中国媒介生物学及控制杂志 ›› 2017, Vol. 28 ›› Issue (1): 85-86.DOI: 10.11853/j.issn.1003.8280.2017.01.025

• 调查研究 • 上一篇    下一篇

气象因素对山东省安丘市肾综合征出血热发病的影响

张清敏1, 禹长兰1, 于世成1, 姜仑涛2, 王成花1   

  1. 1 安丘市疾病预防控制中心, 山东 安丘 262100;
    2 安丘市气象局, 山东 安丘 262100
  • 收稿日期:2016-09-14 出版日期:2017-02-20 发布日期:2017-02-20
  • 作者简介:张清敏,男,中心主任,主治医师,从事传染病防控工作,Email:aqlfy@sina.com

Influence of meteorological factors on hemorrhagic fever with renal syndrome in Anqiu city, Shandong province

ZHANG Qing-min1, YU Chang-lan1, YU Shi-cheng1, JIANG Lun-tao2, WANG Cheng-hua1   

  1. 1 Anqiu Center for Disease Control and Prevention, Anqiu 262100, Shandong Province, China;
    2 Anqiu Bureau of Meteorology
  • Received:2016-09-14 Online:2017-02-20 Published:2017-02-20

摘要:

目的 探索山东省安丘市2000-2014年肾综合征出血热(HFRS)发病情况与气象因素的关系。方法 收集2000-2014年安丘市HFRS流行病学资料和气象资料,采用Excel 2007软件对数据进行汇总,采用SAS 9.2软件进行相关性、多元线性回归分析。结果 年日照时数、年平均风速与HFRS年发病率呈正相关;本月的平均气温、降雨量、平均气压、平均风速、上月平均气温、上2个月平均气温与本月发病率呈负相关,上2个月平均相对湿度、上月日照时数、上月平均气压与本月发病率呈正相关。通过HFRS月发病率与气象因素的相关分析,最终本月日照时数(x2)、上月降雨量(x5)、上2个月平均气温(x8)、上2个月平均相对湿度(x9),与本月发病率(y)建立回归方程:y=0.019 1x2-0.014 2x5-0.239 0x8+0.061 5x9,R2=0.639。结论 安丘市HFRS发病与气象因素有关,可用其预测发病数。

关键词: 肾综合征出血热, 相关分析, 气象因素

Abstract:

Objective The purpose of this study was to explore the relationship between incidence of hemorrhagic fever with renal syndrome (HFRS) and meteorological factors. Methods We collected HFRS epidemiological data and meteorological data from 2000 to 2014 in Anqiu city. Excel 2007 was used for summarizing data and descriptive analysis. SAS 9.2 was used for correlation analysis, multiple linear regression analysis. Results The HFRS incidence was positively associated with annual sunshine hours and the annual average wind speed. The HFRS incidence of this month was inversely associated with this month's temperature, precipitation, average pressure, average wind speed and last one and two month's temperature; but positively was associated with last two month's humidity, last month's sunshine hours and atmospheric pressure. The paper established a regression equation model for multi-analysis of relativities between weather factors and the reported incidence of HFRS. This month's sunlight(x2), last month's precipitation(x5), last two month's temperature (x8) and humidity(x9) as the independent variables, the HFRS incidence of this month as the dependent variables, the regression equation was established, y=0.019 1x2-0.014 2x5-0.239 0x8+0.061 5x9, R2=0.639. Conclusion The HFRS incidence is associated with meteorological factors which can be used to predict the incidence.

Key words: Hemorrhagic fever with renal syndrome, Correlation analysis, Meteorological factors

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