Literature review of data-based models for identification of factors associated with racial disparities in breast cancer mortality. Issue 2 (4th May 2019)

• Record Type: Journal Article
• Authors: Prieto, Diana; Soto-Ferrari, Milton; Tija, Rindy; Peña, Lorena; Burke, Leandra; Miller, Lisa; Berndt, Kelsey; Hill, Brian; Haghsenas, Jafar; Maltz, Ethan; White, Evan; Atwood, Maggie; Norman, Earl
• Is Part Of: Health systems. Volume 8:Issue 2(2019)
• Page Start: 75
• Page End: 98
• Publication Date: 2019-05-04
• Subjects: Breast cancer -- racial disparities -- statistical analysis -- operations research; 610.285
• DOI: 10.1080/20476965.2018.1440925
• Languages: English
• ISSNs: 2047-6965

Abstract

Abstract: In the United States, early detection methods have contributed to the reduction of overall breast cancer mortality but this pattern has not been observed uniformly across all racial groups. A vast body of research literature shows a set of health care, socio-economic, biological, physical, and behavioural factors influencing the mortality disparity. In this paper, we review the modelling frameworks, statistical tests, and databases used in understanding influential factors, and we discuss the factors documented in the modelling literature. Our findings suggest that disparities research relies on conventional modelling and statistical tools for quantitative analysis, and there exist opportunities to implement data-based modelling frameworks for (1) exploring mechanisms triggering disparities, (2) increasing the collection of behavioural data, and (3) monitoring factors associated with the mortality disparity across time.