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Multi-attribute Utility Functions for a Comprehensive Health Status Classification System: Health Utilities Index Mark 2

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Author Info

  • G Torrance
  • Y Zhang
  • D Feeny
  • W Furlong
  • R Barr

Abstract

A seven-attribute health status classification system was developed to record the wide variety of outcomes of childhood cancer and its treatment. The system consists of six core attributes (sensory function, mobility function, emotional function, cognitive function, self care function, and pain) and one disease specific attribute (fertility). The system can be used with or without the fertility attribute. A random sample of parents of children in Kindergarten to Grade 5 was interviewed to determine cardinal preferences for the health states in the system. Values were measured using visual analogue scaling. Utilities were measured using standard gamble. A multiplicative multi-attribute value function is described, based on 203 subjects. A multiplicative multi-attribute utility function is also provided, based on 194 subjects. The system and its preference functions may be useful to other researchers who wish to document health status and assign preference scores.

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Bibliographic Info

Paper provided by Centre for Health Economics and Policy Analysis (CHEPA), McMaster University, Hamilton, Canada in its series Centre for Health Economics and Policy Analysis Working Paper Series with number 1992-18.

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Date of creation: 1992
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Handle: RePEc:hpa:wpaper:199218

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Cited by:
  1. Karen Gerard & Katharine Johnston & Jackie Brown, 1999. "The role of a pre-scored multi-attribute health classification measure in validating condition-specific health state descriptions," Health Economics, John Wiley & Sons, Ltd., vol. 8(8), pages 685-699.
  2. Stevens, K, 2010. "Valuation of the Child Health Utility Index 9D (CHU9D)," MPRA Paper 29938, University Library of Munich, Germany.
  3. Batana, Yélé Maweki, 2010. "Evolution of social inequalities in health in Quebec?," MPRA Paper 20710, University Library of Munich, Germany.
  4. Katherine Stevens, 2012. "Valuation of the Child Health Utility 9D Index," PharmacoEconomics, Springer, vol. 30(8), pages 729-747, August.
  5. Lisa Prosser & James Hammitt & Ron Keren, 2007. "Measuring Health Preferences for Use in Cost-Utility and Cost-Benefit Analyses of Interventions in Children," PharmacoEconomics, Springer, vol. 25(9), pages 713-726, September.
  6. Chantelle Browne & Roberta Longo & Richard Edlin & Jennifer Roberts & Christopher McCabe, 2011. "Challenges in modelling censored health state preference data: Explorations with the EQ-5D and HUI2," Working Papers 1103, Academic Unit of Health Economics, Leeds Institute of Health Sciences, University of Leeds.
  7. Bleichrodt, Han & Quiggin, John, 1999. "Life-cycle preferences over consumption and health: when is cost-effectiveness analysis equivalent to cost-benefit analysis?," Journal of Health Economics, Elsevier, vol. 18(6), pages 681-708, December.
  8. Schuwirth, N. & Reichert, P. & Lienert, J., 2012. "Methodological aspects of multi-criteria decision analysis for policy support: A case study on pharmaceutical removal from hospital wastewater," European Journal of Operational Research, Elsevier, vol. 220(2), pages 472-483.
  9. Martin Dooley & Lori Curtis, . "Child Health and Family Socioeconomic Status in the Canadian National Longitudinal Survey of Children and Youth," Canadian International Labour Network Working Papers 38, McMaster University.
  10. Zafar Hakim & Dev S. Pathak, 1999. "Modelling the EuroQol data: a comparison of discrete choice conjoint and conditional preference modelling," Health Economics, John Wiley & Sons, Ltd., vol. 8(2), pages 103-116.
  11. Musal, R. Muzaffer & Soyer, Refik & McCabe, Christopher & Kharroubi, Samer A., 2012. "Estimating the population utility function: A parametric Bayesian approach," European Journal of Operational Research, Elsevier, vol. 218(2), pages 538-547.
  12. Peter Austin & Michael Escobar, 2003. "The use of finite mixture models to estimate the distribution of the health utilities index in the presence of a ceiling effect," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(8), pages 909-923.

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