The systems modeling methodology of system dynamics is well suited to address the dynamic complexity that characterizes many public health issues. The system dynamics approach involves the development of computer simulation models that portray processes of accumulation and feedback and that may be tested systematically to find effective policies for overcoming policy resistance. System dynamics modeling of chronic disease prevention should seek to incorporate all the basic elements of a modern ecological approach, including disease outcomes, health and risk behaviors, environmental factors, and health-related resources and delivery systems. System dynamics shows promise as a means of modeling multiple interacting diseases and risks, the interaction of delivery systems and diseased populations, and matters of national and state policy.

Health planners in the Division of Diabetes Translation and others from the National Center for Chronic Disease Prevention and Health Promotion of the Centers for Disease Control and Prevention used system dynamics simulation modeling to gain a better understanding of diabetes population dynamics and to explore implications for public health strategy. A model was developed to explain the growth of diabetes since 1980 and portray possible futures through 2050. The model simulations suggest characteristic dynamics of the diabetes population, including unintended increases in diabetes prevalence due to diabetes control, the inability of diabetes control efforts alone to reduce diabetes-related deaths in the long term, and significant delays between primary prevention efforts and downstream improvements in diabetes outcomes.

Proposals to improve the US health system are commonly supported by models that have only a few variables and overlook certain processes that may delay, dilute, or defeat intervention effects. We use an evidence-based dynamic simulation model with a broad national scope to analyze 5 policy proposals. Our results suggest that expanding insurance coverage and improving health care quality would likely improve health status but would also raise costs and worsen health inequity, whereas a strategy that also strengthens primary care capacity and emphasizes health protection would improve health status, reduce inequities, and lower costs. A software interface allows diverse stakeholders to interact with the model through a policy simulation game called HealthBound.

Chronic illness is a large and growing problem throughout the world. Experts agree that the U.S. health care system is poorly organized to care for chronic illnesses and, as a result, is wasteful and unresponsive to the needs of patients. This article describes a program to improve chronic care in a county of Washington State, and how system dynamics models focusing on diabetes and heart failure supported the planning of that program. The models project the program's costs and benefits over 20 years and have given its leadership the ability to do resource planning, set realistic expectations, determine critical success factors, and evaluate the differential impacts on affected parties. Relying upon model projections, the leadership is seeking ways to address concerns about financial “winners” and “losers” so that all parties are willing to participate in and support the program. Copyright © 2004 John Wiley & Sons, Ltd.

Planning programs for the prevention and treatment of cardiovascular disease (CVD) is a challenge to every community that wants to make the best use of its limited resources. Selecting programs that provide the greatest impact is difficult because of the complex set of causal pathways and delays that link risk factors to CVD. We describe a system dynamics simulation model developed for a county health department that incorporates and tracks the effects of those risk factors over time on both first-time and recurrent events. We also describe how the model was used to evaluate the potential impacts of various intervention strategies for reducing the county's CVD burden and present the results of those policy tests.