Abstract
We developed a model to compare the impacts of different lifestyle interventions among prediabetes individuals and to identify the optimal age groups for such interventions. A stochastic simulation was developed to replicate the prediabetes and diabetes trends (1997–2010) in the U.S. adult population. We then simulated the population-wide impacts of three lifestyle diabetes prevention programs, i.e., the Diabetes Prevention Program (DPP), DPP-YMCA, and the Healthy Living Partnerships to Prevent Diabetes (HELP-PD), over a course of 10, 15 and 30 years. Our model replicated the temporal trends of diabetes in the U.S. adult population. Compared to no intervention, the diabetes incidence declined 0.3 per 1,000 by DPP, 0.2 by DPP-YMCA, and 0.4 by HELP-PD over the 15-year period. Our simulations identified HELP-PD as the most cost-effective intervention, which achieved the highest 10-year savings of $38 billion for those aged 25–65, assuming all eligible individuals participate in the intervention and considering intervention achievement rates. Our model simulates the diabetes trends in the U.S. population based on individual-level longitudinal data. However, it may be used to identify the optimal intervention for different subgroups in defined populations.
Highlights
Between disease stages were built upon individual-level data rather than a parameter value extracted from literature
The stochastic progression of each individual is based on a microsimulation model for which the transition probability matrix (TPM) was estimated with data obtained from the PREMIER study[16]
We only simulated the lifestyle intervention which achieved at least 7% body mass index (BMI) reduction for 38% of participants measured some time after the first year of the trial
Summary
Between disease stages were built upon individual-level data rather than a parameter value extracted from literature. This feature enables us to model the progression of each individual reflecting unique demographic characteristics. Our model simulates the entire life history of each individual, which allows us to explore the optimal age range of different interventions. Our life course approach estimates the gain in diabetes-free survival time for any given intervention, i.e., the average time for an individual with prediabetes conditions to develop diabetes
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