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Cited by 22Air Quality and Health ImpactsAsthma and respiratory diseasesSpatial and Panel Data Analysis

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A Closer Look at the Bivariate Association between Ambient Air Pollution and Allergic Diseases: The Role of Spatial Analysis

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Kim, Dohyeong, Seo, SungChul, Min, Soojin, Simoni, Zachary, Kim, Seunghyun, Kim, Myoungkon·2018·International Journal of Environmental Research and Public Health
DOI 10.3390/ijerph15081625PMC PMC6121458
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Abstract

Although previous ecological studies investigating the association between air pollution and allergic diseases accounted for temporal or seasonal relationships, few studies address spatial non-stationarity or autocorrelation explicitly. Our objective was to examine bivariate correlation between outdoor air pollutants and the prevalence of allergic diseases, highlighting the limitation of a non-spatial correlation measure, and suggesting an alternative to address spatial autocorrelation. The 5-year prevalence data (2011–2015) of allergic rhinitis, atopic dermatitis, and asthma were integrated with the measures of four major air pollutants (SO2, NO2, CO, and PM10) for each of the 423 sub-districts of Seoul. Lee’s L statistics, which captures how much bivariate associations are spatially clustered, was calculated and compared with Pearson’s correlation coefficient for each pair of the air pollutants and allergic diseases. A series of maps showing spatiotemporal patterns of allergic diseases at the sub-district level reveals a substantial degree of spatial heterogeneity. A high spatial autocorrelation was observed for all pollutants and diseases, leading to significant dissimilarities between the two bivariate association measures. The local L statistics identifies the areas where a specific air pollutant is considered to be contributing to a type of allergic disease. This study suggests that a bivariate correlation measure between air pollutants and allergic diseases should capture spatially-clustered phenomenon of the association, and detect the local instability in their relationships. It highlights the role of spatial analysis in investigating the contribution of the local-level spatiotemporal dynamics of air pollution to trends and the distribution of allergic diseases.

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Metadata

Authors
Kim, D., Seo, S., Min, S., Simoni, Z., Kim, S., & Kim, M.
Affiliations
  • The University of Texas at Dallas (US)
  • Eulji University (KR)
  • Department of Medical Biochemistry & Molecular Biology, Korea University, Anam-dong, Seongbuk-gu, Seoul 136-701, Korea
  • Korea University (KR)
Corresponding author
Seo, SungChul
ORCID
Venue
International Journal of Environmental Research and Public Health
Publisher
MDPI AG
Published
2018-08-01
Volume
15
Issue
8
Pages
1625
ISSN
1660-4601 (ISSN-L)
DOI
10.3390/ijerph15081625
PMC
PMC6121458
OpenAlex
W2886184476
Cited by
22
References
0
Citations by year
2026: 2 · 2025: 3 · 2024: 3 · 2023: 2 · 2022: 1 · 2021: 4 · 2020: 4 · 2019: 3
FWCI
1.36
Type
journal article
Language
en
Open access
Licence
Locations
  • International Journal of Environmental Research and Public Health (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
The Ministry of Environment, Republic of Korea (2017001350002)
Topics
  • Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Air Quality and Health Impacts
  • Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Asthma and respiratory diseases
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
Keywords
ambient air pollution, allergic diseases, spatial autocorrelation, spatial non-stationarity, allergic rhinitis, atopic dermatitis, asthma, sulfur dioxide, nitrogen dioxide, carbon monoxide, PM10, Pearson correlation, spatiotemporal patterns
MeSH terms
Air Pollutants, Air Pollution, Asthma, Female, Humans, Hypersensitivity, Prevalence, Geographic Information Systems, Spatial Analysis, Rhinitis, Allergic, Seoul

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