Attributable Risk: The Quantitative Contribution of known risk factors
As noted in the first section of this chapter, the search for specific breast cancer risk factors has been stimulated by the large differences in rates of breast cancer among countries, and by changes in rates among migrating populations and within countries over time. The extent to which known risk factors account for these differences in rates is therefore of considerable interest. An often-quoted estimate is that only 30% of breast cancer cases are explained by known risk factors.
This estimate has been widely used to suggest that other major risk factors remain to be discovered, in part fueling the search for environmental pollutants that may be responsible. A study of population-attributable risks in a nationwide survey, however, estimated that at least 45% to 55% of breast cancer cases in the United States may be explained by later age at first birth, nulliparity, family history of breast cancer, higher socioeconomic status, earlier age at menarche, and prior benign breast disease. In another analysis, parity and age at menarche, first birth, and menopause appeared to explain more than one-half of the difference between breast cancer rates in China and the United States.
Among postmenopausal women, just the combination of weight gain after age 18 years and use of postmenopausal hormones accounted for approximately one-third of breast cancer cases. Combined with the reproductive variables, this would clearly account for a large majority of the international differences.
A precise determination of the degree to which changes in the prevalences of known breast cancer risk factors account for the increases in breast cancer rates over time is difficult. Changes in age at first birth do not appear to account for appreciable increases in overall U.S. breast cancer rates through 1990, although more delayed childbearing by women born after 1950 should ultimately contribute to an approximately 9% increase in rates.348 Since the 1940s, however, obesity, use of postmenopausal hormones, and alcohol consumption by women have increased dramatically. Although further work is needed to quantify these contributions to the secular trends, novel risk factors are not required to account for substantial increases in breast cancer rates.
Communication of Risk to Patients
Women and their health care providers are increasingly exposed to information on epidemiologic risk factors for breast cancer, benefits of prevention strategies, and treatment options. The Gail model of breast cancer risk prediction is increasingly used by clinicians to assess breast cancer risk of women with differing risk factor profiles. Evidence suggests that the understanding of risk is poor, however. For example, in a sample of women with a family history of breast cancer, more than two-thirds of women overestimated their lifetime risk of breast cancer, even after participating in a counseling session. The overestimation of risk was substantial and perhaps could lead to inappropriate behaviors, such as overscreening, excessive breast self-examination, or inappropriate decisions regarding prophylactic mastectomy or other strategies.
Factors that appear to influence perception of risk include numeracy. Women with higher numeracy scores had significantly higher accuracy in gauging the benefits of mammography. Importantly, when risk and risk reduction are discussed with an individual, research indicates that both absolute risk and relative risk must be included in the messageto maximize the accuracy of risk perception. Although more effective formats for presentation of risk and benefits are required, the evidence supports discussion of the “risk in 1,000 women exactly like you,” as well as the magnitude of risk reduction, perhaps given as a percentage.
Walter C. Willett, Beverly Rockhill, Susan E. Hankinson, David J. Hunter and Graham A. Colditz
W. C. Willett: Harvard Medical School, Boston, Massachusetts; Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts
B. Rockhill: Harvard Medical School, Brigham and Women’s Hospital, Boston, Massachusetts
S. E. Hankinson: Departments of Medicine and Epidemiology, Harvard Medical School and Harvard School of Public Health, Boston Massachusetts
D. J. Hunter: Departments of Epidemiology and Nutrition, Harvard School of Public Health, Boston, Massachussetts
G. A. Colditz: Department of Medicine, Harvard Medical School, Boston, Massachussetts