Disaggregating Asian American and Pacific Islander Health Data: Opportunities to Advance Health Equity

Comprising about 6% of the US population, Asian American and Pacific Islander (AAPI) people are one of the fastest growing populations in the nation. Even though AAPI people are a diverse group consisting of more 50 ethnicities and 100-plus languages, their health data are often aggregated as one race and treated as a monolith. This grouping masks wide variation in access to care, health status, and health outcomes for different subgroups (such as Native Hawaiian Samoan). For example, one in four Pacific Islander adults report problems paying medical bills (compared to 9% of Asian American and 16% of white adults) and there is wide variation in uninsurance rates across Asian American subgroups.

States with the largest populations of AAPI people include California, New York, Texas, New Jersey, Washington, and Hawaii, but over the last 30 years more states have seen AAPI population booms. There are also higher concentrations of some AAPI subgroups in certain states and localities, reinforcing the need for disaggregated data. For example, North Dakota has become home to a growing number of Bhutanese people.

AAPI health disparities matter and AAPI data disaggregation, or the collection and reporting of AAPI subgroups, is a critical first step for identifying and addressing them. While there has been progress in collection of disaggregated AAPI data in many federal health surveys, adoption at the state level remains inconsistent and suboptimal. Data collection informs policy interventions, funding decisions, and resource allocation, making disaggregated AAPI data collection a critical priority for state policymakers.

The Value of Disaggregated Data During COVID-19

The COVID-19 pandemic underscored the consequences of these data gaps. AAPI people were likely disproportionately impacted by the pandemic because of higher rates of poverty, overrepresentation in frontline and essential workforce, and a higher likelihood of residing in multigenerational households. The pandemic also aligned with an increase in reports of anti-AAPI violence and discrimination against AAPI people. However, poor data quality and limited collection of disaggregated AAPI health data hindered states’ abilities to identify and mitigate COVID-19-related disparities for AAPI subgroups. In addition, it is possible that certain AAPI subgroups faced higher rates of physical assaults or were disproportionately impacted economically (for example, losing employment or health insurance coverage), but nationwide data are not available.

The state or local evidence that is available, such as data from California and New York City, suggest different experiences throughout the COVID-19 pandemic across subgroups. Analysis from the 20 states that collected disaggregated data for Native Hawaiian and Pacific Islander people found a disproportionate COVID-19 mortality burden. In California, this community-driven collection and reporting of Native Hawaiian and Pacific Islander data led Riverside County, California to improve its AAPI data reporting practice and increase COVID-19 invention funding for this population, and the California Department of Health to commit to prioritizing the population in its COVID-19 efforts. To understand the impact of the COVID-19 pandemic on Asian American health center patients, a multilingual survey was developed, which included information about subgroup, COVID-19 testing, mental health challenges, and experience with perceived mistreatment. The findings were used to establish multilingual and multicultural COVID-19 testing sites in the Greater San Francisco Bay Area and informed contact tracing and vaccination outreach.

Current Policies to Disaggregate AAPI Health Data

As part of the Affordable Care Act, the Office of Minority Health (OMH) developed standards for disaggregating AAPI data in population surveys. By 2012, several federal health surveys had expanded subgroup data collection. The Asian American subgroups for which data are most commonly collected are Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese, and Other Asian, and the Pacific Islander subgroups for which data are most commonly collected are Native Hawaiian, Guamanian or Chamorro, Samoan, and Other Pacific Islander. Still, experiences of some subgroups (such as Bangladeshi, Burmese, Hmong, and others) remain masked in the “Other Asian” subgroup. At the December 2021 launch of the White House Initiative on Asian Americans, Native Hawaiians, and Pacific Islanders, lack of disaggregated AAPI data was announced as a key priority.

To advance AAPI health equity, states should expand their data collection categories to, at a minimum, align with OMH-developed standards. States that have higher concentrations of subgroups should consider expanding their data collection to include those specific communities. In recent years, there has been some movement at the state level: California and New York, for example, have passed legislation to require disaggregated AAPI data collection across state agencies and expanded requirements beyond OMH-developed standards to include additional subgroups. For example, in December 2021, New York Governor Kathy Hochul signed a bill mandating all state departments collect information for major AAPI subgroups, including Loatian, Cambodian, Bangladeshi, and Hmong communities.

Barriers & Opportunities for Disaggregated AAPI Health Data Collection and Reporting at the State-Level

There are several hurdles to expanding disaggregated AAPI data collection. In survey data, there are intertwined political, methodological, and financial or administrative barriers that hinder adoption of disaggregated AAPI health data. For example, substantial resources may be required to translate surveys into Asian languages, hire multilingual interviewers, or invest in strategies that increase response rates or sample sizes of AAPI people. Even when disaggregated AAPI data are collected, sometimes it is not possible to report statistics disaggregated by AAPI subgroup or release datasets for public use because of confidentiality concerns. State efforts to collect disaggregated AAPI health data are sometimes opposed because of embedded biases or misperceptions that AAPI people do not experience health inequities. Moreover, some groups that think the resulting policies unfairly target AAPI people relative to other racial groups.

In recognition of such barriers, in November 2021, Centers for Medicare and Medicaid Services leaders included evidence-based investments in improving health equity through data collection on race and ethnicity in an overview of its strategic priorities. Federal agencies should continue to champion the role of granular data collection to advance health equity. There is also value to state-led efforts to collect disaggregated AAPI data and expand collection beyond federal standards. However, state legislative mandates often require advocacy and community organizing to build political support.

It is important to note that some states have already successfully included disaggregated AAPI subgroups in administrative data like public benefit applications and medical claims data. For example, nearly 30 states collect data on disaggregated AAPI subgroups that align with OMH standards through Medicaid applications. Extending such efforts beyond the Medicaid population and integrating in other data sources may provide further insight into state-level inequities in health care access and health outcomes.

In the absence of mandates, there are opportunities for states to learn from one another’s administrative efforts and methodological successes: for example, the California Health Interview Survey (CHIS) is a longtime leader in collecting disaggregated AAPI health data. CHIS employs multiple innovative strategies, including outreach with community organizations to encourage participation, language assistance for people with limited English proficiency, collecting data with multiple modes (cell phone, landline, mail), translating the survey into multiple languages (Cantonese, Mandarin, Korean, Vietnamese, Khmer, and Tagolog), and use of a targeted surname list to oversample for Korean and Vietnamese people. CHIS has continued to adapt during the COVID-19 pandemic and even included new modules measuring prevalence of anti-Asian bias.

Community engagement and stakeholder collaboration are critical to expanding disaggregated AAPI health data collection and reporting. Recently, the New York City Health Department released a first-of-its-kind report of the health and wellbeing of AAPI people in New York City. The report required collaboration with 21 partners, including community-based organizations. Importantly, such efforts require increased funding or investments to support resources required to expand data collection.

Continued Importance of Disaggregated AAPI Health Data Collection

There have been promising improvements in disaggregated AAPI data collection for some subgroups, but opportunities for improvement remain. Through disaggregated AAPI data collection — both administrative sources and surveys — states have the opportunity to identify and eliminate health disparities that can be masked with aggregate grouping. Standardized collection of disaggregated AAPI health data — at a minimum aligning with federal standards —would be a critical first step for states to identify, survey, and address AAPI health inequities. This could also permit cross-state comparisons and encourage collaboration among states to eliminate AAPI inequities.

Without routine data collection and surveillance for AAPI subgroups, it will be impossible to understand their different experiences, define their specific needs, and begin to address disparities. Advancing AAPI health equity will also require other concurrent efforts, including increased funding for AAPI-specific research and increased awareness of persisting AAPI health disparities.