气候变化对长沙市肾综合征出血热发病的影响与预警模型

The warning model and influence of climatic changes on hemorrhagic fever with renal syndrome in Changsha city

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。

Objective To 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 E1 Nifio-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 5.02/100 000 （ 294 cases ）, 2. 55/100 000 （ 150 000 755 cases ） , 9. 92/100 000 （ 578 cases ） , cases , 1.13/100 000 （67 cases）, 1.16/100 000（70 cases）,0.95/100 000 （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. 01,1 -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 = 1.01,95CI： 1.01 - 1.02,P =0. 02） , and MEI （r = 0. 31, P 〈 0. 01,3-month lag period; IRR = 0. 77,95 CI： O. 67 -0. 88, P 〈 0. 01 ） were closely associated with monthly HFRS cases （ 18. 10 cases）. Conclusion Climate factors significantly infuence 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.