Examining How We Collect Race and Ethnicity Data

COVID-19 has disproportionately impacted racial and ethnic minority groups. Public health strategies to help prevent and address these inequities include strategic allocation of resources and culturally appropriate messaging. Such strategies require reliable data to inform decisions. However, several challenges exists with collecting socially constructed data elements such as race and ethnicity.  A recent commentary describes common issues with collecting these data relative to COVID-19 preventative measures and groups key issues into two categories: 1. Missing race/ethnicity data and 2. Contributors to nonresponse. Approximately 50% of COVID-19 vaccination recipients’ race/ethnicity information is missing. Yet, existing data indicates vaccination rates among Black and Hispanic individuals are lower than their share of COVID-19 cases and deaths. “A more robust and inclusive understanding of this disparity requires addressing the significant race/ethnicity data gap.”

Contributors to race/ethnicity nonresponse rates are multidimensional and include lack of inclusive definitions of race and ethnicity, misclassification and mistrust of government.

The Office of Management and Budget (OMB) determines the national standard on race and ethnicity… these were last revised in 1997. Language used to describe ethnicity and race is not universal, particularly among those with intersectional backgrounds… members of certain vulnerable populations may be opposed to identifying with a racial group with whom they may not share linguistic, ethnocultural, and/or physical characteristics.

Multiracial individuals may not indicate all aspects of their identities on surveys due to limitations in the race/ethnicity options presented… Furthermore, racial misclassification by healthcare personnel and other individuals administering in-person health surveys is a concern… [this] may result in leaders’ misunderstanding their local demographics, even when nonresponse rates are low… [and] psychological distress for misidentified persons in addition to inaccurate reporting of diseases such as cancers and adverse birth outcomes.

Because of mistrust between [Black, Indigenous, and People of Color] BIPOC populations and public health authorities, some may not be comfortable disclosing racial and ethnic identity information to contact tracers, healthcare systems, and other collectors of COVID-19 case information.

The authors’ proposed recommendations for addressing these challenges are summarized at the end of this article in this months’ “Tools for the Toolkit.”

Health Inequities Among Racial and Ethnic Minority Groups

It is well known that COVID-19 has disproportionately impacted the morbidity and mortality of racial and ethnic minority groups in the United States (US). However, understanding reasons for this is complex and requires a deeper dive into underlying structural and social determinants of health (SDOH). Two recent studies aimed to do just this by examining individual and county-level data from California and the US, respectively. Of note, this herin review uses terms to describe ethnic groups as stated in their respective articles; however,  more inclusive terminology such as Latinx (gender-neutral or nonbinary alternative to Latino or Latina) are preferred by some individuals. The California-based study estimated the impact of  COVID-19 on deaths among Latino individuals by estimating excess mortality occurring between March and October 2020 compared to the four years prior to the pandemic. Death certificate data and time-series models were used to estimate expected weekly mortality rates. The analytic sample consisted of 43,576 and 220,986 total deaths among Latinos during the pandemic and pre-pandemic period, respectively. Excess mortality was quantified as observed minus expected deaths and risk ratios (RR) as observed to expected death ratio. Subgroup analyses examined additional SDOH such as nativity, education, and occupation. Results indicated death among Latinos in California exceeded expected deaths by 10,316 (31% increase).

Excess death rates were greatest for Latino individuals born in Mexico (RR 1.44; 95% PI, 1.41, 1.48) or a Central American country (RR 1.49; 95% PI, 1.37, 1.64), with less than a high school degree (RR 1.41; 95% PI, 1.35, 1.46), or in food-and-agriculture (RR 1.60; 95% PI, 1.48, 1.74) or manufacturing occupations (RR 1.59; 95% PI, 1.50, 1.69).

These findings align with the US county-level disparities study, which examined poverty-, race- and ethnic-based disparities in COVID-19 cumulative incidence and mortality and associated economic, housing, transit, population health and health care characteristics. Adjusted negative binomial models were used to examine 6-month COVID-19 incidence and mortality among all US counties (n = 3142). Data were obtained from the Hopkins’ Center for Systems Science and Engineering. Findings indicated counties with a larger proportion of Black (IRR = 1.03, 95% CI: 1.02–1.03) or Hispanic (IRR = 1.02, 95% CI: 1.01–1.03) residents had higher incidence of COVID-19 cases while counties with a larger proportion of Black (IRR = 1.02, 95% CI: 1.02–1.03) or Native American (IRR = 1.02, 95% CI: 1.01–1.04) residents had higher mortality.

Higher rates of lacking a high school diploma was associated with higher counts of cases (IRR = 1.03, 95% CI: 1.01–1.05) and deaths (IRR = 1.04, 95% CI: 1.01–1.07). Higher percentages of multi-unit households were associated with higher (IRR = 1.02, 95% CI: 1.01–1.04) and unemployment with lower (IRR = 0.96, 95% CI: 0.94–0.98) incidence. Higher percentages of individuals with limited English proficiency (IRR = 1.09, 95% CI: 1.04–1.14) and households without a vehicle (IRR = 1.04, 95% CI: 1.01–1.07) were associated with more deaths.

The authors’ proposed public health strategies for addressing these SDOH are summarized below in this months’ “Tools for the Toolkit.”

Tools for the Toolkit