Spatial autocorrelation analysis of health care hotspots in Taiwan in 2006

• Published in: BMC public health Vol. 9; no. 1; p. 464
• Main Authors: Tsai, Pui-Jen, Lin, Men-Lung, Chu, Chien-Min, Perng, Cheng-Hwang
• Format: Journal Article
• Language: English
• Published: England BioMed Central 14.12.2009; BioMed Central Ltd; BMC
• Subjects: Adult; Aged; Annual reports; Cause of Death; Classification; Cluster Analysis; Disease; Epidemiologic Methods; Female; Health care; Humans; Hypotheses; Logistic Models; Male; Medical research; Methods; Middle Aged; Mycobacterium; Native peoples; Research article; Risk Factors; Statistical analysis; Statistical Distributions; Statistics; Studies; Taiwan - epidemiology; Tuberculosis; Urban areas; Urban Population; Taiwan
• ISSN: 1471-2458; 1471-2458
• DOI: 10.1186/1471-2458-9-464

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. 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. 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. 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.