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dc.contributor.authorJacobs, Rianne
dc.contributor.authorLesaffre, Emmanuel
dc.contributor.authorTeunis, Peter Fm
dc.contributor.authorHöhle, Michael
dc.contributor.authorvan de Kassteele, Jan
dc.date.accessioned20200413
dc.date.available2018-04-04T09:53:17Z
dc.date.issued2017-01-01
dc.identifier.citationIdentifying the source of food-borne disease outbreaks: An application of Bayesian variable selection. 2017:962280217747311 Stat Methods Med Resen
dc.identifier.issn1477-0334
dc.identifier.pmid29241399
dc.identifier.doi10.1177/0962280217747311
dc.identifier.urihttp://hdl.handle.net/10029/621752
dc.description.abstractEarly identification of contaminated food products is crucial in reducing health burdens of food-borne disease outbreaks. Analytic case-control studies are primarily used in this identification stage by comparing exposures in cases and controls using logistic regression. Standard epidemiological analysis practice is not formally defined and the combination of currently applied methods is subject to issues such as response misclassification, missing values, multiple testing problems and small sample estimation problems resulting in biased and possibly misleading results. In this paper, we develop a formal Bayesian variable selection method to account for misclassified responses and missing covariates, which are common complications in food-borne outbreak investigations. We illustrate the implementation and performance of our method on a Salmonella Thompson outbreak in the Netherlands in 2012. Our method is shown to perform better than the standard logistic regression approach with respect to earlier identification of contaminated food products. It also allows relatively easy implementation of otherwise complex methodological issues.
dc.language.isoenen
dc.titleIdentifying the source of food-borne disease outbreaks: An application of Bayesian variable selection.en
dc.typeArticleen
dc.identifier.journalStat Methods Med Res 2019; 28(4):1126-40en
html.description.abstractEarly identification of contaminated food products is crucial in reducing health burdens of food-borne disease outbreaks. Analytic case-control studies are primarily used in this identification stage by comparing exposures in cases and controls using logistic regression. Standard epidemiological analysis practice is not formally defined and the combination of currently applied methods is subject to issues such as response misclassification, missing values, multiple testing problems and small sample estimation problems resulting in biased and possibly misleading results. In this paper, we develop a formal Bayesian variable selection method to account for misclassified responses and missing covariates, which are common complications in food-borne outbreak investigations. We illustrate the implementation and performance of our method on a Salmonella Thompson outbreak in the Netherlands in 2012. Our method is shown to perform better than the standard logistic regression approach with respect to earlier identification of contaminated food products. It also allows relatively easy implementation of otherwise complex methodological issues.


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