Applying Systems Science Methods to Risk-Based Disease Management and Population Oral Health
- Author(s): Flint MacBride, Robin A.
- Advisor(s): Halfon, Neal
- et al.
Policymakers and public health practitioners face many complex population and health care related problems that cannot be fully understood by simply understanding the component parts. However, the predominant approach in research, practice and policymaking is to approach complex problems with reductionist thinking and methods. Complex systems methods such as agent-based modeling (ABM), System Dynamics (SD) and discrete-event simulations are well suited to deal with the inherent complexity of populations and delivery systems and complement reductionist approaches.
Early childhood caries and the dental care delivery system are good examples of complex systems at many levels. Caries is a complex multifactorial disease that is both infectious and chronic. It is one of the most prevalent chronic conditions in the U.S. and the world, and it is therefore a costly disease, not just financially, but also the impact it can have on learning, development and social engagement of children. Oral health disparities persist in vulnerable populations despite marked improvements in the overall oral health of the nation in the last 50 years. Developments in risk-based care and disease management in the last decades have demonstrated its ability to reduce disease and its consequences, yet it is generally not used to its full potential in dentistry.
An overarching goal of this dissertation is to demonstrate the utility and feasibility of a system-science approach for analyzing interconnected population and health care problems. I draw on the system science toolkit to, first, take a holistic view of the system influences on dentists’ behaviors, and second to use a hybrid simulation model to explore the structure and dynamics of a risk-based and disease management approach to dental care and population oral health. The simulation provides a demonstration of ways to harness the strengths of agent-based models, system dynamics models, and discrete event simulations to represent and learn about the structures driving the system’s behaviors and the underlying interactions at the individual level of patients and providers.
Overall, the modeling process yielded much learning about the disease and dental care process, as well as about the modeling process, its techniques and the language of systems. The findings support the application of these simulation and system thinking tools to aid in learning, planning and to inform the policy making process