Air pollution and hospital admissions for cardiorespiratory diseases in Iran: artificial neural network versus conditional logistic regression

作者：Shakerkhatibi M; Dianat I; Jafarabadi M Asghari^*; Azak R; Kousha A

来源：International Journal of Environmental Science and Technology, 2015, 12(11): 3433-3442.

DOI：10.1007/s13762-015-0884-0

• Case-crossover analysis
• Cardiorespiratory health effects
• Hospital admissions
• Air pollution

This study was conducted to evaluate the relationship between air pollutants (including nitrogen oxides [NO, NO2, NOX], sulfur dioxide [SO2], carbon monoxide [CO], ozone [O-3], and particulate matter of median aerometric diameter < 10 mu m [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, O-3, 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 O-3 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.

• 出版日期2015-11