Why more granular racial, ethnic data could improve maternal outcomes

Payers, providers and—especially—public policy officials might want to take a deeper dive into the race and ethnicity of mothers when weighing what to do about preterm births and low birth weights, according to a study published today in Health Affairs.

“Given that many systematic inequities are historically rooted in state and local-level policies, efforts at these levels may benefit from the development of surveys with disaggregated race and ethnicity information specific to their communities for use in focused place-based initiatives,” the study found. “Such efforts must be careful to ensure that granular race and ethnicity data are used to reduce health inequities and structural barriers, instead of intensifying inequities through racism or racial targeting.”

Researchers at the University of Arkansas posited that understanding the granularity of racial and ethnic disparities will help better address the problems of preterm births and low birth weights. They examined birth certificates from 2016 to 2020 collected by the National Center for Health Statistics (NCHS). Under commonly used racial and ethnic categories such as non-Hispanic Asian, they further divided patients into subgroups such as Korean, Japanese or Filipino.

“Overall, we found large variation between subcategories within broader categories, with rates of low birthweight varying as much as 2.3-fold among the non-Hispanic multiple race category and rates of preterm birth varying as much as 2.0-fold among the Asian category,” the study said.

The study’s corresponding editor, Clare Brown, Ph.D., of the University of Arkansas for Medical Sciences, told Fierce Healthcare that “this analysis was largely inspired by recent articles that I have read regarding the importance of disaggregation of data and how disaggregated data may be helpful in better understanding and mitigating structural racism and discrimination faced by different populations. For example, I have read previous studies about how experiences during COVID-19 may have differed between individuals who are Chinese versus those who are Asian Indian, both of whom are generally categorized as ‘Asian’.”

Clare Brown, Ph.D.
Clare Brown, Ph.D. (University of Arkansas)

Under the broad NCHS category of Hispanic, there are five subcategories. Under non-Hispanic Asian and non-Hispanic American Indian or Alaska Native, there are seven subcategories. Under non-Hispanic Black and non-Hispanic multiple race, there are 21 subcategories. Under non-Hispanic Native Hawaiian and other Pacific Islander, there are four subcategories.

Non-Hispanic white included no subcategories.

The study found that all five Hispanic subcategories and all seven Asian subcategories have statistically significant different rates than the overall rates for the categories.

“Rates of low birthweight among Hispanic subcategories ranged from 5.4% (Cuban) to 7.7% (Puerto Rican), and rates among Asian subcategories ranged from 4.5% (Chinese) to 8.7% (Filipino and Asian Indian),” the study said. “Among the twenty-one multiple race subcategories, fourteen had rates of low birthweight that were different from the overall rate for the multiple race category.”

Brown said both payers and providers should be able to apply the findings of the study in their work.

“Understanding the specific race and ethnicity of the patients that you care for, beneficiaries that you cover or populations that you provide outreach to is critical for improving health equity,” Brown said.

She cites as an example her native Arkansas, which has the largest Marshallese population in the U.S. Providers at the University of Arkansas for Medical Sciences familiarized themselves with Marshallese culture and values and developed health education tools in Marshallese.

“With respect to payers, having information that may indicate risk of adverse outcomes is critical for outreach efforts aimed at improving population health,” said Brown. “Many payers do not collect racial and ethnic information at all, and some populations, such as those of minority race and ethnicity, may be fearful that racial and ethnic information could be used to prevent insurance coverage or raise premiums.”

While collecting granular racial and ethnic data is important, payers should couple that with community outreach to better understand the needs of their beneficiaries.

What most surprised Brown about the study’s findings?

“There was more variation within the multiple race category than the amount of variation between the broader categories themselves,” she said. “This makes sense when you think about it, as the multiple race category aggregates individuals that likely have very different cultures, languages and experiences, such as someone who identifies as Black and White grouped with someone who identifies as Asian and American Indian/Alaskan Native.”