Applied Health Economics and Health Policy

, Volume 9, Issue 1, pp 15–27

Valuing child health utility 9D health states with a young adolescent sample

A feasibility study to compare best-worst scaling discrete-choice experiment, standard gamble and time trade-off methods
  • Julie Ratcliffe
  • Leah Couzner
  • Terry Flynn
  • Michael Sawyer
  • Katherine Stevens
  • John Brazier
  • Leonie Burgess
Practical Application

DOI: 10.2165/11536960-000000000-00000

Cite this article as:
Ratcliffe, J., Couzner, L., Flynn, T. et al. Appl Health Econ Health Policy (2011) 9: 15. doi:10.2165/11536960-000000000-00000

Abstract

QALYs are increasingly being utilized as a health outcome measure to calculate the benefits of new treatments and interventions within cost-utility analyses for economic evaluation. Cost-utility analyses of adolescent-specific treatment programmes are scant in comparison with those reported upon for adults and tend to incorporate the views of clinicians or adults as the main source of preferences. However, it is not clear that the views of adults are in accordance with those of adolescents on this issue. Hence, the treatments and interventions most highly valued by adults may not correspond with those most highly valued by adolescents. Ordinal methods for health state valuation may be more easily understood and interpreted by young adolescent samples than conventional approaches. The availability of young adolescent-specific health state values for the estimation of QALYs will provide new insights into the types of treatment programmes and health services that are most highly valued by young adolescents.

The first objective of this study was to assess the feasibility of applying best-worst scaling (BWS) discrete-choice experiment (DCE) methods in a young adolescent sample to value health states defined by the Child Health Utility 9D (CHU9D) instrument, a new generic preference-based measure of health-related quality of life developed specifically for application in young people. The second objective was to compare BWS DCE questions (where respondents are asked to indicate the best and worst attribute for each of a number of health states, presented one at a time) with conventional time trade-off (TTO) and standard gamble (SG) questions in terms of ease of understanding and completeness.

A feasibility study sample of consenting young adolescent school children (n = 16) aged 11–13 years participated in a face-to-face interview in which they were asked to indicate the best and worst attribute levels from a series of health states defined by the CHU9D, presented one at a time. Participants were also randomly allocated to receive additional conventional TTO or SG questions and prompted to indicate how difficult they found them to complete.

The results indicate that participants were able to readily choose ‘best’ and ‘worst’ dimension levels in each of the CHU9D health states presented to them and provide justification for their choices. Furthermore, when presented with TTO or SG questions and prompted to make comparisons, participants found the BWS DCE task easier to understand and complete.

The results of this feasibility study suggest that BWS DCE methods are potentially more readily understood and interpretable by vulnerable populations (e.g. young adolescents). These findings lend support to the potential application of BWS DCE methods to undertake large-scale health state valuation studies directly with young adolescent population samples.

Supplementary material

40258_2012_90100151_MOESM1_ESM.pdf (203 kb)
Supplementary material, approximately 208 KB.

Copyright information

© Adis Data Information BV 2011

Authors and Affiliations

  • Julie Ratcliffe
    • 1
  • Leah Couzner
    • 1
  • Terry Flynn
    • 2
  • Michael Sawyer
    • 3
  • Katherine Stevens
    • 4
  • John Brazier
    • 4
  • Leonie Burgess
    • 5
  1. 1.Centre for Clinical ChangeFlinders UniversityAdelaideAustralia
  2. 2.Centre for the Study of ChoiceUniversity of Technology, SydneySydneyAustralia
  3. 3.Discipline of PaediatricsUniversity of AdelaideAdelaideAustralia
  4. 4.Health Economics and Decision Science, ScHARRUniversity of SheffieldSheffieldUK
  5. 5.Department of Mathematical SciencesUniversity of Technology, SydneySydneyAustralia
  6. 6.Flinders Clinical EffectivenessFlinders UniversityBedford ParkAustralia