A review of methods used in long-term cost-effectiveness models of diabetes mellitus treatment

Abstract

Diabetes mellitus is a major healthcare concern from both a treatment and a funding perspective. Although decision makers frequently rely on models to evaluate the long-term costs and consequences associated with diabetes interventions, no recent article has reviewed the methods used in long-term cost-effectiveness models of diabetes treatment. The following databases were searched up to April 2008 to identify published economic models evaluating treatments for diabetes mellitus: OVID MEDLINE, EMBASE and the Thomson's Biosis Previews, NHS EED via Wiley's Cochrane Library, and Wiley's HEED database. Identified articles were reviewed and grouped according to unique models. When a model was applied in different settings (e.g. country) or compared different treatment alternatives, only the original publication describing the model was included. In some cases, subsequent articles were included if they provided methodological advances from the original model. The following data were captured for each study: (i) study characteristics; (ii) model structure; (iii) long-term complications, data sources, methods reporting and model validity; (iv) utilities, data sources and methods reporting; (v) costs, data sources and methods reporting; (vi) model data requirements; and (vii) economic results including methods to deal with uncertainty. A total of 17 studies were identified, 12 of which allowed for the conduct of a cost-effectiveness analysis and a cost-utility analysis. Although most models were Markov-based microsimulations, models differed with respect to the number of diabetes-related complications included. The majority of the studies used a lifetime time horizon and a payer perspective. The DCCT for type 1 diabetes and the UKPDS for type 2 diabetes were the trial data sources most commonly cited for the efficacy data, although several non-randomized data sources were used. While the methods used to derive the efficacy data were commonly reported, less information was given regarding the derivation of the utilities or the costs. New interventions were generally deemed cost effective based on ten studies presenting results. Authors relied mostly on univariate sensitivity analyses to test the robustness of their models. Although diabetes is a complex disease, several models have been developed to predict the long-term costs and consequences associated with diabetes treatment. Combined with previous findings, this review supports the development of a reference case that could be used for any new diabetes models. Models could be enhanced if they had the capacity to deal with both first- and second-order uncertainty. Future research should continue to compare economic models for diabetes treatment in terms of clinical outcomes, costs and QALYs when applicable.