title: Diversity of HIV/AIDS epidemic in China: a result from hierarchical clustering analysis and spatial autocorrelation analysis.
abstract: OBJECTIVE
  To find out the diversity of HIV/AIDS epidemic among different areas in China according to their varied epidemic characteristics.
  
  
  METHODS
  Seventeen provincial variables, generated from original HIV/AIDS epidemic data and socioeconomic indicators to indicate HIV/AIDS epidemic characteristics, were introduced to hierarchical clustering analysis to form subepidemic areas. Then spatial autocorrelation analysis was applied to show the clustering distribution of cases from different most-at-risk populations.
  
  
  RESULTS
  Three HIV/AIDS subepidemic areas (A, B, C) were formed, each of which was further divided into two clusters, showing the diversity of HIV/AIDS epidemic in China. A1 was the earliest and severest HIV/AIDS epidemic area and occupied 37% hotspot counties. The epidemic in A1 was driven by IDU in its early period and heterosexual transmission later. Henan, the only province in A2, characterized by its HIV/AIDS epidemic among former plasma donors during the early 1990s, presented strong spatial clustering of blood/plasma transmission occupying 80% blood/plasma hotspots. The epidemic within B3, located in southwest China, was driven by IDU and heterosexual populations, and recently by MSM. The epidemic within B4, covering all four municipalities, had been largely spread among MSM since 2005. B3 and B4 occupied 76% MSM hotspots. For C5 and C6, only sporadic HIV/AIDS infections occurred in the last years among former plasma donors and heterosexual populations, whereas the prevalence among MSM had been increasing.
  
  
  CONCLUSIONS
  China's different HIV/AIDS subepidemic areas had obvious diversity of affected populations, which should be considered when determining prevention policies.
authors.affiliation: aNational Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing bDepartment of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China. *Shasha Qian and Dr Wei Guo contributed equally to the writing of this article.
authors.forename: Shasha
authors.initials: S
authors.lastname: Qian
authors.forename: Wei
authors.initials: W
authors.lastname: Guo
authors.forename: Jiannan
authors.initials: J
authors.lastname: Xing
authors.forename: Qianqian
authors.initials: Q
authors.lastname: Qin
authors.forename: Zhengwei
authors.initials: Z
authors.lastname: Ding
authors.forename: Fangfang
authors.initials: F
authors.lastname: Chen
authors.forename: Zhihang
authors.initials: Z
authors.lastname: Peng
authors.forename: Lu
authors.initials: L
authors.lastname: Wang
mesh_terms: D002681:China / Q000453:epidemiology; D016000:Cluster Analysis; D058872:Epidemics; D005260:Female; D015658:HIV Infections / Q000453:epidemiology*; D006801:Humans; D008297:Male; D062206:Spatial Analysis; D019371:Topography, Medical