Risk assessment strategies as a tool in the application of the Appropriate Level of Protection (ALOP) and Food Safety Objective (FSO) by risk managers

In the course of the last decade, the Appropriate Level of Protection (ALOP), the Food Safety Objective (FSO) and their associated metrics have been proposed by the World Trade Organization and Codex Alimentarius as a means for competent authorities to ultimately translate governmental public health policy regarding food safety into risk-based targets for the food industry. The industry needs to meet these targets through the effective choice of control measures that are part of its operational food safety management system. The aim of this study was to put the practical application of ALOP and FSO to the test in the case of Salmonella in chicken meat in the Netherlands. Two different risk assessment approaches were applied to derive potential ALOP and FSO values, a ‘top-down’ approach based on epidemiological data and a ‘bottom-up’ approach based on food supply chain data. To this end, two stochastic models specific to the Dutch situation were built. Comparisons between 23 countries in Europe were also made using the top-down model. The mean estimated current Level Of Protection values were similar for the two approaches applied, with the bottom-up model yielding 87 cases per 100,000 inhabitants per year (95% CI: 0.03, 904) and the top-down model 71 (95% CI: 9.9, 155). The estimated FSO values on the other hand were considerably different with the mean ‘top down’ FSO being - 4.6 log CFU/g (95% CI: - 5.4, - 4.1) and the mean ‘bottom-up’ FSO - 6.0 log CFU/g (95% CI: - 8.1, - 2.9) reflecting major differences in the output distributions of this parameter obtained with the two approaches. Significant differences were observed between current LOP values for different EU countries, although it was not clear whether this was due to actual differences in the factors influencing the risk of salmonellosis or due to the quality of the available data. Keywords Risk assessment; Stochastic modelling; Salmonellosis; Foodborne disease; Public health targets