Social determinates of health and COVID-19 mortality rates at the county level

Abstract

Understanding how underlying health conditions and social determinants of health affect the severity of COVID-19 is critical for community response planning. Literature reports that groups at higher risk from COVID-19 include those 65 and older, living in nursing homes and long-Term care facilities, and with severe obesity, diabetes, chronic lung disease, or asthma. In addition, other studies has shown that the disease disproportionately affects individuals with lower socio-economic status. Our research seeks to validate these findings and observe the effects of health measures and social determinants of health on COVID-19 mortality at the county-level. In addition to COVID-19 research from hospital population samples, public health officials can leverage county-level factors for novel disease mitigation. We use the Johns Hopkins University COVID-19 reports of confirmed cases and deaths to measure disease mortality for each county in the United States. Then, we compare mortality to multiple county social determinants of health such as age, obesity, diabetes, and smoking in hypothesis testing. We fit multivariate linear models as well as non-linear models to predict mortality as a function of these county measures. The analysis shows that there is little evidence of a relationship between the county health measures of obesity, diabetes, or smoking and COVID-19 mortality as of the date of this publication. However, the analysis does reveal a positive relationship between the percent of a county population that is 65 or older and COVID-19 mortality. Other factors such as overcrowding, the percent uninsured, and the length of time since the virus has been detected in the county are also correlated with county COVID-19 mortality. Potential reasons for these findings, including data quality, are discussed. We also emphasize the advantage of collecting high quality, detailed health data at the county-level and explain how such data could be used to understand factors affecting the outcomes from novel diseases in real-Time, as a disease is progressing.