Social Deprivation and Spatial Clustering of Childhood Asthma in Australia

Overview

Journal Glob Health Res Policy

Publisher Biomed Central

Specialty Public Health

Date 2024 Jun 23

PMID 38910250

Authors

Jahidur Rahman Khan

Raghu Lingam

Louisa Owens

Katherine Chen

Shivanthan Shanthikumar

Steve Oo

Andre Schultz

John Widger

K Shuvo Bakar

Adam Jaffe

Nusrat Homaira

Affiliations

Soon will be listed here.

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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