Improving AAPI Health with Better Data

Recently, a colleague asked me to identify my race. She was collecting diversity information and needed to fill in the field. “Asian,” I said automatically. My parents immigrated to the US from India in the 1980s, and Indian American is the identity I hold most strongly. This option, however, rarely shows up on demographic forms. Presented with the standard options for race (White, Black or African American, American Indian or Alaska Native, Asian), I’ve always selected Asian.

She paused. “Are you sure?”

“What do you mean?” I asked, slightly perplexed.

“I just mean you can choose how you want to identify. We can note whatever categorization feels right to you.”

“Oh!” I shrugged. “Indian American is great, then. Thanks.”

As I left the room, I wondered why the question had caught me off guard. Having worked in numerous healthcare and health-justice spaces, I’m well aware of the importance of being able to self-identify.

And yet, I’ve rarely experienced room for nuance when it comes to Asian American and Pacific Islander—or AAPI—identity in those spaces. That’s especially true regarding data collection, which tends to flatten complex social groups into simple categories. These datasets are then used to shape health interventions that frequently miss the mark for communities with specific experiences, norms, and traditions. As such, AAPI health data approaches that buck traditional racial constructs are crucial to developing solutions that more effectively address this population’s health inequities.

Understanding the AAPI Community

AAPI is an umbrella term that includes people from more than 50 countries who speak over 100 languages. Asians and Asian Americans represent more than 20 different countries in East, South, and Southeast Asia. Native Hawaiians and Pacific Islanders represent dozens of countries and islands, including Polynesia, Melanesia, and more.

Currently, the AAPI community represents 5.7 percent of the American population. It is one of the fastest-growing groups in the country, estimated to reach 9.7 percent of the population, or over 40 million people, by 2050.

Despite this expanding population’s diversity, since the 1960s, AAPIs have frequently been treated as a monolith for official data collection purposes. Prior to this time, people of Asian and Pacific Islander descent tended to self-identify by nationality, language, or ethnicity.

The term Asian American emerged in 1968 out of student organizing. Emma Gee and Yuji Ichioka, two graduate student activists, joined other activists of Asian descent to form the Asian American Political Alliance. Drawing on civil rights and anti-war protests of the time, the Alliance utilized the label, Asian-American, to increase their communities’ visibility and develop unified political power.

The federal government began utilizing “Asian-Pacific Islander” to indicate race in the 1980 Census. This grouping, however, overlooked a plethora of unique histories and experiences. For example, some in the AAPI community came to the United States to escape political persecution in their home countries, while others migrated for white-collar employment opportunities. Some subgroups have been a part of the country’s fabric for over a century, while others represent more recent waves of immigration.

The two “meta-groups” of Asian Americans and Pacific Islanders also have different experiences of colonialism, class, and war. Many Pacific Islanders, for example, continue to fight for decolonization and land sovereignty, whereas colonialism is a historical legacy in most Asian countries. Similarly, as scholars point out, grouping these two populations together masks the fact that Pacific Islanders earn lower median incomes than most Asian Americans and are underrepresented in higher education.

Identifying the Harms of Current Data Approaches

Social and economic differences like these impact health outcomes in the AAPI community. For example, an unemployed political asylee to the United States who lacks a local support network may have very different health outcomes than a work visa-sponsored employee of a Fortune 500 company who comes from relative economic privilege.

Yet, healthcare institutions and systems rarely incorporate such details when collecting data about the AAPI community. Following the lead of the US Census and other federal agencies, they typically gather high-level racial data—if they gather any at all. Without disaggregated data for AAPIs, it is difficult to understand differences in health outcomes between subgroups.

A frequently cited example of the dangers of aggregation is a 1985 report by the US Department of Health and Human Services, which utilized composite data to claim that the “Asian/Pacific Island minority, in the aggregate, is healthier than all other racial groups in the US, including Whites.” This type of generalization is harmful in many ways.

First, it fails to account for subgroups in the AAPI community who consistently experience worse health outcomes than the aggregate. This makes it easy to overlook the structural causes of health inequities, like housing or food insecurity, that impact AAPI subgroups, along with other BIPOC communities. In turn, this analysis limits the attention paid to these groups, the research dollars invested in and resources allocated to them, and targeted public health programming to address their health inequities.

Second, such aggregate data reinforces the longstanding “model minority myth,” according to which Asian Americans and Pacific Islanders are “successful” because they prioritize education, professional achievement, and economic success. This myth is often used to discount the impacts of structural racism on communities of color, reinforcing White supremacy. Similarly, when healthcare institutions and professionals lean on aggregate data to stereotype AAPIs as patients who take personal responsibility for their health outcomes, they often imply that other people of color fail to do the same, leading to further discrimination against those communities. This narrative also distracts from the many challenges that AAPIs face, both to their health and when interacting with the healthcare system.

Take, for example, the COVID-19 pandemic, which disproportionately impacted AAPI communities, both in terms of health outcomes as well as increased anti-Asian sentiment. According to the Centers for Disease Control and Prevention, COVID-19 cases amongst Asian Americans increased by 34 percent from 2020 to 2021. However, this data does not differentiate between different AAPI subgroups or account for factors such as occupation, income, and housing. As a result, the true impact of the pandemic on some AAPI communities may be even greater.

Improving Approaches to AAPI Data

There are several ways that healthcare practitioners and social service providers can address gaps in current approaches to AAPI health data. These include:

• Engaging AAPI advocacy groups and community members. Starting with AAPI groups’ insights on inequities in data design is crucial. AAPI Data, a clearinghouse for data and policy research, created a framework for data equity that calls for incorporation of AAPI voices into research development, collection, analysis, and dissemination. This inclusion can ensure that research objectives and data collection take place in culturally appropriate ways. It can also help to bring granularity to data assessment and the solutions that follow. Rather than health systems assuming that they have Asian patient representation on a research advisory board, for example, a local group could push for specific subgroup representation.
• Disaggregating data. Research shows that Southeast Asian Americans have higher rates of diabetes and liver cancer compared to other AAPI subgroups. Pacific Islanders often have higher rates of obesity compared to other subgroups. These statistics point to the variability of health outcomes among AAPI groups. Providing individuals with the opportunity to self-identify according to nationality, ethnicity, class, or other demographics helps pinpoint and hopefully improve these outcomes. One promising example of this can be seen with the Census, which in the early 2000s separated “Asian” and “Pacific Islander” as options for race. Healthcare institutions, however, have been slow to follow suit.
• Investing in pipelines of underrepresented AAPI health researchers and data interpreters. Some AAPI subgroups are increasingly represented in healthcare spaces, while others continue to face barriers to education and professional opportunity. Their lived experience, however, can inform data analysis in much-needed ways and help develop innovative health programs.

Bringing a more nuanced outlook to health-data collection and analysis can give us a better understanding of AAPI communities’ unique health needs and challenges. In turn, these insights can help us develop more tailored interventions and policies that move the needle in a positive direction for this multifaceted and quickly growing population. To address health disparities in AAPI communities, we must overhaul our AAPI data collection processes.