Department of Large Animal Sciences, Faculty of Life Sciences, University of Copenhagen, Frederiksberg, Denmark.
Zoonoses Public Health. 2010 Nov;57 Suppl 1:49-59. doi: 10.1111/j.1863-2378.2010.01354.x.
Salmonella surveillance-and-control programs in pigs are highly resource demanding, so alternative cost-effective approaches are desirable. The aim of this study was to develop and evaluate a tool for predicting the Salmonella test status in pig herds based on herd information collected from 108 industrial farrow-to-finish pig herds in Portugal. A questionnaire including known risk factors for Salmonella was used. A factor analysis model was developed to identify relevant factors that were then tested for association with Salmonella status. Three factors were identified and labelled: general biosecurity (factor 1), herd size (factor 2) and sanitary gap implementation (factor 3). Based on the loadings in factor 1 and factor 3, herds were classified according to their biosecurity practices. In total, 59% of the herds had a good level of biosecurity (interpreted as a loading below zero in factor 1) and 37% of the farms had good biosecurity and implemented sanitary gap (loading below zero in factor 1 and loading above zero in factor 3). This implied that they, among other things, implemented preventive measures for visitors and workers entering the herd, controlled biological vectors, had hygiene procedures in place, water quality assessment, and sanitary gap in the fattening and growing sections. In total, 50 herds were tested for Salmonella. Logistic regression analysis showed that factor 1 was significantly associated with Salmonella test status (P = 0.04). Herds with poor biosecurity had a higher probability of testing Salmonella positive compared with herds with good biosecurity. This study shows the potential for using herd information to classify herds according to their Salmonella status in the absence of good testing options. The method might be used as a potentially cost-effective tool for future development of risk-based approaches to surveillance, targeting interventions to high-risk herds or differentiating sampling strategies in herds with different levels of infection.
猪的沙门氏菌监测和控制计划需要大量资源，因此需要替代的具有成本效益的方法。本研究旨在开发和评估一种工具，用于根据葡萄牙 108 个工业育肥猪场收集的畜群信息预测猪群中的沙门氏菌检测状态。使用了一份包括沙门氏菌已知风险因素的问卷。开发了一种因素分析模型来识别相关因素，然后测试这些因素与沙门氏菌状态的关联。确定并标记了三个因素：一般生物安全（因素 1）、畜群规模（因素 2）和卫生间隔实施（因素 3）。基于因素 1 和因素 3 的负荷，根据其生物安全实践对畜群进行分类。共有 59%的畜群具有良好的生物安全水平（解释为因素 1 中的负荷值为负），37%的农场具有良好的生物安全水平并实施了卫生间隔（因素 1 中的负荷值为负，因素 3 中的负荷值为正）。这意味着他们除其他外，为进入畜群的访客和工人实施了预防措施，控制了生物媒介，实施了卫生程序，对水质进行了评估，并在育肥和生长区实施了卫生间隔。共有 50 个畜群接受了沙门氏菌检测。逻辑回归分析显示，因素 1 与沙门氏菌检测状态显著相关（P = 0.04）。与具有良好生物安全性的畜群相比，生物安全性差的畜群检测出沙门氏菌阳性的可能性更高。本研究表明，在没有良好检测选择的情况下，利用畜群信息根据沙门氏菌状态对畜群进行分类具有潜力。该方法可作为未来基于风险的监测方法的潜在成本效益工具，针对高风险畜群进行干预，或区分具有不同感染水平的畜群的采样策略。
