title: Space-time clustering characteristics of tuberculosis in China, 2005-2011.
abstract: OBJECTIVE
  China is one of the 22 tuberculosis (TB) high-burden countries in the world. As TB is a major public health problem in China, spatial analysis could be applied to detect geographic distribution of TB clusters for targeted intervention on TB epidemics.
  
  
  METHODS
  Spatial analysis was applied for detecting TB clusters on county-based TB notification data in the national notifiable infectious disease case reporting surveillance system from 2005 to 2011. Two indicators of TB epidemic were used including new sputum smear-positive (SS+) notification rate and total TB notification rate. Global Moran's I by ArcGIS was used to assess whether TB clustering and its trend were significant. SaTScan software that used the retrospective space-time analysis and Possion probability model was utilized to identify geographic areas and time period of potential clusters with notification rates on county-level from 2005 to 2011.
  
  
  RESULTS
  Two indicators of TB notification had presented significant spatial autocorrelation globally each year (p<0.01). Global Moran's I of total TB notification rate had positive trend as time went by (t=6.87, p<0.01). The most likely clusters of two indicators had similar spatial distribution and size in the south-central regions of China from 2006 to 2008, and the secondary clusters in two regions: northeastern China and western China. Besides, the secondary clusters of total TB notification rate had two more large clustering centers in Inner Mongolia, Gansu and Qinghai provinces and several smaller clusters in Shanxi, Henan, Hebei and Jiangsu provinces.
  
  
  CONCLUSIONS
  The total TB notification cases clustered significantly in some special areas each year and the clusters trended to aggregate with time. The most-likely and secondary clusters that overlapped among two TB indicators had higher TB burden and risks of TB transmission. These were the focused geographic areas where TB control efforts should be prioritized.
authors.affiliation: National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
authors.forename: Fei
authors.initials: F
authors.lastname: Zhao
authors.affiliation: National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
authors.forename: Shiming
authors.initials: S
authors.lastname: Cheng
authors.affiliation: National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
authors.forename: Guangxue
authors.initials: G
authors.lastname: He
authors.affiliation: National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
authors.forename: Fei
authors.initials: F
authors.lastname: Huang
authors.affiliation: National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
authors.forename: Hui
authors.initials: H
authors.lastname: Zhang
authors.affiliation: School of Public Health, Fudan University, Shanghai, China.
authors.forename: Biao
authors.initials: B
authors.lastname: Xu
authors.affiliation: AIDS & Tuberculosis Unit, Ministry of Health and Child Welfare, Harare, Zimbabwe.
authors.forename: Tonderayi C
authors.initials: TC
authors.lastname: Murimwa
authors.affiliation: National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
authors.forename: Jun
authors.initials: J
authors.lastname: Cheng
authors.affiliation: National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
authors.forename: Dongmei
authors.initials: D
authors.lastname: Hu
authors.affiliation: National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
authors.forename: Lixia
authors.initials: L
authors.lastname: Wang
mesh_terms: D002681:China / Q000453:epidemiology; D018563:Disease Notification; D006801:Humans; D013029:Space-Time Clustering; D014376:Tuberculosis / Q000453:epidemiology* / Q000517:prevention & control