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AJPH First Look, published online ahead of print Jan 31, 2006
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Understanding Diabetes Population Dynamics Through Simulation Modeling and Experimentation

Andrew P. Jones, MS, Jack B. Homer, PhD, Dara L. Murphy, MPH, Joyce D. K. Essien, MD, MBA, Bobby Milstein, MPH and Donald A. Seville, MS

Dara L. Murphy is with the Division of Diabetes Translation, Joyce D. K. Essien is with the Division of Public Health Partnerships, and Bobby Milstein is with the Division of Adult and Community Health, Centers for Disease Control and Prevention, Atlanta, Ga. Joyce D. K. Essien is also with the Center for Public Health Practice at the Rollins School of Public Health, Emory University, Atlanta. Jack B. Homer is with Homer Consulting, Voorhees, NJ. Andrew P. Jones and Donald A. Seville are with the Sustainability Institute, Asheville, NC, and Hartland, VT, respectively.


Figure 1
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  		FIGURE 1— Overview of model structure, showing primary population stocks (boxes) and flows (arrows with valve symbols and cloud symbols for deaths), modifiable factors affecting flows (roman), and inputs amenable to policy intervention (italics).

 

Figure 2
Figure 2
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  		FIGURE 2— Selected baseline model output, 1980–2050, and comparison to historical data for obesity prevalence (a), diabetes prevalence (b), complication-related deaths per complicated cases (c), and complication-related deaths (d).

Note. Reported obesity prevalence based on National Health and Nutrition Examination Survey,15 and reported diabetes prevalence based on National Health Interview Survey.14 Baseline projection assumes that obesity prevalence rises to 37% in 2006 and remains fixed thereafter, and that disease detection and control efforts all remain fixed after 2004.

 

Figure 3
Figure 3
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  		FIGURE 3— Model output for 3 intervention scenarios compared with the baseline scenario for diabetes prevalence (a) and complication-related deaths (b).

 




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