高级检索
    宋和佳, 黄钰姝, 李永红, 程义斌, 姚孝元. 中国21个地区隔日温差对人群死亡影响的时间序列研究[J]. 环境卫生学杂志, 2022, 12(4): 254-262. DOI: 10.13421/j.cnki.hjwsxzz.2022.04.003
    引用本文: 宋和佳, 黄钰姝, 李永红, 程义斌, 姚孝元. 中国21个地区隔日温差对人群死亡影响的时间序列研究[J]. 环境卫生学杂志, 2022, 12(4): 254-262. DOI: 10.13421/j.cnki.hjwsxzz.2022.04.003
    shu
    SONG He-jia, HUANG Yu-shu, LI Yong-hong, CHENG Yi-bin, YAO Xiao-yuan. Time-series study on the impacts of temperature changes between neighboring days on mortality in 21 regions of China[J]. Journal of Environmental Hygiene, 2022, 12(4): 254-262. DOI: 10.13421/j.cnki.hjwsxzz.2022.04.003
    Citation: SONG He-jia, HUANG Yu-shu, LI Yong-hong, CHENG Yi-bin, YAO Xiao-yuan. Time-series study on the impacts of temperature changes between neighboring days on mortality in 21 regions of China[J]. Journal of Environmental Hygiene, 2022, 12(4): 254-262. DOI: 10.13421/j.cnki.hjwsxzz.2022.04.003
    shu

    中国21个地区隔日温差对人群死亡影响的时间序列研究

    Time-series study on the impacts of temperature changes between neighboring days on mortality in 21 regions of China

      摘要
    • 摘要:
      目的 探索隔日温差(temperature changes between neighboring days, TCN)对人群死亡的影响。
      方法 收集我国21个地区2014—2018年的每日气象因素数据、空气污染物数据和死因统计数据。运用分布滞后非线性模型(distributed lag non-linear model, DLNM)和多元Meta分析，估计不同季节的TCN对每日总死亡人数的影响。
      结果 研究显示，不同季节的TCN对每日总死亡人数均有显著影响，且阈值不同。冷季时，P95 TCN(升温)的14天累积相对危险度(CRR)为0.868 (95%CI: 0.794, 0.948)，而P5(降温)对每日总死亡人数的影响没有统计学意义。暖季时，P95 TCN(升温)的7天CRR为1.078 (95%CI: 1.009, 1.152)，而P5(降温)的7天CRR为0.929 (95%CI: 0.889, 0.971)。冷季时，患有呼吸系统疾病人群更容易受到温度变化的不利影响。暖季时，循环系统疾病人群、女性和≥ 65岁人群对温度变化更为敏感。南北区域的分析显示，北方城市的人群对P95TCN的影响更加敏感。
      结论 不同季节的极端TCN与人群死亡的风险存在关联，冷季时升温可降低人群死亡风险，而暖季时升温可增加人群死亡风险, 降温可降低人群死亡风险。健康促进策略应该考虑相邻两天之间的温度变化对人群死亡影响。

       

      Abstract:
      Objective To investigate the impact of temperature changes between neighboring days (TCN) on mortality.
      Methods Data of daily meteorology, air pollution and cause-of-death were collected for 21 regions of China in 2014—2018. The distributed lag non-linear model(DLNM) and the multivariate meta-analysis were used to investigate the impact of TCN on total daily deaths in different seasons.
      Results The study showed a significant impact of TCN on total daily deaths in different seasons, with different thresholds. During the cold season, P95 TCN (warming) had a 14-day cumulative relative risk (CRR) of 0.868 (95% CI: 0.794-0.948), while P5 TCN (cooling) had no significant impact. During the warm season, P95 TCN (warming) had a 7-day CRR of 1.078 (95% CI: 1.009-1.152), while P5 TCN (cooling) had a 7-day CRR of 0.929 (95% CI: 0.889-0.971). During the cold season, the population with respiratory diseases was more likely to be affected by temperature changes, and during the warm season, the population with circulatory diseases, women, and the population aged ≥65 years were more sensitive to temperature changes. The analysis of the north and south regions showed that the population in northern cities was more sensitive to the impact of P95 TCN.
      Conclusion Extreme TCN in different seasons is associated with the risk of death among populations; warming in the cold season can reduce the risk of death, while in the warm season, warming can increase the risk of death and cooling can reduce the risk of death. Health promotion strategies should take into account the impact of temperature changes between neighboring days on mortality.

       

      • HTML全文
      • 参考文献(23)
      • 相关文章
      • 施引文献
    • 资源附件(0)

    /

    返回文章
    返回