The effect of consecutive ambient air pollution on the hospital admission from chronic obstructive pulmonary disease in the Chengdu region, China

Zhang, Y; Wang, Z; Cao, Y; Zhang, L; Wang, G; Dong, F; Deng, R; Guo, B; Zeng, L; Wang, P; Dai, R; Ran, Y; Lyu, W; Miao, P; Su, S

The effect of consecutive ambient air pollution on the hospital admission from chronic obstructive pulmonary disease in the Chengdu region, China

Zhang, Y Wang, Z Cao, Y Zhang, L Wang, G Dong, F Deng, R Guo, B Zeng, L Wang, P Dai, R Ran, Y Lyu, W Miao, P Su, S

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Publisher:: Springer
Publication Type:: Journal Article
Citation:: Air Quality, Atmosphere and Health, 2021, 14, (7), pp. 1049-1061
Issue Date:: 2021-03-18

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Field	Value	Language
dc.contributor.author	Zhang, Y
dc.contributor.author	Wang, Z
dc.contributor.author	Cao, Y
dc.contributor.author	Zhang, L
dc.contributor.author	Wang, G
dc.contributor.author	Dong, F
dc.contributor.author	Deng, R
dc.contributor.author	Guo, B
dc.contributor.author	Zeng, L
dc.contributor.author	Wang, P
dc.contributor.author	Dai, R
dc.contributor.author	Ran, Y
dc.contributor.author	Lyu, W
dc.contributor.author	Miao, P
dc.contributor.author	Su, S https://orcid.org/0000-0002-5720-8852
dc.date.accessioned	2022-05-23T05:29:07Z
dc.date.available	2021-02-11
dc.date.available	2022-05-23T05:29:07Z
dc.date.issued	2021-03-18
dc.identifier.citation	Air Quality, Atmosphere and Health, 2021, 14, (7), pp. 1049-1061
dc.identifier.issn	1873-9318
dc.identifier.issn	1873-9326
dc.identifier.uri	http://hdl.handle.net/10453/157635
dc.description.abstract	Hospitalisation risks for chronic obstructive pulmonary disease (COPD) have been attributed to ambient air pollution worldwide. However, a rise in COPD hospitalisations may indicate a considerable increase in fatality rate in public health. The current study focuses on the association between consecutive ambient air pollution (CAAP) and COPD hospitalisation to offer predictable early guidance towards estimates of COPD hospital admissions in the event of consecutive exposure to air pollution. Big data analytics were collected from 3-year time series recordings (from 2015 to 2017) of both air data and COPD hospitalisation data in the Chengdu region in China. Based on the combined effects of CAAP and unit increase in air pollutant concentrations, a quasi-Poisson regression model was established, which revealed the association between CAAP and estimated COPD admissions. The results show the dynamics and outbreaks in the variations in COPD admissions in response to CAAP. Cross-validation and mean squared error (MSE) are applied to validate the goodness of fit. In both short-term and long-term air pollution exposures, Z test outcomes show that the COPD hospitalisation risk is greater for men than for women; similarly, the occurrence of COPD hospital admissions in the group of elderly people (> 65 years old) is significantly larger than that in lower age groups. The time lag between the air quality and COPD hospitalisation is also investigated, and a peak of COPD hospitalisation risk is found to lag 2 days for air quality index (AQI) and PM10, and 1 day for PM2.5. The big data-based predictive paradigm would be a measure for the early detection of a public health event in post-COVID-19. The study findings can also provide guidance for COPD admissions in the event of consecutive exposure to air pollution in the Chengdu region.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Springer
dc.relation.ispartof	Air Quality, Atmosphere and Health
dc.relation.isbasedon	10.1007/s11869-021-00998-9
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	1117 Public Health and Health Services
dc.title	The effect of consecutive ambient air pollution on the hospital admission from chronic obstructive pulmonary disease in the Chengdu region, China
dc.type	Journal Article
utslib.citation.volume	14
utslib.location.activity	Netherlands
utslib.for	1117 Public Health and Health Services
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Centre for Health Technologies (CHT)
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-05-23T05:29:03Z
pubs.issue	7
pubs.publication-status	Published
pubs.volume	14
utslib.citation.issue	7

Abstract:

Hospitalisation risks for chronic obstructive pulmonary disease (COPD) have been attributed to ambient air pollution worldwide. However, a rise in COPD hospitalisations may indicate a considerable increase in fatality rate in public health. The current study focuses on the association between consecutive ambient air pollution (CAAP) and COPD hospitalisation to offer predictable early guidance towards estimates of COPD hospital admissions in the event of consecutive exposure to air pollution. Big data analytics were collected from 3-year time series recordings (from 2015 to 2017) of both air data and COPD hospitalisation data in the Chengdu region in China. Based on the combined effects of CAAP and unit increase in air pollutant concentrations, a quasi-Poisson regression model was established, which revealed the association between CAAP and estimated COPD admissions. The results show the dynamics and outbreaks in the variations in COPD admissions in response to CAAP. Cross-validation and mean squared error (MSE) are applied to validate the goodness of fit. In both short-term and long-term air pollution exposures, Z test outcomes show that the COPD hospitalisation risk is greater for men than for women; similarly, the occurrence of COPD hospital admissions in the group of elderly people (> 65 years old) is significantly larger than that in lower age groups. The time lag between the air quality and COPD hospitalisation is also investigated, and a peak of COPD hospitalisation risk is found to lag 2 days for air quality index (AQI) and PM10, and 1 day for PM2.5. The big data-based predictive paradigm would be a measure for the early detection of a public health event in post-COVID-19. The study findings can also provide guidance for COPD admissions in the event of consecutive exposure to air pollution in the Chengdu region.

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http://hdl.handle.net/10453/157635