Wisnieski L, Norby B, Pierce S J, Becker T, Gandy J C, Sordillo L M
Department of Large Animal Clinical Sciences, Michigan State University, 784 Wilson Rd, East Lansing, MI, 48824, USA.
Center for Statistical Training and Consulting, Michigan State University, 293 Farm Lane, East Lansing, MI, 48824, USA.
Prev Vet Med. 2019 Aug 1;169:104701. doi: 10.1016/j.prevetmed.2019.104701. Epub 2019 May 24.
During the transition from late gestation to early lactation, dairy cattle are at increased risk for disease. Herd-level monitoring for disease risk involves evaluating multiple factors, including food intake, cow density, and biomarkers of nutrient metabolism. Biomarkers that are measured include non-esterified fatty acids (NEFA) and beta-hydroxybutyrate (BHB), which are usually measured in a subset of the herd (i.e. cohort). If a certain proportion of cows in the cohort are above a specific threshold for a biomarker, the cohort is considered at high risk of disease. Few previous studies have investigated other methods to aggregate individual cow-level data to the cohort level. We designed a proof-of-concept study to determine if biomarker aggregation methods may be useful to predict cohort incidence of adverse health events including 1) clinical diseases: mastitis, metritis, retained placenta, ketosis, lameness, pneumonia, milk fever, displaced abomasum, 2) and abortion or death of the calf or the cow. The study design was a prospective cohort study that used cows (N = 277) from five Michigan commercial dairy herds. Multiple cohorts of cows (two to four cohorts per farm, 18 total) were enrolled that shared the same dry-off date. We tested three different methods (central, dispersion, and count) to aggregate individual cow data (i.e. biomarkers and covariates) measured at dry-off. The central method consisted of calculating the average value of each variable within a cohort, and the dispersion method involved taking the standard deviation or mean absolute deviation about the median of each variable within a cohort. The count method consisting of counting the number of cows above a specific threshold for each variable within a cohort. We used best subsets selection to select a bouquet of candidate models for each aggregation method and averaged the predictions over the model set. We built 4 sets of Poisson regression models: one for each aggregation method and a combined model that included all three methods. We evaluated the models based on goodness-of-fit, model calibration using scoring rules, and comparison of observed versus predicted counts. The central and the combined method produced models that had good fit and model calibration. These results indicate that it may be possible to use aggregate measures to predict cohort disease incidence as early as dry-off. The next step is to test biomarker aggregation methods in studies with larger sample sizes.
在从妊娠后期到泌乳早期的过渡阶段，奶牛患病风险增加。畜群层面的疾病风险监测涉及评估多个因素，包括采食量、奶牛密度以及营养代谢的生物标志物。所测量的生物标志物包括非酯化脂肪酸（NEFA）和β-羟基丁酸（BHB），通常在畜群的一个子集中（即队列）进行测量。如果队列中的一定比例的奶牛生物标志物高于特定阈值，则该队列被认为处于高疾病风险。以前很少有研究调查将个体奶牛水平的数据汇总到队列水平的其他方法。我们设计了一项概念验证研究，以确定生物标志物汇总方法是否有助于预测不良健康事件的队列发生率，包括1）临床疾病：乳腺炎、子宫炎、胎盘滞留、酮病、跛行、肺炎、产乳热、皱胃移位，2）以及犊牛或奶牛的流产或死亡。研究设计为前瞻性队列研究, 使用来自密歇根州五个商业奶牛场的奶牛（N = 277头）。纳入了多个奶牛队列（每个农场两到四个队列，共18个），这些队列具有相同的干奶日期。我们测试了三种不同的方法（中心法、离散法和计数法）来汇总干奶时测量的个体奶牛数据（即生物标志物和协变量）。中心法包括计算队列中每个变量的平均值，离散法涉及计算队列中每个变量中位数的标准差或平均绝对偏差。计数法包括计算队列中每个变量高于特定阈值的奶牛数量。我们使用最佳子集选择为每种汇总方法选择一组候选模型，并对模型集的预测进行平均。我们构建了4组泊松回归模型：每组汇总方法一组，以及一个包含所有三种方法的组合模型。我们基于拟合优度、使用评分规则的模型校准以及观察计数与预测计数的比较来评估模型。中心法和组合法产生的模型具有良好的拟合度和模型校准。这些结果表明，早在干奶时就有可能使用汇总测量来预测队列疾病发生率。下一步是在更大样本量的研究中测试生物标志物汇总方法。
