Developing adolescent specific health state values for economic evaluation: an application of profile case best worst scaling to the Child Health Utility-9D

Julie Ratcliffe, Terry Flynn, Frances Terlich, Katherine Stevens, John Brazier, Michael Sawyer

    Research output: Contribution to journalArticlepeer-review

    57 Citations (Scopus)

    Abstract

    Background: The way that health is measured and valued is fundamental to economic evaluation. To date, adult health state values have been routinely used in the calculation of QALYs for the economic evaluation of healthcare treatment and preventive programmes, including those targeted at adolescents. Objectives: The main objective of this study was to apply profile case bestworst scaling (BWS) discrete-choice experiment (DCE) methods to obtain adolescent-specific values for the Child Health Utility 9D (CHU9D), a new generic preference-based measure of health-related quality of life developed specifically for application in cost-effectiveness analyses of treatments and interventions targeted at young people. A secondary aim was to assess the feasibility of a web-based method of data collection for the valuation of health states defined by the CHU9D. Methods: A web-based survey was developed including the CHU9D instrument and a series of BWS DCE questions. Specifically, respondents were asked to indicate the best and worst attribute levels from a series of ten health states defined by the CHU9D, presented one at a time. The survey was administered to a community-based sample of consenting adolescents (n = 590) aged 11-17 years. A conditional logistic regression model was applied to estimate values (part-worth utilities) for each level of the nine attributes relating to the CHU9D. A marginal utility matrix was then estimated to generate an adolescent-specific scoring algorithm on the full health = 1 and dead = 0 scale required for the calculation of QALYs. Results: The results indicate that participants were able to readily choose 'best' and 'worst' attribute levels for the CHU9D health states. Large differences in value were found between the first and fifth levels (indicating 'no problems' and 'severe problems', respectively) for all nine attributes relating to the CHU9D. In general, there was little differentiation between the middle levels of all attributes indicating only limited additional value for adolescents of moving between these levels. Comparison of the adolescent-specific algorithm and the existing adult scoring algorithm for the CHU9D revealed some significant differences in values for identical health states, which may have important implications for the application of the CHU9D to value adolescent treatment and service programmes particularly for mental health. In general, adolescents appeared to place more weight upon the CHU9D attributes relating to mental health (worried, sad and annoyed) than would be implied by application of the existing algorithm based upon adult values. Conclusion: This study provides preliminary indications that there may be potentially important and systematic differences in the valuations attached to identical health states by adolescents in comparison with adult population groups. The study findings lend support to the potential future application of profile case BWS DCE methods to undertake large-scale health state valuation studies directly with young adolescent population samples and provide support for the feasibility and acceptability of a web-based mode of administration for this purpose. Adis

    Original languageEnglish
    Pages (from-to)713-727
    Number of pages15
    JournalPharmacoeconomics
    Volume30
    Issue number8
    DOIs
    Publication statusPublished - 2012

    Keywords

    • Adolescents
    • Conjoint-analysis
    • Patient-preference
    • Utility-measurement

    Fingerprint Dive into the research topics of 'Developing adolescent specific health state values for economic evaluation: an application of profile case best worst scaling to the Child Health Utility-9D'. Together they form a unique fingerprint.

    Cite this