Abstract
We have previously described the importance of using multiple indicators for reporting national farm-level antimicrobial use (AMU) information, but the distribution of flock-level AMU and how these indicators relate to each other has not yet been fully explored. Using farm-level surveillance data (2013–2019), for broiler chickens (n = 947 flocks) and turkeys (n = 427), this study aims to (1) characterize flock-level AMU and identify high users, (2) identify appropriate AMU indicators and biomass denominator [population correction unit (PCU) vs. kg weight at pre-slaughter], and (3) make recommendations on the application to veterinarian-producer and national-level reporting. Diverse AMU patterns were identified in broiler chickens (156 patterns) and turkeys (68 patterns); of these, bacitracin, reported by 25% of broiler chicken and 19% of turkey producers, was the most frequently occurring pattern. Depending on the indicator chosen, variations in reported quantity of use, temporal trends and relative ranking of the antimicrobials changed. Quantitative AMU analysis yielded the following results for broiler chickens: mean 134 mg/PCU; 507 number (n) of Canadian (CA) defined daily doses (DDDvet) per 1,000 chicken-days and 18 nDDDvetCA/PCU. Analysis in turkey flocks yielded the following: mean 64 mg/PCU, 99 nDDDvetCA/1,000 turkey-days at risk and 9 nDDDvetCA/PCU. Flocks were categorized based on the percentiles of the mg/PCU distribution: “medium” to “low” users (≤75th percentile) and “high” users (>75th percentile). The odds of being a high user in both broiler chickens and turkeys were significantly increased: if water medications were used, and if trimethoprim-sulfonamides, bacitracins, and tetracyclines were used. Pairwise correlation analysis showed moderate correlation between mg/PCU and the nDDDvetCA/1,000 animal days at risk and between mg/PCU and nDDDvetCA/PCU. Significantly high correlation between nDDDvetCA/1,000 animal days at risk and nDDDvetCA/PCU was observed, suggestive that either of these could be used for routine monitoring of trends in AMU. One source of discrepancy between the indicators was the antimicrobial. Understanding the choice of parameter input and effects on reporting trends in AMU will inform surveillance reporting best practices to help industry understand the impacts of their AMU reduction strategies and to best communicate the information to veterinarians, their producers, and other stakeholders.
Highlights
In recent years, methodologies for monitoring antimicrobials intended for use in animals have advanced and improved, which complements national and global priorities for mitigating the impact of antimicrobial resistance (AMR) using a One Health approach (1)
Multiple metrics and indicators are routinely used by the Canadian Integrated Program for Antimicrobial Resistance Surveillance (CIPARS) to better understand the temporal shifts in antimicrobial use (AMU) and AMR in relation to poultry industry-wide AMU reduction strategies (7)
Building on our methodology for estimating farm-level national AMU data (7, 19) this study further explored AMU characteristics and the utility of different AMU indicators at the flock-level with the intent of informing best practices for surveillance analysis and reporting
Summary
Methodologies for monitoring antimicrobials intended for use in animals have advanced and improved, which complements national and global priorities for mitigating the impact of antimicrobial resistance (AMR) using a One Health approach (1). The OIE has provided guidelines for data collection and reporting (2, 3) and published its 4th annual report with the global data on quantities of antimicrobials intended for use in animals expressed in milligrams per kilogram of animal biomass (mg/kg) (2). The use of multiple antimicrobial use (AMU) metrics (technical units of measurements such as frequency of use, number of medicated rations, days medicated, milligrams, number of DDDs) and indicators (an AMU metric in relation to a denominator such as the animal biomass and animal-time units) for AMU surveillance reporting has been previously described (5, 6). A change in an input parameter, such as the contextualizing denominator (animal biomass), can impact the overall interpretation of the AMU findings (9)
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