Harmonizing Campylobacter risk assessments across European countries – can the pooled process hygiene criteria data be used in the Danish risk assessment model?

IF 3 4区 环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES Microbial Risk Analysis Pub Date : 2024-09-26 DOI:10.1016/j.mran.2024.100325
Francis Lauriau , Maarten Nauta , Nabila Haddad , Sofia Strubbia , Jean-Michel Cappelier , Marianne Sandberg , Sandrine Guillou , Alessandro Foddai
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Abstract

This study investigated the possibility of harmonizing quantitative microbiological risk assessment (QMRA) for Campylobacter spp. across European (EU) countries. French Campylobacter data (2020–2021) from neck skin (NS) pools, sampled at slaughterhouses under the European surveillance component for Food Business Operators (FBOs), were adapted to inform a QMRA model that, among others, has been used within the Danish Action Plan against Campylobacter, on the basis of single leg skins (LS) data. Datasets included culture results (in colony forming unit per gram, CFU/g) from 1,284 broiler flocks slaughtered at 13 slaughterhouses representing broiler production in western France. Five pools (of 2–4 NS samples each) per flock were tested. One pool per tested flock was randomly chosen for the analysis. After conducting descriptive statistics (on flock prevalence and meat contaminations across months and years), three contamination transformation factors (CTFs) were estimated to translate NS pools contaminations into single LS contamination, based on data from French and Danish studies. A reference simulation scenario (ScenRef) was set with CTF = 3.2 (i.e. NS pool concentration divided by 3.2); while other 13 scenarios represented an alternative scenario analysis to investigate the impact of: the CTF value (ScenMin with CTF = 2 and ScenMax with CTF = 10), censored test results (ScenUncens) and random choice of pool per flock (ScenSampling-1 to 10), on the risk estimates. The average monthly/annual risk of human disease per poultry meal and the monthly/annual relative risk (RR) of 2021 compared to 2020, were estimated. In ScenRef, the annual RR was 1.22, suggesting an increase of risk of ≈ 22 % in 2021 compared to 2020. The impact of CTFs, censored data and randomized pool sampling per flock, on the annual and (most) monthly RRs, appeared limited. This study gives an overview of the strengths and limitations to be considered for adapting the French FBO data into the Danish model and to harmonize risk assessments across EU countries, accordingly. To reduce uncertainty in risk estimates, it could be considered increasing representativeness of NS tested flock populations and/or using LS rather than NS samples; because LS samples are more representative of actually retailed meat contaminations. If NS pools are maintained, the relationships between concentrations on NS pools and those on consumed meat requires further investigation.
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统一欧洲各国的弯曲杆菌风险评估 - 丹麦风险评估模型能否使用汇集的工艺卫生标准数据?
本研究探讨了协调欧洲(欧盟)各国弯曲杆菌定量微生物风险评估(QMRA)的可能性。法国弯曲菌数据(2020-2021 年)来自颈皮(NS)池,在欧洲食品企业经营者(FBOs)监控组件下的屠宰场采样,并根据单腿皮(LS)数据进行调整,为 QMRA 模型提供信息。数据集包括在代表法国西部肉鸡生产的 13 家屠宰场屠宰的 1,284 只肉鸡的培养结果(以每克菌落形成单位 CFU/g 表示)。每个鸡群检测了五个培养池(每个培养池有 2-4 个 NS 样品)。每个检测鸡群随机抽取一个样品池进行分析。根据法国和丹麦的研究数据,在进行描述性统计(关于不同月份和年份的鸡群发病率和肉类污染情况)后,估算了三个污染转化因子(CTF),以将 NS 池污染转化为单一 LS 污染。参考模拟情景(ScenRef)设定为 CTF = 3.2(即 NS 池浓度除以 3.2);而其他 13 种情景则代表替代情景分析,以研究 CTF 值(ScenMin,CTF = 2;ScenMax,CTF = 10)、删减测试结果(ScenUncens)和每群随机选择池(ScenSampling-1 至 10)对风险估计值的影响。估算了每顿禽肉平均每月/每年的人类疾病风险,以及 2021 年与 2020 年相比的每月/每年相对风险 (RR)。在 ScenRef 中,年度 RR 为 1.22,表明 2021 年的风险比 2020 年增加了≈22%。CTF、删减数据和每群随机集合取样对年度和(大部分)月度 RR 的影响似乎有限。本研究概述了将法国 FBO 数据纳入丹麦模型以及相应协调欧盟各国风险评估所需考虑的优势和局限性。为减少风险评估的不确定性,可考虑提高受检NS鸡群的代表性,和/或使用LS而非NS样本;因为LS样本更能代表实际零售肉类污染情况。如果保留 NS 池,则需要进一步调查 NS 池浓度与食用肉类浓度之间的关系。
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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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