Bayesian hierarchical modelling to enhance the epidemiological value of abattoir surveys for bovine fasciolosis

Four classes of Bayesian hierarchical models were evaluated using an historical dataset from an abattoir survey for fasciolosis conducted in Victoria, Australia. The purpose of this analysis was to identify areas of high prevalence and to explain these in terms of environmental covariates. The simpl...

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Bibliographic Details
Published in:Preventive veterinary medicine 2005-10, Vol.71 (3), p.157-172
Main Authors: Durr, P.A., Tait, N., Lawson, A.B.
Format: Article
Language:English
Subjects:
Abattoir survey
Abattoirs - statistics & numerical data
Animals
Bayes Theorem
Bayesian statistics
Bayesian theory
Cattle
cattle diseases
Cattle Diseases - epidemiology
Cattle Diseases - etiology
Cattle Diseases - prevention & control
disease detection
disease prevalence
Ecological correlation
environmental factors
epidemiological studies
Fasciola gigantica
Fasciola hepatica
fascioliasis
Fascioliasis - epidemiology
Fascioliasis - prevention & control
Fascioliasis - veterinary
geographic information systems
GIS
Hierarchical models
irrigation
Liver fluke
liver flukes
model validation
NDVI
rain
risk factors
slaughterhouses
statistical models
Surveys and Questionnaires
Victoria - epidemiology
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Summary:Four classes of Bayesian hierarchical models were evaluated using an historical dataset from an abattoir survey for fasciolosis conducted in Victoria, Australia. The purpose of this analysis was to identify areas of high prevalence and to explain these in terms of environmental covariates. The simplest of the Bayesian models, with a single random effect, validated the use of smoothed maps for cartographic display when the sample sizes vary. The model was then extended to partition the random effect into spatially structured and unstructured components, thus allowing for spatial autocorrelation. Rainfall, irrigation, temperature-adjusted rainfall and a remotely sensed surrogate for rainfall, the normalised difference vegetation index (NDVI), were then introduced into the models as explanatory variables. The variable that best explained the observed distribution was irrigation. Associations between prevalence and both rainfall and NDVI that were significant in fixed effects models were shown to be due to spatial confounding. Nevertheless, provided they are used cautiously, confounded variables may be valid predictors for the prevalence of disease.
ISSN:0167-5877
1873-1716
DOI:10.1016/j.prevetmed.2005.07.013