Tawfiles Davidi, Sayan Mutlay, Mahal Brandon A, Tawfiles Miriam, Feliciano Erin Jay G, Nguyen Paul L, Dee Edward Christopher
University of Pennsylvania, Philadelphia, PA, USA.
Department of Radiation Oncology, Brigham and Women's Hospital and Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
J Gen Intern Med. 2025 Sep 18. doi: 10.1007/s11606-025-09879-8.
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.
在美国，癌症治疗结果方面的种族和族裔差异有充分的文献记载，但目前的数据系统往往因依赖过于宽泛的种族分类而掩盖了重要的亚组差异。本文认为，这种将不同人群简单归为“白人”“黑人”或“亚洲人”的汇总方式掩盖了具有临床意义的异质性，并在公共卫生工作中延续了结构上的不可见性。利用美国国立癌症研究所监测、流行病学和最终结果数据库（SEER）和国家癌症数据库（NCDB）等全国性数据库，我们说明了对亚裔美国人亚组进行族裔细分如何已经揭示了癌症发病率和分期方面的显著差异。扩展这种方法，我们强调了地方和区域研究，这些研究表明在黑人、西班牙裔/拉丁裔和白人人群的亚组中，包括非洲移民、波多黎各人以及阿拉伯裔美国人，癌症治疗结果也同样存在差异。这些差异在国家监测系统中仍然隐藏着，破坏了针对癌症筛查、预防和治疗进行调整的努力。我们进一步研究了宽泛的种族分类对基因风险分层、符合文化背景的健康信息传递、公众信任以及公平资金分配的影响。即将在2030年美国人口普查中将中东和北非（MENA）人群作为一个独特类别纳入，这为改革健康数据系统并使其与人口多样性的细微现实相匹配提供了一个及时的契机。最终，我们认为精准公共卫生依赖于能让不可见人群可见的细分数据。解决癌症差异问题，尤其是在未得到充分认识的族裔亚组中，不仅需要更好的数据，还需要致力于文化谦逊、语言包容性以及以公平为中心的研究框架，以弥合身份认同与干预之间的差距。
