Background: Because COVID-19 case data do not capture most SARS-CoV-2 infections, the actual risk of severe disease and death per infection is unknown. Integrating sociodemographic data into analysis can show consequential health disparities.
Methods: Data were merged from September 2020 to November 2021 from 6 national surveillance systems in matched geographic areas and analyzed to estimate numbers of COVID-19-associated cases, emergency department visits, and deaths per 100 000 infections. Relative risks of outcomes per infection were compared by sociodemographic factors in a data set including 1490 counties from 50 states and the District of Columbia, covering 71% of the US population.
Results: Per infection with SARS-CoV-2, COVID-19-related morbidity and mortality were higher among non-Hispanic American Indian and Alaska Native persons, non-Hispanic Black persons, and Hispanic or Latino persons vs non-Hispanic White persons; males vs females; older people vs younger; residents in more socially vulnerable counties vs less; those in large central metro areas vs rural; and people in the South vs the Northeast.
Discussion: Meaningful disparities in COVID-19 morbidity and mortality per infection were associated with sociodemography and geography. Addressing these disparities could have helped prevent the loss of tens of thousands of lives.
Keywords: COVID-19; SARS-CoV-2; health disparities; seroprevalence; sociodemographic factors.
Published by Oxford University Press on behalf of Infectious Diseases Society of America 2023.