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匡文韬, 赵锦, 倪涵, 罗飘异, 戴皓云, 任香, 易尚辉, 洪秀琴, 查文婷, 吕媛. 长沙市2015 — 2019年极端天气事件对循环系统疾病死亡影响[J]. 中国公共卫生, 2023, 39(7): 896-901. DOI: 10.11847/zgggws1139908
引用本文: 匡文韬, 赵锦, 倪涵, 罗飘异, 戴皓云, 任香, 易尚辉, 洪秀琴, 查文婷, 吕媛. 长沙市2015 — 2019年极端天气事件对循环系统疾病死亡影响[J]. 中国公共卫生, 2023, 39(7): 896-901. DOI: 10.11847/zgggws1139908
KUANG Wentao, ZHAO Jin, NI Han, LUO Piaoyi, DAI Haoyun, REN Xiang, YI Shanghui, HONG Xiuqin, ZHA Wenting, LÜ Yuan. Impact of extreme weather events on circulatory system disease mortality in Changsha city: 2015 – 2019[J]. Chinese Journal of Public Health, 2023, 39(7): 896-901. DOI: 10.11847/zgggws1139908
Citation: KUANG Wentao, ZHAO Jin, NI Han, LUO Piaoyi, DAI Haoyun, REN Xiang, YI Shanghui, HONG Xiuqin, ZHA Wenting, LÜ Yuan. Impact of extreme weather events on circulatory system disease mortality in Changsha city: 2015 – 2019[J]. Chinese Journal of Public Health, 2023, 39(7): 896-901. DOI: 10.11847/zgggws1139908

长沙市2015 — 2019年极端天气事件对循环系统疾病死亡影响

Impact of extreme weather events on circulatory system disease mortality in Changsha city: 2015 – 2019

  • 摘要:
      目的  了解湖南省长沙市2015 — 2019年极端天气事件对循环系统疾病死亡的影响,为疾病防控及相关预测预警工作奠定基础。
      方法  收集长沙市2015年1月1日 — 2019年12月31日气象因素和居民循环系统疾病死亡数据,分析长沙市2015 — 2019年极端天气和循环系统疾病死亡的分布特征,并采用Poisson分布u检验、Spearman相关性分析和单滞后效应分析探讨极端天气的发生与循环系统疾病死亡的关系。
      结果  长沙市2015年1月1日 — 2019年12月31日共发生72次低温寒潮事件,集中在每年的12、1和2月;14次高温热浪事件,集中在每年的7和8月;21次阴雨寡照事件,全年各时段分布较平均;长沙市2015 — 2019年共报告循环系统疾病死亡123626例,其中低温寒潮、高温热浪和阴雨寡照期间分别报告循环系统疾病死亡9865、5286和14093例;循环系统疾病总死亡数与低温寒潮、高温热浪、阴雨寡照事件的发生均呈正相关(r = 0.138、0.114、0.097,均P < 0.01);在极端天气中,高温热浪和阴雨寡照对循环系统疾病死亡数的影响滞后效应并不明显,低温寒潮对循环系统疾病总死亡数存在单滞后效应,滞后天数主要集中在2~4 d,滞后效应最强在第2 d,交叉相关系数为0.161;冷季日最低气温对循环系统疾病死亡数影响的单滞后效应最明显,最佳滞后天数为7 d,相关系数为 – 0.518。
      结论  长沙市2015 — 2019年极端天气事件与循环系统疾病死亡存在关联,其中低温寒潮的影响最大,气温对循环系统疾病死亡数的影响存在较为明显的单滞后效应。

     

    Abstract:
      Objective  To explore the impact of extreme weather events on circulatory system disease mortality among residents in Changsha city , Hunan province from 2015 through 2019 for providing evidence to prevention and control and early warning of circulatory system diseases.
      Methods   Daily number of circulatory disease mortality among residents of Changsha city were collected from Changsha Municipal Center for Disease Control and Prevention for the period of 2015 – 2019. Population data and meteorological data of the same period were simultaneously collected from Hunan Provincial Bureau of Statistics and China Meteorological Data Network. The association of extreme weather events with circulatory disease mortality in the population was analyzed with U-test of Poisson distribution, Spearman correlation analysis and single lag effect analysis.
      Results  Following extreme weather events were identified during the 5 year period for the city: 72 low temperature and cold wave events occurring in winter season (between January and February and December of each year), 14 heatwave events in summer season (between July and August of each year), and 21 rainy and sunless weather events occurring throughout a year. During the period, totally 123 626 circulatory disease deaths were reported in the city, including 9 865, 5 286 and 14 093 deaths occurred under extreme weather conditions of low temperature and cold wave, high temperature and heat wave, and rainy and sunless weather, respectively. The daily total number of circulatory disease mortality was positively correlated with the occurrence of low temperature and cold wave (r = 0.138), high temperature and heat wave (r = 0.114), and sunless weather (r = 0.097) (all P < 0.01). The lag effect of high temperature and heat wave and rainy and sunless weather on daily number of circulatory disease mortality was not obvious. There was a single-lag effect of low temperature and cold wave on daily number of circulatory disease mortality, with a strong effect at lag day 2 – lag day 4 and the strongest hysteresis effect at lag day 2 (cross-correlation coefficient = 0.161). The single-lag effect of daily minimum temperature in cold season on daily number of circulatory disease mortality was the most obvious and the effect was the strongest at lag day 7 (correlation coefficient = – 0.518). The effect of low temperature and cold wave was the most obvious at lag day 2.
      Conclusion  From 2015 to 2019, extreme weather events in Changsha were associated with circulatory disease mortality, among which low temperature and cold snaps had the greatest impact, and there was a significant single-day lag effect on daily number of circulatory disease mortality.

     

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