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Dose Response for Infectivity of Several Strains of Campylobacter jejuni in Chickens

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  • Lailai Chen
  • Helena Geys
  • Shaun Cawthraw
  • Arie Havelaar
  • Peter Teunis

Abstract

Although some major risk studies have been done for Campylobacter jejuni, its dose response is not well characterized. Only a single human study is available, providing dose‐response information for only a single isolate. As substantial heterogeneity in infectivity has been acknowledged for other pathogens, it remains unknown how well this single study represents the dose‐response relation for this pathogen. As future human challenge studies with Campylobacter are unlikely, we have to find other means of studying its infectivity. Several dose‐response studies have been done using chickens as host organisms. These studies may be used to obtain quantitative information on the variation in infectivity among different isolates of this pathogen. A hierarchical Bayesian model is well suited to describe heterogeneity, and we demonstrate how the beta‐Poisson model of microbial infection may be adapted to allow for within‐ and between‐isolate variation. Isolates tested in chickens can be categorized into two distinct groups: lab‐adapted and fresh isolates, and we show how the hierarchical dose‐response model can be used to quantitatively describe their differences. Fresh isolates show higher colonization potential and less within‐isolate variation than lab isolates. The results indicate that Campylobacter jejuni is highly infectious in chickens. Different isolates show great variation in infectivity, especially between lab and fresh isolates, indicating that human clinical (volunteer) studies on infectivity must be interpreted cautiously.

Suggested Citation

  • Lailai Chen & Helena Geys & Shaun Cawthraw & Arie Havelaar & Peter Teunis, 2006. "Dose Response for Infectivity of Several Strains of Campylobacter jejuni in Chickens," Risk Analysis, John Wiley & Sons, vol. 26(6), pages 1613-1621, December.
    • Handle: RePEc:wly:riskan:v:26:y:2006:i:6:p:1613-1621
    DOI: 10.1111/j.1539-6924.2006.00850.x
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    References listed on IDEAS

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    1. Peter Teunis & Katsuhisa Takumi & Kunihiro Shinagawa, 2004. "Dose Response for Infection by Escherichia coli O157:H7 from Outbreak Data," Risk Analysis, John Wiley & Sons, vol. 24(2), pages 401-407, April.
    2. Peter F. M. Teunis & Cynthia L. Chappell & Pablo C. Okhuysen, 2002. "Cryptosporidium Dose‐Response Studies: Variation Between Hosts," Risk Analysis, John Wiley & Sons, vol. 22(3), pages 475-485, June.
    3. Peter F. M. Teunis & Cynthia L. Chappell & Pablo C. Okhuysen, 2002. "Cryptosporidium Dose Response Studies: Variation Between Isolates," Risk Analysis, John Wiley & Sons, vol. 22(1), pages 175-185, February.
    4. P. F. M. Teunis & A. H. Havelaar, 2000. "The Beta Poisson Dose‐Response Model Is Not a Single‐Hit Model," Risk Analysis, John Wiley & Sons, vol. 20(4), pages 513-520, August.
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    1. Michael Greenberg & Charles Haas & Anthony Cox & Karen Lowrie & Katherine McComas & Warner North, 2012. "Ten Most Important Accomplishments in Risk Analysis, 1980–2010," Risk Analysis, John Wiley & Sons, vol. 32(5), pages 771-781, May.
    2. Frederick Bloetscher & Daniel Meeroff & Sharon C. Long & Jeanine D. Dudle, 2020. "Demonstrating the Benefits of Predictive Bayesian Dose–Response Relationships Using Six Exposure Studies of Cryptosporidium parvum," Risk Analysis, John Wiley & Sons, vol. 40(11), pages 2442-2461, November.
    3. 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.

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