Identifying the source of food-borne disease outbreaks: An application of Bayesian variable selection

• Type of Material: Article
• Authors: Rianne Jacobs, National Institute of Public Health and the Environment; Emmanuel Lesaffre, KU Leuven; Peter Teunis, Emory University; Micheal Höhle, Stockholm University; Jan van de Kassteele, National Institute of Public Health and the Environment
• Language: English
• Date: 2019-04-01
• Publisher: SAGE Publications (UK and US)
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2017.
• License: Creative Commons Attribution-NonCommercial 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1177/0962280217747311
• Title of Journal or Parent Work: Statistical Methods in Medical Research
• ISSN: 0962-2802
• Volume: 28
• Issue: 4
• Start Page: 1126
• End Page: 1140
• Grant/Funding Information: This work is supported by the National Institute for Public Health and the Environment (RIVM) and through their Strategic Programme (SPR) which contributes to solutions to societal challenges through interdisciplinary research; and by supporting innovation and capacity building at RIVM.
• Keywords: food-borne disease outbreaks; missing value imputation; misclassification; Bayesian variable selection; spike and slab prior
• Research Categories: Biology, Microbiology; Health Sciences, Public Health

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.