Improving racial data equity among minority groups in South Carolina using COVID-19 as an example: application of principal components analysis

Combined single and multiracial categories achieve data equity by avoiding data suppression or aggregation of small diverse populations. Differences in COVID-19 case rates across some age groups between females and males may be biased depending on how race is defined. Younger generations are increasingly multiracial and will be underrepresented if only single race categories are used in public health reporting practices.