不同大气污染物对呼吸系统疾病门诊量的影响

doi:10.3877/cma.j.issn.1674-6902.2020.02.021

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. 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. 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

全文 ( ) 下载PDF ( ) 导出引用

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(PM_2.5、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PM_2.5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)。PM_2.5，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 (PM_2.5 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 PM_2.5 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 PM_2.5, 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
PM_2.5(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
PM_2.5(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	PM_2.5	PM₁₀	CO	SO₂	NO₂	O₃	气温	湿度	风速	气压
PM_2.5	1.000	.910^a	.737^a	.665^a	.677^a	-.439^a	-.477^a	.096^a	-.335^a	.338^a
PM₁₀		1.000	.713^a	.722^a	.702^a	-.396^a	-.468^a	-.130^a	-.273^a	.328^a
CO			1.000	.858^a	.578^a	-.672^a	-.771^a	.052	-.387^a	.571^a
SO₂				1.000	.628^a	-.609^a	-.784^a	-.197^a	-.327^a	.625^a
NO₂					1.000	-.314^a	-.381^a	-.040	-.515^a	.287^a
O₃						1.000	.815^a	-.358^a	.313^a	-.731^a
气温							1.000	-.058	.257^a	-.881^a
湿度								1.000	-.342^a	.099^a
风速									1.000	-.223^a
气压										1.000

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	PM_2.5	PM₁₀	CO	SO₂	NO₂	O₃	气温	湿度	风速	气压
PM_2.5	1.000	.910^a	.737^a	.665^a	.677^a	-.439^a	-.477^a	.096^a	-.335^a	.338^a
PM₁₀		1.000	.713^a	.722^a	.702^a	-.396^a	-.468^a	-.130^a	-.273^a	.328^a
CO			1.000	.858^a	.578^a	-.672^a	-.771^a	.052	-.387^a	.571^a
SO₂				1.000	.628^a	-.609^a	-.784^a	-.197^a	-.327^a	.625^a
NO₂					1.000	-.314^a	-.381^a	-.040	-.515^a	.287^a
O₃						1.000	.815^a	-.358^a	.313^a	-.731^a
气温							1.000	-.058	.257^a	-.881^a
湿度								1.000	-.342^a	.099^a
风速									1.000	-.223^a
气压										1.000

图1 时间趋势自由度的选择

1	Kan H, Chen B, Hong C. Health impact of Outdoor Air Pollution in China: current knowledge and future research needs[J]. Environ Health Perspect, 2009,117(5): A187.
2	Touloumi G, Samoli E, Quenel P, et al. Short-term effects of air pollution on total and cardiovascular mortality[J]. Epidemiology, 2005, 16(1): 49-57.
3	Wang M, Aaron CP, Madrigano J, et al. Association between long-term exposure to ambient air pollution and change in quantitatively assessed emphysema and lung function[J]. JAMA, 2019, 322(6): 546-556.
4	Liu J, Li Y, Li J, et al. Association between ambient PM_2.5 and children′s hospital admissions for respiratory diseases in Jinan, China[J]. Environ Sci Pollut Res, 2019, 26(23): 24112-24120.
5	Chen R, Yin P, Meng X, et al. Associations between Coarse Particulate Matter Air Pollution and Cause-Specific Mortality: A Nationwide Analysis in 272 Chinese Cities[J]. Environ Health Perspect, 2019, 127(1): 17008.
6	Wang B1, Eum KD, Kazemiparkouhi F, et al. The impact of long-term PM exposure on specific causes of death: exposure-response curves and effect modification among 53 million U．S. Medicare beneficiaries.[J]. Environ Health, 2020, 19(1): 20.
7	Stassen FRM, van Eijck PH, Savelkoul PHM, et al. Cell Type- and exposure-specific modulation of CD63/CD81-positive and tissue factor-positive extracellular vesicle release in response to respiratory toxicants[J]. Oxidat Med Cell Longevity, 2019, 2019: 1-9.
8	侯　斌，戴灵真，王　铮，等. 西安市大气污染对城区居民每日死亡率影响的时间序列分析[J]. 环境与健康杂志，2011, 28(12): 1039-1043.
9	Song W, Liu Y, Liu J, et al. The burden of air pollution and weather condition on daily respiratory deaths among older adults in China, Jinan from 2011 to 2017[J]. Medicine, 2019, 98(10): e14694.
10	Davila Cordova JE, Tapia Aguirre V, Vasquez Apestegui V, et al. Association of PM_2.5 concentration with health center outpatient visits for respiratory diseases of children under 5 years old in Lima, Peru[J]. Environmental Health, 2020, 19(1): 7.
11	Rousseau-Ralliard D, Valentino SA, Aubrière M, et al. Effects of first-generation in utero exposure to diesel engine exhaust on second-generation placental function, fatty acid profiles and foetal metabolism in rabbits: preliminary results[J]. Scientific Reports, 2019, 9(1): 9710.
12	Chen G, Li S, Zhang Y, et al. Effects of ambient PM1 air pollution on daily emergency hospital visits in China: an epidemiological study[J]. The Lancet Planetary Health, 2017, 1(6): e221-e229.
13	安　祺，王建强. 新时期陕西省大气污染防治研究[J]. 环境与可持续发展，2018, 43(6): 40-47.
14	杨宇轩，闫思琪，衣颖杰，等. 西安市采暖季与非采暖季PM2.5染毒的人胚肺细胞基因差异表达研究[J]. 西安交通大学学报(医学版), 2015, 36(6): 836-839.
15	韩璐瑶，金发光. 西安市大气污染物影响人群健康的现状[J/CD]. 中华肺部疾病杂志(电子版), 2019, 12(6): 783-785.
16	Liang L, Cai Y, Barratt B, et al. Associations between daily air quality and hospitalisations for acute exacerbation of chronic obstructive pulmonary disease in Beijing, 2013-17: an ecological analysis[J]. The Lancet Planetary Health, 2019, 3(6): e270-e279.
17	Liu J, Li Y, Li J, et al. Association between ambient PM_2.5 and children′s hospital admissions for respiratory diseases in Jinan, China[J]. Environ Sci Pollut Research, 2019, 26(23): 24112-24120.
18	郑　晶，刘晓秋，刘　芳，等. 哈尔滨市大气污染与居民循环系统疾病死亡风险的相关分析[J]. 环境与职业医学，2018, 35(10): 885-891.
19	Mokoena KK, Ethan CJ, Yu Y, et al. Ambient air pollution and respiratory mortality in Xi′an, China: a time-series analysis[J]. Respiratory Research, 2019, 20(1): 139.
20	Agudelo-Castañeda DM, Teixeira EC, Schneider IL, et al. Exposure to polycyclic aromatic hydrocarbons in atmospheric PM_1.0 of urban environments: Carcinogenic and mutagenic respiratory health risk by age groups[J]. Environ Pollut, 2017, 224: 158-170.
21	Kotaki K, Ikeda H, Fukuda T, et al. Trends in the prevalence of COPD in elderly individuals in an air-polluted city in Japan: a cross-sectional study[J]. Int J Chron Obstruct Pulmon Dis, 2019, 14: 791-798.
22	Hvidtfeldt UA, Sørensen M, Geels C, et al. Long-term residential exposure to PM_2.5, PM₁₀, black carbon, NO₂, and ozone and mortality in a Danish cohort[J]. Environ Int, 2019, 123: 265-272.
23	Hastie T, Tibshirani R. Generalized additive models for medical research.[J]. Statistical methods in medical research, 1995, 4(3): 187-196.
24	张镇权，王绍华，吴志乐，等. 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[J]. 中华疾病控制杂志，2019, 23(7): 822-827, 834.
25	张云权，朱耀辉，李存禄，等. 广义相加模型在R软件中的实现[J]. 中国卫生统计，2015, 32(6): 1073-1075.
26	张镇权，王绍华，吴志乐，等. 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[J]. 中华疾病控制杂志，2019, 23(7): 822-827, 834.
27	GB3095-2012. 环境空气质量标准[S]. 北京，中国环境科学出版社，2012: 1-12.
28	Colarusso C, De Falco G, Terlizzi M, et al. The Inhibition of Caspase-1- Does Not revert particulate matter (PM)-induced lung immunesuppression in mice[J]. Front Immunol, 2019, 10: 1329.
29	张　莹，邵　毅，王式功，等. 北京市空气污染物对呼吸系统疾病门诊人数的影响[J]. 中国环境科学，2014, (9): 2401-2407.
30	张江华，郭常义，许慧慧，等. 上海市大气污染与某医院呼吸系统疾病门诊量关系的时间序列研究[J]. 环境与职业医学，2014, 31(11): 846-851.
31	王金玉，李　盛，董继元，等. 兰州市沙尘颗粒物污染与儿童呼吸系统疾病日门诊人次的关联性研究[J]. 中国卫生统计，2018, 35(4): 504-509.
32	Garcia E, Berhane KT, Islam T, et al. Association of changes in air quality with incident qsthma in Children in California, 1993-2014[J]. JAMA, 2019, 321(19): 1906.
33	Zhao Y, Hu J, Tan Z, et al. Ambient carbon monoxide and increased risk of daily hospital outpatient visits for respiratory diseases in Dongguan, China[J]. Sci Total Environ, 2019, 668: 254-260.
34	Chang Q, Zhang H, Zhao Y. Ambient air pollution and daily hospital admissions for respiratory system-related diseases in a heavy polluted city in Northeast China[J]. Environ Sci Pollut Res, 2020, doi: 10.1007/s11356-020-07678-8.
35	Chen G, Li S, Li D, et al. Effects of ambient PM1 air pollution on daily emergency hospital visits in China: an epidemiological study[J]. Lancet Planet Health, 2017, 1(6): e221-e229.
36	Hu K, Guo Y, Hu D, et al. Mortality burden attributable to PM₁ in Zhejiang province, China[J]. Environ Int, 2018, 121: 515-522.

[1]	陈玉莲, 刘瀚旻. 人体生命早期呼吸系统菌群与肺部微生物组发育特征及早期菌群稳态研究现状[J]. 中华妇幼临床医学杂志(电子版), 2023, 19(01): 31-37.
[2]	李琛, 张惟佳, 潘亚萍. 牙周炎与系统性疾病之间关系的应用思考：2022年EFP和WONCA欧洲分部联合研讨会共识报告的解读及启示[J]. 中华口腔医学研究杂志(电子版), 2023, 17(05): 322-327.
[3]	黄利, 唐楠, 廖秀清, 王导新. 冷冻活检在呼吸系统疾病中的应用[J]. 中华肺部疾病杂志(电子版), 2023, 16(01): 121-124.
[4]	刘露梅, 夏世金, 邓思思, 韦雅芹. tRNA衍生的小RNA在呼吸系统疾病中的应用研究[J]. 中华肺部疾病杂志(电子版), 2022, 15(06): 896-899.
[5]	刘凌琳, 章陈晨, 熊玮. 重庆市近三年空气湿度、PM2.5及气温水平对老年呼吸系统疾病的影响[J]. 中华肺部疾病杂志(电子版), 2020, 13(01): 72-74.
[6]	韩璐瑶, 金发光. 西安市大气污染物影响人群健康的现状[J]. 中华肺部疾病杂志(电子版), 2019, 12(06): 783-785.
[7]	顾梅, 王雪梅, 施海彬. 介入导管室手术量季节变动分析[J]. 中华介入放射学电子杂志, 2023, 11(02): 182-185.
[8]	李中中, 布凯林, 秦晋, 张荣, 刘晓云. 大气污染与缺血性脑卒中相关性的研究进展[J]. 中华脑血管病杂志(电子版), 2020, 14(04): 199-203.

阅读次数

全文

摘要

选择文件类型/文献管理软件名称

选择包含的内容

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

模态框（Modal）标题

选择文件类型/文献管理软件名称

选择包含的内容

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