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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.

Aim We studied infants and children with and without special health care needs (SHCN) during the first 8 years of life to compare the (i) types and costs to the government's Medicare system of non-hospital health-care services and prescription medication in each year and (ii) cumulative costs according to persistence of SHCN. Methods Data from the first two biennial waves of the nationally representative Longitudinal Study of Australian Children, comprising two independent cohorts recruited in 2004, at ages 0–1 (n = 5107) and 4–5 (n = 4983) years. Exposure condition: parent-reported Children with Special Health Care Needs Screener at both waves, spanning ages 0–7 years. Outcome measure: Federal Government Medicare expenditure, via data linkage to the Medicare database, on non-hospital health-care attendances and prescriptions from birth to 8 years. Results At both waves and in both cohorts, >92% of children had complete SHCN and Medicare data. The proportion of children with SHCN increased from 6.1% at age 0–1 years to 15.0% at age 6–7 years. Their additional Medicare costs ranged from $491 per child at 6–7 years to $1202 at 0–1 year. This equates to an additional $161.8 million annual cost or 0.8% of federal funding for non-hospital-based health care. In both cohorts, costs were highest for children with persistent SHCNs. Conclusions SHCNs incur substantial non-hospital costs to Medicare, and no doubt other sources of care, from early childhood. This suggests that economic evaluations of early prevention and intervention services for SHCNs should consider impacts on not only the child and family but also the health-care system.

OBJECTIVES: To describe 24-hour time-use patterns and their association with health-related quality of life (HRQoL) in early adolescence. METHODS: The Child Health CheckPoint was a cross-sectional study nested between Waves 6 and 7 of the Longitudinal Study of Australian Children. The participants were 1455 11- to 12-year-olds (39% of Wave 6; 51% boys). The exposure was 24-hour time use measured across 259 activities using the Multimedia Activity Recall for Children and Adolescents. “Average” days were generated from 1 school and 1 nonschool day. Time-use clusters were derived from cluster analysis with compositional inputs. The outcomes were self-reported HRQoL (Physical and Psychosocial Health [PedsQL] summary scores; Child Health Utility 9D [CHU9D] health utility). RESULTS: Four time-use clusters emerged: “studious actives” (22%; highest school-related time, low screen time), “techno-actives” (33%; highest physical activity, lowest school-related time), “stay home screenies” (23%; highest screen time, lowest passive transport), and “potterers” (21%; low physical activity). Linear regression models, adjusted for a priori confounders, showed that compared with the healthiest “studious actives” (mean [SD]: CHU9D 0.84 [0.14], PedsQL physical 86.8 [10.8], PedsQL psychosocial 79.9 [12.6]), HRQoL in “potterers” was 0.2 to 0.5 SDs lower (mean differences [95% confidence interval]: CHU9D −0.03 [−0.05 to −0.00], PedsQL physical −5.5 [−7.4 to −3.5], PedsQL psychosocial −5.8 [−8.0 to −3.5]). CONCLUSIONS: Discrete time-use patterns exist in Australian young adolescents. The cluster characterized by low physical activity and moderate screen time was associated with the lowest HRQoL. Whether this pattern translates into precursors of noncommunicable diseases remains to be determined.