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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/8256

Title: Human salmonellosis: Estimation of dose-illness from outbreak data
Authors: Bollaerts, Kaatje
Aerts, Marc
Faes, Christel
Grijspeerdt, Koen
Dewulf, Jeroen
Mintiens, Koen
Issue Date: 2008
Citation: RISK ANALYSIS, 28(2). p. 427-440
Abstract: The quantification of the relationship between the amount of microbial organisms ingested and a specific outcome such as infection, illness, or mortality is a key aspect of quantitative risk assessment. A main problem in determining such dose-response models is the availability of appropriate data. Human feeding trials have been criticized because only young healthy volunteers are selected to participate and low doses, as often occurring in real life, are typically not considered. Epidemiological outbreak data are considered to be more valuable, but are more subject to data uncertainty. In this article, we model the dose-illness relationship based on data of 20 Salmonella outbreaks, as discussed by the World Health Organization. In particular, we model the dose-illness relationship using generalized linear mixed models and fractional polynomials of dose. The fractional polynomial models are modified to satisfy the properties of different types of dose-illness models as proposed by Teunis et al. Within these models, differences in host susceptibility (susceptible versus normal population) are modeled as fixed effects whereas differences in serovar type and food matrix are modeled as random effects. In addition, two bootstrap procedures are presented. A first procedure accounts for stochastic variability whereas a second procedure accounts for both stochastic variability and data uncertainty. The analyses indicate that the susceptible population has a higher probability of illness at low dose levels when the combination pathogen-food matrix is extremely virulent and at high dose levels when the combination is less virulent. Furthermore, the analyses suggest that immunity exists in the normal population but not in the susceptible population.
Notes: Univ Hasselt, Ctr Stat, B-3590 Diepenbeek, Belgium. Inst Agr & Fisheries Res, Technol & Food Unit, Diepenbeek, Belgium. Univ Ghent, Fac Vet Med, Ghent, Belgium. Vet & Agrochem Res Ctr, Diepenbeek, Belgium.Bollaerts, K, Univ Hasselt, Ctr Stat, Agoralaan 1 Gebouw D, B-3590 Diepenbeek, Belgium.kaatje.bollaerts@uhasselt.be
URI: http://hdl.handle.net/1942/8256
DOI: 10.1111/j.1539-6924.2008.01038.x
ISI #: 000255061900013
ISSN: 0272-4332
Category: A1
Type: Journal Contribution
Validation: ecoom, 2009
Appears in Collections: Research publications

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