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英格兰和苏格兰低风险地区牛结核病感染监测的基于风险的策略。

Risk-based strategies for surveillance of tuberculosis infection in cattle for low-risk areas in England and Scotland.

机构信息

Boyd Orr Centre for Population and Ecosystem Health,Institute of Biodiversity,Animal Health and Comparative Medicine,College of Medical,Veterinary and Life Sciences,University of Glasgow,Glasgow,UK.

Computing Science and Mathematics,Faculty of Natural Sciences,University of Stirling,Stirling,UK.

出版信息

Epidemiol Infect. 2018 Jan;146(1):107-118. doi: 10.1017/S0950268817001935. Epub 2017 Dec 6.

Abstract

Disease surveillance can be made more effective by either improving disease detection, providing cost savings, or doing both. Currently, cattle herds in low-risk areas (LRAs) for bovine tuberculosis (bTB) in England are tested once every 4 years. In Scotland, the default herd testing frequency is also 4 years, but a risk-based system exempts some herds from testing altogether. To extend this approach to other areas, a bespoke understanding of at-risk herds and how risk-based surveillance can affect bTB detection is required. Here, we use a generalized linear mixed model to inform a Bayesian probabilistic model of freedom from infection and explore risk-based surveillance strategies in LRAs and Scotland. Our analyses show that in both areas the primary herd-level risk factors for bTB infection are the size of the herd and purchasing cattle from high-risk areas of Great Britain and/or Ireland. A risk-based approach can improve the current surveillance system by both increasing detection (9% and 7% fewer latent infections), and reducing testing burden (6% and 26% fewer animal tests) in LRAs and Scotland, respectively. Testing at-risk herds more frequently can also improve the level of detection by identifying more infected cases and reducing the hidden burden of the disease, and reduce surveillance effort by exempting low-risk herds from testing.

摘要

疾病监测可以通过提高疾病检测效率、节省成本或两者兼用来实现。目前,英格兰低风险地区(LRAs)的牛群每 4 年进行一次牛结核病(bTB)检测。在苏格兰,默认的牛群检测频率也是 4 年,但基于风险的系统会使一些牛群完全免于检测。为了将这种方法扩展到其他地区,需要对有风险的牛群以及基于风险的监测如何影响 bTB 检测有专门的了解。在这里,我们使用广义线性混合模型为无感染的贝叶斯概率模型提供信息,并在 LRAs 和苏格兰探索基于风险的监测策略。我们的分析表明,在这两个地区,bTB 感染的主要 herd-level 风险因素是牛群的规模以及从英国和/或爱尔兰的高风险地区购买牛只。基于风险的方法可以通过提高检测效率(分别减少 9%和 7%的潜伏感染)和降低检测负担(分别减少 6%和 26%的动物检测)来改进当前的监测系统。更频繁地检测有风险的牛群也可以通过识别更多感染病例和减少疾病的隐性负担来提高检测水平,并通过使低风险牛群免于检测来减少监测工作。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a4ea/5851039/deb4c818a959/S0950268817001935_fig1.jpg

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