Population health metrics · 2025
Improving racial data equity among minority groups in South Carolina using COVID-19 as an example: application of principal components analysis.
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Rubaiya Fnu, O'Connor Janet, Kolev Lyubomir N, Antill James M, Iiams Margaret, Martin LaNaya A, Joseph Chantaezia Z, Youngblood Claire, Almeda-Garrett Jennifer, Kelemen Linda E
Аннотация
BACKGROUND: Data inequity occurs when racial and ethnic groups are aggregated during data collection or reporting despite their differences. To demonstrate racial data equity importance, we re-analyzed South Carolina's (SC) census data and COVID-19 case-rate and death-rate distributions according to age, sex, and new combined single and multiracial categories.
METHODS: The new combined single and multiracial categories included individuals who identified as a single race alone (such as American Indian or Alaska Native, AI-AN) with those who identified as more than one race (such as AI-AN and White) regardless of Hispanic or Latino heritage. We compared those distributions to the single race categories using the American Community Survey 2018-2022 and COVID-19 case and death surveillance data, 2020-2023, for SC. We used principal components analysis to test for differences in age-sex distributions between single race alone and new combined single and multiracial categories for each race.
RESULTS: Compared to the combined single and multiracial categories, single race alone categories lose information, underestimate the population of younger-aged people of AI-AN, Asian, and Native Hawaiian or Other Pacific Islander (NH-OPI) races, and result in COVID-19 case and death rates with extreme values across age groups, particularly for AI-AN and NH-OPI populations. Among AI-AN, certain age groups had different COVID-19 case rate patterns between females and males, but this was explained by race categorization (single race alone vs. combined single and multiracial, P < 0.0001).
CONCLUSIONS: 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.
METHODS: The new combined single and multiracial categories included individuals who identified as a single race alone (such as American Indian or Alaska Native, AI-AN) with those who identified as more than one race (such as AI-AN and White) regardless of Hispanic or Latino heritage. We compared those distributions to the single race categories using the American Community Survey 2018-2022 and COVID-19 case and death surveillance data, 2020-2023, for SC. We used principal components analysis to test for differences in age-sex distributions between single race alone and new combined single and multiracial categories for each race.
RESULTS: Compared to the combined single and multiracial categories, single race alone categories lose information, underestimate the population of younger-aged people of AI-AN, Asian, and Native Hawaiian or Other Pacific Islander (NH-OPI) races, and result in COVID-19 case and death rates with extreme values across age groups, particularly for AI-AN and NH-OPI populations. Among AI-AN, certain age groups had different COVID-19 case rate patterns between females and males, but this was explained by race categorization (single race alone vs. combined single and multiracial, P < 0.0001).
CONCLUSIONS: 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.