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Literature review of data-based models for identification of factors associated with racial disparities in breast cancer mortality

Author

Listed:
  • Diana Prieto
  • Milton Soto-Ferrari
  • Rindy Tija
  • Lorena Peña
  • Leandra Burke
  • Lisa Miller
  • Kelsey Berndt
  • Brian Hill
  • Jafar Haghsenas
  • Ethan Maltz
  • Evan White
  • Maggie Atwood
  • Earl Norman

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.

Suggested Citation

  • Diana Prieto & Milton Soto-Ferrari & Rindy Tija & Lorena Peña & Leandra Burke & Lisa Miller & Kelsey Berndt & Brian Hill & Jafar Haghsenas & Ethan Maltz & Evan White & Maggie Atwood & Earl Norman, 2019. "Literature review of data-based models for identification of factors associated with racial disparities in breast cancer mortality," Health Systems, Taylor & Francis Journals, vol. 8(2), pages 75-98, May.
    • Handle: RePEc:taf:thssxx:v:8:y:2019:i:2:p:75-98
    DOI: 10.1080/20476965.2018.1440925
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