Evidence map›Paper›PMID 38910250›Full record

ArticleGlobal health research and policy2024

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 and 1 more

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Article in Global health research and policy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Worldwide Impact of Human Development and Inequality on the Prevalence of Asthma, Rhinoconjunctivitis and Eczema. Global Asthma Network's Ecological Study.Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology · 2026
    Article
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  6. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Jahidur Rahman KhanSchool of Clinical Medicine, University of New South Wales, Randwick, NSW, 2031, Australia. Jahid.khan@unsw.edu.au.
Raghu LingamSchool of Clinical Medicine, University of New South Wales, Randwick, NSW, 2031, Australia.
Louisa OwensSchool of Clinical Medicine, University of New South Wales, Randwick, NSW, 2031, Australia.
Katherine ChenMurdoch Children's Research Institute, Melbourne, VIC, Australia.
Shivanthan ShanthikumarMurdoch Children's Research Institute, Melbourne, VIC, Australia.
Steve OoPerth Children's Hospital, Perth, WA, Australia.
Andre SchultzPerth Children's Hospital, Perth, WA, Australia.
John WidgerWomen's and Children's Hospital, Adelaide, SA, Australia.
K Shuvo BakarSydney School of Public Health, University of Sydney, Sydney, NSW, Australia.
Adam JaffeSchool of Clinical Medicine, University of New South Wales, Randwick, NSW, 2031, Australia.
Nusrat HomairaSchool of Clinical Medicine, University of New South Wales, Randwick, NSW, 2031, Australia.

Funding

UNSW Philanthropy UNSW Philanthropy
6 · The paper itself

Abstract

backgroundAsthma 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.

methodsData 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.

resultsData 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).

conclusionsWe 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.

Indexed as

AsthmaAdolescentAustraliaBayes TheoremChildChild, PreschoolCluster AnalysisFemaleHumansInfantInfant, NewbornMalePrevalenceRisk FactorsSociodemographic FactorsSocioeconomic FactorsChildhood asthmaSocial deprivationSpatial pattern

Identifiers

PMID38910250
PMCPMC11194868

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.