Colorblind, Data-Blind: Health Equity Demands the Disaggregation of Asian Ethnicities

By: Vivian Lee

In an era that increasingly prioritizes colorblindness, many commentators advocate for the removal of racial and ethnic classifications from public data, arguing that such labels perpetuate division and hinder equality.[1] Yet, as counterintuitive as it may seem, erasing race in healthcare does not eliminate inequality—it conceals it.

When health data is collected without attention to race and ethnicity, it flattens the lived realities of communities of color, producing an incomplete picture. This aggregated approach treats racially and ethnically diverse populations as homogenous groups, hiding disparities in disease prevalence, access to care, and health outcomes. The result is a form of statistical colorblindness: the illusion of equality where inequality persists.

The consequences are particularly visible in how Asian American and Native Hawaiian and Pacific Islander (NHPI) populations are represented in national health data. For decades, these groups have been lumped together under a single “Asian” label.[2] This aggregation, while administratively convenient, masks deep disparities between subgroups. Filipino Americans, for example, experience markedly higher rates of heart disease than East Asian Americans, but this difference is invisible in aggregated data.[3] Without more granular data-collection methods, policymakers cannot identify the communities most at risk, let alone design effective interventions for them.

Historical Roots of Invisible Inequities

The current crisis of invisibility in racial health data is not accidental. It stems from a long history of racial exclusion in American medicine. The Tuskegee syphilis study, the coerced sterilization of Indigenous and Latina women, and the exclusion of Chinese and other Asian immigrants from basic health services under the Chinese Exclusion Act have shaped who received care and how care is measured.[4]

Due to such histories, some nations, including many in the European Union, have resisted collecting racial and ethnic data.[5] Fearing a return to racial profiling, these countries have adopted colorblind data practices.[6] However, as the COVID-19 pandemic revealed, the absence of disaggregated data often harms the very populations it aims to protect. Without granular information on how diseases affect different communities, governments cannot respond effectively to emerging health crises.[7]

The Lessons of COVID-19

The pandemic made the stakes of disaggregated data painfully clear. In the early months of COVID-19, many U.S. states failed to report infection and death rates by race, and fewer still broke down data for Asian and NHPI populations.[8] In early April 2020, the Tennessee Department of Health, for example, reported race for only about 36% of the state’s COVID-19 cases.[9] These inconsistent reporting standards among states make it difficult to accurately assess the pandemic’s impacts on racial groups on a national scale.

A 2020 study of California’s COVID-19 data best illustrates the dangers of data aggregation. Combining Asian and NHPI data produced a crude mortality rate of 75 per 100,000, which appeared lower than the state’s overall rate of 84 per 100,000.[10] However, when viewed separately, the rate for NHPI individuals was significantly higher, at 123 per 100,000.[11] An even deeper dive into the NHPI category unearthed another layer, showing that Samoans had a staggering death rate of 182 per 100,000, which was more than double the statewide average.[12] In another study, California Filipinos were similarly shown to have suffered a disproportionate death rate from COVID-19.[13]

A colorblind data regime would have rendered such disparities invisible, preventing tailored interventions, from targeted vaccination campaigns to culturally tailored public health messaging.

State-Level Progress

Some states have begun to recognize that health equity requires visibility. California’s Assembly Bill 1726, passed in 2016, mandates the disaggregation of public health data for Asian American and NHPI populations.[14] Oregon’s REALD framework requires the state’s health authority to report disaggregated data on race, ethnicity, language, and disability.[15] New York’s “Invisible No More” law, enacted in 2021, compels public agencies to collect and publish detailed demographic breakdowns across ten major Asian ethnic groups.[16]

Yet progress is uneven. Many states lack the infrastructure, funding, or political will to collect disaggregated data. Tennessee, for example, does not currently establish specific minimum standards for racial and ethnic data reporting in healthcare. This absence means that discrepancies among Tennessee’s racial groups often remain undocumented and unaddressed. In a state where racial inequities in access to hospitals, chronic disease rates, and maternal health outcomes are well documented by independent researchers, the failure to collect detailed demographic data perpetuates a state-sponsored cycle of invisibility.

Colorblind Data and the Model Minority Myth

Supporters of colorblind data practices often argue that race-based reporting risks reinforcing division.[17] But data itself is never neutral. When institutions choose not to collect racial information, they are making a political choice—one that favors ignorance over accountability.

The model minority myth provides a striking example. This narrative portrays Asian Americans as universally successful, healthy, and socioeconomically stable, disregarding the actual experiences of marginalized subgroups.[18] From the disaggregated data, however, emerges the sobering truth that Southeast Asians, Pacific Islanders, and other subgroups often face poverty rates and health outcomes worse than the collective Asian population.[19] Colorblind data, in this sense, becomes a tool for denying systemic inequality.

Building a Future of Visibility

The federal government’s Statistical Policy Directive No. 15 (SPD 15) establishes minimum standards for the collection of racial and ethnic data by federal agencies. Although its 2024 revision, which added Middle Eastern and North African as a new category and allowed respondents to specify their ancestry,[20] represents a step toward transparent data, it remains vulnerable to political shifts. Political efforts to dismantle race-conscious policies, including diversity and equity initiatives, threaten to undo decades of work toward equitable data collection.[21]

If colorblind policies succeed in rolling back frameworks like SPD 15, our country risks losing a fundamental tool of collecting uniform data, especially in health. Tennessee and other states should develop clear standards for racial and ethnic data reporting, following the examples of California, Oregon, and New York.

To pretend that race does not matter in healthcare is to ignore reality. Health inequities are not random but patterned along racial lines forged by history, policy, and power. Colorblind data blinds us to suffering. To heal as a nation, we must first learn to see and name where the wounds are deepest.

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[1] Linda M. Hunt & Mary S Megyesi, The Ambiguous Meanings of the Racial/Ethnic Categories Routinely used in Human Genetics Research, 66 Soc. Sci. Medicine, Jan. 2008, at 349–61, https://pmc.ncbi.nlm.nih.gov/articles/PMC2213883/pdf/nihms-37602.pdf; John F. Early, Government Proposes To Make Bad Standards on Race and Ethnicity Worse, CATO Inst. (Apr. 28, 2023, at 12:41 ET), https://www.cato.org/blog/government-proposes-make-bad-standards-race-ethnicity-worse; Alex Nowrasteh, How the Trump Administration Can Obstruct Future Government Attempts to Reestablish Affirmative Action and DEI Policies, CATO Inst. (Jan. 27, 2025, at 13:34 ET), https://www.cato.org/blog/how-trump-administration-can-obstruct-future-government-attempts-reestablish-affirmative.

[2] U.S. Dep’t of Com., Statistical Policy Handbook 37–38 (May 1978), available at https://www2.census.gov/about/ombraceethnicityitwg/1978-statistical-policy-handbook.pdf.

[3] Jed Keenan Obra et al., Achieving Equity in Asian American Health Care: Critical Issues and Solutions, 1 J. Asian Health 1, 5 (2021), https://journalofasianhealth.org/index.php/jasianh/article/view/3/13.

[4] How History Has Shaped Racial and Ethnic Health Disparities: A Timeline of Policies and Events, KFF, https://www.kff.org/how-history-has-shaped-racial-and-ethnic-health-disparities-a-timeline-of-policies-and-events/?entry=1808-to-1890-medical-exploitation-of-enslaved-black-women (last visited Feb. 17, 2025).

[5] Lilla Farkas, European Commission, Analysis and comparative review of equality data collection practices in the European Union: Data collection in the field of ethnicity 31 (2017), https://data.europa.eu/doi/10.2838/447194.

[6] Id.

[7]   Sanni Yaya et al., Commentary, Ethnic and Racial Disparities in COVID-19-related Deaths: Counting the Trees, Hiding the Forest, BMJ Global Health, June 2020, at 1, 2–3, https://gh.bmj.com/content/bmjgh/5/6/e002913.full.pdf.

[8] Gilbert C. Gee et al., Considerations of Racism and Data Equity Among Asian Americans, Native Hawaiians, And Pacific Islanders in the Context of COVID-19, 9 Current Epidemiology Rep. 77, 80–81 (2022), https://link.springer.com/article/10.1007/s40471-022-00283-y#ref-CR41.

[9] TN Department of Health Adds Race and Ethnicity to COVID-19 Data, WBIR (Apr. 8, 2020, at 17:52 ET), https://www.wbir.com/article/news/tn-department-of-health-adds-race-and-ethnicity-to-covid-19-data/51-0ce503e3-1b29-47f3-9590-847b13bccafc.

[10] Ninez A. Ponce et al., Disaggregating California’s COVID-19 Data for Native Hawaiians and Pacific Islanders and Asians, UCLA Center for Health Policy Research 1 (2021), https://healthpolicy.ucla.edu/sites/default/files/2023-04/covid-19-data-nhpi-asians-factsheet-may2021.pdf.

[11] Id.

[12] Id. at 2.

[13] Martin A. Monto & Jordan Marquez, Data Disaggregation Reveals Disproportionate Levels of COVID-19 Risk Among Filipinxs in the USA, 10 J. Racial & Ethnic Health Disparities 1398, 1398 (2023), https://link.springer.com/article/10.1007/s40615-022-01325-3.

[14] Chris Fuchs, California Governor Signs Bill to Disaggregate Asian-American Health Data, NBC News (Sept. 27, 2016, at 9:14 ET) https://www.nbcnews.com/news/asian-america/california-governor-signs-bill-disaggregate-asian-american-health-data-n655361.

[15] Or. Rev. Stat. Ann. § 413.161 (West); Oregon Health Authority 950-030-0030, https://secure.sos.state.or.us/oard/viewSingleRule.action?ruleVrsnRsn=315084 (defining disaggregated racial classifications).

[16] Victory After Over a Decade of Advocacy: Asian American Pacific Islander Community Commends NY Governor Kathy Hochul for Signing Data Disaggregation into Law, CACF (Dec. 23, 2021), https://www.cacf.org/resources/statement-ny-state-aapi-data-disaggregation-law.

[17] See, e.g., Shuang Fu & Kendall King, Data Disaggregation and its Discontents: Discourses of Civil Rights, Efficiency and Ethnic Registry, 42 Discourse: Stud. Cultural Pol. Educ. 199, 204, 210 (2019) (discussing how certain Asian Minnesotans referred to the state’s education data disaggregation law as an “Ethnic Registry Law” that perpetuates the “perpetual foreigner” stereotype); Chris Fuchs, California Data Disaggregation Bill Sparks Debate in Asian-American Community, NBC News (Aug. 26, 2016, at 12:47 ET), https://www.nbcnews.com/news/asian-america/california-data-disaggregation-bill-sparks-debate-asian-american-community-n638286; Alex Nowrasteh, How the Trump Administration Can Obstruct Future Government Attempts to Reestablish Affirmative Action and DEI Policies, CATO Inst. (Jan. 27, 2025, at 1:34 ET), https://www.cato.org/blog/how-trump-administration-can-obstruct-future-government-attempts-reestablish-affirmative.

[18] Obra et al., supra note 3.

[19] Id.; Jens Manuel Krogstad & Carolyne Im, Key Facts About Asians in the U.S., Pew Research Center (May 1, 2025), https://www.pewresearch.org/short-reads/2025/05/01/key-facts-about-asians-in-the-us/.

[20] Revisions to OMB’s Statistical Policy Directive No. 15: Standards for Maintaining, Collecting, and Presenting Federal Data on Race and Ethnicity, 89 Fed. Reg. 22182, 22186, 22192 (Mar. 29, 2024) [hereinafter 2024 SPD 15], https://www.govinfo.gov/content/pkg/FR-2024-03-29/pdf/2024-06469.pdf.

[21] See Exec. Order No. 14224, 90 Fed. Reg. 11363 (Mar. 6, 2025) (designating English as the official language of the United States); Exec. Order No. 14151, 90 Fed. Reg. 8339 (Jan. 29, 2025) (eliminating “illegal and immoral” federal programs promoting diversity, equity, and inclusion).