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Measurement of Antimicrobial Usage: What Can We Learn Across Livestock Sectors?

SEFARI AMR Search Results

Sheep being vaccinated

Microbes (e.g., bacteria, viruses, parasites and fungi) can become resistant to clinical or veterinary drugs (antimicrobials) that are used to treat disease. This has major consequences for how microbial diseases are managed and, therefore, how antimicrobial compounds should be used. Measuring antimicrobial usage (AMU) is a way to monitor the amount of medicines/chemicals that enter the food-chain, and the environment and this could help to reduce antimicrobial resistance (AMR).

This project, funded by the SEFARI Gateway, focused on the use of antimicrobials in livestock production, where it is essential to gauge and direct progress towards reducing AMU as an important part of reducing AMR. More specifically, this project brought together experts from across the livestock sectors, policy and research, through two online workshops, to discuss the current mechanisms for measuring AMU and offer potential lessons that can be learnt. By working together, we saught to provide a collective insight into the fast-changing and diverse set of approaches available in measuring AMU, and this should better inform practice.


Work Completed


Researchers across SEFARI already have considerable experience of bringing experts together to address the problem of antimicrobial resistance (AMR), and there are numerous industry-led projects underway across Scotland & the UK, that are collecting data from farmers or vets to compare antimicrobial usage (AMU), known as benchmarking. Therefore, this project focused on part of the solution – reducing AMU, by bringing the data and expertise together to help inform the livestock industry on best-practice in AMU. If we are to minimise AMR, we need prudent stewardship of the usage of antimicrobials in both humans and animals as a priority.

There has already been substantial progress in monitoring and reducing the usage of antimicrobials in livestock production in the UK. Most of this evidence comes from national aggregated data i.e., estimates of UK veterinary antibiotic resistance and antimicrobial sales surveillance. More recently, data suggests that the decrease in AMU is slowing down. This is to be expected because the most obvious and amenable areas for AMU reduction were understandably tackled first and further reductions are naturally more difficult.

To make further progress in AMU reduction, good data at both the national level and the individual herd (benchmark) level is needed. Obtaining this data requires efficient and accurate systems of measurement and SEFARI researchers have, for example, been working on estimating antimicrobial usage based on sales to beef and dairy farms from UK veterinary practices. However, there also seemed to be a perception that some sectors have better systems for AMU measurement then others. Hence, this project sought to bring together a wide range of stakeholders from across the different livestock sectors to facilitate better cross-sector dialogue, understanding and to find a way to advance best practice together.


The project, undertaken in 2021, involved designing and running two online workshops, themed respectively by livestock sectors:

  • Workshop 1: which focused on AMU for pigs, poultry and game – (9 experts attended + internal facilitators).
  • Workshop 2: which focused on AMU for cows (beef & dairy) and sheep – (11 expert attendees + internal facilitators).

The workshops were planned to be primarily discursive, in order to bring together the practical and commercial expertise of the external participants - as opposed to the SEFARI participants presenting any research. To facilitate discussions, we structured each workshop using the same format.

In each workshop, we began with a presentation of a “thought experiment” to demonstrate the importance of how we calculate AMU. Usage ratios are typically calculated by dividing the total usage (numerator) by an indication of the size of the group receiving the antimicrobial (denominator) and Figure 1 was used to show the sensitivity of this simple ratio when the denominator becomes small.

Figure 1. A graph presented in both workshops of a “thought experiment” in which we demonstrated how sensitive the AMU ratio is to small denominator values.

In each workshop, a discussion then followed on the current choice of data used as the numerator and denominator for national-level and farm-level AMU calculations. Figure 2 summarizes the numerators and denominators most commonly used for each livestock sector.

Figure 2: An illustration of the most commonly used numerators and denominators in different livestock sectors. Note: mg = milligrams, kg = kilograms and PCU = Population Correction Unit, which takes into account the animal population as well as the estimated weight of each particular animal at the time of treatment.

Overall, the workshops provide a simplified and collated overview of AMU in 2021 and the discussions can be summarised as the following take-home messages:

  • General:
    • The measurement of AMU metrics and data collection in livestock is complex.
    • The use of AMU calculations enable discussion about AMU between the vet and the farmer – which is where the immediate determinant of AMU lies.
    • Overly simplified metrics can bias measurement against particular types of livestock production and it is valuable to maintain the reporting of other metrics for context.
    • Measuring AMU in isolation is good but not sufficient. We would gain more benefit from AMU data if we could link the data with other data, such as disease status, in order to identify risk factors.
    • Not all antimicrobials (AMs) are given at the same dose, as the dose is dependent on the AM’s effectiveness. What this means is that some AMs are only required in small amounts (by weight) for a dose. Despite being used in small weights, this does not mean those AMs are contributing any less to the problem of AMR. Therefore, the benchmarking will vary depending on the choice of AM and whether the weight of active ingredient or number of doses is used in AMU calculations. If the metric were to be chosen poorly then, in theory, the farmer could be incentivised to use an AM which would not improve the problem of AMR.
    • Experience from human health AMU, indicates that progress can be made in the recording of use and reason for treatment. However, even in a relatively consolidated industry (human health) that progress took a long time.


  • Importance of industry structure:
    • In order to provide a snap-shot of the current make-up of organisations involved in measuring AMU, we created a map of the different groups that are part of AMU measurement & reduction, see Figure 3.

Figure 3: Groups involved in livestock AMU.

  • During discussions, it became clear the influence between the different players was not as easy to map. The workshop group(s) felt the relationships of influence ranged along a dynamic and complex spectrum.
  • Where there are differences in the demographics of livestock groups, then bench-marking i.e., at the producer level (as started in the pig industry in 2021) can become very sensitive to how the metrics are defined and treat different demographic classes of animal.
    • e.g., dairy calves may consume AMs and, therefore, contribute to their usage (numerator) but they may not be included as being at risk of using AMs (denominator) – and this can artificially penalise farms with a relatively large number of dairy calves.
    • e.g., the pig industry has a lot of animal movements with very different demographic groups in some farms compared to others. Hence AMU by some producers can inherently appear much better than others.
  • In any sector with large numbers of movements (e.g., pigs) or seasonally cyclical sectors (e.g., sheep) there is a high degree of change in the demographic make-up of farms. This means that AMU snap-shots are not necessarily very representative over time.


  • Definitions of numerator (measures of usage) and denominator (measures of animals at risk of using AMs):
    • These definitions appear fairly settled for the national level metrics and are unlikely to change.
    • Changes to national level metrics are not only unlikely but would also disrupt analysis of trends in AMU over time.
    • Depending on the sector, discussions are still ongoing for core metrics at the producer level.


  • Political:
    • It is clear the metrics are sufficiently well established and there is little appetite for any changes. Major changes (political and technological) are happening in the methods of data collection though. For example, there is a shift towards using farm-level usage digital data recording systems like electronic Medicine Books or electronic Medicine Hubs.
    • Assurance schemes are an important class of influencer (sometimes overlooked) in the AMU organisational landscape. Assurance schemes (e.g., Red Tractor) provide a guaranteed set of defined standards for food safety or welfare.
    • Egg packers and processors in the pig and dairy sector are often overlooked, but they are important links in the chain between producers and retailers who influence AMU and the measurement of AMU.
    • Different sectors are at different stages in the “journey” of measuring AMU e.g., there is a perception that the pig sector have paved the way with the use of electronic Medicine Books (eMB) and bench-marking, as have the cattle industry who are now deploying eMHubs. Whilst the sheep sector is hopefully not far behind, and the game sector seems to still be at an earlier stage of measuring AMU.


  • Data collection methods:
    • Egg packers record AMU in detail and this includes zero usage. Whilst zero usage does not contribute to total usage, it does contribute to reported average usage. Recording of zero usage is important in order to avoid over-estimating the average usage of AMs.
    • An eMB allows for recording the reason for treatment – this is useful for identifying future areas for AMU reduction.
    • There is a lot of flux in data gathering, including various competing apps available e.g., HerdWatch; SAHPS; YourHerd. Similarly, there is also different prescribing or billing software (e.g., Teleos  and SAGE ) being used by vet practices. Even within a practice there can be more than one billing software - for example, in a study by SRUC, we found a practice whose software switched during the time period for which we were collecting sales data.
    • There needs to be significant progress on recording the reason for AMU.
    • In mixed units, it is very important to improve the recording of the species for which antimicrobial prescription is made because most AMs are licensed for multiple species and this would provide a more detailed understanding of AMU.


  • Administration of antimicrobials (AMs):
    • In pigs and game there is a shift away from in-feed administration of AMU towards in-water. This makes AMU recording easier because it is managed by the vet rather than dependent on the feed company. It is also a more targeted administration because sick animals may lose their appetite for food but generally do not lose their thirst for water.
    • Intramammary antimicrobials, as used in the dairy industry to treat mastitis, contribute relatively little in total i.e., by weight. Typically reported metrics are different to parenterals (those AMs given by injection). Intramammary antimicrobials are usually defined by the number of doses as defined by DDDvet (the amount of AM to be used per day per adult cow in this case) or DCDvet (the amount of AM to be used for a course for an adult cow in this case). DDDvet and DCDvet are technical units of measurement solely intended for the reporting of antimicrobial consumption data.
    • Pack sizes of antimicrobials in the game industry are getting smaller to encourage less use of surplus antimicrobials arising from big packs of medicated feed.


In summary, through stakeholder discussions this project found that, while most of the definitions of AMU are well established, there is still a great deal of improvement to be achieved, in terms of the data collection mechanisms, in order to achieve more accurate estimates of AMU.


This project brought together experts from across a multitude of different livestock sectors with shared interests and knowledge. By building cross-sectoral and interdisciplinary networks we are seeking to aid our collective understanding of AMU and share best practice.

In collating a general overview of the current state of play in AMU and generating a list of take-home messages, this project will inform ongoing research and practice. More specifically, this opportunity has highlighted that improving methods for collecting data on AMU (e.g., via the use of electronic medicine books) we should be able to improve “benchmarking” at the herd/flock level. This will further our understanding of livestock AMU and result in less dependency on sources such as pharmaceutical sales data held by vet practices, which don’t necessarily offer a complete insight into AMU practice.

Furthermore, by comparing AMU practice across livestock sectors, we have been able to demonstrate the opportunities for some sectors to learn from others.

We now aim to continue to monitor progress in the measurement of AMU and, subject to funding, to assess the level of agreement between the measures made via different data collection mechanisms.



Project Partners

We'd like to thank the partners involved in this work, who were either involved in the steering of the project or by directly contributing their expertise to the workshops. Partners included: Scottish Government; UK Government, SEFARI (SRUC & Moredun), Poultry and Game St David’s, WPS & GW Pig Consultants, Livestock Health Scotland & RHWG, Crowshall Vets, VMD, NFUS, Ruhma, RUMA, Scottish Dairy Hub, PHS, AHDB and National Sheep Association.


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