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Predicting human dose-response relationships from multiple biological models: Issues with Cryptosporidium parvum

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Title: No Slide Title Author: Brenda F. Finch Last modified by: IET Created Date: 3/17/1999 12:44:02 AM Document presentation format: On-screen Show – PowerPoint PPT presentation

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Title: Predicting human dose-response relationships from multiple biological models: Issues with Cryptosporidium parvum


1
Predicting human dose-response relationships from
multiple biological modelsIssues with
Cryptosporidium parvum
Risk Assessment Consortium
2
Cryptosporidiosis Introduction
  • Cryptosporidium parvum
  • coccidian protozoan
  • recognized as human
  • pathogen in 1976
  • In the 1990s
  • one of the leading known causes of waterborne
    disease outbreaks
  • important OI among HIV () persons
  • important cause of diarrhea outbreaks in day care
    centers

3
Outbreaks of Cryptosporidiosis Associated with
Drinking Water - United States, 1984 - 1995
WASHINGTON
WAWALLA WALLA
READING
1994 (113)
1991 (551)
OREGON
MEDFORD
WISCONSIN

MILWAUKEE
TALENT
1993 (400,000)
1992 (3,000)
PENNSYLVANIA
NEVADA
LAS VEGAS
1993 ( ? )
CARROLLTON
1987 (13,000)
NEW MEXICO
GEORGIA
TEXAS
SAN ANTONIO
1984 (2,000)
4
Foodborne Outbreaks of Cryptosporidiosisin the
United States
Suspect Food
Est. Cases
How Contaminated
Location
Chicken Salad Green Onions Green/Fruit
Salad Apple Cider (homemade) Apple Cider
(commercial)
Minnesota Washington Washington DC Maine New
York
15 54 101 160 31
Food Handler ? Food Handler / Field Food
Handler Cattle Feces in Field ? Rinse Water
5
(No Transcript)
6
Dose-response Introduction
  • The determination of the relationship between the
    magnitude of exposure (dose) to a chemical,
    biological or physical agent and the severity
    and/or frequency of associated adverse health
    effects (response). 
  • Relate the level of a biological agent ingested
    with the frequency of infection or disease

7
Dose-response Introduction
  • Relate the level of a biological agent ingested
    with the frequency of infection or disease
  • A variety of endpoints may be considered

8
Dose-response Introduction
  • Pathogen, host and environment are all factors
  • Complex relationship to predict

9
What is the RAC?
  • An inter-agency, interdisciplinary group
  • Working collectively to enhance communication and
    coordination between federal agencies

10
What is the RAC?
  • An inter-agency, interdisciplinary group
  • Promoting the conduct of scientific research that
    will facilitate risk assessments and assist the
    regulatory agencies in fulfilling their specific
    food-safety risk management mandates.

11
Who are the members of the RAC?
  • U.S. Department of Agriculture
  • Animal and Plant Health Inspection
    Service
  • Cooperative State Research, Education,
    and Extension Service
  • Agricultural Research Service
  • Economic Research Service
  • Food Safety and Inspection Service
  • Office of Risk Assessment and Cost
    Benefit Analysis
  •  
  • Environmental Protection Agency
  • Office of Water
  • Office of Prevention, Pesticides and
    Toxic Substances
  • Office of Research and Development
  •  
  • Department of Commerce
  • National Marine Fisheries Service
  • Department of Defense
  • Veterinary Service Activity
  •  
  • Department of Health and Human Services
  • Center for Veterinary Medicine, FDA
  • National Center for Food Safety and
    Technology, FDA
  • National Center for Toxicological Research,
    FDA
  • Centers for Disease Control and Prevention
  • National Institutes of Health
  • Center for Food Safety and Applied
    Nutrition, FDA
  • Office of Womens Health, FDA

12
Understanding Microbial Dose-Response
  • Efforts of the RAC
  • Dose-response workgroup
  • Interest in development of plausible
    dose-response models

13
Understanding Microbial Dose-Response
  • Efforts of the RAC
  • Dose-response workgroup
  • Cooperative Agreements
  • Relating Numbers of Foodborne Pathogens to Human
    Illness

14
Why are we here today?
  • Cryptosporidium was selected for this meeting
    because the body of evidence is extensive

15
Why are we here today?
  • What lessons can we learn from Cryptosporidium
    dose-response modeling that can inform model
    systems for other pathogens?
  • How useful are different biological models as a
    source of data for modeling human dose-response
    relationships?
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