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Health-related quality of life: population epidemiology and concordance in Australian children aged 11–12 years and their parents
  1. Max Catchpool1,
  2. Lisa Gold2,3,
  3. Anneke C Grobler3,4,
  4. Susan A Clifford3,4,
  5. Melissa Wake3,4,5
  1. 1 Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
  2. 2 School of Health and Social Development, Deakin University, Geelong, Victoria, Australia
  3. 3 Murdoch Children’s Research Institute, Parkville, Victoria, Australia
  4. 4 Department of Paediatrics, The University of Melbourne, Parkville, Victoria, Australia
  5. 5 Department of Paediatrics and the Liggins Institute, The University of Auckland, Auckland, New Zealand
  1. Correspondence to Professor Melissa Wake; melissa.wake{at}mcri.edu.au

Abstract

Objectives To describe the distribution of health-related quality of life (HRQL) in a national sample of Australian children aged 11–12 years and their parents, and examine associations within parent–child dyads.

Design The Child Health CheckPoint, a population-based cross-sectional study nested between waves 6 and 7 of the Longitudinal Study of Australian Children (LSAC).

Setting Assessment centres in seven Australian cities and eight regional towns, or home visit; February 2015 to March 2016.

Participants Of all participating CheckPoint families (n=1874), 1853 children (49.0% girls) and 1863 parents (87.7% mothers) with HRQL data were included (1786 pairs).

Outcome measures HRQL was self-reported using preference-based (Child Health Utility 9Dimension, CHU9D) and non-preference-based (Pediatric Quality of Life, PedsQL V.4.0) measures for children and preference-based measures for parents (CHU9D; Assessment of Quality of Life 8 Dimension, AQoL-8D). Utility scores from preference-based measures were calculated using existing Australian algorithms to present a score on a 0–1 scale, where 1 represents full health. Parent–child concordance was assessed using Pearson’s correlation coefficients and adjusted linear regression models. Survey weights and methods were applied to account for LSAC’s complex sample design, stratification and clustering within postcodes.

Results Children’s means and SD were 0.81 (SD 0.16) for CHU9D and 78.3 (SD 13.03) for PedsQL. In adults, mean HRQL for AQoL-8D and CHU9D were 0.78 (SD 0.16) and 0.89 (SD 0.10), respectively. Mean HRQL was similar for boys and girls, but slightly higher for fathers than mothers. The Pearson correlation coefficient for parent–child CHU9D values was 0.13 (95% CI 0.09 to 0.18). Percentiles and concordance are presented for both samples for males and females separately and together.

Conclusions We provide Australian paediatric population values for HRQL measures, and the first national CHU9D values for mid-life adults. At age 11–12 years in this relatively healthy sample, parent–child concordance in HRQL was small.

  • health-related quality of life
  • well-being
  • reference values
  • parents
  • children
  • inheritance patterns
  • correlation studies
  • epidemiologic studies
  • cross-sectional studies

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

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Footnotes

  • Contributors MC, LG, SAC and MW contributed to study conception and interpretation of results, drafted the initial manuscript, critically revised further drafts and approved the final manuscript as submitted. MC and ACG contributed interpretation of results, performed the statistical analysis, drafted the initial manuscript, critically revised further drafts and approved the final manuscript as submitted.

  • Funding This work was supported by the National Health and Medical Research Council (NHMRC) of Australia (Project Grants 1041352, 1109355), The Royal Children’s Hospital Foundation (2014-241), the Murdoch Children’s Research Institute (MCRI), The University of Melbourne and Financial Markets Foundation for Children (2014-055, 2016-310). The following authors were supported by the NHMRC: Early Career Fellowship (1035100) to LG; Senior Research Fellowship (1046518) to MW. MW was supported by Cure Kids, New Zealand. The MCRI administered the research grants for the study and provided infrastructural support (IT and biospecimen management) to its staff and the study, but played no role in the conduct or analysis of the trial. Research at the MCRI is supported by the Victorian Government’s Operational Infrastructure Support Program. The Australian Department of Social Services played a role in study design; however, no other funding bodies had a role in the study design and conduct; data collection, management, analysis and interpretation; preparation, review or approval of the manuscript; and decision to submit the manuscript for publication.

  • Disclaimer The findings and views reported in this paper are those of the authors and should not be attributed to DSS, AIFS or the ABS.

  • Competing interests MW received support from Sandoz to present at a symposium outside the submitted work.

  • Ethics approval The CheckPoint data collection protocol was approved by The Royal Children’s Hospital (Melbourne, Australia) Human Research Ethics Committee (33225D) and The Australian Institute of Family Studies Ethics Committee (14-26).

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data sharing statement The Longitudinal Study of Australian Children datasets and technical documents are available to researchers at no cost via a licence agreement. Data access requests are co-ordinated by the National Centre for Longitudinal Data. More information is available at https://dataverse.ada.edu.au/dataverse/lsac.

  • Patient consent for publication Not required.

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