吉林省长春市大气污染物暴露与脑卒中入院风险的关联性分析

doi:10.13481/j.1671-587X.20210233

摘要/Abstract

摘要： 目的

分析吉林省长春市大气污染物暴露对脑卒中入院风险的影响。

方法

收集2018年10月1日—2019年10月30日在吉林大学第一医院入院的脑卒中病例和同期长春市大气污染物监测数据。采用病例交叉研究进行单污染物和多污染物模型条件Logistic回归分析。通过单污染模型分析各大气污染物对脑卒中入院风险的滞后效应，根据最大比值比（OR）确定各大气污染物最佳滞后期，通过多污染物模型分析大气污染物对脑卒中入院风险的影响。

结果

共纳入病例3 633例。单污染物模型确定的大气污染物对脑卒中入院的最佳滞后期为发病前4 d。多污染物模型分析，发病前4 d的大气细颗粒物（PM_2.5）、二氧化硫（SO₂）、一氧化碳（CO）和臭氧（O₃）浓度可影响脑卒中入院风险。PM_2.5浓度每升高10 μg?m^－3，脑卒中入院风险增加3.0%［95%可信区间（CI）（1.007，1.054）］；SO₂浓度每升高10 μg?m^－3，脑卒中入院风险增加22.6%［95%CI（1.165，1.291）］；CO浓度每升高10 mg?m^－3，脑卒中入院风险增加14%［95%CI（1.101，1.180）］；O₃浓度每升高10 μg?m^—3，脑卒中入院风险增加3.7%［95%CI（1.029，1.046）］。

结论

大气污染物对脑卒中入院的影响有滞后效应。长春市大气中PM_2.5、SO₂、CO和O₃浓度升高可增加当地脑卒中入院风险。

关键词: 大气污染物, 脑卒中, 病例交叉研究, 入院风险, 环境暴露

Abstract: Objective

To analyze the influence of atmospheric pollutant exposure in stroke hospitalization risk in Changchun City， Jilin University.

Methods

The stroke cases admitted at the First Hospital of Jilin University from October 1， 2018 to October 30， 2019 and the atmospheric measurement data of Changchun city were collected. A case-cross study was conducted for Logistic regression analysis on single pollutant and multi-pollutant models.The lag effect of each air pollutant on stroke hospitalization risk was analyzed with single pollutant model， and the best lag of each air pollutant was determined according to the maximum odds ratio （OR）.Multi-pollutant model was used to analyze the influence of air pullutants in the stroke hospitalization risk.

Results

A total of 3 633 cases were included. A single pollutatant model determined that the atmospheric abnormality for stroke admission was the best in the late stage of stay 4 d before onset. Multivariate model analysis found that atmospheric particulate matter 2.5 （PM_2.5），sulfur dioxide （SO₂）， carbon monoxide （CO）， and ozone （O₃） concentrations 4 d before the onset of illness could affect stroke admission risk. Every 10 μg?m^－3 increase in PM_2.5 concentration increased stroke admission risk by 3% ［95% confidence interval（CI）（1.007， 1.054）］； every 10 μg?m^－3 increase in SO₂ concentration increased stroke admission risk by 22.6% ［95%CI （1.165，1.291）］； every 10 mg?m^－3 increase in CO concentration increased stroke admission risk by 14% ［95%CI （1.101，1.180）］； every 10 μg?m^－3 increase in O₃ concentration increased stroke admission risk by 3.7% ［95%CI （1.029，1.046）］.

Conclusion

The impact of air pollutants on stroke admission risk has a lagging effect. The increase of concentrations of PM_2.5， SO₂， CO and O₃ in the atmosphere of Changchun city can increase the risk of local stroke admission.

Key words: air pollutants, stroke, case-cross study, admission risk, environmental exposure

中图分类号:

R122.7

赵天业,周丽婷,张雪婷,齐雯,李旭,王树越,叶琳. 吉林省长春市大气污染物暴露与脑卒中入院风险的关联性分析[J]. 吉林大学学报(医学版), 2021, 47(2): 505-510.

Tianye ZHAO,Liting ZHOU,Xueting ZHANG,Wen QI,Xu LI,Shuyue WANG,Lin YE. Analysis on correlation between exposure to air pollutants and hospitalization risk of stroke in Changchun city of Jilin province[J]. Journal of Jilin University(Medicine Edition), 2021, 47(2): 505-510.

图/表 5

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Characteristic	Number	Percentage(η/%)
Sex
Male	2 386	65.68
Female	1 247	34.32
Age (year)
≥65	1 467	40.38
<65	2 132	58.68
Diagnosis
Ischemic stroke	3 131	86.18
Hemorrhagic stroke	417	11.48
Ischemic stroke combined with hemorrhagic stroke	85	2.34
Admission date
Mar.—May	882	24.28
Jun.—Aug.	901	24.80
Sep.—Nov.	1 074	29.56
Dec.—next Feb.	752	20.70

Pollutant	Min	P₂₅	P₅₀	P₇₅	Max
PM_2.5(μg?m^－3)	3.0	16	27	43.5	197
PM₁₀(μg?m^－3)	6.0	38	54	81	278
SO₂(μg?m^－3)	2.0	4	8	16	37
NO₂(μg?m^－3)	13.0	24	32	39	78
CO(mg?m^－3)	0.3	0.6	0.7	0.8	2.7
O₃(μg?m^－3)	22.0	49	75.7	97	224

Pollutant	PM_2.5	PM₁₀	SO₂	NO₂	CO	O₃
PM_2.5	1
PM₁₀	0.888^**	1
SO₂	0.713^**	0.625^**	1
NO₂	0.824^**	0.695^**	0.638^**	1
CO	0.662^**	0.600^**	0.573^**	0.702^**	1
O₃	0.074	0.155^**	－0.388^**	－0.096	－0.107^*	1

Pollutant	OR (95%CI)	P	Pollutant	OR (95%CI)	P
PM_2.5			PM₁₀
Lag0	1.000 (0.999,1.001)	0.901	Lag0	1.001 (1.000,1.001)	0.114
Lag1	1.000 (0.998,1.001)	0.593	Lag1	1.000 (0.999,1.001)	0.526
Lag2	1.000 (0.999,1.002)	0.850	Lag2	1.000 (0.999,1.001)	0.899
Lag3	1.000 (0.998,1.001)	0.879	Lag3	1.001 (1.000,1.002)	0.299
Lag4	1.001 (1.000,1.003)	0.137	Lag4	1.001 (1.000,1.002)	0.188
Lag5	1.000 (0.998,1.001)	0.810	Lag5	1.000 (0.999,1.001)	0.482
Lag6	1.000 (0.999,1.001)	0.917	Lag6	1.000 (0.999,1.001)	0.896
SO₂			NO₂
Lag0	1.000 (0.991,1.009)	0.979	Lag0	0.974 (0.849,1.117)	0.706
Lag1	0.998 (0.988,1.008)	0.643	Lag1	0.983 (0.839,1.152)	0.833
Lag2	0.996 (0.986,1.006)	0.417	Lag2	0.977 (0.833,1.145)	0.770
Lag3	0.998 (0.988,1.008)	0.701	Lag3	0.932 (0.793,1.096)	0.394
Lag4	1.003 (0.993,1.013)	0.513	Lag4	1.118 (0.951,1.314)	0.176
Lag5	0.999 (0.989,1.009)	0.893	Lag5	0.918 (0.781,1.078)	0.295
Lag6	1.005 (0.996,1.014)	0.261	Lag6	0.985 (0.860,1.129)	0.830
CO			O₃
Lag0	1.000 (0.997,1.003)	0.937	Lag0	1.001 (1.000,1.002)	0.095
Lag1	1.000 (0.996,1.003)	0.866	Lag1	1.000 (0.999,1.002)	0.563
Lag2	0.999 (0.995,1.002)	0.483	Lag2	1.000 (0.999,1.001)	0.850
Lag3	0.999 (0.996,1.003)	0.765	Lag3	1.001 (1.000,1.003)	0.030*
Lag4	1.002 (0.999,1.006)	0.217	Lag4	1.000 (0.999,1.002)	0.648
Lag5	0.999 (0.995,1.002)	0.434	Lag5	0.999 (0.998,1.000)	0.166
Lag6	0.999 (0.996,1.002)	0.450	Lag6	1.001 (1.000,1.002)	0.030*

Pollutant	B	SE	Wald	OR (95%CI)	P
PM_2.5	0.030	0.011	6.645	1.030(1.007, 1.054)	0.01
SO₂	0.204	0.026	60.571	1.226(1.165, 1.291)	<0.01
CO	0.131	0.018	54.707	1.140(1.101, 1.180)	<0.01
O₃	0.037	0.004	73.187	1.037(1.029, 1.046)	<0.01