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Alternative Methods for Grouping Race and Ethnicity to Monitor COVID-19 Outcomes and Vaccination Coverage.
MMWR Morb Mortal Wkly Rep. 2021 Aug 13; 70(32):1075-1080.MM

Abstract

Population-based analyses of COVID-19 data, by race and ethnicity can identify and monitor disparities in COVID-19 outcomes and vaccination coverage. CDC recommends that information about race and ethnicity be collected to identify disparities and ensure equitable access to protective measures such as vaccines; however, this information is often missing in COVID-19 data reported to CDC. Baseline data collection requirements of the Office of Management and Budget's Standards for the Classification of Federal Data on Race and Ethnicity (Statistical Policy Directive No. 15) include two ethnicity categories and a minimum of five race categories (1). Using available COVID-19 case and vaccination data, CDC compared the current method for grouping persons by race and ethnicity, which prioritizes ethnicity (in alignment with the policy directive), with two alternative methods (methods A and B) that used race information when ethnicity information was missing. Method A assumed non-Hispanic ethnicity when ethnicity data were unknown or missing and used the same population groupings (denominators) for rate calculations as the current method (Hispanic persons for the Hispanic group and race category and non-Hispanic persons for the different racial groups). Method B grouped persons into ethnicity and race categories that are not mutually exclusive, unlike the current method and method A. Denominators for rate calculations using method B were Hispanic persons for the Hispanic group and persons of Hispanic or non-Hispanic ethnicity for the different racial groups. Compared with the current method, the alternative methods resulted in higher counts of COVID-19 cases and fully vaccinated persons across race categories (American Indian or Alaska Native [AI/AN], Asian, Black or African American [Black], Native Hawaiian or Other Pacific Islander [NH/PI], and White persons). When method B was used, the largest relative increase in cases (58.5%) was among AI/AN persons and the largest relative increase in the number of those fully vaccinated persons was among NH/PI persons (51.6%). Compared with the current method, method A resulted in higher cumulative incidence and vaccination coverage rates for the five racial groups. Method B resulted in decreasing cumulative incidence rates for two groups (AI/AN and NH/PI persons) and decreasing cumulative vaccination coverage rates for AI/AN persons. The rate ratio for having a case of COVID-19 by racial and ethnic group compared with that for White persons varied by method but was <1 for Asian persons and >1 for other groups across all three methods. The likelihood of being fully vaccinated was highest among NH/PI persons across all three methods. This analysis demonstrates that alternative methods for analyzing race and ethnicity data when data are incomplete can lead to different conclusions about disparities. These methods have limitations, however, and warrant further examination of potential bias and consultation with experts to identify additional methods for analyzing and tracking disparities when race and ethnicity data are incomplete.

Authors+Show Affiliations

CDC COVID-19 Response Team.CDC COVID-19 Response Team.CDC COVID-19 Response Team.CDC COVID-19 Response Team.CDC COVID-19 Response Team.CDC COVID-19 Response Team.CDC COVID-19 Response Team.CDC COVID-19 Response Team.CDC COVID-19 Response Team.CDC COVID-19 Response Team.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

34383729

Citation

Yoon, Paula, et al. "Alternative Methods for Grouping Race and Ethnicity to Monitor COVID-19 Outcomes and Vaccination Coverage." MMWR. Morbidity and Mortality Weekly Report, vol. 70, no. 32, 2021, pp. 1075-1080.
Yoon P, Hall J, Fuld J, et al. Alternative Methods for Grouping Race and Ethnicity to Monitor COVID-19 Outcomes and Vaccination Coverage. MMWR Morb Mortal Wkly Rep. 2021;70(32):1075-1080.
Yoon, P., Hall, J., Fuld, J., Mattocks, S. L., Lyons, B. C., Bhatkoti, R., Henley, J., McNaghten, A. D., Daskalakis, D., & Pillai, S. K. (2021). Alternative Methods for Grouping Race and Ethnicity to Monitor COVID-19 Outcomes and Vaccination Coverage. MMWR. Morbidity and Mortality Weekly Report, 70(32), 1075-1080. https://doi.org/10.15585/mmwr.mm7032a2
Yoon P, et al. Alternative Methods for Grouping Race and Ethnicity to Monitor COVID-19 Outcomes and Vaccination Coverage. MMWR Morb Mortal Wkly Rep. 2021 Aug 13;70(32):1075-1080. PubMed PMID: 34383729.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Alternative Methods for Grouping Race and Ethnicity to Monitor COVID-19 Outcomes and Vaccination Coverage. AU - Yoon,Paula, AU - Hall,Jeffrey, AU - Fuld,Jennifer, AU - Mattocks,S Linda, AU - Lyons,B Casey, AU - Bhatkoti,Roma, AU - Henley,Jane, AU - McNaghten,A D, AU - Daskalakis,Demetre, AU - Pillai,Satish K, Y1 - 2021/08/13/ PY - 2021/8/12/entrez PY - 2021/8/13/pubmed PY - 2021/8/14/medline SP - 1075 EP - 1080 JF - MMWR. Morbidity and mortality weekly report JO - MMWR Morb Mortal Wkly Rep VL - 70 IS - 32 N2 - Population-based analyses of COVID-19 data, by race and ethnicity can identify and monitor disparities in COVID-19 outcomes and vaccination coverage. CDC recommends that information about race and ethnicity be collected to identify disparities and ensure equitable access to protective measures such as vaccines; however, this information is often missing in COVID-19 data reported to CDC. Baseline data collection requirements of the Office of Management and Budget's Standards for the Classification of Federal Data on Race and Ethnicity (Statistical Policy Directive No. 15) include two ethnicity categories and a minimum of five race categories (1). Using available COVID-19 case and vaccination data, CDC compared the current method for grouping persons by race and ethnicity, which prioritizes ethnicity (in alignment with the policy directive), with two alternative methods (methods A and B) that used race information when ethnicity information was missing. Method A assumed non-Hispanic ethnicity when ethnicity data were unknown or missing and used the same population groupings (denominators) for rate calculations as the current method (Hispanic persons for the Hispanic group and race category and non-Hispanic persons for the different racial groups). Method B grouped persons into ethnicity and race categories that are not mutually exclusive, unlike the current method and method A. Denominators for rate calculations using method B were Hispanic persons for the Hispanic group and persons of Hispanic or non-Hispanic ethnicity for the different racial groups. Compared with the current method, the alternative methods resulted in higher counts of COVID-19 cases and fully vaccinated persons across race categories (American Indian or Alaska Native [AI/AN], Asian, Black or African American [Black], Native Hawaiian or Other Pacific Islander [NH/PI], and White persons). When method B was used, the largest relative increase in cases (58.5%) was among AI/AN persons and the largest relative increase in the number of those fully vaccinated persons was among NH/PI persons (51.6%). Compared with the current method, method A resulted in higher cumulative incidence and vaccination coverage rates for the five racial groups. Method B resulted in decreasing cumulative incidence rates for two groups (AI/AN and NH/PI persons) and decreasing cumulative vaccination coverage rates for AI/AN persons. The rate ratio for having a case of COVID-19 by racial and ethnic group compared with that for White persons varied by method but was <1 for Asian persons and >1 for other groups across all three methods. The likelihood of being fully vaccinated was highest among NH/PI persons across all three methods. This analysis demonstrates that alternative methods for analyzing race and ethnicity data when data are incomplete can lead to different conclusions about disparities. These methods have limitations, however, and warrant further examination of potential bias and consultation with experts to identify additional methods for analyzing and tracking disparities when race and ethnicity data are incomplete. SN - 1545-861X UR - https://www.unboundmedicine.com/medline/citation/34383729/Alternative_Methods_for_Grouping_Race_and_Ethnicity_to_Monitor_COVID_19_Outcomes_and_Vaccination_Coverage_ L2 - https://doi.org/10.15585/mmwr.mm7032a2 DB - PRIME DP - Unbound Medicine ER -