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Ambient Air Pollution and Hospitalizations for Ischemic Stroke: A Time Series Analysis Using a Distributed Lag Nonlinear Model in Chongqing, China

ABSTRACT: Short-term exposure to air pollution has been associated with ischemic stroke (IS) hospitalizations, but the evidence of its effects on IS in low- and middle-income countries is limited and inconsistent. We aimed to quantitatively estimate the association between air pollution and hospitalizations for IS in Chongqing, China. This time series study included 2,299 inpatients with IS from three hospitals in Chongqing from January 2015 to December 2016. Generalized linear regression models combined with a distributed lag nonlinear model (DLNM) were used to investigate the impact of air pollution on IS hospitalizations. Stratification analysis was further implemented by sex, age, and season. The maximum lag-specific and cumulative percentage changes of IS were 1.2% (95% CI: 0.4–2.1%, lag 3 day) and 3.6% (95% CI: 0.5–6.7%, lag 05 day) for each 10 μg/m3 increase in PM2.5; 1.0% (95% CI: 0.3–1.7%, lag 3 day) and 2.9% (95% CI: 0.6–5.2%, lag 05 day) for each 10 μg/m3 increase in PM10; 4.8% (95% CI: 0.1–9.7%, lag 4 day) for each 10 μg/m3 increase in SO2; 2.5% (95% CI: 0.3–4.7%, lag 3 day) and 8.2% (95% CI: 0.9–16.0%, lag 05 day) for each 10 μg/m3 increase in NO2; 0.7% (95% CI: 0.0–1.5%, lag 6 day) for each 10 μg/m3 increase in O3. No effect modifications were detected for sex, age, and season. Our findings suggest that short-term exposure to PM2.5, PM10, SO2, NO2, and O3 contributes to more IS hospitalizations, which warrant the government to take effective actions in addressing air pollution issues.

SUBMITTER: Chen H

PROVIDER: S-EPMC8804166 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Stroke, especially ischemic stroke (IS), has been a severe public health problem around the world. However, the association between air pollution and ischemic stroke remains ambiguous. METHODS:A total of 63, 997 IS cases aged 18 years or above in Shenzhen were collected from 2008 to 2014. We used the time-stratified case-crossover design combining with distributed lag nonlinear model (DLNM) to estimate the association between air pollution and IS onset. Furthermore, this study explored the variability across gender and age groups. RESULTS:The cumulative exposure-response curves were J-shaped for SO2, NO2 and PM10, and V-shaped for O3, and crossed over the relative risk (RR) of one. The 99th, 50th (median) and 1st percentiles of concentration (μg/m3) respectively were 37.86, 10.06, 3.71 for SO2, 116.26, 41.29, 18.51 for NO2, 145.94, 48.29, 16.14 for PM10, and 111.57, 49.82, 16.00 for O3. Extreme high-SO2, high-NO2, high-PM10, high-O3, and low-O3 concentration increased the risk of IS, with the maximum RR values and 95% CIs: 1.50(1.22, 1.84) (99th vs median) at 0-12 lag days, 1.37(1.13, 1.67) (99th vs median) at 0-10 lag days, 1.26(1.04, 1.53) (99th vs median) at 0-12 lag days, 1.25(1.04, 1.49) (99th vs median) at 0-14 lag days, and 1.29(1.03, 1.61) (1st vs median) at 0-14 lag days, respectively. The statistically significant minimal RR value and 95% CI was 0.79(0.66,0.94) at 0-10 lag days for extreme low-PM10. The elderly aged over 65 years were susceptible to extreme pollution conditions. Difference from the vulnerability of males to extreme high-SO2, high-NO2 and low-O3, females were vulnerable to extreme high-PM10 and high-O3. Comparing with the elderly, adults aged 18-64 year were immune to extreme low-NO2 and low-PM10. However, no association between CO and IS onset was found. CONCLUSIONS:SO2, NO2, PM10 and O3 exerted non-linear and delayed influence on IS, and such influence varied with gender and age. These findings may have significant public health implications for the prevention of IS.

Project description:BACKGROUND:Evidence of the short-term effects of ambient air pollution on the risk of ischemic stroke in low- and middle-income countries is limited and inconsistent. We aimed to examine the associations between air pollution and daily hospital admissions for ischemic stroke in China. METHODS AND FINDINGS:We identified hospital admissions for ischemic stroke in 2014-2016 from the national database covering up to 0.28 billion people who received Urban Employee Basic Medical Insurance (UEBMI) in China. We examined the associations between air pollution and daily ischemic stroke admission using a two-stage method. Poisson time-series regression models were firstly fitted to estimate the effects of air pollution in each city. Random-effects meta-analyses were then conducted to combine the estimates. Meta-regression models were applied to explore potential effect modifiers. More than 2 million hospital admissions for ischemic stroke were identified in 172 cities in China. In single-pollutant models, increases of 10 ?g/m3 in particulate matter with aerodynamic diameter <2.5 ?m (PM2.5), sulfur dioxide (SO2), nitrogen dioxide (NO2), and ozone (O3) and 1 mg/m3 in carbon monoxide (CO) concentrations were associated with 0.34% (95% confidence interval [CI], 0.20%-0.48%), 1.37% (1.05%-1.70%), 1.82% (1.45%-2.19%), 0.01% (-0.14%-0.16%), and 3.24% (2.05%-4.43%) increases in hospital admissions for ischemic stroke on the same day, respectively. SO2 and NO2 associations remained significant in two-pollutant models, but not PM2.5 and CO associations. The effect estimates were greater in cities with lower air pollutant levels and higher air temperatures, as well as in elderly subgroups. The main limitation of the present study was the unavailability of data on individual exposure to ambient air pollution. CONCLUSIONS:As the first national study in China to systematically examine the associations between short-term exposure to ambient air pollution and ischemic stroke, our findings indicate that transient increase in air pollution levels may increase the risk of ischemic stroke, which may have significant public health implications for the reduction of ischemic stroke burden in China.

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Ambient Air Pollution and Hospitalizations for Ischemic Stroke: A Time Series Analysis Using a Distributed Lag Nonlinear Model in Chongqing, China

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