Weber Maarten F, Aalberts Marian, Dijkstra Thomas, Schukken Ynte H
Royal GD, P.O. Box 9, 7400 AA Deventer, The Netherlands.
Department of Population Health Sciences, Faculty of Veterinary Medicine, Utrecht University, Yalelaan 7, 3584 CL Utrecht, The Netherlands.
Animals (Basel). 2022 Feb 4;12(3):384. doi: 10.3390/ani12030384.
Dairy herds participating in the Dutch milk quality assurance program for paratuberculosis are assigned a herd status on the basis of herd examinations by ELISA of individual serum or milk samples, followed by an optional confirmatory fecal PCR. Test-negative herds are assigned Status A; the surveillance of these herds consists of biennial herd examinations. Farmers falsely believing that their Status A herds are Map-free may inadvertently refrain from preventive measures. Therefore, we aimed to develop a predictive model to alert Status A farmers at increased risk of future positive ELISA results. Using data of 8566 dairy herds with Status A in January 2016, two logistic regression models were built, with the probabilities of ≥1 or ≥2 positive samples from January 2017-June 2019 as dependent variables, and province, soil type, herd size, proportion of cattle born elsewhere, time since previous positive ELISA results, and the 95th percentile of the S/P ratios in 2015-2016, as explanatory variables. As internal validation, both models were applied to predict positive ELISA results from January 2019-June 2021, in 8026 herds with Status A in January 2019. The model predicting ≥1 positive sample had an area under the receiver operating characteristics curve of 0.76 (95% CI: 0.75, 0.77). At a cut-off predicted probability π = 0.40, 25% of Status A herds would be alerted with positive and negative predictive values of 0.52 and 0.83, respectively. The model predicting ≥2 positive samples had lower positive, but higher negative, predictive values. This study indicates that discrimination of Status A herds with high and low risks of future positive ELISA results is feasible. This might stimulate farmers with the highest risks to take additional measures to control any undetected Map infections.
参与荷兰副结核病牛奶质量保证计划的奶牛场，会根据对个体血清或牛奶样本进行酶联免疫吸附测定（ELISA）的牛群检查结果，再加上一项可选的粪便聚合酶链反应（PCR）确认检测，来确定牛群状态。检测呈阴性的牛群被定为A类状态；对这些牛群的监测包括每两年进行一次牛群检查。一些农民错误地认为他们处于A类状态的牛群没有副结核分支杆菌（Map），可能会无意中不采取预防措施。因此，我们旨在开发一种预测模型，以提醒A类状态的农民，其牛群未来ELISA检测结果呈阳性的风险增加。利用2016年1月时8566个处于A类状态的奶牛场的数据，构建了两个逻辑回归模型，将2017年1月至2019年6月期间≥1个或≥2个阳性样本的概率作为因变量，将省份、土壤类型、牛群规模、在其他地方出生的牛的比例、上次ELISA检测呈阳性结果后的时间，以及2015 - 2016年S/P比值的第95百分位数作为解释变量。作为内部验证，这两个模型都被用于预测2019年1月处于A类状态的8026个牛群在2019年1月至2021年6月期间的ELISA阳性结果。预测≥1个阳性样本的模型，其受试者工作特征曲线下面积为0.76（95%置信区间：0.75，0.77）。在预测概率π = 0.40的临界值时，25%的A类状态牛群会收到警报，其阳性预测值和阴性预测值分别为0.52和0.83。预测≥2个阳性样本的模型，其阳性预测值较低，但阴性预测值较高。这项研究表明，区分未来ELISA检测结果呈阳性风险高和低的A类状态牛群是可行的。这可能会促使风险最高的农民采取额外措施，以控制任何未检测到的Map感染。
