BMJ paediatrics open · 2026
Temporal trends, geographic patterns and predictors of chronic health conditions in Australian children: a mixed multilevel analysis.
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Abebe Ayele Mamo, Hoque Zahirul, Khanam Rasheda
Аннотация
OBJECTIVE: This study examined temporal trends, spatial distribution and multilevel predictors of chronic conditions using nationally representative data from the Longitudinal Study of Australian Children.
STUDY DESIGN: Longitudinal population-based cohort study using multilevel and spatial analyses.
METHODS: Multilevel mixed-effects logistic regression models were used to identify child-level, parent-level and context-level predictors across waves 2-8. Model fit was assessed using the intraclass correlation coefficient and Akaike Information Criterion. Spatial mapping was performed using Statistical Area Level 2. Significance was set at p < 0.05.
RESULTS: The prevalence of chronic conditions rose from 32.1% to 58.7%. Significant child-level predictors included severe sleep problems (adjusted odd ratio (AOR) = 2.98), sedentary behaviour (AOR = 1.27), poor diet (low fruit/vegetable intake: AOR = 1.14; high-fat foods: AOR = 1.88), sugary drink intake (AOR = 1.23) and obesity (AOR = 1.43). Parental and contextual predictors included Indigenous status (AOR = 1.68), financial hardship (AOR = 1.16), maternal medical conditions (AOR = 1.48), paternal depression (AOR = 1.61), low household income ($A <500: AOR = 1.26), paternal smoking (AOR = 1.41), heavy alcohol consumption by mothers (AOR = 1.77) and fathers (AOR = 1.72), residence in non-metropolitan areas (AOR = 1.53) and living in moderately to very remote areas (AOR = 2.01).
CONCLUSION: Chronic conditions among Australian children are increasing, driven by interconnecting child, family and geographic risk factors. Multilevel interventions are needed to address these rising inequalities.
STUDY DESIGN: Longitudinal population-based cohort study using multilevel and spatial analyses.
METHODS: Multilevel mixed-effects logistic regression models were used to identify child-level, parent-level and context-level predictors across waves 2-8. Model fit was assessed using the intraclass correlation coefficient and Akaike Information Criterion. Spatial mapping was performed using Statistical Area Level 2. Significance was set at p < 0.05.
RESULTS: The prevalence of chronic conditions rose from 32.1% to 58.7%. Significant child-level predictors included severe sleep problems (adjusted odd ratio (AOR) = 2.98), sedentary behaviour (AOR = 1.27), poor diet (low fruit/vegetable intake: AOR = 1.14; high-fat foods: AOR = 1.88), sugary drink intake (AOR = 1.23) and obesity (AOR = 1.43). Parental and contextual predictors included Indigenous status (AOR = 1.68), financial hardship (AOR = 1.16), maternal medical conditions (AOR = 1.48), paternal depression (AOR = 1.61), low household income ($A <500: AOR = 1.26), paternal smoking (AOR = 1.41), heavy alcohol consumption by mothers (AOR = 1.77) and fathers (AOR = 1.72), residence in non-metropolitan areas (AOR = 1.53) and living in moderately to very remote areas (AOR = 2.01).
CONCLUSION: Chronic conditions among Australian children are increasing, driven by interconnecting child, family and geographic risk factors. Multilevel interventions are needed to address these rising inequalities.