Stroke and vascular neurology · 2026
Short-term exposure to outdoor air pollution significantly associated with severity of acute ischaemic stroke: evidence from the 4S registry.
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Cao Nan, Hong Lan, Fang Kun, Feng Jialiang, Hu Ming, Huang Cheng, Fu Qingyan, Zheng Yang, Yang Qundi, Wang Yuzhuo, Xie Yuankai, Wang Shunyao, Cheng Xin, Dong Qiang
Аннотация
BACKGROUND: While associations between exposure to air pollution and the prevalence of acute ischaemic stroke (AIS) have been investigated, only a few studies have reported the relationship between air pollution and stroke severity. This study aimed to assess the impact of air pollution on AIS severity based on hourly monitoring data and a stroke-specific registry from 2017 to 2021 in Shanghai.
METHODS: Hourly concentrations of particulate matter (PM2.5 and PM10), O3, SO2, CO and NO2 were monitored from 2017 to 2021. A conditional logistic regression model and a Quasi-Poisson model, both coupled with a distributed lag non-linear model and a time-stratified case-crossover design, were used to evaluate a cohort of 106 623 AIS events documented within the Shanghai Stroke Service registry correspondingly. Counterfactual analyses were applied to reveal the potential reducible fractions (PRFs) of air pollution on baseline National Institute of Health Stroke Scale (NIHSS).
RESULTS: Conditional logistic regression model suggested that PM2.5 (excessive risk (ER) 2.31% (95% CI 1.32% to 3.32%), p<0.001), PM10 (ER 2.60% (95% CI 1.97% to 3.23%), p<0.001) and SO2 (ER 4.53% (95% CI 3.37% to 5.70%), p<0.001) have the most significant effects on increased baseline NIHSS score, which remained robust in the two-pollutant model. Counterfactual analysis based on a conditional logistic regression model revealed PRFs (PM2.5 1.77% (95% CI 0.86% to 2.68%), p<0.001; PM10 1.01% (95% CI 0.28% to 1.73%), p<0.001 and SO2 4.04% (95% CI 3.04% to 5.03%), p<0.001). Quasi-Poisson analysis showed similar results.
CONCLUSIONS: Short-term exposure to ambient air pollution, particularly PM2.5, PM10 and SO2, was shown to increase AIS severity. This finding underscored the need for targeted emission controls and stroke prevention strategies.
METHODS: Hourly concentrations of particulate matter (PM2.5 and PM10), O3, SO2, CO and NO2 were monitored from 2017 to 2021. A conditional logistic regression model and a Quasi-Poisson model, both coupled with a distributed lag non-linear model and a time-stratified case-crossover design, were used to evaluate a cohort of 106 623 AIS events documented within the Shanghai Stroke Service registry correspondingly. Counterfactual analyses were applied to reveal the potential reducible fractions (PRFs) of air pollution on baseline National Institute of Health Stroke Scale (NIHSS).
RESULTS: Conditional logistic regression model suggested that PM2.5 (excessive risk (ER) 2.31% (95% CI 1.32% to 3.32%), p<0.001), PM10 (ER 2.60% (95% CI 1.97% to 3.23%), p<0.001) and SO2 (ER 4.53% (95% CI 3.37% to 5.70%), p<0.001) have the most significant effects on increased baseline NIHSS score, which remained robust in the two-pollutant model. Counterfactual analysis based on a conditional logistic regression model revealed PRFs (PM2.5 1.77% (95% CI 0.86% to 2.68%), p<0.001; PM10 1.01% (95% CI 0.28% to 1.73%), p<0.001 and SO2 4.04% (95% CI 3.04% to 5.03%), p<0.001). Quasi-Poisson analysis showed similar results.
CONCLUSIONS: Short-term exposure to ambient air pollution, particularly PM2.5, PM10 and SO2, was shown to increase AIS severity. This finding underscored the need for targeted emission controls and stroke prevention strategies.