Despite trials of mammography and widespread use, optimal screening policy is controversial.
Design and Objective
Six models use common data elements to evaluate US screening strategies.
The models use national data on age-specific incidence, competing mortality, mammography characteristics and treatment effects.
Target Population and Time Horizon
A contemporary population cohort followed over their lifetimes.
We use a societal perspective for analysis.
We evaluate 20 screening strategies with varying initiation and cessation ages applied annually or biennially.
Number of mammograms, breast cancer mortality reduction or life years gained [LYG] (vs. no screening), false positives, unnecessary biopsies and over-diagnosis.
Results of Base Case
The 6 models produce consistent rankings of screening strategies. Screening biennially maintains an average of 81% (range across strategies and models 67–99%) of the benefit of annual screening with almost half the number of false positives. Screening biennially from ages 50 to 69 achieves a median 16.5% (range 15%–23%) breast cancer mortality reduction vs. no screening. Initiating biennial screening at age 40 (vs. 50) reduces mortality by an additional 3% (range 1%–6%), consumes more resources and yields more false positives. Biennial screening after age 69 yields some additional mortality reduction in all models but over-diagnosis increases most substantially at older ages.
Sensitivity Analysis Results
Varying test sensitivity or treatment patterns do not change conclusions.
Results do not include morbidity from false positives, knowledge of earlier diagnosis or under-going unnecessary treatment.
Biennial screening achieves most of the benefit of annual screening with less harm. Decisions about the best strategy depend on program and individual objectives and the weight placed on benefits, harms and resource considerations.