Using risk assessment to evaluate risk-based microbiological criteria Introduction Microbiological criteria (MCs) are increasingly used as an instrument for food safety control. Once the food safety objective (FSO) or performance objective (PO) is defined, sampling schemes and test protocols can be developed which evaluate the efficiency of MCs in achieving this standard 1. This indirect method first requires the definition of PO or FSO before evaluation of the MC. The derivation of “risk-based” MCs goes one step further. It requires quantitative microbiological risk assessment models that directly link the concentrations on food products (at the point where the MC has to be set) with human health risk estimates. Essential models in this context are those that link concentrations at retail with human health risk, the “consumer phase models ”2, and “dose-response models ”3. References 1 Van Schothorst, M. et al (2009). Food Control 20: Nauta, M and Christensen B, The impact of consumer phase models in microbial risk analysis Risk Analysis, in press 3 Teunis P. and Havelaar AH (2000) Risk Analysis 20: Nauta, M.J. et al (2008) Risk Analysis 28: Christensen, B, Rosenquist H and Nauta M, in preparation. Maarten Nauta, Hanne Rosenquist, Bjarke Christensen Method We used a risk assessment model that directly relates Campylobacter concentration at retail with human health risk 2,3,4 (see Fig 1.). We simulated a large number of food lots (batches of chicken meat), with varying distributions of Campylobacter concentrations. These distributions are defined as zero inflated lognormal distributions with a within food lot prevalence between 0 and 1, mean log concentration per gram between 0 and 3, with standard deviation between 0 and 2. For each food lot we - simulated n = 12 random samples, which are analyzed with a test with detection limit 100 cfu/g, 100% test sensitivity and 100% test specificity. The number of positive samples (n det ) is determined. - assessed the risk per serving (Fig. 1) Results This approach provides a relation between the number of positive samples and the associated risk (see Fig 2.) per food lot. On average, the risk is higher in food lots with more positive samples (higher n det ). However, the agreement between n det and risk is weak: per food lot n det is not a good predictor of its risk. Microbiological Criteria (MCs) can be evaluated against Performance Objectives (PO) or Food Safety Objectives (FSO) 1. This requires a definition of PO or FSO first. However, when a risk assessment model is available, the definition of PO or FSO is not needed, and the efficiency of an MC can be directly evaluated in terms of their effect on population health risk. This is illustrated for Campylobacter in broiler meat. We show that in this case refusal of food lots based on setting an MC may not be an efficient way to reduce human health risks. Figure 2. Scatter plot relating simulation results for different food lots: Each dot represents the number of samples where Campylobacters are detected (n det ) and the assessed mean probability of illness for a simulated chicken meat lot. Impact for risk management Risk managers can use this approach to decide on a risk-based MC as defined by n det. The model can relate the percentage of refused food lots, based on various MCs, with the associated relative residual risk. (see Fig 3.) However, refusal of food lots based on MCs does not seem very efficient. Discussion As an alternative to setting an MC, the Danish case-by-case risk assessment methodology demands similar microbiological testing, but uses all quantitative concentration data, not only a treshold value. This method is likely to be more efficient by providing a better association with risk 5. An MC may be effective when it is implemented as a process hygiene criterion (PHC) that aims to stimulate food processing hygiene. It will however be difficult to quantitatively assess the effect of setting such a PHC. Fig 1. The probability of illness consequential to the consumption of chicken meat with the indicated concentration of Campylobacter at purchase, as calculated with the risk assessment model based on consumer chicken meat preparation data 4 and a dose-response relation 3. Figure 3, The relative residual risks and the percentage of refused food lots for different MCs, as defined by n det. This is a hypothetical example, showing how risk managers can use the model to decide on the preferred MC Division of Microbiology and Risk Assessment, We show that the efficiency of the MCs can be evaluated in terms of risk without considering PO or FSO. For Campylobacter in broiler meat, refusal of food lots based on MCs does not seem very efficient.