The short-term association between air pollution and childhood asthma hospital admissions in urban areas of Hefei City in China: A time-series study

Zhang, Yanwu; Ni, Hong; Bai, Lianhua; Cheng, Qiang; Zhang, Heng; Wang, Shusi; Xie, Mingyu; Zhao, Donghui; Su, Hong

doi:10.1016/j.envres.2018.11.043

Cited by 75 publications

(45 citation statements)

References 22 publications

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“…All degrees of freedom of variables were selected according to the empirical researches. In order to completely capture the effects of air pollution and air pollutant concentrations on daily morbidity of scarlet fever, the DLNM was applied for air pollution and air pollutants in our study with both 3 degrees of freedom (DF) [20][21][22]. Using a natural cubic spline, we chose DF as 7 per year for Time to remove long term trends and seasonality [22].…”

Section: Discussionmentioning

confidence: 99%

“…In order to completely capture the effects of air pollution and air pollutant concentrations on daily morbidity of scarlet fever, the DLNM was applied for air pollution and air pollutants in our study with both 3 degrees of freedom (DF) [20][21][22]. Using a natural cubic spline, we chose DF as 7 per year for Time to remove long term trends and seasonality [22]. Additionally, we used smooth function of natural cubic splines with 3 DF in the model for cumulative precipitation, mean temperature and air pressure [23,24]..…”

Section: Discussionmentioning

confidence: 99%

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The association between ambient air pollution and scarlet fever in Qingdao, China, 2014-2018: a quantitative analysis

Tao

Han

et al. 2020

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Background: We conducted a distributed lag non-linear time series analysis to quantify the association between air pollution and scarlet fever in Qingdao city during 2014-2018. Methods: A generalized additive Mixed Model (GAMM) combined with a distributed lag non-linear model (DLNM) was applied to quantify the distributed lag effects of air pollutions on scarlet fever, with daily incidence of scarlet fever as the dependent variable and air pollutions as the independent variable adjusted for potential confounders. Results: A total of 6,316 cases of scarlet fever were notified, and there were 376 days occurring air pollution during the study period. Scarlet fever was significantly associated with air pollutions at a lag of 7 days with different RRs of air pollution degrees (1.172, 95%CI: 1.038-1.323 in mild air pollution; 1.374, 95%CI: 1.078-1.749 in moderate air pollution; 1.610, 95%CI: 1.163-2.314 in severe air pollution; 1.887, 95%CI: 1.163-3.061 in most severe air pollution). Conclusions: Our findings show that air pollution is positively associated with scarlet fever in Qingdao, and the risk of scarlet fever could be increased along with the degrees of air pollution. It contributes to developing strategies to prevent and reduce health impact from scarlet fever and other non-vaccine-preventable respiratory infectious diseases in air polluted areas.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

The association between ambient air pollution and scarlet fever in Qingdao, China, 2014-2018: a quantitative analysis

Tao

Han

et al. 2020

Preprint

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“…Epidemiological studies have shown the links between air pollution exposure and the increased prevalence and severity of asthma [1][2][3]. However, according to China's Environmental Status Bulletin (2014-2017), PM 2.5 concentrations in the Yangtze River Delta was decreased by 34.3% from 2013 to 2017.…”

Section: Introductionmentioning

confidence: 99%

The preliminary investigation of potential response biomarkers to air pollution (PAHs) exposure on childhood asthma

Bao

Jin

et al. 2020

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BackgroundIn recent five years, air quality in China has been gradually improved; however, the prevalence of asthma in China has still increased significantly. This study aims to examine whether air pollution exposure is a significant contributor to childhood asthma and to explore the mechanisms using exposure and response biomarkers. MethodsThe generalized additive model was applied to evaluate the effects of air pollutants on asthma hospitalization. Urinary 1-hydroxypyrene (1-OHPyr) was determined as PAHs exposure biomarker.Metabolic profiles and microbial diversity were screened and selected as response biomarkers. ResultsDaily average concentrations of the six common air pollutants were not significantly associated with daily childhood asthma hospitalization. Urinary 1-OHPyr level showed large variability among asthmatic children and were positively correlated with the abundance of Prevotella (genus level) and purine metabolism. ConclusionsThe biomarker of PAHs exposure is associated with metabolic and microbiological biomarkers of childhood asthma. As the potential health risk, we suggest that PAHs should be included in the national monitoring system aiming to reduce the exposure.in the role of air pollution in the prevalence of asthma [7]. Until now, there has been no update evidence regarding the association between air pollution exposure and childhood asthma in recent five years in China.In addition, polycyclic aromatic hydrocarbons (PAHs) as the most abundant component of atmospheric particulate matters has been found related to the onsets of asthma or exacerbation of asthma symptoms [8][9][10][11][12]. Recent evidence suggests that PAHs exposure altered the abundance of commensal bacteria that were associated with health outcomes. Atmospherica PAHs exposure could influence the composition and abundance of skin microbiota and induce endocrine signaling pathway disrupted [13]. It has been demonstrated that the development of asthma is highly associated with the microbiogical circumstance [14]. Atmospherica PAHs exposure may also induce the aberrant metabolic profiling focused on oxidative stress that leads to airway inflammation [15]. Importantly, studies in childhood asthma linking environmental PAH exposure, human commensal microbiota and metabolic pathways are largely unknown. To better understand how PAHs exerts its effects on childhood asthma, omics analysis based on microbiome and metabolome were performed. Therefore in the present study, we firstly used the public data to analyze the asscociation between the six common air pollutants (SO 2 , NO 2 , CO, O 3 , PM 2.5 , and PM 10 ) and childhood asthma hospitalization in Nanjing for 2015-2018. We aimed to clarify the effects of these air pollutants on asthma under current environment. Then by measuring urinary 1-OHPyr as internal exposure biomarker to PAHs, we expected to find response biomarkers based on the changes of the global metabolic signatures and throat microbial diversity in asthmatic children and explored the underlying mechanisms associate...

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“…In addition, there were lots of studies exploring association between air pollution and asthma in China. However, to our knowledge, regions of studies were often concentrated on areas with high level of air pollution in the north and central China, such as Beijing, Shanghai, Jinan, Hefei, etc [9,11,20,18,32]. Considering geographical differences and air pollution characteristics, it is essential to explore the impacts of air pollution on asthma in areas with low level of air pollution in southern China.…”

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confidence: 99%

“…Guangxi is located in southern China with low level of air pollution. The prevalence of asthma has been reported to be at a moderate level in Guangxi, China [32,33]. In this study, we divided Guangxi into 14 regions according to the city boundary, which included both coastal and inland areas with different geographical and climatic characteristics.…”

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confidence: 99%

Spatiotemporal variations of asthma admission rates and its relationship with environmental factors in Guangxi, China

Liang

Kong

Chen

et al. 2019

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BackgroundThe association between environmental factors and asthma has attracted much attention. Numerous studies have focused on the effects of air pollution or meteorological factors, but the synergistic effects and regional heterogeneity remain unclear. MethodsBetween 2014 and 2015, 8,563 hospital admissions in 2014 and 7,804 hospital admissions in 2015 due to asthma were collected from 14 regions in Guangxi, China. First, we performed a Spearman correlation coefficient model as the single factor analysis to estimate correlation between environmental factors and asthma hospitalization rates in multiple regions. Second, Generalized Additive Model (GAM) was used to analyze the synergistic effects of environmental factors on asthma hospitalization rates in 14 regions, respectively.ResultsThe results indicated that asthma hospitalization rates were high in spring and autumn. There were all nonlinear relationships between air pollutants, meteorological factors and asthma hospitalization rates, and the relationships were different from region to region. According to the single factor analysis, asthma hospitalizations were related to the daily temperature, daily range of temperature, CO, NO2, and PM2.5 in multiple regions. According to the result of synergistic effects analysis, the adjusted R-square was high in Beihai and Nanning, with values of 0.292 and 0.207, which meant that environmental factors were powerful in explaining changes of asthma hospitalization rates in Beihai and Nanning.ConclusionDaily range of temperature is an important factor impact on asthma, which should be considered in the analysis of environmental factors impact on asthma. It is suggested that the relationship between asthma and risk factors in different regions deserves additional study. Governments should develop targeted protective measures for asthma in different regions.

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The short-term association between air pollution and childhood asthma hospital admissions in urban areas of Hefei City in China: A time-series study

Cited by 75 publications

References 22 publications

The association between ambient air pollution and scarlet fever in Qingdao, China, 2014-2018: a quantitative analysis

The association between ambient air pollution and scarlet fever in Qingdao, China, 2014-2018: a quantitative analysis

The preliminary investigation of potential response biomarkers to air pollution (PAHs) exposure on childhood asthma

Spatiotemporal variations of asthma admission rates and its relationship with environmental factors in Guangxi, China

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