Using Mathematical Models to Inform Public Policy for Cancer Prevention and Screening

This chapter introduces the reader to the use of mathematical modeling applied to policy questions in cancer screening and prevention. While randomized trials and observational studies are the mainstays for evaluating the effectiveness of screening and prevention interventions, situations may arise where these methods may not be feasible due to time, ethical, or other constraints. Mathematical models can help to fill these gaps by synthesizing available evidence, often from disparate sources, and estimating outcomes across a range of policy alternatives. Predictions from models can aid policymakers in understanding the trade-offs in benefits and risks among policies under design or evaluation. The goal of this chapter is to help orient policymakers to this methodology. An overview of the methods is provided followed by examples to illustrate the range of policy applications where modeling can be used as well as how policymakers and modelers can collaborate. Real-world examples include the use of modeling to aid the design of the national cervical cancer screening program in the Netherlands, modeling to develop a package of tobacco control policies in the USA, and modeling to evaluate the contributions of cancer screening and treatment to observed trends in breast cancer mortality.