京津冀及周边地区大气细颗粒物和臭氧对医院日门诊量的急性影响及季节性差异

陈晨; 张梦雪; 刘晶; 方建龙; 郭亚菲; 李湉湉; 施小明

doi:10.11676/qxxb2022.040

京津冀及周边地区大气细颗粒物和臭氧对医院日门诊量的急性影响及季节性差异

doi: 10.11676/qxxb2022.040

陈晨^1,,
张梦雪^{1, 2},
刘晶^{1, 3},
方建龙¹,
郭亚菲¹,
李湉湉^{1, 2},
施小明^{1, 2, ,}

1.
中国疾病预防控制中心环境与人群健康重点实验室，中国疾病预防控制中心环境与健康相关产品安全所，北京，100021
2.
南京医科大学公共卫生学院，南京，211166
3.
河北大学公共卫生学院，保定，071002

基金项目: 中国疾病预防控制中心环境所青年科学基金项目（2020YSRF_02）、国家重点研发计划项目（2016YFC0206500）、大气重污染成因与治理攻关项目（DQGG0401）

详细信息

作者简介:
陈晨，主要从事环境流行病学研究。E-mail：chenchen3@nieh.chinacdc.cn

通讯作者:
施小明，主要从事慢性病流行病学研究。E-mail：shixm@chinacdc.cn

中图分类号: P49
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出版历程
- 收稿日期: 2021-12-27
- 录用日期: 2022-04-21
- 修回日期: 2022-03-25
- 网络出版日期: 2022-04-12

Acute impact of ambient fine particulate matter and ozone on daily outpatient visits and its seasonal differences in Beijing-Tianjin-Hebei and surrounding areas

CHEN Chen^1
,,
ZHANG Mengxue^{1, 2},
LIU Jing^{1, 3},
FANG Jianlong¹,
GUO Yafei¹,
LI Tiantian^{1, 2},
SHI Xiaoming^{1, 2
, ,}

1.
China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health，Chinese Center for Disease Control and Prevention，Beijing 100021，China
2.
School of Public Health，Nanjing Medical University，Nanjing 211166，China
3.
College of Public Health，Hebei University，Baoding 071002，China

摘要

摘要: 探究京津冀及周边地区大气细颗粒物（PM_2.5）和臭氧（O₃）短期暴露对人群因病就诊的急性影响及其季节性差异，为区域性大气污染的协同治理提供流行病学证据。收集2013年1月1日—2018年12月31日京津冀及周边地区共14个城市100家医院门诊的日就诊量，以及大气PM_2.5和O₃日均浓度和气象因子数据，基于时间序列研究设计，采用二阶段统计分析策略（广义相加模型联合meta分析），在控制气象因子和时间趋势等混杂因素的基础上构建双污染物模型，分析大气PM_2.5和O₃短期暴露对人群因病就诊的影响。研究期间，大气PM_2.5和O₃日均浓度平均分别为 72.2±56.8 μg/m³和 58.2±36.9 μg/m³，医院门诊就诊量达6257万人 · 次。双污染物模型结果显示，移动平均滞后0—1 d的PM_2.5和O₃暴露浓度每升高10 μg/m³，医院门诊就诊量分别增加0.25%（95%置信区间（95%CI）：0.20%—0.29%）和0.15%（95%CI：0.07%—0.22%）；拟合季节分层模型发现，冷季PM_2.5暴露对门诊就诊量的急性影响较强，而O₃相关效应则呈现出暖季较强的特征。京津冀及周边地区大气PM_2.5和O₃短期暴露均增大人群因病就诊的风险，提示应采取积极措施协同治理大气PM_2.5和O₃复合污染，同时重视污染物冷、暖季风险的差异。
- 细颗粒物 /
- 臭氧 /
- 短期暴露 /
- 因病就诊 /
- 季节模式
Abstract: This study explores acute effects of short-term exposure to ambient fine particulate matter (PM_2.5) and ozone (O₃) on hospital visits in the Beijing-Tianjin-Hebei region and surrounding areas and provides epidemiological evidence for the coordinated management of regional air pollution. Daily outpatient visits at 100 hospitals in 14 cities across the Beijing-Tianjin-Hebei region and surrounding areas and daily mean concentrations of PM_2.5 and O₃ as well as meteorological factors for the period from 1 January 2013 to 31 December 2018 are collected. Based on time series studies, a two-stage statistical analysis strategy (generalized additive model combined with meta analysis) is adopted to construct a dual-pollutant model by adjusting confounding factors (such as meteorological factors and time trends) to analyze effects of short-term exposure to ambient PM_2.5 and O₃ on hospital visits. During the study period, the average daily concentrations of ambient PM_2.5 and O₃ are 72.2±56.8 μg/m³ and 58.2±36.9 μg/m³, respectively, and the number of outpatient visits is 62.57 million. The results of the dual-pollutant model show that per 10 μg/m³ increases in 2 d moving average PM_2.5 and O₃ concentrations are associated with excess risks of 0.25% (95%CI: 0.20%—0.29%) and 0.15% (95%CI: 0.07%—0.22%) for daily outpatient visits with 0 to 1 d lag, respectively. Fitting the model of seasonal stratification, the acute effect of PM_2.5 exposure on outpatient visits is strong in cold season, while the O₃-related effect shows a strong effect in warm season. It is found that short-term exposure to ambient PM_2.5 and O₃ in the Beijing-Tianjin-Hebei region and surrounding areas both can increase the risk of outpatient visits. It is recommended to take active measures to coordinately control the combined pollution of PM_2.5 and O₃, and pay attention to different risk characteristics of pollutants between the cold and warm seasons.
- Fine particulate matter /
- Ozone /
- Short-term exposure /
- Hospital visits /
- Seasonal pattern

HTML全文

图 1 京津冀及周边地区大气细颗粒物短期暴露对医院门诊每日就诊量的急性影响

Figure 1. Acute effects of short-term exposure to ambient fine particulate matter on daily outpatient visits in Beijing-Tianjin-Hebei and surrounding areas

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图 2 京津冀及周边地区大气臭氧短期暴露对医院门诊每日就诊量的急性影响

Figure 2. Acute effects of short-term exposure to ambient ozone on daily outpatient visits in Beijing-Tianjin-Hebei and surrounding areas

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图 3 京津冀及周边地区大气细颗粒物和臭氧短期暴露对医院门诊就诊影响的滞后效应

Figure 3. Lagged effect of short-term exposure to ambient fine particulate matter and ozone on hospital outpatient visits in Beijing-Tianjin-Hebei and surrounding areas

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表 1 京津冀及周边地区纳入研究的100家医院类型

Table 1. Type of 100 hospitals in Beijing-Tianjin-Hebei and surrounding areas

省/直辖市	医院类型（家）
省/直辖市	三级综合医院	二级及社区综合医院	儿童医院
北京市	4	6	1
天津市	4	6	1
河北省	6	10	1
山西省	4	8	2
山东省	7	13	3
河南省	8	13	3

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表 2 2013—2018年京津冀及周边地区100家医院每日门诊就诊量、大气污染物浓度和气象因子统计

Table 2. Summary statistics of daily outpatient visits at 100 hospitals and air pollutants and meteorological factors across the Beijing-Tianjin-Hebei region and surrounding areas from 2013 to 2018

	2013—2018年		冷季	暖季
	Mean±SD	Median （P25，P75）	Mean±SD	Mean±SD
每日门诊就诊量（人·次）	282±463	103（24，347）	287±477	277±449
细颗粒物（μg/m³）	72.2±56.8	56.0 （35.5，90.8）	90.9±67.4	53.8±35.2
臭氧（μg/m³）	58.2±36.9	52.2 （28.6，81.5）	39.6±26.3	76.4±36.7
日均气温（℃）	14.1±10.7	15.6 （4.5，23.7）	5.5±7.1	22.7±5.3
相对湿度（%）	60.6±18.8	62.0 （47.0，75.0）	54.8±19.6	66.2±16.1
注：P25和P75分别为25%和75%分位数；Mean为平均值；Median为中位数；SD为标准差。

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表 3 不同污染物模型对全年大气细颗粒物和臭氧短期暴露急性效应的影响

Table 3. Influences of different pollutant-models on acute effects of ambient fine particulate matter and ozone short-term exposure during the entire year

污染物及暴露滞后	门诊就诊人次增加百分比[%（95%CI）]
污染物及暴露滞后	双污染物模型	单污染物模型
细颗粒物 lag0	0.23 （0.20—0.27）	0.24 （0.20—0.27）
细颗粒物 lag1	0.12 （0.09—0.15）	0.12 （0.09—0.15）
细颗粒物 lag01	0.25 （0.20—0.29）	0.25 （0.21—0.30）
臭氧 lag0	0.08 （0.02—0.14）	0.09 （0.03—0.16）
臭氧 lag1	0.16 （0.10—0.23）	0.17 （0.11—0.24）
臭氧 lag01	0.15 （0.07—0.22）	0.18 （0.10—0.26）

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表 4 模型自由度调整对全年大气细颗粒物和臭氧移动平均滞后0—1 d暴露急性效应的影响

Table 4. Influences of degree of freedom adjustment on acute effects of annual ambient fine particulate matter and ozone exposure with 0 to 1 d lag

自由度调整	门诊就诊人次增加百分比[%（95%CI）]
自由度调整	细颗粒物	臭氧
df_time=6，df_tem=3，df_rh=3	0.25 （0.20—0.29）	0.15 （0.07—0.22）
df_time=7，df_tem=3，df_rh=3	0.26 （0.21—0.30）	0.11 （0.04—0.19）
df_time=6，df_tem=5，df_rh=5	0.24 （0.20—0.29）	0.13 （0.06—0.20）
df_time=7，df_tem=5，df_rh=5	0.25 （0.21—0.30）	0.11 （0.03—0.18）

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