Air pollution and hospital admissions for critical illness in emergency department: a tertiary-center research in Changsha, China, 2016–2020
We aimed to comprehensively investigate the associations of air pollutants with hospital admissions for critical illness in ED. Patients with critical illness including level 1 and level 2 of the Emergency Severity Index admitted in ED of Changsha Central Hospital from January 2016 to December 2020...
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| Veröffentlicht in: | Environmental science and pollution research international 2022-03, Vol.29 (15), p.21440-21450 |
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| creator | Lin, Hang Long, Yong Su, Yingjie Song, Kun Li, Changluo Ding, Ning |
| description | We aimed to comprehensively investigate the associations of air pollutants with hospital admissions for critical illness in ED. Patients with critical illness including level 1 and level 2 of the Emergency Severity Index admitted in ED of Changsha Central Hospital from January 2016 to December 2020 were enrolled. Meteorological and air pollutants data source were collected from the National Meteorological Science Data Center. A Poisson generalized linear regression combined with a polynomial distributed lag model (PDLM) was utilized to explore the effect of air pollution on hospital admissions for critical illness in ED. Benchmarks as references (25th) were conducted for comparisons with high levels of pollutant concentrations (75th). At first, lagged effects of all different air pollutants were analyzed. Then, based on the most significant factor, analyses in subgroups were performed by gender (male and female), age ( 65), disorders (cardiovascular, neurological, respiratory), and seasons (spring, summer, autumn, and winter). A total of 47,290 patients with critical illness admitted in ED were included. The effects of air pollutants (PM
2.5
, PM
10
, SO
2
, NO
2
, O
3
and CO) on critical illness ED visits were statistically significant. Strong collinearity between PM
2.5
and PM
10
(
r
= 0.862) was found. Both single-day lag and cumulative-day lag day models showed that PM
2.5
had the strongest effects (lag 0, RR = 1.025, 95% CI 1.008–1.043, and lag 0–14, RR = 1.067, 95% CI 1.017–1.120, respectively). In both PM
2.5
and PM
10
, the risks of critical illness in male, > 65 ages, respiratory diseases, and winter increased the most significant. Air pollutants, especially PM
2.5
and PM
10
exposure, could increase the risk of critical illness admission. |
| doi_str_mv | 10.1007/s11356-021-17295-8 |
| format | Article |
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2.5
, PM
10
, SO
2
, NO
2
, O
3
and CO) on critical illness ED visits were statistically significant. Strong collinearity between PM
2.5
and PM
10
(
r
= 0.862) was found. Both single-day lag and cumulative-day lag day models showed that PM
2.5
had the strongest effects (lag 0, RR = 1.025, 95% CI 1.008–1.043, and lag 0–14, RR = 1.067, 95% CI 1.017–1.120, respectively). In both PM
2.5
and PM
10
, the risks of critical illness in male, > 65 ages, respiratory diseases, and winter increased the most significant. Air pollutants, especially PM
2.5
and PM
10
exposure, could increase the risk of critical illness admission.</description><identifier>ISSN: 0944-1344</identifier><identifier>EISSN: 1614-7499</identifier><identifier>DOI: 10.1007/s11356-021-17295-8</identifier><identifier>PMID: 34761317</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Air pollution ; Air pollution effects ; Aquatic Pollution ; Atmospheric Protection/Air Quality Control/Air Pollution ; Benchmarks ; Collinearity ; Data centers ; Earth and Environmental Science ; Ecotoxicology ; Emergency medical care ; Emergency medical services ; Environment ; Environmental Chemistry ; Environmental Health ; Environmental science ; Illnesses ; Males ; Neurological diseases ; Nitrogen dioxide ; Particulate matter ; Patient admissions ; Patients ; Pollutants ; Pollution levels ; Polynomials ; Research Article ; Respiratory diseases ; Statistical analysis ; Subgroups ; Sulfur dioxide ; Waste Water Technology ; Water Management ; Water Pollution Control ; Winter</subject><ispartof>Environmental science and pollution research international, 2022-03, Vol.29 (15), p.21440-21450</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021</rights><rights>2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.</rights><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c375t-e50018135ee727f797bd88339506fb7bfe1f0026cad4cbf30151694938dae033</citedby><cites>FETCH-LOGICAL-c375t-e50018135ee727f797bd88339506fb7bfe1f0026cad4cbf30151694938dae033</cites><orcidid>0000-0001-7805-2191</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11356-021-17295-8$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11356-021-17295-8$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34761317$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Lin, Hang</creatorcontrib><creatorcontrib>Long, Yong</creatorcontrib><creatorcontrib>Su, Yingjie</creatorcontrib><creatorcontrib>Song, Kun</creatorcontrib><creatorcontrib>Li, Changluo</creatorcontrib><creatorcontrib>Ding, Ning</creatorcontrib><title>Air pollution and hospital admissions for critical illness in emergency department: a tertiary-center research in Changsha, China, 2016–2020</title><title>Environmental science and pollution research international</title><addtitle>Environ Sci Pollut Res</addtitle><addtitle>Environ Sci Pollut Res Int</addtitle><description>We aimed to comprehensively investigate the associations of air pollutants with hospital admissions for critical illness in ED. Patients with critical illness including level 1 and level 2 of the Emergency Severity Index admitted in ED of Changsha Central Hospital from January 2016 to December 2020 were enrolled. Meteorological and air pollutants data source were collected from the National Meteorological Science Data Center. A Poisson generalized linear regression combined with a polynomial distributed lag model (PDLM) was utilized to explore the effect of air pollution on hospital admissions for critical illness in ED. Benchmarks as references (25th) were conducted for comparisons with high levels of pollutant concentrations (75th). At first, lagged effects of all different air pollutants were analyzed. Then, based on the most significant factor, analyses in subgroups were performed by gender (male and female), age (< 45, 45–65, and > 65), disorders (cardiovascular, neurological, respiratory), and seasons (spring, summer, autumn, and winter). A total of 47,290 patients with critical illness admitted in ED were included. The effects of air pollutants (PM
2.5
, PM
10
, SO
2
, NO
2
, O
3
and CO) on critical illness ED visits were statistically significant. Strong collinearity between PM
2.5
and PM
10
(
r
= 0.862) was found. Both single-day lag and cumulative-day lag day models showed that PM
2.5
had the strongest effects (lag 0, RR = 1.025, 95% CI 1.008–1.043, and lag 0–14, RR = 1.067, 95% CI 1.017–1.120, respectively). In both PM
2.5
and PM
10
, the risks of critical illness in male, > 65 ages, respiratory diseases, and winter increased the most significant. Air pollutants, especially PM
2.5
and PM
10
exposure, could increase the risk of critical illness admission.</description><subject>Air pollution</subject><subject>Air pollution effects</subject><subject>Aquatic Pollution</subject><subject>Atmospheric Protection/Air Quality Control/Air Pollution</subject><subject>Benchmarks</subject><subject>Collinearity</subject><subject>Data centers</subject><subject>Earth and Environmental Science</subject><subject>Ecotoxicology</subject><subject>Emergency medical care</subject><subject>Emergency medical services</subject><subject>Environment</subject><subject>Environmental Chemistry</subject><subject>Environmental Health</subject><subject>Environmental science</subject><subject>Illnesses</subject><subject>Males</subject><subject>Neurological diseases</subject><subject>Nitrogen dioxide</subject><subject>Particulate matter</subject><subject>Patient admissions</subject><subject>Patients</subject><subject>Pollutants</subject><subject>Pollution levels</subject><subject>Polynomials</subject><subject>Research Article</subject><subject>Respiratory diseases</subject><subject>Statistical analysis</subject><subject>Subgroups</subject><subject>Sulfur dioxide</subject><subject>Waste Water Technology</subject><subject>Water Management</subject><subject>Water Pollution 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Int</addtitle><date>2022-03-01</date><risdate>2022</risdate><volume>29</volume><issue>15</issue><spage>21440</spage><epage>21450</epage><pages>21440-21450</pages><issn>0944-1344</issn><eissn>1614-7499</eissn><abstract>We aimed to comprehensively investigate the associations of air pollutants with hospital admissions for critical illness in ED. Patients with critical illness including level 1 and level 2 of the Emergency Severity Index admitted in ED of Changsha Central Hospital from January 2016 to December 2020 were enrolled. Meteorological and air pollutants data source were collected from the National Meteorological Science Data Center. A Poisson generalized linear regression combined with a polynomial distributed lag model (PDLM) was utilized to explore the effect of air pollution on hospital admissions for critical illness in ED. Benchmarks as references (25th) were conducted for comparisons with high levels of pollutant concentrations (75th). At first, lagged effects of all different air pollutants were analyzed. Then, based on the most significant factor, analyses in subgroups were performed by gender (male and female), age (< 45, 45–65, and > 65), disorders (cardiovascular, neurological, respiratory), and seasons (spring, summer, autumn, and winter). A total of 47,290 patients with critical illness admitted in ED were included. The effects of air pollutants (PM
2.5
, PM
10
, SO
2
, NO
2
, O
3
and CO) on critical illness ED visits were statistically significant. Strong collinearity between PM
2.5
and PM
10
(
r
= 0.862) was found. Both single-day lag and cumulative-day lag day models showed that PM
2.5
had the strongest effects (lag 0, RR = 1.025, 95% CI 1.008–1.043, and lag 0–14, RR = 1.067, 95% CI 1.017–1.120, respectively). In both PM
2.5
and PM
10
, the risks of critical illness in male, > 65 ages, respiratory diseases, and winter increased the most significant. Air pollutants, especially PM
2.5
and PM
10
exposure, could increase the risk of critical illness admission.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>34761317</pmid><doi>10.1007/s11356-021-17295-8</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0001-7805-2191</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0944-1344 |
| ispartof | Environmental science and pollution research international, 2022-03, Vol.29 (15), p.21440-21450 |
| issn | 0944-1344 1614-7499 |
| language | eng |
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| source | SpringerLink Journals - AutoHoldings |
| subjects | Air pollution Air pollution effects Aquatic Pollution Atmospheric Protection/Air Quality Control/Air Pollution Benchmarks Collinearity Data centers Earth and Environmental Science Ecotoxicology Emergency medical care Emergency medical services Environment Environmental Chemistry Environmental Health Environmental science Illnesses Males Neurological diseases Nitrogen dioxide Particulate matter Patient admissions Patients Pollutants Pollution levels Polynomials Research Article Respiratory diseases Statistical analysis Subgroups Sulfur dioxide Waste Water Technology Water Management Water Pollution Control Winter |
| title | Air pollution and hospital admissions for critical illness in emergency department: a tertiary-center research in Changsha, China, 2016–2020 |
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