This paper examines factors associated with screening mammography and screening's impact on a breast cancer prediction model in the Nurses’ Health Study, a cohort of largely white female health professionals. The rate of screening rose from 77% to 92% across 2-year periods from 1988 to 2000, which was higher than in representative national samples in 1987–1989 (54%) (24
), 1995–1997 (71%) (24
), and 1998–2000 (76%) (1
), possibly because of greater access to mammography among nurses. Even with such high rates, the lack of screening experienced by roughly 20% of the cohort could potentially distort estimated effects. Age and menopausal status were among the strongest predictors of screening mammography. Women aged 50–54 years were most likely to be screened, with lower rates among both younger and older women, particularly postmenopausal women. A similar decrease with age was also seen among a cohort of Medicare beneficiaries aged 65 or over in 2000, even after adjustment for current health status (7
). There, the screening rate was only 39% over 2 years. In a cohort from Ontario studied from 1999 to 2002, 46.5% of the women had a screening mammogram within a 2-year period, a rate that also decreased with age (8
). The Hawaii and Los Angeles Multiethnic Cohort also found the highest mammography use among women in their 50’s (5
As anticipated, a history of benign breast disease or a family history of breast cancer was a strong predictor of mammography use. In addition, women with other medical conditions, such as hypertension, elevated cholesterol, and osteoarthritis, were more likely to undergo mammography, perhaps because of more frequent contact with medical professionals. Those with the more serious diagnosis of cardiovascular disease, however, were less likely to pursue breast cancer screening. The Ontario study also found lower screening rates among diabetics (8
), a finding that was not observed here.
We found reduced use of mammography screening among women who were overweight or obese, but only if premenopausal. Results from the 1998 National Health Interview Survey also saw decreased rates with higher body mass index among white women only (25
), and data from the 1998 Behavioral Risk Factor Surveillance Survey found reduced use among both obese and underweight women (26
). Analyses of more recent Behavioral Risk Factor Surveillance Survey data from 2004, however, found no reduction in use among overweight or obese women, although lower use persisted among underweight women (9
). None of these previous reports examined this relation by age or menopausal status.
Among the strongest predictors of screening mammography was hormone therapy, which is not unexpected given prior work suggesting a link between such hormones and increased breast cancer risk (2
). The odds of screening mammography were 2–3 times higher among current users of hormone therapy, an increase that grew larger over the years from 1988 to 1998.
How to best control for screening remains subject to debate. Weiss (12
) suggested stratified or multivariable analyses adjusting for the effect of screening. Because screening may also play the role of intermediate variable, however, such means of control may not be sufficient (13
). Joffe et al. (21
) suggest restricting the analysis to those previously screened, which could reduce the degree of confounding but also restrict the generalizablility of predictive models. Others argue that such restriction to screened populations may not be enough if screening is not complete (27
). For example, hormone therapy users who undergo screening may be different from those who do not. These authors suggest using sensitivity analysis to develop a range of plausible values for the effects of such exposures. Although Joffe et al. used only subsets in which the particular exposure effect on screening was small, we explored the use of a simple restriction to all those who had a screening mammogram in the previous 2-year period. This restriction led to estimates similar to those from the unweighted model. For example, the estimated rate ratio for 10 years of current use of estrogen plus progesterone was 2.01, and that for estrogen alone was 1.45, similar to the unweighted estimates of 2.00 and 1.44, respectively.
The current analysis first estimates the association of several factors with screening mammography and then uses these to compute weights for a predetermined breast cancer prediction model. This procedure is similar but not identical to marginal structural models using inverse probability of treatment weights (23
). Here, the aim is to remove the influence of screening from the estimated effects of other risk predictors, rather than to estimate the effect of screening itself. The computed model estimates the risk of breast cancer in a population of women of whom 80% undergo screening mammography, with such screening evenly distributed by the selected risk factors. The structural model attempts to estimate the direct effects of risk factors on breast cancer apart from their effect on screening (22
Weighting led to some attenuation of estimated effects in the breast cancer model, such as for history of benign breast disease, family history of breast cancer, and smoking. The largest impact was on the estimated effects of hormone therapy, likely due to its strong impact on screening. The weighted results tend to agree more closely with randomized results from the Women's Health Initiative, a trial of the health effects of hormone therapy. There, women assigned to estrogen plus progesterone experienced a 26% increase in risk of breast cancer compared with women assigned to placebo over an average 5.6 years of follow-up (29
), while those with a prior hysterectomy assigned to estrogen alone had a lower risk of breast cancer over an average 7.1 years (31
). Because of the lack of full compliance in the Women's Health Initiative, the results are difficult to compare directly, however. More recent analyses of both the combined estrogen-progesterone (32
) and estrogen-only (33
) data from the Women's Health Initiative examined time from menopause to initiation of hormone therapy and suggested closer agreement between the trial and observational findings.
Limitations of the current analysis include the strong assumption of no unmeasured confounding, which is necessary to determine causality. The Nurses’ Health Study collects a wide range of variables every 2 years of follow-up, including many known risk factors for breast cancer. It is possible that others are either not available or not well controlled. The validity of any causal inference depends on this control.
Detection bias associated with screening must be considered in analyses of any predictors when routine screening examinations are advised. Although randomized trial data are available for some exposures, such as hormone therapy, it is impossible to conduct trials to study the effects of other risk factors, including reproductive factors or medical history. In addition, when examining changing rates of breast cancer incidence (34
), researchers must consider screening, as well as its relation to risk factors such as hormone therapy. Causal inference models and sample reweighting, particularly when time-varying data are available, can attempt to address these complex issues.