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Cryptosporidium Dose‐Response Studies: Variation Between Hosts

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  • Peter F. M. Teunis
  • Cynthia L. Chappell
  • Pablo C. Okhuysen

Abstract

The issue of variation is highly important in dose‐response analysis: variation among genetically related pathogens infecting the same host, but also variation among hosts, in susceptibility to infection by the same pathogen. This latter issue is addressed here for the protozoan parasite Cryptosporidium parvum, the causative agent for many outbreaks of water‐borne gastrointestinal illness. In human feeding studies, infectivity has been shown to be low in subjects with high preexisting anti‐Cryptosporidium IgG‐levels. Here we adapt the hit theory model of microbial infection to incorporate covariables, characterizing the immune status of the susceptible host. The probability of any single oocyst in the inoculum to cause infection appears to depend on preexisting IgG‐levels. This does not necessarily imply direct protection by the humoral immune system; high IgG‐levels may reflect a recent episode of infection/illness, and be an epi‐phenomenon associated with other protective responses. The IgG‐dependence of the dose‐response relation can be easily applied in quantitative risk analysis. The distribution of anti‐Cryptosporidium IgG levels in the general population is accessible by analyzing serum banks, which are maintained in many Western countries. Using such an approach provides first insights into the variation of susceptibility to infection in the general population.

Suggested Citation

  • 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.
  • Handle: RePEc:wly:riskan:v:22:y:2002:i:3:p:475-485
    DOI: 10.1111/0272-4332.00046
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    References listed on IDEAS

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    1. Peter F. M. Teunis & Nico J. D. Nagelkerke & Charles N. Haas, 1999. "Dose Response Models For Infectious Gastroenteritis," Risk Analysis, John Wiley & Sons, vol. 19(6), pages 1251-1260, December.
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    1. K. D. M. Pintar & A. Fazil & F. Pollari & D. Waltner‐Toews & D. F. Charron & S. A. McEwen & T. Walton, 2012. "Considering the Risk of Infection by Cryptosporidium via Consumption of Municipally Treated Drinking Water from a Surface Water Source in a Southwestern Ontario Community," Risk Analysis, John Wiley & Sons, vol. 32(7), pages 1122-1138, July.
    2. 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.
    3. 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.
    4. 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.
    5. S. R. Petterson, 2016. "Application of a QMRA Framework to Inform Selection of Drinking Water Interventions in the Developing Context," Risk Analysis, John Wiley & Sons, vol. 36(2), pages 203-214, February.
    6. Vegard Nilsen & John Wyller, 2016. "QMRA for Drinking Water: 1. Revisiting the Mathematical Structure of Single‐Hit Dose‐Response Models," Risk Analysis, John Wiley & Sons, vol. 36(1), pages 145-162, January.
    7. James D. Englehardt & Jeff Swartout, 2006. "Predictive Bayesian Microbial Dose‐Response Assessment Based on Suggested Self‐Organization in Primary Illness Response: Cryptosporidium parvum," Risk Analysis, John Wiley & Sons, vol. 26(2), pages 543-554, April.
    8. Tingting Gao & Rong Chen & Yanzheng Liu & Xiaochang C. Wang & Yuyou Li, 2018. "Construction of a Dose–Illness Relationship via Modeling Morbidity and Application to Risk Assessment of Wastewater Reuse," Risk Analysis, John Wiley & Sons, vol. 38(8), pages 1672-1684, August.
    9. 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.
    10. Anna Makri & Reza Modarres & Rebecca Parkin, 2004. "Cryptosporidiosis Susceptibility and Risk: A Case Study," Risk Analysis, John Wiley & Sons, vol. 24(1), pages 209-220, February.

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