Microbial Risk Assessment
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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 [email protected]

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Microbial Risk Assessment Frameworks, Principles, and Approaches

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Microbial risk assessment frameworks principles and approaches

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

[email protected]

202-994-5482


Purposes of mra

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


Frameworks

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


Microbial risk assessment frameworks principles and approaches

ILSI/EPA Framework


Guiding concepts

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


Key challenges

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


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