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.

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.