Evaluating hospital costs in type 2 diabetes care: does the choice of the model matter?

BACKGROUND: Awareness of the economic burden of diabetes has led to a number of studies on economic issues. However, comparison among cost-of-illness studies is problematic because different methods are used to arrive at a final cost estimate. OBJECTIVE: The aim of the study is to show how estimates of hospitalisation costs for diabetic patients can vary significantly in relation to the statistical method adopted in the analysis. RESEARCH DESIGN AND METHODS: The study analyses diabetic patients' costs as a function of demographic and clinical covariates, by applying the following statistical survival models: the parametric survival model assuming Weibull distribution, the Cox proportional hazard (PH) model and the Aalen additive regression for modelling costs. The Aalen approach is robust both for the non proportionality in hazard and for departures from normality. In addition it is able to easily model the effect of covariates on the extreme costs. This cost analysis is based on data collected for a retrospective observational study analysing repeated hospitalisations (N = 4816) in a cohort of 3892 diabetic patients. RESULTS: There is agreement in all models with the effects of the considered covariates (age, sex, duration of disease and presence of other pathologies). An effect of over- or under-estimation, according to the chosen model due to arguably inappropriate model fitting, was observed, being more evident for some specific profiles of the patients, and overall accounting for as much as 20% of the estimated effect. The Aalen model was able to cope with all the other models in furnishing unbiased estimates with the advantage of a greater flexibility in representing the covariates' effect on the cost process. CONCLUSIONS: An appropriate choice of the model is crucial in avoiding misinterpretation of cost determinants of type 2 diabetes care. For our data set the Aalen model proved itself to be a realistic and informative way to characterise the effect of covariates on costs