大气颗粒物对成年居民患心血管疾病的影响及植被覆盖的效应修饰研究

褚一凡; 施恒远; 刘超; 刘成榕; 董意麟; 张珮瑶; 贾贤杰; 吴学森; 高怀荃

doi:10.16462/j.cnki.zhjbkz.2023.05.006

大气颗粒物对成年居民患心血管疾病的影响及植被覆盖的效应修饰研究

doi: 10.16462/j.cnki.zhjbkz.2023.05.006

1.
233030 蚌埠，蚌埠医学院公共卫生学院流行病与卫生统计学教研室
2.
230000 合肥，安徽中医药大学党委巡察工作办公室

基金项目:

蚌埠医学院创新团队项目 BYK201901

蚌埠医学院“512人才培育计划” BY51201202

详细信息

通讯作者:
高怀荃，E-mail: 935677399@qq.com

中图分类号: R54
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- 被引次数: 0
出版历程
- 收稿日期: 2022-11-24
- 修回日期: 2023-01-28
- 刊出日期: 2023-05-10

Effects of atmospheric particulate matter on the prevalence of cardiovascular diseases in adult residents and effect modification of vegetation coverage

1.
Department of Epidemiology and Statistics, School of Public Health, Bengbu Medical College, Bengbu 233030, China
2.
Anhui University of Chinese Medicine, Office of Internal Inspection, Hefei 230000, China

Funds:

Innovation Team Project of Bengbu Medical College BYK201901

Bengbu Medical College "512 Talent Cultivation Program" BY51201202

More Information

Corresponding author: GAO Huaiquan, E-mail: 935677399@qq.com

摘要

摘要: 目的评估城市大气颗粒物(particulate matter, PM)对成年居民患心血管疾病(cardiovascular disease, CVD)的影响及植被覆盖的效应修饰研究。方法 2015年在蚌埠市抽取4 000名成年居民，使用问卷调查和居民体检获得基本信息。通过卫星反演技术，获得参与者家庭所在1 km缓冲区的大气PM浓度，应用logistic回归分析模型分析PM与患CVD的关联。通过分层分析，探讨年龄、性别、BMI和植被覆盖对这种关联的潜在作用。结果最终纳入3 190名对象，其中CVD患者1 080例。亚微米颗粒物(submicrometer particles, PM₁)、细颗粒物(fine particulate matter, PM_2.5)、可吸入颗粒物(inhalable particles, PM₁₀)不同组之间患病率差异均有统计学意义(均有P < 0.05)。植被覆盖指标归一化植被指数(normalized difference vegetation index, NDVI)、增强型植被指数(enhanced vegetation index, EVI)、土壤调整植被指数(soil adjusted vegetation index, SAVI)不同组之间患病率差异有统计学差异(均有P＜0.05)。logistic回归分析模型分析显示：调整协变量后，PM_2.5高浓度组患病风险为低浓度组的1.352倍(OR=1.352, 95% CI: 1.078~1.696)。PM₁、PM₁₀与CVD的调整模型差异无统计学意义(均有P＞0.05)。分层分析显示：PM_2.5与植被覆盖的交互作用差异均有统计学意义(均有P＜0.05)，在植被覆盖处于较低水平时，PM_2.5与CVD患病风险的关联更强。结论 PM污染越严重，CVD患病风险可能越高。植被覆盖可能缓解了PM污染与CVD之间的关联。
- 大气颗粒物 /
- 植被覆盖 /
- 心血管疾病 /
- logistic回归分析模型
Abstract: Objective To evaluate the effects of atmospheric particulate matter on the prevalence of cardiovascular diseases in adult residents and effect modification of vegetation coverage. Methods A total of 4 000 adult residents were selected in Bengbu City in 2015. The basic information was obtained through a questionnaire survey and residents' physical examination. The concentration of atmospheric particulate matter (PM) in the 1 km buffer zone where the participants' homes were located was obtained through satellite retrieval technology. Logistic regression was used to analyze the association between PM and CVD. Stratified analyzes were performed to explore the potential confounding effect of age, sex, BMI, and the vegetation cover rate. Results Among the 3 190 subjects included, 1 080 were CVD patients. There were statistical differences in the prevalence of CVD among submicrometer particles (PM₁), fine particulate matter (PM_2.5), and inhalable particles (PM₁₀) groups (all P < 0.05). There were statistically differences in the prevalence of CVD among normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), soil adjusted vegetation index (SAVI) (all P < 0.05). Logistic regression showed that after adjusting covariates, the risk of the PM_2.5-high concentration group was 1.352 times that of the low concentration group (OR=1.352, 95% CI: 1.078-1.696). There was no statistically significant difference between the adjusted models of PM₁ and PM₁₀ (all P > 0.05). Stratified analysis showed that the interaction between PM_2.5 and vegetation coverage was statistically significant (all P < 0.05). And the association between PM_2.5 and the risk of cardiovascular disease was stronger when the vegetation coverage was at a lower level compared to higher level. Conclusions The more serious the atmospheric PM pollution, the higher the risk of CVD could be. Vegetation cover may moderate the association between PM pollution and CVD.
- Atmospheric particulate matter /
- Vegetation cover /
- Cardiovascular disease /
- Logistic regression analysis model

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表 1 研究对象一般人口学特征[n(%)]

Table 1. Demographic characteristics of the research subjects [n(%)]

变量	总人数(N=3 190)	CVD组(n=1 080)	NCVD组(n=2 110)	X²值	P值	变量	总人数(N=3 190)	CVD组(>n=1 080)	NCVD组(n=2 110)	X²值	P值
性别				4.809	0.028	经常运动				44.631	<0.001
男	1 391(43.6)	500(35.9)	891(64.1)			否	1 589(49.6)	447(28.2)	1 137(71.8)
女	1 799(56.4)	580(32.2)	1 219(67.8)			是	1 615(50.4)	633(39.4)	973(60.6)
年龄组(岁)				296.619	<0.001	PM_2.5				24.122	<0.001
18~60	2 297(72.0)	571(24.9)	1 726(75.1)			低浓度组	1 124(35.2)	330(29.4)	794(70.6)
≥60	893(28.0)	509(57.0)	384(43.0)			中浓度组	1 400(43.9)	479(34.2)	921(65.8)
BMI(kg/m²)				122.583	<0.001	高浓度组	666(20.9)	271(40.7)	395(59.3)
<24.0	1 424(44.6)	335(23.5)	1 089(76.5)			PM₁				24.833	<0.001
≥24.0	1 766(55.4)	745(42.2)	1 021(57.8)			低浓度组	1 133(35.5)	326(28.8)	807(71.2)
文化程度				48.195	<0.001	中浓度组	996(31.2)	388(39.0)	608(61.0)
小学及以下	946(29.7)	405(42.8)	541(57.2)			高浓度组	1 061(33.3)	366(34.5)	695(65.5)
中学	1 924(60.3)	580(30.1)	1 344(69.9)			PM₁₀				16.796	<0.001
大专及以上	320(10.0)	95(29.7)	225(70.3)			低浓度组	1 104(34.6)	373(33.8)	731(66.2)
婚姻状况				83.192	<0.001	中浓度组	1 332(41.8)	494(37.1)	838(62.9)
未婚	116(3.6)	6(5.2)	110(94.8)			高浓度组	754(23.6)	213(28.2)	541(71.8)
已婚	2 702(84.7)	898(33.2)	1 804(66.8)			NDVI-500 m				12.556	<0.001
丧偶	304(9.5)	155(51.0)	149(49.0)			低水平组	1 065(33.4)	374(35.1)	691(64.9)
离异	68(2.1)	21(30.9)	47(69.1)			中水平组	1 081(33.9)	396(36.6)	685(63.4)
月收入(元)				17.422	<0.001	高水平组	1 044(32.7)	310(29.7)	734(70.3)
0~<1 000	750(23.5)	228(30.4)	522(69.6)			EVI-500 m				13.988	<0.001
1 000~<3 000	1 899(59.5)	697(36.7)	1 202(63.3)			低水平组	1 134(35.5)	409(36.1)	725(63.9)
≥3 000	541(17.0)	155(28.7)	386(71.3)			中水平组	1 029(32.3)	370(36.0)	659(64.0)
吸烟				2.563	0.109	高水平组	1 027(32.2)	301(29.3)	726(70.7)
否	2 197(68.9)	724(33.0)	1 473(67.0)			SAVI-500 m				13.035	<0.001
是	993(31.1)	356(35.9)	637(64.1)			低水平组	1 121(35.1)	404(36.0)	717(64.0)
						中水平组	1 047(32.8)	375(35.8)	672(64.2)
						高水平组	1 022(32.0)	301(29.5)	721(70.5)

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表 2 PM与CVD关联性分析

Table 2. Association between PM and CVD

PM	模型1 ^a			模型2 ^b
PM	Wald值	OR值(95% CI)	P值	Wald值	OR值(95% CI)	P值
PM_2.5
低浓度组	1.000			1.000
中浓度组	6.737	1.251(1.056~1.482)	0.009	1.155	1.114(0.915~1.356)	0.282
高浓度组	23.900	1.651(1.350~2.018)	＜0.001	6.837	1.352(1.078~1.696)	0.009
PM₁
低浓度组	1.000			1.000
中浓度组	6.252	1.281(1.055~1.556)	0.012	3.753	1.222(0.998~1.497)	0.053
高浓度组	2.223	1.159(0.955~1.407)	0.136	0.677	1.089(0.889~1.334)	0.411
PM₁₀
低浓度组	1.000			1.000
中浓度组	2.868	1.155(0.978~1.365)	0.090	0.634	1.078(0.897~1.295)	0.426
高浓度组	6.347	0.772(0.631~0.944)	0.012	0.427	0.925(0.731~1.170)	0.513
注：^a表示未调整变量；^b表示调整变量为年龄、性别、BMI、文化程度、婚姻状况、月收入和运动情况。低浓度组、中浓度组、高浓度组为PM按照三分位数分组。

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表 3 PM_2.5与CVD的分层分析

Table 3. Stratified analysis on the association between PM_2.5 and the risk of CVD

变量	PM_2.5组
变量	Wald值	OR值(95% CI)	P值
年龄组(岁)			0.550
<60	4.085	1.104 (1.003~1.216)
≥60	1.845	1.094 (0.961~1.244)
性别			0.004
男	10.295	1.210 (1.077~1.360)
女	0.141	1.020 (0.920~1.130)
BMI(kg/m²)			0.647
<24	3.067	1.088 (0.990~1.196)
≥24	4.205	1.145 (1.006~1.303)
NDVI
低水平组	5.797	1.271 (1.046~1.544)
中水平组	0.149	1.035 (0.871~1.229)	0.017
高水平组	2.526	1.070 (0.984~1.162)	0.016
EVI
低水平组	7.022	1.323 (1.076~1.628)
中水平组	0.499	1.060 (0.901~1.248)	0.041
高水平组	1.766	1.060 (0.973~1.154)	0.035
SAVI
低水平组	4.890	1.244 (1.025~1.509)
中水平组	0.441	1.058 (0.896~1.250)	0.032
高水平组	1.998	1.064 (0.976~1.160)	0.044
注：低水平组、中水平组、高水平组为植被指数按照三分位数分组。

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变量	总人数(N=3 190)	CVD组(n=1 080)	NCVD组(n=2 110)	X²值	P值	变量	总人数(N=3 190)	CVD组(>n=1 080)	NCVD组(n=2 110)	X²值	P值
性别				4.809	0.028	经常运动				44.631	<0.001
男	1 391(43.6)	500(35.9)	891(64.1)			否	1 589(49.6)	447(28.2)	1 137(71.8)
女	1 799(56.4)	580(32.2)	1 219(67.8)			是	1 615(50.4)	633(39.4)	973(60.6)
年龄组(岁)				296.619	<0.001	PM_2.5				24.122	<0.001
18~60	2 297(72.0)	571(24.9)	1 726(75.1)			低浓度组	1 124(35.2)	330(29.4)	794(70.6)
≥60	893(28.0)	509(57.0)	384(43.0)			中浓度组	1 400(43.9)	479(34.2)	921(65.8)
BMI(kg/m²)				122.583	<0.001	高浓度组	666(20.9)	271(40.7)	395(59.3)
<24.0	1 424(44.6)	335(23.5)	1 089(76.5)			PM₁				24.833	<0.001
≥24.0	1 766(55.4)	745(42.2)	1 021(57.8)			低浓度组	1 133(35.5)	326(28.8)	807(71.2)
文化程度				48.195	<0.001	中浓度组	996(31.2)	388(39.0)	608(61.0)
小学及以下	946(29.7)	405(42.8)	541(57.2)			高浓度组	1 061(33.3)	366(34.5)	695(65.5)
中学	1 924(60.3)	580(30.1)	1 344(69.9)			PM₁₀				16.796	<0.001
大专及以上	320(10.0)	95(29.7)	225(70.3)			低浓度组	1 104(34.6)	373(33.8)	731(66.2)
婚姻状况				83.192	<0.001	中浓度组	1 332(41.8)	494(37.1)	838(62.9)
未婚	116(3.6)	6(5.2)	110(94.8)			高浓度组	754(23.6)	213(28.2)	541(71.8)
已婚	2 702(84.7)	898(33.2)	1 804(66.8)			NDVI-500 m				12.556	<0.001
丧偶	304(9.5)	155(51.0)	149(49.0)			低水平组	1 065(33.4)	374(35.1)	691(64.9)
离异	68(2.1)	21(30.9)	47(69.1)			中水平组	1 081(33.9)	396(36.6)	685(63.4)
月收入(元)				17.422	<0.001	高水平组	1 044(32.7)	310(29.7)	734(70.3)
0~<1 000	750(23.5)	228(30.4)	522(69.6)			EVI-500 m				13.988	<0.001
1 000~<3 000	1 899(59.5)	697(36.7)	1 202(63.3)			低水平组	1 134(35.5)	409(36.1)	725(63.9)
≥3 000	541(17.0)	155(28.7)	386(71.3)			中水平组	1 029(32.3)	370(36.0)	659(64.0)
吸烟				2.563	0.109	高水平组	1 027(32.2)	301(29.3)	726(70.7)
否	2 197(68.9)	724(33.0)	1 473(67.0)			SAVI-500 m				13.035	<0.001
是	993(31.1)	356(35.9)	637(64.1)			低水平组	1 121(35.1)	404(36.0)	717(64.0)
						中水平组	1 047(32.8)	375(35.8)	672(64.2)
						高水平组	1 022(32.0)	301(29.5)	721(70.5)

表(3)

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大气颗粒物对成年居民患心血管疾病的影响及植被覆盖的效应修饰研究

doi: 10.16462/j.cnki.zhjbkz.2023.05.006

通讯作者:
高怀荃，E-mail: 935677399@qq.com

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Effects of atmospheric particulate matter on the prevalence of cardiovascular diseases in adult residents and effect modification of vegetation coverage

Corresponding author: GAO Huaiquan, E-mail: 935677399@qq.com

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大气颗粒物对成年居民患心血管疾病的影响及植被覆盖的效应修饰研究

doi: 10.16462/j.cnki.zhjbkz.2023.05.006

通讯作者: 高怀荃，E-mail: 935677399@qq.com

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Effects of atmospheric particulate matter on the prevalence of cardiovascular diseases in adult residents and effect modification of vegetation coverage

Corresponding author: GAO Huaiquan, E-mail: 935677399@qq.com

计量

出版历程

目录

通讯作者:
高怀荃，E-mail: 935677399@qq.com