Investigating COVID-19 Testing in North Carolina: Racial, Ethnic, and Geographic Disparities

Authors: Katerina Brandt*, University of North Carolina - Chapel Hill, Varun Goel, University of North Carolina at Chapel Hill
Topics: Medical and Health Geography, Quantitative Methods, Spatial Analysis & Modeling
Keywords: COVID-19, health disparities, intersectionality, North Carolina
Session Type: Virtual Paper
Day: 4/9/2021
Start / End Time: 1:30 PM / 2:45 PM
Room: Virtual 8
Presentation File: No File Uploaded


While recent evidence consistently shows higher rates of COVID-19 morbidity and mortality among marginalized racial and ethnic groups in the US, access to COVID-19 testing, and the structural barriers that might limit access, have not yet been extensively analyzed. In this paper, we use a comprehensive SARS-CoV-2 testing dataset to trace the spatio-temporal trajectory of testing access in North Carolina and highlight disparities along the intersections of race, ethnicity, gender and rurality. We observe higher test positivity and lower number of tests per capita among Latinx, non-Latinx (NL) Black, NL American Indian, and rural communities. Additionally, while test positivity is higher in rural areas the disparities along racial-ethnic lines persist. We explore how the mechanisms that produce these disparities vary across geography and race using spatio-temporal methods and spatial divergence indices. For example, using anonymized cell phone records and census data we find that even in areas with low testing rates, higher NL Black and Latinx populations were associated with more travel to work, suggesting these populations are at higher risk of exposure and in greater need of testing. Additionally, we find that higher testing among white communities trended with lower positivity rates, while higher testing among Latinx populations trended with higher positivity rates, suggesting that testing is driven by different processes for different groups. Our study demonstrates that access to COVID-19 testing is more than a simple function of infection rates and test positivity; ongoing testing efforts should incorporate long standing inequalities across space and time.

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