ArticleStatistical methods in medical research2019
Identifying the source of food-borne disease outbreaks: An application of Bayesian variable selection.
Article in Statistical methods in medical research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Exploring the Interplay of Food Security, Safety, and Psychological Wellness in the COVID-19 Era: Managing Strategies for Resilience and Adaptation.Foods (Basel, Switzerland) · 2024Review
- Simulation and identification of foodborne outbreaks in a large supermarket consumer purchase dataset.Scientific reports · 2022Article
- Matched Versus Unmatched Analysis of Matched Case-Control Studies.American journal of epidemiology · 2021Article
- Bayesian variable selection for high dimensional predictors and self-reported outcomes.BMC medical informatics and decision making · 2020Article
- Tracing the Origin of Food-borne Disease Outbreaks: A Network Model Approach.Epidemiology (Cambridge, Mass.) · 2020Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early 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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