Hierarchical Bayesian analysis of censored microbiological contamination data for use in risk assessment and mitigation

Microbiological contamination data often is censored because of the presence of non-detects or because measurement outcomes are known only to be smaller than, greater than, or between certain boundary values imposed by the laboratory procedures. Therefore, it is not straightforward to fit distributi...

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Bibliographic Details
Published in:Food microbiology 2011-06, Vol.28 (4), p.712-719
Main Authors: Busschaert, P., Geeraerd, A.H., Uyttendaele, M., Van Impe, J.F.
Format: Article
Language:English
Subjects:
Animals
Bayes Theorem
Biological and medical sciences
Campylobacter - isolation & purification
Colony Count, Microbial
Fish Products - microbiology
Food industries
Food microbiology
Food Microbiology - methods
Fundamental and applied biological sciences. Psychology
Hierarchical Bayesian modeling
Listeria monocytogenes - isolation & purification
Poultry Products - microbiology
Quantitative microbiological risk assessment
Risk Assessment - methods
Uncertainty
Variability
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Summary:Microbiological contamination data often is censored because of the presence of non-detects or because measurement outcomes are known only to be smaller than, greater than, or between certain boundary values imposed by the laboratory procedures. Therefore, it is not straightforward to fit distributions that summarize contamination data for use in quantitative microbiological risk assessment, especially when variability and uncertainty are to be characterized separately. In this paper, distributions are fit using Bayesian analysis, and results are compared to results obtained with a methodology based on maximum likelihood estimation and the non-parametric bootstrap method. The Bayesian model is also extended hierarchically to estimate the effects of the individual elements of a covariate such as, for example, on a national level, the food processing company where the analyzed food samples were processed, or, on an international level, the geographical origin of contamination data. Including this extra information allows a risk assessor to differentiate between several scenario’s and increase the specificity of the estimate of risk of illness, or compare different scenario’s to each other. Furthermore, inference is made on the predictive importance of several different covariates while taking into account uncertainty, allowing to indicate which covariates are influential factors determining contamination.
ISSN:0740-0020
1095-9998
DOI:10.1016/j.fm.2010.06.006