Breast cancer is the mostly diagnosed cancer and the second leading cause of cancer death among American women of all races. Despite improvement of recurrence and survival rates of breast cancer in the U.S., a significant difference between white and black women remains. Previous studies have found that more advanced and aggressive tumors and less than optimal treatment may explain the lower survival rates for black women as compared to white women. However, there has been no research that jointly considers potential factors that include not only individual level information such as treatment history and tumor characteristics, but also provider characteristics, and social environmental information, due to limitations of current analytic methods and the lack of comprehensive data sets.
Louisiana State University Health Sciences Center School of Public Health in New Orleans (LSUHSC-NO) was recently awarded funding by National Institute on Minority Health and Health Disparities (NIMHD) to examine the determinant of racial disparities in breast cancer survival and recurrence. Dr. Qingzhao Yu, associate professor of biostatistics in the School of Public Health, will serve as the principal investigator of the grant entitled “Multilevel Mediation Analysis to Explore Racial Disparities in Breast Cancer Recurrence and Survival using CDC Special Studies”. The researchers will develop a novel multilevel nonparametric mediation analysis method to differentiate indirect effects from risk factors that may explain disparities in health outcomes.
“The team proposes a novel statistical method that can jointly consider potentially correlated multiple mediators, and differentiate the effect from every individual factor from multiple levels that may contribute to the race-breast cancer survival and recurrence relationship” notes Dr. Yu. “This proposal will provide among the most comprehensive explorations of racial disparities in breast cancer patients’ recurrence and survival in the United States. Moreover, the statistical method is generalized so that it can be used to explain a wide range of health disparities.”