| Abstract: |
<p>Racial and ethnic disparities in cancer outcomes are well documented in the USA, yet current data systems often obscure important subgroup differences by relying on overly broad racial classifications. This paper argues that such aggregation-labeling diverse populations simply as "White," "Black," or "Asian"-masks clinically significant heterogeneity and perpetuates structural invisibility in public health efforts. Drawing on national databases like SEER and NCDB, we illustrate how ethnic disaggregation among Asian American subgroups has already revealed marked disparities in cancer incidence and staging. Extending this approach, we highlight local and regional studies showing similarly divergent cancer outcomes among subgroups within Black, Hispanic/Latino, and White populations-including African immigrants, Puerto Ricans, and Arab Americans. These disparities remain hidden in national surveillance systems, undermining efforts to tailor cancer screening, prevention, and treatment. We further examine the consequences of broad racial classification for genetic risk stratification, culturally appropriate health messaging, public trust, and equitable funding allocation. The forthcoming inclusion of Middle Eastern and North African (MENA) populations as a distinct category in the 2030 U.S. Census offers a timely opportunity to reform health data systems and align them with the nuanced realities of population diversity. Ultimately, we argue that precision public health depends on disaggregated data that make invisible populations visible. Addressing cancer disparities-particularly in under-recognized ethnic subgroups-requires not only better data, but also a commitment to cultural humility, linguistic inclusivity, and equity-centered research frameworks that bridge the gap between identity and intervention.</p> |
