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Quality of Life Research

, Volume 22, Issue 1, pp 173–183 | Cite as

Measuring preference-based quality of life in children aged 6–7 years: a comparison of the performance of the CHU-9D and EQ-5D-Y—the WAVES Pilot Study

  • Alastair G. CanawayEmail author
  • Emma J. Frew
Article

Abstract

Purpose

To examine the performance of the child health utility 9D (CHU-9D) and EuroQol 5D-youth (EQ-5D-Y) in children aged 6–7 years.

Method

The CHU-9D and EQ-5D-Y were interviewer-administered to 160 children aged 6–7 years at six schools across the West Midlands. Missing values, time taken to complete instruments and interviewer ratings were recorded to assess feasibility/acceptability. Construct validity was assessed by testing convergent validity hypotheses. Reliability was examined via a test–retest of a sub-sample. Psychometric properties were further examined by exploring distributions of utility scores, qualitative notes and design of the questionnaires.

Results

No missing responses were recorded with over 80% of children’s understanding being rated as good/excellent for both questionnaires. The average completion time for both instruments was less than 3 minutes, demonstrating excellent feasibility/acceptability. Evidence of construct validity was recorded with 12 of the 13 convergent hypotheses being supported. Test–retest reliability was relatively poor for both instruments with weighted kappa coefficients ranging from fair to moderate.

Conclusion

Children aged 6–7 years can feasibly complete utility instruments when interviewer-administered. The reliability of the instruments is of concern and requires further study. With respect to content validity and other psychometric properties, the CHU-9D is favoured to the EQ-5D-Y. Until the EuroQol group produces tariff values for the EQ-5D-Y, we recommend that the EQ-5D-Y is not used for utility elicitation in this age group.

Keywords

HRQoL QALY Child health EQ-5D-Y CHU-9D 

Abbreviations

CHU-9D

Child health utility 9D

EQ-5D-Y

EuroQol 5D-youth

EQ-5D

EuroQol 5D

GPBM

Generic preference-based measure

NICE

National Institute for Health and Clinical Excellence

HRQL

Health-related quality of life

PedsQL

Paediatric quality of life inventory

WAVES

West Midlands active lifestyle and healthy eating in schools

TTO

Time trade off

SG

Standard gamble

SF-6D

Short form 6D

Notes

Acknowledgments

This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme (project number 06/85/11). The views and opinions expressed therein are those of the authors and do not necessarily reflect those of the HTA programme, NIHR, NHS or the Department of Health.

References

  1. 1.
    NICE (2004), Guide to the methods of technology appraisal. Available online: http://www.nice.org.uk/niceMedia/pdf/TAP_Methods.pdf.
  2. 2.
    NICE (2008), Guide to the methods of technology appraisal. Available Online: http://www.nice.org.uk/media/B52/A7/TAMethodsGuideUpdatedJune2008.pdf.
  3. 3.
    Räsänen, P., Roine, E., Sinotenen, H., Semberg-Konttinen, V., Ryynänen, P., & Roine, R. (2006). Use of quality-adjusted life years for the estimation of effectiveness of health care: A systematic literature review. International Journal of Technology Assessment in Health Care, 22, 235–241.PubMedCrossRefGoogle Scholar
  4. 4.
    Varni, J., Burwinkle, T., & Seid, M. (2006). The PedsQL™ 4.0 as a school population health measure: Feasibility, reliability, and validity. Quality of Life Research, 15, 203–215.PubMedCrossRefGoogle Scholar
  5. 5.
    Ravens-Sieberer, U., Gosch, A., Kilroe, J., et al. (2005). KIDSCREEN-52 quality-of-life measure for children and adolescents. Pharmacoeconomics, 5(3), 353–364.Google Scholar
  6. 6.
    Landgraf, J., Maunsell, K., Ware, J., et al. (1998). Canadian-French, German and UK versions of the child health questionnaire: Methodology and preliminary item scaling results. Quality of Life Research, 7, 433–445.PubMedCrossRefGoogle Scholar
  7. 7.
    Riley, A. (2004). Evidence that school-age children can self-report on their health. Ambulatory Pediatrics, 4(4), 371–376.PubMedCrossRefGoogle Scholar
  8. 8.
    Petrou, S. (2003). Methodological issues raised by preference-based approaches to measuring the health status of children. Health Economics, 12, 697–702.PubMedCrossRefGoogle Scholar
  9. 9.
    McAuley, K., Taylor, R., Farmer, V., Hansen, P., Williams, S., Booker, C., et al. (2010). Economic evaluation of a community-based obesity prevention program in children: The APPLE project. Obesity, 1, 131–136.CrossRefGoogle Scholar
  10. 10.
    Pal, D. (1996). Quality of life assessment in children: A review of conceptual and methodological issues in multidimensional health status measures. Journal of Epidemiology and Community Health, 50, 391–396.PubMedCrossRefGoogle Scholar
  11. 11.
    Binger, C., Ablin, A., Feverstein, R., Kushner, J., & Zoger, S. M. C. (1969). Childhood leukaemia: Emotional impact on patient and family. New England Journal of Medicine, 280, 414–418.PubMedCrossRefGoogle Scholar
  12. 12.
    Ball, A., Russell, E., Seymour, D., et al. (2001). Problems in using health survey questionnaires in older patients with physical disabilities. Gerontology, 47, 334–340.PubMedCrossRefGoogle Scholar
  13. 13.
    Achenbach, T., McConaughy, S., & Howell, C. (1987). Child/adolescent behavioural and emotional problems: Implications of cross-informant correlations for situational specificity. Psychological Bulletin, 101, 213–232.PubMedCrossRefGoogle Scholar
  14. 14.
    Eieser, C., & Morse, R. (2001). Quality-of-life measures in chronic diseases of childhood. Health Technology Assessment, 5(4), 1–156.Google Scholar
  15. 15.
    McGrath, P. (1990). Pain in children: Nature, assessment, and treatment. New York: Guilford.Google Scholar
  16. 16.
    Varni, J., Thompson, K., & Hanson, V. (1987). The Varni/Thompson pediatric pain questionnaire: I. Chronic musculoskeletal pain in juvenile rheumatoid arthritis. Pain, 28, 27–38.PubMedCrossRefGoogle Scholar
  17. 17.
    Varni, J., & Bernstein, B. (1991). Evaluation and management of pain in children with rheumatic diseases. Rheumatic Disease Clinics of North America, 17, 985–1000.PubMedGoogle Scholar
  18. 18.
    Varni, J., Limbers, C., & Burwinkle, T. (2007). How young can children reliably and validly self-report their health-related quality of life?: An analysis of 8591 children across age subgroups with the PedsQL™4.0 Generic Core Scales. Health and Quality of Life Outcomes, 5(1), 1–13.PubMedCrossRefGoogle Scholar
  19. 19.
    Wille, N., Badia, X., et al. (2010). Development of the EQ-5D-Y: A child-friendly version of the EQ-5D. Quality of Life Research, 19, 875–886.PubMedCrossRefGoogle Scholar
  20. 20.
    Stevens, K. (2010). Assessing the performance of a new generic measure of health related quality of life for children and refining it for use in health state valuation. Applied Health Economics and Health Policy, 8(3), 157–169.Google Scholar
  21. 21.
    Ravnes-Sieberer, U., Willie, N., et al. (2011). Feasibility, reliability, and validity of the EQ-5D-Y: Results from a multinational study. Quality of Life Research, 19, 887–897.CrossRefGoogle Scholar
  22. 22.
    Ratcliffe, J., Stevens, K., Sawyer, M., et al. (2011). An assessment of the construct validity of the CHU9D in the Australian adolescent general population. [Published online ahead of print Aug 12, 2011]. Quality of life research, http://www.springerlink.com/content/m63102v5n327p017/.
  23. 23.
    Boyle, S., Jones, G., & Walters, S. (2010). Physical activity, quality of life, weight status and diet in adolescents. Quality of Life Research, 19(7), 943–954.PubMedCrossRefGoogle Scholar
  24. 24.
    Thomas, K., Koller, K., Williams, H., et al. (2011). A multicentre randomised controlled trial and economic evaluation of ion-exchange water softeners for the treatment of eczema in children: The Softened Water Eczema Trial (SWET). Health Technology Assessment, 15(8), 1–176.Google Scholar
  25. 25.
    Szende, A., Oppe, M., & Devlin, N. (2007). EQ-5D valuation sets: An inventory, comparative review and users’ guide. Rotterdam: EuroQol Foundation, Springer.Google Scholar
  26. 26.
    Stevens, K. (2010). Working with children to develop dimensions for a preference based generic paediatric health related quality of life measures. Qualitative Health Research, 20(3), 340–351.PubMedCrossRefGoogle Scholar
  27. 27.
    Stevens, K. (2010). Valuation of the child health utility index 9D (CHU-9D). Health Economics and Decision Science Discussion Paper 10/07.Google Scholar
  28. 28.
    Perreault, W. (1975). Controlling order-effect bias. Public Opinion Quarterly pp. 544–551.Google Scholar
  29. 29.
    Essink-Bot, M., Krabbe, P., & Bonsel, G. A. N. (1997). An empirical comparison of four generic health status measures: The Nottingham health profile, the medical outcomes study 36-item short-form health survey, the COOP/WONCA charts, and the EuroQol Instrument. Medical Care, 35, 522–537.PubMedCrossRefGoogle Scholar
  30. 30.
    Brazier, J., & Deverill, M. (1999). A checklist for judging preference-based measures of health related quality of life: Learning from psychometric measures. Health Economics, 8, 41–51.PubMedCrossRefGoogle Scholar
  31. 31.
    Badia, X., Monserrat, S., Roset, M., & Herdman, M. (1999). Feasibility, validity and test-retest reliability of scaling methods for health states: The visual analogue scale and the time trade-off. Quality of Life Research, 8(4), 303–310.PubMedCrossRefGoogle Scholar
  32. 32.
    McHorney, C., Ware, J., Lu, J., & Sherbourne, C. (1994). The MOS 36-item short-form health survey (SF-36): III Tests of data quality, scaling assumptions, and reliability across diverse patient groups. Medical Care, 32, 40–66.PubMedCrossRefGoogle Scholar
  33. 33.
    Cohen, J. (1968). Weighted kappa: Nominal scale agreement with provision for scaled disagreement or partial credit. Psychological Bulletin, 70, 213–220.PubMedCrossRefGoogle Scholar
  34. 34.
    Landis, J., & Koch, G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33, 159–174.PubMedCrossRefGoogle Scholar
  35. 35.
    Cohen, J. (1988). Statistical power analysis for the behavioural sciences (2nd ed.). NJ: Lawrence Erlbaum Publishers.Google Scholar
  36. 36.
    Brod, M., Tesler, L., & Christensen, T. (2009). Qualitative research and content validity: Developing best worst practices based on science and experience. Quality of Life Research, 18, 1263–1278.PubMedCrossRefGoogle Scholar
  37. 37.
    Brazier, J., Roberts, J., Tsuchiya, A., & Busschbach, J. (2011). A comparison of the EQ-5D and SF6D across seven patient groups. Health Economics, 13, 873–884.CrossRefGoogle Scholar
  38. 38.
    Ratcliffe, J., Flynn, T., Terlich, F., Brazier, J., Stevens, K., & Sawyer, M. Developing adolescent specific health state values for economic evaluation: an application of profile case best worst scaling to the Child Health Utility-9D. Pharmacoeconomics, Forthcoming.Google Scholar
  39. 39.
    Al-Janabi, H., Flynn, T., & Coast, J. (in press), Development of a self-report measure of capability wellbeing for adults: The ICECAP-A. Quality of Life Research. Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.Health Economics Unit, Public Health BuildingUniversity of BirminghamBirminghamUK

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