The Health Utilities Index (HUI®): concepts, measurement properties and applications

Health Utilities Inc, Dundas, ON, Canada.
Health and Quality of Life Outcomes (Impact Factor: 2.1). 02/2003; 1:54. DOI: 10.1186/1477-7525-1-54
Source: PubMed

ABSTRACT This is a review of the Health Utilities Index (HUI) multi-attribute health-status classification systems, and single- and multi-attribute utility scoring systems. HUI refers to both HUI Mark 2 (HUI2) and HUI Mark 3 (HUI3) instruments. The classification systems provide compact but comprehensive frameworks within which to describe health status. The multi-attribute utility functions provide all the information required to calculate single-summary scores of health-related quality of life (HRQL) for each health state defined by the classification systems. The use of HUI in clinical studies for a wide variety of conditions in a large number of countries is illustrated. HUI provides comprehensive, reliable, responsive and valid measures of health status and HRQL for subjects in clinical studies. Utility scores of overall HRQL for patients are also used in cost-utility and cost-effectiveness analyses. Population norm data are available from numerous large general population surveys. The widespread use of HUI facilitates the interpretation of results and permits comparisons of disease and treatment outcomes, and comparisons of long-term sequelae at the local, national and international levels.

  • [Show abstract] [Hide abstract]
    ABSTRACT: Standard gamble (SG) and time trade-off (TTO) are two methods used for obtaining health utility values (utilities). Whether the order in which the methods are applied alters the relative utilities obtained by each method is unknown.Objective We sought to determine whether the order in which SG and TTO utilities were obtained affects the relative values of the utilities obtained by each technique.Methods Utilities were assessed for 29 health states from 4016 parents by using SG and TTO. The assessment order was randomized by respondent. For analysis by health state, we calculated (SG –TTO) for each assessment and tested whether the SG – TTO difference was significantly different between the two groups (SG first and TTO first). For analysis by individual, we calculated a risk-posture coefficient, γ, defined by the utility curve, SG = TTOγ. We predicted γ through regression analysis with the covariates: child age, child sex, birth order, respondent age, respondent education level, and assessment method order.ResultsIn 19 of 29 health states, the SG − TTO difference was significantly greater (more risk averse) when TTO was assessed first. In the regression analysis, “child age” and “assessment method order” were significant predictors of risk attitude. The risk posture coefficient γ was higher (more risk-seeking) with increasing child age and in the SG-first respondents.Conclusion The order in which the SG versus TTO method is used strongly influences the relative values of the utilities obtained.
    Value in Health 09/2012; 15(6):926-932. · 2.89 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Patients with venous thromboembolism (VTE) commonly have an underlying genetic predisposition. However, genetic tests nowadays in use have very low sensitivity for identifying subjects at risk of VTE. Thrombo inCode(®) is a new genetic tool that has demonstrated very good sensitivity, thanks to very good coverage of the genetic variants that modify the function of the coagulation pathway. To conduct an economic analysis of risk assessment of VTE from the perspective of the Spanish National Health System with Thrombo inCode(®) (a clinical-genetic function for assessing the risk of VTE) versus the conventional/standard method used to date (factor V Leiden and prothrombin G20210A). An economic model was created from the National Health System perspective, using a decision tree in patients aged 45 years with a life expectancy of 81 years. The predictive capacity of VTE, based on identification of thrombophilia using Thrombo inCode(®) and using the standard method, was obtained from two case-control studies conducted in two different populations (S. PAU and MARTHA; 1,451 patients in all). Although this is not always the case, patients who were identified as suffering from thrombophilia were subject to preventive treatment of VTE with warfarin, leading to a reduction in the number of VTE events and an increased risk of severe bleeding. The health state utilities (quality-adjusted life-years [QALYs]) and costs (in 2013 EUR values) were obtained from the literature and Spanish sources. On the basis of a price of EUR 180 for Thrombo inCode(®), this would be the dominant option (more effective and with lower costs than the standard method) in both populations. The Monte Carlo probabilistic analyses indicate that the dominance would occur in 100 % of the simulations in both populations. The threshold price of Thrombo inCode(®) needed to reach the incremental cost-effectiveness ratio (ICER) generally accepted in Spain (EUR 30,000 per QALY gained) would be between EUR 3,950 (in the MARTHA population) and EUR 11,993 (in the S. PAU population). According to the economic model, Thrombo inCode(®) is the dominant option in assessing the risk of VTE, compared with the standard method currently used.
    Applied Health Economics and Health Policy 02/2015;
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: IntroductionAccurate measurement of health inequities is indispensable to track progress or to identify needs for health equity policy interventions. A key empirical task is to measure the extent to which observed inequality in health ¿ a difference in health ¿ is inequitable. Empirically operationalizing definitions of health inequity has generated an important question not considered in the conceptual literature on health inequity. Empirical analysis can explain only a portion of observed health inequality. This paper demonstrates that the treatment of unexplained inequality is not only a methodological but ethical question and that the answer to the ethical question ¿ whether unexplained health inequality is unfair ¿ determines the appropriate standardization method for health inequity analysis and can lead to potentially divergent estimates of health inequity.Methods We use the American sample of the 2002¿03 Joint Canada/United States Survey of Health and measure health by the Health Utilities Index (HUI). We model variation in the observed HUI by demographic, socioeconomic, health behaviour, and health care variables using Ordinary Least Squares. We estimate unfair HUI by standardizing fairness, removing the fair component from the observed HUI. We consider health inequality due to factors amenable to policy intervention as unfair. We contrast estimates of inequity using two fairness-standardization methods: direct (considering unexplained inequality as ethically acceptable) and indirect (considering unexplained inequality as unfair). We use the Gini coefficient to quantify inequity.ResultsOur analysis shows that about 75% of the variation in the observed HUI is unexplained by the model. The direct standardization results in a smaller inequity estimate (about 60% of health inequality is inequitable) than the indirect standardization (almost all inequality is inequitable).Conclusions The choice of the fairness-standardization method is ethical and influences the empirical health inequity results considerably. More debate and analysis is necessary regarding which treatment of the unexplained inequality has the stronger foundation in equity considerations.
    International Journal for Equity in Health 01/2015; 14(1):11. · 1.71 Impact Factor

Full-text (2 Sources)

Available from
May 31, 2014