评估屠宰场丹麦肉鸡群弯曲杆菌交叉污染——考虑真实的鸡群流行率估计和特别抽样

IF 3 4区 环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES
Alessandro Foddai, Nao Takeuchi-Storm, Birgitte Borck Høg, Jette Sejer Kjeldgaard, Jens Kirk Andersen, Johanne Ellis-Iversen
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引用次数: 3

摘要

利用来自两个国家监测组成部分和临时抽样的数据,对屠宰场丹麦肉鸡群的弯曲杆菌交叉污染进行了调查。比较了2018年的动物水平(AL)和食品安全(FS)指标。AL部分包含了在两个丹麦屠宰场(S1-S2)处理的3012只鸡的粪腔拭子池的PCR结果,而FS部分包含了999/ 3012只鸡的腿皮培养测试。每月“表观”(AP)和“真实”群流行(TP)估计。成分之间的一致性以百分比和加权kappa值来衡量。通过广义线性混合效应(GLM)模型评估交叉污染的发生情况(仅FS成分为阳性或CC,而两个成分均为阴性或NegBoth为阴性)、屠宰场和监测期(季度:第一季度至第四季度)之间的关系。随后,采用线性混合效应(LME)模型研究胴体阳性禽群的肉污染水平(y = log10菌落形成单位/克,cfu/g)、屠宰场、监测期和禽群类型(CC vs.阳性或PosBoth)之间的关系。对于这两个模型,农场都是随机效应。最后,在2019年秋季,对S1和S2连续两个群的盲肠和颈部皮肤样本进行了特别现场调查。对分离株进行全基因组测序(WGS)、多位点序列分型(MLST)和单核苷酸多态性(SNP)分析。FS分量的月TP始终高于AL分量。各组成部分之间的一致性很强,但有8.1-8.6%的鸡群为CC,它们的中位数cfu/g比PosBoth鸡群低21-28倍。在GLM模型中,解释变量均显著(p值<0.05)。例如,比值比(or)为8.4 (95% CI: 4.0;Q3与Q1的比值为17.6),3.1 (1.8;5.2), S2 vs. S1。在LME模型中,群体类型和其他两个变量之间的交互作用显著。在实地研究中,在S2的五次采样中有一次,盲肠阳性群被最初的阴性群接替。盲肠阴性群颈部皮肤58.3%呈阳性,分离物基因与盲肠阳性群相似。结果表明,交叉污染受监测期和屠宰场的影响,对胴体阳性禽TP有显著影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessing Campylobacter cross-contamination of Danish broiler flocks at slaughterhouses considering true flock prevalence estimates and ad-hoc sampling

Campylobacter cross-contamination of Danish broiler flocks at slaughterhouses was investigated using data from two national surveillance components and from ad-hoc sampling. The animal level (AL) and food safety (FS) components from 2018 were compared. The AL component contained results of PCR on pools of cloacal swabs from 3,012 flocks processed at two Danish slaughterhouses (S1-S2), while the FS component regarded culture testing of leg skins from 999/3,012 flocks. The monthly “apparent” (AP) and “true” flock prevalence (TP) were estimated. Agreement between components was measured in percentage and in weighted-Kappa values. The relationship between the occurrence of cross-contamination (flock positive only in the FS component = cross-contaminated or CC, vs. flock negative in both components or NegBoth), slaughterhouse and surveillance period (quarter: Q1 to Q4) was evaluated by a generalized linear mixed effects (GLM) model. Thereafter, a linear mixed effects (LME) model was used to investigate the relationship between the level of meat contamination of carcass positive flocks (y = log10 colony forming units per gram, cfu/g), slaughterhouse, surveillance period, and flock type (CC vs. positive in both components or PosBoth). For both models, the farm was the random effect. Finally, in autumn 2019, ad-hoc field investigations were carried out testing caecal and neck skin samples, from two consecutive flocks at S1 and S2. Whole genome sequencing (WGS) was performed on isolates, for multilocus sequence typing (MLST) and single nucleotide polymorphisms (SNP) analysis. The monthly TP was always higher for the FS than for the AL component. Agreement between the components was substantial, but 8.1–8.6% of the flocks were CC. Those had median cfu/g 21–28 times lower than that of PosBoth flocks. In the GLM model, the explanatory variables were both significant (P-value <0.05). For example, the odds ratios (ORs) were 8.4 (95% CI: 4.0; 17.6) for Q3 vs. Q1, and 3.1 (1.8; 5.2) for S2 vs. S1. In the LME model, the flock type and the interaction between the other two variables, were significant. In the field study, a caecal positive flock was succeeded by an initially negative flock, in one out of five sampling sessions at S2. The cecal negative flock was positive in 58.3% of the neck skins with the isolate genetically similar to that from the caecal positive flock. Those results show that cross-contamination can be affected by surveillance periods and slaughterhouses, and it can contribute significantly to the TP of carcass positive flocks.

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来源期刊
Microbial Risk Analysis
Microbial Risk Analysis Medicine-Microbiology (medical)
CiteScore
5.70
自引率
7.10%
发文量
28
审稿时长
52 days
期刊介绍: The journal Microbial Risk Analysis accepts articles dealing with the study of risk analysis applied to microbial hazards. Manuscripts should at least cover any of the components of risk assessment (risk characterization, exposure assessment, etc.), risk management and/or risk communication in any microbiology field (clinical, environmental, food, veterinary, etc.). This journal also accepts article dealing with predictive microbiology, quantitative microbial ecology, mathematical modeling, risk studies applied to microbial ecology, quantitative microbiology for epidemiological studies, statistical methods applied to microbiology, and laws and regulatory policies aimed at lessening the risk of microbial hazards. Work focusing on risk studies of viruses, parasites, microbial toxins, antimicrobial resistant organisms, genetically modified organisms (GMOs), and recombinant DNA products are also acceptable.
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