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

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.

Five Year Trends of Particulate Matter Concentrations in Korean Regions (2015–2019): When to Ventilate?

“…According to the 2017 Global Burden of Disease Study, exposure to outdoor air pollution is one of the leading risk factors for premature death, accounting for 3.4 million deaths each year [ 2 ]. One of the most critical air pollutants is particulate matters (PM) which are considered as one of major reasons for increased prevalence or exacerbation of respiratory diseases [ 3 , 4 ], cardiovascular diseases [ 5 ], and diabetes [ 6 ]. The International Agency for Research on Cancer (IARC) has designated the atmospheric PM as a carcinogen of the same class as asbestos and those found in tobacco smoke [ 7 ].…”

Spatiotemporal variations of asthma admission rates and its relationship with environmental factors in Guangxi, China

“…Among the air pollution factors, PM 2.5 , NO 2 and CO were positively correlated with asthma in multiple regions. To our knowledge, there was no consensus among various studies on the air pollutants that affect the onset of asthma [11,15,18,[48][49][50]. Although the relationship between asthma and exposure to PM 2.5 has been frequently measured, reported conclusions have not been consistent [42].…”

“…First, the 14 regions in our study are all in Guangxi province in China, which are geographically adjacent. Spatial autocorrelation may exist in the environmental data and asthma hospitalization rates in 14 regions [50]. Second, we did not consider the effect of pollen on asthma due to the lack of pollen data.…”

Modelling and Comparing Shading Effects of 3D Tree Structures with Virtual Leaves

“…The global L measure presented similar trend for the paired comparison of spatial distribution of shadows (RL shadows versus other modalities), which revealed a strong spatial similarity of the shading patterns RL-EL, and RL-NoL, and dissimilarity of RL-HP. Moreover, L statistic worked as a superior alternative to Pearson's correlation coefficient, as it captured the magnitude of the spatially clustering of the bivariate spatial associations [63].…”