Integrated surveillance of extended-spectrum beta-lactamase (ESBL)-producing Salmonella and Escherichia coli from humans and animal species raised for human consumption in Canada from 2012 to 2017.
Courtney A PrimeauAmrita BharatNicol JaneckoCarolee A CarsonMichael MulveyRichard Reid-SmithScott McEwenJennifer E McWhirterElizabeth Jane ParmleyPublished in: Epidemiology and infection (2022)
Resistance to beta-lactam antimicrobials caused by extended-spectrum beta-lactamase (ESBL)-producing organisms is a global health concern. The objectives of this study were to (1) summarise the prevalence of potential ESBL-producing Escherichia coli (ESBL-EC) and Salmonella spp. (ESBL-SA) isolates from agrifood and human sources in Canada from 2012 to 2017, and (2) describe the distribution of ESBL genotypes among these isolates. All data were obtained from the Canadian Integrated Program for Antimicrobial Resistance Surveillance (CIPARS). CIPARS analysed samples for the presence of ESBLs through phenotypic classification and identified beta-lactamase genes ( bla TEM , bla SHV , bla CTX , bla OXA , bla CMY-2 ) using polymerase chain reaction (PCR) and whole genome sequencing (WGS). The prevalence of PCR-confirmed ESBL-EC in agrifood samples ranged from 0.5% to 3% across the surveillance years, and was detected most frequently in samples from broiler chicken farms. The overall prevalence of PCR-confirmed ESBL-SA varied between 1% and 4% between 2012 and 2017, and was most frequently detected in clinical isolates from domestic cattle. The TEM-CMY2 gene combination was the most frequently detected genotype for both ESBL-EC and ESBL-SA. The data suggest that the prevalence of ESBL-EC and ESBL-SA in Canada was low (i.e. <5%), but ongoing surveillance is needed to detect emerging or changing trends.
Keyphrases
- klebsiella pneumoniae
- escherichia coli
- multidrug resistant
- public health
- risk factors
- biofilm formation
- endothelial cells
- gram negative
- antimicrobial resistance
- global health
- risk assessment
- gene expression
- deep learning
- drinking water
- pseudomonas aeruginosa
- induced pluripotent stem cells
- genome wide identification