摘要目的 了解气象因素变化对肾综合征出血热(HFRS)发病的影响，探索应用气象因素对HFRS发病进行预警。方法 收集长沙市2000-2009年HFRS病例（共2171例），同时收集同期气象数据，构建基于气象因素的长沙市HFRS传播预测模型，使用Cochran-Armitage趋势检验分析HFRS年发病率的变化趋势，采用交叉相关分析法计算气象因素[包括月平均温度、相对湿度、降水量及厄尔尼诺南方涛动指数(MEI)]与每月HFRS发病人数之间的时滞周期，最后采用时间序列泊松回归模型分析不同气象因素对HFRS传播的影响。结果 2000-2009年长沙市HFRS年发病率分别为13.09/10万（755例）、9.92/10万（578例）、5.02/10万（294例）、2.55/10万（150例）、1.13/10万(67例)、1.16/10万（70例）、0.95/10万（58例）、1.40/10万（87例）、0.75/10万（47例）、1.02/10万（65例），整体呈下降趋势（Z= -5.78,P＜0.01）。模型分析显示，月平均气温[18.00℃,r=0.26,P ＜0.01，1个月时滞周期；发病率比(IRR)=1.02，95％CI:1.00～1.03，P＜0.01]、相对湿度（75.50％，r=0.62,P＜0.01，3个月时滞周期；IRR= 1.03,95％CI:1.02～ 1.04，P＜ 0.01）、降水量（112.40mm,r=0.25，P＜0.01，6个月时滞周期；IRR= 1.01，95％CI:1.01～1.02,P=0.02）和MEI（r=0.31，P＜0.01，3个月时滞周期；IRR =0.77,95％ CI:0.67～0.88，P＜0.01）与HFRS月发病人数（18.10例）紧密相关。结论 气象因素对HFRS发病存在明显影响，在控制变量自相关、季节性及长期趋势的影响后，长沙市时间序列泊松回归模型预测精度较高，可以实现对长沙市HFRS的提前预警。

abstractsObjectiveTo realize the influence of climatic changes on the transmission of hemorrhagic fever with renal syndrome ( HFRS), and to explore the adoption of climatic factors in warning HFRS. Methods A total of 2171 cases of HFRS and the synchronous climatic data in Changsha from 2000 to 2009 were collected to a climate-based forecasting model for HFRS transmission. The Cochran-Armitage trend test was employed to explore the variation trend of the annual incidence of HFRS. Cross-correlations analysis was then adopted to assess the time-lag period between the climatic factors, including monthly average temperature,relative humidity,rainfall and Multivariate El Ni(n)o-Southern Oscillation Index (MEI) and the monthly HFRS cases. Finally the time-series Poisson regression model was constructed to analyze the influence of different climatic factors on the HFRS transmission. Results The annual incidence of HFRS in Changsha between 2000 - 2009 was 13.09/100 000 ( 755 cases ), 9. 92/100 000 ( 578 cases ),5.02/100 000 ( 294 cases ), 2. 55/100 000 ( 150 cases ), 1.13/100 000 ( 67 cases ), 1.16/100 000 (70 cases),0. 95/l00 000 (58 cases), 1.40/100 000 (87 cases),0. 75/100 000 (47 cases) and 1.02/100 000 (65 cases), respectively. The incidence showed a decline during these years (Z = -5.78,P＜ 0. 01 ). The results of Poisson regression model indicated that the monthly average temperature (18.00 ℃ ,r =0. 26,P ＜0. 01 ,l-month lag period; IRR= 1.02,95％CI: 1.00 - 1.03,P ＜0. 01 ) ,relative humidity (75.50％ ,r =0. 62 ,P ＜0. 01,3-month lag period; IRR = 1.03,95％ CI: 1.02 - 1.04 ,P ＜0. 01 ),rainfall ( 112.40 mm, r = 0.25, P ＜ 0. 01,6-month lag period; IRR = 1.01,95 CI: 1.01 - 1.02, P = 0. 02 ),and MEI (r =0. 31 ,P ＜0. 01,3-month lag period; IRR =0. 77,95CI: 0. 67 -0. 88,P ＜0. 01 ) were closely associated with monthly HFRS cases ( 18. 10 cases). ConclusionClimate factors significantly influence the incidence of HFRS. If the influence of variable-autocorrelation, seasonality, and long-term trend were controlled,the accuracy of forecasting by the time-series Poisson regression model in Changsha would be comparatively high,and we could forecast the incidence of HFRS in advance.