scholarly journals Population health bio-phenotypes in 11–12 year old children and their midlife parents: Growing Up in Australia’s Child Health CheckPoint

BMJ Open ◽  
2019 ◽  
Vol 9 (Suppl 3) ◽  
pp. 1-2 ◽  
Author(s):  
Melissa Wake ◽  
Susan A Clifford

In an ambitious undertaking, Growing Up in Australia’s Child Health CheckPoint streamlined and implemented wide-ranging population phenotypes and biosamples relevant to non-communicable diseases in nearly 1900 parent–child dyads throughout Australia at child aged 11–12 years. This BMJ Open Special Issue describes the methodology, epidemiology and parent–child concordance of 14 of these phenotypes, spanning cardiovascular, respiratory, bone, kidney, hearing and language, body composition, metabolic profiles, telomere length, sleep, physical activity, snack choice and health-related quality of life. The Special Issue also includes a cohort summary and study methodology paper.

2016 ◽  
Vol 10 (3) ◽  
pp. 781-795 ◽  
Author(s):  
Paul Lanier ◽  
Shenyang Guo ◽  
Wendy Auslander ◽  
Kathleen Gillespie ◽  
Allison Dunnigan ◽  
...  

BMJ Open ◽  
2019 ◽  
Vol 9 (Suppl 3) ◽  
pp. 157-164 ◽  
Author(s):  
Max Catchpool ◽  
Lisa Gold ◽  
Anneke C Grobler ◽  
Susan A Clifford ◽  
Melissa Wake

ObjectivesTo 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.DesignThe Child Health CheckPoint, a population-based cross-sectional study nested between waves 6 and 7 of the Longitudinal Study of Australian Children (LSAC).SettingAssessment centres in seven Australian cities and eight regional towns, or home visit; February 2015 to March 2016.ParticipantsOf 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 measuresHRQL 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.ResultsChildren’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.ConclusionsWe 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.


Rheumatology ◽  
2020 ◽  
Vol 59 (Supplement_2) ◽  
Author(s):  
Andrew Smith ◽  
Bishma Saqib ◽  
Rebecca Lee ◽  
Wendy Thomson ◽  
Lis Cordingley

Abstract Background Juvenile idiopathic arthritis (JIA) is a heterogeneous group of arthritic conditions presenting in children and young people, in which physical limitations and associated complications can have detrimental effects on physical and psychosocial wellbeing. This study aims to investigate the impact of living with JIA on different aspects of health-related quality of life (HRQoL) and to explore how this changes over time, using data from the Childhood Arthritis Prospective Study (CAPS). Methods Longitudinal data collected as part of CAPS were analysed. HRQoL was assessed at baseline, 1 year and 3 years’ post-diagnosis using the Child Health Questionnaire (CHQ), a parent-completed form for children from 5 years of age. The CHQ measures physical, emotional and social components of child health status. Raw domain scores were transformed via algorithm into values ranging from 0-100, with higher scores indicating better health status. Mean (standard deviation) and median (interquartile range) for each domain were determined, both for the full cohort and by gender. Differences between median scores at baseline and 3 years were assessed using the Wilcoxon signed-rank test. Mean scores of each domain were visually compared with a reference population sample of healthy children from the United States. Results 184 participants completed the questionnaire at all 3 time points. At baseline, compared to the reference population, children with JIA scored lower in every domain although scores were closer between the 2 groups at 3 years. Median scores improved over time, the exception being the general health perceptions domain which decreased after baseline. Domains with the greatest improvement were physical functioning,“bodily pain and social-physical. The largest changes occurred from baseline to 1 year. Statistically significant differences between baseline and 3-year scores were found for all domains. Domain scores for male and female participants were very similar at baseline, though scores for male participants indicated slightly better health at 1 and 3 years for both physical and psychosocial domains. Conclusion JIA has significant impact on HRQoL, which improves within 3 years of diagnosis with the greatest improvement occurring within the first year. Physical health domains show greater improvement over time than psychosocial domains, although psychosocial scores were generally higher throughout the study. Male participants tend to score slightly higher than female participants in both physical and psychosocial domains after baseline. Further research should explore measurable patient, age or disease-related drivers of HRQoL. Disclosures A. Smith None. B. Saqib None. R. Lee None. W. Thomson None. L. Cordingley None.


2015 ◽  
Vol 18 (4) ◽  
pp. 432-438 ◽  
Author(s):  
Gang Chen ◽  
Terry Flynn ◽  
Katherine Stevens ◽  
John Brazier ◽  
Elisabeth Huynh ◽  
...  

2015 ◽  
Vol 29 (5) ◽  
pp. 402-412
Author(s):  
Mary C. O'Laughlen ◽  
Patricia J. Hollen ◽  
Karen Rance ◽  
Virginia Rovnyak ◽  
Ivora Hinton ◽  
...  

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