Studying the effects of POs and MCs on the Salmonella ALOP with a quantitative risk assessment model for beef production.
The Finnish Salmonella Control Programme and the special guarantees (SG) of import concerning Salmonella in the beef production chain were examined within the risk analysis framework. The appropriate level of protection (ALOP de facto since not referred to as ALOP in regulation), performance objectives (PO), and microbiological criteria (MC) were identified along the beef production chain. A quantitative microbiological risk assessment (QMRA) model using the Bayesian probabilistic method was developed for the beef chain to evaluate the capability of different POs to contribute to the ALOP. The influence of SGs was studied as an intervention protecting Finnish consumers. The QMRA made it possible to translate an ALOP without a stated food safety objective (FSO) to POs when implemented for both ready-to-eat (RTE) and non-RTE products. According to the results, the Finnish ALOP de facto for beef, beef preparations and products (10 human Salmonella cases/100,000) was reached in all of the years 1996-2004. However, if the prevalence at the slaughter, domestic cut beef, and retail levels would increase to the level of POs set (maximum 1%), the ALOP de facto would be exceeded by a factor of roughly two. On the other hand, the zero tolerance applied to MCs would keep the true Salmonella prevalence at production steps with POs clearly below 1%, and the ALOP would then be achievable. The influence of SGs on the total exposure was so small (average 0.1% added to the total prevalence of beef-derived foods at retail) that their relevance may be doubted with the current amount and Salmonella prevalence in beef-derived imports. On the other hand, a change in import profile could increase the protective effect of the SGs. Although practical follow-up has to be carried out as apparent prevalences, the objectives and criteria should be estimated as true prevalences and incidences with quantified uncertainties in order to achieve a sound, transparent scientific-based understanding of the risk. The QMRA model developed here provided a useful tool for studying the connection between the ALOP and other targets in the production chain.
Tuominen
Pirkko
P
Food Safety Authority (EVIRA), Risk Assessment Unit, Mustialankatu 3, 00790 Helsinki, Finland. pirkko.tuominen@evira.fi
Ranta
Jukka
J
Maijala
Riitta
R
D000818:Animals; D001499:Bayes Theorem; D002417:Cattle; D015169:Colony Count, Microbial; D003132:Commerce; D003257:Consumer Product Safety; D005506:Food Contamination / Q000032:analysis* / Q000517:prevention & control; D005516:Food Microbiology; D006801:Humans; D008460:Meat / Q000382:microbiology*; D008461:Meat Products / Q000382:microbiology; D015995:Prevalence; D018570:Risk Assessment; D012475:Salmonella / Q000254:growth & development / Q000302:isolation & purification*