The impact of ambient ozone pollution on pneumonia: A nationwide time-series analysis

Tian, Yaohua; Wu, Yiqun; Liu, Hui; Si, Yaqin; Wu, Yao; Wang, Xiaowen; Wang, Mengying; Wu, Junhui; Chen, Libo; Chen, Wei; Wu, Tao; Gao, Pei; Hu, Yonghua

doi:10.1016/j.envint.2020.105498

Cited by 51 publications

(32 citation statements)

References 39 publications

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“…This fact was observed even in Bogotá in spite of the seasonal pattern in the time series of attentions in this city [Additional le 2]. This reduction in the number of attentions in 2015 coincided with a decrease in the gross domestic product of Colombia and it is consistent with the relationship observed by other authors between pneumonia and economic growth measured as gross domestic product [49,50]. In the case of Colombia, the country experienced a contraction of its economy in 2015 as result of the reduction in oil prices [51], the country's main export.…”

Section: Discussionsupporting

confidence: 85%

Machine Learning to Forecast Medical Attentions of Pneumonia Cases in Colombian Cities: An implementation with Air Quality, Meteorological and Admission Data

Gutiérrez

2020

Preprint

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Background: Previous authors have evidenced the relationship between air pollution-aerosols and meteorological variables with the occurrence of pneumonia. Forecasting the number of attentions of pneumonia cases may be useful to optimize the allocation of healthcare resources and support public health authorities to implement emergency plans to face an increase in patients. The purpose of this study is to implement four machine-learning methods to forecast the number of attentions of pneumonia cases in the five largest cities of Colombia by using air pollution-aerosols, and meteorological and admission data.Methods: The number of attentions of pneumonia cases in the five most populated Colombian cities was provided by public health authorities between January 2009 and December 2019. Air pollution-aerosols and meteorological data were obtained from remote sensors. Four machine-learning methods were implemented for each city. We selected the machine-learning methods with the best performance in each city and implemented two techniques to identify the most relevant variables in the forecasting developed by the best-performing machine-learning models. Results: According to R2 metric, random forest was the machine-learning method with the best performance for Bogotá, Medellín and Cali; whereas for Barranquilla, the best performance was obtained from the Bayesian adaptive regression trees, and for Cartagena, extreme gradient boosting had the best performance. The most important variables for the forecasting were related to the admission data.Conclusions: The results obtained from this study suggest that machine learning can be used to efficiently forecast the number of attentions of pneumonia cases, and therefore, it can be a useful decision-making tool for public health authorities.

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Section: Discussionsupporting

confidence: 85%

Machine Learning to Forecast Medical Attentions of Pneumonia Cases in Colombian Cities: An implementation with Air Quality, Meteorological and Admission Data

Gutiérrez

2020

Preprint

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“…After removing 835 studies during title and abstract screening, 103 were selected for full-text review, and 82 studies were excluded for the following reasons: reviews or letter (n = 4), different outcomes (n = 3), combined outcomes (n = 31), irrelevant studies (n = 10), not providing short-term effects (n = 3), not providing overall effects (n = 10), conducted in children (n = 1), unable to extract data (n = 1), and overlapping studies (n = 19). Finally, 21 studies were included for meta-analysis [9,10,[21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39].…”

Section: Resultsmentioning

confidence: 99%

“…The ranges of increase were 0.9-1.1% for PM 2.5 , 0.4-0.5% for PM 10 , and 2.4-4.0% for NO 2 . When excluding the three largest studies, which had more than 1 million cases [10,37,38], the pooled % increase of hospital admission or ER visit per 10 μg/m 3 increase in PM 2.5 and PM 10 and 10 ppb in O 3 was 1.1% (95% CI: 0.6-1.6; I 2 = 46.5%), 0.5% (95% CI: 0.3-0.7; I 2 = 0%), and 5.5% (95% CI: -1.1-2.2; I 2 = 56%), respectively. As there were six and five studies for PM 2.5 conducted in North America and East Asia, respectively, we performed a subgroup analysis.…”

Section: Resultsmentioning

confidence: 99%

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

et al. 2021

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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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“…Ground-level ozone (O3) is one of major air pollutants that has negative impacts on human health and can result in eye and nose irritation, respiratory disease, and lung function impairment (Jerrett et al, 2009;Khaniabadi et al, 2017;Huang et al, 2018). Tian et al (2020b) observed increased admissions for pneumonia associated with O3 exposure, especially for elderly people. In addition, it also has important impacts on climate as a greenhouse gas by absorbing thermal radiation (Fishman et al, 1979;IPCC, 2014).…”

Section: Introductionmentioning

confidence: 99%

Spatial and temporal changes of the ozone sensitivity in China based on satellite and ground-based observations

Wang¹,

A²,

Ding³

et al. 2020

Preprint

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Abstract. Ground-level ozone (O3) pollution has been steadily getting worse in most part of eastern China during the past five years. The non-linearity of O3 formation with its precursors like nitrogen oxides (NOx = NO + NO2) and volatile organic compounds (VOCs) are complicating effective O3 abatement plans. The diagnosis from space-based observations, the ratio of formaldehyde (HCHO) columns to tropospheric NO2 columns (HCHO / NO2), has previously been proved to be highly consistent with our current understanding of surface O3 chemistry. HCHO / NO2 ratio thresholds distinguishing O3 formation sensitivity depend on regions and O3 chemistry interactions with aerosol. To shed more light on current the O3 formation sensitivity over China, we have derived HCHO / NO2 ratio thresholds by directly connecting satellite-based HCHO / NO2 observations and ground-based O3 measurements over the major Chinese cities in this study. We find that a VOC-limited regime occurs for HCHO / NO2 4.2. The HCHO / NO2 between 2.3 and 4.2 reflects the transition between the two regimes. Our method shows that the O3 formation sensitivity tends to be VOC-limited over urban areas and NOx-limited over rural and remote areas in China. We find that there is a shift in some cities from the VOC-limited to the transitional regime that is associated with a rapid drop of anthropogenic NOx emissions owing to the widely-applied rigorous emission control strategies between 2016 and 2019. This detected spatial expansion of the transitional regime is supported by rising surface O3 concentrations. The enhanced O3 concentrations in urban areas during the COVID-19 lockdown in China indicate that a protocol with simultaneous anthropogenic NOx emissions and VOC emissions controls is essential for O3 abatement plans.

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The impact of ambient ozone pollution on pneumonia: A nationwide time-series analysis

Cited by 51 publications

References 39 publications

Machine Learning to Forecast Medical Attentions of Pneumonia Cases in Colombian Cities: An implementation with Air Quality, Meteorological and Admission Data

Machine Learning to Forecast Medical Attentions of Pneumonia Cases in Colombian Cities: An implementation with Air Quality, Meteorological and Admission Data

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

Spatial and temporal changes of the ozone sensitivity in China based on satellite and ground-based observations

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