Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, United Kingdom.
PLoS One. 2024 May 15;19(5):e0280173. doi: 10.1371/journal.pone.0280173. eCollection 2024.
In a recent study (doi: 10.1371/journal.pone.0265662), associations were identified between owner-reported dog health status and diet, whereby those fed a vegan diet were perceived to be healthier. However, the study was limited because it did not consider possible confounding from variables not included in the analysis. The aim of the current study was to extend these earlier findings, using different modelling techniques and including multiple variables, to identify the most important predictors of owner perceptions of dog health. From the original dataset, two binary outcome variables were created: the 'any health problem' distinguished dogs that owners perceived to be healthy ("no") from those perceived to have illness of any severity; the 'significant illness' variable distinguished dogs that owners perceived to be either healthy or having mild illness ("no") from those perceived to have significant or serious illness ("yes"). Associations between these health outcomes and both owner-animal metadata and healthcare variables were assessed using logistic regression and machine learning predictive modelling using XGBoost. For the any health problem outcome, best-fit models for both logistic regression (area under curve [AUC] 0.842) and XGBoost (AUC 0.836) contained the variables dog age, veterinary visits and received medication, whilst owner age and breed size category also featured. For the significant illness outcome, received medication, veterinary visits, dog age and were again the most important predictors for both logistic regression (AUC 0.903) and XGBoost (AUC 0.887), whilst breed size category, education and owner age also featured in the latter. Any contribution from the dog vegan diet variable was negligible. The results of the current study extend the previous research using the same dataset and suggest that diet has limited impact on owner-perceived dog health status; instead, dog age, frequency of veterinary visits and receiving medication are most important.
在最近的一项研究中（doi: 10.1371/journal.pone.0265662），确定了犬主报告的犬健康状况与饮食之间的关联，即那些喂食纯素饮食的犬被认为更健康。然而，该研究存在局限性，因为它没有考虑到分析中未包含的可能存在的混杂变量。本研究旨在使用不同的建模技术和纳入多个变量，扩展这些早期发现，以确定影响犬主对犬健康感知的最重要预测因素。从原始数据集中，创建了两个二项式结果变量：“任何健康问题”区分了犬主认为健康的犬（“否”）与那些被认为患有任何严重程度疾病的犬；“重大疾病”变量区分了犬主认为健康或患有轻度疾病的犬（“否”）与那些被认为患有重大或严重疾病的犬（“是”）。使用逻辑回归和 XGBoost 机器学习预测模型评估这些健康结果与犬主-动物元数据和医疗保健变量之间的关联。对于任何健康问题的结果，逻辑回归（曲线下面积 [AUC] 0.842）和 XGBoost（AUC 0.836）的最佳拟合模型都包含了犬年龄、兽医就诊次数和接受药物治疗这三个变量，而犬主年龄和犬种体型类别也有影响。对于重大疾病的结果，接受药物治疗、兽医就诊次数、犬年龄再次成为逻辑回归（AUC 0.903）和 XGBoost（AUC 0.887）的最重要预测因素，而犬种体型类别、教育程度和犬主年龄也在后者中起作用。犬纯素饮食变量的任何影响都可以忽略不计。本研究的结果扩展了使用相同数据集的先前研究，表明饮食对犬主感知的犬健康状况的影响有限；相反，犬年龄、兽医就诊次数和接受药物治疗的频率是最重要的。
