A procedure for surveillance data-driven risk assessment to inform Campylobacter risk-based control

IF 3 4区 环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES
Alessandro Foddai , Marianne Sandberg , Maarten Nauta
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引用次数: 0

Abstract

In this study is presented a procedure for surveillance data-driven risk assessment, which can be used to inform inter-sectorial Campylobacter risk-based control, e.g. within National Action Plans and One Health (OH) systems. Campylobacter surveillance data (2019 to 2022) and a published quantitative microbial risk assessment (QMRA) model were used, to show the procedure. Moreover, an interface tool was developed in Excel for showing descriptive statistics on measured apparent flock prevalence (AP) and concentrations (colony forming units per gram, cfu/g) on the meat, together with their related QMRA outputs. Currently (mid-2024), Danish fresh broiler meat is produced by four slaughterhouse companies (A, B, C and D), where approximately 30 % of the annually slaughtered broiler flocks are randomly culture tested, on one leg skin (LS) sample per flock sampled from chilled carcasses. Data variables were: date of sampling, farm-ID, within farm house-ID, flock-ID, slaughterhouse name, sample-ID, and Campylobacter concentrations. Flocks were classified as carcass positive with a concentration ≥ 10 cfu/g. The data was fed into the QMRA model to assess: a) the average risk of human campylobacteriosis per serving (during a month or year), and b) the monthly/annual risk of 2022 relative (RR) to the baseline (average) risk from the previous three years. The descriptive statistics and the risk assessment (RA) were carried out at national level and for each slaughterhouse. In 2022, the national RR was 1.03, implying that the average annual risk increased by approximately 3 % compared to the baseline. Nevertheless, for slaughterhouses A, B and D, the annual risk decreased by ≈ 22 %, 21 % and 43 %, respectively; whereas for slaughterhouse C it increased by 48 %. Monthly risk estimates showed seasonal variations, according to the visualized changes of AP and meat contaminations. The national monthly RR was >1 in July and from September to December. During those months: slaughterhouse C had always RR > 1, slaughterhouse A had a relative increase of risk in July, slaughterhouse B in July and November, and slaughterhouse D in October and December. The procedure and the tools used in this study, allow identifying the impact of seasonality and food-chain stages (i.e. slaughterhouses and their broilers sourcing farms) on the risk per serving, so that Campylobacter risk-based control could be implemented accordingly, from farm to fork, across consecutive surveillance periods. The same principles could be applied in other countries, food chains, and/or for other foodborne pathogens, when similar data and QMRA models are available.

监测数据驱动的风险评估程序,为基于风险的弯曲杆菌控制提供依据
本研究介绍了一种监测数据驱动的风险评估程序,可用于为部门间基于风险的弯曲杆菌控制提供信息,例如在国家行动计划和 "同一健康"(OH)系统中。为展示该程序,使用了弯曲杆菌监测数据(2019 年至 2022 年)和已发布的微生物定量风险评估(QMRA)模型。此外,还在 Excel 中开发了一个界面工具,用于显示肉类上测量到的表观菌群流行率(AP)和浓度(每克菌落形成单位,cfu/g)的描述性统计数据,以及相关的 QMRA 输出结果。目前(2024 年中期),丹麦鲜肉鸡肉由四家屠宰场公司(A、B、C 和 D)生产,每年屠宰的肉鸡群中约有 30% 接受随机培养检测,每个鸡群从冷藏胴体中抽取一个腿皮(LS)样本。数据变量包括:采样日期、农场标识、农场内部标识、鸡群标识、屠宰场名称、样本标识和弯曲杆菌浓度。屠体弯曲菌浓度≥ 10 cfu/g的鸡群被归类为阳性。这些数据被输入 QMRA 模型,以评估:a)每份(一个月或一年内)人类弯曲杆菌病的平均风险;b)与前三年的基线(平均)风险相比,2022 年的每月/每年风险。对全国和每个屠宰场进行了描述性统计和风险评估(RA)。2022 年,全国 RR 值为 1.03,这意味着与基线相比,年平均风险增加了约 3%。然而,A、B 和 D 屠宰场的年风险分别降低了 22%、21% 和 43%,而 C 屠宰场的年风险则增加了 48%。根据可视化的屠宰前处理和肉类污染变化,月度风险估计值显示出季节性变化。7 月和 9 月至 12 月的全国月风险估计值为 1。在这些月份中:C 屠宰场的 RR 值始终为 1,A 屠宰场在 7 月份的风险相对增加,B 屠宰场在 7 月和 11 月,D 屠宰场在 10 月和 12 月。这项研究中使用的程序和工具可以确定季节性和食物链阶段(即屠宰场及其肉鸡采购场)对每份肉鸡风险的影响,从而在连续监测期间,从农场到餐桌,实施相应的基于风险的弯曲杆菌控制。如果有类似的数据和 QMRA 模型,同样的原则也可应用于其他国家、食物链和/或其他食源性病原体。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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