How we measure America’s rapidly expanding diversity has critical implications for the nation’s health. A new PolicyLink report offers recommendations for improving how we collect and report data about racial and ethnic subgroups.
Does the kind of data we collect and report ensure everyone has a fair and just opportunity to live their healthiest life possible?
As the country grows more ethnically and racially diverse, there is a growing debate among health researchers about the value of breaking down data in more refined ways. The argument is that simply looking at health outcomes through the lens of broad racial or ethnic categories (e.g., black people or Asian Americans) doesn’t paint an accurate enough picture of health and well-being. It masks what’s happening within subgroups and glosses over the nuanced experiences that greatly influence outcomes in these populations.
Recently, the Robert Wood Johnson Foundation (RWJF) partnered with PolicyLink to identify the needs and gaps in how ethnic and racial data are collected, analyzed, and reported for each of the major aggregated ethnic and racial groups.
When data are broken down by detailed racial and ethnic subgroups, or disaggregated, a more representative picture of health emerges.
In fact, for decades, the research community has agreed that it would be better to disaggregate health data. The reality is, however, that there are challenges to doing this well. Disaggregating data in research studies can result in very small sample sizes, for instance, which makes it difficult to measure outcomes accurately. Consequently, study participants are often lumped together into one of five distinct groups: black, white, American Indian, Latinx, or Asian American/Pacific Islander.
But the U.S. population is much more diverse than that and each person’s history and experiences are more nuanced, all of which can influence risk factors and health outcomes.
Case in point: Asian Americans account for 17 million people and nearly half of all refugees who arrived in the United States between 2000 and 2010. The experiences of someone who is a refugee, for example, is very different than a fourth-generation American or someone who immigrated for graduate studies.
Use of aggregated data points related to health, education, and economics perpetuate the model minority myth that all Asians are healthy, affluent, and well-educated. It obscures the very real challenges that many people within Asian American communities face. Consequently, fewer resources are devoted to subgroups who are faring poorly.
There are many other examples of how broad racial/ethnic designations mask the social, cultural, and economic complexity of a group:
There are 562 federally recognized Indian nations in the United States. In addition to members of these tribes differing ethnically, culturally, and linguistically, they can live on or off reservations, which influences their access to health services and other major resources.
Forty-two million people in the United States self-identify as black or African-American. While most of them have lived in the United States for generations, more than three million are immigrants, mostly from different parts of Africa or the Caribbean.
The Hispanic/Latino population makes up about 16 percent of the U.S. population, and about three-quarters self-identify as Mexican, Puerto Rican, or Cuban, which represent strikingly different cultures and histories.
Individuals self-identifying as white represent more than two-thirds of the U.S. population and the cultural diversity of three continents (North America, Europe, and Africa).
By producing disaggregated data for detailed groups, you can always combine the data to produce summarized data on the entire group. However, the reverse is not true. You can’t get detailed data from aggregated data.
And without accurate data by detailed racial/ethnic group, some of the most disadvantaged in our communities are rendered invisible to policy makers, leaving their critical needs unmet.
Everyone has a part to play. We started working with the Asian and Pacific Islander American Health Forum to look at disaggregated rates of Asian American children who are overweight and obese. While prior research has concluded that childhood overweight/obesity rates were low among Asian Americans as a whole, this project documented significant differences across Asian American subgroups.
We’re exploring options for the next phase of our work in this space. Possible activities would include supporting advocacy for policy change at the state level, as well as state- and local-level training for demographers to learn how to better collect, analyze, and report on health data across different ethnic and racial groups.
Data drives action. And the way we collect those data will determine who gets the resources to make change happen. Further, as a research community, we must look for opportunities to disaggregate data in ways that are sensitive to the cultural, political, and social issues in our environment. In doing so, we need to collect, analyze, and report data in ways that do not put respondents at risk or perpetuate existing stereotypes.
Tina Kauh joined the Robert Wood Johnson Foundation in 2012. In her role as a Research-Evaluation-Learning senior program officer, she evaluates the work of grantees, develops new research and evaluation programs, helps to develop and monitor performance indicators, and disseminates lessons learned.