Kelly M Kenzik1, Sanjeev Y Tuli2, Dennis A Revicki3, Elizabeth A Shenkman4, I-Chan Huang4. 1. Center for Outcomes and Effectiveness Research and Education, University of Alabama at Birmingham, AL, USA (KMK) 2. Division of General Pediatrics, Department of Pediatrics, University of Florida, Gainesville, FL, USA (SYT) 3. Center for Health Outcomes Research, Evidera, Bethesda, MD, USA (DAR) 4. Institute for Child Health Policy, Department of Health Outcomes & Policy, University of Florida, Gainesville, FL, USA (EAS, I-CH).
Abstract
BACKGROUND: Few studies have compared multiple health-related quality-of-life (HRQOL) instruments simultaneously for pediatric populations. This study aimed to test psychometric properties of 4 legacy pediatric HRQOL instruments: the Child Health and Illness Profile (CHIP), the KIDSCREEN-52, the KINDL, and the Pediatric Quality of Life Inventory (PedsQL). METHODS: This study used data from 908 parents whose children (ages 2-19 years) were enrolled in Florida Medicaid. Parents were asked via telephone interview to complete each instrument appropriate to the age of their children. Structural, convergent/discriminant, and known-group validities were investigated. We examined structural validity using confirmatory factor analyses. We examined convergent/discriminant validity by comparing Spearman rank correlation coefficients of homogeneous (physical functioning and physical well-being) versus heterogeneous (physical and psychological functioning) domains of the instruments. We assessed known-groups validity by examining the extent to which HRQOL differed by the status of children with special health needs (CSHCN). RESULTS: Domain scores of the 4 instruments were not normally distributed, and ceiling effects were significant in most domains. The KIDSCREEN-52 demonstrates the best structural validity, followed by the CHIP, KINDL, and PedsQL. The PedsQL and the KIDSCREEN-52 show better convergent/discriminant validity than the other instruments. Known-groups validity in discriminating CSHCN versus no needs was the best for the PedsQL, followed by the KIDSCREEN-52, the CHIP, and the KINDL. CONCLUSION: No one instrument was fully satisfactory in all psychometric properties. Strategies are recommended for future comparison of item content and measurement properties across different HRQOL instruments for research and clinical use.
BACKGROUND: Few studies have compared multiple health-related quality-of-life (HRQOL) instruments simultaneously for pediatric populations. This study aimed to test psychometric properties of 4 legacy pediatric HRQOL instruments: the Child Health and Illness Profile (CHIP), the KIDSCREEN-52, the KINDL, and the Pediatric Quality of Life Inventory (PedsQL). METHODS: This study used data from 908 parents whose children (ages 2-19 years) were enrolled in Florida Medicaid. Parents were asked via telephone interview to complete each instrument appropriate to the age of their children. Structural, convergent/discriminant, and known-group validities were investigated. We examined structural validity using confirmatory factor analyses. We examined convergent/discriminant validity by comparing Spearman rank correlation coefficients of homogeneous (physical functioning and physical well-being) versus heterogeneous (physical and psychological functioning) domains of the instruments. We assessed known-groups validity by examining the extent to which HRQOL differed by the status of children with special health needs (CSHCN). RESULTS: Domain scores of the 4 instruments were not normally distributed, and ceiling effects were significant in most domains. The KIDSCREEN-52 demonstrates the best structural validity, followed by the CHIP, KINDL, and PedsQL. The PedsQL and the KIDSCREEN-52 show better convergent/discriminant validity than the other instruments. Known-groups validity in discriminating CSHCN versus no needs was the best for the PedsQL, followed by the KIDSCREEN-52, the CHIP, and the KINDL. CONCLUSION: No one instrument was fully satisfactory in all psychometric properties. Strategies are recommended for future comparison of item content and measurement properties across different HRQOL instruments for research and clinical use.
Authors: Mirella De Civita; Dean Regier; Abul H Alamgir; Aslam H Anis; Mark J Fitzgerald; Carlo A Marra Journal: Pharmacoeconomics Date: 2005 Impact factor: 4.981
Authors: Anne W Riley; Christopher B Forrest; Barbara Starfield; George W Rebok; Judith A Robertson; Bert F Green Journal: Med Care Date: 2004-03 Impact factor: 2.983
Authors: Elina Kyösti; Tero I Ala-Kokko; Pasi Ohtonen; Outi Peltoniemi; Paula Rautiainen; Janne Kataja; Hanna Ebeling; Janne H Liisanantti Journal: Intensive Care Med Date: 2018-08-22 Impact factor: 17.440
Authors: Carrie R Howell; Lindsay A Thompson; Heather E Gross; Bryce B Reeve; Darren A DeWalt; I-Chan Huang Journal: Value Health Date: 2016-02-16 Impact factor: 5.725
Authors: Pranav K Gandhi; Lindsay A Thompson; Sanjeev Y Tuli; Dennis A Revicki; Elizabeth Shenkman; I-Chan Huang Journal: PLoS One Date: 2014-09-30 Impact factor: 3.240
Authors: Erik Grasaas; Sølvi Helseth; Liv Fegran; Jennifer Stinson; Milada Småstuen; Kristin Haraldstad Journal: Health Qual Life Outcomes Date: 2020-01-30 Impact factor: 3.186
Authors: Ming Chen; Conor M Jones; Hailey E Bauer; Onyekachukwu Osakwe; Pavinarmatha Ketheeswaran; Justin N Baker; I-Chan Huang Journal: Children (Basel) Date: 2022-02-02
Authors: Michiel A J Luijten; Lotte Haverman; Raphaële R L van Litsenburg; Leo D Roorda; Martha A Grootenhuis; Caroline B Terwee Journal: Eur J Pediatr Date: 2022-02-15 Impact factor: 3.860