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Multiplicative Multi-Attribute Utility Function for the Health Utilities Index Mark 3 (HUI3) System: A Technical Report


  • W Furlong
  • D Feeny
  • G Torrance
  • C Goldsmith
  • S DePauw
  • Z Zhu
  • M Denton
  • M Boyle


This paper is of interest to analysts, policy makers and decision makers involved with descriptive clinical studies, clinical trials, programme evaluations, measuring population health, and planning assessments. It describes a recently developed system for measuring the overall health status and health-related quality of life of individuals, clinical groups and general populations. The measurement system is the Health Utilities Index Mark 3 (HUI3) and it consists of two components: the health status classification system; and the preference-based scoring system. The health status classification system was first published in 1995 and this paper focuses on the scoring system. The HUI3 is the latest member of the Health Utilities Index family developed by researchers at McMaster University during the past 20 years. HUI3 is a brief but comprehensive system for describing the health status of individuals and for assigning a preference score to that health status. The HUI3 is generic in the sense that it is designed to be applicable to all people. The scores are based on preference measures from a random sample of the general population. HUI3 is founded directly on multi-attribute utility theory. These scores are, therefore, referred to as utility scores and represent community preferences. Community preferences are considered an appropriate source of preferences for calculating quality-adjusted life years (QALYs) for use in cost-effectiveness or cost-utility analyses and for use in the measurement of population health. The HUI3 is also useful in clinical studies as a method of describing the health status of patients and tracking it over time. The HUI has been included in studies being undertaken by more than one hundred investigative teams based in major centres around the world. The HUI3 has also been included in every major Canadian general population health survey since 1990. The early inclusion of the HUI3 in population health surveys has placed Canada in the forefront of regional and local surveillance of population health, including health-related quality of life (HRQL) and health-adjusted life expectancy (HALE) measures. This technical report provides the first public release of the multiplicative multi-attribute utility function (MAUF) for the Health Utilities Index Mark 3 (HUI3). The report provides details of the study design, preference survey results, and modelling techniques. It also includes an appendix which presents the HUI3 utility scoring systems concisely for use by data managers and analysts. The purpose of the HUI3 MAUF is to provide a formula for calculating scores for all of the 972,000 health states defined by the HUI3 health status classification system. This report presents a HUI3 utility function on the conventional Dead = 0.00 to Perfect Health = 1.00 scale. This is the most appropriate scale for calculating aggregated indices of morbidity and mortality such as quality-adjusted life years (QALYs). Evidence to date indicates that the HUI3 measurement system is acceptable, reliable, valid, responsive and useful.

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  • W Furlong & D Feeny & G Torrance & C Goldsmith & S DePauw & Z Zhu & M Denton & M Boyle, 1998. "Multiplicative Multi-Attribute Utility Function for the Health Utilities Index Mark 3 (HUI3) System: A Technical Report," Centre for Health Economics and Policy Analysis Working Paper Series 1998-11, Centre for Health Economics and Policy Analysis (CHEPA), McMaster University, Hamilton, Canada.
  • Handle: RePEc:hpa:wpaper:199811

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    Cited by:

    1. Carmen Herrero Blanco & Juan D. Moreno Ternero, 2002. "Economic Evaluation Of Newborn Hearing Screening Procedures," Working Papers. Serie AD 2002-06, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    2. Piérard, Emmanuelle, 2014. "The effect of physician supply on health status: Canadian evidence," Health Policy, Elsevier, vol. 118(1), pages 56-65.
    3. Stengos, Thanasis & Thompson, Brennan S., 2012. "Testing for bivariate stochastic dominance using inequality restrictions," Economics Letters, Elsevier, vol. 115(1), pages 60-62.
    4. Bleichrodt, Han & Herrero, Carmen & Pinto, Jose Luis, 2002. "A proposal to solve the comparability problem in cost-utility analysis," Journal of Health Economics, Elsevier, vol. 21(3), pages 397-403, May.
    5. Doorslaer, Eddy van & Jones, Andrew M., 2003. "Inequalities in self-reported health: validation of a new approach to measurement," Journal of Health Economics, Elsevier, vol. 22(1), pages 61-87, January.
    6. Dionne, Georges & Lebeau, Martin, 2010. "Le calcul de la valeur statistique d’une vie humaine," L'Actualité Economique, Société Canadienne de Science Economique, vol. 86(4), pages 487-530, décembre.
    7. Jorgen Lauridsen & Terkel Christiansen & Unto Häkkinen, 2004. "Measuring inequality in self-reported health-discussion of a recently suggested approach using Finnish data," Health Economics, John Wiley & Sons, Ltd., vol. 13(7), pages 725-732.
    8. Dolores Jiménez-Rubio & Peter C. Smith & Eddy Van Doorslaer, 2008. "Equity in health and health care in a decentralised context: evidence from Canada," Health Economics, John Wiley & Sons, Ltd., vol. 17(3), pages 377-392.
    9. D. Stratmann-Schoene & T. Kuehn & R. Kreienberg & R. Leidl, 2006. "A preference-based index for the SF-12," Health Economics, John Wiley & Sons, Ltd., vol. 15(6), pages 553-564.
    10. Charles M. Harvey & Lars Peter Østerdal, 2010. "Cardinal Scales for Health Evaluation," Decision Analysis, INFORMS, vol. 7(3), pages 256-281, September.

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