The short-term association between air pollution and asthma hospitalization: a time-series analysis

Abstract

Exposure to air pollutants has been linked to the exacerbation of the respiratory disease. The air quality index and air quality health index (AQHI) were used to evaluate the number of asthma hospitalization attributed to air quality using time-series regression analysis with a distributed lag model. This study was performed from March 2014 to February 2019 in Hamadan, Iran. A generalized linear model (GLM) and distributed lag model (DLM) with the family of Poisson distribution were used to investigate the asthma hospitalization associated with air pollution. The seasonal effect of air pollution was considered in data analysis. Results showed that during the study period, 671 people were hospitalized. The mean (SD) values for AQI, AQHI, and PM2.5 levels were 70.66 (37.77), 3.58 (2.22), and 38.59 (17.43), respectively. The highest adverse effects on asthma hospitalization were observed for AQHI at lag 1 (RR\u2009=\u20091.052, 95% CI 0.998–1.109) and for AQI at lag 1 (RR\u2009=\u20091.002, 95% CI 0.997–1.007), for 1-unit increase of the indexes, and were observed for PM2.5 at lag 0 (RR\u2009=\u20091.005, 95% CI 1.001–1.009) and lag 7 (RR\u2009=\u20091.005, 95% CI 1.001–1.009) for 10 µg/m3 increase in PM2.5 levels. Seasonal analysis results showed that hospitalization in the warm season was significantly higher than that of the cold season for AQI and PM2.5 levels, while RR obtained for AQHI was higher in the cold season. The results confirm the association between air pollutants and an increase in a load of adult asthma hospitalization. To confirm the effects of seasonal variation of PM level and other possible factors (such as meteorological factors) on hospitalization due to asthma, more research will be needed. Our findings provide valuable data to increasing policymakers’ awareness and developing a strategy to improve air quality.