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.
作者
石凌
李叶兰
胡伟红
SHI Ling;LI Yelan;HU Weihong(Changsha Center for Disease Control and Prevention,Changsha 410000,China)
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
关键词
人群死亡风险
相邻两日温差
时间序列
滞后效应
Death risk
Temperatures variation between neighboring days
Time series
Lag effect
