ArticleBMC veterinary research2019
Additive Bayesian networks for antimicrobial resistance and potential risk factors in non-typhoidal Salmonella isolates from layer hens in Uganda.
Article in BMC veterinary research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed, 28 citations in OpenAlex.
- Household satisfaction with health services and response strategies to malaria in mountain communities of Uganda.Transactions of the Royal Society of Tropical Medicine and Hygiene · 2025Article
- Evaluation of risk factors for treatment failure in canine patients undergoing photoactivated chromophore for keratitis - corneal cross-linking (PACK-CXL): a retrospective study using additive bayesian network analysis.BMC veterinary research · 2023Article
- Bayesian network modeling of patterns of antibiotic cross-resistance by bacterial sample source.Communications medicine · 2023Article
- Identifying patient-level risk factors associated with non-β-lactam resistance outcomes in invasive MRSA infections in the United States using chain graphs.JAC-antimicrobial resistance · 2022Article
- Bovine Respiratory Disease: Conventional to Culture-Independent Approaches to Studying Antimicrobial Resistance in North America.Antibiotics (Basel, Switzerland) · 2022Review
- Identifying Associations in Minimum Inhibitory Concentration Values ofFrontiers in veterinary science · 2022Article
- Phages for Africa: The Potential Benefit and Challenges of Phage Therapy for the Livestock Sector in Sub-Saharan Africa.Antibiotics (Basel, Switzerland) · 2021Review
- Bayesian Network Modeling Applied to Feline Calicivirus Infection Among Cats in Switzerland.Frontiers in veterinary science · 2020Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 3 countries.
Funding
Abstract
backgroundMulti-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.
methodsWe 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.
resultsThe 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.
conclusionsAlthough 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.
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