Framework for microbial food-safety risk assessments amenable to Bayesian modeling

Area of Science:

• Food Safety
• Risk Assessment
• Microbiology
• Regulatory Science

Background:

• Regulatory agencies conduct microbial risk assessments to predict human illness changes due to food safety policies.
• Current assessments often occur upstream in the food supply chain, prior to consumption.
• National disease surveillance data is typically summarized annually.

Purpose of the Study:

• To propose a quantitative framework for microbial food safety risk assessments.
• To enable the annual evaluation of national regulatory policies.
• To align illness estimates with national disease surveillance data.

Main Methods:

• Development of a framework for quantitative microbial risk assessment.
• Utilizing probabilistic models incorporating uncertainty and variability.
• Application of Bayesian methods, specifically the Bayesian synthesis method.

Main Results:

• The framework generates consistent estimates with national disease surveillance data.
• It allows for the collapse or elimination of intermediate modeling steps.
• Bayesian synthesis effectively produces relevant posterior distributions for policy assessment.
• An example using Campylobacter in chicken estimated illnesses avoided by a hypothetical policy.

Conclusions:

• The proposed framework provides a robust method for assessing the annual impact of food safety policies.
• It facilitates the quantitative evaluation of policy effects on human illnesses.
• The framework supports economic benefit analysis of regulatory interventions.