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中华肺部疾病杂志(电子版)

中华肺部疾病杂志(电子版) ›› 2020, Vol. 13 ›› Issue (02) : 229 -235. doi: 10.3877/cma.j.issn.1674-6902.2020.02.021

论著

不同大气污染物对呼吸系统疾病门诊量的影响
韩璐瑶1, 吴克坚2, 高永恒1, 鱼高乐1, 李志超3, 王在强1, 高彦军1, 林红卫1, 金发光1,†()   
  1. 1. 710032　西安，空军（第四）军医大学第二附属医院呼吸与危重症医学科
    2. 710032　西安，空军（第四）军医大学基础医学院数学物理教研室
    3. 710032　西安，空军（第四）军医大学基础医学院生理与病理生理教研室
  • 收稿日期:2019-12-21 出版日期:2020-04-25
  • 通信作者: 金发光
  • 基金资助:
    陕西省重点研发项目(2017ZDL-SF-14-6)

Effects of different air pollutants on the outpatient quantity of respiratory diseases

Luyao Han1, Kejian Wu2, Yongheng Gao1, Gaole Yu1, Zhichao Li3, Zaiqiang Wang1, Yanjun Gao1, Hongwei Lin1, Faguang Jin1,†()   

  1. 1. Department of Respiratory, Second Affiliated Hospital, Air Force Medical University, Xi′an 710038, China
    2. Department of Mathematics and Physics, School of Basic Medicine, Air Force Medical University, Xi′an 710038, China
    3. Department of physiology and pathophysiology, Air Force Medical University, Xi′an 710038, China
  • Received:2019-12-21 Published:2020-04-25
  • Corresponding author: Faguang Jin
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引用本文：

韩璐瑶, 吴克坚, 高永恒, 鱼高乐, 李志超, 王在强, 高彦军, 林红卫, 金发光. 不同大气污染物对呼吸系统疾病门诊量的影响[J]. 中华肺部疾病杂志(电子版), 2020, 13(02): 229-235.

Luyao Han, Kejian Wu, Yongheng Gao, Gaole Yu, Zhichao Li, Zaiqiang Wang, Yanjun Gao, Hongwei Lin, Faguang Jin. Effects of different air pollutants on the outpatient quantity of respiratory diseases[J]. Chinese Journal of Lung Diseases(Electronic Edition), 2020, 13(02): 229-235.

目的

探讨西安不同大气污染物对呼吸系统疾病门诊量的影响。

方法

收集2015-2016年西安市每天六种大气污染物数据，即颗粒污染物(PM2.5、PM10)、二氧化硫(SO2)、二氧化氮(NO2)、一氧化碳(CO) 、臭氧(O3)，2015-2016年西安市每日气象数据(气温、相对湿度、气压、风速)，以及同期西安市两所综合性三甲医院呼吸科每日的门急诊量数据。采用Spearman秩相关检验评价同期大气污染物与气象因素的相关性；采用平滑样条函数控制日平均气温、相对湿度、气压和长期趋势等混杂因素对呼吸系统疾病门诊量的影响。将星期效应(Day of the week effect, DOW)即周末，作为哑变量纳入多元回归模型。在考虑了0～7 d的滞后效应后，采用以泊松分布为连接函数的广义相加模型(Generalized Additive Model, GAM)，通过构建单污染物模型对年龄、疾病的亚组进行分析。

结果

：西安市大气中细颗粒物PM2.5浓度每升高10 μg/m3，呼吸系统门急诊量增加0.39%(ER＝0.39，95%CI：0.37～0.41，P<0.05)，且在第6天达到最强效应；可吸入颗粒物PM10浓度每升高10 μg/m3，西安市呼吸系统门急诊量增加0.34%(ER＝0.34，95%CI: 0.32～0.35，P<0.05)；CO浓度每升高10 μg/m3，呼吸系统门急诊量增加48.53%(ER＝48.53，95%CI: 46.07～51.03，P<0.05)，且最强效应在第6天；NO2浓度每升高10 μg/m3，西安市呼吸系统门急诊量增加0.96%(ER＝0.96，95%CI: 0.9～1.02，P<0.05)；SO2浓度每升高10 μg/m3，西安市呼吸系统门急诊量增加2.13%(ER＝2.13，95%CI: 2.04～2.23，P<0.05)；O3浓度每升高10 μg/m3，西安市呼吸系统门急诊量增加0.07%(ER＝0.07，95%CI：0.04～0.10，P<0.05)。PM2.5，PM10，CO，SO2，NO2、O3浓度的增加均会引起呼吸系统相关疾病门诊量的增加，且多在滞后第6天影响最大，CO对呼吸系统疾病门诊量的影响尤为明显。

结论

西安市6种主要大气污染物浓度暴露会增加呼吸系统疾病的门诊就诊量，其中污染物CO对门诊量的影响最大，且对65岁以上人群影响更大。

关键词: 大气污染, 呼吸系统疾病, 时间序列, 广义相加模型
Objective

To explore the effects of different air pollutants on the outpatient quantity of respiratory diseases, with the city of xi′an as the research object.

Methods

collected relevant data, including the daily data of six kinds of air pollutants in Xi′an from 2015 to 2016 (PM2.5 and PM10, CO, SO2, NO2 and O3), the daily meteorological data in Xi′an from 2015 to 2016 (average temperature, daily average relative humidity and daily average air pressure, wind speed), and the daily outpatient and emergency data of respiratory departments of two comprehensive 3A grade hospitals in Xi′an during the same period. Spearman′s rank correlation test was used to evaluate the correlation between air pollutants and meteorological factors during the same period. A smooth spline function was used to control the influence of daily mean temperature, relative humidity, air pressure and long-term trend on the outpatient volume of respiratory diseases. The Day of the Week effect (DOW) was incorporated into the multivariate regression model as a dummy variable. Considering the lag effect of 0 to 7 days, this paper adopted the Generalized Additive Model (GAM) taking Poisson distribution as the connection function.Through the construction of single pollutant models and the analysis of age and disease subgroups, the influence of air pollutants on the number of outpatient and emergency visits of respiratory diseases were studied. This paper explained the specific effect of air pollutants on population health in Xi′an.

Results

This study showed that for every 10 μg/m3 increase in the concentration of PM2.5 in xi′an, the emergency department volume of respiratory diseases increased by 0.39% (ER=0.39, 95%CI: 0.37~0.41, P<0.05), and the strongest effect was achieved on the 6th day. For every 10 μg/m3 increase in concentration of PM10, the emergency department volume of respiratory diseases in xi′an increased by 0.34% (ER=0.34, 95%CI: 0.32~0.35, P<0.05). For every 10 mg/m3 increase in concentration of CO, e emergency department volume of respiratory diseases increased by 48.53%(ER=48.53, 95%CI: 46.07~51.03, P<0.05), and the strongest effect was at day 6.For every 10 μg/m3 increase in concentration of NO2, the emergency department volume of respiratory diseases in xi′an increased by 0.96% (ER=0.96, 95%CI: 0.9~1.02, P<0.05). For every 10 μg/m3 increase in the concentration of SO2, the emergency department volume of respiratory system in xi′an increased by 2.13% (ER=2.13, 95%CI: 2.04~2.23, P<0.05). For every 10 μg/m3 increase in the concentration of O3, the emergency department volume of respiratory diseases in xi′an increased by 0.07% (ER=0.07, 95%CI: 0.04~0.10, P<0.05). The increase of the concentrations of PM2.5, PM10, CO, SO2, NO2 and O3 will all cause an increase in the outpatient volume of respiratory diseases, and the effect is the biggest on the 6th day of lag. The effect of CO on the outpatient volume of respiratory system is particularly obvious.

Conclusion

The results of this study showed that exposure to the concentration of six major air pollutants in xi′an would increase the number of outpatient visits for respiratory diseases, among which pollutant CO had the greatest impact on the number of outpatient visits.

Key words: Air pollution, Respiratory disease, Generalized additive model, Time series
表1 2015-2016年西安市大气污染物、气象资料和门诊量情况
变　量 平均数 最小值 P25 P75 最大值
日门诊量(n) 78.45 15 49 101 171
PM2.5(μg/m3) 64.68 12 33 75 437
PM10(μg/m3) 131.2 23 77 166 496
CO(mg/m3) 1.684 0.6 1.1 2 5.4
SO2(μg/m3) 21.78 3 11 29 96
NO2(μg/m3) 48.51 14 35 60 115
O3(μg/m3) 80.49 8 37 122.5 247
日均气温(℃) 14.65 -8 6.371 22.971 32.529
湿度(%RH) 67.88 23.57 57.07 79 97.86
风速(m/s) 1.6328 0.5429 1.2429 1.9143 3.8286
气压(hPa) 965.3 948.2 957.7 972.1 996.8
表2 2015-2016年西安市大气污染物浓度与气象因素间Spearman相关分析
指　标 PM2.5 PM10 CO SO2 NO2 O3 气温 湿度 风速 气压
PM2.5 1.000 .910a .737a .665a .677a -.439a -.477a .096a -.335a .338a
PM10   1.000 .713a .722a .702a -.396a -.468a -.130a -.273a .328a
CO     1.000 .858a .578a -.672a -.771a .052 -.387a .571a
SO2       1.000 .628a -.609a -.784a -.197a -.327a .625a
NO2         1.000 -.314a -.381a -.040 -.515a .287a
O3           1.000 .815a -.358a .313a -.731a
气温             1.000 -.058 .257a -.881a
湿度               1.000 -.342a .099a
风速                 1.000 -.223a
气压                   1.000
图1 时间趋势自由度的选择
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