A health inequality analysis of childhood asthma prevalence in urban Australia - 12/03/24
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Abstract |
Background |
Long-standing health inequalities in Australian society that were exposed by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic were described as “fault lines” in a recent call to action by a consortium of philanthropic organizations. With asthma a major contributor to childhood disease burden, studies of its spatial epidemiology can provide valuable insights into the emergence of health inequalities early in life.
Objective |
The aims of this study were to characterize the spatial variation of asthma prevalence among children living within Australia’s 4 largest cities and quantify the relative contributions of climatic and environmental factors, outdoor air pollution, and socioeconomic status in determining this variation.
Methods |
A Bayesian model with spatial smoothing was developed to regress ecologic health status data from the 2021 Australian Census against groups of explanatory covariates intended to represent mechanistic pathways.
Results |
The prevalence of asthma in children aged 5 to 14 years averages 7.9%, 8.2%, 8.5%, and 7.6% in Sydney, Melbourne, Brisbane, and Perth, respectively. This small inter-city variation contrasts against marked intracity variation at the small-area level, which ranges from 6% to 12% between the least and most affected locations in each. Statistical variance decomposition on a subsample of Australian-born, nonindigenous children attributes 66% of the intracity spatial variation to the assembled covariates. Of these covariates, climatic and environmental factors contribute 30%, outdoor air pollution contributes 19%, and areal socioeconomic status contributes the remaining 51%.
Conclusion |
Geographic health inequalities in the prevalence of childhood asthma within Australia’s largest cities reflect a complex interplay of factors, among which socioeconomic status is a principal determinant.
Le texte complet de cet article est disponible en PDF.Key words : Asthma, health inequalities, Bayesian statistics
Abbreviations used : ABS, EVI, IRSAD, NHS, PM2.5, UCB
Plan
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