Why U.S. Clinical Research Must Disaggregate Identities

Posted on: Thursday 21 May 2026

This blog article has been prepared by Dr Anas Malik Radif Alubaidi.

The article presents the views of the author and not necessarily those of the Faculty of Pharmaceutical Medicine.

As a globally engaged clinician and health advocate, I have witnessed firsthand how identity categories in U.S. clinical research are routinely oversimplified, flattening rich cultural, linguistic, and geographic distinctions into broad racial labels. This undermines both patient care and scientific validity. While the conflation of  “Latino” and “Hispanic” remains a persistent issue, another equally damaging misclassification persists: the automatic categorisation of Arab and Mediterranean populations as “white” or “Caucasian” despite presenting with unique cardiovascular, metabolic, and psychosocial risk profiles. Similarly, Latino and Hispanic individuals ranging from Puerto Rican to Central American backgrounds are treated as a monolith, even though their health determinants and cultural contexts differ significantly.

These oversimplifying practices that occur across the electronic health record, research protocols, and demographic surveys, obscure meaningful differences in health outcomes, access to care, and lived experience, undermining both equity and scientific rigor.

These patterns are not abstract, but they shape real-world data, influence funding priorities, and perpetuate gaps in care.

Latino and Hispanic Misclassification

  • Hispanic refers to individuals from Spanish-speaking countries, regardless of geography.
  • Latino refers to people from Latin America, including non-Spanish-speaking countries like Brazil.
  • These terms reflect distinct migration histories, cultural norms, and health determinants, yet they are often used interchangeably in research protocols.

Arab and Mediterranean Misclassification

  • Individuals from the Middle East, North Africa, and parts of Southern Europe (e.g., Lebanon, Egypt, Syria, Greece, Turkey) are classified as “white” in clinical datasets.
  • This racial categorisation erases unique genetic, environmental, and sociopolitical factors that influence disease prevalence and treatment response.
  • Arab Americans, for example, face elevated risks for diabetes and cardiovascular disease, yet their outcomes are buried under generalised “Caucasian” data1.

Underrepresentation and Data Erasure

Despite comprising significant portions of the U.S. population, Latino, Hispanic, Arab, and Mediterranean communities remain underrepresented in clinical trials. Even when included, their identities are often misclassified or aggregated in ways that dilute meaningful subgroup analysis.

This leads to:

  • Inaccurate prevalence estimates for chronic conditions.
  • Poorly tailored public health interventions.
  • Missed opportunities for culturally competent care delivery.

Implications for Equity and Scientific Validity

Aggregated racial categories mask critical differences in health behaviours, genetic predispositions, and environmental exposures. For instance, Puerto Rican populations may face distinct cardiovascular risks compared to Central American migrants, just as Lebanese Americans may differ significantly from Italian Americans in terms of dietary patterns and stress-related illness2. When these groups are lumped together as “Hispanic” or “white”, research loses its precision – and communities lose visibility.

A compelling example comes from the broader critique outlined by Dr Hadi Danawi, Chair of the Arab Board for Clinical Research, in his 2023 article3 ‘Where Are All the Arabs? And How a U.S. Census Change Could Improve Clinical Trial Diversity’. While not naming a single trial, the article highlights a systemic issue as “Arabs are included in clinical research. However, they are currently categorized as whites. And as such, there’s no real way to tell how many are participating in clinical research.”

 

This misclassification has occurred across numerous federally funded and industry-sponsored trials, particularly those relying on U.S. Census definitions, which historically grouped individuals from the Middle East and North Africa under the “white” racial category. This includes trials on cardiovascular disease, diabetes, and pharmacogenomics, fields where Arab populations show distinct genetic and environmental risk profiles.

For example, studies involving CYP2C19 polymorphisms, which affect drug metabolism, have shown significant variation among Arab populations. Yet these nuances are frequently subsumed within broad “Caucasian” aggregates, diminishing their precision and clinical relevance3.

Recommendations

To advance equity and improve clinical relevance, I propose the following:

  • Disaggregate identity data in federally funded trials to reflect Latino vs. Hispanic, and Arab vs. Mediterranean distinctions.
  • Educate researchers and Institutional Review Boards on the sociopolitical and cultural nuances of identity terminology.
  • Include Arab and other subgroups within the Mediterranean categories in demographic reporting, especially in studies involving cardiovascular, metabolic, and mental health outcomes.
  • Engage community leaders in recruitment and consent processes to ensure cultural alignment and trust.

Equity in research begins with precision in language and classification. The FDA’s guidance on the collection of race and ethnicity data anchored in terminology defined by the U.S. Office of Management and Budget underscores why this precision matters as without it, population‑specific signals remain hidden. The 2024 OMB update, which introduced a new minimum reporting category for “Middle Eastern and North African” populations, further illustrates how evolving standards aim to correct long‑standing gaps. Yet even within current FDA guidance, Middle Eastern populations are still grouped under “white,” a reflection of pre‑updated terminology that no longer aligns with lived identity or emerging regulatory expectations.

By acknowledging the distinct identities of Latino, Hispanic, Arab, and other sub‑Mediterranean communities, and by ensuring the data systems reflect these distinctions, we then move closer to a research landscape capable of detecting real differences in risk, response, and access.

Misclassification is not merely a technical flaw; it is a barrier to justice, obscuring the very inequities that rigorous science is meant to illuminate4.

References

  1. Lababidi H, Lababidi G, Rifai MA, Nasir K, Al-Kindi S (2024). Cardiovascular disease in Arab Americans: A literature review of prevalence, risk factors, and directions for future research. American Journal of Preventive Cardiology. 2024 Jun;18:100665. 100665. doi: 10.1016/j.ajpc.2024.100665.
  2. Daviglus ML, Talavera GA, Avilés-Santa ML, et al. (2012). Prevalence of Major Cardiovascular Risk Factors and Cardiovascular Diseases Among Hispanic/Latino Individuals of Diverse Backgrounds in the United States. JAMA. 2012;308(17):1775–1784. doi:10.1001/jama.2012.14517.
  3. Danawi H (2023). Where Are All the Arabs? And how a U.S. Census Change Could Improve Clinical Trial Diversity. Available at: https://www.clinicalleader.com/doc/where-are-all-the-arabs-and-how-a-u-s-census-change-could-improve-clinical-trial-diversity-0001.
  4. Marks R, Jones N, and Battle K (2024). Updates to OMB’s Race/Ethnicity Standards. U.S. Census Bureau, Random Samplings Blog. Available at: https://www.census.gov/newsroom/blogs/random-samplings/2024/04/updates-race-ethnicity-standards.html