To advance health equity, the research field and philanthropy must address underlying racism present in data collection.
Community members representing various races, ethnicities, ages, genders, and people with disabilities stand on rectangular bars from a bar graph while holding up a line graph, conveying representative data collection. Photo credit: Gracia Lam
Editor’s Note, February 2025: Communities across the country are doing remarkable work to improve health and well-being. Access to data that accurately reflects their experiences is essential to advancing health equity for all. That’s why it is deeply concerning that the Trump administration removed key health data and information from a range of federal health websites—information that should never be hidden for political reasons. While some of it is being restored, this moment underscores an urgent need to protect and expand access to disaggregated data that reflects the identities of our country’s diverse population. Let’s continue pushing for transparency and honoring the strength and resilience of communities working to advance health equity.
My parents, like so many other immigrants, moved to America with dreams of a better life, full of opportunity for themselves and their children. My parents were Korean immigrants who left nearly everything when they came to the United States. For over three decades, they owned a corner store in Philadelphia where they worked long, physically demanding days while navigating numerous cultural and language barriers. False narratives about Asian Americans perpetuate beliefs that people like my parents were thriving because they were such “hard workers.” But I saw first-hand how the challenges they faced negatively impacted their social, emotional, and physical health and wellbeing.
As a graduate student, I wanted to study issues important to Asian Americans, but I couldn’t find data to support my work—because it did not exist. As a researcher, I developed proposals to collect data from Asian Americans, but the projects were not funded. Working in philanthropy, I’m interested in seeing research based on the experiences of Asian Americans. However, funding such projects has been challenging because there is a status quo belief about how much data collection should cost. Researchers who were willing to pursue this work reported legitimate and frustrating barriers: too often, it was perceived as time consuming or otherwise difficult to collect the needed data. And trying to meaningfully study a smaller subpopulation within the broader Asian American population was nearly impossible.
Today’s deep racial and ethnic inequities are a direct result of structural racism: the policies, practices, and norms that create and maintain White supremacy, including those that guide how researchers collect, analyze, and report on data.
Making So-Called Subpopulations More Visible
One promising policy shift: The Office of Management and Budget (OMB) is exploring updates to the federal minimum standards for data collection (last revised in a quarter century ago). These updates might include:
- Requiring detailed race and ethnicity categories by default, beyond existing broad racial/ethnic groups.
- Adding a new ethnic category for Middle Eastern or North African (MENA) individuals, disaggregating them from the White category.
The Robert Wood Johnson Foundation submitted comments and I was encouraged to see over 20,000 comments during the comment period ending in April 2023. I applaud the OMB; these potential changes in federal standards are one critical lever that can accelerate how institutions live into commitments to dismantle structural racism.
Researchers Have a Role to Play
Alongside larger changes like the OMB standards, it is critical for researchers to interrogate how systemic racism is embedded in how we do research. Disaggregated data are essential to better understand the circumstances of all populations, particularly those that are small in number and more challenging to engage in research studies.
The lack of adequately disaggregated data contributes to the failure to meet the needs of underrepresented populations by rendering them invisible when policies are made, resources are allocated, and programs are designed and implemented.
Due to inadequate data disaggregation, the diverse Asian American population is lumped together in data analyses and, as an aggregate group, are largely seen as doing well. This conclusion is driven by data for two or three of the largest Asian American subgroups, missing the needs and lived experiences of countless others. Evidence has illustrated the wide variation in characteristics, including household income and poverty rates, among the nation’s culturally and linguistically distinct Asian American communities.
What Researchers Can Do
While the research community has long documented the importance of data disaggregation, there has been relatively little action taken to actually do it. Health equity will not be achieved without changing the research field’s norms and practices.
It is vital that researchers push for more funding that allows them to recruit more participants for adequate sample sizes (both for research purposes as well as to ensure data privacy). We also have to translate all data collection tools, including surveys and consent forms, into multiple languages. It will take more time and money to recruit participants who are less apt to engage with researchers because of the lack of a trusted relationship or participants who don't see themselves or their community in the identity of the researchers. Researchers often decide that added time and expense aren’t “worth” the cost because funders and policymakers aren’t pushing for or paying for the data.
Decisions about whether including certain racial or ethnic groups in research is worth the expense are guided, at least in part, by racial stereotypes and the notion that a particular ethnic or racial group is so small that their data don't matter, or at least don’t matter enough.
Systemic Solutions to a Systemic Issue
Researchers must deeply understand and fight back against the racist principles that guide which data we collect, analyze, and report. The Data Disaggregation Action Network has compiled a number of valuable resources that provide recommendations for how to achieve meaningful ethnic and racial data disaggregation through policy, infrastructure, and practice change.
Investing in data disaggregation should be a common agenda for researchers and funders and updated federal data standards are a promising start. Fully understanding challenges and clearly defining opportunities requires access to information that reflects everyone, even if it costs more. Researchers and philanthropy alike need to invest time and resources into overlooked communities.
What RWJF is Doing
In addition to submitting comments on the proposed federal standards, the Robert Wood Johnson Foundation is making strategic investments in organizations engaged in advocacy, technical assistance, and legal guidance related to disaggregated data. For example:
- State and national coalitions advocating for more inclusive disaggregated data standards.
- The Data Equity Center at UCLA aimed at providing technical assistance and training to advance more equitable research practices.
- The Network for Public Health Law, which provides guidance on addressing legal barriers and issues related to disaggregated data.
- The NHPI Data Policy Lab, established initially in response to inequities in data collection during the COVID pandemic, has evolved to focus more broadly on identifying data gaps for NHPI communities.
RWJF is committed to sharing our learnings from these investments alongside our grantees, supporting advocates calling for more meaningful additional racial/ethnic data categories, and encouraging our philanthropic peers to invest in data disaggregation. If we truly hope to achieve health equity, we must individually and collectively rethink how we make decisions about who’s represented in data, how and why we represent people in data, and commit to formal, nationwide collection of disaggregated data. Personally, I am committed to future generations of researchers having access to data I didn’t that tells a more complete story about communities needs and assets to meaningfully inform policies and priority setting.
Improving the collection of disaggregated race and ethnicity data will require more than adding fields in electronic data capture systems. It requires engagement at multiple levels. All of us—federal and local government, philanthropy, and the people who collect, analyze, and use data—need to consider what is truly possible when we recognize historically overlooked communities. Together, we can achieve better health and greater equity for all of our communities.
Explore resources for collecting data on community conditions that influence health.
About the Author
Tina Kauh, a senior program officer in the Research-Evaluation-Learning Unit, develops new research and evaluation programs, supports the development of team strategy, evaluates the work of grantees, and disseminates key learnings.