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AJPH First Look, published online ahead of print Jan 31, 2006
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96/3/488    most recent
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Andrew P. Jones
Don A. Seville
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American Journal of Public Health, 10.2105/AJPH.2005.063529


Analytic Essay Forum

Understanding Diabetes Population Dynamics Through Simulation Modeling and Experimentation

Andrew P. Jones 1*, Jack B. Homer 2, Dara L. Murphy 3, Joyce D. Essien 4, Bobby Milstein 3, Don A. Seville 1, Michael Engelgau 3

1 Sustainability Institute
2 Homer Consulting
3 CDC
4 Rollins School of Public Health, Emory University

* To whom correspondence should be addressed. E-mail: apjones{at}sustainer.org.


   Abstract

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 system dynamics simulation model was developed to explain the growth of diabetes since 1980 and portray possible futures through 2050. This model was used to conduct simulated experiments involving interventions for disease management and primary prevention.

The model simulations suggested characteristic dynamics of the diabetes population, including unintended increases in diabetes or prediabetes prevalence due to diabetes control or prediabetes management, 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. Simulated diabetes interventions often produce impacts that look different in the short term than they do in the long term. For example, intervention strategies that focus entirely on diabetes management may quickly reduce diabetes-related complications and deaths but are less effective in the long term than strategies that balance disease management with primary prevention.

System dynamics modeling can help diabetes planners identify more effective public health strategies and set appropriate goals.

Key Words: Diabetes, Chronic Disease, Obesity, Overweight, Underweight, Prevention, Social Science




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