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Prevalence of Antibiotic-Resistant Seafood-Borne Pathogens in Retail Seafood Sold in Malaysia: A Systematic Review and Meta-Analysis.

Omowale A OdeyemiMuhamad AminFera R DewiNor Azman KasanHelen OnyeakaDeyan StratevOlumide Adedokun Odeyemi
Published in: Antibiotics (Basel, Switzerland) (2023)
The objective of this study was to examine the frequency and extent of antibiotic-resistant pathogens in seafood sold in Malaysia, using a systematic review and meta-analysis approach to analyze primary research studies. Four bibliographic databases were systematically searched for primary studies on occurrence. Meta-analysis using a random-effect model was used to understand the prevalence of antibiotic-resistant bacteria in retail seafood sold in Malaysia. A total of 1938 primary studies were initially identified, among which 13 met the inclusion criteria. In the included primary studies, a total of 2281 seafoods were analyzed for the presence of antibiotic-resistant seafood-borne pathogens. It was observed that 51% (1168/2281) of the seafood was contaminated with pathogens. Overall, the prevalence of antibiotic-resistant seafood-borne pathogens in retail seafood was 55.7% (95% CI: 0.46-0.65). Antibiotic-resistant Salmonella species had an overall prevalence of 59.9% (95% CI: 0.32-0.82) in fish, Vibrio species had an overall prevalence of 67.2% (95% CI: 0.22-0.94) in cephalopods, and MRSA had an overall prevalence of 70.9% (95% CI: 0.36-0.92) in mollusks. It could be concluded that there is a high prevalence of antibiotic-resistant seafood-borne pathogens in the retail seafood sold in Malaysia, which could be of public health importance. Therefore, there is a need for proactive steps to be taken by all stakeholders to reduce the widespread transmission of antibiotic-resistant pathogens from seafood to humans.
Keyphrases
  • risk factors
  • gram negative
  • public health
  • antimicrobial resistance
  • escherichia coli
  • case control
  • staphylococcus aureus
  • machine learning
  • pseudomonas aeruginosa
  • tyrosine kinase
  • big data
  • deep learning
  • risk assessment