Without Data Equity, We Will Not Achieve Health Equity

Data equity refers to the production and distribution of high-quality, inclusive, actionable, and accessible data. Improving data equity involves changes to the ways in which data are collected, processed and recoded, analyzed, reported, and shared.

A fundamental tenet of data equity affirms that communities are fairly represented and that the inclusivity and accuracy in data collection informs decisions addressing diverse community needs.¹ Data collection strategies affect which populations can be identified within the data.² Data coding and processing decisions determine the representativeness of the data, their comparability to other sources, and how the data can be used.³ The ways in which data are analyzed and reported, including how the results are interpreted, affect how populations are represented in research and in policy.⁴  And, decisions about how data are shared has to strike a balance between expanding accessibility to data about specific populations and how harmful unauthorized disclosure of respondents’ data can occur.⁵

As public and private sectors place racial justice as a top priority, how race and ethnicity are measured is a prominent example calling for data equity principles to be applied in health and other data ecosystems.

Federal guidelines through the Office of Management and Budget (OMB) have historically provided a framework on minimum race and ethnicity categories to be collected in federal and federally funded datasets. On March 28, 2024, the Office of Management and Budget (OMB) published its revised Statistical Policy Directive No. 15, nearly three decades after its last update in 1997. This directive, originally introduced in 1977, established minimum standards to ensure the federal government’s ability to compare data across federal agencies and document the effectiveness of federal programs in serving diverse populations. This directive includes standards for maintaining, collecting, and presenting data on race and ethnicity.

In its revision, OMB replaces the two-part Latino/Hispanic ethnicity and 5 category race question (American Indian/Alaska Native, Asian, Black or African American, Native Hawaiian or Pacific Islander, White) into a single question prompt that will include new categories, for example, a new Middle Eastern or North African group. It also now requires more disaggregated data collection on subgroup identities within race and ethnicity categories. These categories matter because the choices made in data collection mold our comprehension of what is happening in communities and influences the distribution of resources to address these needs.

The revisions have generated considerable excitement, but with uncertainty surrounding questions, such as: how to implement the new minimum standards, how to prioritize data collection in historically marginalized communities, how to create tabulations with small sample sizes, how to roll-up categories into acceptable intermediate racialized category groupings, and how to develop and sustain long-term relationships with communities. The revisions could necessitate major comprehensive updates and revisions to many data systems.

Recognizing a need to build resources and tools to help organizations achieve their data equity goals and navigate the upcoming changes to the OMB categories, the Robert Wood Johnson Foundation funded the UCLA Data Equity Center (DEC) to offer technical assistance and resources to turn data equity goals into concrete actions. The DEC promotes equity in data by offering resources and technical assistance that will inform, support and promote data systems becoming more representative, inclusive, and collaborative with the communities they serve. The overall strategy includes compiling curated resources that outline actionable steps organizations can take to improve data equity, developing targeted trainings for staff, and providing technical assistance to organizations as they improve their data collection, processing, reporting, and dissemination.

The DEC is housed at the UCLA Center for Health Policy Research (the Center), which has a 30-year history in advancing health equity through high-quality research and data production. In addition to the broad experience of our multidisciplinary team at the Center, the DEC partners with a network of subject-matter experts who are dedicated to advancing survey and data science methodologies and practice that is more equitable. We are developing a compendium of resources that demonstrates how to apply a data equity lens across a data ecosystem, including customizing an organization’s data equity framework, engaging with relevant communities, designing a data collection system, designing and translating a questionnaire, and developing appropriate methods such as sample design, weighting, imputation, analysis, and dissemination.

Although the movement to improve data equity began with efforts to disaggregate racial and ethnic categories to better represent the diversity within racial categories, advancing health equity requires expanding beyond race and ethnicity to include other social drivers of health. Each person is a member of many communities that intersect to structure and define their identity and the social experiences and contexts that shape their well-being. These may be defined by their personal characteristics, such as their gender, disability status or type, sexual orientation, veteran status, immigration status, the language(s) they speak, and the environments in which they live and work.

No single data set can measure everything or reach every community; those collecting data have to weigh the costs and benefits of different decisions and make choices about where and how to focus their inclusion efforts. Several strategies can make this process move more smoothly, with community engagement being pivotal. Data leaders must collaborate and maintain sustainable long-term relationships with communities in order to understand and represent their unique opportunities, challenges, and data needs.

We all have a role to play in achieving this reality. Decision-makers should seek meaningful, representative data. Data leaders should work together to advance practice and share their methods broadly. Organizations that collect and/or use data should build a data equity plan and principles. Community leaders should help data leaders understand their community’s history, lived experience, and trusted channels for sharing information.

While data equity is only one component of achieving health equity, it is fundamental to that goal. Without data equity, we will not achieve health equity. This is where commitment from leaders, engagement with community, and efforts to build tools and resources to operationalize data equity principles can help propel the work forward.

References

1. Ponce NA, Shimkhada R, Adkins-Jackson PB. Making Communities More Visible: Equity-Centered Data to Achieve Health Equity. Milbank Q. Apr 2023;101(S1):302-332. doi:10.1111/1468-0009.12605. 
2. Ponce N. Centering Health Equity in Population Health Surveys. JAMA Health Forum. 2020;1(12):e201429. doi:10.1001/jamahealthforum.2020.1429
3. Becker T BS, Dorsey R, Ponce NA. Data Disaggregation with American Indian/Alaska Native Population Data. . Population Research and Policy Review. 2021;40(1):103-125. doi:10.1007/s11113-020-09635-2
4. Ponce NA, Bautista R, Sondik EJ, et al. Championing partnerships for data equity. J Health Care Poor Underserved. May 2015;26(2 Suppl):6-15. doi:10.1353/hpu.2015.0058
5. Shimkhada R, Scheitler AJ, Ponce NA. Capturing Racial/Ethnic Diversity in Population-Based Surveys: Data Disaggregation of Health Data for Asian American, Native Hawaiian, and Pacific Islanders (AANHPIs). Population Research and Policy Review. 2021/02/01 2021;40(1):81-102. doi:10.1007/s11113-020-09634-3

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