COVID-19疫情对厦门市气象因素与流行性感冒发病关联分析的影响  

Impact of the COVID-19 epidemic on the association between meteorological factors and influenza in Xiamen,China

作　　者：欧剑鸣[1,2] 陈玉檐 阿依努尔·要力达西 陈娟娟[5] 冯喻琳 陈思[6] 谢忠杭[1,2] 黄文龙[1] 杨子豪 李玲芳 祝寒松[1,2] OU Jian-ming;CHEN Yu-yan;AYNUR Joldo-sh;CHEN Juan-juan;FENG Yu-lin;CHEN Si;XIE Zhong-hang;HUANG Wen-long;YANG Zi-hao;LI Ling-fang;ZHU Han-song(Fujian Provincial Center for Disease Control and Prevention/Fujian Provincial Key Laboratory of Zoonosis Research,Fuzhou 350012,China;School of Public Health,Fujian Medical University;Fujian Provincial Judicial Drug Rehabilitation Hospital;School of Public Health,Xiamen University;Xiamen City Center for Disease Control and Prevention;Fujian Climate Center;Sinosoft Company Limited)

机构地区：[1]福建省疾病预防控制中心福建省人兽共患病研究重点实验室,福州350012 [2]福建医科大学公共卫生学院 [3]福建省司法戒毒医院 [4]厦门大学公共卫生学院 [5]厦门市疾病预防控制中心 [6]福建省气候中心 [7]中科软科技股份有限公司

出　　处：《环境卫生学杂志》2023年第5期319-327,共9页JOURNAL OF ENVIRONMENTAL HYGIENE

基　　金：福建省自然科学基金(2020J01094,2021J01350);福建省卫生健康委员会中青年骨干人才培养项目(2021GGA037);福建省科技创新平台建设项目(2019Y2001)。

摘　　要：目的目前尚不清楚新型冠状病毒感染(coronavirus disease 2019,COVID-19)疫情是否影响气象因素与流行性感冒(流感)的关联分析,本研究旨在探讨此问题及其影响特征,为今后精确评估气象因素对流感的风险效应提供依据。方法对厦门市气象因素和流感发病的每日数据分为三部分:全年期(2010年1月1日—2021年12月31日)、非COVID-19疫情期(2010年1月1日—2020年1月21日)和COVID-19疫情期(2020年1月22日—2021年12月31日),采用R 4.2.1软件通过分布滞后非线性模型(distributed lag nonlinear model,DLNM)进行气象因素与流感的关联分析。结果非COVID-19疫情期低(<42%)和高(>85%)相对湿度(relative humidity,RH)是流感的危险因素,高RH风险累计效应随滞后时间的增加而增加,随RH的增加而先增加后减弱,RH达到93%滞后14 d时最显著(RR=1.41)。COVID-19疫情期RHU<75%对流感的风险累计效应随RH的减少和滞后时间的增加而增加,RH为35%滞后14 d时最显著(RR=1.86×10^(6))。非COVID-19疫情期降水量(precipitation,PRE)处于25~75 mm对流感的风险累计效应先增加后减少,随滞后时间的增加而增加,滞后14 d,PRE达到40 mm时最显著(RR=1.49)。COVID-19疫情期PRE对流感的风险累计效应随PRE的增加而增加。COVID-19疫情期流感发病数的大幅下降严重影响气象因素与流感发病关联分析,致研究结果与全年期和非COVID-19疫情期差别较大;对全年期的研究结果影响较小。结论COVID-19疫情封控政策导致了厦门市COVID-19疫情期流感数大幅下降,从而显著影响了气象因素与流感发病关联分析客观性和科学性,但对长期多年度的分析结果影响有限。此外,极端天气例如RHU、PRE等对流感发病关联的分析也产生一定影响。Objective It is still unclear whether the coronavirus disease 2019(COVID-19)epidemic affects the association between meteorological factors and influenza,and this study aims to investigate this issue and its impact characteristics,so as to provide a basis for accurate assessment of the risk effect of meteorological factors on influenza in the future.Methods The daily data of meteorological factors and influenza in Xiamen were analyzed in three datasets:the whole year period(from January 1,2010 to December 31,2021),the non-COVID-19 epidemic period(from January 1,2010 to January 21,2020),and the COVID-19 epidemic period(from January 22,2020 to December 31,2021).Association between meteorological factors and influenza were analyzed through the distributed lag non-linear model(DLNM)of R 4.2.1 software.Results Low(<42%)and high(>85%)relative humidity(RH)were risk factors for influenza during the non-COVID-19 epidemic period.The cumulative risk effect of high RH increased with the lag time,first increasing and then decreasing with the increase of RH.with the most significant effect when RH reached 93% and lagged for 14 days(relative risk[RR]=1.41).During the COVID-19 epidemic period,the cumulative risk effect of RH<75% on influenza increased with the reduction of RH and the increase of lag time,with the most significant effect when RH decreased to 35% and lagged for 14 days(RR=1.86×10^(6)).During the non-COVID-19 epidemic period,the cumulative risk effect of precipitation(PRE)at 25-75 mm on influenza increased first and then decreased and increased with the increase of lag time,with the most significant effect when PRE reached 40 mm and lagged for 14 days(RR=1.49).The cumulative risk effect of PRE on influenza increased with the increase of PRE during the COVID-19 epidemic period.The sharp reduction in the number of influenza cases during the COVID-19 epidemic period seriously affected the association between meteorological factors and influenza,leading to the significant difference in analysis re-sult between this pe

关 键 词：气象 气温 相对湿度 流行性感冒 新型冠状病毒感染(COVID-19) 分布滞后非线性模型(DLNM) 

分 类 号：R184.1[医药卫生—流行病学]