title: Spatial autocorrelation analysis of health care hotspots in Taiwan in 2006.
abstract: BACKGROUND
  Spatial analytical techniques and models are often used in epidemiology to identify spatial anomalies (hotspots) in disease regions. These analytical approaches can be used to not only identify the location of such hotspots, but also their spatial patterns.
  
  
  METHODS
  In this study, we utilize spatial autocorrelation methodologies, including Global Moran's I and Local Getis-Ord statistics, to describe and map spatial clusters, and areas in which these are situated, for the 20 leading causes of death in Taiwan. In addition, we use the fit to a logistic regression model to test the characteristics of similarity and dissimilarity by gender.
  
  
  RESULTS
  Gender is compared in efforts to formulate the common spatial risk. The mean found by local spatial autocorrelation analysis is utilized to identify spatial cluster patterns. There is naturally great interest in discovering the relationship between the leading causes of death and well-documented spatial risk factors. For example, in Taiwan, we found the geographical distribution of clusters where there is a prevalence of tuberculosis to closely correspond to the location of aboriginal townships.
  
  
  CONCLUSIONS
  Cluster mapping helps to clarify issues such as the spatial aspects of both internal and external correlations for leading health care events. This is of great aid in assessing spatial risk factors, which in turn facilitates the planning of the most advantageous types of health care policies and implementation of effective health care services.
authors.affiliation: College of Liberal and General Education, Aletheia University, Republic of China, Taiwan. puijentsai@gmail.com
authors.forename: Pui-Jen
authors.initials: PJ
authors.lastname: Tsai
authors.forename: Men-Lung
authors.initials: ML
authors.lastname: Lin
authors.forename: Chien-Min
authors.initials: CM
authors.lastname: Chu
authors.forename: Cheng-Hwang
authors.initials: CH
authors.lastname: Perng
mesh_terms: D000328:Adult; D000368:Aged; D002423:Cause of Death; D016000:Cluster Analysis; D004812:Epidemiologic Methods; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D012307:Risk Factors; D016008:Statistical Distributions; D013624:Taiwan / Q000453:epidemiology; D014505:Urban Population