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Clustering of equine grass sickness cases in the United Kingdom: a study considering the effect of position-dependent reporting on the space-time K-function.

French, NP, McCarthy, HE, Diggle, PJ and Proudman, CJ (2005) Clustering of equine grass sickness cases in the United Kingdom: a study considering the effect of position-dependent reporting on the space-time K-function. Epidemiol Infect, 133 (2). pp. 343-348.

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Abstract

Equine grass sickness (EGS) is a largely fatal, pasture-associated dysautonomia. Although the aetiology of this disease is unknown, there is increasing evidence that Clostridium botulinum type C plays an important role in this condition. The disease is widespread in the United Kingdom, with the highest incidence believed to occur in Scotland. EGS also shows strong seasonal variation (most cases are reported between April and July). Data from histologically confirmed cases of EGS from England and Wales in 1999 and 2000 were collected from UK veterinary diagnostic centres. The data did not represent a complete census of cases, and the proportion of all cases reported to the centres would have varied in space and, independently, in time. We consider the variable reporting of this condition and the appropriateness of the space-time K-function when exploring the spatial-temporal properties of a 'thinned' point process. We conclude that such position-dependent under-reporting of EGS does not invalidate the Monte Carlo test for space-time interaction, and find strong evidence for space time clustering of EGS cases (P < 0.001). This may be attributed to contagious or other spatially and temporally localized processes such as local climate and/or pasture management practices.

Item Type: Article
Authors :
NameEmailORCID
French, NPUNSPECIFIEDUNSPECIFIED
McCarthy, HEUNSPECIFIEDUNSPECIFIED
Diggle, PJUNSPECIFIEDUNSPECIFIED
Proudman, CJc.proudman@surrey.ac.ukUNSPECIFIED
Date : April 2005
Uncontrolled Keywords : Animals, Autonomic Nervous System Diseases, Botulism, Clostridium botulinum, Disease Outbreaks, England, Horse Diseases, Horses, Humans, Models, Theoretical, Monte Carlo Method, Plants, Edible, Wales
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 10:10
Last Modified : 17 May 2017 14:47
URI: http://epubs.surrey.ac.uk/id/eprint/826665

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