北京市延庆区大气污染物对呼吸系统疾病门急诊量的影响

张镇权; 王绍华; 吴志乐; 郭秀花

doi:10.16462/j.cnki.zhjbkz.2019.07.016

北京市延庆区大气污染物对呼吸系统疾病门急诊量的影响

doi: 10.16462/j.cnki.zhjbkz.2019.07.016

张镇权^{1, 2},
王绍华²,
吴志乐²,
郭秀花^1, ,

1.
100069 北京, 首都医科大学公共卫生学院, 临床流行病学北京市重点实验室
2.
102100 北京, 北京市延庆区疾病预防控制中心, 传染病地方病控制科

详细信息

通讯作者:
郭秀花, E-mail: guoxiuh@ccmu.edu.cn

中图分类号: R122.7
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出版历程
- 收稿日期: 2019-01-08
- 修回日期: 2019-04-08
- 刊出日期: 2019-07-10

Effects of air pollutants on outpatient and emergency number of respiratory diseases in Yanqing District of Beijing

1.
School of Public Health in Capital Medical University, Beijing Key Laboratory of Clinical Epidemiology, Beijing 100069, China
2.
Infectious and Endemic Disease Control Section of Yanqing District, Beijing 102100, China

More Information

Corresponding author: GUO Xiu-hua, E-mail: guoxiuh@ccmu.edu.cn

摘要

摘要: 目的探讨北京市延庆区大气污染对人群呼吸系统疾病门急诊量影响。方法收集2014-2017年北京市延庆区二级医疗机构的呼吸系统疾病每日门急诊量数据和同期气象数据资料以及大气污染数据资料，运用时间序列的广义相加模型，在控制混杂因素的基础上，分析大气污染物浓度与呼吸系统门急诊量的关系以及滞后效应。结果研究表明，大气中空气动力学直径当量直径≤ 2.5 μm的颗粒物（particulate matter 2.5，PM_2.5）每增加10 μg/m³，呼吸系统门急诊量增加0.11%（RR=0.11，95%CI：0.09~0.14，P=0.001）；大气中空气动力学直径当量直径≤ 10 μm的颗粒物（particulate matter 10，PM₁₀）每增加10 μg/m³，呼吸系统门急诊量增加0.17%（RR=0.17，95%CI：0.15~0.19，P=0.001；二氧化氮（nitrogen dioxide，NO₂）每增加10 μg/m³，呼吸系统门急诊量增加0.44%（RR=0.44，95%CI：0.37~0.50，P=0.001）；一氧化碳（carbon monoxide，CO）每增加10 μg/m³，呼吸系统门急诊量增加3.34%（RR=3.34，95%CI：1.57~5.15，P=0.001），且最强效应期均在第0 d。二氧化硫（sulfur ioxide，SO₂）每增加10 μg/m³呼吸系统门急诊量增加-1.69%（RR=-1.69，95%CI：-1.80~-1.57，P=0.001），且在第1 d达到最强效应值。臭氧（ozone，O₃）每增加10 μg/m³呼吸系统门急诊量增加-0.12%（RR=0.12，95%CI：-0.15~-0.10，P=0.001），且在第5 d达到最强效应值。双污染物模型分析中，CO在SO₂的影响下，对呼吸系统疾病门急诊人数影响尤为明显。结论本研究结果提示延庆区PM_2.5、PM₁₀、NO₂、CO浓度的增加均会导致呼吸系统门急诊量的增加，且不同污染物之间存在相互作用。
- 大气污染 /
- 时间序列 /
- 广义相加模型 /
- 呼吸系统疾病
Abstract: Objective To explore the association between air pollutants and the number of outpatient and emergency visits for respiratory diseases in Yanqing District, Beijing. Methods Data from 2014-2017 of the number of outpatient and emergency visits for respiratory diseases was collected from secondary medical institutions in Yanqing District of Beijing, and data of meteorological monitoring and atmospheric pollution were also collected. On the basis of controlling confounding factor, a generalized additive model was used to analyze the relationship between air pollutant concentration and the number of outpatient and emergency visits for respiratory diseases, and lag effect. Results The results showed that, Particulate Matter 2.5 (PM_2.5) increased by 10 μg/m³, the number of respiratory outpatient and emergency visits would increase 0.11% (95% CI: 0.09-0.14, P=0.001); Particulate Matter 10 (PM10) increased by 10 μg/m³, the number of respiratory outpatient and emergency visits would increase 0.17% (95% CI: 0.15-0.19, P=0.001); Nitrogen dioxide(NO₂) increased by 10 μg/m³, the number of respiratory outpatient and emergency visits would increase 0.44% (95% CI: 0.37-0.50, P=0.001); carbon monoxide (CO) increased by 10 μg/m³, the number of respiratory outpatient and emergency visits would increase 3.34% (95% CI: 1.57-5.15, P=0.001), and the most effective period was on the Zeroth day. The number of respiratory outpatient and emergency visits would increase -1.69%(95% CI: -1.80--1.57, P=0.001) with sulfur ioxide (SO₂) increased 10 μg/m3, and the most effective period was on the 1th day. The number of respiratory outpatient and emergency visits would increase -0.12% (95% CI: -0.15--0.10, P=0.001) with O₃ increased 10 μg/m³, and the most effective period was on the 5th day. In the analysis of double pollutant model, under the influence of SO₂, CO had a significant effect on the number of outpatient and emergency visits with respiratory diseases. Conclusion The results of this study suggest that in Yanqing District, increasing concentrations of PM_2.5, PM₁₀, NO₂ and CO will cause an increase in the number of outpatient and emergency visits for respiratory diseases, and there are interactions between different pollutants.
- Air pollution /
- Time series /
- Generalized additive model /
- Respiratory diseases

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图 1 大气污染物日均浓度每增加10 μg/m³对呼吸系统疾病日门急诊人数影响的RR值及95%CI

Figure 1. RR and 95% CI effect of 10 μg/m³ increase in daily concentration of atmospheric pollutants on number of patients with respiratory diseases in daily emergency

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表 1 2014-2017年延庆区大气污染物、气象资料和门急诊量情况

Table 1. Atmospheric pollutants, meteorological data and number of outpatient and emergency cases in Yanqing District from 2014 to 2017

变量	M	最小值	P₂₅	P₇₅	最大值
门急诊量(人)	1 560	392	1 367.5	1 747	3 007
PM_2.5(μg/m³)	47.00	3	22.25	87	406
PM₁₀(μg/m³)	67.75	5	42	105	1 004
CO(μg/m³)	0.80	0.1	0.5	1.4	7.8
SO₂(μg/m³)	6.70	1	3	15	88
NO₂(μg/m³)	31.00	2	21	47.65	127
O₃(μg/m³)	66.70	2	28	104.25	500
日均气温(℃)	11.88	-18.74	-1.18	20.46	29.52
最高温(℃)	16.90	-15.4	4.06	25.5	38.36
最低温(℃)	6.60	-20.78	-5.26	15.78	26.7
湿度(%RH)	56.00	9.8	40.4	71.2	97
风速(m/s)	1.70	0	1.26	2.3	8
气压(hPa)	969.20	944.2	962.9	977.1	995.12

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表 2 2014-2017年延庆区大气污染物浓度和气象因素的相关性分析

Table 2. Correlation analysis of atmospheric pollutant concentrations and meteorological factors in Yanqing District from 2014 to 2017

指标	PM_2.5	PM₁₀	CO	SO₂	NO₂	O₃	气温	湿度	风速	气压
PM_2.5	1	0.702^a	0.763^a	0.450^a	0.607^a	0.029	-0.074^a	0.204^a	-0.030	0.087^a
PM₁₀		1	0.550^a	0.434^a	0.553^a	0.026	-0.051	0.009	-0.026	-0.008
CO			1	0.644^a	0.652^a	-0.209^a	-0.369^a	0.103^a	-0.054^b	0.327
SO₂				1	0.525^a	-0.091^a	-0.522^a	-0.319^a	0.148^a	0.437^a
NO₂					1	-0.386^a	-0.261^a	0.131^a	-0.234^a	0.108^a
O₃						1	0.477^a	-0.095^a	0.277^a	-0.262^a
气温							1	0.325^a	-0.054^b	-0.649^a
湿度								1	-0.570^a	-0.311^a
风速									1	0.307^a
气压										1
注：^aP＜0.001;^bP＜0.050。

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表 3 延庆区大气污染物每增加10 μg/m³时呼吸系统疾病门急诊量增加百分比(PI%)及95%CI值

Table 3. Percent increase(PI %) and 95% CI in acute respiratory diseases per 10μg/m³ increase in atmospheric pollutants in Yanqing District

污染物	最强效应期	单污染物模型	双污染物模型
污染物	最强效应期	单污染物模型	PM_2.5	PM₁₀	SO₂	NO₂	CO	O₃
PM_2.5	lag0	0.11(0.09~0.14)^a	—	-0.11(-0.14~-0.07)^a	0.26(0.23~0.29)^a	-0.12(-0.15~--0.09)^a	-0.04(-0.08~-0.01)^b	0.02(-0.01~0.04)
PM₁₀	lag0	0.17(0.15~0.19)^a	0.17(0.14-0.19)^a	—	0.27(2.25~0.29)^a	0.02(-0.00~0.04)	0.13(0.11~0.15)^a	0.13(0.11~0.15)^a
SO₂	lag1	-1.69(-1.80~-1.57)^a	-2.39(-2.52~-2.26)^a	-2.46(-2.58~-2.34)^a	—	-3.33(-3.46~-3.20)^a	-3.30(-3.46~-3.15)	-1.70(-1.81~-1.58)^a
NO₂	lag0	0.44(0.37-0.50)^a	0.62(0.54~0.70)^a	0.63(0.56~0.70)^a	1.64(1.57~1.72)^a	—	1.06(0.97~1.14)^a	0.55(0.48~0.62)^a
CO	lag5	-0.12(-0.15~-0.10)^a	5.71(3.19~8.29)^a	-3.82(-5.69~-1.92)^a	42.72(39.43~46.09)^a	-14.94(-16.80~13.03)	—	1.91(0.16~3.69)^b
O₃	lag0	3.34(1.57~5.15)^b	-0.29(-0.31~-0.26)^a	-0.30(-0.32~-0.27)^a	-0.24(-0.27~-0.21)	-0.25(-0.27~-0.22)^a	-0.22(-0.24~-0.19)	—
注：^aP＜0.001;^bP＜0.05

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