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Purpose To test the effect of race/ethnicity on Social Security Administration (SSA) life tables’ life expectancy (LE) predictions in localized prostate cancer (PCa) patients treated with either radical prostatectomy (RP) or external beam radiotherapy (EBRT). We hypothesized that LE will be affected by race/ethnicity. Patients and Methods We relied on the 2004–2006 Surveillance, Epidemiology, and End Results database to identify D’Amico intermediate- and high-risk PCa patients treated with either RP or EBRT. SSA life tables were used to compute 10-year LE predictions and were compared to OS. Stratification was performed according to treatment type (RP/EBRT) and race/ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic/Latino, and Asian). Results Of 55,383 assessable patients, 40,490 were non-Hispanic White (RP 49.3% vs. EBRT 50.7%), 7194 non-Hispanic Black (RP 41.3% vs. EBRT 50.7%), 4716 Hispanic/Latino (RP 51.0% vs. EBRT 49.0%) and 2983 were Asian (RP 41.6% vs. EBRT 58.4%). In both RP and EBRT patients, OS exceeded life tables’ LE predictions, except for non-Hispanic Blacks. However, in RP patients, the magnitude of the difference was greater than in EBRT. Moreover, in RP patients, OS of non-Hispanic Blacks virtually perfectly followed predicted LE. Conversely, in EBRT patients, the OS of non-Hispanic Black patients was worse than predicted LE. Conclusions When comparing SEER-derived observed OS with SSA life table–derived predicted life expectancy, we recorded a survival disadvantage in non-Hispanic Black RP and EBRT patients, which was not the case in the three other races/ethnicities (non-Hispanic Whites, Hispanic/Latinos, and Asians). This discrepancy should ideally be confirmed within different registries, countries, and tumor entities. Furthermore, the source of these discrepant survival outcomes should be investigated and addressed by health care politics.

journal of racial and ethnic health disparities

Race/Ethnicity may be an Important Predictor of Life Expectancy in Localized Prostate Cancer Patients: Novel Analyses Using Social Security Administration Life Tables