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

  • Rohan SweeneyEmail author
  • Gang Chen
  • Lisa Gold
  • Fiona Mensah
  • Melissa Wake



The Paediatric Quality of Life InventoryTM 4.0 Generic Core Scales (PedsQL) is a non-preference based instrument for assessing health related quality of life (HRQoL) in children. Recent papers presented algorithms of parental proxy and short-form versions of the PedsQL onto the validated preference-based Child Health Utility 9D (CHU9D) instrument, to enable conversion of PedsQL scores to quality adjusted life years for use in economic evaluation. However, further research was needed to both validate these algorithms, and assess if use of the full 23-item PedsQL self-report instrument is preferable to other PedsQL versions for mapping onto child self-report CHU9D utilities.


To develop a mapping algorithm for converting the 23-item PedsQL instrument onto the CHU9D instrument and provide an external validation of two recently published algorithms that might be considered alternatives.


Data from children in the Longitudinal Study of Australian Children (LSAC) were used (N = 1801). Six econometric methods were compared to identify the best algorithms, assessed against a series of goodness-of-fit criteria. The same data and goodness-of-fit criteria were used in the external validation exercise for previously published mapping algorithms.


The optimal mapping algorithm was identified, which used PedsQL dimension scores to predict the CHU9D utilities. It performed well against standard goodness-of-fit tests. The external validation exercise revealed the recently published alternative algorithms also performed relatively well.


The identified mapping algorithms can be used to facilitate cost-utility analysis in comparable populations when only the PedsQL instrument is available. Results from this population indicate the algorithms identified in this paper are well suited for estimating CHU9D self-report utilities when the full 23-item self-report PedsQL instrument has been used.


CHU9D PedsQL Mapping Utility 



This paper uses unit record data from Growing Up in Australia, the Longitudinal Study of Australian Children. The study is conducted in partnership between the Department of Social Services (DSS), the Australian Institute of Family Studies (AIFS) and the Australian Bureau of Statistics (ABS). The findings and views reported in this paper are those of the author and should not be attributed to DSS, AIFS or the ABS. Some study data were collected and managed using REDCap (Research Electronic Data Capture) electronic data capture tools. REDCap is a secure, web-based application designed to support data capture for research studies.


The collection and data management of the Longitudinal Study of Australian Children’s (LSAC) Child Health CheckPoint data was supported by the National Health and Medical Research Council (NHMRC) of Australia (Project Grants 1041352, 1109355), The Royal Children’s Hospital Foundation (Grant No. 2014-241), the Murdoch Children’s Research Institute, The University of Melbourne, the National Heart Foundation of Australia (100660), Financial Markets Foundation for Children (Grant Nos. 2014-055, 2016-310) and the Victoria Deaf Education Institute. Research at the Murdoch Children’s Research Institute is supported by the Victorian Government’s Operational Infrastructure Program. The following authors are supported by the NHMRC: MW, Principal Research Fellowship 1160906; FM (Early Career Fellowship #1037449; Career Development Fellowship #1111160).

Compliance with ethical standards

Conflict of interest

The authors of this paper have no conflicts of interest to declare.

Ethical approval

The source of data for this study, the CheckPoint study, was approved by The Royal Children’s Hospital Melbourne Human Research Ethics Committee (33225D) and the Australian Institute of Family Studies Ethics Committee (14-26).

Informed consent

Parent or guardian provided written consent for their child’s (and their own) participation in the CheckPoint study.

Supplementary material

11136_2019_2357_MOESM1_ESM.docx (971 kb)
Supplementary material 1 (DOCX 971 kb)


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Centre for Health Economics, Monash Business SchoolMonash UniversityCaulfield EastAustralia
  2. 2.Deakin Health Economics, School of Health and Social DevelopmentDeakin UniversityGeelongAustralia
  3. 3.Murdoch Children’s Research InstituteParkvilleAustralia
  4. 4.Department of PaediatricsThe University of MelbourneParkvilleAustralia
  5. 5.Department of Paediatrics and The Liggins InstituteThe University of AucklandGraftonNew Zealand

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