Comparative Modeling of Effective Policies for Colorectal Cancer Control

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ABSTRACT Colorectal cancer (CRC) is the second leading cause of cancer death in the United States. The long-term goal of our proposed project is to reduce the population burden of CRC by providing the information needed to address key policy questions across the CRC control continuum in an accessible and transparent manner. To accomplish this goal we will use our population-based microsimulation models to: 1) Evaluate the impact of screening as practiced in the US; 2) Inform the debate about the increase in CRC incidence before age 50; 3) Consider the effectiveness of precision of screening and surveillance; 4) Address other emerging issues and opportunities in CRC control; and 5) Use novel methods to improve model accessibility and transparency. Our team will fill critical gaps in knowledge, enabling decision makers to act. New evidence that we will incorporate in our models to better inform CRC control opportunities will be 1) updated information on screening patterns in the US (in collaboration with the Population-based Research Optimizing Screening through Personalized Regimen, or PROSPR), 2) data on the increased risk of CRC in persons under age 50 (in collaboration with Rebecca Siegal of the American Cancer Society, who did the seminal work in this area), and 3) state-of-the art colonoscopy screening data to incorporate alternative carcinogenesis pathways in the natural history models (in collaboration with the New Hampshire Colonoscopy Registry). We will synthesize and incorporate the growing body of evidence in the literature to assess the clinical utility of personalized screening and treatment, as well as the potential role for novel computer-aided detection and diagnosis modalities. We will expand our models to project clinical and resource-based outcomes for middle-income countries that are considering the implementation of a screening program. Lastly, there is a critical need to make our models assessible and transparent. To this end we will use high performance computing approaches to develop and apply deep- learning methods for model calibration and model emulation, which will aid in model sharing. The three participating modeling groups are well positioned to carry out this work, bringing a wealth of experience, expertise, and insight to issues related to microsimulation modeling of CRC, and have a proven track record of collaboration and disseminating our work to health policy decision makers.