Pandit Pranav S, Williams Deniece R, Rossitto Paul, Adaska John M, Pereira Richard, Lehenbauer Terry W, Byrne Barbara A, Li Xunde, Atwill Edward R, Aly Sharif S
EpiCenter for Disease Dynamics, One Health Institute, School of Veterinary Medicine, University of California Davis, Davis, CA, The United States of America.
Veterinary Medicine Teaching and Research Center, University of California, Davis, Tulare, CA, The United States of America.
PeerJ. 2021 Jul 16;9:e11732. doi: 10.7717/peerj.11732. eCollection 2021.
Understanding the effects of herd management practices on the prevalence of multidrug-resistant pathogenic and commensals spp. in dairy cattle is key in reducing antibacterial resistant infections in humans originating from food animals. Our objective was to explore the herd and cow level features associated with the multi-drug resistant, and resistance phenotypes shared between , and spp. using machine learning algorithms.
Randomly collected fecal samples from cull dairy cows from six dairy farms in central California were tested for multi-drug resistance phenotypes of and spp. Using data on herd management practices collected from a questionnaire, we built three machine learning algorithms (decision tree classifier, random forest, and gradient boosting decision trees) to predict the cows shedding multidrug-resistant and commensal bacteria.
The decision tree classifier identified rolling herd average milk production as an important feature for predicting fecal shedding of multi-drug resistance in or commensal bacteria. The number of culled animals, monthly culling frequency and percentage, herd size, and proportion of Holstein cows in the herd were found to be influential herd characteristics predicting fecal shedding of multidrug-resistant phenotypes based on random forest models for and commensal bacteria. Gradient boosting models showed that higher culling frequency and monthly culling percentages were associated with fecal shedding of multidrug resistant or commensal bacteria. In contrast, an overall increase in the number of culled animals on a culling day showed a negative trend with classifying a cow as shedding multidrug-resistant bacteria. Increasing rolling herd average milk production and spring season were positively associated with fecal shedding of multidrug- resistant . Only six individual cows were detected sharing tetracycline resistance phenotypes between and either of the commensal bacteria.
Percent culled and culling rate reflect the increase in culling over time adjusting for herd size and were associated with shedding multidrug resistant bacteria. In contrast, number culled was negatively associated with shedding multidrug resistant bacteria which may reflect producer decisions to prioritize the culling of otherwise healthy but low-producing cows based on milk or beef prices (with respect to dairy beef), amongst other factors. Using a data-driven suite of machine learning algorithms we identified generalizable and distant associations between antimicrobial resistance in and fecal commensal bacteria, that can help develop a producer-friendly and data-informed risk assessment tool to reduce shedding of multidrug-resistant bacteria in cull dairy cows.
了解畜群管理措施对奶牛中多重耐药病原菌和共生菌流行率的影响，是减少源自食用动物的人类抗菌药物耐药性感染的关键。我们的目标是使用机器学习算法，探索与多重耐药以及在[具体菌属1]、[具体菌属2]和[具体菌属3]之间共享的耐药表型相关的畜群和奶牛水平特征。
从加利福尼亚州中部六个奶牛场随机收集的淘汰奶牛粪便样本，检测了[具体菌属1]和[具体菌属2]的多重耐药表型。利用从问卷中收集的畜群管理措施数据，我们构建了三种机器学习算法（决策树分类器、随机森林和梯度提升决策树），以预测排出多重耐药[具体菌属1]和共生菌的奶牛。
决策树分类器确定滚动畜群平均产奶量是预测[具体菌属1]或共生菌粪便中多重耐药性排出的一个重要特征。基于[具体菌属1]和共生菌的随机森林模型，发现淘汰动物数量、每月淘汰频率和百分比、畜群规模以及畜群中荷斯坦奶牛的比例是预测多重耐药表型粪便排出的有影响的畜群特征。梯度提升模型表明，较高的淘汰频率和每月淘汰百分比与多重耐药[具体菌属1]或共生菌的粪便排出有关。相比之下，淘汰日淘汰动物数量的总体增加与将奶牛分类为排出多重耐药菌呈负相关。滚动畜群平均产奶量的增加和春季与多重耐药[具体菌属1]的粪便排出呈正相关。仅检测到六头个体奶牛在[具体菌属1]与任一共生菌之间共享四环素耐药表型。
淘汰百分比和淘汰率反映了在考虑畜群规模的情况下随时间推移淘汰率的增加，并且与排出多重耐药菌有关。相比之下，淘汰数量与排出多重耐药菌呈负相关，这可能反映了生产者基于牛奶或牛肉价格（对于奶牛牛肉而言）等因素，优先淘汰原本健康但低产奶牛的决策。通过一套数据驱动的机器学习算法，我们确定了[具体菌属1]中的抗菌药物耐药性与粪便共生菌之间可推广且有区别的关联，这有助于开发一种生产者友好且基于数据的风险评估工具，以减少淘汰奶牛中多重耐药菌的排出。
