Making Communities More Visible: Equity-Centered Data to Achieve Health Equity

Centennial Issue
Health Equity Population Health

Policy Points:

  • Despite decades of research exposing health disparities between populations and communities in the US, health equity goals remain largely unfulfilled. We argue these failures call for applying an equity lens in the way we approach data systems, from collection and analysis to interpretation and distribution. Hence, health equity requires data equity.
  • There is notable federal interest in policy changes and federal investments to improve health equity. With this, we outline the opportunities to align these health equity goals with data equity by improving the way communities are engaged and how population data are collected, analyzed, interpreted, made accessible, and distributed.
  • Policy priority areas for data equity include increasing the use of disaggregated data, increasing the use of currently underused federal data, building capacity for equity assessments, developing partnerships between government and community, and increasing data accountability to the public.

The US Center for Disease Control and Prevention regards “health equity” as a state when every person has the opportunity to “attain his or her full health potential” and no one is “disadvantaged from achieving their potential because of social position or other socially determined circumstances.”1 The COVID-19 pandemic exposed the disproportionate toll on historically marginalized and underresourced segments of the US population through systemic inequities in employment, education, housing, food security, and health care access. These segments of individuals and families were rendered invisible in policy decisions and public investments in health and health care because their assigned social categories—racialized and minoritized, socioeconomic, sexual orientation and gender identity, and differently abled—were already unfairly allocated limited resources.2–5 Policy neglect for these “invisible” groups is also compounded, and at times misinformed, by differential investments by geographic location. Many of such investments are historically rooted in de facto segregation, redlining, and present-day gentrification.6 The United States, as a historically inequitable nation, warrants reparative and restorative efforts in addressing the disproportional disadvantages faced by individuals, families, communities, and subsequently, populations. These structural exposures and the consequent experiences of marginalized populations are not documented in policy-setting data. A foundational step toward health equity is getting the measurement, interpretation, and use of structural and systemic bias in health data systems right, especially among populations who are “invisible” in the evidence platforms that inform policies.5 Measurement and evaluation of systemic bias is a strong step toward achieving health equity, but the findings yielded by measuring and evaluating systemic biases must be paired with investments in infrastructure, protocols, and practice to build an equity centered data ecosystem. In order to capture systemic bias, we must first ensure data are collected equitably. The collection of data by the federal government classified by racialized or ethnic group has a long and contentious history in the United States.6 Variations in classification approaches in federal and state health statistics have substantial implications for measuring health status, access, and health care quality. Recent work suggests health disparities research has aided in preserving systems rooted in systemic racism.7

Although we have made strides in health disparities and health equity in the past 30 years, the defining goal of population health is to maintain and improve the health of the entire population and we have yet to reduce inequalities between population groups.8 Doing so requires making the transformative structural changes needed to tackle health disparities. Therefore, a closer look at our data is deeply needed in the conversation on health equity. Our first step is to improve the ways we collect data on racialized and ethnic groups, how we engage with such communities, how data are democratized, and how data can be used in the pathways to policy change.9

In this commentary, we focus on data equity in racialized and minoritized groups by commenting on the institutional commitments, notably community-partnered initiatives put forth as priorities by the Biden Administration in 2021.10 We begin with definitions to frame our commentary. We acknowledge there may not be a consensus on these definitions and the ones we use may be more reproduced by established entities with blind spots in health equity. However, we engage these terms and this commentary to contribute to the momentum of change being led by federal entities in partnership with the communities for whom this change is most salient. Thus, for this reason, we have also chosen to make visible the names of racialized groups that are often made invisible by acronyms.

Open Access

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  1. Centers for Disease Control and Prevention. Health Equity. National Center for Chronic Disease Prevention and Health Promotion (NCCDPHP). December 8, 2022. Accessed Oct 14, 2022.
  2. Jones CP. Action and allegories. In: Jones CP, Ford CL, Griffith DM, Bruce MA, Gilbert KL, eds. Racism: Science & Tools for the Public Health Professional. APHA Press; 2019. doi:10.2105/9780875533049ch11
  3. Krieger N, Kim R, Feldman J, Waterman PD. Using the Index of Concentration at the Extremes at multiple geographical levels to monitor health inequities in an era of growing spatial social polarization: Massachusetts, USA (2010-14). Int J Epidemiol. 2018;47(3):788-819.
  4. Mays VM, Ponce NA, Washington DL, Cochran SD. Classification of race and ethnicity: implications for public health. Annu Rev Public Health. 2003;24(1):83-110.
  5. Ponce NA. Centering health equity in population health surveys. JAMA Health Forum. 2020;1(12):e201429.
  6. Rothstein R. The Color of Law: A Forgotten History of How Our Government Segregated America. W.W. Norton & Company, Inc.;2017.
  7. Hardeman RR, Karbeah J. Examining racism in health services research: a disciplinary self-critique. Health Serv Res. 2020;55(Suppl 2):777-780.
  8. Kindig D, Stoddart G. What is population health? Am J Public Health. 2003;93(3):380-383.
  9. Ponce NA, Bautista R, Sondik EJ, et al. Championing partnerships for data equity. J Health Care Poor Underserved. 2015;26(2Suppl):6-15.
  10. Executive Office of the President. Executive order 13985: Advancing racial equity and support for underserved communities through the federal government. Washington, DC: The White House, January 20, 2021. Accessed Oct 14, 2022.

Ponce N, Shimkhada R, Adkins-Jackson PB. Making Communities More Visible: Equity-Centered Data to Achieve Health Equity. Milbank Q. 2023;101(S1): 302-332.