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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

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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.

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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

References

  1. NICE (2004), Guide to the methods of technology appraisal. Available online: http://www.nice.org.uk/niceMedia/pdf/TAP_Methods.pdf.

  2. NICE (2008), Guide to the methods of technology appraisal. Available Online: http://www.nice.org.uk/media/B52/A7/TAMethodsGuideUpdatedJune2008.pdf.

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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. 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.

    Article  PubMed  CAS  Google Scholar 

  7. Riley, A. (2004). Evidence that school-age children can self-report on their health. Ambulatory Pediatrics, 4(4), 371–376.

    Article  PubMed  Google Scholar 

  8. Petrou, S. (2003). Methodological issues raised by preference-based approaches to measuring the health status of children. Health Economics, 12, 697–702.

    Article  PubMed  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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. McGrath, P. (1990). Pain in children: Nature, assessment, and treatment. New York: Guilford.

    Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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. 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.

    Article  Google Scholar 

  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. 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.

    Article  PubMed  Google Scholar 

  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. 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. 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.

    Article  PubMed  Google Scholar 

  27. Stevens, K. (2010). Valuation of the child health utility index 9D (CHU-9D). Health Economics and Decision Science Discussion Paper 10/07.

  28. Perreault, W. (1975). Controlling order-effect bias. Public Opinion Quarterly pp. 544–551.

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  33. Cohen, J. (1968). Weighted kappa: Nominal scale agreement with provision for scaled disagreement or partial credit. Psychological Bulletin, 70, 213–220.

    Article  PubMed  CAS  Google Scholar 

  34. Landis, J., & Koch, G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33, 159–174.

    Article  PubMed  CAS  Google Scholar 

  35. Cohen, J. (1988). Statistical power analysis for the behavioural sciences (2nd ed.). NJ: Lawrence Erlbaum Publishers.

    Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  Google Scholar 

  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.

  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.

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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.

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Authors and Affiliations

Authors

Corresponding author

Correspondence to Alastair G. Canaway.

Additional information

This study is conducted on behalf of the WAVES investigators. The full details are given in ‘Appendix’.

Appendix

Appendix

WAVES trial investigators

University of Birmingham:

Peymane Adab (Chief Investigator), Tim Barrett (Professor of Paediatrics), KK Cheng (Professor of Epidemiology) Amanda Daley (NIHR Senior Research Fellow), Jon Deeks (Professor of Health Statistics), Joan Duda (Professor of Sport and Exercise Psychology), Emma Frew (Senior lecturer in Health Economics), Paramjit Gill (Clinical Reader in Primary Care Research), Miranda Pallan (Clinical Research Fellow), Jayne Parry (Professor of Policy and Health).

Cambridge MRC Epidemiology Unit:

Ulf Ekelund (Programme Leader for Physical Activity Epidemiology Programme).

University of Leeds:

Janet Cade (Professor of Nutritional Epidemiology and Public Health).

The University of Edinburgh:

Raj Bhopal (Bruce and John Usher Chair in Public Health).

Trial collaborators

Birmingham East and North PCT:

Eleanor McGee (Public Health Nutrition Lead).

Birmingham local education authority:

Sandra Passmore (Personal, Social and Health Education Advisor).

WAVES trial management group

Emma Lancashire (trial co-ordinator), Miranda Pallan, Peymane Adab (chair).

WAVES research team:

Behnoush Aranjani, Jo Clark, Tania Griffin, Kiya Kelleher, Emma Lancashire (trial co-ordinator), Alastair Canaway, Karla Hemming.

Trial steering committee:

Peymane Adab, John Bennett, Kelvin Jordan (chair), Karla Hemming, Louise Longworth, Peter Whincup.

REC number:

10/H1202/69.

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Canaway, A.G., Frew, E.J. 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. Qual Life Res 22, 173–183 (2013). https://doi.org/10.1007/s11136-012-0119-5

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  • DOI: https://doi.org/10.1007/s11136-012-0119-5

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