title: Spatial analysis of hemorrhagic fever with renal syndrome in China.
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 < or = 20% and < or = 10% of the total population, respectively.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Beijing Institute of Microbiology and Epidemiology, State Key Laboratory of Pathogen and Biosecurity, Beijing, China. fang_lq@163.com
authors.forename: Liqun
authors.initials: L
authors.lastname: Fang
authors.forename: Lei
authors.initials: L
authors.lastname: Yan
authors.forename: Song
authors.initials: S
authors.lastname: Liang
authors.forename: Sake J
authors.initials: SJ
authors.lastname: de Vlas
authors.forename: Dan
authors.initials: D
authors.lastname: Feng
authors.forename: Xiaona
authors.initials: X
authors.lastname: Han
authors.forename: Wenjuan
authors.initials: W
authors.lastname: Zhao
authors.forename: Bing
authors.initials: B
authors.lastname: Xu
authors.forename: Ling
authors.initials: L
authors.lastname: Bian
authors.forename: Hong
authors.initials: H
authors.lastname: Yang
authors.forename: Peng
authors.initials: P
authors.lastname: Gong
authors.forename: Jan Hendrik
authors.initials: JH
authors.lastname: Richardus
authors.forename: Wuchun
authors.initials: W
authors.lastname: Cao
mesh_terms: D002681:China / Q000453:epidemiology; D016000:Cluster Analysis; D003710:Demography; D040362:Geographic Information Systems; D005843:Geography; D006480:Hemorrhagic Fever with Renal Syndrome / Q000453:epidemiology*; D006801:Humans; D015994:Incidence; D011159:Population Surveillance; D011634:Public Health / Q000379:methods*; D018570:Risk Assessment / Q000379:methods; D013223:Statistics as Topic