A meta analysis with meta regression
Check out this meta-analysis by Nikki Ow and Nancy Mayo about the Pediatric Quality of Life Inventory, the PedsQL™ developed by Dr. James W. Varni. This review was conducted on the PedsQL™ 4.0 Generic Core Scales using the self-report version of the PedsQL™ for children aged 5 to 18 years. It estimated a global mean total score on the PedsQL™ and highlighted that many factors were associated with HRQL. Results [..] provide ranges of pediatric HRQL across personal factors and regions that can be used for normative comparisons of HRQL. Importantly, this review confirms that children as young as 5 years of age can self-report a complex construct such as HRQOL with the proper measurement instrument.
Health‑related quality of life scores of typically developing children and adolescents around the world: a meta‑analysis with meta‑regression by Nikki Ow and Nancy E. Mayo
Purpose: In the context of identifying consequences and evaluating interventions for rare diseases, health-related quality of life (HRQL) measures are often used. Conclusions about HRQL are difficult to make as the participants are likely drawn from different countries. A global estimate of HRQL with estimates of variation would permit pooling of data from diverse sources. The aim was to estimate a global HRQL score of typically developing children and adolescents on the Pediatric Quality of Life Inventory (PedsQL™) and to identify sources of variation across studies.
Methods: A systematic search was conducted in December 2018 on four databases: MEDLINE, EMBASE, CINAHL and PsycINFO. Inclusion criteria were all population health studies or validation studies using the self-report version of the PedsQL™ for typically developing children aged 5 to 18 years. Quality appraisal was conducted using the Appraisal Tool for Cross-Sectional Studies. Meta-analysis and meta-regression were conducted.
Results: A total of 66 studies with a sample size of 67,805 participants were included in this analysis. The average QOL score across all studies was 80.9 (CI 78.6–83.2). Univariate analyses showed that region, minimum age of sample and income of country, was associated with the total HRQL score. Subgroup analysis showed that there was an effect of age and region on HRQL scores.
Conclusion: Results of this review provide ranges of pediatric HRQL across personal factors and regions that can be used for normative comparisons of HRQL. Interpretation of scores on generic measures should always take into consideration the contextual influences in the child’s life.
Thank you very much to the Authors for this comprehensive and qualitative work. The PedsQL™ and its derivatives are copyrighted by Dr. James W. Varni, with all rights reserved. Do not use without permission. For information and conditions to access and use of the PedsQL™ and its translations, please contact Mapi Research Trust, Lyon, France via ePROVIDE. To access the full review, please visit the publisher’s website.