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Microbial Risk Assessment Frameworks, Principles, and Approaches. Rebecca Parkin, PhD, MPH Department of Environmental and Occupational Health Center for Risk Science and Public Health, School of Public Health and Health Services The George Washington University Medical Center parkinr@gwu.edu

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Microbial Risk Assessment Frameworks, Principles, and Approaches


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    1. Microbial Risk AssessmentFrameworks, Principles, and Approaches Rebecca Parkin, PhD, MPH Department of Environmental and Occupational Health Center for Risk Science and Public Health, School of Public Health and Health Services The George Washington University Medical Center parkinr@gwu.edu 202-994-5482

    2. Predict population health risks linked to microbial pathogens Food (fresh and processed) Drinking water Recreational water Wastewater, runoff Biosolids, manure Modified organisms (contamination, cleanup) Estimate magnitude of disease burdens Attributable risk Source-specific risk Identify high risk sub-populations Immune compromised Dialysis, cancer patients Infants, children Elderly Guide policy decisions Purposes of MRA

    3. Frameworks • Underlying forms • Chemical risk assessment, 1983 (traditional) • Ecological risk assessment, 1990’s (systems) • Microbial Risk AssessmentFrameworks • National Research Council (NRC), 1983 • Modified NRC (used internationally), 1990’s • International Life Sciences Institute (ILSI) & EPA, 2000

    4. ILSI/EPA Framework

    5. Ordered, iterative process Complete source-response pathway assessments Use field & clinical measurements Build modules Flexible structure Accommodate different pathogens, contexts, scenarios, dynamics Assess impacts Assumptions, data limitations Meaningful,useful products Open, transparent processes Guiding Concepts

    6. Problem formulation What is THE problem? How can it be modeled? For what purpose? Comprehensiveness of approach Analytic plan (sources & use of data) Model development Linkages of dynamic ecological & population scale disease models Compatibility of model structure & components with complexity of the problem Evaluation of the model Analysis of uncertainty & variability Explicit identification of assumptions Use of probabilistic methods Key Challenges