Routinely collected testing data has been a vital resource for public health response during the COVID-19 pandemic and has revealed the extent to which Black and Hispanic persons have borne a disproportionate burden of SARS-CoV-2 infections and hospitalizations in the United States. However, missing race and ethnicity data and missed infections due to testing disparities limit the interpretation of testing data and obscure the true toll of the pandemic. We investigated potential bias arising from these two types of missing data through a case study in Holyoke, Massachusetts during the pre-vaccination phase of the pandemic. First, we estimated SARS-CoV-2 testing and case rates by race/ethnicity, imputing missing data using a joint modelling approach. We then investigated disparities in SARS-CoV-2 reported case rates and missed infections by comparing case rate estimates to estimates derived from a COVID-19 seroprevalence survey. Compared to the non-Hispanic white population, we found that the Hispanic population had similar testing rates (476 vs. 480 tested per 1,000) but twice the case rate (8.1% vs. 3.7%). We found evidence of inequitable testing, with a higher rate of missed infections in the Hispanic population compared to the non-Hispanic white population (77 vs. 58 infections missed per 1,000).
Keywords: COVID-19; bias; health disparities; missing data; multiple imputation.
© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.