A considerable amount of the research on health in America divides people into only six racial and ethnic categories: Black/African American, American Indian/Alaska Native, Asian, Native Hawaiian/Other Pacific Islander (NHPI), white, and Latino/Hispanic. This system of classification, established by the federal Office of Management and Budget (OMB), is the minimum standard for all federally funded studies, and is used in many private ones. But a new report from the Robert Wood Johnson Foundation argues that these categories are far too broad, erasing the experiences of minority groups and preventing the achievement of health equity.
According to the report, the OMB classification, which hasn’t been updated since 1997, lumps together people with very different cultural backgrounds and experiences. The Asian category, for example, contains everyone from Hmong tribespeople to highly-educated Taiwanese immigrants, and the white category contains many people of Middle Eastern origin.
The report argues that these broad groupings can conceal important differences. Diabetes, for example, is a major cause of death among American Indian/Alaska Natives, but death rates vary greatly between regions, from 16.3 per 100,000 among Alaskans to 129.7 in the Great Plains area. Without data about these specific subcategories, the problem would remain invisible.
Statistics about large groups also mask great variation in social determinants of health, such as income. On average, Asian-Americans earn over $15,000 per year more than a typical U.S. individual. But this obscures a high level of income inequality within the group, with Indian-Americans averaging $119,000 and Burmese-Americans averaging only $44,000. This difference is correlated with very different health outcomes.
This lack of data creates a vicious cycle that can make researching smaller groups impossible. “Say you want to do research on Asian-Americans and their well-being,” said Dr. Tina Kauh, a co-author of the report. “Funders aren’t interested in Asian-Americans because there’s no [data] showing that they need to invest resources, but there’s no data because no one’s willing to fund that research.”
The OMB guidelines can also cause smaller groups, such as Native Hawaiian/Other Pacific Islanders, to be combined with Asians, or placed into an ambiguous “Other” category. This makes it harder to do research that would address their needs. And sometimes the standards of categorization are ignored. Months into the pandemic, 30 states and the District of Columbia were not reporting COVID-19 data on Native Hawaiian/Other Pacific Islanders, despite it being required. This prevented researchers from studying what turned out to be a real problem: a 2021 study of data from California showed that the NHPI COVID-19 mortality rate was 1.5 times higher than the state average.
In order to combat these issues, the report calls for the collection of significantly more detailed racial and ethnic data, along with data about related factors that can influence health, such as education, income, and country or region of birth. It calls for this data to always be disaggregated—separated into meaningful subgroups.
Accomplishing this, however, is going to take money. Getting meaningful data on smaller populations will require recruiting larger samples of them into studies. There are also costs associated with ensuring the privacy of subjects, designing new systems of categorization, and updating health records systems to accommodate the additional data.
It will also require political will. In the past several years, state-level efforts to pass laws requiring more detailed categories for Asians have met resistance from some Asian groups who believe that being classified into sub-groups could diminish their political power or be used as part of affirmative action programs that would prevent them from getting into schools. Others have expressed fear that disaggregated data could be used to pathologize ethnicity and culture, or to promote ideas about the biological superiority or inferiority of certain races.
However, Kauh thinks that the present offers an excellent chance for progress.
“COVID was a horrible experience, but it created a real opportunity to make change around disaggregated data. It shined a really bright light on why it’s important. It honed in on the inequities that exist for certain populations,” she said. “It takes longer for policy change at the federal level, but organizations can change what they’re doing. Philanthropy can increase the budgets that allow for disaggregated data. Researchers can start testing different ways to ask those questions.”
Kauh believes that by taking these steps, researchers and policy-makers can strike a blow against health inequity and the systemic racism that underlies it.
“Researchers ask themselves, ‘Is it worth the cost of disaggregating data?’” she said. “Well, if you keep going down that line of questioning, it gets you to the point of ‘Is it worth spending money on this group of people? Are they deserving?’ And the answer is yes.”