Global Health Research and Policy (Jun 2024)

Social deprivation and spatial clustering of childhood asthma in Australia

  • Jahidur Rahman Khan,
  • Raghu Lingam,
  • Louisa Owens,
  • Katherine Chen,
  • Shivanthan Shanthikumar,
  • Steve Oo,
  • Andre Schultz,
  • John Widger,
  • K. Shuvo Bakar,
  • Adam Jaffe,
  • Nusrat Homaira

DOI
https://doi.org/10.1186/s41256-024-00361-2
Journal volume & issue
Vol. 9, no. 1
pp. 1 – 10

Abstract

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Abstract Background Asthma is the most common chronic respiratory illness among children in Australia. While childhood asthma prevalence varies by region, little is known about variations at the small geographic area level. Identifying small geographic area variations in asthma is critical for highlighting hotspots for targeted interventions. This study aimed to investigate small area-level variation, spatial clustering, and sociodemographic risk factors associated with childhood asthma prevalence in Australia. Methods Data on self-reported (by parent/carer) asthma prevalence in children aged 0–14 years at statistical area level 2 (SA2, small geographic area) and selected sociodemographic features were extracted from the national Australian Household and Population Census 2021. A spatial cluster analysis was used to detect hotspots (i.e., areas and their neighbours with higher asthma prevalence than the entire study area average) of asthma prevalence. We also used a spatial Bayesian Poisson model to examine the relationship between sociodemographic features and asthma prevalence. All analyses were performed at the SA2 level. Results Data were analysed from 4,621,716 children aged 0–14 years from 2,321 SA2s across the whole country. Overall, children’s asthma prevalence was 6.27%, ranging from 0 to 16.5%, with significant hotspots of asthma prevalence in areas of greater socioeconomic disadvantage. Socioeconomically disadvantaged areas had significantly higher asthma prevalence than advantaged areas (prevalence ratio [PR] = 1.10, 95% credible interval [CrI] 1.06–1.14). Higher asthma prevalence was observed in areas with a higher proportion of Indigenous individuals (PR = 1.13, 95% CrI 1.10–1.17). Conclusions We identified significant geographic variation in asthma prevalence and sociodemographic predictors associated with the variation, which may help in designing targeted asthma management strategies and considerations for service enhancement for children in socially deprived areas.

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