The impact of outdoor air pollutants on outpatient visits for respiratory diseases during 2012–2016 in Jinan, China

Wang, Shanshan; Li, Yifan; Niu, Aimin; Liu, Yao; Song, Lili; Song, Wan-mei; Liu, Jinyue; Li, Huai-chen

doi:10.1186/s12931-018-0958-x

Cited by 27 publications

(14 citation statements)

References 39 publications

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“…However, there is lack of consistent results for sex differences in health effects of various air pollutants. Many similar studies conducted in Beijing for asthma children [43], in Ningbo for respiratory infections children [44], in Jinan for outpatient respiratory diseases [45] and in Hanoi for children lower respiratory infections [18] found that there was no obvious difference between boys and girls. Thus, additional studies are needed to clarify whether there are sex differences for the associations between air pollutants and respiratory diseases among children.…”

Section: Discussionmentioning

confidence: 96%

Association of air pollution with outpatient visits for respiratory diseases of children in an ex-heavily polluted Northwestern city, China

Liu

et al. 2020

BMC Public Health

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Background: A great number of studies have confirmed that children are a particularly vulnerable population to air pollution. Methods: In the present study, 332,337 outpatient visits of 15 hospitals for respiratory diseases among children (0-13 years), as well as the simultaneous meteorological and air pollution data, were obtained from 2014 to 2016 in Lanzhou, China. The generalized additive model was used to examine the effects of air pollutants on children's respiratory outpatient visits, including the stratified analysis of age, gender and season. Results: We found that PM 2.5 , NO 2 and SO 2 were significantly associated with the increased total respiratory outpatient visits. The increments of total respiratory outpatient visits were the highest in lag 05 for NO 2 and SO 2 , a 10 μg/m 3 increase in NO 2 and SO 2 was associated with a 2.50% (95% CI: 1.54, 3.48%) and 3.50% (95% CI: 1.51, 5.53%) increase in total respiratory outpatient visits, respectively. Those associations remained stable in twopollutant models. Through stratification analysis, all air pollutants other than PM 10 were significantly positive associated with the outpatients of bronchitis and upper respiratory tract infection. Besides, both NO 2 and SO 2 were positively related to the pneumonia outpatient visits. PM 2.5 and SO 2 were significantly related to the outpatient visits of other respiratory diseases, while only NO 2 was positively associated with the asthma outpatients. We found these associations were stronger in girls than in boys, particularly in younger (0-3 years) children. Interestingly, season stratification analysis indicated that these associations were stronger in the cold season than in the transition or the hot season for PM 10 , PM 2.5 and SO 2. Conclusions: Our results indicate that the air pollution exposure may account for the increased risk of outpatient visits for respiratory diseases among children in Lanzhou, particularly for younger children and in the cold season.

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Section: Discussionmentioning

confidence: 96%

Association of air pollution with outpatient visits for respiratory diseases of children in an ex-heavily polluted Northwestern city, China

Liu

et al. 2020

BMC Public Health

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“… 49 Another Chinese time series study of 1.4 million patient visits for respiratory diseases that included pneumonia and acute bronchitis, reported associations with higher outdoor PM 2.5 , PM 10 , NO 2 and ozone. 50 In a less-polluted part of China, PM 2.5 , PM 10 and NO 2 exposures, over the past 7 days were associated with outpatient visits for respiratory disease in a 4year period. 51 …”

Section: Discussionmentioning

confidence: 98%

Ambient air pollution and risk of respiratory infection among adults: evidence from the multiethnic study of atherosclerosis (MESA)

et al. 2021

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BackgroundAir pollution may affect the risk of respiratory infection, though research has focused on uncommon infections or infections in children. Whether ambient air pollutants increase the risk of common acute respiratory infections among adults is uncertain, yet this may help understand whether pollutants influence spread of pandemic respiratory infections like COVID-19.ObjectiveTo estimate the association between ambient air pollutant exposures and respiratory infections in adults.MethodsDuring five study examinations over 12 years, 6536 participants in the multiethnic study of atherosclerosis (MESA) reported upper respiratory tract infections, bronchitis, pneumonia or febrile illness in the preceding 2 weeks. Using a validated spatiotemporal model, we estimated residential concentrations of ambient PM2.5, NOx and NO2 for the 2–6 weeks (short-term) and year (long-term) prior to each examination.ResultsIn this population aged 44–84 years at baseline, 10%–32% of participants reported a recent respiratory infection, depending on month of examination and study region. PM2.5, NOx and NO2 concentrations over the prior 2–6 weeks were associated with increased reporting of recent respiratory infection, with risk ratios (95% CIs) of 1.04 (1.00 to 1.09), 1.15 (1.10 to 1.20) and 1.21 (1.10 to 1.33), respectively, per increase from 25th to 75th percentile in residential pollutant concentration.ConclusionHigher short-term exposure to PM2.5 and traffic-related pollutants are associated with increased risk of symptomatic acute respiratory infections among adults. These findings may provide an insight into the epidemiology of COVID-19.

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“…Based on GAM, researchers find the exposure effect of PM 2.5 is significant in 2 days, 5 days and 7 days, respectively [12]. In another research, the lag effect increases from the same day and reaches a maximum on the 3rd day, and becomes insignificant after 5 days, by applying GAM in Jinan, China [13].…”

Section: Introductionmentioning

confidence: 96%

A New Deep Learning Algorithm for Detecting the Lag Effect of Fine Particles on Hospital Emergency Visits for Respiratory Diseases

Bu²,

Xia³

et al. 2020

IEEE Access

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There exists a time lag between short-term exposure to fine particulate matter (PM 2.5) and incidence of respiratory diseases. The quantification of length of the time lag is significant for preparation and allocation of relevant medical resources. Several classic lag analysis methods have been applied to determine this length. However, different models often lead to distinct results and which one is better is subtle. The prerequisite of obtaining the reliable length is that the model can truly reveal the underlying pattern hidden in the above relationship. Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the field of deep learning, whose strong capability makes it widely applied in many fields. In this study, we manage to exploit it to acquire the time-lag length in the exposure-response relationship. The relationship between exposure and response is assumed as linear and non-linear, and models with and without confounding factors are performed under these two assumptions. Results of DLNM model show that the best hospital emergency visit prediction appears in 3 lag days, with the maximum RR value of 1.004357 (95% CI: 1.000938-1.009563). Then, a vary of LSTM models with different time steps are performed, which are evaluated by mean absolute error (MAE), the mean absolute percentage error (MAPE), the root of mean square error (RMSE) and R square (R 2). The results show that LSTM of time step 3 achieves the lowest MAE (33), MAPE (9.86), RMSE (42) and the highest R 2 (0.78), consistent with the result of DLNM model. Also, the proposed model is compared with ARIMA model, one of the commonly used forecasting models, showing better accuracy. This demonstrates that LSTM can be used as a new method to detect the lag effect of PM 2.5 on respiratory diseases.

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The impact of outdoor air pollutants on outpatient visits for respiratory diseases during 2012–2016 in Jinan, China

Cited by 27 publications

References 39 publications

Association of air pollution with outpatient visits for respiratory diseases of children in an ex-heavily polluted Northwestern city, China

Association of air pollution with outpatient visits for respiratory diseases of children in an ex-heavily polluted Northwestern city, China

Ambient air pollution and risk of respiratory infection among adults: evidence from the multiethnic study of atherosclerosis (MESA)

A New Deep Learning Algorithm for Detecting the Lag Effect of Fine Particles on Hospital Emergency Visits for Respiratory Diseases

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