Risk-based control of Campylobacter spp. in broiler farms and slaughtered flocks to mitigate risk of human campylobacteriosis – A One Health approach

IF 3 4区 环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES
Alessandro Foddai, Maarten Nauta, Johanne Ellis-Iversen
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引用次数: 5

Abstract

Effects of risk-based control of Campylobacter spp. in Danish broiler farms and flocks were simulated, to assess potential reductions of human risk of campylobacteriosis, associated to the consumption of poultry meat produced in Denmark. Two national data streams were used and represented: Flock status by testing cloacal swabs (CS, 2018–2019) and carcass status by testing leg skin samples (LS, 2019). In the CS surveillance component all flocks slaughtered at the two major Danish slaughterhouses were tested with a polymerase chain reaction (PCR), while in LS one third randomly selected flocks were tested by culture (results in colony forming units per gram, cfu/g). Each farm was identified by its Central Husbandry Register (CHR) number. Two risk farm classification strategies (I-II) were based on CS data from 2018. Farms were classified as: always negative (Neg-CHRs), low risk (LowR-CHRs) and high risk (HighR-CHRs) farms. In strategy I, HighR-CHRs had more than five positive flocks, while in strategy II; they had more than 27.8% of the slaughtered flocks positive. Those two cut-offs were the annual 3rd quartiles across positive farms. Thereafter, a risk assessment model was used to estimate the annual relative risk (RR) of human campylobacteriosis in 2019, compared to that of 2013. Three hypothetical levels of cfu/g reductions (A, B and C) were simulated on the LS positive flocks (> 10 cfu/g) slaughtered by HighR-CHRs and were pairwise combined with the two classification strategies, yielding six risk-mitigation scenarios (A I-II; B I-II; C I-II). In scenarios A I-II, zero cfu/g were simulated, while in scenarios B and C, the original cfu/g were divided by three and by two. For each scenario, RRs were compared to the RR of the original cfu/g (scenario O).

In 2018, if all flocks from HighR-CHRs had been negative, the annual CS flock prevalence would have reduced from 19.7% to 7.6% (strategy I) or 9.6% (strategy II). Whereas in 2019, it would have reduced from 17.1% to 7.8% or 11.6%. In both years, HighR-CHRs delivered a high percentage of the total annual positive flocks (61.4–54.4% under strategy I and 51.2–32.6% with strategy II). In 2019, if HighR-CHRs had delivered only LS negative flocks, the RR would have reduced from 0.94 (scenario “O”) to 0.51 (A-I). Other scenarios showed smaller RR reductions. Targeting high risk farms/flocks for intensive control could improve One Health-ness of national action plans against Campylobacter spp.

肉鸡养殖场和屠宰群中弯曲杆菌属的风险控制,以降低人类弯曲杆菌病的风险-一种健康的方法
模拟了在丹麦肉鸡养殖场和鸡群中基于风险控制弯曲杆菌的效果,以评估与丹麦生产的禽肉消费相关的弯曲杆菌病人类风险的潜在降低。使用并表示了两种国家数据流:通过测试肛肠拭子检测羊群状态(CS, 2018-2019)和通过测试腿部皮肤样本检测胴体状态(LS, 2019)。在CS监测部分,所有在丹麦两个主要屠宰场屠宰的鸡群都用聚合酶链反应(PCR)进行检测,而在LS中,随机选择三分之一的鸡群进行培养检测(结果以每克菌落形成单位计算,cfu/g)。每个农场由其中央畜牧登记(CHR)编号确定。两种风险农场分类策略(I-II)基于2018年的CS数据。养殖场分为:始终阴性(阴性- chrs)、低风险(低r - chrs)和高风险(高r - chrs)。在策略1中,HighR-CHRs有5个以上的阳性鸡群,而在策略2中;屠宰禽群阳性率超过27.8%。这两个临界值是正值农场的年度第三个四分位数。随后,采用风险评估模型对2019年与2013年相比的人类弯曲杆菌病年度相对风险(RR)进行了估算。在LS阳性鸡群(>10 cfu/g)被高r - chrs屠宰,并与两种分类策略成对结合,产生六种风险缓解情景(A I-II;B i ii;C i ii)。在情景A - I-II中,模拟零cfu/g,而在情景B和C中,原始cfu/g分别除以3和2。对于每种情景,将RR与原始cfu/g(情景O)的RR进行比较。2018年,如果来自高r - chrs的所有禽群均为阴性,CS禽群的年患病率将从19.7%降至7.6%(策略I)或9.6%(策略II),而在2019年,它将从17.1%降至7.8%或11.6%。在这两年中,高r - chrs在年度阳性禽群总数中所占的比例都很高(在策略I下为61.4-54.4%,在策略II下为51.2-32.6%)。2019年,如果高r - chrs只交付LS阴性禽群,那么RR将从0.94(情景“O”)降至0.51(情景a -I)。其他情景的RR降低幅度较小。针对高风险养殖场/禽群进行集约化控制,可提高国家弯曲杆菌防治行动计划的健康度。
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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