Predicting mammography and breast self-examination in African American women

Cancer Nurs. 1997 Oct;20(5):315-22. doi: 10.1097/00002820-199710000-00002.


Breast cancer mortality is significantly greater in African American women than in their Caucasian counterparts. The purpose of this study was to identify variables associated with the breast cancer screening behaviors of mammography utilization and breast self-examination (BSE) in a convenience sample of low income African American women. A total of 328 African American women, living in a large midwestern metropolitan area, who were at < or = 150% of poverty level, and between the ages of 45 and 64 years were included in this study. Data were collected over a period of 18 months. Predisposing, enabling, and need variables from Anderson's theoretical framework included perceived susceptibility, benefits, barriers, confidence, knowledge, physician recommendation, demographic characteristics, and past experiences, as well as health-care and insurance information. Variables that significantly predicted mammography utilization included perceived barriers, mammography suggested by health-care professionals, recent thoughts about mammography, and a regular medical doctor. Variables that significantly predicted either frequency or proficiency of BSE included susceptibility, benefits, confidence, knowledge, barriers, and a regular physician. Implications for clinical practice include (a) recognizing predictors of screening among low-income African American women; (b) addressing culturally specific barriers, e.g., cancer fatalism, in order to increase compliance with screening; (c) establishing consistency in primary care providers; and (d) increasing confidence and knowledge through education.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • African Americans / psychology*
  • Breast Neoplasms / prevention & control*
  • Breast Self-Examination / psychology*
  • Female
  • Humans
  • Mammography / psychology*
  • Middle Aged
  • Midwestern United States
  • Multivariate Analysis
  • Patient Acceptance of Health Care*
  • Regression Analysis