Effects of Mammography Screening Under Different Screening Schedules: Model Estimates of Potential Benefits and Harms

Abstract

Background 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. Data Sources 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. Perspective We use a societal perspective for analysis. Interventions We evaluate 20 screening strategies with varying initiation and cessation ages applied annually or biennially. Outcome Measures 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. Limitations Results do not include morbidity from false positives, knowledge of earlier diagnosis or under-going unnecessary treatment. Conclusion 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.

Simulating Strategies for Improving Control of Hypertension Among Patients with Usual Source of Care in the United States: The Blood Pressure Control Model

“…Computer simulation modeling has been instrumental in developing clinical guidelines and policy in general, 14,[23][24][25][26] and for hypertension in particular. 27 Farley and colleagues 27 used mathematical modeling to project the potential mortality benefits of increasing hypertension control, while Turner and Schalkwyk 28 used computer simulation of BP variability to examine spurious identification of hypertension in clinical studies.…”

Malpractice claims following screening mammography in The Netherlands

“…Screening interval length may influence screening outcome parameters such as screening sensitivity and tumor size of screen-detected cancers and interval cancers. 28,29 Furthermore, it was not possible to compare the low number of claims in our screening region with a nation-wide number of claims following breast cancer screening. Details of claims are not routinely registered by screening organizations in The Netherlands.…”

Failure of researchers, reviewers, editors, and the media to understand flaws in cancer screening studies: Application to an article in Cancer

“…These models have been used by the US Preventive Services Task Force to aid in understanding whether the benefits of screening observed in the RCTs extend into the modern era of adjuvant therapy, the relative effects of annual versus biennial screening, and the effectiveness of screening depending on age. 24 …”