Health equity actions: understanding data disaggregation

For decades, researchers have agreed data should be broken down by racial and ethnic subgroups to obtain a more representative picture of results. So, when health care providers and others working on health initiatives see health data segregated by race or ethnicities, they likely assume the data will be useful when working to advance health equity.

However, disaggregating data in research studies can result in very small sample sizes, which makes it difficult to measure outcomes accurately. As a result, study participants are often lumped together into one of five groups: Black, white, American Indian, Latinx, or Asian American/Pacific Islander. The problem with that is the U.S. population is much more diverse than five groups and each person’s history and experiences are more nuanced – all of which can influence risk factors, health outcomes and efforts to meet the health needs of specific segments of the population.

In the Talking Pediatrics episode, “Health Equity Actions: Understanding Data Disaggregation,” guest host Adriene Thornton discusses this complex area of health equity with Dr. Nathan Chomilo, pediatrician and medical director for the State of Minnesota’s Medicaid and MinnesotaCare programs. Thornton is the manager of health equity at Children’s Minnesota.

Gathering accurate health data is complex

Issues with using race and ethnic data start with the difficult task of defining groups. Race is a social category, not a biological category, so from the start race is a complex research category to define. Generations of historical and structural inequities due to race have led to disparities in every aspect of society, with health disparities among the greatest. All of these factors contribute to the definitions of groups. Also, to have accurate data the full community being surveyed must be included, which can be complicated and time intensive, particularly if the definition of groups is unclear or insufficient.

Beyond race and ethnic definitions, disaggregation of data for health equity might also consider definitions for rural, urban, gender, disability, sexual orientation, zip code, and the list goes on. Once groups being surveyed are defined, then the data source(s) can be more clearly understood and developed.

Going beyond the health data

Consider many Black and Indigenous people distrust the health care system after generations of abuse by the medical community. It’s important to acknowledge these factors when asking people of color to share information about their race and ethnicity for health research. Taking the time to talk about what the information is being used for, how they will or won’t be personally identified in the research and creating a survey experience with questions that allow participants to “feel seen” all contribute to better data collection that reflects the population you’re trying to learn more about.

“Data is the initial signal,” said Dr. Chomilo. “It shows us there is a gap in either an outcome or access to a resource. Then from there we should ask more questions. We should go to the communities that we are identifying as experiencing that gap or inequity and ask them what’s the lived reality, and does this data match up with what they’re seeing.”

Health equity requires a multi-pronged approach

Race and ethnic information are important for health care providers and others working on health initiatives to take into consideration when trying to advance health equity and provide culturally relevant and responsive care and solutions in a broader context. They can be helpful markers of the impacts of structural racism at the population level and inform public policy efforts to improve opportunities for certain groups.

“The data is helpful to capture these experiences and having a broad understanding,” said Dr. Chomilo. “But if we’re talking about getting down to the person level, we always want to start with asking that person what their experience is and being open to it being different from what we see in the data.”

Race and ethnic data aren’t as helpful in a clinical setting. “As a provider, I find it helpful to know the cultural experiences and differences with my patients,” said Dr. Chomilo. “But I wouldn’t ever want to assume just because I have a Somali patient or a Hmong patient or Vietnamese or Liberian or African American in front of me that I know exactly everything I need to know about them and their lived experience.”