Surveillance is an important aspect of antimicrobial stewardship. Knowing where we stand, whether it’s the amount of drug used, the types of drugs that are used, the prevalence resistance or various other types of data, is important. That’s why there’s a lot of effort put into surveillance.
However, data need to be used to have a real impact. That can involve setting targets, identifying areas where interventions are needed, developing and testing interventions and similar things that aim to reduce or optimize antimicrobial use, not just tell us what’s happening.
Sometimes the action component gets lost, since analyzing data can be a lot easier (and less expensive, and less time consuming) than effecting change.
So, linking those areas is important, as is figuring out how to get better data (and…not-so-subtle CANresist plug here….something we need to do more of.)
- A commentary in the Veterinary Record (Coyne et al, Dec 2017) entitled Antimicrobial use in dairy cattle; what gets measured gets improved highlights some important issues.. A research paper in the same edition of that journal (Hyde et al) reports on changes in antimicrobial use on UK dairy farms.
A few highlights:
They looked at a few different ways to assess antimicrobial use, including mass (mg) of antimicrobial active ingredient per population correction unit (mg/PCU), defined daily doses (DDDvet) and defined course doses (DCDvet).
- It’s a challenge to accurately capture use on farms (and other places) because of the ways antimicrobials are dispensed and used, but with a standard approach, changes over time can be assessed with a good degree of confidence. It also highlighted some of the problems we have with surveillance, including how to report data. Reporting kg of drug used is pretty crude. Defined daily doses and other metrics used in humans are hard to accurately apply in animals, with markedly different weights and limited animal-specific use data. In dairy cattle, intramammary antibiotics are commonly used, something that defies accurate capture using some reporting approaches.
There was a lot of variation, with use of farms ranging from 0.36 to 97.79 mg/PCU and 0.05 to 20.29 DDDvet.
- That gives a snapshot of the farms that were studied, and it shows that there is at least a subset of farms that are ripe for an intervention. That’s step 1. Figuring out why their antibiotic use is so high is step 2, helping them drop it is step 3, and more surveillance to see if the rates actually drop is step 4. All are important and it’s critical to keep going after step 1.
25% of farms accounted for over 50% of antibiotic use.
- Targeting those farms can obviously have a big impact overall.
The commentary states “This work emphasises the need for accurate baseline antimicrobial use data which is representative of the national dairy herd . Identifying such high antimicrobial use behaviours will allow the industry to better target antimicrobial use interventions to reduce use at a farm, veterinary practice and national level. Due to the wide spectrum of antimicrobial use recorded by Hyde and others, there is a need to approach antimicrobial reduction interventions at an individual farm level. For example, benchmarking can improve farmer awareness of their current antimicrobial use practices and may play a role in reducing high antimicrobial use behaviours and promoting prudent use.”
We can replace “national dairy herd” with every other species (including people), and add humans healthcare facilities and the community and this statement would still be accurate.