Objective:

To examine temporal trends, spatial distribution, and multilevel predictors of chronic conditions in Australian children using nationally representative data.

Approach:

• Study Design: Longitudinal population-based cohort study using multilevel and spatial analyses.
• Methods: Multilevel mixed-effects logistic regression models were used to identify predictors across waves 2–8, with model fit assessed using intraclass correlation coefficient and Akaike Information Criterion.

Key Findings:

• Prevalence of chronic conditions rose from 32.1% to 58.7%.
• Significant child-level predictors included severe sleep problems (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 (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).

Interpretation:

Chronic conditions among Australian children are increasing, driven by interconnecting child, family, and geographic risk factors.

Limitations:

• Limited research has explored how chronic conditions evolve over time and vary geographically.
• Most prior studies focused on static prevalence, overlooking temporal trends and spatial variation.

Conclusion:

Multilevel interventions are needed to address rising inequalities in chronic health conditions among Australian children.