Figure 3. Diabetes conceptual model Reprinted with permission from the American Public Health Association58
management; and (3) prevent diabetes (through the prevention of obesity). These three hypothetical scenarios are compared to “baseline,” a predictive model in which the status quo of diabetes clinical practices and preven- tion activities is maintained at baseline levels.
The following outcomes were predicted under each of the three scenarios:
The improved clinical management of diabetes leads to short-term improvements in diabetes con- trol, complications, and associated deaths. However, following these improvements in the first few years, there is a rapid rise in complication deaths. Improve- ments in complications are rapidly overtaken by the growth in diabetes prevalence because nothing has been done to reduce diabetes onset.
Efforts to manage persons with prediabetes would lead to reductions in the onset of diabetes initially, and ultimately would reduce deaths from diabetes compli- cations. But without prediabetes prevention efforts, the amount of reduction in deaths is less than optimal.
Finally, the primary prevention of diabetes shows the most drastic and lasting reductions in deaths.
However, even this powerful step alone (i.e., reducing rates of obesity without concurrent changes in prediabe- tes management or clinical diabetes management) would not reduce the overall burden of diabetes in terms of both the number of unhealthy days (not pictured) and the number of deaths due to diabetes right away (Figure 4). In fact, the number of deaths attributable to diabetes would actually rise through at least the year 2020, al- though during subsequent decades, a significant decrease in diabetes prevalence and deaths would occur. Thus, the time perspective is vital to determining the value of a strategy—that is, disease management works in the short term, but primary prevention is more effective in the long term. This example illustrates the potential of systems science to inform healthcare and policy decisions to improve population health.
In another example of adopting a systems approach to improving the understanding of a public health problem, Levy and colleagues developed SimSmoke,59 a simulation model for guiding policy to make a population impact on reducing smoking prevalence. SimSmoke uses historical and current data to model
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