Combined DES/SD simulaton model of breast cancer screening for older women: An overview

Abstract

We develop a simulation modeling framework for evaluating the effectiveness of breast cancer screening policies for US women of age 65+. We introduce a two-phase simulation approach to modeling the main components in the breast cancer screening process. The first phase is a natural-history model of the incidence and progression of untreated breast cancer in randomly sampled individuals from the designated population. Combining discrete event simulation (DES) and system dynamics (SD) submodels, the second phase is a screening-and-treatment model that uses information about the genesis of breast cancer in the sampled individuals as generated by the natural-history model to estimate the benefits of different policies for screening the designated population and treating the affected women. Based on extensive simulation-based comparisons of alternative screening policies, we concluded that annual screening from age 65 to age 80 is the best policy for minimizing breast cancer deaths or for maximizing quality-adjusted life-years saved.