2018—2022年盐城市气象因素对人群死亡的影响——基于分布滞后非线性模型的研究  

Effect of Meteorological Factors on Population Mortality in Yancheng City from 2018 to 2022:Research Based on a Distribution Lag Nonlinear Model

作　　者：蔡伟 王瑞 王大力 刘付东[3] 梁季[3] CAI Wei;WANG Rui;WANG DaLI;LIU Fudong;LIANG Ji(Chronic Disease Department,Binhai County Center for Disease Control and Prevention,Yancheng,Jiangsu 224500;Chronic Disease Department,Yancheng Center for Disease Control and Prevention,Yancheng,Jiangsu 224100;Office,Binhai County Center for Disease Control and Prevention,Yancheng,Jiangsu 224500)

机构地区：[1]滨海县疾病预防控制中心慢性病科,江苏盐城224500 [2]滨海县疾病预防控制中心办公室,江苏盐城224500 [3]盐城市疾病预防控制中心慢性病科,江苏盐城224100

出　　处：《智慧健康》2024年第34期4-7,共4页Smart Healthcare

基　　金：盐城市科技项目研究资助《盐城市淮河流域地区胃癌危险因素精准研究》(项目编号:YK2021033)。

摘　　要：目的分析环境气温对盐城市逐日非意外死亡的影响,为降低人群死亡风险提供依据。方法收集2018—2022年盐城市气象资料和人口非意外死亡数据,并基于R软件的分布滞后非线性模型对日均气温和死亡数据进行时间滞后效应分析。结果2018—2022年盐城市环境气温与日死亡人数暴露-反应曲线近似呈“U”型,低温和高温引起死亡风险增加,高温表现为急性效应,RRLag0=1.17(95%CI:1.13~1.21),低温作用更为持久。极低温在滞后0~5 d出现保护性效应,RRLag0-5=0.90(95%CI:0.82~0.99),极高温在滞后0~7 d累积死亡风险最大,RRLag0-7=1.32(95%CI:1.26~1.38),高温在各滞后期的累积滞后效应均高于低温。因气温暴露导致死亡的归因分值为11.17(95%CI:0.74~19.90),人群死亡归因人数为33757人(95%CI:2754~59449),低温和高温暴露导致死亡的累积归因风险较低。结论2018—2022年盐城市环境气温对人群死亡存在滞后影响,应针对高危人群提前做好监测预警,以降低气温变化对人群死亡的影响。Objective To analyze the effect of environmental temperature on daily non accidental deaths in Yancheng City and provide basis for reducing risks of mortality of the population.Methods The paper collected meteorological data and non accidental death data of Yancheng City from 2018 to 2022 and analyzed time lag effect of daily temperature and death data based on distribution lag nonlinear model of R software.The exposure response curve of environmental temperature and daily death toll in Yancheng City from 2018 to 2022 showed approximate"U"shape.Low and high temperatures caused increase of death risks,while high temperatures showed acute effect,with RRLag0=1.17(95%CI1.13~1.21).Low temperatures showed persistent effect.Extreme low temperature showed protective effect with lag of 0~5 days,with RRLag0-5=0.90(95%CI0.82~0.99).Extreme high temperature had the highest cumulative risk of death,with lag of 0-7 days,with RRLag0-7=1.32(95%CI1.26~1.38).The cumulative lag effect of high temperature was higher than low temperature in each lag period.Attribution scores for deaths caused by temperature exposure were 11.17(95%CI0.74~19.90)and number of population with death attribution was 33757(95%CI2754~59449).Cumulative attribution risk for deaths caused by low and high temperature exposure was relatively low.Conclusion Environmental temperature has lag effect for population mortality in Yancheng City from 2018 to 2022.Early monitoring and warning should be carried out for high-risk populations to reduce effect of temperature changes on population mortality.

关 键 词：气温 死亡 归因风险 分布滞后非线性模型 

分 类 号：G63[文化科学—教育学]