基础医学与临床 ›› 2016, Vol. 36 ›› Issue (6): 805-810.

• 研究论文 • 上一篇    下一篇

中国3城市气温与人群死亡的关系

马新明1,李润奎2,罗凯1,张瑞明1,王宗爽3,许群4   

  1. 1. 北京协和医学院基础学院
    2. 中国科学院大学资源与环境学院
    3. 中国环境科学研究院
    4. 中国医学科学院基础医学研究所 北京协和医学院基础学院
  • 收稿日期:2016-03-11 修回日期:2016-04-19 出版日期:2016-06-05 发布日期:2016-05-27
  • 通讯作者: 许群 E-mail:xuqun@ibms.cams.cn
  • 基金资助:
    环保公益项目

Association between temperature and mortality in three cities in China

  • Received:2016-03-11 Revised:2016-04-19 Online:2016-06-05 Published:2016-05-27

摘要: 目的 探讨中国3城市(北京、成都和南京)气温与居民死亡的关系,评估气温相关的非意外死亡、心血管疾病死亡及呼吸系统疾病死亡的风险。方法 收集2008年1月1日至2010年12月31日北京、成都和南京的每日死亡数、同期气象数据和空气污染数据,用分布滞后非线性模型(DLNM),在控制长期趋势和季节效应以及其他混杂因素后,研究气温与死因别死亡的关系。结果 北京、成都和南京的气温与死因别死亡存在非线性关系,在较高温度时存在明显的急性效应,在较低温度时存在滞后效应。北京、成都和南京3城市极高气温对非意外死亡滞后0d的累积热效应存在差异 (p <0.05),RR值分别为1.09 (95% CI:1.04,1.14)、1.03 (95% CI:1.01,1.05)和 1.17 (95% CI:1.10,1.25)。极低气温对非意外死亡滞后0~15d的累积冷效应存在差异(p <0.05),RR值分别为1.71 (95% CI:1.43,2.04)、3.09 (95% CI:1.57,6.10)和1.95 (95% CI:1.21,3.16)。 结论 北京、成都和南京高温和低温均引起居民死亡风险升高,高温引起急性效应,低温产生的效应相对滞后,但持续时间较长。

关键词: 气温, 死因别死亡, 分布滞后非线性模型

Abstract: Objective To explore the relationship between temperature and mortality in three cities (Beijing, Chengdu and Nanjing) in China and evaluate the temperature-related risk of non-accidental, cardiovascular and respiratory mortality. Methods Data on daily deaths of the three cities as well as meteorological factors and air pollution were collected from January 1, 2008 to December 31, 2010. Distributed lag non-linear model (DLNM) was used to assess the effects of temperature on cause-specific mortality after controlling the long term, seasonal trend and other confounders. Results Non-linear relationships between the temperature and cause-specific mortality were observed in Beijing, Chengdu and Nanjing. The associations of the cumulative hot effects of extremely hot temperature for non-accidental mortality at lag 0 in Beijing, Chengdu, Nanjing were statistically significant (p <0.05), and the relative risk (RR) were 1.09 (95% CI:1.04,1.14), 1.03 (95% CI:1.01,1.05) and 1.17 (95% CI:1.10,1.25), respectively. Meanwhile, the associations of the cumulative cold effects of extremely cold temperature for non-accidental mortality at lag 0-15 in Beijing, Chengdu and Nanjing were statistically significant (p <0.05), the RRs were 1.71 (95% CI:1.43,2.04), 3.09 (95% CI:1.57,6.10) and 1.95 (95% CI:1.21,3.16), respectively. Conclusions Extremely cold and hot temperature could increase the risk of daily cause-specific mortality in Beijing, Chengdu and Nanjing. Hot temperature could cause acute effect, the effect of cold temperature had a several days delay, but a longer persistence.

Key words: temperature, cause-specific mortality, distributed lag non-linear model