A comparative analysis of meat inspection data as an information source of the health and welfare of broiler chickens based on Finnish data

Abstract

The comprehensive, reliable, and comparable meat inspection (MI) data of broiler chickens (i.e. broilers) are essential for the monitoring and surveillance of broiler health and welfare at the national and European Union (EU) levels. We compared the condemnation causes issued to broiler carcasses during MI in four large Finnish broiler slaughterhouses (SHs) by investigating the similarities and differences between local MI instructions used in the SHs. The way in which MI condemnations were recorded in the Finnish Food Authority's (FFA's) MI statistics were also explored. We additionally analysed the FFA's official MI data from the 2015–2019 period. The study showed that the MI criteria used in the SHs differed from one another regarding how severe or extensive a broiler defect or disease must be to cause condemnation during MI. In Finland, the annual total condemnation prevalence of whole broilers varied between 2.6% and 4.8% in 2015–2019, and a significant difference was observed between the SHs' monthly total condemnation prevalences, except in two SH pairs. Mistakes in recording the FFA's MI statistics and differences in the SH operators' reasons to reject broilers from the food chain affect the comparability of the condemnation prevalences between the SHs. Only half of the SHs partially condemned broiler carcasses and collected data concerning these condemnations. Cellulitis (0.3–1.0%), ascites (0.3–0.4%), and body cavity disorders (0.2–0.3%) were the most common causes for condemning whole broiler carcasses in 2015–2019. The MI data can be used for monitoring and surveillance purposes only once the differences between the SH data and data reliability are known. Although the harmonization of all condemnation causes is impossible, harmonizing the condemnations of carcasses with diseases that most threaten broiler health and welfare and cause the largest economic losses would be important.