, Volume 30, Issue 8, pp 729–747 | Cite as

Valuation of the Child Health Utility 9D Index

  • Katherine StevensEmail author
Original Research Article


Background and Objectives

The aim of this study was to test the feasibility of estimating preference weights for all health states defined by the Child Health Utility 9D (CHU9D), a new generic measure of health-related quality of life for children aged 7–11 years. The estimation of preference weights will allow the calculation of QALYs for use in paediatric economic evaluation.


Valuation interviews were undertaken with 300 members of the UK adult general population to obtain preference weights for a sample of the health states in the CHU9D descriptive system. Both standard gamble and ranking valuation methods were used. Regression modelling was undertaken to estimate models that could predict a value for every health state defined by the system. A range of models were tested and were evaluated based on their predictive performance.


Models estimated on the standard gamble data performed better than the rank model. All models had a few inconsistencies or insignificant levels and so further modelling was done to estimate a parsimonious consistent regression model using the general-to-specific approach, by combining inconsistent levels and removing non-significant levels. The final preferred model was an ordinary least squares (OLS) model. All the coefficients in this model were significant, there were no inconsistencies and the model had the best predictive performance and a low mean absolute error.


This research has demonstrated it is feasible to value the CHU9D descriptive system, and preference weights for each health state can be generated to allow the calculation of QALYs. The CHU9D can now be used in the economic evaluation of paediatric healthcare interventions. Further research is needed to investigate the impact of children’s preferences for the health states and what methods could be used to obtain these preferences.


Root Mean Square Error Ordinary Little Square Descriptive System Standard Gamble Rank Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research was funded as part of a UK Medical Research Council Special Training Fellowship in Health Services and Health of the Public Research. The work carried out is independent of the funders. Thanks from the author go to the CRE at Sheffield Hallam University and to all the participants who took part.

This paper is part of a theme issue co-edited by Lisa Prosser, University of Michigan, USA, and no external funding was used to support the publication of this theme issue.


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Copyright information

© Springer International Publishing AG 2012

Authors and Affiliations

  1. 1.Health Economics and Decision Science (HEDS), School of Health and Related Research (ScHARR)University of Sheffield, Regent CourtSheffieldUK

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