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Human Salmonellosis: Estimation of Dose‐Illness from Outbreak Data

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  • Kaatje Bollaerts
  • Marc Aerts
  • Christel Faes
  • Koen Grijspeerdt
  • Jeroen Dewulf
  • Koen Mintiens

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.

Suggested Citation

  • Kaatje Bollaerts & Marc Aerts & Christel Faes & Koen Grijspeerdt & Jeroen Dewulf & Koen Mintiens, 2008. "Human Salmonellosis: Estimation of Dose‐Illness from Outbreak Data," Risk Analysis, John Wiley & Sons, vol. 28(2), pages 427-440, April.
  • Handle: RePEc:wly:riskan:v:28:y:2008:i:2:p:427-440
    DOI: 10.1111/j.1539-6924.2008.01038.x
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    References listed on IDEAS

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    1. Christel Faes & Marc Aerts & Helena Geys & Geert Molenberghs, 2007. "Model Averaging Using Fractional Polynomials to Estimate a Safe Level of Exposure," Risk Analysis, John Wiley & Sons, vol. 27(1), pages 111-123, February.
    2. Patrick Royston & Douglas G. Altman, 1994. "Regression Using Fractional Polynomials of Continuous Covariates: Parsimonious Parametric Modelling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 43(3), pages 429-453, September.
    3. Christel Faes & Niel Hens & Marc Aerts & Ziv Shkedy & Helena Geys & Koen Mintiens & Hans Laevens & Frank Boelaert, 2006. "Estimating herd‐specific force of infection by using random‐effects models for clustered binary data and monotone fractional polynomials," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 55(5), pages 595-613, November.
    4. Clifford M. Hurvich & Jeffrey S. Simonoff & Chih‐Ling Tsai, 1998. "Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(2), pages 271-293.
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    Cited by:

    1. Ides Boone & Yves Van der Stede & Kaatje Bollaerts & David Vose & Dominiek Maes & Jeroen Dewulf & Winy Messens & Georges Daube & Marc Aerts & Koen Mintiens, 2009. "NUSAP Method for Evaluating the Data Quality in a Quantitative Microbial Risk Assessment Model for Salmonella in the Pork Production Chain," Risk Analysis, John Wiley & Sons, vol. 29(4), pages 502-517, April.
    2. Régis Pouillot* & Karin Hoelzer & Yuhuan Chen & Sherri B. Dennis, 2015. "Listeria monocytogenes Dose Response Revisited—Incorporating Adjustments for Variability in Strain Virulence and Host Susceptibility," Risk Analysis, John Wiley & Sons, vol. 35(1), pages 90-108, January.
    3. Armand Maul, 2014. "Heterogeneity: A Major Factor Influencing Microbial Exposure and Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 34(9), pages 1606-1617, September.

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