Comparison of recommendations for screening mammography using CISNET models.
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abstract
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BACKGROUND: Currently, there are several different recommendations for screening mammography from major national health care organizations, including: 1) annual screening at ages 40 to 84 years; 2) screening annually at ages 45 to 54 years, then biennially at ages 55 to 79 years; and 3) biennial screening at ages 50 to 74 years. METHODS: Mean values of six Cancer Intervention and Surveillance Modeling Network (CISNET) models were used to compare these three screening mammography recommendations in terms of benefits and risks. RESULTS: Mean mortality reduction was greatest with the recommendation of annual screening at ages 40 to 84 years (39.6%), compared with the hybrid recommendation of screening annually at ages 45 to 54 years, then biennially at ages 55 to 79 years (30.8%), and the recommendation of biennial screening at ages 50 to 74 years (23.2%). For a single-year cohort of US women aged 40 years, assuming 100% compliance, more breast cancers deaths would be averted over their lifetime with annual screening starting at age 40 (29,369) than with the hybrid recommendation (22,829) or biennial screening ages 50-74 (17,153 based on 2009 CISNET estimates, 15,599 based on 2016 CISNET estimates). To achieve the greatest mortality benefit, this single-year cohort of women would have the greatest total number of screening mammograms, benign recalls, and benign biopsies performed over the course of screening by following annual screening starting at age 40 years (90.2 million, 6.8 million, and 481,269, respectively) than by following the hybrid recommendation (49.0 million, 4.1 million, and 286,288, respectively) or biennial screening at ages 50 to 74 years (27.3 million, 2.3 million, and 162,885, respectively). CONCLUSION: CISNET models demonstrate that the greatest mortality reduction is achieved with annual screening of women starting at age 40 years. Cancer 2017;123:3673-3680. © 2017 American Cancer Society.
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Breast Neoplasms
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Mammography
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Models, Theoretical
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Practice Guidelines as Topic
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