Health-related quality of life: population epidemiology and concordance in Australian children aged 11-12 years and their parents

Brazilian children's quality of life during the COVID-19 pandemic: analysis of contextual factors and dimensions

OBJECTIVE

The aim of this study was to investigate the contextual factors associated with the quality of life (QOL) of Brazilian children aged 0-12 years during the strict period of social isolation.

METHODS

This observational cross-sectional study was conducted between July and September 2020 using an online questionnaire on QOL-related family factors and the Pediatric Quality of Life Inventory (PedsQL™). Results were analyzed by multinomial logistic regression analysis.

RESULTS

The sample had 849 children, mostly from the South Region of Brazil (75%), white (83%), with typical development (79%), sedentary (68%), using screen (85%) for >3 h/day (44%). Their mothers were their main caregivers (90%). The following variables were significantly associated with high scores of QOL: typical health status (OR 2.38; 95%CI 1.60-3.55; screen time ≤2 h/day (OR 1.62; 95%CI 1.17-2.24); social distancing considered as "easy" (OR 1.67; 95%CI 1.20-2.32), and stimulation of the child by the family (OR 1.93; 95%CI 1.08-3.45).

CONCLUSIONS

This study indicates that the family context can influence children's QOL, especially during the COVID-19 pandemic and home environment reorganization.

An Investigation of Inter-Rater and Intra-Proxy Agreement in Measuring Quality of Life of Children in the Community Using the EQ-5D-Y-3L

BACKGROUND

Self-reporting of health-related quality of life (HRQoL) in children is not always feasible. To date, proxy perspectives (Proxy versions 1 and 2) using the EQ-5D-Y-3L have not been explored for its impact on agreement with child self-report. Proxy version 1 requires the proxy to consider their own view of the child's HRQoL (proxy-proxy), while with Proxy version 2, the proxy is asked to respond as they believe their child would self-report their HRQoL (proxy-child). This study compared the inter-rater and intra-proxy agreement (overall and dimension level) using the EQ-5D-Y-3L self, proxy-proxy, and proxy-child reports.

METHODS

A community-based sample of child (aged 6-12 years) and parent dyads were invited to participate in a semi-structured interview. The child self-completed the EQ-5D-Y-3L independently of the parent who completed the EQ-5D-Y-3L from proxy-proxy and proxy-child perspectives. Agreement was determined using Concordance Correlation Coefficients (CCCs) for the overall (preference-weighted) HRQoL, while agreement at the dimension level was evaluated using Gwet's agreement coefficient (AC1). To assess the differences between the self and the two proxy reports, the Wilcoxon matched-pair signed-rank test was used.

RESULTS

This study involved 85 child-parent dyads. The agreement between self and proxy overall HRQoL was low (fair) with both proxy-proxy (CCC = 0.28) and proxy-child (CCC = 0.26) reports. The largest discrepancy in the child-proxy agreement at dimension level with both the proxy versions was observed for 'feeling worried, sad or unhappy'. Within this dimension, the proxy-child perspective resulted in a stronger agreement (AC1 = 0.7, good) with child self-report compared with the traditional proxy-proxy perspective (AC1 = 0.58, moderate). Although the preference-weighted HRQoL was consistent across both the proxy perspectives, a significant difference was observed in the EQ VAS scores (p = 0.02).

CONCLUSIONS

This study demonstrates that choice of proxy perspective may have an impact on the problems reported on HRQoL dimensions and EQ VAS scores. However, in this community-based sample of generally healthy children, no significant difference was observed in the inter-rater agreement for child-self and proxy preference-weighted EQ-5D-Y-3L values based on proxy perspectives. While this suggests that preference-weighted data are not sensitive to the choice of perspective, these findings may differ for different HRQoL instruments and for alternative value sets with different properties.

Enrolment of the first paediatric cohort into the Australian lupus registry and biobank: A single-centre experience

INTRODUCTION

We aim to report on the feasibility of establishment of the first paediatric cohort as part of the longitudinal database of the Australian Lupus Registry and Biobank (ALRB) and to describe the enrolment data with a focus on clinical characteristics, serological data, treatment strategies and patient/parent-reported outcome measures.

METHODS

All patients under the age of 18 years with a diagnosis of systemic lupus erythematosus (SLE) attending the paediatric rheumatology service of a single, tertiary hospital were identified. Patients were enrolled in the ALRB if they met ≥4/11 of the American College of Rheumatology (ACR) 1997 SLE classification criteria or the Systemic Lupus International Collaborating Clinics (SLICC) 2012 classification criteria. Enrolment data including demographics, clinical characteristics, serological profiles, disease activity and damage assessments were recorded. Peds-QL Rheumatology and General Modules were used to assess patient and parent-reported outcomes.

RESULTS

Twenty-seven patients were eligible for inclusion, with 26 patients (96%) consenting for enrolment. Twenty-five patients (92%) consented for biobanking. Twenty patients (77%) were female. The median age at enrolment was 16 years (interquartile range (IQR) 13.7, 17.4). The median disease duration from diagnosis was 3.2 years (IQR 1.4, 5.3). Sixteen patients (62%) had synovitis, 16 (62%) had cutaneous involvement, 4 (15%) had serositis, 17 (65%) had haematological involvement and 7 (27%) had renal involvement at enrolment. Nineteen patients (73%) were prescribed at least two disease-modifying anti-rheumatic medications (DMARDs). Hydroxychloroquine (n = 22, 85%) and mycophenolate mofetil (n = 9, 35%) were the most commonly prescribed DMARDs. The median SLEDAI-2K score was 2 (IQR 2, 4). Six patients (23%) had active disease (SLEDAI-2K ≥6) at enrolment. Three patients (11.5%) had reported damage using the SLICC/ACR Damage Index. Twenty-three children (88%) and eighteen parents (69%) completed the Paediatric Quality of Life Inventory. Quality of life scores reported across domains of physical, emotional, social and school functioning at enrolment were comparable to previously studied paediatric cohorts with SLE and other chronic diseases.

CONCLUSION

We have established our centre as the first paediatric participating site of the ALRB, providing contemporary data on the clinical characteristics, serological profile and health-related quality of life outcomes of Australian children with SLE. Paediatric involvement with this national registry will provide a unique perspective for future clinical and scientific research. Collection of Australian-specific paediatric longitudinal data will also enable a broader understanding of SLE within a multicultural Australian population.

Child-Parent Agreement in the Assessment of Health-Related Quality of Life Using the CHU9D and the PedsQLTM

OBJECTIVE

This study examined the inter-rater agreement between child-self and parental proxy health-related quality of life (HRQoL) ratings (overall and domain level) using two different generic child-specific measures, the Child Health Utility 9D (CHU9D) and the Pediatric Quality of Life Inventory (PedsQLTM), in a community-based sample of Australian children. A secondary objective was to investigate the impact of age on child-parent agreement across the dimensions of the two measures.

METHODS

A total of 85 child-parent dyads (children aged 6-12 years) recruited from the community completed the self and proxy versions of the CHU9D and the PedsQLTM, respectively. The inter-rater agreement was estimated using Concordance Correlation Coefficients (CCC) and Gwet's Agreement Coefficient (AC1) for the overall sample and across age-groups.

RESULTS

Agreement was low for overall HRQoL for both the CHU9D (CCC = 0.28) and the PedsQLTM (CCC = 0.39). Across the CHU9D dimensions, agreement was the highest for 'sad' (AC1 = 0.83) and lowest for 'tired' (AC1 = 0.31). The PedsQLTM demonstrated stronger agreement (AC1 = 0.41-0.6) for the physical health dimension but weaker for the psychosocial dimensions (AC1 < 0.4). Except for the 'tired' dimension, agreement was consistent across age-groups with the CHU9D, whilst the PedsQLTM showed poor agreement for most of the psychosocial health items among the older age-groups only (8-10 and 11-12 years).

CONCLUSION

This study highlights that the agreement between child and parent proxy reported HRQoL may be influenced by both the measure used and the age of the child. These findings may have implications for the economic evaluation of healthcare interventions and services in child populations when both child and proxy perspectives are considered in the assessment of child HRQoL.

Psychometric Evaluation of the PedsQL GCS and CHU9D in Australian Children and Adolescents with Common Chronic Health Conditions

BACKGROUND

Generic instruments such as the Pediatric Quality of Life Inventory™ v4.0 Generic Core Scales (PedsQL GCS) and Child Health Utility 9D (CHU9D) are widely used to assess health-related quality of life (HRQOL) of the general childhood population, but there is a paucity of information about their psychometric properties in children with specific health conditions. This study assessed psychometric properties, including acceptability, reliability, validity, and responsiveness, of the PedsQL GCS and the CHU9D in children and adolescents with a range of common chronic health problems.

METHODS

We used data from the Longitudinal Study of Australian Children (LSAC), for children aged 10-17 years with at least one of the following six parent-reported health conditions: asthma, anxiety/depression, attention deficit hyperactivity disorder (ADHD), autism/Asperger's, epilepsy, and type 1 diabetes mellitus. The LSAC used parent proxy-reported PedsQL GCS and child self-reported CHU9D assessments. The performance of each instrument (PedsQL GCS and CHU9D) for each psychometric property (acceptability, reliability, validity, and responsiveness) was assessed against established criteria.

RESULTS

The study sample included 7201 children and adolescents (mean age = 14 years; range 10.1-17.9 years; 49% female) with 15,568 longitudinal observations available for analyses. Across the six health conditions, acceptability of the PedsQL GCS was high, while acceptability for the CHU9D was mixed. Both the PedsQL GCS and CHU9D showed strong internal consistency (Cronbach's alpha range: PedsQL GCS = 0.70-0.95, CHU9D = 0.76-0.84; item-total correlations range: PedsQL GCS = 0.35-0.84, CHU9D = 0.32-0.70). However, convergent validity for both the PedsQL GCS and CHU9D was generally weak (Spearman's correlations ≤ 0.3). Known group validity was strong for the PedsQL GCS (HRQOL differences were detected for children with and without asthma, anxiety/depression, ADHD, autism/Asperger's, and epilepsy). CHU9D was only able to discriminate between children with and without anxiety/depression, ADHD, and autism/Asperger's. The responsiveness of both the PedsQL GCS and CHU9D was variable across the six conditions, and most of the estimated effect sizes were relatively small (< 0.5).

CONCLUSION

This study expands the evidence base of psychometric performance of the PedsQL GCS and CHU9D and can aid in appropriate HRQOL instrument selection for the required context by researchers and clinicians.

Assessment of Quality of Life of Transgender and Gender-Diverse Children and Adolescents in Melbourne, Australia, 2017-2020

IMPORTANCE

Transgender and gender-diverse (TGD) children and adolescents may experience not only gender dysphoria but also depression and anxiety, all of which are likely to be associated with reduced quality of life (QOL). Despite this, little is known about QOL in this population.

OBJECTIVES

To identify demographic, social, and clinical characteristics associated with reduced QOL in TGD children and adolescents; compare their QOL with age-matched population-based norms and that of young people with common mental health problems; and evaluate the association between gender dysphoria and QOL.

DESIGN, SETTING, AND PARTICIPANTS

In this cohort study, baseline data were derived from questionnaires completed in a prospective cohort study (Trans20) of TGD children aged 6 to 12 years and adolescents aged 13 to 17 years first seen at the Royal Children's Hospital Gender Service (Melbourne, Australia) between February 2017 and February 2020.

MAIN OUTCOMES AND MEASURES

The main outcome was QOL, measured using the Child Health Utility 9D instrument (CHU-9D). Data collection included demographic information, social factors (eg, bullying, lack of support, and social transition), and clinical characteristics (eg, gender identity, gender dysphoria, and mental health difficulties). Population norms and CHU-9D data for Australian youths with mental health diagnoses were derived from published literature.

RESULTS

The TGD cohort comprised 525 children and adolescents aged 6 to 17 years (median age, 14 years [IQR, 12-16 years]; 364 [69.33%] presumed female at birth). The mean (SD) CHU-9D score was 0.46 (0.26). Compared with population norms, TGD children (0.58 [0.27] vs 0.81 [0.16]; P < .001) and adolescents (0.41 [0.25] vs 0.80 [0.14]; P < .001) had significantly lower scores. Within the TGD cohort, mean (SD) scores were significantly lower in adolescents (0.41 [0.24] vs 0.62 [0.25]; P < .001), those assigned female at birth (0.43 [0.26] vs 0.55 [0.25]; P < .001), those reporting mental health problems (0.37 [0.23] vs 0.57 [0.25]; P < .001) and physical health problems (0.41 [0.26] vs 0.48 [0.26]; P = .04), and those who were bullied (0.38 [0.24] vs 0.52 [0.25]; P < .001). Gender dysphoria alone was associated with a lower mean (SD) CHU-9D score (0.51 [0.23]) than that in control adolescents with serious mental health conditions such as depression (0.64 [0.26]) and anxiety (0.70 [0.24]) and was an independent factor associated with QOL.

CONCLUSIONS AND RELEVANCE

In this cohort study of TGD children and adolescents in Australia, QOL was worse in this population than in age-matched, population-based peers. Quality of life associated with gender dysphoria was substantially worse than that seen in young people with common mental health conditions. These findings emphasize the risk of poor QOL among TGD young people and the need to better support them.

Australian adolescent population norms for the child health utility index 9D-results from the young minds matter survey

OBJECTIVE

Patient-reported outcomes of health-related quality-of-life (HRQoL) are important descriptors of population health. A recent Australian adolescent population survey provided a unique opportunity to derive preference-based HRQoL.

METHODS

Data from 2967 adolescents aged 11-17 years were analysed. An interviewer-led parent/carer questionnaire was administered for demographic variables and mental disorders of adolescents during previous 12 months using the Diagnostic Interview Schedule for Children. A self-report survey was administered to derive HRQoL using the child health utility nine-dimensions instrument (CHU-9D). Weighted HRQoL was derived for several demographic groups, mental disorder diagnosis, and youth risk behaviours.

RESULTS

The total population had a mean utility of 0.78 [standard deviation (SD): 0.20]. Males had a significantly higher mean utility (0.81, SD 0.18) than females (0.76, SD: 0.21) (Cohen's d = 0.23, p < 0.001), and utility decreased with age for both males and females (p < 0.001). Family type and some parent/carer variables were associated with significant lower HRQoL scores with small effect size. Youth risk behaviours were associated with reduced HRQoL with moderate effect sizes. Adolescents who self-harmed, had suicidal ideation, or had a mental disorder had significantly lower utilities scores with moderate to large effect sizes compared to those who did not have such conditions.

CONCLUSIONS

This study has provided contemporary Australian population norms for HRQoL in adolescents that may be used as cross comparison between studies as well as indicators allowing estimation of population health (e.g. estimation of the burden of disease) and can be used to populate future economic models.

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

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.

Child Health CheckPoint: cohort summary and methodology of a physical health and biospecimen module for the Longitudinal Study of Australian Children

OBJECTIVES

'Growing Up in Australia: The Longitudinal Study of Australian Children' (LSAC) is Australia's only nationally representative children's longitudinal study, focusing on social, economic, physical and cultural impacts on health, learning, social and cognitive development. LSAC's first decade collected wide-ranging repeated psychosocial and administrative data; here, we describe the Child Health CheckPoint, LSAC's dedicated biophysical module.

DESIGN, SETTING AND PARTICIPANTS

LSAC recruited a cross-sequential sample of 5107 infants aged 0-1 year and a sample of 4983 children aged 4-5 years in 2004, since completing seven biennial visits. CheckPoint was a cross-sectional wave that travelled Australia in 2015-2016 to reach LSAC's younger cohort at ages 11-12 years between LSAC waves 6 and 7. Parent-child pairs participated in comprehensive assessments at 15 Assessment Centres nationwide or, if unable to attend, a shorter home visit.

MEASURES

CheckPoint's intergenerational, multidimensional measures were prioritised to show meaningful variation within normal ranges and capture non-communicable disease (NCD) phenotype precursors. These included anthropometry, physical activity, fitness, time use, vision, hearing, and cardiovascular, respiratory and bone health. Biospecimens included blood, saliva, buccal swabs (also from second parent), urine, hair and toenails. The epidemiology and parent-child concordance of many measures are described in separate papers.

RESULTS

1874 (54% of eligible) parent-child pairs and 1051 second parents participated. Participants' geographical distribution mirrored the broader Australian population; however, mean socioeconomic position and parental education were higher and fewer reported non-English-speaking or Indigenous backgrounds. Application of survey weights partially mitigates that the achieved sample is less population representative than previous waves of LSAC due to non-random attrition. Completeness was uniformly high for phenotypic data (>92% of eligible), biospecimens (74%-97%) and consent (genetic analyses 98%, accessing neonatal blood spots 97%, sharing 96%).

CONCLUSIONS

CheckPoint enriches LSAC to study how NCDs develop at the molecular and phenotypic levels before overt disease emerges, and clarify the underlying dimensionality of health in childhood and mid-adulthood.