Dataset Information

Short-term associations between ambient air pollution and stroke hospitalisations: time-series study in Shenzhen, China.

ABSTRACT:

Objective

To investigate the association between ambient air pollution and stroke morbidity in different subgroups and seasons.

Methods

We performed a time-series analysis based on generalised linear models to study the short-term exposure-response relationships between air pollution and stroke hospitalisations, and conducted subgroup analyses to identify possible sensitive populations.

Results

For every 10 µg/m³ increase in the concentration of air pollutants, across lag 0-3 days, the relative risk of stroke hospitalisation was 1.029 (95% CI 1.013 to 1.045) for PM_2.5, 1.054 (95% CI 1.031 to 1.077) for NO₂ and 1.012 (95% CI 1.002 to 1.022) for O₃. Subgroup analyses showed that statistically significant associations were found in both men and women, middle-aged and older populations, and both cerebral infarction and intracerebral haemorrhage. The seasonal analyses showed that statistically significant associations were found only in the winter.

Conclusions

Our study indicates that short-term exposure to PM_2.5, NO₂ and O₃ may induce stroke morbidity, and the government should take actions to mitigate air pollution and protect sensitive populations.

SUBMITTER: Guo Y

PROVIDER: S-EPMC7103818 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Short-term associations between ambient air pollution and stroke hospitalisations: time-series study in Shenzhen, China.

Guo Yanfang Y Xie Xiufang X Lei Lin L Zhou Haibin H Deng Shizhou S Xu Ying Y Liu Zheng Z Bao Junzhe J Peng Ji J Huang Cunrui C

BMJ open 20200319 3

<h4>Objective</h4>To investigate the association between ambient air pollution and stroke morbidity in different subgroups and seasons.<h4>Methods</h4>We performed a time-series analysis based on generalised linear models to study the short-term exposure-response relationships between air pollution and stroke hospitalisations, and conducted subgroup analyses to identify possible sensitive populations.<h4>Results</h4>For every 10 µg/m<sup>3</sup> increase in the concentration of air pollutants, a ...[more]

PMID: 32198300

Similar Datasets

Project description:BackgroundInfluenza imposes a heavy burden on public health. Little is known, however, of the associations between detailed measures of exposure to ambient air pollution and influenza at an individual level.ObjectiveWe examined individual-level associations between six criteria air pollutants and influenza using case-crossover design.MethodsIn this individual-level time-stratified case-crossover study, we linked influenza cases collected by the Guangzhou Center for Disease Control and Prevention from 1 January 2013 to 31 December 2019 with individual residence-level exposure to particulate matter (PM2.5 and PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3) and carbon monoxide (CO). The exposures were estimated for the day of onset of influenza symptoms (lag 0), 1-7 d before the onset (lags 1-7), as well as an 8-d moving average (lag07), using a random forest model and linked to study participants' home addresses. Conditional logistic regression was developed to investigate the associations between short-term exposure to air pollution and influenza, adjusting for mean temperature, relative humidity, public holidays, population mobility, and community influenza susceptibility.ResultsN=108,479 eligible cases were identified in our study. Every 10-μg/m3 increase in exposure to PM2.5, PM10, NO2, and CO and every 5-μg/m3 increase in SO2 over 8-d moving average (lag07) was associated with higher risk of influenza with a relative risk (RR) of 1.028 (95% CI: 1.018, 1.038), 1.041 (95% CI: 1.032, 1.049), 1.169 (95% CI: 1.151, 1.188), 1.004 (95% CI: 1.003, 1.006), and 1.134 (95% CI: 1.107, 1.163), respectively. There was a negative association between O3 and influenza with a RR of 0.878 (95% CI: 0.866, 0.890).ConclusionsOur findings suggest that short-term exposure to air pollution, except for O3, is associated with greater risk for influenza. Further studies are necessary to decipher underlying mechanisms and design preventive interventions and policies. https://doi.org/10.1289/EHP12145.

Project description:BackgroundAir pollution is a major risk factor for planetary health and has long been suspected of predisposing humans to respiratory diseases induced by pathogens like influenza viruses. However, epidemiological evidence remains elusive due to lack of longitudinal data from large cohorts.ObjectiveOur aim is to quantify the short-term association of influenza incidence with exposure to ambient air pollutants in Chinese cities.MethodsBased on air pollutant data and influenza surveillance data from 82 cities in China over a period of 5 years, we applied a two-stage time series analysis to assess the association of daily incidence of reported influenza cases with six common air pollutants [particulate matter with aerodynamic diameter ≤2.5μm (PM2.5), particulate matter with aerodynamic diameter ≤10μm (PM10), NO2, SO2, CO, and O3], while adjusting for potential confounders including temperature, relative humidity, seasonality, and holiday effects. We built a distributed lag Poisson model for one or multiple pollutants in each individual city in the first stage and conducted a meta-analysis to pool city-specific estimates in the second stage.ResultsA total of 3,735,934 influenza cases were reported in 82 cities from 2015 to 2019, accounting for 72.71% of the overall case number reported in the mainland of China. The time series models for each pollutant alone showed that the daily incidence of reported influenza cases was positively associated with almost all air pollutants except for ozone. The most prominent short-term associations were found for SO2 and NO2 with cumulative risk ratios of 1.094 [95% confidence interval (CI): 1.054, 1.136] and 1.093 (95% CI: 1.067, 1.119), respectively, for each 10 μg/m3 increase in the concentration at each of the lags of 1-7 d. Only NO2 showed a significant association with the daily incidence of influenza cases in the multipollutant model that adjusts all six air pollutants together. The impact of air pollutants on influenza was generally found to be greater in children, in subtropical cities, and during cold months.DiscussionIncreased exposure to ambient air pollutants, particularly NO2, is associated with a higher risk of influenza-associated illness. Policies on reducing air pollution levels may help alleviate the disease burden due to influenza infection. https://doi.org/10.1289/EHP12146.

Project description:BackgroundAir pollution disparities by socioeconomic status (SES) are well documented for the United States, with most literature indicating an inverse relationship (i.e., higher concentrations for lower-SES populations). Few studies exist for China, a country accounting for 26% of global premature deaths from ambient air pollution.ObjectiveOur objective was to test the relationship between ambient air pollution exposures and SES in China.MethodsWe combined estimated year 2015 annual-average ambient levels of nitrogen dioxide (NO2) and fine particulate matter [PM ≤2.5μm in aerodynamic diameter (PM2.5)] with national demographic information. Pollution estimates were derived from a national empirical model for China at 1-km spatial resolution; demographic estimates were derived from national gridded gross national product (GDP) per capita at 1-km resolution, and (separately) a national representative sample of 21,095 individuals from the China Health and Retirement Longitudinal Study (CHARLS) 2015 cohort. Our use of global data on population density and cohort data on where people live helped avoid the spatial imprecision found in publicly available census data for China. We quantified air pollution disparities among individual's rural-to-urban migration status; SES factors (education, occupation, and income); and minority status. We compared results using three approaches to SES measurement: individual SES score, community-averaged SES score, and gridded GDP per capita.ResultsAmbient NO2 and PM2.5 levels were higher for higher-SES populations than for lower-SES population, higher for long-standing urban residents than for rural-to-urban migrant populations, and higher for the majority ethnic group (Han) than for the average across nine minority groups. For the three SES measurements (individual SES score, community-averaged SES score, gridded GDP per capita), a 1-interquartile range higher SES corresponded to higher concentrations of 6-9 μg/m3 NO2 and 3-6 μg/m3 PM2.5; average concentrations for the highest and lowest 20th percentile of SES differed by 41-89% for NO2 and 12-25% for PM2.5. This pattern held in rural and urban locations, across geographic regions, across a wide range of spatial resolution, and for modeled vs. measured pollution concentrations.ConclusionsMultiple analyses here reveal that in China, ambient NO2 and PM2.5 concentrations are higher for high-SES than for low-SES individuals; these results are robust to multiple sensitivity analyses. Our findings are consistent with the idea that in China's current industrialization and urbanization stage, economic development is correlated with both SES and air pollution. To our knowledge, our study provides the most comprehensive picture to date of ambient air pollution disparities in China; the results differ dramatically from results and from theories to explain conditions in the United States. https://doi.org/10.1289/EHP9872.

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.

Dataset Information

Short-term associations between ambient air pollution and stroke hospitalisations: time-series study in Shenzhen, China.

Objective

Methods

Results

Conclusions

Publications

Short-term associations between ambient air pollution and stroke hospitalisations: time-series study in Shenzhen, China.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets