, which employs machine learning techniques, analyzed and visualized associations between SDoH and high COVID-19 fatality rate at the county level. Results Of the 23 county-level SDoH considered, population density, residential segregation (between white and non-white populations), and preventable hospitalization rates were key predictors of COVID-19 fatalities. Segregation was an important predictor of COVID-19 fatalities in counties of low population density. The model area under the curve (AUC) was 0.79, with a sensitivity of 74% and specificity of 76%. Conclusion Our findings, using a novel analytical lens, suggest that COVID-19 fatality is high in areas of high population density. While population density correlates to COVID-19 fatality, our study also finds that segregation predicts COVID-19 fatality in less densely populated counties. These findings have implications for COVID-19 resource planning and require appropriate attention.