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Why Antibiotic Use Data in Animals Needs to Be Collected and How This Can Be Facilitated

Overview of attention for article published in Frontiers in Veterinary Science, December 2017
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  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

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8 X users

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92 Mendeley
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Title
Why Antibiotic Use Data in Animals Needs to Be Collected and How This Can Be Facilitated
Published in
Frontiers in Veterinary Science, December 2017
DOI 10.3389/fvets.2017.00213
Pubmed ID
Authors

Jorge Pinto Ferreira

Abstract

Antimicrobial resistance (AMR) is currently recognized as one of the most significant threats to public health worldwide. It is a phenomenon that highlights the interconnectivity between human and animal health since any use of antibiotics in humans can eventually lead to resistance in the microbial populations colonizing animals and vice versa. In recent years, our understanding of the relationship between the use of antibiotics and the consequent development of resistance in microbial populations to these (or similar) antibiotics has increased. Having accurate data, ideally in a digital format, on the use of antibiotics are therefore of paramount importance. Current obstacles to having such data include, among others, the lack of consensual and harmonized technical methods and units that represent antimicrobial use (AMU), the insufficient incentives to motivate primary producers to report their use of antibiotics, and the inexistence of user-friendly technologies for the collection of such data, despite the generalized use of Internet and electronic devices. Further development and adoption of the units proposed by the European Surveillance of Veterinary Antimicrobial Consumption will contribute to the long-desired harmonization. Rewarding the animal producers (via tax incentives, for example) that use less antibiotics and the development of an app, to which producers could orally report the used antibiotics are among the solutions that could help to overcome the current challenges. I here also argue that having mandatory electronic veterinary prescriptions and awareness campaings, funded via public-private partnerships, should also be considered as methods that could help for the control of societal problems like AMR.

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 92 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 92 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 25%
Researcher 14 15%
Student > Master 13 14%
Other 7 8%
Student > Bachelor 6 7%
Other 10 11%
Unknown 19 21%
Readers by discipline Count As %
Veterinary Science and Veterinary Medicine 22 24%
Agricultural and Biological Sciences 15 16%
Medicine and Dentistry 9 10%
Environmental Science 5 5%
Social Sciences 4 4%
Other 15 16%
Unknown 22 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 June 2018.
All research outputs
#5,912,416
of 23,011,300 outputs
Outputs from Frontiers in Veterinary Science
#950
of 6,320 outputs
Outputs of similar age
#116,779
of 439,142 outputs
Outputs of similar age from Frontiers in Veterinary Science
#17
of 70 outputs
Altmetric has tracked 23,011,300 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 6,320 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.9. This one has done well, scoring higher than 84% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 439,142 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 70 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.