The Forgotten Underrepresented Minorities: A Call for Data Disaggregation

In the July-August 2021 issue of Family Medicine, Drs Jabbarpour and Westfall discussed the importance of racial diversity in the family medicine workforce.¹ I applaud the authors’ commentary highlighting that the field of family medicine can address racial inequalities in our patient population by addressing structural racism and by promoting diversity and inclusion in residency training. One way to continue our progress is to advocate for racial data disaggregation. 

Data disaggregation is the breakdown of data into detailed subgroups. This can reveal inequalities that may not have been fully reflected in the aggregated data. Data inequity is a form of structural racism, as it ignores vulnerable subgroups and denies allocation of much-needed resources. This is especially true in the case of Asian Americans.

In most racial/ethnic data collections, Asian Americans are seen as a monolith, when the reality is that they encompass a diverse array of nationalities, languages, immigration histories, and socioeconomic backgrounds. For instance, Japanese Americans have lower poverty rates than White Americans, while Hmong, Khmer, Laotian, and Vietnamese Americans have higher poverty rates.² When Asian American data are aggregated, the conclusions misleadingly suggest that Asian Americans as a singular population are thriving, perpetuating the harmful myth on Asian Americans being the model minority, where they are assumed to be doing better than other minority groups.^3,4

Consequently, Asian American physicians are excluded from the designation of underrepresented minority (URM) in medicine as they make up 7% of the nation’s overall population, yet 17% of active physicians, implying that Asian American physicians are the overrepresented minority. However, data disaggregation would reveal that while Filipinos make up 18% of the nation’s Asian American population, they made up only 4.3% of the Asian American medical school applicants in 2019. Additionally, Laotians, Indonesians, and Cambodians altogether made up only 0.5% of the Asian American applicants.^5,6 I am only able to provide breakdown examples of the medical school applicants as disaggregated data of active Asian American physicians are not even collected. 

As the original authors mentioned, patient/provider racial concordance leads to improved patient-provider communication, medical adherence, and patient satisfaction.1 With all the nuances within a large racial categorization, data disaggregation allows us family physicians to see and serve marginalized communities that may have been invisible otherwise. Only when we begin to collect this data can we recruit and train family physicians that look like the diverse patients we serve.

doi: 10.22454/FamMed.2022.653419