title: Clinical outcomes of modelling mammography screening strategies.
abstract: BACKGROUND
  A validated breast cancer model can be used to compare health outcomes associated with different screening strategies.
  
  
  METHODS
  The University of Wisconsin Cancer Intervention and Surveillance Modeling Network (CISNET) breast cancer microsimulation model was adapted to simulate breast cancer incidence, screening performance and delivery of optimal therapies in Canada. The model considered effects of breast density on incidence and screening performance. Model predictions of incidence, mortality and life-years (LY) gained for a 1960 birth cohort of women for No Screening were compared with 11 digital mammography screening strategies that varied by starting and stopping age and frequency.
  
  
  RESULTS
  In the absence of screening, the estimate of LYs lost from breast cancer was 360.1 per 1,000 women, and each woman diagnosed with breast cancer after age 40 who dies of breast cancer would lose an average of 19.1 years. Biennial screening at ages 50 to 74 resulted in 116.3 LYs saved. Annual screening at ages 40 to 49, followed by biennial screening to age 74, resulted in 170.3 LY saved. Screening annually at ages 40 to 74 recovered the most: 214 LY saved. Annual screening at age 40 resulted in 54 LY gained per 1,000 women. More frequent screening was associated with an increased ratio of detection of ductal in situ to invasive cancers, more abnormal recalls and more negative biopsies, but a reduction in the number of women required to be screened per life saved or per LY saved.
  
  
  CONCLUSIONS
  In general, mortality reduction was found to be associated with the total number of lifetime screens for breast cancer. However, for the same number of screens, more frequent screening after age 50 appeared to have a greater impact than beginning screening earlier. When the number of LYs saved by screening was considered, a greater impact was achieved by screening women in their 40s than by reducing the interval between screens.
authors.affiliation: Physical Sciences Program, Sunnybrook Research Institute; Departments of Medical Biophysics and Medical Imaging, University of Toronto.
authors.forename: Martin J
authors.initials: MJ
authors.lastname: Yaffe
authors.affiliation: Cancer Care Ontario.
authors.forename: Nicole
authors.initials: N
authors.lastname: Mittmann
authors.affiliation: Institute for Technology Assessment, Massachusetts General Hospital.
authors.forename: Pablo
authors.initials: P
authors.lastname: Lee
authors.affiliation: Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth.
authors.forename: Anna N A
authors.initials: AN
authors.lastname: Tosteson
authors.affiliation: Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin.
authors.forename: Amy
authors.initials: A
authors.lastname: Trentham-Dietz
authors.affiliation: Department of Population Health Sciences and Carbone Cancer Center and the Department of Industrial and Systems Engineering, University of Wisconsin.
authors.forename: Oguzhan
authors.initials: O
authors.lastname: Alagoz
authors.affiliation: Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute.
authors.forename: Natasha K
authors.initials: NK
authors.lastname: Stout
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000453:epidemiology / Q000401:mortality; D002170:Canada / Q000453:epidemiology; D015331:Cohort Studies; D003362:Cost-Benefit Analysis; D055088:Early Detection of Cancer / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D015994:Incidence; D015993:Life Tables; D008327:Mammography / Q000706:statistics & numerical data*; D008403:Mass Screening / Q000706:statistics & numerical data*; D008875:Middle Aged; D015233:Models, Statistical; D008962:Models, Theoretical
keywords: Breast screening; health outcomes; microsimulation model; preventive health