Now That We Are Disaggregating Race and Ethnicity Data, We Need to Start Understanding What They Mean
Authors: Joshua J. Quint, Joseph Keawe‘aimoku Kaholokula
Journal: JAMA Network Open (2024)

The study by Santi and Verhoef 1 examined racial and ethnic disparities for in-hospital COVID-19 mortality in Hawai'i using disaggregated data.The study by Santi and Verhoef 1 highlights the importance of disaggregating Asian, Native Hawaiian, and Pacific Islander individuals in clinical research.It also provides an opportunity to discuss some of the nuances and complexities involved with data disaggregation. 1 Data Disaggregation StandardsRepresenting some of the fastest growing race and ethnicity groups and with a population of more than 25 million in the United States, there is an increasing need for disaggregated Asian, Native Hawaiian, and Pacific Islander data.Since 1997, federal data standards have separated Asian individuals and Native Hawaiian and Pacific Islander individuals into distinct categories. 2 The US Department of Health and Human Services recommended the collection of detailed data for Asian and Native Hawaiian and Pacific Islander populations in 2011, adopting the detailed list used in the 2010 Census.Yet, many studies continue to combine Asian, Native Hawaiian, and Pacific Islander data into a single group, and very few studies use more detailed categories.Aggregating diverse race and ethnicity groups can conceal underlying disparities and is a barrier to health equity. 3When aggregate statistics are reported, heterogeneity in the experiences of smaller groups become invisible, which can inhibit the ability of these communities to advocate for resources.The value of disaggregated data is readily apparent in Hawai'i, where more than two-thirds of the population identifies as Asian, Native Hawaiian, or Pacific Islander.The distinct cultural, historical, and socioeconomic backgrounds of these populations contribute to varying life expectancies and other health disparities. 4Specificity (ie, level of detail) and context (eg, social determinants of health) are important factors to consider when disaggregating data to identify and address health inequities.Social epidemiology and causal inference methods are valuable tools when going beyond mere descriptions of health disparities to identify root causes and understand the social determinants of health.On March 28, 2024, the US Office of Management and Budget published a historic revision to the 1997 Statistical Policy Directive, aimed at improving federal race and ethnicity statistics and ensuring that data more accurately reflect the racial and ethnic diversity of the US population.The standard now includes a requirement to collect more detailed categories and for tabulation procedures to result in the production of as much information as possible.With regards to persons who select multiple race or ethnicity categories, the standard describes the nonmutually exclusive alone or in combination approach for tabulation and reporting.Since we advocate here for the use of more detailed categories and the alone or in combination approach, this commentary may serve as a timely and useful resource for agencies and researchers seeking to implement the new standards. Collecting Granular Race and Ethnicity DataThe study by Santi and Verhoef 1 goes well beyond the minimum standard by further disaggregating among Asian, Native Hawaiian, and Pacific Islander groups (eg, Chinese, Filipino, Japanese, Native