文章摘要
邓源,任翔,郭玉清,耿梦杰,张翠红,黄硕,林帆,王丽萍.2008-2020年我国北方15个城市流感与气象因素的关联性研究[J].中华流行病学杂志,2023,44(5):765-771
2008-2020年我国北方15个城市流感与气象因素的关联性研究
The correlations between influenza and meteorological factors in 15 cities of northern China, 2008-2020
收稿日期:2022-10-07  出版日期:2023-05-13
DOI:10.3760/cma.j.cn112338-20221007-00862
中文关键词: 流感  气象因素  面板数据
英文关键词: Influenza  Meteorological factors  Panel data
基金项目:国家自然科学基金(91846302);国家科技重大专项(2018ZX10713001-001)
作者单位E-mail
邓源 中国疾病预防控制中心传染病管理处/传染病监测预警重点实验室, 北京 102206  
任翔 中国疾病预防控制中心传染病管理处/传染病监测预警重点实验室, 北京 102206  
郭玉清 中国疾病预防控制中心传染病管理处/传染病监测预警重点实验室, 北京 102206  
耿梦杰 中国疾病预防控制中心传染病管理处/传染病监测预警重点实验室, 北京 102206  
张翠红 中国疾病预防控制中心传染病管理处/传染病监测预警重点实验室, 北京 102206  
黄硕 中国疾病预防控制中心传染病管理处/传染病监测预警重点实验室, 北京 102206  
林帆 中国疾病预防控制中心传染病管理处/传染病监测预警重点实验室, 北京 102206  
王丽萍 中国疾病预防控制中心传染病管理处/传染病监测预警重点实验室, 北京 102206 wanglp@chinacdc.cn 
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中文摘要:
      目的 定量评估我国北方城市气象因素对流感的发病影响,探索北方15个城市的气象因素对流感发病影响的差异。方法 收集西安市、兰州市、西宁市、银川市和乌鲁木齐市(西北5市)、北京市、天津市、石家庄市、太原市、呼和浩特市、济南市和郑州市(华北7市)、沈阳市、长春市和哈尔滨市(东北3市)共15个省会城市2008-2020年流感的月报告发病率和月度气象数据,运用面板数据回归模型定量分析气象因素对流感的发病影响。结果 综合单因素和多因素面板回归分析结果显示,在控制人口密度及其他气象因素的影响后,月均气温每下降5 ℃,东北3市、华北7市以及西北5市流感的发病率变化百分比(MCP)分别为11.35%、34.04%和25.04%,最佳滞后期月数分别为1、0和1个月;月均相对湿度每下降10%,东北3市和华北7市的MCP分别为15.84%和14.80%,最佳滞后期月数分别为2和1个月;月累计降雨量每减少10 mm,西北5市的MCP为4.50%,最佳滞后期月数为1个月;月累计日照时长每减少10 h,东北3市和西北5市的MCP分别为4.19%和5.97%,最佳滞后期月数均为1个月。结论 2008-2020年我国北方15个城市的气温、相对湿度、降雨量及日照时长与流感的发病均为负关联,气温和相对湿度是主要的敏感气象因素。气温对华北7市流感的发病有较强的直接影响作用,相对湿度对东北3市流感的发病有较强的滞后作用,西北5市的日照时长对流感的发病影响高于东北3市。
英文摘要:
      Objective To understand the influence of meteorological factors on the morbidity of influenza in northern cities of China and explore the differences in the influence of meteorological factors on the morbidity of influenza in 15 cities. Methods The monthly reported morbidity of influenza and monthly meteorological data from 2008 to 2020 were collected in 15 provincial capital cities, including Xi 'an, Lanzhou, Xining, Yinchuan and Urumqi (5 northwestern cities), Beijing, Tianjin, Shijiazhuang, Taiyuan, Hohhot, Ji'nan, Zhengzhou (7 northern cities), Shenyang, Changchun and Harbin (3 northeastern cities). The panel data regression model was applied to conduct quantitative analyze on the influence of meteorological factors on influenza morbidity. Results The univariate and multivariate panel regression analysis showed that after controlling the population density and other meteorological factors, for each 5 ℃ drop of monthly average temperature, the morbidity change percentage (MCP) of influenza was 11.35%, 34.04% and 25.04% in the 3 northeastern cities, 7 northern cities and 5 northwestern cities, respectively, and the best lag period months was 1, 0 and 1 month; When the monthly average relative humidity decreased by 10%, the MCP was 15.84% in 3 cities in northeastern China and 14.80% in 7 cities in northern China respectively, and the best lag period months was 2 and 1 months respectively; The MCP of 5 cities in northwestern China was 4.50% for each 10 mm reduction of monthly accumulated precipitation, and the best lag period months was 1 month; The MCPs of 3 cities in northeastern China and 5 cities in northwestern China were 4.19% and 5.97% respectively when the accumulated sunshine duration of each month decreased by 10 hours, the best lag period months was 1 month. Conclusions In northern cities of China from 2008 to 2020, the temperature, relative humidity, precipitation and sunshine duration all had negatively impact on the morbidity of influenza, and temperature and relative humidity were the main sensitive meteorological factors. Temperature had a strong direct impact on the morbidity of influenza in 7 cities in northern China, and relative humidity had a strong lag effect on the morbidity of influenza in 3 cities in northeastern China. The duration of sunshine in 5 cities in northwestern China had a greater impact on the morbidity of influenza compared with 3 cities in northeastern China.
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