Alternative Methods for Grouping Race and Ethnicity to Monitor COVID-19 Outcomes and Vaccination Coverage

Paula Yoon; Jeffrey Hall; Jennifer Fuld; S. Linda Mattocks; B. Casey Lyons; Roma Bhatkoti; Jane Henley; A. D. McNaghten; Demetre Daskalakis; Satish K. Pillai

doi:10.15585/mmwr.mm7032a2

Alternative Methods for Grouping Race and Ethnicity to Monitor COVID-19 Outcomes and Vaccination Coverage

Paula Yoon, Jeffrey Hall, Jennifer Fuld, S. Linda Mattocks, B. Casey Lyons, Roma Bhatkoti, Jane Henley, A. D. McNaghten, Demetre Daskalakis, Satish K. Pillai

Research output: Contribution to journal › Article › peer-review

32 Scopus citations

Abstract

What is already known about this topic? Analyses of race and ethnicity in COVID-19 data to identify and monitor disparities are complicated by missing or unknown data. What is added by this report? Methods that use more race information when ethnicity information is missing resulted in higher estimated COVID-19 case counts, incidence, and vaccination coverage for most racial groups studied; however, these methods have limitations and warrant further examination of potential bias. What are the implications for public health practice? Ongoing work with experts is needed to identify methods for optimizing race and ethnicity data when data are incomplete. Multiple data sources are needed to monitor disparities and continued efforts are needed to strengthen the reporting of these data, consistent with CDC's Data Modernization Initiative.

Original language	English
Pages (from-to)	1075-1080
Number of pages	6
Journal	Morbidity and Mortality Weekly Report
Volume	70
Issue number	32
DOIs	https://doi.org/10.15585/mmwr.mm7032a2
State	Published - 2021
Externally published	Yes

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title = "Alternative Methods for Grouping Race and Ethnicity to Monitor COVID-19 Outcomes and Vaccination Coverage",

abstract = "What is already known about this topic? Analyses of race and ethnicity in COVID-19 data to identify and monitor disparities are complicated by missing or unknown data. What is added by this report? Methods that use more race information when ethnicity information is missing resulted in higher estimated COVID-19 case counts, incidence, and vaccination coverage for most racial groups studied; however, these methods have limitations and warrant further examination of potential bias. What are the implications for public health practice? Ongoing work with experts is needed to identify methods for optimizing race and ethnicity data when data are incomplete. Multiple data sources are needed to monitor disparities and continued efforts are needed to strengthen the reporting of these data, consistent with CDC's Data Modernization Initiative.",

author = "Paula Yoon and Jeffrey Hall and Jennifer Fuld and Mattocks, {S. Linda} and Lyons, {B. Casey} and Roma Bhatkoti and Jane Henley and McNaghten, {A. D.} and Demetre Daskalakis and Pillai, {Satish K.}",

year = "2021",

doi = "10.15585/mmwr.mm7032a2",

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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.

PY - 2021

Y1 - 2021

N2 - What is already known about this topic? Analyses of race and ethnicity in COVID-19 data to identify and monitor disparities are complicated by missing or unknown data. What is added by this report? Methods that use more race information when ethnicity information is missing resulted in higher estimated COVID-19 case counts, incidence, and vaccination coverage for most racial groups studied; however, these methods have limitations and warrant further examination of potential bias. What are the implications for public health practice? Ongoing work with experts is needed to identify methods for optimizing race and ethnicity data when data are incomplete. Multiple data sources are needed to monitor disparities and continued efforts are needed to strengthen the reporting of these data, consistent with CDC's Data Modernization Initiative.

AB - What is already known about this topic? Analyses of race and ethnicity in COVID-19 data to identify and monitor disparities are complicated by missing or unknown data. What is added by this report? Methods that use more race information when ethnicity information is missing resulted in higher estimated COVID-19 case counts, incidence, and vaccination coverage for most racial groups studied; however, these methods have limitations and warrant further examination of potential bias. What are the implications for public health practice? Ongoing work with experts is needed to identify methods for optimizing race and ethnicity data when data are incomplete. Multiple data sources are needed to monitor disparities and continued efforts are needed to strengthen the reporting of these data, consistent with CDC's Data Modernization Initiative.

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Alternative Methods for Grouping Race and Ethnicity to Monitor COVID-19 Outcomes and Vaccination Coverage

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