Primary preventive care model for Type 2 Diabetes: Input calibration with response data

Type 2 Diabetes Mellitus (T2DM) and its complications account for 11% of the global health expenditure (IDF 2012). Different primary, secondary, and tertiary preventive interventions promise better health outcomes and cost savings but are often studied separately. This paper proposes a simulation model for T2DM that comprehends the nonlinear interactions of multiple interventions for various stages of T2DM on population dynamics, health outcomes, and costs. We summarize the model, then demonstrate how we addressed the important challenge of fitting input parameters given that data needed to be combined from disparate sources of data sources in a way that calibrates input parameters to output metrics over a range of decision variables (a form of model calibration to achieve a response model match to clinical data). We present preliminary numerical results to inform policies for T2DM prevention and management.