Correlation of particulate ambient air pollutants (PM10, PM2.5) and respiratory emergency room admissions in Urmia using case- crossover analysis

Abstract

Abstract Introduction: Air pollution has become as one of the most serious environmental problems worldwide, due to its detrimental consequences that couse about 7 million deaths per year. Materials and Methods: This study was conducted to investigate the association of particulate air pollutants with emergency hospital admission due to the respiratory symptoms from 1394 to the end of 1396 using the case-crossover method in Urmia city. According to the International Classification of Diseases, the codes of 5 types of respiratory diseases including pneumonia, acute bronchitis, COPD, asthma, shortness of breath along with information about age, sex, address and time of admission of patients, from the information system registered in Hospital reports were collected. Raw data obtained from hospitals were reviewed and initially edited. In Excel software, duplicate patient data was identified by sorting data and deleting duplicate records in Excel software. The codes of the studied respiratory diseases were checked and in case of discrepancies with the studied codes, they were removed. Hourly data related to PM10 and PM2.5 pollutants, which were measured and recorded by the mobile station, were also collected. Data from other air pollution monitoring stations were not included in the data summary because they contained a high percentage of lost data. Invalid and erroneous data were checked and deleted. In order to remove the outdated data, the method of calculating the mean 3 times the standard deviation was used and the data that were outside the mentioned range were deleted. The effect of airborne particulate pollution indices on hospital admission of respiratory patients in this study was considered as 2 days from the date of hospital admission as exposure time and other days of the month as control days. SPSS software version 25 was used to create the database. The mixed symmetric composite covariance structure and the estimating the correctness of the maximum bound using probability distribution as well as the multinomial and link function generalized logistic regression were used. Generalized mixed linear modeling was also used to analyze the relationship between hospital admission of respiratory patients and particulate pollutants (PM10, PM2.5).

Results: The findings showed a direct and significant relationship between the cases of hospital admissions due to the pneumonia, COPD, asthma and shortness of breath with PM10 particles. So that with the increase of every 10 µg/m3 of PM10, the chances of developing, pneumonia,COPD, asthma and shortness of breath increased by 0.2, 0.2, 0.7 and 0.1%, respectively.

Conclusion: The results obtained from this study showed that in Urmia city, the concentrations of PM10 and PM2.5 were higher than the national and international standards during the study period. There was a direct and significant relationship between the hospital admission in each of the pneumonia, COPD, asthma and shortness of breath diseases with PM10 value. There was no significant relationship between PM2.5 and hospital admission with any of the studied outcomes.

Keywords: Respiratory effects, Hospital admission, Air pollution, Suspended particles, Case-crossover analysis