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Race, explicit racial attitudes, implicit racial attitudes, and COVID-19 cases and deaths: An analysis of counties in the United States

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  • George B Cunningham
  • Lisa T Wigfall

Abstract

Objectives: To examine the potential moderating effects of explicit racial attitudes and implicit racial attitudes on the relationship between percent of Black county residents and COVID-19 cases and deaths. Methods: We collected data from a variety of publicly available sources for 817 counties in the US. (26% of all counties). Cumulative COVID-19 deaths and cases from January 22 to August 31, 2020 were the dependent variables; explicit racial attitudes and implicit racial attitudes served as the moderators; subjective poor or fair health, food insecurity, percent uninsured, percent unemployed, median family income, percent women, percent of Asian county resident, percent of Hispanic county residents, and percent of people 65 or older were controls. Results: The percent of Black county residents was positively associated with COVID-19 cases and deaths at the county level. The relationship between percent of Black residents and COVID-19 cases was moderated by explicit racial attitudes and implicit racial attitudes. Conclusions: Implicit racial attitudes can take on a shared property at the community level and effectively explain racial disparities. COVID-19 cases are highest when both the percent of Black county residents and implicit racial attitudes are high.

Suggested Citation

  • George B Cunningham & Lisa T Wigfall, 2020. "Race, explicit racial attitudes, implicit racial attitudes, and COVID-19 cases and deaths: An analysis of counties in the United States," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.
    • Handle: RePEc:plo:pone00:0242044
    DOI: 10.1371/journal.pone.0242044
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    1. Miller, Chad A. & Wilkins, Clara L. & de Paula Couto, Clara & Farias, Jéssica & Lisnek, Jaclyn A., 2023. "Anti-Black attitudes predict decreased concern about COVID-19 among Whites in the U.S. and Brazil," Social Science & Medicine, Elsevier, vol. 320(C).
    2. Tianchu Lyu & Nicole Hair & Nicholas Yell & Zhenlong Li & Shan Qiao & Chen Liang & Xiaoming Li, 2021. "Temporal Geospatial Analysis of COVID-19 Pre-Infection Determinants of Risk in South Carolina," IJERPH, MDPI, vol. 18(18), pages 1-18, September.
    3. Andrew C Stokes & Dielle J Lundberg & Irma T Elo & Katherine Hempstead & Jacob Bor & Samuel H Preston, 2021. "COVID-19 and excess mortality in the United States: A county-level analysis," PLOS Medicine, Public Library of Science, vol. 18(5), pages 1-18, May.

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