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2013-2017 年英国兽医实验室提交的牛样本中“未确诊”结果的风险因素分析。

Risk factor analysis for "diagnosis not reached" results from bovine samples submitted to British veterinary laboratories in 2013-2017.

机构信息

Veterinary Epidemiology, Economics and Public Health, Department of Pathobiology and Population Sciences, The Royal Veterinary College, Hawkshead Lane, North Mymms, Hatfield, Herts, AL9 7TA, UK.

Department of Epidemiological Sciences, Animal and Plant Health Agency (APHA) - Weybridge, Addlestone, Surrey, KT15 3NB, UK.

出版信息

Prev Vet Med. 2020 Sep;182:105099. doi: 10.1016/j.prevetmed.2020.105099. Epub 2020 Jul 21.

DOI:10.1016/j.prevetmed.2020.105099
PMID:32731091
Abstract

Routine diagnostic data from laboratories are an important source of information for passive animal health surveillance. In Great Britain, the Veterinary Investigation Diagnosis Analysis (VIDA) database includes records of diagnostic submissions made to a nationwide network of 28 veterinary post-mortem facilities (VPFs). Data on "diagnosis not reached" (DNR), i.e. where submissions do not lead to a confirmed diagnosis, are analysed quarterly to look for unexpectedly high incidences of DNRs which could indicate the presence of a new or emerging disease in British livestock populations. The objective of the present study was to provide a better understanding about the reasons of DNR occurrence and to inform improvements of the coverage and reporting of this kind of surveillance data. A subset of the VIDA database comprising diagnostic submissions from cattle received from 2013 to 2017 (122,444 records) was analysed. A mixed-effects multivariable logistic regression model, accounting for clustering by farm and county, was used to investigate associations between potential predictors and DNR. The variables included in the model were: VPF identity, animal sex, age, production purpose, main presenting sign of the animal from which the sample was obtained, and sample submission type. The variable that showed the strongest association with DNR was the main presenting sign of the animal, followed by submission type, VPF identity, animal age, sex, and production purpose, in that order. Submissions from animals with abortion as the main clinical sign had the highest odds ratio (OR 21.6, 95 % confidence interval [CI] 19.6-23.9, with mastitis taken as the baseline). Submissions where neither carcasses (i.e. a whole dead animal provided for post-mortem examination) nor foetuses (i.e. an unborn dead animal) were provided had approximately 12 times the odds of being DNR, compared to submissions of a carcass (OR 11.6, 95 % CI 10.7-12.5). In addition, submission type and main presenting sign can be considered as important confounders in the association between the other predictors and DNR. This study has helped characterise DNR occurrence and suggests some possible improvements that could be made to the passive surveillance system investigated, such as encouraging greater carcase submission, accounting for identified issues when interpreting increased occurrence of DNR and further investigating reduced submissions or greater DNR occurrence in some geographical regions.

摘要

常规诊断数据是被动动物健康监测的重要信息来源。在英国,兽医调查诊断分析(VIDA)数据库包括向全国 28 个兽医剖检设施(VPF)网络提交的诊断记录。每季度对“未确诊”(DNR)的数据进行分析,即提交的样本没有导致确诊诊断,以寻找 DNR 发生率异常升高的情况,这可能表明英国牲畜群体中存在新的或新兴疾病。本研究的目的是更好地了解 DNR 发生的原因,并为改进这种监测数据的覆盖范围和报告提供信息。分析了 VIDA 数据库中的一个子集,该子集包括 2013 年至 2017 年期间从牛中收到的诊断提交记录(122444 条记录)。使用包含农场和郡聚类的混合效应多变量逻辑回归模型,研究了潜在预测因子与 DNR 之间的关联。纳入模型的变量包括:VPF 身份、动物性别、年龄、生产目的、获得样本的动物的主要表现体征,以及样本提交类型。与 DNR 关联最强的变量是动物的主要表现体征,其次是提交类型、VPF 身份、动物年龄、性别和生产目的。以流产为主要临床体征的动物的提交样本具有最高的优势比(OR 21.6,95%置信区间[CI] 19.6-23.9,以乳腺炎为基线)。既未提供尸体(即提供用于剖检的整只死动物)也未提供胎儿(即未出生的死动物)的提交样本,其 DNR 发生的可能性约为仅提供尸体提交样本的 12 倍(OR 11.6,95%CI 10.7-12.5)。此外,提交类型和主要表现体征可被视为其他预测因子与 DNR 之间关联的重要混杂因素。本研究有助于描述 DNR 的发生情况,并提出了一些可能的改进措施,可以应用于所研究的被动监测系统,例如鼓励更多地提交尸体,在解释 DNR 发生率增加时考虑到已识别的问题,并进一步调查某些地理区域减少的提交或更高的 DNR 发生率。

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