Additive Bayesian networks for antimicrobial resistance and potential risk factors in non-typhoidal Salmonella isolates from layer hens in Uganda.
Antimicrobial resistance
Multi-drug resistance
Patterns of antimicrobial resistance
Salmonella
Journal
BMC veterinary research
ISSN: 1746-6148
Titre abrégé: BMC Vet Res
Pays: England
ID NLM: 101249759
Informations de publication
Date de publication:
24 Jun 2019
24 Jun 2019
Historique:
received:
07
12
2018
accepted:
16
06
2019
entrez:
26
6
2019
pubmed:
27
6
2019
medline:
6
8
2019
Statut:
epublish
Résumé
Multi-drug resistant bacteria are seen increasingly and there are gaps in our understanding of the complexity of antimicrobial resistance, partially due to a lack of appropriate statistical tools. This hampers efficient treatment, precludes determining appropriate intervention points and renders prevention very difficult. We re-analysed data from a previous study using additive Bayesian networks. The data contained information on resistances against seven antimicrobials and seven potential risk factors from 86 non-typhoidal Salmonella isolates from laying hens in 46 farms in Uganda. The final graph contained 22 links between risk factors and antimicrobial resistances. Solely ampicillin resistance was linked to the vaccinating person and disposal of dead birds. Systematic associations between ampicillin and sulfamethoxazole/trimethoprim and chloramphenicol, which was also linked to sulfamethoxazole/trimethoprim were detected. Sulfamethoxazole/trimethoprim was also directly linked to ciprofloxacin and trimethoprim. Trimethoprim was linked to sulfonamide and ciprofloxacin, which was also linked to sulfonamide. Tetracycline was solely linked to ciprofloxacin. Although the results needs to be interpreted with caution due to a small data set, additive Bayesian network analysis allowed a description of a number of associations between the risk factors and antimicrobial resistances investigated.
Sections du résumé
BACKGROUND
BACKGROUND
Multi-drug resistant bacteria are seen increasingly and there are gaps in our understanding of the complexity of antimicrobial resistance, partially due to a lack of appropriate statistical tools. This hampers efficient treatment, precludes determining appropriate intervention points and renders prevention very difficult.
METHODS
METHODS
We re-analysed data from a previous study using additive Bayesian networks. The data contained information on resistances against seven antimicrobials and seven potential risk factors from 86 non-typhoidal Salmonella isolates from laying hens in 46 farms in Uganda.
RESULTS
RESULTS
The final graph contained 22 links between risk factors and antimicrobial resistances. Solely ampicillin resistance was linked to the vaccinating person and disposal of dead birds. Systematic associations between ampicillin and sulfamethoxazole/trimethoprim and chloramphenicol, which was also linked to sulfamethoxazole/trimethoprim were detected. Sulfamethoxazole/trimethoprim was also directly linked to ciprofloxacin and trimethoprim. Trimethoprim was linked to sulfonamide and ciprofloxacin, which was also linked to sulfonamide. Tetracycline was solely linked to ciprofloxacin.
CONCLUSIONS
CONCLUSIONS
Although the results needs to be interpreted with caution due to a small data set, additive Bayesian network analysis allowed a description of a number of associations between the risk factors and antimicrobial resistances investigated.
Identifiants
pubmed: 31234834
doi: 10.1186/s12917-019-1965-y
pii: 10.1186/s12917-019-1965-y
pmc: PMC6591809
doi:
Substances chimiques
Anti-Bacterial Agents
0
Types de publication
Journal Article
Langues
eng
Pagination
212Subventions
Organisme : NORHED
ID : UGA-13/0031
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