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Home / Equine Research Bank / Front Vet Sci / Antimicrobial Usage in Horses: The Use of Electronic Data, D...
Frontiers in veterinary science2020; 7; 216; doi: 10.3389/fvets.2020.00216

Antimicrobial Usage in Horses: The Use of Electronic Data, Data Curation, and First Results.

Abstract: The usage of antimicrobial drugs (AMs) leads to an increase in antimicrobial resistance (AMR). Although different antimicrobial usage (AMU) monitoring programs exist for livestock animals in Germany, there is no such system for horses. However, with the increasing usage of electronic practice management software (EPMS), it is possible to analyze electronic field data generated for routine purposes. The aim of this study was to generate AMU data for German horses with data from the Clinic for Horses (CfH), University of Veterinary Medicine Hannover (TiHo), and in addition to show that different processes of data curation are necessary to provide results, especially considering quantitative indices. In this investigation, the number of antimicrobial doses used and the amount and percentage of active ingredients applied were calculated. Data contained all drugs administered between the 1st of January and the 31st of December 2017. A total of 2,168 horses were presented for veterinary care to the CfH and 34,432 drug applications were documented for 1,773 horses. Of these, 6,489 (18.85%) AM applications were documented for 837 (47.21%) horses. In 2017, 162.33 kg of active ingredients were documented. The most commonly used antibiotic classes were sulfonamides (84.32 kg; 51.95 %), penicillins (30.11 kg; 18.55%) and nitroimidazoles (24.84 kg; 15.30%). In 2017, the proportion of Critically Important Antibiotics (CIA)-Highest Priority used was 0.15% (0.24 kg) and the proportion of CIA-High Priority used was 20.85% (33.85 kg). Of the total 9,402 entries of antimicrobial active ingredients, the three with the largest number used were sulfonamides [ = 2,798 (29.76%)], trimethoprim [ = 2,757 (29.76%)] and aminoglycosides [ = 1,381 (14.69%)]. Comparison between Administered Daily Dose (ADA) and Recommended Daily Dose of CfH (RDD), showed that 3.26% of ADA were below RDD, 3.18% exceeded RDD and 93.55% were within the range around RDD. This study shows that data generated by an EPMS can be evaluated once the method is set up and validated. The method can be transferred to evaluate data from the EPMS of other clinics or animal species, but the transferability depends on the quality of AMU documentation and close cooperation with respective veterinarians is essential.
Copyright © 2020 Schnepf, Bienert-Zeit, Ertugrul, Wagels, Werner, Hartmann, Feige and Kreienbrock.
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Publication Date: 2020-04-29 PubMed ID: 32411737PubMed Central: PMC7200993DOI: 10.3389/fvets.2020.00216Google Scholar: Lookup
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  • Journal Article

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The research article centers around collating and analyzing data on the usage of antimicrobial drugs in horses of Germany. Through data gathered from a University’s Clinic for Horses, the study aims to understand prevalent antimicrobial usage patterns and interpret the potential implications on antimicrobial resistance.

Objective of the Research

  • The primary objective of this research is to generate data on antimicrobial usage (AMU) in horses, given the absence of any existing monitoring systems. The researchers have used data secured from an electronic practice management software (EPMS) used at the Clinic for Horses (CfH) at the University of Veterinary Medicine Hannover.

Data Curation and Analysis

  • Data curation was an important aspect of the study, with the team interpreting the data to provide relevant results. Two key areas were targeted – the number of antimicrobial drugs used, and the amount and percentage of active ingredients used in the medication.
  • Data from the entirety of the year 2017 was used, documenting all drugs given to the horses during the period.

Findings of the Study

  • The study found that out of 34,432 drug applications recorded for 1,773 horses, 18.85% were antimicrobial applications covering 47.21% of the horses.
  • In terms of ingredients, 162.33 kg of active ingredients were used in 2017. The most heavily utilized antibiotic classes were sulfonamides (51.95%), penicillins (18.55%), and nitroimidazoles (15.30%).
  • When it came to the usage of Critically Important Antibiotics (CIA), the proportion of the highest priority was minuscule at 0.15% while high priority stood at 20.85%.
  • The three most popular antimicrobial active ingredients used were sulfonamides (29.76%), trimethoprim (29.76%), and aminoglycosides (14.69%).

Significance of the Study

  • The research indicates that data generated via EPMS can offer valuable insight once an evaluation method is established and validated. Such a method can be expanded to assess data from other clinics and even other animal species. However, data quality and close cooperation with the relevant veterinarians are critical.
  • The findings can help in understanding the patterns and prevalence of AMU in horses. This information could be vital in formulating practices and policies that restrict antimicrobial resistance.

Cite This Article

APA
Schnepf A, Bienert-Zeit A, Ertugrul H, Wagels R, Werner N, Hartmann M, Feige K, Kreienbrock L. (2020). Antimicrobial Usage in Horses: The Use of Electronic Data, Data Curation, and First Results. Front Vet Sci, 7, 216. https://doi.org/10.3389/fvets.2020.00216

Publication

Frontiers in veterinary science
ISSN: 2297-1769
NlmUniqueID: 101666658
Country: Switzerland
Language: English
Volume: 7
Pages: 216
PII: 216

Researcher Affiliations

Schnepf, Anne
  • Department of Biometry, Epidemiology and Information Processing, WHO Collaborating Centre for Research and Training for Health in the Human-Animal-Environment Interface, University for Veterinary Medicine Hannover, Hanover, Germany.
Bienert-Zeit, Astrid
  • Clinic for Horses, University for Veterinary Medicine Hannover, Hanover, Germany.
Ertugrul, Hatice
  • Department of Biometry, Epidemiology and Information Processing, WHO Collaborating Centre for Research and Training for Health in the Human-Animal-Environment Interface, University for Veterinary Medicine Hannover, Hanover, Germany.
Wagels, Rolf
  • Information and Data Service (TiHo-IDS), University for Veterinary Medicine Hannover, Hanover, Germany.
Werner, Nicole
  • Department of Biometry, Epidemiology and Information Processing, WHO Collaborating Centre for Research and Training for Health in the Human-Animal-Environment Interface, University for Veterinary Medicine Hannover, Hanover, Germany.
Hartmann, Maria
  • Department of Biometry, Epidemiology and Information Processing, WHO Collaborating Centre for Research and Training for Health in the Human-Animal-Environment Interface, University for Veterinary Medicine Hannover, Hanover, Germany.
Feige, Karsten
  • Clinic for Horses, University for Veterinary Medicine Hannover, Hanover, Germany.
Kreienbrock, Lothar
  • Department of Biometry, Epidemiology and Information Processing, WHO Collaborating Centre for Research and Training for Health in the Human-Animal-Environment Interface, University for Veterinary Medicine Hannover, Hanover, Germany.

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Citations

This article has been cited 9 times.
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