Contamination of raw poultry meat with foodborne pathogens could occur because of improper handling at primary production and slaughterhouse levels. Low microbial prevalence data often consists of a high amount of non-detections (zero positives), so a flexible framework is required to characterise the underlying microbial distribution and conduct reliable inferential statistics. Thus, the objective of this work was to evaluate the performance of zero-inflated binomial (ZIB) regression models to describe the effects of sampling site (carcass, thigh, breast, wings) on the measured incidences of Salmonella, Listeria monocytogenes and Staphylococcus aureus on chicken meat. For each pathogen, four regression models based on the zero-inflated binomial ZIB (p, w₀) distribution were fitted to the presence/absence data with sampling site as covariate and random-effects due to sampling occasion either in the binomial probability (p) or in the extra-proportion of non-detections (w₀). For the three pathogens, the sampling site exerted a greater effect on w₀ than on p itself, with breast bearing the lowest prevalence estimates of Salmonella spp. (mean: 0.88%; 95% CI: 0.02–1.95%) and S. aureus (mean 1.48%; 95% CI: 0.01–4.00%). The fitting capacity of the models was further improved when random effects due to sampling occasion were placed in w₀ (deviances decreased from 146.7–156.7 to 140.2–140.6). This would imply that, theoretically, the variability in pathogens’ occurrence from batch to batch mainly arises from the variability in non-contaminated zones. At any sampling site, the mean prevalence was estimated as 1.35 (95% CI: 0.15 – 2.70) for Salmonella, 2.11 (95% CI: 0.04 – 5.63) for L. monocytogenes and 2.36 (95% CI: 0.04 – 5.12) for S. aureus. Sampling performance analysis showed that wings were mostly suitable to detect Salmonella and S. aureus with higher probability (0.016 and 0.035 respectively), while for L. monocytogenes, sampling of thigh could be more effective (0.032).

由于初级生产和屠宰场处理不当，可能会导致生禽肉被食源性病原菌污染。较低的微生物患病率数据通常包含大量未检出（零阳性），因此需要一个灵活的框架来表征潜在的微生物分布并进行可靠的推论统计。因此，这项工作的目的是评估零膨胀二项式（ZIB）回归模型的性能，以描述采样部位（car体，大腿，乳房，翅膀）对沙门氏菌，单核细胞增生李斯特菌和金黄色葡萄球菌发生率的影响。在鸡肉上。对于每种病原体，基于零膨胀二项式ZIB（p，w ₀）分布适合于存在/不存在的数据，并且由于抽样的机会（二项式概率（p）或未检测到的超比例（w ₀）），采样点作为协变量和随机效应。对于这三种病原体，采样点对w _0的影响大于对p本身的影响，而乳房中沙门氏菌的患病率最低。（平均值：0.88％； 95％CI：0.02–1.95％）和金黄色葡萄球菌（平均值1.48％； 95％CI：0.01–4.00％）。当将由于采样时机引起的随机效应置于w ₀时，模型的拟合能力得到了进一步提高。（差异从146.7-156.7降低至140.2-140.6）。从理论上讲，这意味着每批之间病原体发生的变异性主要是由未污染区域的变异性引起的。在任何采样点，沙门氏菌的平均患病率估计为1.35（95％CI：0.15 – 2.70），单核细胞增生李斯特菌为2.11（95％CI：0.04 – 5.63），沙门氏菌为2.36（95％CI：0.04 – 5.12）。金黄色葡萄球菌。抽样性能分析表明，翅膀最适合于检出沙门氏菌和金黄色葡萄球菌的可能性更高（分别为0.016和0.035），而对于单核细胞增生李斯特菌，大腿采样可能更有效（0.032）。