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International Journal of Environment Science and Technology
Center for Environment and Energy Research and Studies (CEERS)
ISSN: 1735-1472
EISSN: 1735-2630
Vol. 12, No. 11, 2015, pp. 3433-3442
Bioline Code: st15322
Full paper language: English
Document type: Research Article
Document available free of charge

International Journal of Environment Science and Technology, Vol. 12, No. 11, 2015, pp. 3433-3442

 en Air pollution and hospital admissions for cardiorespiratory diseases in Iran: artificial neural network versus conditional logistic regression
Shakerkhatibi, M.; Dianat, I.; Asghari Jafarabadi, M.; Azak, R. & Kousha, A.

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.

Keywords
Case–crossover analysis; Cardiorespiratory health effects; Hospital admissions; Air pollution

 
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