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

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.

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Associations of Exposure to Air Pollution with Insulin Resistance: A Systematic Review and Meta-Analysis

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph15112593 ◽

2018 ◽

Vol 15 (11) ◽

pp. 2593 ◽

Cited By ~ 9

Author(s):

Jiajia Dang ◽

Mengtong Yang ◽

Xinge Zhang ◽

Haotian Ruan ◽

Guiyu Qin ◽

...

Keyword(s):

Insulin Resistance ◽

Air Pollution ◽

Particulate Matter ◽

Cohort Studies ◽

Air Pollutants ◽

English Language ◽

Meta Analysis ◽

Percentage Change ◽

Model Assessment ◽

Aerodynamic Diameter

In this article, we review the available evidence and explore the association between air pollution and insulin resistance (IR) using meta-analytic techniques. Cohort studies published before January 2018 were selected through English-language literature searches in nine databases. Six cohort studies were included in our sample, which assessed air pollutants including PM2.5 (particulate matter with an aerodynamic diameter less than or equal to 2.5 μm), NO2(nitrogen dioxide), and PM10 (particulate matter with an aerodynamic diameter less than 10 μm). Percentage change in insulin or insulin resistance associated with air pollutants with corresponding 95% confidence interval (CI) was used to evaluate the risk. A pooled effect (percentage change) was observed, with a 1 μg/m3 increase in NO2 associated with a significant 1.25% change (95% CI: 0.67, 1.84; I2 = 0.00%, p = 0.07) in the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) and a 0.60% change (95% CI: 0.17, 1.03; I2 = 30.94%, p = 0.27) in insulin. Similar to the analysis of NO2, a 1 μg/m3 increase in PM10 was associated with a significant 2.77% change (95% CI: 0.67, 4.87; I2 = 94.98%, p < 0.0001) in HOMA-IR and a 2.75% change in insulin (95% CI: 0.45, 5.04; I2 = 58.66%, p = 0.057). No significant associations were found between PM2.5 and insulin resistance biomarkers. We conclude that increased exposure to air pollution can lead to insulin resistance, further leading to diabetes and cardiometabolic diseases. Clinicians should consider the environmental exposure of patients when making screening and treatment decisions for them.

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Short-term effects of outdoor air pollution on acute ischaemic stroke occurrence: a case-crossover study in Tianjin, China

Occupational and Environmental Medicine ◽

10.1136/oemed-2019-106301 ◽

2020 ◽

Vol 77 (12) ◽

pp. 862-867

Author(s):

Xuemei Qi ◽

Zhongyan Wang ◽

Xiaokun Guo ◽

Xiaoshuang Xia ◽

Juanjuan Xue ◽

...

Keyword(s):

Air Pollution ◽

Particulate Matter ◽

Ischaemic Stroke ◽

Acute Ischaemic Stroke ◽

Ambient Air ◽

Air Pollutant ◽

Aerodynamic Diameter ◽

Stroke Incidence ◽

Case Crossover ◽

Increased Risk

ObjectiveAmbient air pollution is associated with ischaemic stroke incidence. However, most of the previous studies used stroke-related hospital admission rather than stroke onset itself. This study aimed to evaluate the relationship between ambient air pollutant exposures and acute ischaemic stroke based on the timing of symptom onset.MethodsA time-stratified, case-crossover analysis was performed among 520 patients who had ischaemic stroke admitted to the Second Hospital of Tianjin Medical University (Tianjin, China) between 1 April 2018 and 31 March 2019 (365 days). Daily air pollutant concentrations of particulate matter with aerodynamic diameter 2.5 µm, particulate matter with aerodynamic diameter 10 µm (PM10), sulfur dioxide, nitrogen dioxide, carbon monoxide and ozone were obtained from fixed-site monitoring stations. We used conditional logistic regression to estimate OR and 95% CI corresponding to an increase in IQR of each air pollutant after adjusting for the effects of temperature and relative humidity.ResultsOverall, a higher risk of ischaemic stroke was found between April and September. During this period PM10 was associated with an increased risk of ischaemic stroke (1-day lag: OR=1.49, 95% CI 1.09 to 2.02; 3-day mean: OR=1.58, 95% CI 1.09 to 2.29) among patients between 34 and 70 years old. Positive associations were also observed between PM10 (1-day lag: OR=1.51, 95% CI 1.10 to 2.07; 3-day mean: OR=1.57, 95% CI 1.08 to 2.29), ozone (1-day lag: OR=1.83, 95% CI 1.16 to 2.87; 3-day mean: OR=1.90, 95% CI 1.06 to 3.42) and ischaemic stroke occurrence among those with hyperlipidaemia.ConclusionOur results suggest that air pollution is associated with a higher risk of ischaemic stroke in younger people or people with hyperlipidemia. These findings still need to be further investigated.

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Associations of Ambient Air Pollutants and Meteorological Factors With COVID-19 Transmission in 31 Chinese Provinces: A Time Series Study

INQUIRY The Journal of Health Care Organization Provision and Financing ◽

10.1177/00469580211060259 ◽

2021 ◽

Vol 58 ◽

pp. 004695802110602

Author(s):

Han Cao ◽

Bingxiao Li ◽

Tianlun Gu ◽

Xiaohui Liu ◽

Kai Meng ◽

...

Keyword(s):

Air Pollution ◽

Relative Humidity ◽

Wind Velocity ◽

Air Pollutants ◽

Meteorological Factors ◽

Meta Analysis ◽

Ambient Air ◽

Air Pollutant ◽

Pollutant Concentrations ◽

Air Pollutant Concentrations

Evidence regarding the effects of environmental factors on COVID-19 transmission is mixed. We aimed to explore the associations of air pollutants and meteorological factors with COVID-19 confirmed cases during the outbreak period throughout China. The number of COVID-19 confirmed cases, air pollutant concentrations, and meteorological factors in China from January 25 to February 29, 2020, (36 days) were extracted from authoritative electronic databases. The associations were estimated for a single-day lag as well as moving averages lag using generalized additive mixed models. Region-specific analyses and meta-analysis were conducted in 5 selected regions from the north to south of China with diverse air pollution levels and weather conditions and sufficient sample size. Nonlinear concentration–response analyses were performed. An increase of each interquartile range in PM2.5, PM10, SO2, NO2, O3, and CO at lag4 corresponded to 1.40 (1.37–1.43), 1.35 (1.32–1.37), 1.01 (1.00–1.02), 1.08 (1.07–1.10), 1.28 (1.27–1.29), and 1.26 (1.24–1.28) ORs of daily new cases, respectively. For 1°C, 1%, and 1 m/s increase in temperature, relative humidity, and wind velocity, the ORs were 0.97 (0.97–0.98), 0.96 (0.96–0.97), and 0.94 (0.92–0.95), respectively. The estimates of PM2.5, PM10, NO2, and all meteorological factors remained significantly after meta-analysis for the five selected regions. The concentration–response relationships showed that higher concentrations of air pollutants and lower meteorological factors were associated with daily new cases increasing. Higher air pollutant concentrations and lower temperature, relative humidity and wind velocity may favor COVID-19 transmission. Controlling ambient air pollution, especially for PM2.5, PM10, NO2, may be an important component of reducing risk of COVID-19 infection. In addition, as winter months are arriving in China, the meteorological factors may play a negative role in prevention. Therefore, it is significant to implement the public health control measures persistently in case another possible pandemic.

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Prediction of Air Pollutant Concentration Based on One-Dimensional Multi-Scale CNN-LSTM Considering Spatial-Temporal Characteristics: A Case Study of Xi’an, China

Atmosphere ◽

10.3390/atmos12121626 ◽

2021 ◽

Vol 12 (12) ◽

pp. 1626

Author(s):

Hongbin Dai ◽

Guangqiu Huang ◽

Jingjing Wang ◽

Huibin Zeng ◽

Fangyu Zhou

Keyword(s):

Air Pollution ◽

Air Pollutants ◽

Prediction Models ◽

Air Pollutant ◽

Pollutant Concentration ◽

Concentration Data ◽

Spatial Characteristics ◽

Data Prediction ◽

Temporal And Spatial ◽

Temporal And Spatial Characteristics

Air pollution has become a serious problem threatening human health. Effective prediction models can help reduce the adverse effects of air pollutants. Accurate predictions of air pollutant concentration can provide a scientific basis for air pollution prevention and control. However, the previous air pollution-related prediction models mainly processed air quality prediction, or the prediction of a single or two air pollutants. Meanwhile, the temporal and spatial characteristics and multiple factors of pollutants were not fully considered. Herein, we establish a deep learning model for an atmospheric pollutant memory network (LSTM) by both applying the one-dimensional multi-scale convolution kernel (ODMSCNN) and a long-short-term memory network (LSTM) on the basis of temporal and spatial characteristics. The temporal and spatial characteristics combine the respective advantages of CNN and LSTM networks. First, ODMSCNN is utilized to extract the temporal and spatial characteristics of air pollutant-related data to form a feature vector, and then the feature vector is input into the LSTM network to predict the concentration of air pollutants. The data set comes from the daily concentration data and hourly concentration data of six atmospheric pollutants (PM2.5, PM10, NO2, CO, O3, SO2) and 17 types of meteorological data in Xi’an. Daily concentration data prediction, hourly concentration data prediction, group data prediction and multi-factor prediction were used to verify the effectiveness of the model. In general, the air pollutant concentration prediction model based on ODMSCNN-LSTM shows a better prediction effect compared with multi-layer perceptron (MLP), CNN, and LSTM models.

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

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17051522 ◽

2020 ◽

Vol 17 (5) ◽

pp. 1522 ◽

Cited By ~ 2

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.

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Short-term exposure to air pollution and hospital admission for pneumonia: a systematic review and meta-analysis

Environmental Health ◽

10.1186/s12940-020-00687-7 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Jeong Yee ◽

Young Ah Cho ◽

Hee Jeong Yoo ◽

Hyunseo Yun ◽

Hye Sun Gwak

Keyword(s):

Systematic Review ◽

Air Pollution ◽

Hospital Admission ◽

Subgroup Analysis ◽

Air Pollutants ◽

Respiratory Health ◽

Meta Analysis ◽

Sensitivity Analyses ◽

Short Term ◽

Short Term Exposure

Abstract Background Air pollution is a major issue that poses a health threat worldwide. Although several studies investigated the adverse effects of air pollution on various diseases, few have directly demonstrated the effects on pneumonia. Therefore, we performed a systematic review and meta-analysis on the associations between short-term exposure of air pollutants and hospital admission or emergency room (ER) visit for pneumonia. Methods A literature search was performed using PubMed, Embase, and Web of Science up to April 10, 2020. Pooled estimates were calculated as % increase with 95% confidence intervals using a random-effects model. A sensitivity analysis using the leave-one-out method and subgroup analysis by region were performed. Results A total of 21 studies were included in the analysis. Every 10 μg/m3 increment in PM2.5 and PM10 resulted in a 1.0% (95% CI: 0.5–1.5) and 0.4% (95% CI: 0.2–0.6) increase in hospital admission or ER visit for pneumonia, respectively. Every 1 ppm increase of CO and 10 ppb increase of NO2, SO2, and O3 was associated with 4.2% (95% CI: 0.6–7.9), 3.2% (95% CI: 1.3–5.1), 2.4% (95% CI: − 2.0-7.1), and 0.4% (95% CI: 0–0.8) increase in pneumonia-specific hospital admission or ER visit, respectively. Except for CO, the sensitivity analyses yielded similar results, demonstrating the robustness of the results. In a subgroup analysis by region, PM2.5 increased hospital admission or ER visit for pneumonia in East Asia but not in North America. Conclusion By combining the inconsistent findings of several studies, this study revealed the associations between short-term exposure of air pollutants and pneumonia-specific hospital admission or ER visit, especially for PM and NO2. Based on the results, stricter intervention policies regarding air pollution and programs for protecting human respiratory health should be implemented.

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Air Pollution Is Associated with Poor Cognitive Function in Taiwanese Adults

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18010316 ◽

2021 ◽

Vol 18 (1) ◽

pp. 316

Author(s):

Meng-Chieh Chen ◽

Chen-Feng Wang ◽

Bo-Cheng Lai ◽

Sun-Wung Hsieh ◽

Szu-Chia Chen ◽

...

Keyword(s):

Air Pollution ◽

Particulate Matter ◽

Cognitive Function ◽

Cognitive Decline ◽

Cognitive Functioning ◽

Air Pollutants ◽

Protective Factor ◽

Linear Regression Analysis ◽

Aerodynamic Diameter ◽

High Nitrogen

The issue of air pollution is gaining increasing attention worldwide, and mounting evidence has shown an association between air pollution and cognitive decline. The aim of this study was to investigate the relationships between air pollutants and cognitive impairment using the Mini-Mental State Exam (MMSE) and its sub-domains. In this study, we used data from the Taiwan Biobank combined with detailed daily data on air pollution. Cognitive function was assessed using the MMSE and its five subgroups of cognitive functioning. After multivariable linear regression analysis, a high level of particulate matter with an aerodynamic diameter of ≤2.5 μm (PM2.5), low ozone (O3), high carbon monoxide (CO), high sulfur dioxide (SO2), high nitric oxide (NO), high nitrogen dioxide (NO2), and high nitrogen oxide (NOx) were significantly associated with low total MMSE scores. Further, high SO2 and low O3 were significantly associated with low MMSE G1 scores. Low O3, high CO, high SO2, high NO2, and high NOx were significantly associated with low MMSE G4 scores, and high PM2.5, high particulate matter with an aerodynamic diameter of ≤10 μm (PM10), high SO2, high NO2, and high NOx were significantly associated with low MMSE G5 scores. Our results showed that exposure to different air pollutants may lead to general cognitive decline and impairment of specific domains of cognitive functioning, and O3 may be a protective factor. These findings may be helpful in the development of policies regarding the regulation of air pollution.

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Impact of air pollution on renal outcomes: a systematic review and meta-analysis protocol

BMJ Open ◽

10.1136/bmjopen-2020-041088 ◽

2021 ◽

Vol 11 (1) ◽

pp. e041088

Author(s):

Aghilès Hamroun ◽

Aurore Camier ◽

Jean Joel Bigna ◽

François Glowacki

Keyword(s):

Chronic Kidney Disease ◽

Air Pollution ◽

Kidney Disease ◽

Air Pollutants ◽

Meta Analysis ◽

Air Pollutant ◽

Cochrane Library ◽

Final Report ◽

Kidney Graft ◽

Renal Outcomes

IntroductionChronic kidney disease is a serious and a frequent disease associated with a high risk of morbi-mortality. Although several risk factors have already been well addressed, mostly diabetes and hypertension, many remain underappreciated, such as chronic exposure to air pollution.Methods and analysisWe will search EMBASE, PubMed, Web of Science, Cochrane Library and CINAHL database, from inception to 31 March 2020, for relevant records using a combination of keywords related to the type of exposure (ozone, carbon monoxide, nitrogen oxides and dioxide, sulfur dioxide, PM2.5, PMcoarse and PM10) and to the type of outcome (chronic kidney disease, end-stage renal/kidney disease, kidney failure, proteinuria/albuminuria, renal function, renal transplant, kidney graft, kidney transplant failure, nephrotic syndrome and kidney cancer). The review will be reported according to the guidelines of the Meta-analysis Of Observational Studies in Epidemiology. Two independent reviewers will select studies without design or language restrictions, using original data and investigating the association between exposure to one or more of the prespecified air pollutants and subsequent risk of renal outcomes. Using random-effects meta-analyses, we will present pooled summary statistics (HR, OR or beta-coefficients with their respective 95% CI) associated with a standardised increase in each pollutant level. The results will be presented by air pollutant and outcome. Heterogeneity will be assessed using the χ2 test on Cochran’s Q statistic and quantified by calculating I2. The Egger’s test and visual inspection of funnel plots will be used to assess publication bias.Ethics and disseminationSince primary data are not collected in this study, ethical approval is not required. This review is expected to provide relevant data on the associations between various air pollutants’ exposure and renal outcomes. The final report will be published in an international peer-reviewed journal.PROSPERO registration numberCRD42020187956.

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Long-term Exposure to Air Pollution and Lipometabolic Disturbance: A Systematic Review and Meta-Analysis

10.1101/2020.05.19.20106849 ◽

2020 ◽

Author(s):

Haohao Chen ◽

Ye Zhu ◽

Liuhua Shi ◽

Andrew Rosenberg ◽

Lixin Tao ◽

...

Keyword(s):

Air Pollution ◽

Air Pollutants ◽

Meta Analysis ◽

Random Effect ◽

Random Effect Model ◽

Blood Lipid ◽

The United States ◽

Air Pollutant ◽

Lipid Parameter

Abstract Background: Growing evidence suggests that long-term exposure to air pollutants is associated with cardiovascular morbidity, including lipometabolic disturbance. Objectives: To explore the chronic effects of air pollutants on lipometabolic disturbance via detectable lipoprotein parameters. Methods: Seven online databases were searched to conduct a meta-analysis of epidemiological studies examining the relationship between air pollution and lipid parameter levels. Subgroup analysis was additionally carried out for each air pollutant studied. Results: A total of 2,274 records were retrieved, resulting in 10 studies included in the final quantitative meta-analysis, comprising seven studies in Europe and the United States and three studies in mainland China. Using a random-effect model, the results showed that for each 10 μg/m3 increase in PM2.5, TC, LDL-C, and HDL-C levels and metabolic syndrome (MetS) incidence increased by 3.31% (95% CI: 2.29%, 8.91%), 2.34% (95% CI: 1.30%, 3.39%), -1.57% (95% CI: -1.85%, -1.28%), and 4.33% (95% CI: 2.69%, 5.98%), respectively; for each 10 μg/m3 increase in PM10, TG, HDL-C, and LDL-C levels increased by 5.27% (95% CI: 2.03%, 8.50%), -0.24% (95% CI: -0.95%, -0.47%), and 0.45% (95% CI: -0.57%, 1.47%), respectively; for each 10 μg/m3 increase in NO2, TG and HDL-C levels increased by 4.18% (95% CI: 1.12%, 7.23%) and -0.51% (95% CI: -2.61%, 1.58%), respectively. No significant associations were detected for combinations of air pollutants on lipometabolic disturbance. Conclusion: Increased air pollutant exposure was significantly associated with changes in blood lipid parameter levels, which can be an indicator of the body's lipometabolic disturbance.

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The Long-Term Effect of Exposure to Particulate Matter Air Pollution on the Incidence of Myocardial Infarction: A Systematic Review and Meta-Analysis

10.21203/rs.3.rs-440374/v1 ◽

2021 ◽

Author(s):

Masoud Khosravipour ◽

Roya Safari-Faramani ◽

Fatemeh Rajati ◽

Fariborz Omidi

Keyword(s):

Myocardial Infarction ◽

Air Pollution ◽

Particulate Matter ◽

Subgroup Analysis ◽

Meta Analysis ◽

Aerodynamic Diameter ◽

Long Term Effect ◽

Inverse Variance ◽

Long Term Impact

Abstract This study systematically reviews the long-term impact of exposure to particulate matter (PM) air pollution with aerodynamic diameter ≤ 10 µm on the incidence of myocardial infarction (MI). The relevant databases were searched with appropriate keywords on February 29, 2020. A random-effects model through a generic inverse-variance method was used to calculate the pooled hazard ratio (HR) and 95% confidence interval (CI) of MI. The number of 17 cohort studies with more than 18 million participants and 800,000 cases of MI were included. A significantly higher risk of MI was observed per 1 µg/m3 increment of PM with aerodynamic diameter ≤ 10 µm (HR= 1.02,95 % CI = 1.01, 1.03). Subgroup analysis according to the study population indicates subjects with cardiovascular diseases history had a significantly greater risk of MI per 1 µg/m3 increase in PM with aerodynamic diameter ≤ 10 µm level (HR= 1.05,95% CI= 1.01, 1.08). Subgroup analysis according to aerodynamic diameter of PM showed only a significant stronger risk of MI per 1 µg/m3 increase in PM with aerodynamic diameter < 2.5 µm (HR= 1.01,95% CI= 1.00, 1.02). The pooled result confirms a significant association between the long-term exposure to PM air pollution and the developing of MI.

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