Journal of global antimicrobial resistance · 2026
Antibiotic practices and AMR trends in livestock farms of Mymensingh region in Bangladesh.
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Sohan Mohammad, Ashikuzzaman Md, Hasan Mahmudul, Taifunnahar Sara, Taluckder Mohsin, Fasina Folorunso Oludayo, Shahiduzzaman Md
Аннотация
OBJECTIVE: In Bangladesh, the unregulated use of antimicrobials in livestock poses a significant threat to the effectiveness of these drugs, yet multi-sectoral data to guide stewardship are scarce. This study investigated veterinary drug use, antibiotic awareness, and risk factors contributing to antimicrobial resistance (AMR) in the Mymensingh region of Bangladesh.
METHODS: A cross-sectional survey was conducted among livestock farmers (n = 101) and retail pharmacy sellers (n = 104), complemented by veterinary hospital prescription records (n = 330). Field data were collected and descriptive and analytical statistics were conducted in Python to determine predictors of antibiotic choice. Principal Component Analysis (PCA), Hierarchical Clustering, and network analysis were performed to characterize antibiotic usage patterns and co-prescription structures.
RESULTS: Provider qualification and knowledge of withdrawal periods significantly influenced antibiotic practices in both farm and pharmacy settings. PCA captured major variance components in farm (41.06%), pharmacy (51.89%), and hospital (40.85%) datasets. Hierarchical Clustering demonstrated sector-specific preferences among 27 antibiotic compounds, with Amoxicillin dominating farm use and Tetracycline-class drugs prevalent in hospital prescriptions. Network analysis revealed tightly connected co-prescription clusters, with Amoxicillin and Tetracycline functioning as central nodes.
CONCLUSIONS: Significant variation exists in antibiotic awareness and usage across sectors, shaped largely by provider qualification and withdrawal knowledge. The identified predictors and usage networks provide critical evidence for designing targeted antimicrobial stewardship interventions, highlighting the need for stricter pharmacy regulation and enhanced farmer education.
METHODS: A cross-sectional survey was conducted among livestock farmers (n = 101) and retail pharmacy sellers (n = 104), complemented by veterinary hospital prescription records (n = 330). Field data were collected and descriptive and analytical statistics were conducted in Python to determine predictors of antibiotic choice. Principal Component Analysis (PCA), Hierarchical Clustering, and network analysis were performed to characterize antibiotic usage patterns and co-prescription structures.
RESULTS: Provider qualification and knowledge of withdrawal periods significantly influenced antibiotic practices in both farm and pharmacy settings. PCA captured major variance components in farm (41.06%), pharmacy (51.89%), and hospital (40.85%) datasets. Hierarchical Clustering demonstrated sector-specific preferences among 27 antibiotic compounds, with Amoxicillin dominating farm use and Tetracycline-class drugs prevalent in hospital prescriptions. Network analysis revealed tightly connected co-prescription clusters, with Amoxicillin and Tetracycline functioning as central nodes.
CONCLUSIONS: Significant variation exists in antibiotic awareness and usage across sectors, shaped largely by provider qualification and withdrawal knowledge. The identified predictors and usage networks provide critical evidence for designing targeted antimicrobial stewardship interventions, highlighting the need for stricter pharmacy regulation and enhanced farmer education.