Breast cancer screening is a topic of hot debate, and currently no general consensus has been reached on starting and ending ages and screening intervals, in part because of a lack of precise estimations of the benefit-harm ratio. Simulation models are often applied to account for the expected benefits and harms of regular screening; however, the degree to which the model outcomes are reliable is not clear. In a recent systematic review, we therefore aimed to assess the quality of published simulation models for breast cancer screening of the general population. The models were scored according to a framework for qualitative assessment. We distinguished seven original models that utilized a common model type, modelling approach, and input parameters. The models predicted the benefit of regular screening in terms of mortality reduction; and overall, their estimates compared well to estimates of mortality reduction from randomized controlled trials. However, the models did not report on the expected harms associated with regular screening. We found that current simulation models for population breast cancer screening are prone to many pitfalls; their outcomes bear a high overall risk of bias, mainly because of a lack of systematic evaluation of evidence to calibrate the input parameters and a lack of external validation. Our recommendations concerning future modelling are therefore to use systematically evaluated data for the calibration of input parameters, to perform external validation of model outcomes, and to account for both the expected benefits and the expected harms so as to provide a clear balance and cost-effectiveness estimation and to adequately inform decision-makers.
Keywords: Breast cancer; modelling; mortality reduction; screening.