张莉君, 东春阳, 许慧慧, 吴春峰, 郭常义, 许东, 程昱. 上海市大气污染与某综合医院儿科呼吸系统疾病门诊人次的关系[J]. 环境与职业医学, 2020, 37(8): 741-746. DOI: 10.13213/j.cnki.jeom.2020.20119
引用本文: 张莉君, 东春阳, 许慧慧, 吴春峰, 郭常义, 许东, 程昱. 上海市大气污染与某综合医院儿科呼吸系统疾病门诊人次的关系[J]. 环境与职业医学, 2020, 37(8): 741-746. DOI: 10.13213/j.cnki.jeom.2020.20119
ZHANG Li-jun, DONG Chun-yang, XU Hui-hui, WU Chun-feng, GUO Chang-yi, XU Dong, CHENG Yu. Association between ambient air pollution and pediatric respiratory disease outpatients in a hospital in Shanghai[J]. Journal of Environmental and Occupational Medicine, 2020, 37(8): 741-746. DOI: 10.13213/j.cnki.jeom.2020.20119
Citation: ZHANG Li-jun, DONG Chun-yang, XU Hui-hui, WU Chun-feng, GUO Chang-yi, XU Dong, CHENG Yu. Association between ambient air pollution and pediatric respiratory disease outpatients in a hospital in Shanghai[J]. Journal of Environmental and Occupational Medicine, 2020, 37(8): 741-746. DOI: 10.13213/j.cnki.jeom.2020.20119

上海市大气污染与某综合医院儿科呼吸系统疾病门诊人次的关系

Association between ambient air pollution and pediatric respiratory disease outpatients in a hospital in Shanghai

  • 摘要: 背景

    国内外研究显示,大气污染物浓度升高与儿童肺功能降低、呼吸系统疾病及症状的发生密切相关。

    目的

    分析上海市某区大气污染物日均浓度与儿童呼吸系统疾病日门诊人次的相关性。

    方法

    收集2013—2018年上海市某综合医院儿科呼吸系统疾病日门诊人数,及距离最近的1个空气质量监测点的大气污染物监测资料,包括细颗粒物(PM2.5)、可吸入颗粒物(PM10)、二氧化氮(NO2)、二氧化硫(SO2)、臭氧(O3)和一氧化碳(CO)及气象指标(温度、相对湿度)的日均值,采用基于Poisson分布的广义线性时间序列模型,分析大气污染对儿科呼吸系统疾病门诊人次的滞后效应(lag0~lag7)和累积滞后效应(lag01~lag07)。

    结果

    6种污染物浓度在不同时间每增加10 μg·m-3对儿童呼吸系统疾病门诊量有不同程度的影响。PM2.5在lag4的滞后效应和lag07的累积滞后效应最为显著,超额就诊风险(ER)为0.32%(95% CI:0.12%~0.53%)和0.59%(95% CI:0.15%~1.03%);PM10在lag3对门诊量有滞后影响,ER为0.19%(95% CI:0.00%~0.37%);NO2在lag3的滞后效应和lag07的累积滞后效应最为显著,ER分别为1.11%(95% CI:0.71%~1.51%)和3.05%(95% CI:2.24%~3.87%);SO2在lag4的滞后效应和lag07的累积滞后效应最为显著,ER分别为1.86%(95% CI:1.08%~2.65%)和4.90%(95% CI:3.31%~6.51%);O3与门诊量呈负相关,lag6的滞后效应和lag07的累积滞后效应最显著,ER分别为-0.21%(95% CI:-0.38%~-0.05%)和-0.56%(95% CI:-0.93%~-0.20%);CO对儿童呼吸系统疾病的滞后效应主要体现在lag4和lag5,ER分别为0.30%(95% CI:0.07%~0.53%)和0.24%(95% CI:0.01%~0.47%)(P < 0.05或P < 0.01)。多污染物模型分析显示:当SO2、NO2与PM2.5、O3和CO共存时,SO2和NO2的效应更稳健,ER分别为1.54%(95% CI:0.49%~2.60%)和1.21%(95% CI:0.67%~1.74%);当NO2与PM10、O3和CO共存时,NO2对门诊量影响最大,ER为1.41%(95% CI:0.88%~1.95%)(P < 0.01)。

    结论

    上海市大气污染物PM2.5、PM10、NO2、SO2和CO水平与儿科呼吸系统疾病门诊人次间存在正相关关系,O3与门诊人次呈负相关,SO2和NO2在污染物联合效应中作用更稳健。

     

    Abstract: Background

    Both domestic and foreign studies have shown that ambient air pollutant concentrations are closely related to the decrease of lung function and the occurrence of respiratory diseases and symptoms in children.

    Objective

    The study aims to evaluate the association between daily air pollutant concentrations and hospital pediatric respiratory disease outpatients in Shanghai.

    Methods

    Generalized linear models (GLM) were used to evaluate the lag effects (lag0 to lag7) and cumulative lag effects (lag01 to lag07) between daily outpatients of pediatric respiratory diseases in a general hospital and daily air pollutant concentrationsincluding fine particulate matters (PM2.5), inhalable particulate matters (PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), and carbon monoxide (CO) from a nearest air quality monitoring station in Shanghai in 2013-2018. Daily meteorological variables were also collected.

    Results

    Per 10 μg·m-3 increase of the six pollutants at different lag time points had different effect sizes on pediatric respiratory disease outpatients. Per 10 μg·m-3 increase in concentrations of PM2.5 at lag4excess risk (ER)=0.32%, 95% CI:0.12%-0.53% and lag07 (ER=0.59%, 95% CI:0.15%-1.03%), PM10 at lag3 (ER=0.19%, 95% CI:0.00%-0.37%), NO2 at lag 3 (ER=1.11%, 95% CI:0.71%-1.51%) and lag07 (ER=3.05%, 95% CI:2.24%-3.87%), SO2 at lag4 (ER=1.86%, 95% CI:1.08%-2.65%) and lag07 (ER=4.90%, 95% CI:3.31%-6.51%), O3 at lag6 (ER=-0.21%, 95% CI:-0.38%--0.05%) and lag07 (ER=-0.56%, 95% CI:-0.93%--0.20%), and CO at lag4 (ER=0.30%, 95% CI:0.07%-0.53%), and lag5 (ER=0.24%, 95% CI:0.01%-0.47%) showed the largest effects among the respective categories (P < 0.05 or P < 0.01). The results of multi-pollutant model analysis showed that SO2 and NO2 had more stable and significant impacts on the pediatric respiratory disease outpatients when they coexisted with PM2.5, O3, and CO, and the related ERs (95% CIs) were 1.54% (0.49%-2.60%) and 1.21% (0.67%-1.74%), respectively; NO2 showed the strongest effect when it coexisted with PM10, O3, and CO, and the related ER (95% CIs) was 1.41% (0.88%-1.95%) (P < 0.01).

    Conclusion

    The ambient PM2.5, PM10, NO2, SO2, and CO concentrations are positively and O3 concentration is negatively associated with daily hospital pediatric outpatients due to respiratory diseases in Shanghai. The ambient NO2 and SO2 have more significant and stable synergistic effects in multi-pollutant model analysis compared with single-pollutant model analysis.

     

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