146-OR: A New Type 2 Diabetes Microsimulation Model to Estimate Long-Term Health Outcomes, Costs, and Cost-Effectiveness

Abstract

We developed a new microsimulation model to estimate health effects, costs, and cost-effectiveness of public health interventions for type 2 diabetes. The model combined risk equations for complications and mortality, risk factor progression equations, and patient utility equations based on the U.S. ACCORD and Look AHEAD studies with a cost equation estimated from a large panel dataset of privately insured U.S. adults. The model features a web-based interface that allows users to change model parameters. The model includes interventions for glycemic, blood pressure, and cholesterol control; smoking cessation; and a generic intervention that users can modify to represent control of multiple risk factors. To demonstrate how the model estimates cost-effectiveness, we analyzed an intervention to reduce HbA1c from 9% to 7%. We estimated the long-run health effects, costs, and quality-adjusted life-years (QALYs) for a simulated cohort of 10,000 U.S. adults with type 2 diabetes. On average, the simulated cohort of U.S. patients was projected to have 20.16 remaining life-years (from a mean age of 61), incur $187,435 in discounted medical costs, and experience 8.89 discounted QALYs. Results were most sensitive to age and duration of diabetes at baseline and the discount factor. The intervention to reduce HbA1c produced $3,151 in incremental costs and gained 0.4 QALYs, yielding an incremental cost-effectiveness ratio of $7,838 per QALY. The model performed well in internal validation exercises. External validation results were more mixed, with the model better at predicting clinical trial results than at predicting observational study results. As the model’s risk progression, patient utility, and costs are all derived using recent U.S. studies, our new model can more accurately project the long-run health impact, costs, and cost-effectiveness of interventions for type 2 diabetes in the United States than existing models. Disclosure T. J. Hoerger: None. M. Kaufmann: None. H. Chen: None. A. M. Anderson: None. L. R. Staimez: None. K. Narayan: None. P. Zhang: None. R. Hilscher: None. S. Neuwahl: None. Y. J. Cheng: None. S. R. Benoit: None. H. Shao: Research Support; Self; Sanofi. M. Laxy: None. W. Yang: None. I. Cintina: None. Funding Centers for Disease Control and Prevention (200-2016-92270)