SCH: EXP: Smart Adaptive Adherence-Enhancing Intervention Strategies for Breast Cancer Prevention

Georgia Tech Research Corporation, Atlanta GA

Investigators

Abstract

Each year about 200,000 women are diagnosed with and more than 40,000 die from breast cancer, the most common female cancer in the US. Late detection significantly reduces survival; while 5-year survival is about 97 percent for early stage breast cancers, it is only about 20 percent for advanced stage cancers. Numerous clinical trials and community setting analyses have shown that repeat mammography use can significantly reduce breast cancer mortality. The reduction in breast cancer mortality due to screening however, is contingent upon adhering to screening recommendations and having consecutive on-schedule mammograms. Therefore, women who do not adhere to receiving repeat mammograms are at risk for developing advanced stage or incurable breast cancers. Indeed, adherence to cancer screening has been identified as a national top priority to reduce cancer mortality. In line with this initiative, the research objective of this project is to optimize the design and allocation of adaptive adherence-enhancing intervention (AEI) strategies to improve overall adherence to mammography screening, while reducing unnecessary costs. From a societal perspective, this research has the potential to significantly improve the efficiency of adherence-enhancing intervention strategies for more effective breast cancer prevention. Results from this research can inform breast cancer prevention policies at the level of the individual health plan, a state's comprehensive cancer control plan, and also at the national level in terms of guideline development. This project will also have an immediate impact on integration of research and learning, and enhancing diversity. Under this project, a PhD student will be trained to apply systems modeling methodologies to healthcare area. In addition, the investigators will engage several minority students into these research activities, and aim to attract them to engineering with a focus on healthcare. This research will apply machine learning and adaptive stochastic dynamic control methodologies to learn patients' responses to adherence-enhancing interventions and optimize the use of intervention strategies accordingly. If successful, this project will make several intellectual contributions. First, this will be the first study to optimize the design and allocation of adaptive AEI strategies for sustained mammography use. The team will develop flexible adaptive stochastic control models that capture key disease and intervention dynamics, conduct in depth structural analysis of analytical models, and develop tailored solution algorithms. In parameterizing such models, the team will use large national datasets to inform the models. Further, the team will test policies derived by the analytical models against some actual policies through a detailed simulation model to evaluate possible solutions and estimate the impact. The project's approaches are general and could be applied to other chronic diseases with historically low adherence rates to screening.