title: Modelling mammography screening for breast cancer in the Canadian context: Modification and testing of a microsimulation model.
abstract: BACKGROUND
  Modelling is a flexible and efficient approach to gaining insight into the trade-offs surrounding a complex process like breast screening, which involves more variables than can be controlled in an experimental study.
  
  
  METHODS
  The University of Wisconsin Cancer Intervention and Surveillance Modeling Network (CISNET) breast cancer microsimulation model was adapted to simulate breast cancer incidence and screening performance in Canada. The model considered effects of breast density on the sensitivity and specificity of screening. The model's ability to predict age-specific incidence of breast cancer was assessed.
  
  
  RESULTS
  Predictions of age-adjusted incidence over calendar years and age-specific incidence of breast cancer in Canadian women are presented. Based on standard screening strategies, ratios of in situ to invasive disease and stage distribution of disease at diagnosis are compared with data from the British Columbia provincial screening program.
  
  
  CONCLUSIONS
  The adapted model performs well in predicting age-specific incidence and cross-sectional incidence in the absence of screening. The ratios of detection of in situ to invasive cancers and the overall stage distribution of detected cancers are in reasonable agreement with empirical data from British Columbia.
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; D000368:Aged; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000453:epidemiology; D001955:British Columbia; D002170:Canada / Q000453:epidemiology; D003430:Cross-Sectional Studies; D055088:Early Detection of Cancer / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D015994:Incidence; D008327:Mammography / Q000706:statistics & numerical data*; D008875:Middle Aged; D015233:Models, Statistical; D012680:Sensitivity and Specificity
keywords: Breast screening; incidence; microsimulation model; preventive health; sensitivity; specificity