摘要
目的评价威海市气象干旱与风疹发病风险的关系。方法采用时间序列方法,基于分布滞后非线性模型对2005—2013年威海市风疹发病数据及同期干旱数据、气象数据进行回归分析。分析气象干旱对风疹发病影响的即时效应与滞后效应。结果威海市9年期间共计发病526人,年均发病率为2.18/10万。分布滞后非线性模型结果显示气象干旱对风疹发病的影响呈非线性关系,气象干旱在滞后2旬和3旬时,气象干旱导致的风疹发病增加,其RR值分别为1.11(95%CI:1.04~1.19)和1.10(95%CI:1.04~1.16);在滞后2~3旬时,其累计RR值最大(RR=1.09),但无统计学意义(P>0.05)。结论气象干旱对风疹的发病影响存在着滞后性,可导致风疹发病风险增加。
Objective:To evaluate the relationship between meteorological droughts and the morbidity of rubella in Weihai.Methods: A regression analysis was built by the time-series analysis method between meteorological droughts,meteorological factors and rubella in Weihai from 2005 to 2013 on the basis of the distributed lag non-linear model in order to analyze the immediate effect and lag effect of meteorological droughts on the morbidity of rubella.Results: There were 526 reported cases of rubella in Weihai during the study period,with an average incidence of 2.18/105 .The model showed that the effect of meteorological droughts on rubella was nonlinear.The risk of rubella was increased after meteorological droughts,and the lagged periods were 2 and 3 ten-day.The RRs on rubella were 1.11 (95% CI: 1.04~1.19) and 1.10 (95% CI: 1.04~1.16) at lagged 2nd and 3rd ten-days,respectively.Although strongest cumulative lag effects on rubella were observed at 2nd and 3rd ten-days (RR=1.09),there was no statistical significance ( P >0.05).Conclusion: The meteorological drought has a lag effect on the morbidity of rubella,and may increase risk of rubella in the study area.
作者
薛晓嘉
李学文
刘雪娜
侯海峰
姜宝法
丁国永
李晓梅
XUE Xiao-jia;LI Xue-wen;LIU Xue-na;HOU Hai-feng;JIANG Bao-fa;DING Guo-yong;LI Xiao-mei(School of Public Health,Shandong First Medical University&Shandong Academy of Medical Sciences,Taian 271016,China;School of Public Health,Shandong University,Jinan 250012,China)
出处
《泰山医学院学报》
CAS
2019年第5期321-324,共4页
Journal of Taishan Medical College
基金
山东省医药卫生科技发展计划项目(2016WS0605
2016WS0602)
泰安市科技发展计划项目(2016NS1206
2015NS2136)
泰山医学院高层次课题(2015GCC16
2016GCC05)
泰山医学院博士科研启动基金(刘雪娜)
关键词
气象干旱
风疹
时间序列分析
分布滞后非线性模型
meteorological drought
rubella
time-series analysis
distributed lag non-linear model