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Shifting from traditional hazard-based food safety management towardrisk-based management requires statistical methods for evaluating intermediatetargets in food production, such as microbiological criteria (MC), in terms oftheir effects on human risk of illness. A fully risk-based evaluation of MCinvolves several uncertainties that are related to both the underlyingQuantitative Microbiological Risk Assessment (QMRA) model and theproduction-specific sample data on the prevalence and concentrations ofmicrobes in production batches. We used Bayesian modeling for statisticalinference and evidence synthesis of two sample data sets. Thus, parameteruncertainty was represented by a joint posterior distribution, which we thenused to predict the risk and to evaluate the criteria for acceptance ofproduction batches. We also applied the Bayesian model to compare alternativecriteria, accounting for the statistical uncertainty of parameters, conditionalon the data sets. Comparison of the posterior mean relative risk,$E(\mathit{RR}|\mathrm{data})=E(P(\mathrm{illness}|\mathrm{criterion ismet})/P(\mathrm{illness})|\mathrm{data})$, and relative posterior risk,$\mathit{RPR}=P(\mathrm{illness}|\mathrm{data, criterion ismet})/P(\mathrm{illness}|\mathrm{data})$, showed very similar results, butcomputing is more efficient for RPR. Based on the sample data, together withthe QMRA model, one could achieve a relative risk of 0.4 by insisting that thedefault criterion be fulfilled for acceptance of each batch.

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