YING Sheng-jie, GU Yi-qin, WANG Xi, HE Dan-dan, GONG Zhi-min, JIANG Li-hua, XU Ying, ZHANG Ying, ZHANG Xiong-wei, ZHEN Ling-yan. Time-series analysis on association between air pollution and student absence caused by respiratory disorders in Minhang District of Shanghai[J]. Journal of Environmental and Occupational Medicine, 2018, 35(5): 394-399. DOI: 10.13213/j.cnki.jeom.2018.17683
Citation: YING Sheng-jie, GU Yi-qin, WANG Xi, HE Dan-dan, GONG Zhi-min, JIANG Li-hua, XU Ying, ZHANG Ying, ZHANG Xiong-wei, ZHEN Ling-yan. Time-series analysis on association between air pollution and student absence caused by respiratory disorders in Minhang District of Shanghai[J]. Journal of Environmental and Occupational Medicine, 2018, 35(5): 394-399. DOI: 10.13213/j.cnki.jeom.2018.17683

Time-series analysis on association between air pollution and student absence caused by respiratory disorders in Minhang District of Shanghai

  • Objective To evaluate the short-term effects of air pollution on student absence caused by respiratory disorders in Minhang District of Shanghai.

    Methods Daily data on student absence caused by respiratory disorders, meteorological data, and air pollution data of Minhang District from September 1, 2013 to June 30, 2016 were collected. A time-series analysis by generalized additive model was conducted to examine the relationship between air pollutant concentrations on single lag days from the current day to previous 5 days (lag0-lag5) and on cumulative lag days from the current day to previous 1, 3, and 5 days (lag01, lag03, and lag05) and student absence caused by respiratory disorders after controlling for time trend, day-of-week effect, holiday effect, and weather conditions.

    Results During the study period, the unqualified rates of NO2, PM2.5, PM10, and O3 were 8.51%, 20.79%, 5.84%, and 8.12%, respectively, while SO2 and CO were within the national limits. In the single-pollutant models, AQI, PM2.5, PM10, SO2, and NO2 were positively correlated with both emerging and total student absence caused by respiratory disorders (P < 0.05). The NO2, PM2.5, and PM10 concentrations on lag1RR and 95%CI were 3.53 (2.15-4.90), 11.80 (8.85-14.75), and 4.04 (2.48-5.60), respectively and the SO2 concentration on lag5 (RR=18.20; 95%CI:13.95-22.45) showed the most significant effects on total student absence caused by respiratory disorders. The NO2 concentration on lag0 (RR=11.65; 95%CI:8.59-14.71) and the SO2, PM2.5, and PM10 concentrations on lag1RR and 95%CI were 3.39 (1.91-4.88), 17.90 (12.96-22.84), and 3.89 (2.20-5.58), respectively showed the most significant effects on emerging student absence caused by respiratory disorders. Cumulative effects of all pollutants on lag05 were most significant for both emerging and total student absences. In the multiple-pollutant models, the effects of PM2.5 and PM10 on student absence caused by respiratory disorders were not statistically significant after adjusting for main air particulate matters (PM10 or PM2.5) and gaseous pollutants (SO2 and NO2).

    Conclusion The ambient air pollutant concentrations of PM2.5, PM10, SO2, and NO2 are positively associated with student absence caused by respiratory disorders.

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