A Bayesian approach to the evaluation of risk-based microbiological criteria for \uppercaseCampylobacter in broiler meat

TL;DR

This paper introduces a Bayesian framework for evaluating microbiological criteria in broiler meat, integrating uncertainty in risk assessment to improve decision-making in food safety management.

Contribution

It develops a Bayesian modeling approach to synthesize data and quantify uncertainty in risk-based microbiological criteria evaluation, enhancing traditional methods.

Findings

Achieved a relative risk of 0.4 with the proposed criteria.

Demonstrated the Bayesian method's efficiency in risk evaluation.

Compared Bayesian risk metrics, showing similar results with improved computation.

Abstract

Shifting from traditional hazard-based food safety management toward risk-based management requires statistical methods for evaluating intermediate targets in food production, such as microbiological criteria (MC), in terms of their effects on human risk of illness. A fully risk-based evaluation of MC involves several uncertainties that are related to both the underlying Quantitative Microbiological Risk Assessment (QMRA) model and the production-specific sample data on the prevalence and concentrations of microbes in production batches. We used Bayesian modeling for statistical inference and evidence synthesis of two sample data sets. Thus, parameter uncertainty was represented by a joint posterior distribution, which we then used to predict the risk and to evaluate the criteria for acceptance of production batches. We also applied the Bayesian model to compare alternative criteria, accounting for the statistical uncertainty of parameters, conditional on the data sets. Comparison of the posterior mean relative risk, $E(\mathit{RR}|\mathrm{data})=E(P(\mathrm{illness}|\mathrm{criterion is met})/P(\mathrm{illness})|\mathrm{data})$, and relative posterior risk, $\mathit{RPR}=P(\mathrm{illness}|\mathrm{data, criterion is met})/P(\mathrm{illness}|\mathrm{data})$, showed very similar results, but computing is more efficient for RPR. Based on the sample data, together with the QMRA model, one could achieve a relative risk of 0.4 by insisting that the default criterion be fulfilled for acceptance of each batch.