Clinical outcomes of modelling mammography screening strategies.
BACKGROUND
A validated breast cancer model can be used to compare health outcomes associated with different screening strategies.


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, 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.


INTERPRETATION
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.
['Yaffe|Martin J|MJ|', 'Mittmann|Nicole|N|', 'Lee|Pablo|P|', 'Tosteson|Anna N A|AN|', 'Trentham-Dietz|Amy|A|', 'Alagoz|Oguzhan|O|', 'Stout|Natasha K|NK|']
D000328:Adult D000367:Age Factors D000368:Aged D001943:Breast Neoplasms D002170:Canada D015331:Cohort Studies D003362:Cost-Benefit Analysis D055088:Early Detection of Cancer D005260:Female D006801:Humans D015994:Incidence D015993:Life Tables D008327:Mammography D008403:Mass Screening D008875:Middle Aged D015233:Models, Statistical D008962:Models, Theoretical
health outcomes microsimulation model Breast screening preventive health