用不同指标度量隔日气温变异对居民寿命损失年影响的比较研究  

Comparison of indicators in predicting impact of temperature variability between neighboring days on years of life lost

作　　者：陈思齐 许燕君[2] 胡建雄 许晓君[2] 刘涛[1] 肖建鹏[1] 曾韦霖[1] 郭凌川 李杏[1] 马文军[1] CHEN Si-qi;XU Yan-jun;HU Jian-xiong;XU Xiao-jun;LIU Tao;XIAO Jian-peng;ZENG Wei-lin;GUO Ling-chuan;LI Xing;MA Wen-jun(Guangdong Provincial Institute of Public Health,Guangdong Provincial Center for Disease Control and Prevention,Guangzhou,Guangdong 511430,China;Institute of Chronic and Noncommunicable Disease Control and Prevention,Guangdong Provincial Center for Disease Control and Prevention,Guangzhou,Guangdong 511430,China)

机构地区：[1]广东省疾病预防控制中心广东省公共卫生研究院,广东广州511430 [2]广东省疾病预防控制中心慢性非传染性疾病预防控制所,广东广州511430

出　　处：《环境与职业医学》2020年第7期636-642,共7页Journal of Environmental and Occupational Medicine

基　　金：国家重点研发计划(2018YFA0606200)。

摘　　要：[背景]大量研究表明气温是影响人群健康的重要因素,而气温变异,尤其隔日气温变异对人群健康影响的研究较少。[目的]比较隔日温差(TCN,隔日平均气温之差)、气温变异(TV,隔日最高气温和最低气温的标准差)以及本研究新提出的根据隔日气温变异的方向和效应大小计算得到的隔日温度总变异(TTV)这三个隔日气温变异指标与居民寿命损失年(YLL)的暴露-反应关系,探索能更好反映隔日气温变异对居民死亡影响的指标。[方法]收集2013—2017年广东省40个区(县)气象数据以及死亡登记资料。采用分布滞后非线性模型(DLNM)和多变量meta分析的两阶段分析方法,分别拟合日夜温差和夜日温差与YLL率(每10万人口YLL值)的暴露-反应关系,提取日夜温差和夜日温差的归因YLL率作为各自权重计算TTV。计算Pearson相关系数,分析三个隔日气象变异指标间的相关性。采用DLNM和多变量meta分析两阶段分析方法,分别分析TCN、TV和TTV与居民YLL率的暴露-反应关系,比较不同隔日气温变异指标对人群死亡影响的差异。[结果]研究期间内广东省40个区(县)日均YLL率为22.3/10万。经计算,TCN平均值为(0.0±1.8)℃,TV为4.6±1.5,TTV平均值为(8.1±2.7)℃,三个指标均趋近正态分布。TCN与TV和TTV相关性较弱(r=0.0979,r=0.0880),而TV与TTV相关性较强(r=0.8891)。在控制平均气温的滞后效应后,TCN与YLL率的暴露-反应关系无统计学意义,而TV和TTV与YLL率的暴露-反应关系有统计学意义。TV-YLL和TTV-YLL的暴露-反应关系曲线相似,均呈类似"U"型关系,过低或过高的TV和TTV均会增加人群的YLL率。极端低(第5百分位数)的TV(TV=2.2)和TTV(TTV=2.8℃)的归因YLL率及其95%CI依次为1.0/10万(0.1/10万~1.9/10万)和2.1/10万(0.2/10万~4.0/10万),极端高(第95百分位数)的TV(TV=7.2)和TTV(TTV=12.1℃)的归因YLL率效应值及其95%CI依次为3.1/10万(1.2/10万~5.1/10万)和4.1/10万(2.3/10万~5.8/10万),�[Background]Numerous epidemiological studies have demonstrated a significant association between ambient temperature and population health,but evidence is limited for the health impact of temperature variability between neighboring days.[Objective]The study compares exposure-response associations of years of life lost(YLL)with different indicators of temperature variability between neighboring days,including temperature change between neighboring days(TCN,difference of mean temperature between neighboringdays),temperature variability(TV,the standard deviation of maximum and minimum temperatures between neighboring days),and total temperature variability between neighboring days(TTV)according to the directions and effects of temperature variability between neighboring days which was developed in the present study.The study aims to explore which measure can better assess the impact of temperature variability between neighboring days on mortality.[Methods]Death registration data and meteorological data during 2013-2017 were collected from 40 districts/counties in Guangdong,China.The exposure-response association of diurnal temperature range with YLL rate(YLL per 100000 population)and the association of nocturnal temperature range with YLL rate were investigated using a two-stage approach including distributed lag non-linear model(DLNM)and multivariable meta-analysis.Then TTV was weighted by attributable YLL rate of diurnal temperature range and nocturnal temperature range.The correlations of the three indicators of temperature variability between neighboring days were examined by Pearson correlation analysis.The exposure-response associations of YLL rate with TCN,TV,and TTV were evaluated by DLNM model and multivariable meta-analysis.The effects of different indicators of temperature variability between neighboring days on mortality were compared.[Results]The daily average YLL rate of the 40 study locations in Guangdong Province was 22.3 per 10~5 inhabitants during the study period.The means of TCN,TV,and TTV were(0

关 键 词：温度变异 隔日温差 夜日温差 日夜温差 寿命损失年 分布滞后非线性模型 

分 类 号：R12[医药卫生—环境卫生学]