IDEAS home Printed from https://ideas.repec.org/a/wly/riskan/v34y2014i9p1606-1617.html
   My bibliography  Save this article

Heterogeneity: A Major Factor Influencing Microbial Exposure and Risk Assessment

Author

Listed:
  • Armand Maul

Abstract

Microbial risk assessment is dependent on several biological and environmental factors that affect both the exposure characteristics to the biological agents and the mechanisms of pathogenicity involved in the pathogen‐host relationship. Many exposure assessment studies still focus on the location parameters of the probability distribution representing the concentration of the pathogens and/or toxin. However, the mean or median by themselves are insufficient to evaluate the adverse effects that are associated with a given level of exposure. Therefore, the effects on the risk of disease of a number of factors, including the shape parameters characterizing the distribution patterns of the pathogen in their environment, were investigated. The statistical models, which were developed to provide a better understanding of the factors influencing the risk, highlight the role of heterogeneity and its consequences on the commonly used risk assessment paradigm. Indeed, the heterogeneity characterizing the spatial and temporal distribution of the pathogen and/or the toxin contained in the water or food consumed is shown to be a major factor that may influence the magnitude of the risk dramatically. In general, the risk diminishes with higher levels of heterogeneity. This scheme is totally inverted in the presence of a threshold in the dose‐response relationship, since heterogeneity will then have a tremendous impact, namely, by magnifying the risk when the mean concentration of pathogens is below the threshold. Moreover, the approach of this article may be useful for risk ranking analysis, regarding different exposure conditions, and may also lead to improved water and food quality guidelines.

Suggested Citation

  • 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.
  • Handle: RePEc:wly:riskan:v:34:y:2014:i:9:p:1606-1617
    DOI: 10.1111/risa.12184
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/risa.12184
    Download Restriction: no

    File URL: https://libkey.io/10.1111/risa.12184?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Régis Pouillot & Pascal Beaudeau & Jean‐Baptiste Denis & Francis Derouin & AFSSA Cryptosporidium Study Group, 2004. "A Quantitative Risk Assessment of Waterborne Cryptosporidiosis in France Using Second‐Order Monte Carlo Simulation," Risk Analysis, John Wiley & Sons, vol. 24(1), pages 1-17, February.
    2. 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.
    3. Emilie Rieu & Koenraad Duhem & Elisabeth Vindel & Moez Sanaa, 2007. "Food Safety Objectives Should Integrate the Variability of the Concentration of Pathogen," Risk Analysis, John Wiley & Sons, vol. 27(2), pages 373-386, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Géraldine Boué & Enda Cummins & Sandrine Guillou & Jean‐Philippe Antignac & Bruno Le Bizec & Jeanne‐Marie Membré, 2017. "Development and Application of a Probabilistic Risk–Benefit Assessment Model for Infant Feeding Integrating Microbiological, Nutritional, and Chemical Components," Risk Analysis, John Wiley & Sons, vol. 37(12), pages 2360-2388, December.
    2. A. H. Havelaar & A. N. Swart, 2014. "Impact of Acquired Immunity and Dose‐Dependent Probability of Illness on Quantitative Microbial Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 34(10), pages 1807-1819, October.
    3. Fanny Tenenhaus‐Aziza & Jean‐Jacques Daudin & Alexandre Maffre & Moez Sanaa, 2014. "Risk‐Based Approach for Microbiological Food Safety Management in the Dairy Industry: The Case of Listeria monocytogenes in Soft Cheese Made from Pasteurized Milk," Risk Analysis, John Wiley & Sons, vol. 34(1), pages 56-74, January.
    4. Régis Pouillot & Nicolas Miconnet & Anne‐Laure Afchain & Marie Laure Delignette‐Muller & Annie Beaufort & Laurent Rosso & Jean‐Baptiste Denis & Marie Cornu, 2007. "Quantitative Risk Assessment of Listeria monocytogenes in French Cold‐Smoked Salmon: I. Quantitative Exposure Assessment," Risk Analysis, John Wiley & Sons, vol. 27(3), pages 683-700, June.
    5. 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.
    6. Philip J. Schmidt & Katarina D. M. Pintar & Aamir M. Fazil & Edward Topp, 2013. "Harnessing the Theoretical Foundations of the Exponential and Beta‐Poisson Dose‐Response Models to Quantify Parameter Uncertainty Using Markov Chain Monte Carlo," Risk Analysis, John Wiley & Sons, vol. 33(9), pages 1677-1693, September.
    7. Régis Pouillot & Véronique Goulet & Marie Laure Delignette‐Muller & Aurélie Mahé & Marie Cornu, 2009. "Quantitative Risk Assessment of Listeria monocytogenes in French Cold‐Smoked Salmon: II. Risk Characterization," Risk Analysis, John Wiley & Sons, vol. 29(6), pages 806-819, June.
    8. Allison C. Reilly & Andrea Staid & Michael Gao & Seth D. Guikema, 2016. "Tutorial: Parallel Computing of Simulation Models for Risk Analysis," Risk Analysis, John Wiley & Sons, vol. 36(10), pages 1844-1854, October.
    9. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:riskan:v:34:y:2014:i:9:p:1606-1617. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1111/(ISSN)1539-6924 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.