Of the breast cancer risk factors assessed in the participants, high-density mammographic parenchymal patterns, as measured by the proportion of breast area composed of epithelial and stromal tissue, had the greatest impact on breast cancer risk. Of the breast cancers in this study, 28% were attributable to having 50% or greater breast density.

The Gail et al. model 2 fit well in this sample in terms of predicting numbers of breast cancer cases in specific risk factor strata but had modest discriminatory accuracy at the individual level. This finding has implications for use of the model in clinical counseling of individual women.

Background: Few estimates of the fraction of cases of breast cancer attributable to recognized risk factors have been published. All estimates are based on selected groups, making their generalizability to the U.S. population uncertain. Purpose: Our goal was to estimate the fraction of breast cancer cases in the United States attributable to well-established risk factors (i.e., later age at first birth, nulliparity, higher family income, and first-degree family history of breast cancer), using data from the first National Health and Nutrition Examination Survey (NHANES I) Epidemiologic Follow-up Study (NHEFS), the survey and follow-up of a probability sample of the U.S. population. Methods: From a cohort of 7508 female participants surveyed in the early 1970s, and followed up between 1982 and 1984 and again in 1987, 193 breast cancer cases were accrued for study. We calculated incidence rates, relative risks (RRs), and population attributable risks (PARs) for breast cancer risk factors and extended our results to the U.S. female population by using sample weights from the NHANES I survey. Results: Our PAR estimates suggest that later age at first birth and nulliparity accounted for a large fraction of U.S. breast cancer cases, 29.5% (95% confidence interval [CI] = 5.6%-53.3%); higher income contributed 18.9% (95% CI = -4.3% to 42.1%), and family history of breast cancer accounted for 9.1% (95% CI = 3.0%-15.2%). Taken together, these wellestablished risk factors accounted for approximately 47% (95% CI = 17%-77%) of breast cancer cases in the NHEFS cohort and about 41% (95% CI = 2%-80%) in the U.S. population. Conclusions: The RRs for most of these risk factors were modest, but their prevalence as a group was high, leading to estimates that suggest that a substantial proportion of breast cancer cases in the United States are explained by well-established risk factors. Implications: Elucidation of the determinants underlying recognized factors and study of other factors that may confer risk or protection are needed in efforts to advance understanding of breast cancer etiology and to aid in devising strategies for prevention. [J Natl Cancer Inst 1987;87:1681-5]

Background: To improve the discriminatory power of the Gail model for predicting absolute risk of invasive breast cancer, we previously developed a relative risk model that incorporated mammographic density (DENSITY) from data on white women in the Breast Cancer Detection Demonstration Project (BCDDP). That model also included the variables age at birth of fi rst live child (AGEFLB), number of aff ected mother or sisters (NUMREL), number of previous benign breast biopsy examinations (NBIOPS), and weight (WEIGHT). In this study, we developed the corresponding model for absolute risk. ( 1 ) used data from the Breast Cancer Detection Demonstration Project (BCDDP) to develop a model for the absolute risk of breast cancer for women in a given age interval. This model, known as the Gail model, included age at menarche (AGEMEN), age at birth of fi rst live child (AGEFLB), number of previous benign breast biopsy examinations (NBIOPS), and number of fi rst-degree relatives (mother or sisters) with breast cancer (NUMREL). We call these standard risk factors. This model was recalibrated to data from National Cancer Institute's (NCI's) Surveillance, Epidemiology, and End Results (SEER) program ( 2 ) , and the resulting model, called Gail model 2, is available at http://www.cancer.gov/bcrisktool/ . This model has been used to design prevention trials, such as the Breast Cancer