UK Valuation


The HUI2 is the only preference based multi-attribute health related quality of life instrument specifically developed for use with children. It consists of seven dimensions (sensation, mobility, emotion, cognition, self care, pain and fertility), each of which has between three and five levels. The levels range from "normal functioning for age" to "extreme disability".

Preference based quality of life weights can be calculated for all health states in the descriptive system using a multiplicative multi-attribute utility function (MAUF) developed by Torrance and colleagues. This is based on interviews with parents of school age children in Hamilton, Ontario, Canada. (Torrance GW et al. A multi-attribute utility function for a comprehensive health status classification system: Health Utilities Mark 2 Medical Care 1996;34(7):702-722)

As part of an MRC funded study on risk adjustment in paediatric intensive care (the UK PICOS project), we undertook a UK valuation of the HUI2.

Three health state valuation surveys were undertaken with 450 members of the UK general population. We estimated a multi-attribute utility function algorithm in the first survey, a statistical inference valuation algorithm in the second survey and compared the predictive performance of these algorithms in the third validation survey. We proposed alternative methods and models to the original Canadian algorithm and identified the new UK statistical inference valuation algorithm to be superior to both the Canadian and UK Multi-attribute utility valuation models in terms of predictive performance.

The results of the different UK models and associated work are published in the papers listed below.


  • Kharroubi, S. McCabe, C. Modelling HUI 2 health state preference data using a nonparametric Bayesian method. Medical Decision Making (forthcoming)
  • Stevens K, McCabe C J, Brazier J E, Roberts J. Multi-attribute utility function or statistical inference models: a comparison of health state valuation models using the HUI2 health state classification system. Journal of Health Economics. 2007; 26 (5); 992-1002.
  • Stevens K, McCabe C J, Brazier J E. Response to Shmueli. Health Economics Letters. 16: 759-761 (2007)
  • McCabe C, Stevens K, Roberts J, Brazier J E. Health State Values for the HUI2 descriptive system: results from a UK Survey. Health Economics 2005; 14 (3). [Link]

    Please note: Since this paper was published, some errors in Table 1 and Table 3 have come to our attention. Most notably the health state descriptors in Table 1 were incorrect; and the co-efficients reported in Table 3 for the Random Effects model, were the same as those for the OLS model. In order to ensure that these were in fact errors of presentation and transcription we have redone the analyses reported in the original paper. Corrected versions of Table 1 and Table 3 can be downloaded above.
    The discussion and conclusions of the original paper, including the preferred model for UK HUI2 health state values, are not affected by these errors.
  • McCabe C, Stevens K, Brazier J E. Utility values for the Health Utility Index Mark 2: An empirical assessment of alternative mapping functions. Medical Care, 2005, 43:627-635. [Medical Care Website]
  • McCabe C., Brazier JE. Gilks P. et al Estimating population cardinal health state valuation models from individual ordinal (rank) health state preference data. JHE, 2006, 25(3):418-431
  • Stevens K, McCabe C, Brazier J. Mapping between Visual Analogue Scale and Standard Gamble; Results from the UK Health Utilities Index 2 valuation survey. Health Economics, 2006, 15(5):527-533 [Link]
  • HEDS Discussion Paper 04/6 McCabe C, Stevens K. Visual Analogue Scales: do they have a role in the measurement of preferences for Health States? 2004.


Our preferred algorithm is the UK statistical inference valuation algorithm; however, should you wish to use a MAUF model, our preferred algorithm is the cubic MAUF. To obtain either algorithm, please use this link

Further Information

If you would like further information about this work, please contact Katherine Stevens.