Estimating health state utility values for comorbid health conditions: a synopsis of the current evidence base

Abstract

Background: Analysts frequently estimate the health state utility values (HSUVs) for combined health conditions (CHCs) using data from cohorts with single health conditions. The methods used to estimated the HSUVs can produce very different results and there is currently no consensus on the most appropriate technique that should be used. Objective: To conduct a detailed critical review of existing empirical literature to gain an understanding of the reasons for differences in results and identify where uncertainty remains that may be addressed by further research. Results: Of the eleven studies identified, ten assessed the additive method, ten the multiplicative method, seven the minimum method, and three the combination model. Two studies evaluated just one of the techniques while the others compared results generated using two or more. The range of the HSUVs can influence general findings and methods are sometimes compared using descriptive statistics that may not be appropriate for assessing predictive ability. None of the proposed methods gave consistently accurate results across the full range of possible HSUVs and the values assigned to normal health influence the accuracy of the methods. Conclusions: While there is no unequivocal evidence for supporting one particular method, the combination linear model appeared to give more accurate results in the studies reviewed. However, before a method can be recommended, research is required in datasets covering the full range of the preference-based indices and health conditions typically defined in decision analytic models. The methods used to assess performance and the statistics used when reporting results require improvement in general