Antimicrobial resistance (AMR) has emerged as a global health threat, which has elicited a
high-level political declaration at the United Nations General Assembly, 2016. In response, member
countries agreed to pay greater attention to the surveillance and implementation of antimicrobial stewardship.
TheNigeria Centre forDisease Control called for a reviewofAMR inNigeria using a “OneHealth approach”.
As anecdotal evidence suggests that food animal health and production rely heavily on antimicrobials,
it becomes imperative to understand AMR trends in food animals and the environment. We reviewed
previous studies to curate data and evaluate the contributions of food animals and the environment
(2000–2016) to the AMR burden in Nigeria using a Preferred Reporting Items for Systematic Reviews
and Meta-Analyses (PRISMA) flowchart focused on three areas: Antimicrobial resistance, residues, and
antiseptics studies. Only one of the 48 antimicrobial studies did not reportmultidrug resistance. At least
18 bacterial spp. were found to be resistant to various locally available antimicrobials. All 16 residue
studies reported high levels of drug residues either in the form of prevalence or concentration above the
recommended international limit. Fourteen different “resistotypes”were found in some commonly used
antiseptics. High levels of residues and AMRwere found in food animals destined for the human food chain.
High levels of residues and antimicrobials discharged into environments sustain the AMR pool. These had
evolved into potential public health challenges that need attention. These findings constitute public health
threats forNigeria’s teeming population and require attention.
Figure S1: Flow chart of the methodological strategy (PRISMA 2009 Flow Diagram), Figure S2: Nigeria geopolitical zonal spread of the AMRS reports, Figure S3: Geopolitical zonal spread of the Antimicrobial Residue reports, Figure S4a: Level of resistance within generation of antimicrobials tested, Figure S4b: Proportional (%) pattern of resistance levels within generation of antimicrobials tested, Figure S5a: Frequency of Antimicrobial Resistance levels of classes of antibiotics, Figure S5b: Antimicrobial resistance patterns within classes along generation of antibiotics, Figure S6: Antimicrobial resistance patterns of β-lactam derivatives antibiotics, Figure S7: Antimicrobial resistance patterns of Quinolones, Figure S8: Antimicrobial resistance patterns of Aminoglycosides, Figure S9: Antimicrobial resistance patterns of Macrolide, Phenicol, and Tetracycline, Figure S10: Antimicrobial resistance patterns of Sulfonamides derivatives, Figure S11: Frequency of antimicrobial resistance levels of other classes of antibiotics, Figure S12: Antimicrobial resistance patterns of other classes of antibiotics, Figure S13: Pattern of antimicrobial resistance of Escherichia coli, Figure S14: Pattern of antimicrobial resistance of Salmonella, Figure S15: Pattern of antimicrobial resistance of Staphylococcus, Figure S16: Pattern of antimicrobial resistance of Pseudomonas, Figure S17: Pattern of antimicrobial resistance of Klebsiella, Figure S18: Pattern of antimicrobial resistance of other bacteria, Table excel S1: Raw data AMRS, S2: Comprehensive AMRS data, S3: Categorized AMRS data analytical.