Detecting clusters of disease with R

One of the main concerns of Public Health surveillance is the detection of clusters of disease, i. e., the presence of high incidence rates around a particular location, which usually means a higher risk of suffering from the disease under study (Aylin et al. 1999). Many methods have been proposed f...

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Bibliographic Details
Published in:Journal of geographical systems 2005-06, Vol.7 (2), p.189-206
Main Authors: GOMEZ-RUBIO, V, FERRANDIZ-FERRAGUD, J, LOPEZ-QUILEZ, A
Format: Article
Language:English
Subjects:
Analysis. Health state
Areal geology. Maps
Bayesian analysis
Biological and medical sciences
Disease
Disease control
Earth sciences
Earth, ocean, space
Epidemiology
Exact sciences and technology
General aspects
Geologic maps, cartography
Medical sciences
Population
Programming languages
Public health
Public health. Hygiene
Public health. Hygiene-occupational medicine
Statistics
Studies
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Summary:One of the main concerns of Public Health surveillance is the detection of clusters of disease, i. e., the presence of high incidence rates around a particular location, which usually means a higher risk of suffering from the disease under study (Aylin et al. 1999). Many methods have been proposed for cluster detection, ranging from visual inspection of disease maps to full Bayesian models analysed using MCMC. In this paper we describe the use and implementation, as a package for the R programming language, of several methods which have been widely used in the literature, such as Openshaw's GAM, Stone's test and others. Although some of the statistics involved in these methods have an asymptotical distribution, bootstrap will be used to estimate their actual sampling distributions. [PUBLICATION ABSTRACT]
ISSN:1435-5930
1435-5949
DOI:10.1007/s10109-005-0156-5