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Showing content from https://pubmed.ncbi.nlm.nih.gov/19920274/ below:

Effects of mammography screening under different screening schedules: model estimates of potential benefits and harms

. 2009 Nov 17;151(10):738-47. doi: 10.7326/0003-4819-151-10-200911170-00010. Effects of mammography screening under different screening schedules: model estimates of potential benefits and harms Kathleen A Cronin, Stephanie Bailey, Donald A Berry, Harry J de Koning, Gerrit Draisma, Hui Huang, Sandra J Lee, Mark Munsell, Sylvia K Plevritis, Peter Ravdin, Clyde B Schechter, Bronislava Sigal, Michael A Stoto, Natasha K Stout, Nicolien T van Ravesteyn, John Venier, Marvin Zelen, Eric J Feuer; Breast Cancer Working Group of the Cancer Intervention and Surveillance Modeling Network

Affiliations

Affiliation

¹ Georgetown University Medical Center and Lombardi Comprehensive Cancer Center, Washington, DC, USA. mandelbj@georgetown.edu

PMID: 19920274
PMCID: PMC3515682
DOI: 10.7326/0003-4819-151-10-200911170-00010

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Effects of mammography screening under different screening schedules: model estimates of potential benefits and harms

Jeanne S Mandelblatt et al. Ann Intern Med. 2009.

. 2009 Nov 17;151(10):738-47. doi: 10.7326/0003-4819-151-10-200911170-00010. Authors Jeanne S Mandelblatt¹, Kathleen A Cronin, Stephanie Bailey, Donald A Berry, Harry J de Koning, Gerrit Draisma, Hui Huang, Sandra J Lee, Mark Munsell, Sylvia K Plevritis, Peter Ravdin, Clyde B Schechter, Bronislava Sigal, Michael A Stoto, Natasha K Stout, Nicolien T van Ravesteyn, John Venier, Marvin Zelen, Eric J Feuer; Breast Cancer Working Group of the Cancer Intervention and Surveillance Modeling Network Affiliation

¹ Georgetown University Medical Center and Lombardi Comprehensive Cancer Center, Washington, DC, USA. mandelbj@georgetown.edu

PMID: 19920274
PMCID: PMC3515682
DOI: 10.7326/0003-4819-151-10-200911170-00010

Item in Clipboard

Erratum in

Ann Intern Med. 2010 Jan 19;152(2):136

Abstract

Background: Despite trials of mammography and widespread use, optimal screening policy is controversial.

Objective: To evaluate U.S. breast cancer screening strategies.

Design: 6 models using common data elements.

Data sources: National data on age-specific incidence, competing mortality, mammography characteristics, and treatment effects.

Target population: A contemporary population cohort.

Time horizon: Lifetime.

Perspective: Societal.

Interventions: 20 screening strategies with varying initiation and cessation ages applied annually or biennially.

Outcome measures: Number of mammograms, reduction in deaths from breast cancer or life-years gained (vs. no screening), false-positive results, unnecessary biopsies, and overdiagnosis.

Results of base-case analysis: The 6 models produced consistent rankings of screening strategies. Screening biennially maintained an average of 81% (range across strategies and models, 67% to 99%) of the benefit of annual screening with almost half the number of false-positive results. Screening biennially from ages 50 to 69 years achieved a median 16.5% (range, 15% to 23%) reduction in breast cancer deaths versus no screening. Initiating biennial screening at age 40 years (vs. 50 years) reduced mortality by an additional 3% (range, 1% to 6%), consumed more resources, and yielded more false-positive results. Biennial screening after age 69 years yielded some additional mortality reduction in all models, but overdiagnosis increased most substantially at older ages.

Results of sensitivity analysis: Varying test sensitivity or treatment patterns did not change conclusions.

Limitation: Results do not include morbidity from false-positive results, patient knowledge of earlier diagnosis, or 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.

Primary funding source: National Cancer Institute.

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Figures
Figure 1. Percent Breast Cancer Mortality Reduction…

Figure 1. Percent Breast Cancer Mortality Reduction vs. Number of Mammography Screens per Woman by…
Figure 1. Percent Breast Cancer Mortality Reduction vs. Number of Mammography Screens per Woman by Model and Screening Strategy

The panels in this figure show an efficiency frontier graph for each model. The graph plots the average number of mammograms performed per women against the percent mortality reduction for each screening strategy (vs. no screening). We plot efficient strategies (i.e., those where increases in use of mammography resources result in greater mortality reduction than the next least intensive strategy) in all six models. We also plot “borderline” strategies (approaches that are efficient in some models but not in others). The line between strategies that is drawn represents the “efficiency frontier”. Strategies on this line would be considered efficient in that they achieve the greatest gain per use of mammography resources compared to 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 prior strategies and could indicate a point at which additional investment (use of screening) might be considered as having a low return (benefit). To highlight efficient strategies that decision makers might want to consider, we have color coded the strategies that might be considered most efficient overall across the models. We also highlight one common current approach (annual screening 40–79), although it is below the efficiency frontier in most models. Blue represents biennial screening from age 50–69 Green represents biennial screening from age 50–74 Pink represents biennial screening from age 50–79 Red represents annual screening from age 40–79 Model Group Abbreviations: D (Dana Farber Cancer Center), E (Erasmus Medical Center), G (Georgetown U.), M (M.D. Anderson Cancer Center), S (Stanford U.), W (U. of Wisconsin/Harvard)

Appendix Figure 1. Life Years Gained vs.…

Appendix Figure 1. Life Years Gained vs. Number of Mammography Screens per Woman by Model…
Appendix Figure 1. Life Years Gained vs. Number of Mammography Screens per Woman by Model and Screening Strategy

The panels in this figure show an efficiency frontier graph for each model. The graph plots the average number of mammograms performed per women against the percent mortality reduction for each screening strategy (vs. no screening). We plot efficient strategies (i.e., those where increases in use of mammography resources result in greater mortality reduction than the next least intensive strategy) in all six models. We also plot “borderline” strategies (approaches that are efficient in some models but not in others). The line between strategies that is drawn represents the “efficiency frontier”. Strategies on this line would be considered efficient in that they achieve the greatest gain per use of mammography resources compared to 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 prior strategies and could indicate a point at which additional investment (use of screening) might be considered as having a low return (benefit). To highlight efficient strategies that decision makers might want to consider, we have color coded the strategies that might be considered most efficient overall across the models. We also highlight one common current approach (annual screening 40–79), although it is below the efficiency frontier in most models. Blue represents biennial screening from age 50–69 Green represents biennial screening from age 50–74 Pink represents biennial screening from age 50 to 79 Red represents annual screening from age 40 to 79 Model Group Abbreviations: D (Dana Farber Cancer Center), E (Erasmus Medical Center), G (Georgetown U.), M (M.D. Anderson Cancer Center), S (Stanford U.), W (U. of Wisconsin/Harvard)