Diabetes mellitus is a chronic and complex disease, increasing in prevalence and consequent health expenditure. Cost-effectiveness models with long time horizons are commonly used to perform economic evaluations of diabetes’ treatments. As such, prediction accuracy and structural uncertainty are important features in cost-effectiveness models of chronic conditions.
The aim of this systematic review is to identify and review published cost-effectiveness models of diabetes treatments developed between 2011 and 2022 regarding their methodological characteristics. Further, it also appraises the quality of the methods used, and discusses opportunities for further methodological research.
A systematic literature review was conducted in MEDLINE and Embase to identify peer-reviewed papers reporting cost-effectiveness models of diabetes treatments, with time horizons of more than 5 years, published in English between 1 January 2011 and 31 of December 2022. Screening, full-text inclusion, data extraction, quality assessment and data synthesis using narrative synthesis were performed. The Philips checklist was used for quality assessment of the included studies. The study was registered in PROSPERO (CRD42021248999).
The literature search identified 30 studies presenting 29 unique cost-effectiveness models of type 1 and/or type 2 diabetes treatments. The review identified 26 type 2 diabetes mellitus (T2DM) models, 3 type 1 DM (T1DM) models and one model for both types of diabetes. Fifteen models were patient-level models, whereas 14 were at cohort level. Parameter uncertainty was assessed thoroughly in most of the models, whereas structural uncertainty was seldom addressed. All the models where validation was conducted performed well. The methodological quality of the models with respect to structure was high, whereas with respect to data modelling it was moderate.
Models developed in the past 12 years for health economic evaluations of diabetes treatments are of high-quality and make use of advanced methods. However, further developments are needed to improve the statistical modelling component of cost-effectiveness models and to provide better assessment of structural uncertainty.