Asian American are lumped into a single category
Imagine a doctor treating you based on stereotypes, not your unique needs. This is the reality for many Asian Americans, lumped into a single category that masks their diverse experiences. Aggregated data on health, income, and social determinants presents a misleading view of Asian American communities, perpetuating the damaging model minority myth. Asian Americans are perceived as healthy, affluent, and academically successful.
This oversimplification masks the genuine challenges experienced by many Asian American subgroups, rendering them invisible in health discussions and depriving them of essential resources.
“My parents were first generation Korean immigrants who came to the United States in the late 70s, and for over 30 years they owned a corner store in Philadelphia, where they worked 14 or 15 hour days, 7 days a week, and those days were physically demanding and socially isolating. On a daily basis they had to navigate numerous cultural and language barriers with customers, teachers, doctors, and others they encountered on a regular basis. The well-known model minority myth would claim people like my parents were thriving because they were such quote hard workers. But, I saw firsthand how the challenges that they face impacted their social, emotional, and fiscal health and well-being,” says Tina J. Kauh, Senior Program Officer within the Research-Evaluation-Learning Unit of the Robert Wood Johnson Foundation.
Health disparities impact marginalized groups
Health disparities have long been a challenge in public health, manifesting in higher rates of illness, disability, and mortality among marginalized groups. To address these inequities effectively, it is essential to understand the nuanced factors contributing to them. At the Ethnic Media Services briefing on May 17, sponsored by the Robert Wood Johnson Foundation, experts and advocates explored how better data collection help direct resources and interventions where they are needed most, especially to communities facing the greatest health challenges.
Disaggregating data is a critical step in this process, providing a clearer, more detailed picture of health outcomes across different populations. For the first time since 1997, the Office of Management and Budget (OMB) expanded its race and ethnicity standards to capture historically excluded communities who will now be visible in federal data collection.
Aggregated data masks differences
All too often, health data in the U.S. has been collected and reported at an aggregate level that masks vital differences between subpopulations. When data lumps together broad categories like “White,” “Black,” “Asian,” or “Hispanic/Latino,” important nuances about distinct populations are lost, explains Kauh. For example, the “Asian American” category encompasses an enormously diverse range of peoples and cultures including Chinese Americans, Indian Americans, Vietnamese Americans, Filipino Americans and many others. These subgroups can have starkly different socioeconomic status, behaviors, risks and health outcomes that get obscured in aggregate data.
Similarly, the “Hispanic/Latino” grouping merges together populations as diverse as Mexican Americans, Puerto Ricans, Cubans, Dominicans and more. Each of these communities may face very different realities and challenges impacting their health that only become visible when data is disaggregated. Disaggregating data allows us to see whether certain subgroups face higher rates of conditions like diabetes, heart disease, or mental health issues and determine the underlying causes.
Experience of loss
Gail C. Christopher, Executive Director of the National Collaborative for Health Equity and Director of the Robert Wood Johnson Foundation’s National Commission to Transform Public Health Data Systems recounts her experiences of loss like so many African-American women. Recently she was a candidate for surgery and her well-meaning physician decided on a course of action to prevent her disease from worsening, based on a stereotype.
“The extreme inflammatory response that had been associated with African-Americans and many of you may know there are many algorithms and clinical protocols that are based on stereotypes and not accurate perceptions of many diverse communities. But based on a particular stereotype, he decided that he was going to really increase the dosage of the medication post-surgery dramatically. And the result of that was the loss of the vision in one eye because it was ocular surgery.”
Now think of how scary that is, we as consumers are conditioned to do as told to do by our practicing physician. This underscores the importance of making sure that more granular data on distinct ethnicities, tribal affiliations, nationalities, and localized communities is recognized as crucial for pinpointing disparities and their root causes.
The path to improve data collection
Fortunately, the federal government and many states and municipalities have begun prioritizing the collection of more granular, disaggregated data to better identify disparities and monitor the impact of public health programs. Advocacy groups representing diverse communities have also pushed for improved data collection and reporting standards.
Meeta Anand, Senior Program Director, Census and Data Equity, at The Leadership Conference Education Fund, recalls a story of when she was growing up, “ My mother is from Haiti, my father is from India. Growing up, I did not have much of a choice of what to put on a form. There was no such thing as multi-select. There was no opportunity to truly reflect who I was and I hung out in that, you know, famous ‘other’ box. But in recent history, you can multi-select, and that’s what we want. We want to get as close to people being able to be seen and felt.”
Juan Rosa, National Director of Civic Engagement at NALEO Educational Fund recalls a similar narrative. “When I started to complete the census for my family first and then for myself, here in the United States, I was very proud to mark that my race was black. In the Dominican Republic, I wouldn’t have had that choice. I would have been asked to, put myself down as in as an indio, which in English is Indian. In the Dominican Republic that is a color. Between white and black. I don’t have the problem in the United States.”
Tailoring strategies to data insights
With detailed, disaggregated data, public health officials and policymakers can develop more effective, tailored strategies and allocate resources to the communities in greatest need. Community organizations and local leaders can leverage this data to raise awareness, secure funding, and design culturally appropriate interventions. Healthcare systems and providers can use these insights to improve preventive care, disease management, and patient outreach.
While data disaggregation is a crucial first step, fully addressing health disparities requires sustained, multifaceted efforts to tackle systemic, socioeconomic, and environmental factors. However, having clear, detailed data on all populations is essential for creating effective, equity-focused policies and programs.
The push for robust, standardized demographic data disaggregation across the U.S. healthcare system is vital for achieving health equity for all communities. With clearer and more actionable data, the nation can make significant progress in closing long-standing gaps and improving health outcomes for all Americans.