Conventional and unsupervised artificial intelligence analyses identified risk factors for antimicrobial resistance on dairy farms in the province of Québec, Canada

Abstract

Antimicrobial resistance (AMR) is one of the greatest threats to global health worldwide and is threatening not only humans, but also animal production systems, including dairy farms. The objective of this paper was to describe risks factors associated with AMR on dairy farms in Québec, Canada. This observational cross-sectional study included 101 commercial dairy farms and took place between the springs of 2017 and 2018 for a one-year period. We explored risk factors such as farm practices and producer’s knowledge, antimicrobial use, and the presence of Salmonella Dublin. We evaluated AMR with fecal Escherichia coli retrieved from pre-weaned calves and lactating cows using the following outcomes, the presence of extended-spectrum-β-lactamase/AmpC resistance and the number of resistances to antimicrobial classes. Unsupervised analyses naturally categorized the sample of farms according to their health/treatment status and herd size. Three clusters of farms were identified (cluster 1 mainly healthy herds and low ceftiofur users, cluster 2 relatively high ceftiofur users, cluster 3 farms with a higher incidence of diseases and higher antimicrobial treatment rates). Dimension 1 and cluster membership were statistically associated with the presence of extended-spectrum-β-lactamase/AmpC resistance in lactating cows and in pre-weaned calves. Dimension 1 was also statistically associated with the number of resistances in lactating cows and in pre-weaned calves. This study highlights the complexity of analyzing risk factors associated with AMR. Our results suggest that the herd health status and the AMU-related practices used are associated with AMR in dairy farms.