Spatial Analysis in Epidemiology

Spatial Analysis in Epidemiology

Language: English

Pages: 162

ISBN: 0198509898

Format: PDF / Kindle (mobi) / ePub


This book provides a practical, comprehensive and up-to-date overview of the use of spatial statistics in epidemiology - the study of the incidence and distribution of diseases. Used appropriately, spatial analytical methods in conjunction with GIS and remotely sensed data can provide significant insights into the biological patterns and processes that underlie disease transmission. In turn, these can be used to understand and predict disease prevalence. This user-friendly text brings together the specialised and widely-dispersed literature on spatial analysis to make these methodological tools accessible to epidemiologists for the first time.

With its focus on application rather than theory, Spatial Analysis in Epidemiology includes a wide range of examples taken from both medical (human) and veterinary (animal) disciplines, and describes both infectious diseases and non-infectious conditions. Furthermore, it provides worked examples of methodologies using a single data set from the same disease example throughout, and is structured to follow the logical sequence of description of spatial data, visualisation, exploration, modelling and decision support. This accessible text is aimed at graduate students and researchers dealing with spatial data in the fields of epidemiology (both medical and veterinary), ecology, zoology and parasitology, environmental science, geography and statistics

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when the locations of events are known, they present problems where there are either large numbers of events or multiple events at the same location. In such situations the resulting maps tend to be cluttered, making it difficult to appreciate the density of events. Further difficulties with point maps arise when attribute information needs to be displayed at each location. The use of different symbols to represent attribute values is one solution, but large numbers of points and a wide range of

existence of space–time clustering. Kulldorff and Hjalmars (1999) review the Knox method, and discuss in some detail its statistical limitations, such as the problems of populationshift bias and the subjective choice of critical thresholds. They propose a modification of the test that overcomes these problems which they demonstrate using cases of lung cancer in New Mexico (1973–1991). Using the standard Knox test, significant space–time clustering is indicated at a range of critical distances

Lawson (2006a) have covered the whole subject area. There have also been several textbooks that are collections of chapters authored by different experts in the field (Elliott et al. 1992a; Gatrell and Löytönen 1998; Lawson et al. 1999a; Elliott et al. 2000; Lawson and Denison 2002; Durr and Gatrell 2004; Lawson and Kleinman 2005a; Hay et al. 2006). Despite these developments, general epidemiology texts typically do not include even a basic introduction to spatial analysis, apart from using maps

beginning with the least significant, until the estimated regression coefficients for all retained explanatory variables were significant at the alpha level of 0.05. A check for collinearity was carried out by calculating the variance inflation factor for each variable (Armitage et al. 2002). The final model is shown in Table 7.1. The results of this linear regression model show that median herd size is negatively associated with elevation and the maximum channel 3 amplitude (a measure of emitted

Computational and Graphical Statistics. 15, 428–442. Durr, P.A., Froggatt, A.E., 2002. How best to geo-reference farms? A case study from Cornwall, England. Preventive Veterinary Medicine 56, 51–62. Durr, P., Gatrell, A., 2004. GIS and Spatial Analysis in Veterinary Science. CABI Publishing, Wallingford. Dwass, M., 1957. Modified randomization tests for nonparametric hypotheses. The Annals of Mathematical Statistics 28, 181–187. Eastman, J.R., 2001. Guide to GIS and Image Processing. Clark Labs,

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