TY - JOUR
T1 - A closer look at the bivariate association between ambient air pollution and allergic diseases
T2 - The role of spatial analysis
AU - Kim, Dohyeong
AU - Seo, Sung Chul
AU - Min, Soojin
AU - Simoni, Zachary
AU - Kim, Seunghyun
AU - Kim, Myoungkon
N1 - Funding Information:
This research was funded by Ministry of Environment, Republic of Korea (grant number 2017001350002). Acknowledgments: This study was supported by a fund (“Environmental Technology Development Project: Development of Receptor-centered Exposure Assessment Methodology and Service platform, project No. 2017001350002) by Ministry of Environment, Republic of Korea. This support is greatly appreciated. All authors declare they have no actual or potential competing financial interest.
Publisher Copyright:
© 2018 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2018/8/1
Y1 - 2018/8/1
N2 - 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.
AB - 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.
KW - Air pollution
KW - Allergic disease
KW - Bivariate association
KW - Geographic information systems
KW - Spatial analysis
UR - http://www.scopus.com/inward/record.url?scp=85051299119&partnerID=8YFLogxK
U2 - 10.3390/ijerph15081625
DO - 10.3390/ijerph15081625
M3 - Article
C2 - 30071675
AN - SCOPUS:85051299119
SN - 1661-7827
VL - 15
JO - International journal of environmental research and public health
JF - International journal of environmental research and public health
IS - 8
M1 - 1625
ER -