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Psychometric Evaluation of the PedsQL GCS and CHU9D in Australian Children and Adolescents with Common Chronic Health Conditions

Author

Listed:
  • Rakhee Raghunandan

    (University of Sydney
    University of Sydney)

  • Kirsten Howard

    (University of Sydney
    University of Sydney)

  • Sarah Smith

    (London School of Hygiene & Tropical Medicine)

  • Anagha Killedar

    (University of Sydney)

  • Erin Cvejic

    (University of Sydney)

  • Martin Howell

    (University of Sydney
    University of Sydney)

  • Stavros Petrou

    (University of Oxford)

  • Emily Lancsar

    (Australian National University)

  • Germaine Wong

    (University of Sydney)

  • Jonathan Craig

    (Flinders University)

  • Alison Hayes

    (University of Sydney)

Abstract

Background Generic instruments such as the Pediatric Quality of Life Inventory™ v4.0 Generic Core Scales (PedsQL GCS) and Child Health Utility 9D (CHU9D) are widely used to assess health-related quality of life (HRQOL) of the general childhood population, but there is a paucity of information about their psychometric properties in children with specific health conditions. This study assessed psychometric properties, including acceptability, reliability, validity, and responsiveness, of the PedsQL GCS and the CHU9D in children and adolescents with a range of common chronic health problems. Methods We used data from the Longitudinal Study of Australian Children (LSAC), for children aged 10–17 years with at least one of the following six parent-reported health conditions: asthma, anxiety/depression, attention deficit hyperactivity disorder (ADHD), autism/Asperger’s, epilepsy, and type 1 diabetes mellitus. The LSAC used parent proxy-reported PedsQL GCS and child self-reported CHU9D assessments. The performance of each instrument (PedsQL GCS and CHU9D) for each psychometric property (acceptability, reliability, validity, and responsiveness) was assessed against established criteria. Results The study sample included 7201 children and adolescents (mean age = 14 years; range 10.1–17.9 years; 49% female) with 15,568 longitudinal observations available for analyses. Across the six health conditions, acceptability of the PedsQL GCS was high, while acceptability for the CHU9D was mixed. Both the PedsQL GCS and CHU9D showed strong internal consistency (Cronbach’s alpha range: PedsQL GCS = 0.70–0.95, CHU9D = 0.76–0.84; item-total correlations range: PedsQL GCS = 0.35–0.84, CHU9D = 0.32–0.70). However, convergent validity for both the PedsQL GCS and CHU9D was generally weak (Spearman’s correlations ≤ 0.3). Known group validity was strong for the PedsQL GCS (HRQOL differences were detected for children with and without asthma, anxiety/depression, ADHD, autism/Asperger’s, and epilepsy). CHU9D was only able to discriminate between children with and without anxiety/depression, ADHD, and autism/Asperger’s. The responsiveness of both the PedsQL GCS and CHU9D was variable across the six conditions, and most of the estimated effect sizes were relatively small (

Suggested Citation

  • Rakhee Raghunandan & Kirsten Howard & Sarah Smith & Anagha Killedar & Erin Cvejic & Martin Howell & Stavros Petrou & Emily Lancsar & Germaine Wong & Jonathan Craig & Alison Hayes, 2023. "Psychometric Evaluation of the PedsQL GCS and CHU9D in Australian Children and Adolescents with Common Chronic Health Conditions," Applied Health Economics and Health Policy, Springer, vol. 21(6), pages 949-965, November.
    • Handle: RePEc:spr:aphecp:v:21:y:2023:i:6:d:10.1007_s40258-023-00836-2
    DOI: 10.1007/s40258-023-00836-2
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    References listed on IDEAS

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    1. John Brazier & Mark Deverill, 1999. "A checklist for judging preference‐based measures of health related quality of life: Learning from psychometrics," Health Economics, John Wiley & Sons, Ltd., vol. 8(1), pages 41-51, February.
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