Comparing human and animal antimicrobial usage: a critical appraisal of the indicators used is needed

Comparisons between antimicrobial usage (AMU) in humans and food-producing animals are regularly made. The accuracy of such comparisons depends on the indicators used to quantify AMU. Indicators for AMU quantitatively relate use data (the numerator) to population data (the denominator). The denominator should be a proxy for the population at risk in a certain period when comparing the exposure of different populations to antimicrobials. Denominators based on numbers of animals slaughtered, such as the commonly used population correction unit, do not consider the time at risk of antimicrobial treatment. Production-based indicators underestimate animal AMU. Additionally, production-based indicators are fundamentally different from indicators used to quantify human AMU. Using such indicators to compare human and animal AMU therefore leads to biased results. More caution should be taken in selecting the indicator to quantify AMU when comparing AMU in food-producing animals and humans.

Monitoring of Farm-Level Antimicrobial Use to Guide Stewardship: Overview of Existing Systems and Analysis of Key Components and Processes

The acknowledgment of antimicrobial resistance (AMR) as a major health challenge in humans, animals and plants, has led to increased efforts to reduce antimicrobial use (AMU). To better understand factors influencing AMR and implement and evaluate stewardship measures for reducing AMU, it is important to have sufficiently detailed information on the quantity of AMU, preferably at the level of the user (farmer, veterinarian) and/or prescriber or provider (veterinarian, feed mill). Recently, several countries have established or are developing systems for monitoring AMU in animals. The aim of this publication is to provide an overview of known systems for monitoring AMU at farm-level, with a descriptive analysis of their key components and processes. As of March 2020, 38 active farm-level AMU monitoring systems from 16 countries were identified. These systems differ in many ways, including which data are collected, the type of analyses conducted and their respective output. At the same time, they share key components (data collection, analysis, benchmarking, and reporting), resulting in similar challenges to be faced with similar decisions to be made. Suggestions are provided with respect to the different components and important aspects of various data types and methods are discussed. This overview should provide support for establishing or working with such a system and could lead to a better implementation of stewardship actions and a more uniform communication about and understanding of AMU data at farm-level. Harmonization of methods and processes could lead to an improved comparability of outcomes and less confusion when interpreting results across systems. However, it is important to note that the development of systems also depends on specific local needs, resources and aims.