Individual and composite socioeconomic inequalities in diagnosis, treatment, and survival of kidney cancer: a population-based analysis of the SEER database.

PURPOSE: To evaluate individual and composite socioeconomic inequalities in kidney cancer diagnosis, treatment, and survival. METHODS: Patients with kidney cancer were identified from the SEER database. Ordinal logistic regression, binary logistic regression, and Cox proportional hazards models were used to evaluate associations of socioeconomic variables of interest (sex, race, marital status, household income, and residence) with stage at diagnosis, surgical treatment in localized disease, and survival outcomes. A composite socioeconomic status (SES) risk score and risk stratification were further constructed. RESULTS: Male sex, non-White race, unmarried status, low income, and rural residence were associated with more advanced stage at diagnosis (all OR > 1, p < 0.05), a lower likelihood of receiving partial nephrectomy (except sex; all OR < 1, p < 0.05), and worse survival (all HR > 1, p < 0.05). Higher SES scores and risk categories showed graded associations with advanced-stage diagnosis, lower partial nephrectomy use, and worse survival (all p < 0.05). The magnitudes of inequalities in diagnosis (p < 0.001), surgical treatment (p = 0.046), and survival (all p < 0.05) attenuated with increasing age. There was no evidence of temporal change in the associations with stage at diagnosis (p = 0.132) or survival outcomes (all p > 0.05), whereas the association with surgical treatment became more pronounced across diagnostic periods (p < 0.001). CONCLUSION: Among US patients with kidney cancer in SEER, SES disadvantage was associated with later diagnosis, lower partial nephrectomy use, and worse survival. Further validation is needed before applying the composite SES score beyond the US healthcare context.