Development of a Health-State Classification System for the Pediatric Quality-of-Life Inventory Version 4.0 Generic Core Scales for Preference-Based Valuation in Australia

Joseph Kwon; Rakhee Raghunandan; Son Hong Nghiem; Kirsten Howard; Emily Lancsar; Elisabeth Huynh; Martin Howell; Stavros Petrou; Sarah Smith

doi:10.1016/j.jval.2024.08.005

Development of a Health-State Classification System for the Pediatric Quality-of-Life Inventory Version 4.0 Generic Core Scales for Preference-Based Valuation in Australia

Value Health. 2024 Sep 27:S1098-3015(24)02849-3. doi: 10.1016/j.jval.2024.08.005. Online ahead of print.

Authors

Joseph Kwon¹, Rakhee Raghunandan², Son Hong Nghiem³, Kirsten Howard², Emily Lancsar³, Elisabeth Huynh³, Martin Howell², Stavros Petrou⁴, Sarah Smith⁵

Affiliations

¹ Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, England, UK.
² Menzies Centre for Health Policy and Economics, University of Sydney, Sydney, NSW, Australia.
³ Department of Health Economics, Wellbeing and Society, Australian National University, Canberra, NSW, Australia.
⁴ Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, England, UK. Electronic address: stavros.petrou@phc.ox.ac.uk.
⁵ Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, England, UK.

PMID: 39343089
DOI: 10.1016/j.jval.2024.08.005

Abstract

Objectives: Pediatric Quality-of-Life Inventory Version 4.0 Generic Core Scales (PedsQL GCS), comprising 23 items covering 4 subscales (physical, emotional, social, and school functioning), is a widely applied generic measure of childhood health-related quality of life but does not provide health utilities for cost-effectiveness-based decision making. This study aimed to develop a reduced item version of PedsQL GCS amenable to health utility derivation in Australia.

Methods: Data sources were 2 cohorts of the Longitudinal Study of Australian Children, including proxy responses for all PedsQL GCS versions (Toddlers, Young Children, Children, and Teens), and the CheckPoint sample containing child self-report to the Children version. Three analytic samples were CheckPoint sample (n = 1874); Mallinson sample containing 1 measurement per child from one of the Young Children, Children, or Teens versions (n = 7855); and Toddlers sample (n = 7401). Exploratory and confirmatory factor analyses assessed dimensionality. Psychometric analyses used Rasch and classical criteria on 3 randomly selected subsamples (n = 500) per sample. Item selection prioritized psychometric performance in the CheckPoint sample, also considering performance in other samples and conceptual content.

Results: Dimensionality assessments did not generate an alternative empirical structure for the measure, and psychometric analyses were conducted on the original 4 subscales. The selected items were: "Get aches and pains" for physical functioning; "Feel sad/blue" for emotional functioning; "Other kids not friends" for social functioning; and "Keeping up with school work" for school functioning.

Conclusions: The final 4-item set, pending further psychometric validation and valuation, can generate health utilities from the widely used PedsQL GCS to inform cost-effectiveness-based decision making.

Keywords: Pediatric Quality of Life Inventory Version 4.0 Generic Core Scales; health utility; health-state classification system; item set selection; psychometric evaluation.