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As the fastest-growing racial/ethnic group in the U.S., Asian Americans, Native Hawaiians, and Pacific Islanders (AANHPI) make up about 7% of the U.S. population. AANHPIs represent more than 50 ethnic groups and speak over 100 different languages, but national health data rarely collect information needed to disaggregate AANHPI into smaller groups to capture this diversity. This study reviewed AANHPI disaggregated data collection methods in large, U.S. population-based surveys, summarized disaggregated data reporting practices in the peer-reviewed literature, and conducted key informant interviews with 23 members of the National Network of Health Surveys who lead state/local and national surveys to identify challenges to data collection. We found that while large national surveys may include AANHPI subpopulation in data collection, sample sizes are often too small to permit meaningful data analysis. In our interviews, barriers cited included the lack of stakeholder interest, inadequate funding, lack of space on questionnaires, as well as methodologic challenges, such as the inability to obtain an adequate sample size and poor questionnaire design. Survey administration efforts to improve disaggregated health data are challenging, but when done, have led to a proliferation of new population-representative knowledge on the health of AANHPI subgroups. This study provides evidence of the population health value of disaggregating AANHPI data to identify the needs of vulnerable subpopulations. It also advances the field by offering key informants’ insights in overcoming implementation challenges.

Population-level health outcomes and measures of well-being are often described relative to broad racial/ethnic categories such as White or Caucasian; Black or African American; Latino or Hispanic; Asian American; Native Hawaiian and Pacific Islander; or American Indian and Alaska Native. However, the aggregation of data into these groups masks critical within-group differences and disparities, limiting the health and social services fields’ abilities to target their resources where most needed. While researchers and policymakers have recognized the importance of disaggregating racial/ethnic data—and many organizations have advocated for it over the years—progress has been slow and disparate. The ongoing lack of racial/ethnic data disaggregation perpetuates existing inequities in access to much-needed resources that can ensure health and well-being. In its efforts to help build a Culture of Health and promote health equity, the Robert Wood Johnson Foundation has supported activities aimed to advance the meaningful disaggregation of racial/ethnic data—at the collection, analysis, and reporting phases. This special issue presents further evidence for the importance of disaggregation, the technical and policy challenges to creating change in practice, and the implications of improving the use of race and ethnicity data to identify and address gaps in health.