Title: 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 important for community response planning. The Center for Disease Control and Prevention (CDC) states that groups at higher risk from COVID-19 include those 65 and older, those living in nursing homes and long-term care facilities, those with severe obesity, diabetes, chronic lung disease, or asthma. In addition, sources have shown that the disease disproportionately affects those with lower socio-economic status. We use Johns Hopkins University COVID-19 reports of confirmed cases and deaths to measure disease mortality for each county in the United States.

The measure is then compared to county social determinants of health such as age, obesity, diabetes, and smoking. 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 discuss 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.