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American Journal of Epidemiology Vol. 148, No. 3: 276-281
Copyright © 1998 by The Johns Hopkins University School of Hygiene and Public Health


other

Seasonal Rainfall Variability, the Incidence of Hemorrhagic Fever with Renal Syndrome, and Prediction of the Disease in Low-lying Areas of China

Peng Bi1,2, Xike Wu2, Fangzhen Zhang2, Kevin A. Parton3 and Shilu Tong4

1 Department of Social and Preventive Medicine, University of Queensland Brisbane, Australia
2 Department of Epidemiology, Anhui Medical University Hefel, People's Republic of China
3 Department of Health Studies, University of New England Armidale, Australia
4 School of Public Health, Queensland University of Technology Kelvin Grove, Australia

To investigate determinants of hemorrhagic fever with renal syndrome (HFRS) in low-lying areas of China, the authors studied Chuigang and Wanggang communities in Anhui Province. These adjacent farming communities have a population of about 100,000. Data were collected from the two communities in 1961–1977 and from Yingshang County in 1983–1995; information covered the incidence of HFRS, amount of precipitation, differences in the water level of the Huai River, density of Apodemus agrarius, autumn crop production, and areas of inundated farmland. Correlation and multiple linear regression analyses were used to estimate the relation between seasonal rainfall, density of mice, occupational factors, and occurrence of the disease. Associations were observed between the incidence of HFRS and the amount of precipitation, the water level of the Huai River, and the areas of inundated farmland in Chuigang community. The smaller the water-level difference, the less farmland was inundated and the higher the incidence of HFRS. In Wanggang community, the density of A. agrarius (r1 = 0.63, p = 0.02), the water-level difference in the Huai River (r2 = –0.81, p = 0.007), and crop production (r3 = 0.96, p = 0.005) were correlated with the incidence of HFRS. The regression analyses based on Wanggang community suggested that these indexes could be used as predictive variables, and the results from the model were well calibrated with the actual incidence of HFRS in that community (R2 = 0.88, p < 0.01) and Yingshang County (R2 = 0.91, p < 0.01). Am J Epidemiol 1998;148:276–81.

forecasting; hemorrhagic fever with renal syndrome; precipitation; topography, medical


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