Spatial autocorrelation may bias the risk estimation: An application of eigenvector spatial filtering on the risk of air pollutant on asthma

Abstract

© 2022 Elsevier B.V.Air pollutants are major risk factors for respiratory diseases, particularly asthma, socially and spatially correlated. Many existing environment-asthma-related studies, however, have evaluated the impact of crude trends at the largest district level, which accounts only for temporal effects and may produce biased results with spatial autocorrelation. This study aimed to investigate how the spatial autocorrelation affects the air pollution effect estimations (sulfur dioxide [SO2], nitrogen dioxide [NO2], carbon monoxide [CO], and particulate matter [PM10]) on daily asthma emergency department (ED) visits in two metropolitan areas in Korea (Seoul Metropolitan Area [SMA] and Busan Metropolitan City, Ulsan Metropolitan City, Gyeongsangnamdo [BUG]). We applied eigenvector spatial filter (ESF) to the spatio-temporal model to remove spatial autocorrelation and distributed lag nonlinear model (DLNM) to explore nonlinear patterns between air pollutant concentration and lagged days on the three models including aggregated model (a temporal model), spatial model without ESF, and spatial model with ESF (both are spatio-temporal models). The effect of SO2 was not statistically significant for asthma ED visits in the aggregated model for SMA (cumulative relative risks [CRR] = 0.99, confidence intervals [CI]: 0.93–1.05), while the effect was statistically significant in the spatial model with ESF (CRR = 1.10, CI: 1.08–1.12). NO2 and CO were positively correlated to asthma ED visits in the spatial model without ESF (CRR = 0.84, CI: 0.81–0.86; 0.91, 0.89–0.94, respectively), but the spatial model with ESF showed significant risks (CRR = 1.21, CI: 1.18–1.24; 1.13, 1.11–1.16). Moreover, the spatial model with ESF successfully removed spatial autocorrelation (P-values for Moran's I 0.83–0.98) and demonstrated the highest model fit (McFadden's pseudo R2 0.42–0.43 for SMA and 0.26–0.27 for BUG) among the three models. Our findings demonstrate how ESF can be introduced into spatial correlation to remove bias and construct more reliable models.