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

M. Shakerkhatibi, I. Dianat, M. Asghari Jafarabadi, R. Azak, A. Kousha

Research output: Contribution to journalArticleResearchpeer-review

26 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)3433-3442
Number of pages10
JournalInternational Journal of Environmental Science and Technology
Volume12
Issue number11
DOIs
Publication statusPublished - 1 Nov 2015
Externally publishedYes

Keywords

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

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