北京大学学报(医学版) ›› 2017, Vol. 49 ›› Issue (3): 403-408. doi: 10.3969/j.issn.1671-167X.2016.03.006

• 论著 • 上一篇    下一篇

北京市颗粒物污染对慢性阻塞性肺疾病急性加重住院的影响

曹宇1,刘徽2,张俊3,黄克武4,赵厚宇1,杨羽1,詹思延1△   

  1. (1. 北京大学公共卫生学院流行病与卫生统计学系, 北京 100191; 2. 北京大学医学信息学中心, 北京 100191; 3. 北京大学人民医院神经内科, 北京 100044; 4. 北京朝阳医院呼吸与危重症医学科, 北京 100020)
  • 出版日期:2017-06-18 发布日期:2017-06-18
  • 通讯作者: 詹思延 E-mail: siyan-zhan@bjmu.edu.cn
  • 基金资助:
    国家重点研发计划(2016YFC0901105)项目资助

Effect of particulate air pollution on hospital admissions for acute exacerbation of chronic obstructive pulmonary disease in Beijing

CAO Yu 1, LIU Hui 2, ZHANG Jun3, HUANG Ke-wu4, ZHAO Hou-yu1, YANG Yu1, ZHAN Si-yan1△   

  1. (1. Department of Epidemiology and Biostatistics, Peking University School of Public Health, Beijing 100191, China; 2. Medical Informatics Center, Peking University, Beijing 100191, China; 3. Department of Nephrology, Peking University People’s Hospital, Beijing 100044, China; 4. Department of Respiratory and Critical Care Medicine, Beijing ChaoYang Hospital, Beijing 100020, China)
  • Online:2017-06-18 Published:2017-06-18
  • Contact: ZHAN Si-yan E-mail: siyan-zhan@bjmu.edu.cn
  • Supported by:
    Supported by National Key Research and Development Program of China (2016YFC0901105)

摘要: 目的:研究北京市颗粒物污染对慢性阻塞性肺疾病急性加重(acute exacerbation of chronic obstructive pulmonary disease,AECOPD)的影响,并分析颗粒物污染对不同特征人群的影响差异。方法:从国家卫生与计划生育委员会医疗管理服务指导中心获取北京市2014年至2015年三甲医院AECOPD患者的住院资料,从中国空气质量监测平台获取同期大气污染资料,从中国气象局获取同期气象资料。采用广义相加Poisson回归模型,在控制长期趋势、周效应、节假日效应、气象条件等混杂因素后,评估PM2.5、PM10对AECOPD住院人次的影响。根据患者不同特征(性别、年龄)进行亚组分析,确定颗粒物污染的高危人群。结果:纳入15家医院,共7 884例住院患者,男女比例2.3 ∶1,65~79岁患者最多(45.5%)、≥80岁(37.1%)次之、<65岁(17.4%)最少。PM2.5、PM10日均浓度分别为(77.1±66.6) μg/m3、(111.9±75.8) μg/m3,两者均在移动平均滞后4 d时对AECOPD的影响最大,即PM2.5日均浓度每增加10 μg/m3,AECOPD住院人次增加0.53%(95%CI:0.01%~1.06%,P=0.0478), PM10日均浓度每增加10 μg/m3,AECOPD住院人次增加0.53%(95%CI:0.07%~1.00%,P=0.025 0)。亚组分析结果显示,PM2.5、PM10日均浓度每增加10 μg/m3,女性患者住院人次分别增加1.13%(95%CI:0.19%~2.07%,P=0.018 3)、1.06%(95%CI:0.22%~1.91%,P=0.013 6);≥80岁患者住院人次分别增加1.25%(95%CI:0.40%~2.11%,P=0.004 0)、1.18%(95%CI:0.42%~1.95%,P=0.002 4);而男性、<65岁、65~79岁患者中,PM2.5、PM10与AECOPD的关联无统计学意义。由此可见,女性、≥80岁患者对颗粒物污染更敏感。结论:颗粒物污染会增加AECOPD住院风险,且女性、年老者风险更高。

关键词: 空气污染, 肺疾病, 慢性阻塞性, 颗粒物, 广义相加模型

Abstract: Objective:To assess the association between particulate air pollution and hospital admissions for acute exacerbation of chronic obstructive pulmonary disease (AECOPD) in Beijing, and to eva-luate the differences of association among different subgroups.Methods: Hospital admissions to intertiary hospitals for AECOPD from  January 1,2014, through  December 31, 2015 were obtained from the electronic hospitalization summary reports (HSRs).We obtained the data on air pollution during the study period from the national air pollution monitoring system. The data on meteorological variables were obtained from the Chinese meteorological bureau. A poison generalized additive model was used to assess the effects of particulate pollution on AECOPD with adjustment for the long term trend, day of week, holiday effect and meteorological variables. Subgroup analyses were also conducted by age and gender, which would help identify higher-risk groups. Results: A total of 7 884 hospitalizations from 15 tertiary hospitals were recorded during the study period, and 69.3% were male patients, 37.1% were ≥80 years of age, 45.5% were 65-79 years of age, and 17.4% were younger than 65. The mean (SD) daily concentrations of PM2.5, PM10 were 77.1 (66.6) μg/m3, 111.9 (75.8) μg/m3. Every 10 μg/m3 increase in particulate pollution concentration for a lag of 4 d was associated with an increase in hospital admissions for AECOPD as follows: 0.53% (95% CI: 0.01%-1.06%, P=0.0478) of PM2.5, 0.53% (95% CI: 0.07%-1.00%, P=0.0250) of PM10, respectively. We found differences in risk for AECOPD admissions among the different subgroups. For every 10 μg/m3 increase in PM2.5, PM10 exposure in the female group there was a 1.13% (95% CI: 0.19%-2.07%, P=0.018 3) increase, 1.06% (95% CI: 0.22%-1.91%, P=0.013 6) increase in admissions, respectively, while in the male group, the association was non-significant. The patients of 80 years of age and older demonstrated a hi-gher risk of AECOPD, 1.25% (95% CI: 0.40%-2.11%, P=0.004 0) increase of PM2.5, 1.18% (95% CI: 0.42%-1.95%, P=0.002 4) increase of PM10, respectively, while other subgroups didn’t find significant association. Conclusion: Our findings showed that particulate air pollution was significantly associated with hospital admissions for AECOPD in Beijing. The susceptibility to particulate pollution varied by gender and age.

Key words: Air pollution, Particulate maller, Pulmonary disease, chronic obstructive, Generalized additive model

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