Publication Abstract

Authors: Tewari AK, Gold HT, Demers RY, Johnson CC, Yadav R, Wagner EH, Yood MU, Field TS, Divine G, Menon M

Title: Effect of socioeconomic factors on long-term mortality in men with clinically localized prostate cancer.

Journal: Urology 73(3):624-30

Date: 2009 Mar

Abstract: OBJECTIVES: To examine the effect of socioeconomic factors on survival in black and white patients with local or regional prostate cancer. METHODS: All cases (n = 2046) of clinically localized prostate cancer diagnosed from 1990 to 2000 at the Henry Ford Health System and the Henry Ford Medical Group, equal access health centers, were included. Data on the stage, grade, age at diagnosis, socioeconomic status, treatment given, comorbidities, and vital statistics were gathered from the Henry Ford Medical Group tumor registry and computerized databases, pathologic reports, patient charts, Surveillance, Epidemiology, and End Results database, and the national death registry. The endpoints were the overall and cancer-specific survival. Survival was calculated using Cox proportional hazards regression models. RESULTS: Of the 2046 cases, 1243 were white and 803 were black. Black patients were more likely to have lower incomes, a greater baseline prostate-specific antigen level, and greater comorbidities. They were also more likely to undergo radiotherapy and less likely to undergo radical prostatectomy. Univariate analysis, with white race as the baseline hazard, showed that black patients had significantly increased cancer-specific (hazard ratio [HR] 1.47, 95% confidence interval [CI] 1.01-2.13) and overall (HR 1.29, 95% CI 1.09-1.53) mortality. However, adjusting for insurance status and income on multivariate analysis revealed no significant differences in cancer-specific (HR 1.04, 95% CI 0.66-1.64) and overall (HR 0.96, 95% CI 0.78-1.18) survival. CONCLUSIONS: In this cohort, socioeconomic factors were sufficient to explain the disparity in survival between white and black patients. Survival differences disappeared after adjusting for income status on multivariate analysis.