Research article

Spatial analysis of hemorrhagic fever with renal syndrome in China

Liqun Fang¹ , Lei Yan² , Song Liang^2,5 , Sake J de Vlas³ , Dan Feng¹ , Xiaona Han¹ , Wenjuan Zhao¹ , Bing Xu² , Ling Bian⁴ , Hong Yang¹ , Peng Gong² , Jan Hendrik Richardus³ and Wuchun Cao¹

¹  Beijing Institute of Microbiology and Epidemiology, State Key Laboratory of Pathogen and Biosecurity, Beijing, China

²  Institute of Remote Sensing Applications, Chinese Academy of Science, Beijing, China

³  Department of Public Health, Erasmus MC, University Medical Center, Rotterdam, the Netherlands

⁴  Department of Geography, University at Buffalo, USA

⁵  School of Public Health, University of California, Berkeley, USA

author email corresponding author email

BMC Infectious Diseases 2006, 6:77doi:10.1186/1471-2334-6-77

Published:	26 April 2006

Abstract

Background

Hemorrhagic fever with renal syndrome (HFRS) is endemic in many provinces with high incidence in mainland China, although integrated intervention measures including rodent control, environment management and vaccination have been implemented for over ten years. In this study, we conducted a geographic information system (GIS)-based spatial analysis on distribution of HFRS cases for the whole country with an objective to inform priority areas for public health planning and resource allocation.

Methods

Annualized average incidence at a county level was calculated using HFRS cases reported during 1994–1998 in mainland China. GIS-based spatial analyses were conducted to detect spatial autocorrelation and clusters of HFRS incidence at the county level throughout the country.

Results

Spatial distribution of HFRS cases in mainland China from 1994 to 1998 was mapped at county level in the aspects of crude incidence, excess hazard and spatial smoothed incidence. The spatial distribution of HFRS cases was nonrandom and clustered with a Moran's I = 0.5044 (p = 0.001). Spatial cluster analyses suggested that 26 and 39 areas were at increased risks of HFRS (p < 0.01) with maximum spatial cluster sizes of ≤ 20% and ≤ 10% of the total population, respectively.

Conclusion

The application of GIS, together with spatial statistical techniques, provide a means to quantify explicit HFRS risks and to further identify environmental factors responsible for the increasing disease risks. We demonstrate a new perspective of integrating such spatial analysis tools into the epidemiologic study and risk assessment of HFRS.