Abstract

Excerpted From: Leslie Zellers, Amy Vertal, Lloyd Feng and Mar Velez, Disaggregating Public Health Data by Race and Ethnicity to Improve Public Health, 54 Journal of Law, Medicine & Ethics 70 (Spring, 2026) (34 Footnotes) (Full Document)

Having high-quality data is a necessary first step toward effective policy and programs. As the diversity of the nation’s population grows, broad race and ethnicity categories are increasingly insufficient to capture the diversity of experiences and outcomes across different communities. “Data disaggregation” refers to the collection, reporting, and analysis of information on more detailed subgroups by race, ethnicity, and other characteristics. While aggregated data can mask disparities, disaggregated data allow researchers to better analyze differences between groups, and the relationships among multiple variables. Disaggregation of public health data by race and ethnicity is therefore critical to understanding and addressing health disparities and driving progress toward health equity.

The COVID-19 pandemic surfaced some important examples of how disaggregated data allow health officials to tailor health interventions to the most at-risk communities and maximize their impact and efficiency. In late 2020, for example, California’s Santa Clara County began collecting disaggregated race and ethnicity data on COVID-19 infections, revealing that Vietnamese and Filipino residents experienced higher rates of infections compared to other Asian American groups. Asian Health Services, a federally-qualified health center in Alameda County, also began collecting disaggregated data for patients coming in for COVID-19 testing and found that Vietnamese residents were testing positive at nearly twice the rate of Asian Americans overall. Asian Health Services responded by conducting targeted in-language outreach in areas with large Vietnamese populations. Following the tailored interventions informed by this disaggregated data collection, COVID-19 positivity rates for Vietnamese residents dropped to levels similar to other groups, demonstrating the importance of granular data in informing effective public health responses.

In spite of the clear benefits of granular data to inform policies and programs, many existing state and federal data collection systems do not collect sufficiently disaggregated race and ethnicity data. This masks the nuanced realities of many communities behind larger trends and makes it more difficult to identify and address health inequities.

The US Office of Management and Budget’s (OMB’s) Statistical Policy Directive No. 15 (SPD 15) defines the minimum set of categories that federal agencies must use when collecting race and ethnicity data. Until OMB revised SPD 15 in 2024, these standards had not been updated since 1997. The categories and question format that the 1997 SPD 15 standards required agencies to use posed significant issues for the accuracy of federal race and ethnicity data. First, the 1997 SPD 15 standards used separate questions to collect information about Hispanic origin and race (see Figure 1). This two-question format led many individuals of Latino/Latine origin who did not identify with any of the available race categories (Black, White, etc.), to skip the race question, select “some other race” when that option was available, or write in their Hispanic origin under one of the race categories. Second, the 1997 SPD 15 standards did not include a distinct Middle Eastern or North African (MENA) category, instead classifying individuals of MENA origin under the “White” racial category. And while the 1997 standards encouraged federal agencies to collect more detailed race and ethnicity data beyond the minimum categories, they did not require agencies to do so. These issues reduced the accuracy of federal data not only for the groups mentioned above, but also for the nation’s population overall. In the health context, these data gaps likely led to missed opportunities to identify and respond to disparities in health outcomes, such as those observed during the COVID-19 pandemic.

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Detailed data on race and ethnicity is vital to support data equity and provide insights into health outcomes. Collection of race and ethnicity data at the state level is more important than ever given significant cuts to federal data collection efforts in 2025.

States have the potential to require the collection and disaggregation of race and ethnicity data to make informed decisions about services and funding that reflect their specific populations. Thirteen states have adopted laws in this area. Although the laws represent a tremendous success reflecting the education and advocacy of many groups, the laws are only a starting point. To ensure that the laws are implemented -- that forms are updated, that data are collected and analyzed, and that programs or services are changed -- requires an ongoing and long-term commitment.

Leslie Zellers, JD, is an attorney with more than 20 years of experience in public health law and policy for government and nonprofit organizations. She has a JD from UC Law San Francisco.

Amy Vertal, MPH, is Senior Program Manager for Census and Data Equity at The Leadership Conference on Civil and Human Rights. She has an MPH in Community Health and Social Sciences from the City University of New York Graduate School of Public Health & Health Policy.

Lloyd Feng is Senior Data Policy Coordinator at the Coalition for Asian American Children and Families (CACF). He received a BA in Art and Archaeology with a minor in East Asian Studies from Princeton University in 2019.

Mar Velez, MPH, is Director of Policy at the Latino Coalition for a Healthy California. She has a dual Masters degree in Public Health and City Planning from UC Berkeley.