• Journal Article

Using costs in cost-effectiveness models for chronic diseases: lessons from diabetes

Citation

Hoerger, T. (2009). Using costs in cost-effectiveness models for chronic diseases: lessons from diabetes. Medical Care, 47(7 Suppl 1), S21-S27.

Abstract

BACKGROUND: Cost-effectiveness models for chronic diseases frequently require simulating the development of disease complications over a long period. Model development often focuses on disease progression, with less attention devoted to costs. OBJECTIVE: To identify key challenges in incorporating costs in cost-effectiveness models for chronic diseases. RESEARCH DESIGN: We use our experience in developing and applying a diabetes cost-effectiveness model to illustrate the challenges in incorporating costs in cost-effectiveness models for chronic diseases. RESULTS: Costs used in cost-effectiveness analyses for chronic diseases are sometimes drawn from a variety of published sources with little concern about consistency between sources or the underlying functional form for costs. Identifying costs of complications in chronic disease modeling often receives inadequate attention compared to the time and effort devoted to modeling disease progression. Costs of averted complications typically cannot be estimated during a trial, because these complications begin to accrue years after the intervention. Complication costs may be estimated through gross-costing, using an additive cost function with individual complication costs derived from difference sources, or through cost regressions that apply a multiplicative functional form using a single data source. The choice between additive and multiplicative cost functions may affect the cost-effectiveness ratios generated by the model. Current guidelines do not provide much guidance on choosing between the costing approaches. CONCLUSIONS: Developing a set of standard cost estimates might streamline the modeling process for chronic diseases, but standardization will require careful attention to functional form and the selection of appropriate data sets