Literature DB >> 27060541

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

Julie Ratcliffe1, Elisabeth Huynh2, Gang Chen3, Katherine Stevens4, Joffre Swait2, John Brazier4, Michael Sawyer5, Rachel Roberts6, Terry Flynn7.   

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.
Copyright © 2016. Published by Elsevier Ltd.

Entities:  

Keywords:  Adolescents; Australia; Best worst scaling; Economic evaluation; Health; Quality adjusted life years; Valuation

Mesh:

Year:  2016        PMID: 27060541     DOI: 10.1016/j.socscimed.2016.03.042

Source DB:  PubMed          Journal:  Soc Sci Med        ISSN: 0277-9536            Impact factor:   4.634


  44 in total

1.  Measuring Health-Related Quality of Life in Adolescent Populations: An Empirical Comparison of the CHU9D and the PedsQLTM 4.0 Short Form 15.

Authors:  Karin Dam Petersen; Gang Chen; Christine Mpundu-Kaambwa; Katherine Stevens; John Brazier; Julie Ratcliffe
Journal:  Patient       Date:  2018-02       Impact factor: 3.883

2.  Assessing capability in economic evaluation: a life course approach?

Authors:  Joanna Coast
Journal:  Eur J Health Econ       Date:  2019-08

3.  Mapping CHU9D Utility Scores from the PedsQLTM 4.0 SF-15.

Authors:  Christine Mpundu-Kaambwa; Gang Chen; Remo Russo; Katherine Stevens; Karin Dam Petersen; Julie Ratcliffe
Journal:  Pharmacoeconomics       Date:  2017-04       Impact factor: 4.981

4.  Giving a Voice to Marginalised Groups for Health Care Decision Making.

Authors:  Richard De Abreu Lourenço; Nancy Devlin; Kirsten Howard; Jason J Ong; Julie Ratcliffe; Jo Watson; Esther Willing; Elisabeth Huynh
Journal:  Patient       Date:  2020-10-01       Impact factor: 3.883

5.  Using Latent Class Analysis to Model Preference Heterogeneity in Health: A Systematic Review.

Authors:  Mo Zhou; Winter Maxwell Thayer; John F P Bridges
Journal:  Pharmacoeconomics       Date:  2018-02       Impact factor: 4.981

6.  Estimating Age- and Sex-Specific Utility Values from the CHU9D Associated with Child and Adolescent BMI z-Score.

Authors:  Anagha Killedar; Thomas Lung; Stavros Petrou; Armando Teixeira-Pinto; Alison Hayes
Journal:  Pharmacoeconomics       Date:  2020-04       Impact factor: 4.981

7.  Mapping the PedsQL™ onto the CHU9D: An Assessment of External Validity in a Large Community-Based Sample.

Authors:  Christine Mpundu-Kaambwa; Gang Chen; Elisabeth Huynh; Remo Russo; Julie Ratcliffe
Journal:  Pharmacoeconomics       Date:  2019-09       Impact factor: 4.981

8.  Psychometric evaluation of the Chinese version of the Child Health Utility 9D (CHU9D-CHN): a school-based study in China.

Authors:  Peirong Yang; Gang Chen; Peng Wang; Kejian Zhang; Feng Deng; Haifeng Yang; Guihua Zhuang
Journal:  Qual Life Res       Date:  2018-05-05       Impact factor: 4.147

9.  Mapping PedsQLTM scores onto CHU9D utility scores: estimation, validation and a comparison of alternative instrument versions.

Authors:  Rohan Sweeney; Gang Chen; Lisa Gold; Fiona Mensah; Melissa Wake
Journal:  Qual Life Res       Date:  2019-11-19       Impact factor: 4.147

10.  Economic Evaluations of Childhood Hearing Loss Screening Programmes: A Systematic Review and Critique.

Authors:  Rajan Sharma; Yuanyuan Gu; Teresa Y C Ching; Vivienne Marnane; Bonny Parkinson
Journal:  Appl Health Econ Health Policy       Date:  2019-06       Impact factor: 2.561

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