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Valuing the Child Health Utility 9D: Using profile case best worst scaling methods to develop a new adolescent specific scoring algorithm

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  • Ratcliffe, Julie
  • Huynh, Elisabeth
  • Chen, Gang
  • Stevens, Katherine
  • Swait, Joffre
  • Brazier, John
  • Sawyer, Michael
  • Roberts, Rachel
  • Flynn, Terry

Abstract

In contrast to the recent proliferation of studies incorporating ordinal methods to generate health state values from adults, to date relatively few studies have utilised ordinal methods to generate health state values from adolescents. This paper reports upon a study to apply profile case best worst scaling methods to derive a new adolescent specific scoring algorithm for the Child Health Utility 9D (CHU9D), a generic preference based instrument that has been specifically designed for the estimation of quality adjusted life years for the economic evaluation of health care treatment and preventive programs targeted at young people. A survey was developed for administration in an on-line format in which consenting community based Australian adolescents aged 11–17 years (N = 1982) indicated the best and worst features of a series of 10 health states derived from the CHU9D descriptive system. The data were analyzed using latent class conditional logit models 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. It was evident that different decision processes were being used in the best and worst choices. Whilst respondents appeared readily able to choose ‘best’ attribute levels for the CHU9D health states, a large amount of random variability and indeed different decision rules were evident for the choice of ‘worst’ attribute levels, to the extent that the best and worst data should not be pooled from the statistical perspective. The optimal adolescent-specific scoring algorithm was therefore derived using data obtained from the best choices only. The study provides important insights into the use of profile case best worst scaling methods to generate health state values with adolescent populations.

Suggested Citation

  • Ratcliffe, Julie & Huynh, Elisabeth & Chen, Gang & Stevens, Katherine & Swait, Joffre & Brazier, John & Sawyer, Michael & Roberts, Rachel & Flynn, Terry, 2016. "Valuing the Child Health Utility 9D: Using profile case best worst scaling methods to develop a new adolescent specific scoring algorithm," Social Science & Medicine, Elsevier, vol. 157(C), pages 48-59.
  • Handle: RePEc:eee:socmed:v:157:y:2016:i:c:p:48-59
    DOI: 10.1016/j.socscimed.2016.03.042
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    References listed on IDEAS

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    2. Richard De Abreu Lourenço & Nancy Devlin & Kirsten Howard & Jason J. Ong & Julie Ratcliffe & Jo Watson & Esther Willing & Elisabeth Huynh, 2021. "Giving a Voice to Marginalised Groups for Health Care Decision Making," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 14(1), pages 5-10, January.
    3. Chen, Gang & Ratcliffe, Julie & Milte, Rachel & Khadka, Jyoti & Kaambwa, Billingsley, 2021. "Quality of care experience in aged care: An Australia-Wide discrete choice experiment to elicit preference weights," Social Science & Medicine, Elsevier, vol. 289(C).
    4. Aizaki, Hideo & Fogarty, James, 2019. "An R package and tutorial for case 2 best–worst scaling," Journal of choice modelling, Elsevier, vol. 32(C), pages 1-1.
    5. Tosin Lambe & Emma Frew & Natalie J. Ives & Rebecca L. Woolley & Carole Cummins & Elizabeth A. Brettell & Emma N. Barsoum & Nicholas J. A. Webb, 2018. "Mapping the Paediatric Quality of Life Inventory (PedsQL™) Generic Core Scales onto the Child Health Utility Index–9 Dimension (CHU-9D) Score for Economic Evaluation in Children," PharmacoEconomics, Springer, vol. 36(4), pages 451-465, April.
    6. Mo Zhou & Winter Maxwell Thayer & John F. P. Bridges, 2018. "Using Latent Class Analysis to Model Preference Heterogeneity in Health: A Systematic Review," PharmacoEconomics, Springer, vol. 36(2), pages 175-187, February.
    7. Coast, Joanna, 2018. "A history that goes hand in hand: Reflections on the development of health economics and the role played by Social Science & Medicine, 1967–2017," Social Science & Medicine, Elsevier, vol. 196(C), pages 227-232.
    8. Valentina Prevolnik Rupel & Marko Ogorevc, 2021. "EQ-5D-Y Value Set for Slovenia," PharmacoEconomics, Springer, vol. 39(4), pages 463-471, April.
    9. Joanna Coast, 2019. "Assessing capability in economic evaluation: a life course approach?," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 20(6), pages 779-784, August.
    10. Karin Dam Petersen & Gang Chen & Christine Mpundu-Kaambwa & Katherine Stevens & John Brazier & Julie Ratcliffe, 2018. "Measuring Health-Related Quality of Life in Adolescent Populations: An Empirical Comparison of the CHU9D and the PedsQLTM 4.0 Short Form 15," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 11(1), pages 29-37, February.
    11. Osman, Ahmed M.Y. & Wu, Jing & He, Xiaoning & Chen, Gang, 2021. "Eliciting SF-6Dv2 health state utilities using an anchored best-worst scaling technique," Social Science & Medicine, Elsevier, vol. 279(C).
    12. Rajan Sharma & Yuanyuan Gu & Teresa Y. C. Ching & Vivienne Marnane & Bonny Parkinson, 2019. "Economic Evaluations of Childhood Hearing Loss Screening Programmes: A Systematic Review and Critique," Applied Health Economics and Health Policy, Springer, vol. 17(3), pages 331-357, June.

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