2010—2019年长沙市相邻两日温差对人群死亡影响的时间序列研究  

Effects of temperatures variation between neighboring days on mortality risk in Changsha 2010-2019:a time series analysis

作　　者：石凌 李叶兰[1] 胡伟红[1] SHI Ling;LI Yelan;HU Weihong(Changsha Center for Disease Control and Prevention,Changsha 410000,China)

机构地区：[1]长沙市疾病预防控制中心,长沙410000

出　　处：《公共卫生与预防医学》2021年第4期7-11,共5页Journal of Public Health and Preventive Medicine

摘　　要：目的通过描述2010—2019年长沙市气象因素与人群非意外死亡情况,探索相邻两日温差对人群死亡影响的关联强度和模式,为制定人群健康保护策略提供针对性的参考。方法通过泊松广义线性回归模型和分布滞后非线性模型,研究相邻两日温差对不同人群的死亡风险和滞后模式。结果长沙市2010—2019年非意外死亡人数为404328人,其中65岁以上占74.18%,男性占58.98%,呼吸系统疾病死亡占11.11%,心脑血管疾病死亡占54.47%。该研究时长为3652 d,日最高平均温度为35.8℃,日最低温度为-2.80℃。相邻两日温差的变化范围为-12.30℃~10.80℃,每增加1℃能增加人群1.12%的死亡风险(RR=1.0112,95%CI:1.0061~1.0164),其影响在暴露后第4 d达到最大。通过年龄、性别、病因分组研究发现,相邻两日温差对65岁以上、男性、患有呼吸系统疾病人群影响更大。结论相邻两日温差和长沙市非意外死亡人数呈现正相关,且具有明显的滞后效应;当相邻两日温差发生巨大变化时,应该加强患有呼吸系统疾病男性年老人群的保护,以减少相邻两日温差变化的带来影响。Objectives To analyze the features on temperature and mortality of Changsha in 2009-2019,and to explore the association between temperatures variation between neighboring days(TVN)and mortality by using time-series analysis.Methods A Poisson generalized linear regression model combined with a distributed lag non-linear model was used to analyse the association between TVN and mortality.Results A total of 404328 deaths were studied in Changsha during 2010-2019,the proportion of people aged over 65 years,males respiratory disease,and cardiovascular disease were 74.18%,58.98%,11.11%and 54.47%,respectively.During the 3652-day study period,the daily mean maximum and minimum temperature were 35.8℃and-2.8℃.The TVN varied from-12.30℃to 10.8℃,and a significant correlation was found between TVN and mortality risk,with 1.12%(RR=1.0112,95%CI:1.0061~1.0164)mortality risk increased for 1℃rise in TVN,and the greatest effect of TVN on mortality was at 4 days lag.According to the analysis on age,gender and death-cause,the elderly man over 65 years old,respiratory disease people were more vulnerable to the temperature change between day by day.Conclusion This study provides a comprehensive picture of the non-linear associations between temperature variation and mortality,and there is a certain lag effect.The findings on vulnerability characteristics can help improve clinical and public health practices to reduce disease burden associated with current and future abnormal weather.

关 键 词：人群死亡风险 相邻两日温差 时间序列 滞后效应 

分 类 号：R122[医药卫生—环境卫生学]