| The objective of this research is to develop, validate, and exploit a simulation modeling framework for evaluating the effectiveness of breast cancer screening policies in the near future (that is, over the period 2012–2020) for US women who are at least 65 years old. This work includes an examination of key components in the breast cancer screening process for older women, and an approach to defining and modeling those components using simulation. In the near future, it is expected that half of newly diagnosed breast cancer cases will be in women 65 and older. This development, along with the aging US population, is evidence that older women will become the prevalent patient cohort in the breast cancer population of the United States. This research utilizes a two-phase simulation modeling approach. The first simulation is a natural history model of breast cancer incidence and progression in randomly sampled individuals from the designated population of older US women. The second simulation is an integrated screening-and-treatment model that uses knowledge about the genesis of breast cancer within the same population gained from the natural history model to estimate the benefits of different policies for screening the designated population and treating the relevant individuals. Both simulation models are composed of interacting submodels that represent key aspects of the incidence, progression, screening, treatment, survival, and cost of breast cancer in the population of older US women as well as the overall structure of the system for detecting and treating this disease. We discuss the rationale for combining the discrete-event and system-dynamics modeling techniques for the analysis of this problem, with an underlying goal of identifying the benefit of using this integrated approach. Our methodology is “individualized” in the sense that we simulate the lives of individual women who are representative of the designated population, and each woman’s risk of being diagnosed with breast cancer is based on her individual risk factors. Other problem areas are explored in this research, including the development of techniques for input modeling, general systems modeling, and output analysis that are specifically adapted to address the special needs of simulation-based health care decision making. |
