Trying To Predict the Future for People with Diabetes: A Tough but Important Task

With growing health care costs, policymakers and health care providers need information on the cost-effectiveness of interventions. This issue contains an economic evaluation (1) of a prevention policy for diabetes—a condition that affects more than 18 million persons in the United States at a cost of approximately $132 billion annually.

To assist policymakers, researchers have developed models that simulate the progression of diabetes, expenditures on diabetes care, and effects of interventions. The outputs of these models include costs and health outcomes, such as length of life (often expressed as quality-adjusted life-years [QALYs]), a measure that considers the quality of life in each health state. Two principal types of diabetes models exist. Most researchers use a Markov model, which comprises disease states (for example, normal, impaired glucose tolerance [IGT], and diabetes) represented in a computer program. The computer simulates transitions from one disease state to another as chance events. A second novel type of model, named Archimedes (2, 3), uses object-oriented computer programming and complex differential equations to simulate pathophysiologic processes (for example, hepatic glucose output after a meal) that change over time and can lead to disease. For a technical explanation of the Archimedes model, I refer to the accompanying editorial in this issue (4). My editorial focuses on the clinical and policy aspects of the Archimedes model.

The Archimedes model calculates the changes in costs and quality-adjusted length of life (in QALYs) from implementing the lifestyle intervention of the Diabetes Prevention Program (DPP) study (5) or prescribing metformin to a cohort with IGT like the enrollees in the DPP. Eddy and colleagues (1) took a societal perspective and used time horizons of 10, 20, and 30 years. Compared …