As health care resources are finite it is important to evaluate and compare different interventions for health problems in terms of costs and consequences.

An important method of doing this involves conducting cost-utility analyses. Such studies relate the cost of treatment to the benefits that accrue. This is often expressed in terms of quality adjusted life years (QALYS). Consequently QALYS should be a composite metric of both longevity and quality of life.   

In order to calculate QALYS it is necessary to generate data using preference-based utility measures. These assign values to health states that range from ‘1’ for full health to ‘0’ for death. Some health states are valued below 0 as they are considered worse than death. The data are generated by means of patient-reported outcome measures. Generic preference-based utility measures are the mostly widely used. These include the EQ-5D [1], SF-6F [2] and the Health Utility Index (HUI-3) [3]. Typically these have several domains that describe different dimensions of health such as pain, mobility and usual activities. Scores on the different dimensions are then used to create a number of different possible health states.

Although generic preference-based utility measures are the most commonly used they lack sensitivity and relevance [4,5] due to their generic nature. Consequently, they are unlikely to show the real benefits of effective interventions. There is evidence that disease-specific utility measures are more responsive than generic ones [6,7].

GR have experience of generating disease-specific utility measures for a number of different conditions. One way in which this can be done is by converting responses on existing high quality, disease-specific quality of life measures. An advantage of converting a disease-specific measure is that the resulting utility values will be specific to the condition in question.  If a disease specific measure has been developed carefully all the items will be relevant to the respondents' condition and no important issue will have been omitted. Use of such disease-specific utility values maximizes the likelihood of showing the benefits of clinical interventions.


  1. Brooks R, Rabin R, De Charro F: The Measurement and Valuation of Health Status Using EQ-5D: A European Perspective. London: Kluwer Academic publishers; 2004.

  2. Brazier J, Roberts J, Deverill M: The estimation of a preference based single index measure for health from the SF-36. J Health Econ 2002, 21:271-92.

  3. Feeny DH, Furlong WJ, Torrance GW, Goldsmith CH, Zhu Z, DePauw S, Denton M, Boyle Ml: Multiattribute and single attribute utility function – the health utility index mark 3 system. Med Care 2002, 40:113-28.

  4. Brazier JE, Severill M, Harper R, Booth A: A review of the use of health status measures in economic evaluation. Health Technology and Assessment 1999, 3(9):.

  5. Guyatt GH, King DR, Feeny DH, Stubbing D, Goldstein RS: Generic and specific measurement of health-related quality of life in a clinical trial of respiratory rehabilitation. J Clin Epidemiol 1999, 52(3):187-92.

  6. McTaggart-Cowan HM, Marra CA, Yang Y, Brazier JE, Kopec JA, Fitzgerald JM, Anis AH, Lynd LD: The validity of generic and condition-specific preference-based instruments: the ability to discriminate asthma control status. Qual Life Res 2008, 17(3):453-62.

  7. Krahn M, Bremner KE, Tomlinson G, Ritvo P, Irvine J, Naglie G: Responsiveness of disease-specific and generic utility instruments in prostate cancer patients. Qual Life Res 2007, 16(3):509-22.