Sampling design and inference of the caecal-skin Campylobacter relationship in broilers

Abstract

The relationship between Campylobacter levels in broiler caeca and on carcass skin is central to quantitative microbial risk assessment along the poultry production chain, underpinning modelling of intervention impacts, including EFSA assessments of the public health impact of control measures. However, this relationship is typically inferred from monitoring data generated under sampling designs that do not preserve pairing between specimens and may involve pooling. In this study, we used a simulation framework to evaluate whether commonly used sampling strategies allow reliable recovery of the caecal-skin relationship. A simulated broiler population was generated, assigning caecal and skin loads to individual birds based on a specified linear relationship. Sampling was conducted under paired and unpaired designs, with and without pooling, reflecting approaches used in surveillance programmes and in policy-oriented models. Regression models were fitted to sampled data across 1,000 simulations for a range of assumed slopes. Under paired sampling, estimated slopes closely matched the true relationship across most scenarios. In contrast, unpaired sampling consistently failed to recover the association, with estimated slopes centred around zero regardless of the true slope. These findings were robust to variation in within-flock prevalence, residual error, and intercept. The results show that sampling design fundamentally affects identifiability of relationships between stages of the production chain. This has implications for interpretation of parameters derived from monitoring data and used in quantitative Campylobacter risk assessments informing policy. Parameters derived from unpaired and pooled monitoring data should therefore be interpreted with caution when used to support risk assessment and decision-making. Campylobacter; broiler chickens; sampling strategy; unpaired sampling; carcass contamination; quantitative microbial risk assessment; simulation.