Background: This is a part of a larger effort to characterize the effects on socio-economic factors (SEFs)on cancer outcome. Surveillance, Epidemiology and End Result (SEER) bone and joint sarcoma (BJS) datawere used to identify potential disparities in cause specific survival (CSS). Materials and
Methods: This studyanalyzed SEFs in conjunction with biologic and treatment factors. Absolute BJS specific risks were calculatedand the areas under the receiver operating characteristic (ROC) curve were computed for predictors. Actuarialsurvival analysis was performed with Kaplan-Meier method. Kolmogorov-Smirnov’s 2-sample test was used tofor comparing two survival curves. Cox proportional hazard model was used for multivariate analysis.
Results:There were 13501 patients diagnosed BJS from 1973 to 2009. The mean follow up time (SD) was 75.6 (90.1)months. Staging was the highest predictive factor of outcome (ROC area of 0.68). SEER stage, histology, primarysite and sex were highly significant pre-treatment predictors of CSS. Under multivariate analysis, patientsliving in low income neighborhoods and rural areas had a 2% and 5% disadvantage in cause specific survivalrespectively.
Conclusions: This study has found 2-5% decrement of CSS of BJS due to SEFs. These data maybe used to generate testable hypothesis for future clinical trials to eliminate BJS outcome disparities.