Duke DataFest Analysis Reveals How COVID-19 Impacts Communities Already Suffering from Health Disparities

August 13, 2020

This is the first installment in a series of articles profiling winners of the Duke ASA DataFest: COVID-19 Virtual Data Challenge, co-sponsored by the Duke University Department of Statistical Science and the Duke AI Health Institute.

Aside from altering the very fabric of daily life across the United States and the world, the COVID-19 pandemic has exposed the many existing shortcomings and inequities of the American healthcare system. The burgeoning public health crisis has resulted in more than 5 million confirmed cases nationwide and close to 163,000 deaths as of the beginning of August. However, some communities and groups have been disproportionately impacted, as a prize-winning analysis by Duke’s Meredith Brown, Matt Feder, and Pouya Mohammadi, presented at this year’s Duke American Statistical Association (ASA) DataFest: COVID-19 Virtual Data Challenge.

In “Regression Analysis of COVID-19’s Effect on Different Communities”, which won in the “Judge’s Pick” category in the DataFest Challenge, Brown, Feder, and Mohammadi examined how the COVID-19 pandemic is disproportionately affecting low-income communities and people of color in the United States. Combining data collected by the New York Times and the U.S. Census, the researchers found that the proportion of Black persons living in a given county is highly correlated with deaths per capita. They also found that there was a strong correlation between the number of cases in a county and the proportion of county residents living in poverty. And because the relationship is logarithmic, as the number of cases multiply in a county, the deaths per capita in impoverished counties increase at a greater rate than the deaths per capita of wealthier counties.