Air pollution and hospital admissions for cardiorespiratory diseases in Iran: artificial neural network versus conditional logistic regression
This study was conducted to evaluate the relationship between air pollutants (including nitrogen oxides [NO, NO2, NOX], sulfur dioxide [SO2], carbon monoxide [CO], ozone [O3], and particulate matter of median aerometric diameter
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| Veröffentlicht in: | International journal of environmental science and technology (Tehran) 2016-02, Vol.12 (11) |
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| container_title | International journal of environmental science and technology (Tehran) |
| container_volume | 12 |
| creator | Shakerkhatibi, M Dianat, I Asghari Jafarabadi, M Azak, R Kousha, A |
| description | This study was conducted to evaluate the relationship between air
pollutants (including nitrogen oxides [NO, NO2, NOX], sulfur dioxide
[SO2], carbon monoxide [CO], ozone [O3], and particulate matter of
median aerometric diameter |
| format | Article |
| fullrecord | <record><control><sourceid>bioline</sourceid><recordid>TN_cdi_bioline_primary_cria_bioline_st_st15322</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>cria_bioline_st_st15322</sourcerecordid><originalsourceid>FETCH-bioline_primary_cria_bioline_st_st153223</originalsourceid><addsrcrecordid>eNqVjV1KA0EQhOdBwWi8Q18gkv2JAd9EFH33fWlnZmPpZHrpnlVyCc_sRPQAQkFBfVTViVs0226zavpte-bOzd7W6_6675uF-7qF0iQpzQWSiXOgV7EJhRNx2MOsxkajKHnWANFYqXIRPVCARbZohExPyvmGWAtGeNR2jrP-WPkUfaePqDYbeckBx6uKkuxgBZ407urs8WnpTkdOFi9__cJdPdw_3z2uXiAJOQ6TYs96GLyCh7_QSlWz6dq2-3fhG0JkYk4</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Air pollution and hospital admissions for cardiorespiratory diseases in Iran: artificial neural network versus conditional logistic regression</title><source>Free Full-Text Journals in Chemistry</source><source>SpringerLink Journals - AutoHoldings</source><creator>Shakerkhatibi, M ; Dianat, I ; Asghari Jafarabadi, M ; Azak, R ; Kousha, A</creator><creatorcontrib>Shakerkhatibi, M ; Dianat, I ; Asghari Jafarabadi, M ; Azak, R ; Kousha, A</creatorcontrib><description>This study was conducted to evaluate the relationship between air
pollutants (including nitrogen oxides [NO, NO2, NOX], sulfur dioxide
[SO2], carbon monoxide [CO], ozone [O3], and particulate matter of
median aerometric diameter<10 µm [PM10]) and hospital
admissions for cardiovascular and respiratory diseases. The study had a
case-crossover design which was conducted in Tabriz, Iran. Daily
hospital admissions and air quality data from March 2009 to March 2011
were analyzed using the artificial neural networks (ANNs) and
conditional logistic regression modeling. The results showed
significant associations between gaseous air pollutants including NO2,
O3, and NO and hospital admissions for cardiovascular disease. Gaseous
air pollutants of NO2, NO, and CO were associated with hospital
admissions for chronic obstructive pulmonary disease, while PM10 was
associated with hospitalizations due to respiratory infections. PM10
and O3 were also associated with asthmatic hospital admissions. There
was no significant association between SO2 and studied health outcomes.
Comparing the results of logistic regressions and ANNs confirmed the
optimality of the ANNs for detection of the best predictors of hospital
admissions caused by air pollution. Further research is required to
investigate the effects of seasonal variations on air pollution-related
health outcomes.</description><identifier>ISSN: 1735-1472</identifier><language>eng</language><publisher>Center for Environment and Energy Research and Studies (CEERS)</publisher><subject>Air pollution ; Cardiorespiratory health effects ; Case-crossover analysis ; Hospital admissions</subject><ispartof>International journal of environmental science and technology (Tehran), 2016-02, Vol.12 (11)</ispartof><rights>Copyright 2015 - International Journal of Environment Science and Technology</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780</link.rule.ids></links><search><creatorcontrib>Shakerkhatibi, M</creatorcontrib><creatorcontrib>Dianat, I</creatorcontrib><creatorcontrib>Asghari Jafarabadi, M</creatorcontrib><creatorcontrib>Azak, R</creatorcontrib><creatorcontrib>Kousha, A</creatorcontrib><title>Air pollution and hospital admissions for cardiorespiratory diseases in Iran: artificial neural network versus conditional logistic regression</title><title>International journal of environmental science and technology (Tehran)</title><description>This study was conducted to evaluate the relationship between air
pollutants (including nitrogen oxides [NO, NO2, NOX], sulfur dioxide
[SO2], carbon monoxide [CO], ozone [O3], and particulate matter of
median aerometric diameter<10 µm [PM10]) and hospital
admissions for cardiovascular and respiratory diseases. The study had a
case-crossover design which was conducted in Tabriz, Iran. Daily
hospital admissions and air quality data from March 2009 to March 2011
were analyzed using the artificial neural networks (ANNs) and
conditional logistic regression modeling. The results showed
significant associations between gaseous air pollutants including NO2,
O3, and NO and hospital admissions for cardiovascular disease. Gaseous
air pollutants of NO2, NO, and CO were associated with hospital
admissions for chronic obstructive pulmonary disease, while PM10 was
associated with hospitalizations due to respiratory infections. PM10
and O3 were also associated with asthmatic hospital admissions. There
was no significant association between SO2 and studied health outcomes.
Comparing the results of logistic regressions and ANNs confirmed the
optimality of the ANNs for detection of the best predictors of hospital
admissions caused by air pollution. Further research is required to
investigate the effects of seasonal variations on air pollution-related
health outcomes.</description><subject>Air pollution</subject><subject>Cardiorespiratory health effects</subject><subject>Case-crossover analysis</subject><subject>Hospital admissions</subject><issn>1735-1472</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>RBI</sourceid><recordid>eNqVjV1KA0EQhOdBwWi8Q18gkv2JAd9EFH33fWlnZmPpZHrpnlVyCc_sRPQAQkFBfVTViVs0226zavpte-bOzd7W6_6675uF-7qF0iQpzQWSiXOgV7EJhRNx2MOsxkajKHnWANFYqXIRPVCARbZohExPyvmGWAtGeNR2jrP-WPkUfaePqDYbeckBx6uKkuxgBZ407urs8WnpTkdOFi9__cJdPdw_3z2uXiAJOQ6TYs96GLyCh7_QSlWz6dq2-3fhG0JkYk4</recordid><startdate>20160209</startdate><enddate>20160209</enddate><creator>Shakerkhatibi, M</creator><creator>Dianat, I</creator><creator>Asghari Jafarabadi, M</creator><creator>Azak, R</creator><creator>Kousha, A</creator><general>Center for Environment and Energy Research and Studies (CEERS)</general><scope>RBI</scope></search><sort><creationdate>20160209</creationdate><title>Air pollution and hospital admissions for cardiorespiratory diseases in Iran: artificial neural network versus conditional logistic regression</title><author>Shakerkhatibi, M ; Dianat, I ; Asghari Jafarabadi, M ; Azak, R ; Kousha, A</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-bioline_primary_cria_bioline_st_st153223</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Air pollution</topic><topic>Cardiorespiratory health effects</topic><topic>Case-crossover analysis</topic><topic>Hospital admissions</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shakerkhatibi, M</creatorcontrib><creatorcontrib>Dianat, I</creatorcontrib><creatorcontrib>Asghari Jafarabadi, M</creatorcontrib><creatorcontrib>Azak, R</creatorcontrib><creatorcontrib>Kousha, A</creatorcontrib><collection>Bioline International</collection><jtitle>International journal of environmental science and technology (Tehran)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shakerkhatibi, M</au><au>Dianat, I</au><au>Asghari Jafarabadi, M</au><au>Azak, R</au><au>Kousha, A</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Air pollution and hospital admissions for cardiorespiratory diseases in Iran: artificial neural network versus conditional logistic regression</atitle><jtitle>International journal of environmental science and technology (Tehran)</jtitle><date>2016-02-09</date><risdate>2016</risdate><volume>12</volume><issue>11</issue><issn>1735-1472</issn><abstract>This study was conducted to evaluate the relationship between air
pollutants (including nitrogen oxides [NO, NO2, NOX], sulfur dioxide
[SO2], carbon monoxide [CO], ozone [O3], and particulate matter of
median aerometric diameter<10 µm [PM10]) and hospital
admissions for cardiovascular and respiratory diseases. The study had a
case-crossover design which was conducted in Tabriz, Iran. Daily
hospital admissions and air quality data from March 2009 to March 2011
were analyzed using the artificial neural networks (ANNs) and
conditional logistic regression modeling. The results showed
significant associations between gaseous air pollutants including NO2,
O3, and NO and hospital admissions for cardiovascular disease. Gaseous
air pollutants of NO2, NO, and CO were associated with hospital
admissions for chronic obstructive pulmonary disease, while PM10 was
associated with hospitalizations due to respiratory infections. PM10
and O3 were also associated with asthmatic hospital admissions. There
was no significant association between SO2 and studied health outcomes.
Comparing the results of logistic regressions and ANNs confirmed the
optimality of the ANNs for detection of the best predictors of hospital
admissions caused by air pollution. Further research is required to
investigate the effects of seasonal variations on air pollution-related
health outcomes.</abstract><pub>Center for Environment and Energy Research and Studies (CEERS)</pub></addata></record> |
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| identifier | ISSN: 1735-1472 |
| ispartof | International journal of environmental science and technology (Tehran), 2016-02, Vol.12 (11) |
| issn | 1735-1472 |
| language | eng |
| recordid | cdi_bioline_primary_cria_bioline_st_st15322 |
| source | Free Full-Text Journals in Chemistry; SpringerLink Journals - AutoHoldings |
| subjects | Air pollution Cardiorespiratory health effects Case-crossover analysis Hospital admissions |
| title | Air pollution and hospital admissions for cardiorespiratory diseases in Iran: artificial neural network versus conditional logistic regression |
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