Modelling mammography screening for breast cancer in the Canadian context: Modification and testing of a microsimulation model

Health Rep. 2015 Dec;26(12):3-8.

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.

Data and 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.

Interpretation: 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.

Keywords: Breast screening; incidence; microsimulation model; preventive health; sensitivity; specificity.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Breast Neoplasms / diagnostic imaging*
  • Breast Neoplasms / epidemiology
  • British Columbia
  • Canada / epidemiology
  • Cross-Sectional Studies
  • Early Detection of Cancer / statistics & numerical data*
  • Female
  • Humans
  • Incidence
  • Mammography / statistics & numerical data*
  • Middle Aged
  • Models, Statistical
  • Sensitivity and Specificity