Short-Term Air Pollution as a Risk for Stroke Admission: A Time-Series Analysis

2020 ◽  
Vol 49 (4) ◽  
pp. 404-411
Author(s):  
Colm Patrick Byrne ◽  
Kathleen E. Bennett ◽  
Anne Hickey ◽  
Paul Kavanagh ◽  
Brian Broderick ◽  
...  
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Meta Analysis ◽  
Air Pollutant ◽  
Pollution Level ◽  
Short Term ◽  
Urban Centre ◽  
Stroke Incidence ◽  
Relative Risks ◽  
The Impact

Background: The harmful effects of outdoor air pollution on stroke incidence are becoming increasingly recognised. We examined the impact of different air pollutants (PM2.5, PM10, NO2, ozone, and SO2) on admission for all strokes in two Irish urban centres from 2013 to 2017. Methods: Using an ecological time series design with Poisson regression models, we analysed daily hospitalisation for all strokes and is­chaemic stroke by residence in Dublin or Cork, with air pollution level monitoring data with a lag of 0–2 days from exposure. Splines of temperature, relative humidity, day of the week, and time were included as confounders. Analysis was also performed across all four seasons. Data are presented as relative risks (RRs) and 95% confidence intervals (95% CI) per interquartile range (IQR) increase in each pollutant. Results: There was no significant association between all stroke admission and any individual air pollutant. On seasonal analysis, during winter in the larger urban centre (Dublin), we found an association between all stroke cases and an IQR increase in NO2 (RR 1.035, 95% CI: 1.003–1.069), PM10 (RR 1.032, 95% CI: 1.007–1.057), PM2.5 (RR 1.024, 95% CI: 1.011–1.039), and SO2 (RR 1.035, 95% CI: 1.001–1.071). There was no significant association found in the smaller urban area of Cork. On meta-analysis, there remained a significant association between NO2 (RR 1.013, 95% CI: 1.001–1.024) and PM2.5 (1.009, 95% CI 1.004–1.014) per IQR increase in each. Discussion: Short-term air pollution in winter was found to be associated with hospitalisation for all strokes in a large urban centre in Ireland. As Ireland has relatively low air pollution internationally, this highlights the need to introduce policy changes to reduce air pollution in all countries.

scholarly journals The Impact of COVID-19 on NO2 and PM2.5 Levels and Their Associations with Human Mobility Patterns in Singapore

2021 ◽  
Author(s):  
Yangyang Li ◽  
Yihan Zhu ◽  
Jia Yu Karen Tan ◽  
Hoong Chen Teo ◽  
Andrea Law ◽  
...  
Keyword(s):  
Air Pollution ◽  
East Coast ◽  
Human Mobility ◽  
Regional Scale ◽  
Air Pollutant ◽  
Pollution Level ◽  
The South ◽  
High Association ◽  
Pollutant Level ◽  
The Impact

AbstractThe decline in NO2 and PM2.5 pollutant levels were observed during COVID-19 around the world, especially during lockdowns. Previous studies explained such observed decline with the decrease in human mobility, whilst overlooking the meteorological changes (e.g., rainfall, wind speed) that could mediate air pollution level simultaneously. This pitfall could potentially lead to over-or under-estimation of the effect of COVID-19 on air pollution. Consequently, this study aims to re-evaluate the impact of COVID-19 on NO2 and PM2.5 pollutant level in Singapore, by incorporating the effect of meteorological parameters in predicting NO2 and PM2.5 baseline in 2020 using machine learning methods. The results found that NO2 and PM2.5 declined by a maximum of 38% and 36%, respectively, during lockdown period. As two proxies for change in human mobility, taxi availability and carpark availability were found to increase and decrease by a maximum of 12.6% and 9.8%, respectively, in 2020 from 2019 during lockdown. To investigate how human mobility influenced air pollutant level, two correlation analyses were conducted: one between PM2.5 and carpark availability changes at regional scale and the other between NO2 and taxi availability changes at a spatial resolution of 0.01°. The NO2 variation was found to be more associated with the change in human mobility, with the correlation coefficients vary spatially across Singapore. A cluster of stronger correlations were found in the South and East Coast of Singapore. Contrarily, PM2.5 and carpark availability had a weak correlation, which could be due to the limit of regional analyses. Drawing to the wider context, the high association between human mobility and NO2 in the South and East Coast area can provide insights into future NO2 reduction policy in Singapore.Graphical Abstract

scholarly journals Air pollution, SARS-CoV-2 transmission, and COVID-19 outcomes: A state-of-the-science review of a rapidly evolving research area

2020 ◽  
Author(s):  
Anushka Bhaskar ◽  
Jay Chandra ◽  
Danielle Braun ◽  
Jacqueline Cellini ◽  
Francesca Dominici
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Health Outcomes ◽  
Study Design ◽  
Air Pollutant ◽  
Short Term ◽  
Cross Sectional ◽  
Short And Long Term ◽  
Long Term Studies

Background: As the coronavirus pandemic rages on, 692,000 (August 7, 2020) human lives and counting have been lost worldwide to COVID-19. Understanding the relationship between short- and long-term exposure to air pollution and adverse COVID-19 health outcomes is crucial for developing solutions to this global crisis. Objectives: To conduct a scoping review of epidemiologic research on the link between short- and long-term exposure to air pollution and COVID-19 health outcomes. Method: We searched PubMed, Web of Science, Embase, Cochrane, MedRxiv, and BioRxiv for preliminary epidemiological studies of the association between air pollution and COVID-19 health outcomes. 28 papers were finally selected after applying our inclusion/exclusion criteria; we categorized these studies as long-term studies, short-term time-series studies, or short-term cross-sectional studies. One study included both short-term time-series and a cross-sectional study design. Results: 27 studies of the 28 reported evidence of statistically significant positive associations between air pollutant exposure and adverse COVID-19 health outcomes; 11 of 12 long-term studies and all 16 short-term studies reported statistically significant positive associations. The 28 identified studies included various confounders, spatial and temporal resolutions of pollution concentrations, and COVID-19 health outcomes. Discussion: We discuss methodological challenges and highlight additional research areas based on our findings. Challenges include data quality issues, ecological study design limitations, improved adjustment for confounders, exposure errors related to spatial resolution, geographic variability in testing, mitigation measures and pandemic stage, clustering of health outcomes, and a lack of publicly available data and code.

scholarly journals Association between exposure to ambient air pollution and hospital admission, incidence, and mortality of stroke: an updated systematic review and meta-analysis of more than 23 million participants

2021 ◽  
Vol 26 (1) ◽  
Author(s):  
Zhiping Niu ◽  
Feifei Liu ◽  
Hongmei Yu ◽  
Shaotang Wu ◽  
Hao Xiang
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Hospital Admission ◽  
Air Pollutants ◽  
Meta Analysis ◽  
Air Pollutant ◽  
Pollutant Concentration ◽  
Aerodynamic Diameter ◽  
Stroke Incidence ◽  
Incidence And Mortality

Abstract Background Previous studies have suggested that exposure to air pollution may increase stroke risk, but the results remain inconsistent. Evidence of more recent studies is highly warranted, especially gas air pollutants. Methods We searched PubMed, Embase, and Web of Science to identify studies till February 2020 and conducted a meta-analysis on the association between air pollution (PM2.5, particulate matter with aerodynamic diameter less than 2.5 μm; PM10, particulate matter with aerodynamic diameter less than 10 μm; NO2, nitrogen dioxide; SO2, sulfur dioxide; CO, carbon monoxide; O3, ozone) and stroke (hospital admission, incidence, and mortality). Fixed- or random-effects model was used to calculate pooled odds ratios (OR)/hazard ratio (HR) and their 95% confidence intervals (CI) for a 10 μg/m3 increase in air pollutant concentration. Results A total of 68 studies conducted from more than 23 million participants were included in our meta-analysis. Meta-analyses showed significant associations of all six air pollutants and stroke hospital admission (e.g., PM2.5: OR = 1.008 (95% CI 1.005, 1.011); NO2: OR = 1.023 (95% CI 1.015, 1.030), per 10 μg/m3 increases in air pollutant concentration). Exposure to PM2.5, SO2, and NO2 was associated with increased risks of stroke incidence (PM2.5: HR = 1.048 (95% CI 1.020, 1.076); SO2: HR = 1.002 (95% CI 1.000, 1.003); NO2: HR = 1.002 (95% CI 1.000, 1.003), respectively). However, no significant differences were found in associations of PM10, CO, O3, and stroke incidence. Except for CO and O3, we found that higher level of air pollution (PM2.5, PM10, SO2, and NO2) exposure was associated with higher stroke mortality (e.g., PM10: OR = 1.006 (95% CI 1.003, 1.010), SO2: OR = 1.006 (95% CI 1.005, 1.008). Conclusions Exposure to air pollution was positively associated with an increased risk of stroke hospital admission (PM2.5, PM10, SO2, NO2, CO, and O3), incidence (PM2.5, SO2, and NO2), and mortality (PM2.5, PM10, SO2, and NO2). Our study would provide a more comprehensive evidence of air pollution and stroke, especially SO2 and NO2.

scholarly journals Sepsis-related hospital admissions and ambient air pollution: a time series analysis in 6 Chinese cities

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yu Wang ◽  
Zhen Liu ◽  
Lian Yang ◽  
Jiushun Zhou ◽  
Jia Li ◽  
...  
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Hospital Admissions ◽  
Ambient Air ◽  
Air Pollutant ◽  
Additive Models ◽  
Short Term ◽  
Short Term Exposure ◽  
Hospital Stays ◽  
Age And Sex

Abstract Background Some prevalent but rarely studied causes of hospital admissions, such as sepsis is still unknown whether affected by air pollution. Methods We used time-series regression within generalized additive models to estimate the effect of air pollutant level on the sepsis-related hospital admissions, for the years 2017–18, using data from six cities in Sichuan, China. Potential effect modifications by age and sex were also explored. The effects of air pollutant on hospital stays for sepsis were also quantified. Results Positive associations between short-term exposure to NO2 and O3 and risk of sepsis-related hospital admissions and stays were found. Each 10 μg/m3 increase in short-term NO2 at lag 03 and O3 at lag 4 was associated with an increase of 2.76% (95% CI: 0.67, 4.84%) and 0.64% (95% CI: 0.14, 1.14%) hospital admissions, respectively. An increase of 0.72% (95% CI: 0.05, 1.40%) hospital stay was associated with 10 μg/m3 increase in O3 concentration at lag 4. Besides, the adverse effect of exposure to NO2 was more significant in males and population aged less than 14 years; while more significant in females and population aged 14 ~ 65 and over 65 years for exposure to O3. These associations remained stable after the adjustment of other air pollutants.8. Conclusion Exposure to ambient NO2 and O3 may cause substantial sepsis hospitalizations, and hospital stays in Sichuan, China. These associations were different in subgroup by age and sex.

scholarly journals Short-Term Effect of Air Pollution on Tuberculosis Based on Kriged Data: A Time-Series Analysis

2020 ◽  
Vol 17 (5) ◽  
pp. 1522 ◽  
Author(s):  
Shuqiong Huang ◽  
Hao Xiang ◽  
Wenwen Yang ◽  
Zhongmin Zhu ◽  
Liqiao Tian ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Pollutants ◽  
Additive Model ◽  
Ground Level ◽  
Air Pollutant ◽  
Aerodynamic Diameter ◽  
Short Term ◽  
Short Term Exposure ◽  
Incident Cases ◽  
The Impact

Tuberculosis (TB) has a very high mortality rate worldwide. However, only a few studies have examined the associations between short-term exposure to air pollution and TB incidence. Our objectives were to estimate associations between short-term exposure to air pollutants and TB incidence in Wuhan city, China, during the 2015–2016 period. We applied a generalized additive model to access the short-term association of air pollution with TB. Daily exposure to each air pollutant in Wuhan was determined using ordinary kriging. The air pollutants included in the analysis were particulate matter (PM) with an aerodynamic diameter less than or equal to 2.5 micrometers (PM2.5), PM with an aerodynamic diameter less than or equal to 10 micrometers (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ground-level ozone (O3). Daily incident cases of TB were obtained from the Hubei Provincial Center for Disease Control and Prevention (Hubei CDC). Both single- and multiple-pollutant models were used to examine the associations between air pollution and TB. Seasonal variation was assessed by splitting the all-year data into warm (May–October) and cold (November–April) seasons. In the single-pollutant model, for a 10 μg/m3 increase in PM2.5, PM10, and O3 at lag 7, the associated TB risk increased by 17.03% (95% CI: 6.39, 28.74), 11.08% (95% CI: 6.39, 28.74), and 16.15% (95% CI: 1.88, 32.42), respectively. In the multi-pollutant model, the effect of PM2.5 on TB remained statistically significant, while the effects of other pollutants were attenuated. The seasonal analysis showed that there was not much difference regarding the impact of air pollution on TB between the warm season and the cold season. Our study reveals that the mechanism linking air pollution and TB is still complex. Further research is warranted to explore the interaction of air pollution and TB.

scholarly journals A Review on Short-Term Prediction of Air Pollutant Concentrations

2018 ◽  
Vol 7 (3.23) ◽  
pp. 32
Author(s):  
Ahmad Fauzi Raffee ◽  
Siti Nazahiyah Rahmat ◽  
Hazrul Abdul Hamid ◽  
Muhammad Ismail Jaffar
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Multivariate Time Series ◽  
Air Pollutant ◽  
Short Term ◽  
Pollutant Concentrations ◽  
Univariate Time Series ◽  
Time Series Modelling ◽  
Air Pollutant Concentrations ◽  
Short Term Prediction

In the attempt to increase the production of the industrial sector to accommodate human needs; motor vehicles and power plants have led to the decline of air quality. The tremendous decline of air pollution levels can adversely affect human health, especially children, those elderly, as well as patients suffering from asthma and respiratory problems. As such, the air pollution modelling appears to be an important tool to help the local authorities in giving early warning, apart from functioning as a guide to develop policies in near future. Hence, in order to predict the concentration of air pollutants that involves multiple parameters, both artificial neural network (ANN) and principal component regression (PCR) have been widely used, in comparison to classical multivariate time series. Besides, this paper also presents comprehensive literature on univariate time series modelling. Overall, the classical multivariate time series modelling has to be further investigated so as to overcome the limitations of ANN and PCR, including univariate time series methods in short-term prediction of air pollutant concentrations.  

scholarly journals Short-term association of air pollution and health in Poland. Results from multi-city study

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
D Rabczenko ◽  
T Madej ◽  
B Wojtyniak
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Time Series Analysis ◽  
Acute Effect ◽  
Term Effect ◽  
Age Group ◽  
Short Term ◽  
Short Term Effect ◽  
Relative Risks ◽  
Pm10 And Pm2.5

Abstract Background Although time-series studies of the impact of air pollution on mortality are numerous in the literature there is noticeable lack of the results from multi-city studies in Poland. The aim of our analysis is to fill this gap and give estimates based on the most actual data. Methods A multi-city ecological time series analysis was carried out. A database contained a daily number of deaths from all causes (excluding external), by sex and age groups (below 65, 65 and more), PM10 and PM2.5 levels as well as meteorological conditions in 3 agglomerations and 19 big cities in Poland. The analysis was performed using generalized additive models. The distributed lag model method was used to investigate the mortality displacement phenomenon. Results Two versions of time-series analysis considering the level of air pollution from the current and previous day (acute effect) as well as from the past 30 days (allowing for mortality displacement) in the total population as well as in sub-populations determined by sex and age-group were done in each localization. The pooled effect estimate was calculated based on estimates obtained in each city. The value of the relative risk of death from all causes associated with an increase of air pollution with PM10 and PM2.5 was equal to (respectively) 1.005 (1.003-1.006) and 1.004 (1.002-1.005) for acute effect and 1.016 (1.011-1.021) and 1.021 (1.015-1.026) for delayed pollution effect. Higher relative risks were obtained for females and in the older age group. Conclusions A statistically significant short-term effect of air pollution with both PM10 and PM2.5 on mortality was found. The relative risks for PM2.5 were higher than for PM10. Population of females and people aged 65 years and above were more at risk. Key messages The existence of the short-term effect of dust air pollution on mortality in Poland has been confirmed. The air pollution effect modification by sex and age was found.

scholarly journals The impact of acute air pollution fluctuations on bronchiectasis pulmonary exacerbation: a case-crossover analysis

2018 ◽  
Vol 52 (1) ◽  
pp. 1702557 ◽  
Author(s):  
Pieter C. Goeminne ◽  
Bianca Cox ◽  
Simon Finch ◽  
Michael R. Loebinger ◽  
Pallavi Bedi ◽  
...  
Keyword(s):  
Air Pollution ◽  
Linkage Study ◽  
Air Pollutant ◽  
High Resolution Computed Tomography ◽  
Distributed Lag ◽  
Relative Risks ◽  
Case Crossover ◽  
Air Pollution Exposure ◽  
Pollution Exposure ◽  
The Impact

In bronchiectasis, exacerbations are believed to be triggered by infectious agents, but often no pathogen can be identified. We hypothesised that acute air pollution exposure may be associated with bronchiectasis exacerbations.We combined a case-crossover design with distributed lag models in an observational record linkage study. Patients were recruited from a specialist bronchiectasis clinic at Ninewells Hospital, Dundee, UK.We recruited 432 patients with clinically confirmed bronchiectasis, as diagnosed by high-resolution computed tomography. After excluding days with missing air pollution data, the final model for particles with a 50% cut-off aerodynamic diameter of 10 µm (PM10) was based on 6741 exacerbations from 430 patients and for nitrogen dioxide (NO2) it included 6248 exacerbations from 426 patients. For each 10 µg·m−³ increase in PM10 and NO2, the risk of having an exacerbation that same day increased significantly by 4.5% (95% CI 0.9–8.3) and 3.2% (95% CI 0.7–5.8) respectively. The overall (lag zero to four) increase in risk of exacerbation for a 10 μg·m−3 increase in air pollutant concentration was 11.2% (95% CI 6.0–16.8) for PM10 and 4.7% (95% CI 0.1–9.5) for NO2. Subanalysis showed higher relative risks during spring (PM10 1.198 (95% CI 1.102–1.303), NO2 1.146 (95% CI 1.035–1.268)) and summer (PM10 2.142 (95% CI 1.785–2.570), NO2 1.352 (95% CI 1.140–1.602)) when outdoor air pollution exposure would be expected to be highest.In conclusion, acute air pollution fluctuations are associated with increased exacerbation risk in bronchiectasis.

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