A health inequality analysis of childhood asthma prevalence in urban Australia

J Allergy Clin Immunol. 2024 Aug;154(2):285-296. doi: 10.1016/j.jaci.2024.01.023. Epub 2024 Mar 12.

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.

Keywords: Asthma; Bayesian statistics; health inequalities.

MeSH terms

  • Adolescent
  • Air Pollution / adverse effects
  • Asthma* / epidemiology
  • Australia / epidemiology
  • Bayes Theorem
  • COVID-19 / epidemiology
  • Child
  • Child, Preschool
  • Cities / epidemiology
  • Female
  • Health Status Disparities*
  • Humans
  • Male
  • Prevalence
  • SARS-CoV-2
  • Social Class
  • Socioeconomic Factors
  • Urban Population