Climate variability and hemorrhagic fever with renal syndrome transmission in northeastern China

Wen Yi Zhang, Wei Dong Gu, 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, Wu Chun Cao

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)915-920
Number of pages6
JournalEnvironmental Health Perspectives
Volume118
Issue number7
DOIs
StatePublished - 2010

Fingerprint

Hemorrhagic Fever with Renal Syndrome
Climate
China
Humidity
Temperature
Meteorology
Centers for Disease Control and Prevention (U.S.)

Keywords

  • China
  • Cross-correlation
  • Forecast
  • Hemorrhagic fever with renal syndrome
  • Risk factors
  • Time-series Poisson regression

ASJC Scopus subject areas

  • Health, Toxicology and Mutagenesis
  • Public Health, Environmental and Occupational Health
  • Medicine(all)

Cite this

Zhang, W. Y., Gu, W. D., Fang, L. Q., Li, C. P., Bi, P., Glass, G. E., ... Cao, W. C. (2010). Climate variability and hemorrhagic fever with renal syndrome transmission in northeastern China. Environmental Health Perspectives, 118(7), 915-920. https://doi.org/10.1289/ehp.0901504

Climate variability and hemorrhagic fever with renal syndrome transmission in northeastern China. / Zhang, Wen Yi; Gu, Wei Dong; Fang, Li Qun; Li, Chang Ping; Bi, Peng; Glass, Gregory E.; Jiang, Jia Fu; Sun, Shan Hua; Qian, Quan; Liu, Wei; Yan, Lei; Yang, Hong; Tong, Shi Lu; Cao, Wu Chun.

In: Environmental Health Perspectives, Vol. 118, No. 7, 2010, p. 915-920.

Research output: Contribution to journalArticle

Zhang, WY, Gu, WD, Fang, LQ, Li, CP, Bi, P, Glass, GE, Jiang, JF, Sun, SH, Qian, Q, Liu, W, Yan, L, Yang, H, Tong, SL & Cao, WC 2010, 'Climate variability and hemorrhagic fever with renal syndrome transmission in northeastern China', Environmental Health Perspectives, vol. 118, no. 7, pp. 915-920. https://doi.org/10.1289/ehp.0901504
Zhang, Wen Yi ; Gu, Wei Dong ; Fang, Li Qun ; Li, Chang Ping ; Bi, Peng ; Glass, Gregory E. ; Jiang, Jia Fu ; Sun, Shan Hua ; Qian, Quan ; Liu, Wei ; Yan, Lei ; Yang, Hong ; Tong, Shi Lu ; Cao, Wu Chun. / Climate variability and hemorrhagic fever with renal syndrome transmission in northeastern China. In: Environmental Health Perspectives. 2010 ; Vol. 118, No. 7. pp. 915-920.
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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 Ni{\~n}o 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.",
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T1 - Climate variability and hemorrhagic fever with renal syndrome transmission in northeastern China

AU - Zhang, Wen Yi

AU - Gu, Wei Dong

AU - Fang, Li Qun

AU - Li, Chang Ping

AU - Bi, Peng

AU - Glass, Gregory E.

AU - Jiang, Jia Fu

AU - Sun, Shan Hua

AU - Qian, Quan

AU - Liu, Wei

AU - Yan, Lei

AU - Yang, Hong

AU - Tong, Shi Lu

AU - Cao, Wu Chun

PY - 2010

Y1 - 2010

N2 - 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.

AB - 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.

KW - China

KW - Cross-correlation

KW - Forecast

KW - Hemorrhagic fever with renal syndrome

KW - Risk factors

KW - Time-series Poisson regression

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SN - 0091-6765

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