Estimating the Unknown: Greater Racial and Ethnic Disparities in COVID-19 Burden After Accounting for Missing Race and Ethnicity Data

Epidemiology. 2021 Mar 1;32(2):157-161. doi: 10.1097/EDE.0000000000001314.

Abstract

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.

MeSH terms

  • Asian / statistics & numerical data
  • Black or African American / statistics & numerical data*
  • COVID-19 / ethnology*
  • COVID-19 / mortality
  • Data Collection
  • Georgia / epidemiology
  • Health Status Disparities
  • Hispanic or Latino / statistics & numerical data*
  • Hospitalization / statistics & numerical data*
  • Humans
  • Indigenous Peoples / statistics & numerical data*
  • Mortality / ethnology*
  • Native Hawaiian or Other Pacific Islander / statistics & numerical data
  • SARS-CoV-2
  • Statistics as Topic
  • United States / epidemiology
  • White People / statistics & numerical data