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[温度对中国五个城市死亡率的滞后效应分析]

[Analysis of the lag-effects of temperature on the five cities' mortality in China].

作者信息

Sun Yun-zong, Li Li-ping, Zhou Mai-geng

机构信息

Injury Prevention Research Center in Medical College of Shantou University, Shantou 515041, China.

出版信息

Zhonghua Yu Fang Yi Xue Za Zhi. 2012 Nov;46(11):1015-9.

Abstract

OBJECTIVE

To study the characteristics of the effect of different temperatures on mortality of different cities through analyzing the relationship between mortality and meteorology of five Chinese cities.

METHODS

We get the demography and climate data of Beijing, Tianjin, Shanghai, Nanjing and Changsha cities from National Center of Disease Control and Prevention and Climate net respectively. Then we applied the R software and Distributed Lag Non-linear Models (DLNM) package to analyze our data and find the nonlinear and lag effects on mortality using DLNM.

RESULTS

The city of Beijing and Tianjin are located in the temperate zone. And the climate of Shanghai, Nanjing, Changsha belong to subtropical monsoon climate. When the daily mean temperature arrived 30°C and on lag 0 day, the values of relative risk of effect of high mean temperature on mortality in Nanjing (1.31, 95%CI: 1.21 - 1.41) and Changsha (1.25, 95%CI: 1.13 - 1.39) are larger than that in Beijing (1.18, 95%CI: 1.12 - 1.25), Tianjin (1.18, 95%CI: 1.10 - 1.26) and Shanghai(1.15, 95%CI: 1.06 - 1.24). While the relative risk of effect of low mean temperature on mortality is lower and lasts for a longer lag time. During the whole lag time, the relative risk of effect of the lowest daily mean temperature of each city on mortality in Tianjin, Changsha, Beijing, Nanjing, and Shanghai is 3.41, 95%CI: 1.60 - 7.27, 2.15, 95%CI: 1.11 - 4.15, 2.24, 95%CI: 1.12 - 4.48, 2.80, 95%CI: 1.75 - 4.48, 1.53, 95%CI: 1.12 - 2.03, respectively. The cumulative effect of mean temperature on mortality appears like a U-shape. When on lag 0-1 day, the value of relative risk of effect of extremely high temperature and the highest mean temperature on mortality is larger than 1. While the effect of low temperature on mortality becomes obvious after lag 2 days.

CONCLUSION

Depending on this research, extremely low temperature and the lowest mean temperature has a more obvious impact on mortality in the northern area than in the south. Extremely high temperature and the highest daily mean temperature is on the contrary. Meanwhile, different temperatures have different impacts on mortality in the same city: high temperature has an acute impact while there is a longer lag time in low temperature.

摘要

目的

通过分析中国五个城市的死亡率与气象因素之间的关系,研究不同温度对不同城市死亡率影响的特征。

方法

我们分别从国家疾病预防控制中心和气候网获取北京、天津、上海、南京和长沙的人口统计学和气候数据。然后应用R软件和分布滞后非线性模型(DLNM)包对数据进行分析,并使用DLNM找出对死亡率的非线性和滞后效应。

结果

北京和天津位于温带。上海、南京、长沙的气候属于亚热带季风气候。当日平均气温达到30°C且滞后0天时,南京(1.31,95%CI:1.21 - 1.41)和长沙(1.25,95%CI:1.13 - 1.39)的高温对死亡率影响的相对风险值大于北京(1.18,95%CI:1.12 - 1.25)、天津(1.18,95%CI:1.10 - 1.26)和上海(1.15,95%CI:1.06 - 1.24)。而低温对死亡率影响的相对风险较低且持续更长的滞后时间。在整个滞后时间内,天津、长沙、北京、南京和上海各城市最低日平均气温对死亡率影响的相对风险分别为3.41,95%CI:1.60 - 7.27、2.15,95%CI:1.11 - 4.15、2.24,95%CI:1.12 - 4.48、2.80,95%CI:1.75 - 4.48、1.53,95%CI:1.12 - 2.03。平均气温对死亡率的累积效应呈U形。当滞后0 - 1天时,极高温度和最高日平均气温对死亡率影响的相对风险值大于1。而低温对死亡率的影响在滞后2天后变得明显。

结论

根据本研究,极低温度和最低日平均气温对北方地区死亡率的影响比南方更明显。极高温度和最高日平均气温则相反。同时,不同温度对同一城市的死亡率有不同影响:高温有急性影响,而低温的滞后时间更长。

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