Spatiotemporal Analysis of Ambient Air Pollution Exposure and Respiratory Infections Cases in Beijing

Zhang, An; Zhou, Fang; Jiang, Lili; Qi, Qingwen; Wang, Jinfeng

doi:10.21101/cejph.a4247

Cited by 6 publications

(4 citation statements)

References 16 publications

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“…It is estimated that 50-150 µg/m 3 increases in PM 2.5 concentration caused a nearly one-fold increase in the risk of lower respiratory tract infection (Cohen et al, 2017). In our study, air pollutants were associated with the risk of outpatient visits for ILI over a period without the influenza pandemic, which was consistent with most studies (Chen et al, 2017;Feng et al, 2016;Liang et al, 2014;Liu et al, 2018;Nhung et al, 2017;Zhang et al, 2015). Feng et al (2016) suggested a strong positive relationship between PM 2.5 and ILI risk during the flu season after adjusting for potential variables.…”

Section: Discussionsupporting

confidence: 91%

“…Air pollution leads to many adverse health conditions, mainly manifested by respiratory or cardiac symptoms (Schraufnagel et al, 2018). In China, many studies have already demonstrated that exposure to PM 2.5 , PM 10 and other air pollutants, such as nitrogen dioxide (NO 2 ) and sulfur dioxide (SO 2 ), can induce great adverse impacts on human health, such as ischemic heart disease, cerebrovascular disease, chronic obstructive pulmonary disease (COPD), and lung cancer (Chen et al, 2018;Li et al, 2017;Song et al, 2017;Xia et al, 2017;Zhang et al, 2015). One study reported that PM 2.5 and PM 10 levels were associated with an increased risk of arrhythmia admissions among 26 Chinese cities, especially in elderly and diabetic patients (Zheng et al, 2018).…”

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confidence: 99%

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Air pollutants and outpatient visits for influenza-like illness in Beijing, China

Wang

et al. 2021

PeerJ

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Background Air pollution leads to many adverse health conditions, mainly manifested by respiratory or cardiac symptoms. Previous studies are limited as to whether air pollutants were associated to influenza-like illness (ILI). This study aimed to explore the association between air pollutants and outpatient visits for ILI, especially during an outbreak of influenza. Methods Daily counts of hospital visits for ILI were obtained from Peking University Third Hospital between January 1, 2015, and March 31, 2018. A generalized additive Poisson model was applied to examine the associations between air pollutants concentrations and daily outpatient visits for ILI when adjusted for the meteorological parameters. Results There were 35862 outpatient visits at the fever clinic for ILI cases. Air quality index (AQI), PM2.5, PM10, CO and O3 on lag0 days, as well as nitrogen dioxide (NO2) and sulfur dioxide (SO2) on lag1 days, were significantly associated with an increased risk of outpatient visits for ILI from January 2015 to November 2017. From December 2017 to March 2018, on lag0 days, air pollutants PM2.5 [risk ratio (RR) = 0.971, 95% CI: 0.963-0.979], SO2 (RR = 0.892, 95% CI: 0.840–0.948) and CO (RR = 0.306, 95% CI: 0.153–0.612) were significantly associated with a decreased risk of outpatient visits for ILI. Interestingly, on the lag2 days, all the pollutants were significantly associated with a reduced risk of outpatient visits for ILI except for O3. We did not observe the linear correlations between the outpatient visits for ILI and any of air pollutants, which were instead associated via a curvilinear relationship. Conclusions We found that the air pollutants may be associated with an increased risk of outpatient visits for ILI during the non-outbreak period and with a decreased risk during the outbreak period, which may be linked with the use of disposable face masks and the change of outdoor activities. These findings expand the current knowledge of ILI outpatient visits correlated with air pollutants during an influenza pandemic.

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Section: Discussionsupporting

confidence: 91%

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confidence: 99%

Air pollutants and outpatient visits for influenza-like illness in Beijing, China

Wang

et al. 2021

PeerJ

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“…Zhang et al [ 29 ] reported that a low concentration of PM 2·5 was related to acute respiratory infections 3 days before a visit, while a high concentration of PM 2·5 was related to the infections on the day before a visit. In this study, PM 2·5 influenced acute respiratory infections in lag 0 and lag 2.…”

Section: Discussionmentioning

confidence: 99%

A Machine Learning-Based Study of the Effects of Air Pollution and Weather in Respiratory Disease Patients Visiting Emergency Departments

Lee

Kim

Yoon

et al. 2022

Emergency Medicine International

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Background. To date, investigating respiratory disease patients visiting the emergency departments related with fined dust is limited. This study aimed to analyze the effects of two variable-weather and air pollution on respiratory disease patients who visited emergency departments. Methods. This study utilized the National Emergency Department Information System (NEDIS) database. The meteorological data were obtained from the National Climate Data Service. Each weather factor reflected the accumulated data of 4 days: a patient’s visit day and 3 days before the visit day. We utilized the RandomForestRegressor of scikit-learn for data analysis. Result. The study included 525,579 participants. This study found that multiple variables of weather and air pollution influenced the respiratory diseases of patients who visited emergency departments. Most of the respiratory disease patients had acute upper respiratory infections [J00–J06], influenza [J09–J11], and pneumonia [J12–J18], on which PM10 following temperature and steam pressure was the most influential. As the top three leading causes of admission to the emergency department, pneumonia [J12–J18], acute upper respiratory infections [J00–J06], and chronic lower respiratory diseases [J40–J47] were highly influenced by PM10. Conclusion. Most of the respiratory patients visiting EDs were diagnosed with acute upper respiratory infections, influenza, and pneumonia. Following temperature, steam pressure and PM10 had influential relations with these diseases. It is expected that the number of respiratory disease patients visiting the emergency departments will increase by day 3 when the steam pressure and temperature values are low, and the variables of air pollution are high. The number of respiratory disease patients visiting the emergency departments will increase by day 3 when the steam pressure and temperature values are low, and the variables of air pollution are high.

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“…As systematic PM 2.5 data measurement in China became available and frequently used for research since 2012, recent studies looked into health effects of high PM 2.5 concentrations by cause-specific mortality indications [ 18 , 24 – 27 ]. A most recent publication of a nationwide time-series study in China evaluated the short-term associations between PM 2.5 and daily mortality across 272 representative Chinese cities between January 2013 and December 2015.…”

Section: Introductionmentioning

confidence: 99%

Concentration-Response Relationship between PM_2.5and Daily Respiratory Deaths in China: A Systematic Review and Metaregression Analysis of Time-Series Studies

Ren

Fang

et al. 2017

BioMed Research International

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The association between the particulate matters with aerodynamic diameter ≤ 2.5 μm (PM2.5) and daily respiratory deaths, particularly the concentration-response pattern, has not been fully examined and established in China. We conducted a systematic review of time-series studies to compile information on the associations between PM2.5 concentration and respiratory deaths and used metaregression to assess the concentration-response relationship. Out of 1,957 studies screened, eleven articles in English and two articles in Chinese met the eligibility criteria. For single-day lags, per 10 μg/m3 increase in PM2.5 concentration was associated with 0.30 [95% confidence interval (CI): 0.10, 0.50] percent increase in daily respiratory deaths; for multiday lags, the corresponding increase in respiratory deaths was 0.69 (95% CI: 0.55, 0.83) percent. Difference in the effects was observed between the northern cities and the south cities in China. No statistically significant concentration-response relationship between PM2.5 concentrations and their effects was found. With increasingly wider location coverage for PM2.5 data, it is crucial to further investigate the concentration-response pattern of PM2.5 effects on respiratory and other cause-specific mortality for the refinement and adaptation of global and national air quality guidelines and targets.

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Spatiotemporal Analysis of Ambient Air Pollution Exposure and Respiratory Infections Cases in Beijing

Cited by 6 publications

References 16 publications

Air pollutants and outpatient visits for influenza-like illness in Beijing, China

Air pollutants and outpatient visits for influenza-like illness in Beijing, China

A Machine Learning-Based Study of the Effects of Air Pollution and Weather in Respiratory Disease Patients Visiting Emergency Departments

Concentration-Response Relationship between PM_2.5and Daily Respiratory Deaths in China: A Systematic Review and Metaregression Analysis of Time-Series Studies

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Spatiotemporal Analysis of Ambient Air Pollution Exposure and Respiratory Infections Cases in Beijing

Cited by 6 publications

References 16 publications

Air pollutants and outpatient visits for influenza-like illness in Beijing, China

Air pollutants and outpatient visits for influenza-like illness in Beijing, China

A Machine Learning-Based Study of the Effects of Air Pollution and Weather in Respiratory Disease Patients Visiting Emergency Departments

Concentration-Response Relationship between PM2.5and Daily Respiratory Deaths in China: A Systematic Review and Metaregression Analysis of Time-Series Studies

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Concentration-Response Relationship between PM_2.5and Daily Respiratory Deaths in China: A Systematic Review and Metaregression Analysis of Time-Series Studies