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The Canadian Food Inspection Agency is developing an Establishment-based Risk Assessment (ERA) model for commercial and on-farm mills involved in the manufacture, storage, packaging, labeling, or distribution of livestock feed (ERA–Feed Mill model). This model will help inform the allocation of inspection resources on the basis of feed safety risk, including animal health and food safety risk. In a previous study, 34 risk factors, grouped into inherent, mitigation, and compliance clusters, along with assessment criteria were selected. The objective of this current study was to estimate the relative risk (RR) of the 203 assessment criteria on the basis of the impact on feed safety to design an ERA–Feed Mill model algorithm. Furthermore, the intent of this study was to assess the maximum increase or decrease of risk obtained when multiple criteria belonging to a same cluster were identified in a specific feed mill. To do so, a two-round face-to-face expert elicitation was conducted with 28 Canadian feed experts. Results showed no significant association between respondent profiles (years of experience and work sector) and estimated RR. Uniformity of answers between experts improved between rounds. Criteria having the highest increase in risk (median RR ≥ 4) included the presence of materials prohibited to be fed to ruminants in a facility that produces ruminant feed, the presence of multiple livestock species on-site, and historical noncompliances related to the inspection of the feed mill's process control and end-product control programs. Risk mitigation criteria having the highest impact on decreasing the risk were the implementation of feed safety certifications, the use of dedicated manufacturing lines (prohibited materials or medications), and having a hazard sampling plan in place for finished feed. The median RR assigned to each criterion and cluster will be used to build an algorithm of the Canadian Food Inspection Agency's ERA–Feed Mill model.

The Canadian Food Inspection Agency is developing an Establishment-based Risk Assessment model for Hatcheries to allocate inspection resources according to the food safety risk associated with each hatchery falling under its jurisdiction. In a previous study, 29 factors contributing to the food safety risk of hatcheries were identified and grouped into three clusters (inherent risk, risk mitigation, and compliance) and assessment criteria were defined. The objective of the current study was to estimate the relative risk (RR) of these criteria. Two rounds of expert elicitations were conducted to allow 13 Canadian experts to estimate the RR of each criterion (n= 96) based on its potential impact on human health, with a specific focus on Salmonellaspp. This process also aimed to estimate the maximum increase or decrease in the overall food safety risk of a hatchery when considering multiple criteria belonging to a specific cluster and to assess the risk attribution of Salmonellaspp. at the hatchery and bird-type levels. Results showed that the respondent profile had no influence on the importance given to a majority of criteria. Uniformity of answers among experts improved from the first to the second round. Overall, 62.5%, 32.3%, and 5.2% of the criteria were attributed to an RR that was less than 2, between 2 and 3, and greater than 3, respectively. Mixing eggs from different supply flocks when placed into the same hatching unit, hatching multiple species, and importing eggs with unknown quality status were identified as having the highest contribution to a hatchery's inherent risk. Requiring information on the foodborne pathogen status of supplying flocks and the occurrence of regulatory enforcement actions were the most impactful risk mitigation and compliance factors, respectively. The median RR value assigned to each criterion and cluster will be used to build this new model.

•A list of 155 risk factors for food establishment risk assessment model is proposed.•Company's commitment towards food safety was one of the most important factors.•The survey outcomes are useful information for the design of risk assessment models.