Abstract
Antimicrobial resistance (AMR) is a major concern for “One Health”. Any use of antimicrobials can potentially contribute to the spread of multidrug-resistant bacteria. Antimicrobial use (AMU) in food animals is projected to increase over time. Thus, it is important to monitor AMU and changes in the efficacy of antimicrobials. The objectives of this study were to determine (i) AMU over time in chicken production in Canada, (ii) to examine the advantages of using minimum inhibitory concentration (MIC) data to characterize temporal trends in AMR and (iii) to forecast the AMR to commonly used antimicrobials in Salmonella spp. from chicken production.
Farm-level data, including AMU and other farm management practices, were collected by the Canadian Integrated Program for Antimicrobial Resistance (CIPARS). Using CLSI guidelines, Salmonella spp. were isolated and susceptibility tested using broth microdilution. Statistical analysis was performed to determine the effect of AMU and management factors on MIC over time. Important risk factors included in the generalized linear model were selected by LASSO regression. The ability to predict subtle changes on MIC over time was compared using two different models (i) logistic and (ii) multinomial regression. The best model fit was determined by Akaike information criteria. Forecasting for the effect of AMU on AMR over time in Salmonella spp. was performed using ARIMA model.
Overall, AMU in chickens has decreased over the last decade. However, the use of some antimicrobials has fluctuated over time. The effect of time was most evident on the use of streptomycin, sulfamethoxazole, and tetracycline. In terms of AMR, there was decrease in the MIC’s of streptomycin and sulfamethoxazole and an increase in tetracycline over time. Both logistic and multinomial regression models showed similar results on the effect of AMU and time on MIC, however, the multinomial model was more sensitive to subtle MIC time trends. We found that the use of lincosamides-aminocyclitols or third generation cephalosporins contributed to tetracycline resistance. Forecasting model results suggest that if antimicrobials continue to be used at the current rate tetracycline resistance will continue to increase in Salmonella spp. isolated from Canadian chicken farms in the next 4 years.
Reduction in AMU in chicken production mitigates AMR in Salmonella spp., an important pathogen for human and animal health. Analyzing the AMR surveillance data by multinomial model is more accurate than logistic regression in detecting early subtle changes in MIC’s. Continued monitoring of AMU and AMR is necessary to evaluate the effect of measures to reduce AMU on food production and safety and to preserve the efficacy of antimicrobials to treat infections.