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Factors Associated with Antimicrobial Use in Fijian Livestock Farms.

Xavier KhanCaroline RymerRosemary H M LimPartha Ray
Published in: Antibiotics (Basel, Switzerland) (2022)
Antimicrobial stewardship (AMS) programmes in human health and livestock production are vital to tackling antimicrobial resistance (AMR). Data on antimicrobial use (AMU), resistance, and drivers for AMU in livestock are needed to inform AMS efforts. However, such data are limited in Fiji. Therefore, this study aimed to evaluate the association between farmer (socio-economic, demographic) and livestock production and management factors with AMU. Information was collected using purposive and snowball sampling from 236 livestock farmers and managers located in Central and Western divisions, Viti Levu, Fiji. Multinomial logistic regression was used to determine the factors associated with AMU in farms using an aggregated livestock farm model. Farms that raised cattle only for dairy (farm factor) were more likely to use antibiotics and anthelmintics ( p = 0.018, OR = 22.97, CI 1.713, 308.075) compared to mixed cattle and poultry farms. Farms that maintained AMU records were more likely to use antibiotics ( p = 0.045, OR = 2.65, CI 1.024, 6.877) compared to farms that did not. Other livestock production and management factors had no influence on AMU on the livestock farms. AMU in livestock farms was not influenced by the socio-economic and demographic characteristics of the farmer. There were differences between livestock enterprises regarding their management. The lack of association between management system and AMU could be because there was so much variation in management system, levels of farmer knowledge and awareness of AMU, and in management of farm biosecurity. Future studies exploring farmers' knowledge and awareness of AMU and livestock management are required to design AMS programmes promoting prudent AMU in all livestock farms locally.
Keyphrases
  • antimicrobial resistance
  • healthcare
  • risk assessment
  • human health
  • machine learning
  • quality improvement
  • health information
  • deep learning
  • data analysis
  • case control