title: A Geographic Analysis about the Spatiotemporal Pattern of Breast Cancer in Hangzhou from 2008 to 2012.
abstract: BACKGROUND
  Breast cancer (BC) is the most common female malignant tumor. Previous studies have suggested a big incidence disparity among different cities in China. The present work selected a typical city, Hangzhou, to study BC incidence disparity within the city.
  
  
  METHODS
  Totally, 8784 female breast cancer cases were obtained from the Hangzhou Center for Disease Control and Prevention during the period 2008-2012. Analysis of Variance and Poisson Regression were the statistical tools implemented to compare incidence disparity in the space-time domain (reference group: township residents during 2008, area: subdistrict, town, and township, time frame: 2008-2012), space-time scan statistics was employed to detect significant spatiotemporal clusters of BC compared to the null hypothesis that the probability of cases diagnosed at a particular location was equal to the probability of cases diagnosed in the whole study area. Geographical Information System (GIS) was used to generate BC spatial distribution and cluster maps at the township level.
  
  
  RESULTS
  The subdistrict populations were found to have the highest and most stable BC incidence. Although town and township populations had a relatively low incidence, it displayed a significant increasing trend from 2008 to 2012. The BC incidence distribution was spatially heterogeneous and clustered with a trend-surface from the southwest low area to the northeast high area. High clusters were located in the northeastern Hangzhou area, whereas low clusters were observed in the southwestern area during the time considered.
  
  
  CONCLUSIONS
  Better healthcare service and lifestyle changes may be responsible for the increasing BC incidence observed in towns and townships. One high incidence cluster (Linping subdistrict) and two low incidence clusters (middle Hangzhou) were detected. The low clusters may be attributable mainly to developmental level disparity, whereas the high cluster could be associated with other risk factors, such as environmental pollution.
authors.affiliation: College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, China.
authors.forename: Xufeng
authors.initials: X
authors.lastname: Fei
authors.affiliation: Institute of Islands and Coastal Ecosystems, Zhejiang University, Zhoushan, China.
authors.forename: Zhaohan
authors.initials: Z
authors.lastname: Lou
authors.affiliation: Institute of Islands and Coastal Ecosystems, Zhejiang University, Zhoushan, China.
authors.forename: George
authors.initials: G
authors.lastname: Christakos
authors.affiliation: Hangzhou Center for Disease Control and Prevention, Hangzhou, China.
authors.forename: Qingmin
authors.initials: Q
authors.lastname: Liu
authors.affiliation: Hangzhou Center for Disease Control and Prevention, Hangzhou, China.
authors.forename: Yanjun
authors.initials: Y
authors.lastname: Ren
authors.affiliation: Institute of Islands and Coastal Ecosystems, Zhejiang University, Zhoushan, China.
authors.forename: Jiaping
authors.initials: J
authors.lastname: Wu
mesh_terms: D001943:Breast Neoplasms / Q000453:epidemiology*; D002681:China / Q000453:epidemiology; D002947:Cities; D016000:Cluster Analysis; D005260:Female; D040362:Geographic Information Systems; D006801:Humans; D015994:Incidence; D012307:Risk Factors
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