Why Antibiotic Use Data in Animals Needs to Be Collected and How This Can Be Facilitated.
Why Antibiotic Use Data in Animals Needs to Be Collected and How This Can Be Facilitated.
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DOI:
10.3389/fvets.2017.00213
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发表时间:
2017
影响因子:
3.2
通讯作者:
Pinto Ferreira J
中科院分区:
文献类型:
--
作者:
Pinto Ferreira J
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.
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影响因子:
10.4
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Ashbolt NJ;Amézquita A;Backhaus T;Borriello P;Brandt KK;Collignon P;Coors A;Finley R;Gaze WH;Heberer T;Lawrence JR;Larsson DG;McEwen SA;Ryan JJ;Schönfeld J;Silley P;Snape JR;Van den Eede C;Topp E
通讯作者:
Topp E
影响因子:
2.6
作者:
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通讯作者:
van Geijlswijk, Ingeborg M.
影响因子:
3.3
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56.3
作者:
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通讯作者:
Shen, Jianzhong
影响因子:
3.8
作者:
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通讯作者:
Staerk, Katharina