Min Rex Cheung1. 1. FROS Radiation Oncology CyberKnife Center, 40-20 Main St., 4 Fl, Flushing NY, USA E-mail : cheung.r100@gmail.com.
Abstract
BACKGROUND: We studied Surveillance, Epidemiology and End Results (SEER) breast cancer data of Georgia USA to analyze the impact of socio-economic factors on the disparity of breast cancer treatment outcome. MATERIALS AND METHODS: This study explored socio-economic, staging and treatment factors that were available in the SEER database for breast cancer from Georgia registry diagnosed in 2004-2009. An area under the receiver operating characteristic curve (ROC) was computed for each predictor to measure its discriminatory power. The best biological predictors were selected to be analyzed with socio-economic factors. Survival analysis, Kolmogorov- Smirnov 2-sample tests and Cox proportional hazard modeling were used for univariate and multivariate analyses of time to breast cancer specific survival data. RESULTS: There were 34,671 patients included in this study, 99.3% being females with breast cancer. This study identified race and education attainment of county of residence as predictors of poor outcome. On multivariate analysis, these socio-economic factors remained independently prognostic. Overall, race and education status of the place of residence predicted up to 10% decrease in cause specific survival at 5 years. CONCLUSIONS: Socio-economic factors are important determinants of breast cancer outcome and ensuring access to breast cancer treatment may eliminate disparities.
BACKGROUND: We studied Surveillance, Epidemiology and End Results (SEER) breast cancer data of Georgia USA to analyze the impact of socio-economic factors on the disparity of breast cancer treatment outcome. MATERIALS AND METHODS: This study explored socio-economic, staging and treatment factors that were available in the SEER database for breast cancer from Georgia registry diagnosed in 2004-2009. An area under the receiver operating characteristic curve (ROC) was computed for each predictor to measure its discriminatory power. The best biological predictors were selected to be analyzed with socio-economic factors. Survival analysis, Kolmogorov- Smirnov 2-sample tests and Cox proportional hazard modeling were used for univariate and multivariate analyses of time to breast cancer specific survival data. RESULTS: There were 34,671 patients included in this study, 99.3% being females with breast cancer. This study identified race and education attainment of county of residence as predictors of poor outcome. On multivariate analysis, these socio-economic factors remained independently prognostic. Overall, race and education status of the place of residence predicted up to 10% decrease in cause specific survival at 5 years. CONCLUSIONS: Socio-economic factors are important determinants of breast cancer outcome and ensuring access to breast cancer treatment may eliminate disparities.
Authors: Shivani Khanna; Kristine N Kim; Muhammad M Qureshi; Ankit Agarwal; Divya Parikh; Naomi Y Ko; Alexander E Rand; Ariel E Hirsch Journal: Int J Womens Health Date: 2017-12-06
Authors: Esmat Davoudi Monfared; Maryam Mohseny; Farzaneh Amanpour; Alireza Mosavi Jarrahi; Mohammad Moradi Joo; Mohammad Ali Heidarnia Journal: Asian Pac J Cancer Prev Date: 2017-04-01