Estimating the unknown: greater racial and ethnic disparities in COVID-19 burden after accounting for missing race and ethnicity data.

Labgold, Katie; Hamid, Sarah; Shah, Sarita; Gandhi, Neel R; Chamberlain, Allison; Khan, Fazle; Khan, Shamimul; Smith, Sasha; Williams, Steve; Lash, Timothy L; Collin, Lindsay J

BACKGROUND Black, Hispanic, and Indigenous persons in the United States have an increased risk of SARS-CoV-2 infection and death from COVID-19, due to persistent social inequities. However, the magnitude of the disparity is unclear because race-ethnicity information is often missing in surveillance data. METHODS We quantified the burden of SARS-CoV-2 notification, hospitalization, and case fatality rates in an urban county by racial-ethnic group using combined race-ethnicity imputation and quantitative bias analysis for misclassification. RESULTS The ratio of the absolute racial-ethnic disparity in notification rates after bias adjustment, compared with the complete case analysis, increased 1.3-fold for persons classified Black and 1.6-fold for those classified Hispanic, in reference to classified White persons. CONCLUSIONS These results highlight that complete case analyses may underestimate absolute disparities in notification rates. Complete reporting of race-ethnicity information is necessary for health equity. When data are missing, quantitative bias analysis methods may improve estimates of racial-ethnic disparities in the COVID-19 burden.