Association of short‐term exposure to ambient air pollution with mortality from ischemic and hemorrhagic stroke

Abstract

Background Short‐term exposure to ambient air pollution has been linked to increased risk of stroke mortality, but its adverse effects on mortality from specific types of stroke including ischemic stroke and hemorrhagic stroke remain poorly understood. Methods Using the China National Mortality Surveillance System, we conducted a time‐stratified case‐crossover study among 412,567 stroke deaths in Jiangsu province, China during 2015–2019. Residential daily PM2.5, PM10, SO2, NO2, CO, and O3 exposure concentrations were extracted from the ChinaHighAirPollutants dataset for each subject. Conditional logistic regression models were performed to conduct exposure–response analyses. Results Each 10 μg/m3 increase of PM2.5, PM10, SO2, NO2, CO, and O3 was respectively associated with a 1.44%, 0.93%, 5.55%, 2.90%, 0.148%, and 0.54% increase in odds of mortality from ischemic stroke, which was significantly stronger than that from hemorrhagic stroke (percent change in odds: 0.74%, 0.51%, 3.11%, 1.15%, 0.090%, and 0.10%). The excess fraction of ischemic stroke mortality associated with PM2.5, PM10, SO2, NO2, CO, and O3 exposure was 6.90%, 6.48%, 8.21%, 8.61%, 9.67%, and 4.76%, respectively, which was also significantly higher than that of hemorrhagic stroke mortality (excess fraction: 3.49%, 3.48%, 4.69%, 3.48%, 5.86%, and 0.88%). These differences in adverse effects generally remained across sex, age, and season. Conclusions Short‐term exposure to ambient air pollution was significantly associated with increased risk of both ischemic and hemorrhagic stroke mortality and posed considerable excess mortality. Our results suggest that air pollution exposure may lead to substantially greater adverse effects on mortality from ischemic stroke than that from hemorrhagic stroke.

Short‐Term Exposure to Ambient Air Pollution and Hospital Admissions for Sequelae of Stroke in Chinese Older Adults

“…We calculated excess hospital admissions using excess fraction of admissions and number of excess admissions to quantify the hospital admission burden of sequelae of stroke attributable to air pollution according to the established associations with the following formula: where is the point estimate in the conditional logistic regression model; is the count of subjects; is the concentration of each air pollutant on case days with pollutant concentration levels above ; is the minimum exposure level for each air pollutant (Cohen et al., 2017 ; Xu et al., 2022 ). The corresponding number of excess admissions was calculated as .…”

“…We calculated excess hospital admissions using excess fraction of admissions and number of excess admissions to quantify the hospital admission burden of sequelae of stroke attributable to air pollution according to the established associations with the following formula: $$$\mathrm{E}\mathrm{x}\mathrm{c}\mathrm{e}\mathrm{s}\mathrm{s}\,\mathrm{f}\mathrm{r}\mathrm{a}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}=\frac{\sum \nolimits_{i=1}^{N}1-\frac{1}{{\mathrm{e}}^{\beta \times \left({C}_{i}-{C}_{0}\right)}}}{N}\times 100\%$$$ where $$\beta $$ is the point estimate in the conditional logistic regression model; $$N$$ is the count of subjects; $${C}_{i}$$ is the concentration of each air pollutant on case days with pollutant concentration levels above $${C}_{0}$$; $${C}_{0}$$ is the minimum exposure level for each air pollutant (Cohen et al., 2017; Xu et al., 2022). The corresponding number of excess admissions was calculated as $$\mathrm{e}\mathrm{x}\mathrm{c}\mathrm{e}\mathrm{s}\mathrm{s}\,\mathrm{f}\mathrm{r}\mathrm{a}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\times N$$.…”

Short-term exposure to ambient air pollution and readmissions for heart failure among 3660 post-discharge patients with hypertension in older Chinese adults

“…where β indicates the point estimate in conditional logistic regression models; N indicates the number of readmission cases; C i indicates the ambient air pollution exposure on case days with pollutant concentration levels exceeding C 0 ; C 0 indicates the minimum exposure level of each air pollutant; or the reference levels of each air pollutant according to the 2021 WHO air quality guidelines (AQGs) and the ambient air quality standards in China 25. The corresponding number of excess hospital readmissions was calculated as excess fraction×N.…”

Ground-level gaseous pollutants across China: daily seamless mapping and long-term spatiotemporal variations

“…Strong associations and negative effects between ambient gaseous pollution (e.g., NO2, SO2, and CO) and a variety of diseases has been demonstrated for people of all ages through multiregional and national cohort studies in China. These diseases include general mortality , cause-specific cardiovascular disease (Xu et al, 2022a), ischemic and hemorrhagic stroke (Xu et al, 2022b;Wu et al, 2022b;Cai et al, 2022;He et al, 2022), dementia mortality (Liu et al, 2022), blood pressure (Song et al, 2022;Wu et al, 2022a), renal function (Li et al, 2022a), neurodevelopmental delay (Su et al, 2022), serum liver enzymes , overweight and obesity , insomnia (Xu et al, 2021), subjective sleep quality (Wang et al, 2022), and visual impairment (Chen et al, 2022a). These studies attest well to the value of the CHAP dataset with some unique merits for the sake of public health, among others, now and in the future.…”