Scientific Reports (Sep 2024)
Trends in 5-year cancer survival disparities by race and ethnicity in the US between 2002–2006 and 2015–2019
Abstract
Abstract Racial and ethnic disparities persist in cancer survival rates across the United States, despite overall improvements. This comprehensive analysis examines trends in 5-year relative survival rates from 2002–2006 to 2015–2019 for major cancer types, elucidating differences among racial/ethnic groups to guide equitable healthcare strategies. Data from the SEER Program spanning 2000–2020 were analyzed, focusing on breast, colorectal, prostate, lung, pancreatic cancers, non-Hodgkin lymphoma, acute leukemia, and multiple myeloma. Age-standardized relative survival rates were calculated to assess racial (White, Black, American Indian/Alaska Native, Asian/Pacific Islander) and ethnic (Hispanic, Non-Hispanic) disparities, utilizing period analysis for recent estimates and excluding cases identified solely through autopsy or death certificates. While significant survival improvements were observed for most cancers, notable disparities persisted. Non-Hispanic Blacks exhibited the largest gain in breast cancer survival, with an increase of 5.2% points (from 77.6 to 82.8%); however, the survival rate remained lower than that of Non-Hispanic Whites (92.1%). Colorectal cancer survival declined overall (64.7–64.1%), marked by a 6.2% point drop for Non-Hispanic American Indian/Alaska Natives (66.3–60.1%). Prostate cancer survival declined across all races, with Non-Hispanic American Indian/Alaska Natives showing a decrease of 7.7% points (from 96.9 to 89.2%). Lung cancer, acute leukemia, and multiple myeloma showed notable increases across groups. Substantial racial/ethnic disparities in cancer survival underscore the notable need for tailored strategies ensuring equitable access to advanced treatments, particularly addressing significant trends in colorectal and pancreatic cancers among specific minority groups. Careful interpretation of statistical significance is warranted given the large dataset.
Keywords