Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/61806
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Type: Journal article
Title: Climate variability and Hemorrhagic Fever with renal syndrome transmission in Northeastern China
Author: Zhang, W.
Guo, W.
Fang, L.
Li, C.
Bi, P.
Glass, G.
Jiang, J.
Sun, S.
Qian, Q.
Liu, W.
Yan, L.
Yang, H.
Tong, S.
Cao, W.
Citation: Environmental Health Perspectives, 2010; 118(7):915-920
Publisher: U.S. Dept of Health and Human Services Public Health Science
Issue Date: 2010
ISSN: 0091-6765
1552-9924
Statement of
Responsibility: 
Wen-Yi Zhang, Wei-Dong Guo, Li-Qun Fang, Chang-Ping Li, Peng Bi, Gregory E. Glass, Jia-Fu Jiang, Shan-Hua Sun, Quan Qian, Wei Liu, Lei Yan, Hong Yang, Shi-Lu Tong and Wu-Chun Cao
Abstract: Background: The transmission of hemorrhagic fever with renal syndrome (HFRS) is influenced by climatic variables. However, few studies have examined the quantitative relationship between climate variation and HFRS transmission. Objective: We examined the potential impact of climate variability on HFRS transmission and developed climate-based forecasting models for HFRS in northeastern China. Methods: We obtained data on monthly counts of reported HFRS cases in Elunchun and Molidawahaner counties for 1997–2007 from the Inner Mongolia Center for Disease Control and Prevention and climate data from the Chinese Bureau of Meteorology. Cross-correlations assessed crude associations between climate variables, including rainfall, land surface temperature (LST), relative humidity (RH), and the multivariate El Niño Southern Oscillation (ENSO) index (MEI) and monthly HFRS cases over a range of lags. We used time-series Poisson regression models to examine the independent contribution of climatic variables to HFRS transmission. Results: Cross-correlation analyses showed that rainfall, LST, RH, and MEI were significantly associated with monthly HFRS cases with lags of 3–5 months in both study areas. The results of Poisson regression indicated that after controlling for the autocorrelation, seasonality, and long-term trend, rainfall, LST, RH, and MEI with lags of 3–5 months were associated with HFRS in both study areas. The final model had good accuracy in forecasting the occurrence of HFRS. Conclusions: Climate variability plays a significant role in HFRS transmission in northeastern China. The model developed in this study has implications for HFRS control and prevention.
Keywords: China; cross-correlation; forecast; hemorrhagic fever with renal syndrome; risk factors; time-series Poisson regression.
Rights: Copyright status unknown
RMID: 0020100283
DOI: 10.1289/ehp.0901504
Appears in Collections:Public Health publications
Environment Institute publications

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