Fall Nils, Ohlson Anna, Emanuelson Ulf, Dohoo Ian
Department of Clinical Sciences, Swedish University of Agricultural Sciences, PO Box 7054, SE-75007 Uppsala, Sweden.
Växa Sverige, PO Box 210, SE-101 24 Stockholm, Sweden.
Prev Vet Med. 2018 Jun 1;154:23-28. doi: 10.1016/j.prevetmed.2018.03.012. Epub 2018 Mar 19.
The use of routinely recorded data for research purposes and disease surveillance is an attractive proposition. However, this requires that the validity and reliability of the data be evaluated for the purpose for which they are to be used. This manuscript reports an evaluation of milk shipment data for evaluating their usefulness in disease monitoring and the resilience of organic and conventional dairy herds in Sweden. A large number of inconsistencies were observed in the data, necessitating substantial efforts to "clean" the data. Given that the selection of rules used in the cleaning process was subjective in nature, a sensitivity analysis was carried out to determine if different cleaning routines produced substantially different results. Despite the cleaning efforts we observed far more large residuals at the shipment level than expected. Thus, it was concluded that the data were too "noisy" to be used for identification of short term impacts on milk production. Resilience was evaluated by examining the residual variance in milk shipped per cow per day under the assumption that herds with high resilience would have lower residual variance. The effects on residual variance of organic status or whether or not the herd used an automatic milking system were evaluated in models in which the residual variance was stratified or not by these factors. We did not find consistent evidence to suggest that organic herds had higher resilience than conventional herds, but this could be partly due to using residual variance as the measure indicating resilience.
将常规记录数据用于研究目的和疾病监测是一个颇具吸引力的提议。然而，这要求针对数据的使用目的评估其有效性和可靠性。本手稿报告了对牛奶运输数据的评估，以评价其在疾病监测以及瑞典有机和传统奶牛群恢复力方面的有用性。在数据中观察到大量不一致之处，因此需要付出巨大努力来“清理”数据。鉴于清理过程中使用的规则选择本质上是主观的，进行了敏感性分析以确定不同的清理程序是否会产生显著不同的结果。尽管进行了清理工作，但我们在运输层面观察到的大残差比预期的要多得多。因此，得出的结论是，这些数据太“嘈杂”，无法用于识别对牛奶生产的短期影响。通过在假设恢复力高的牛群具有较低残差方差的情况下，检查每头奶牛每天运输的牛奶中的残差方差来评估恢复力。在残差方差是否按有机状态或牛群是否使用自动挤奶系统等因素分层的模型中，评估了这些因素对残差方差的影响。我们没有找到一致的证据表明有机牛群比传统牛群具有更高的恢复力，但这可能部分归因于将残差方差用作表示恢复力的指标。
