Modeling the impact of population screening on breast cancer mortality in the United States

Abstract

Objective: Optimal US screening strategies remain controversial. We use six simulation models to evaluate screening outcomes under varying strategies.

Methods: The models incorporate common data on incidence, mammography characteristics, and treatment effects. We evaluate varying initiation and cessation ages applied annually or biennially and calculate mammograms, mortality reduction (vs. no screening), false-positives, unnecessary biopsies and over-diagnosis.

Results: The lifetime risk of breast cancer death starting at age 40 is 3% and is reduced by screening. Screening biennially maintains 81% (range 67% to 99%) of annual screening benefits with fewer false-positives. Biennial screening from 50-74 reduces the probability of breast cancer death from 3% to 2.3%. Screening annually from 40 to 84 only lowers mortality an additional one-half of one percent to 1.8% but requires substantially more mammograms and yields more false-positives and over-diagnosed cases.

Conclusion: Decisions about screening strategy depend on preferences for benefits vs. potential harms and resource considerations.

Fig. 1

Percentage of breast cancer mortality reduction versus number of mammographies per woman, by model and screening strategy. The panels show an efficiency frontier graph for an exemplar model (Model G). The graph plots the average number of mammograms performed per 1000 women against the percentage of mortality reduction for each screening strategy (vs. no screening). We plot efficient strategies (that is, those in which increases in use of mammography resources result in greater mortality reduction than the next least-intensive strategy). We also plot “borderline” strategies (approaches that are efficient in some models but not others). The line between strategies represents the “efficiency frontier.” Strategies on this line would be considered efficient because they achieve the greatest gain per use of mammography resources compared with the point (or strategy) immediately below it. Points that fall below the line are not considered as efficient as those on the line. When the slope in the efficiency frontier plot levels off, the additional reductions in mortality per unit increase in use of mammography are small relative to the previous strategies and could indicate a point at which additional investment (use of screening) might be considered as having a low return (benefit). Adapted from Mandelblatt et al., 2009.