Blog Post

Busting Myths About the Asian American “Model Minority”

More Detailed Health Data Needed

January 6, 2022

By:

Chris Tachibana, PhD, MS
and Janet Weiner, PhD, MPH

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If asked to guess which racial or ethnic group has higher rates of COVID-19 deaths per infection than others, most people wouldn’t choose Asian Americans. The perception is that Americans of Asian background are a “model minority” with superior income, education, and health—a myth refuted in recent work by LDI fellows Lan Ðoàn, PhD, MPH and Lola Fayanju, MD, MA, MPHS.

In fact, one of the biggest misconceptions is the aggregation of diverse groups of people into a single category of “Asian American.” Two recent studies highlight the problem.

In Journals of Gerontology, Ðoàn and colleagues investigate cardiovascular disease in Asian Americans and Native Hawaiians and Pacific Islanders (NH/PIs), in aggregate and by ethnic subgroups. The researchers analyzed data from the 2011-2015 Medicare Health Outcomes Study, which included nearly 640,000 Medicare Advantage recipients and about 27,000 who self-identified as Asian American, Native Hawaiian, or Pacific Islander. The data allowed the researchers to analyze cardiovascular risk factors (such as diabetes or smoking) and outcomes (such as heart attack or stroke) in eight Asian American and two NH/PI subgroups.

Previous U.S. research showed mortality from cardiovascular disease decreasing for white adults but not improving for Asian Americans. The more granular analysis by Ðoàn and colleagues showed that, in particular, including NH/PIs in the Asian American category was problematic. NH/PIs reported more obesity, diabetes, high blood pressure, and smoking than people who identified as white or other Asian American subgroups. The researchers had sufficient data to reveal specific ethnic group disparities. For example, compared to white adults, NH/PIs as a whole reported less cardiovascular disease. On closer examination, though, Native Hawaiian adults were more likely to report having a stroke—a result that was masked by considering NH/PIs in aggregate. Within the NH/PI group, smoking was higher in Native Hawaiian women (15%) than women in the Other Pacific Islander group (6%), reflecting the need for more detailed data on NH/PI populations. Similar trends were observed when disaggregating data for Asian Americans, where Filipino adults had the greatest prevalence of overweight, obesity and hypertension compared to white and other Asian American adults. The authors note that we have much to learn about small-population subgroups such as Americans with backgrounds from Pakistan, Sri Lanka, or Guam, although working with data on small groups is challenging, in part because of difficulties preserving anonymity.

In a similar vein, in the journal Cancer, Fayanju and colleagues examine differences in breast cancer characteristics and outcomes among Asian women. Using data from the Surveillance, Epidemiology, and End Results (SEER) database, they identified more than 900,000 women diagnosed with breast cancer from 1990-2016. More than 63,000 of them were “Asian,” which the researchers subclassified as Chinese, Japanese, Korean, Filipino, Vietnamese, South Asian (Asian Indian or Pakistani), Southeast Asian (Cambodian, Laotian, Hmong, or Thai), or other Asian.

The researchers found that breast cancer characteristics (such as tumor subtypes) and outcomes vary significantly among Asian women. For example, in aggregate, “Asian” women had the highest 10-year overall survival rates of all racial and ethnic groups. But Southeast Asian women had the worst overall survival of any Asian group, and had no better outcomes than white women. The authors recommend that researchers disaggregate data by country or region of origin to identify subgroups that are at risk for worse outcomes than aggregated data may suggest.

We should not be surprised by these findings. “Asian” and Asian American” encompass groups that have important differences, including country of origin, ancestry, culture, and immigration patterns. As Ðoàn and colleagues note, the “Asian American” category represents more than 40 communities and countries of origin.

Among factors contributing to observed subgroup differences, Ðoàn and coauthors cite histories of colonization and structural racism, poor health care access, and overrepresentation in hazardous environments, for example, as health workers during the COVID-19 pandemic. Unlike many Asian Americans with an immigrant or refugee background, Native Hawaiians are an Indigenous people. Many Pacific Islanders enter the U.S. with nonimmigrant status because of political relationships between the U.S. and their country of origin. This status can affect access to U.S. health care.

In an EClinical Medicine commentary, Ðoàn and colleagues call for higher-quality data collection, analysis, and reporting to help eliminate these disparities and biases. They have three recommendations for funders and researchers:

Invest in improved, standardized data collection and reporting practices by race/ethnicity. In federal COVID-19 data, more than one-third of death reports lack race or ethnicity information. Previous research on health data showed that, in general, individuals in Asian American groups are often misclassified. Among proposed solutions, the authors suggested using algorithms to account for missing data and increasing research funding, with only 0.17% of National Institutes of Health-funded clinical research traditionally focused on Asian American groups.
Place equal weight on community stories and published qualitative/quantitative literature. The authors recommend that researchers recognize the value of both quantitative data and information from Asian American community listening sessions. Qualitative data will lead to health materials, programs, and policies that are meaningful to the communities they intend to support.
Address systems-level implicit and explicit Asian American bias. The myth of Asian Americans as an exceptional group with few health problems and disparities means their risks and needs are overlooked and their communities excluded from equity policies, for example, about vaccine allocation. Education, training, and better data could help correct and prevent these biases.

Lan Ðoàn, PhD, MPH, is a postdoctoral fellow in the Department of Population Health Section for Health Equity at NYU Grossman School of Medicine and a Scholar at the University of Pennsylvania’s Center for Improving Care Delivery for the Aging (CICADA).

Oluwadamilola (Lola) Fayanju, MD, MA, MPHS is the inaugural Helen O. Dickens Presidential Associate Professor in the Perelman School of Medicine at the University of Pennsylvania. She is the Chief of Breast Surgery for the University of Pennsylvania Health System.