全群的可重复性评分:对新西兰数据集的分析
R A Laven1, W A Mason2, L J Laven1
1Tāwharau Ora - School of Veterinary Science, Massey University, Palmerston North, New Zealand.
New Zealand veterinary journal
|September 3, 2024
概括
一个单一的全群率得分不能可靠地预测未来的率或确定乳牛的可接受福利状况. 这种评分方法最好用于识别治疗的个体牛,而不是评估整体群体福利.
科学领域:
- 兽医流行病学 兽医流行病学
- 动物福利科学动物福利科学
- 乳牛的健康状况 乳牛的健康状况
背景情况:
- 是乳牛的重大福利问题.
- 准确评估的患病率对于群体管理和福利至关重要.
- 需要对季节性的趋势进行预测工具.
研究的目的:
- 评估春季全群的得分是否预测新西兰奶牛场的夏季的流行 (反之亦然).
- 为了确定单个群体的分数是否可以识别两个季节率<5%的群体.
主要方法:
- 来自120家新西兰奶牛场的病流行数据分析,在春季和夏季得分.
- 应用协议限制分析来评估季节性预测的准确性.
- 单个分数的准确性和特异性的计算,用于识别具有可接受福利的群体 (<5%的).
主要成果:
- 预计夏季的患病率是春季患病率的0.23-4.3倍,占95%的时间.
- 关于可接受福利的单一分数的准确性在春季为92%,夏季为87%.
- 特异性较低,春季为74%,夏季为59%,表明福利状况的预测值较差.
结论:
- 一个单一的,全群的率得分不足以准确预测未来的率.
- 一个季节可接受的状态不能可靠地预测后续季节的福利状态.
- 整群评分最好用于识别需要治疗的个体牛,而不是确定整群福利状况.
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