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Original Investigation
February 2, 2017

Population-Attributable Risk Proportion of Clinical Risk Factors for Breast Cancer

Author Affiliations
  • 1Department of Epidemiology and Biostatistics, University of California, San Francisco
  • 2Department of Public Health Sciences, University of California, Davis
  • 3Group Health Research Institute, Group Health Cooperative, Seattle, Washington
  • 4Department of Surgery and University of Vermont Cancer Center, University of Vermont, Burlington
  • 5Department of Medicine, University of California, San Francisco
JAMA Oncol. Published online February 2, 2017. doi:10.1001/jamaoncol.2016.6326
Key Points

Question  What proportion of premenopausal and postmenopausal breast cancers are attributed to commonly collected clinical risk factors?

Findings  In this population-based, case-control, cohort study of 202 746 women, breast density and body mass index had the largest individual population-attributable risk proportion. Thirty-nine percent of premenopausal and 26% of postmenopausal breast cancers could be prevented if breast density in women with dense breasts was reduced to scattered fibroglandular densities on the Breast Imaging Reporting and Data System scale, and postmenopausal breast cancer incidence would be reduced by 23% if all women achieved a body mass index less than 25.

Meaning  Clinical breast cancer risk factors explain a large proportion of breast cancer incidence and should be used in the clinical setting for risk stratification and targeted screening and prevention efforts.


Importance  Many established breast cancer risk factors are used in clinical risk prediction models, although the proportion of breast cancers explained by these factors is unknown.

Objective  To determine the population-attributable risk proportion (PARP) for breast cancer associated with clinical breast cancer risk factors among premenopausal and postmenopausal women.

Design, Setting, and Participants  Case-control study with 1:10 matching on age, year of risk factor assessment, and Breast Cancer Surveillance Consortium (BCSC) registry. Risk factor data were collected prospectively from January 1, 1996, through October 31, 2012, from BCSC community-based breast imaging facilities. A total of 18 437 women with invasive breast cancer or ductal carcinoma in situ were enrolled as cases and matched to 184 309 women without breast cancer, with a total of 58 146 premenopausal and 144 600 postmenopausal women enrolled in the study.

Exposures  Breast Imaging Reporting and Data System (BI-RADS) breast density (heterogeneously or extremely dense vs scattered fibroglandular densities), first-degree family history of breast cancer, body mass index (>25 vs 18.5-25), history of benign breast biopsy, and nulliparity or age at first birth (≥30 years vs <30 years).

Main Outcomes and Measures  Population-attributable risk proportion of breast cancer.

Results  Of the 18 437 women with breast cancer, the mean (SD) age was 46.3 (3.7) years among premenopausal women and 61.7 (7.2) years among the postmenopausal women. Overall, 4747 (89.8%) premenopausal and 12 502 (95.1%) postmenopausal women with breast cancer had at least 1 breast cancer risk factor. The combined PARP of all risk factors was 52.7% (95% CI, 49.1%-56.3%) among premenopausal women and 54.7% (95% CI, 46.5%-54.7%) among postmenopausal women. Breast density was the most prevalent risk factor for both premenopausal and postmenopausal women and had the largest effect on the PARP; 39.3% (95% CI, 36.6%-42.0%) of premenopausal and 26.2% (95% CI, 24.4%-28.0%) of postmenopausal breast cancers could potentially be averted if all women with heterogeneously or extremely dense breasts shifted to scattered fibroglandular breast density. Among postmenopausal women, 22.8% (95% CI, 18.3%-27.3%) of breast cancers could potentially be averted if all overweight and obese women attained a body mass index of less than 25.

Conclusions and Relevance  Most women with breast cancer have at least 1 breast cancer risk factor routinely documented at the time of mammography, and more than half of premenopausal and postmenopausal breast cancers are explained by these factors. These easily assessed risk factors should be incorporated into risk prediction models to stratify breast cancer risk and promote risk-based screening and targeted prevention efforts.