Keyword: imicrobial Resistance

ABSTRACT: Background: Antimicrobial resistance (AMR) surveillance is essential for estimating resistance burden and guiding public-health responses, yet coverage remains uneven across geographic regions, populations, healthcare settings, and One Health domains. Low observed resistance may therefore reflect limited detection and reporting rather than genuinely low resistance.
Objectives: To map the nature and determinants of AMR under-surveillance, examine computational approaches for identifying surveillance blind spots, and develop an evidence-informed framework for AI-enabled detection of hidden AMR surveillance gaps.
Methods: PubMed and Dimensions were systematically searched for literature published from 2015–2026. Eligible studies addressed AMR surveillance coverage, capacity, representativeness, completeness, or computational approaches relevant to surveillance gaps. Study selection and synthesis followed JBI scoping-review methodology and PRISMA-ScR guidance.
Results: Twenty-two studies were included. Under-surveillance encompassed geographic and population underrepresentation, diagnostic and laboratory limitations, fragmented information systems, delayed reporting, weak genomic coverage, and incomplete One Health integration. Computational approaches included spatial and statistical modelling, machine learning, geographic information systems, genomic analytics, digital laboratory systems, and environmental surveillance. These approaches could identify or partially address surveillance incompleteness but remained constrained by data quality and representativeness. The synthesis informed a framework for detecting discordance between expected AMR risk and observed surveillance intensity.
Conclusions: AMR under-surveillance is a systems-level problem that can distort the apparent distribution of resistance. AI should complement microbiological surveillance by identifying uncertainty and prioritizing settings requiring strengthened surveillance.

ABSTRACT: Post-caesarean surgical site infections (SSIs) remain a major cause of maternal morbidity, particularly in low-resource settings, and are increasingly complicated by antimicrobial resistance. This study aimed to determine the antimicrobial susceptibility patterns and prevalence of multidrug resistance among bacterial isolates from post-caesarean SSIs at University College Hospital, Ibadan. A retrospective analysis of 66 laboratory records was conducted, out of which 52 records met the inclusion criteria. Bacterial isolates were identified using appropriate culture plates, and their antimicrobial susceptibility profiles were analysed using standard disk diffusion (Kirby–Bauer) method. A total of 75 isolates were identified, with Gram-negative organisms (68%) predominating over Gram-positive organisms (32%). The most common isolate was Pseudomonas aeruginosa, followed by Escherichia coli, Klebsiella spp, and Staphylococcus aureus. Antimicrobial susceptibility testing revealed high resistance rates to commonly used antibiotics such as amoxicillin, erythromycin, and azithromycin, while better susceptibility was observed with gentamicin, levofloxacin, and vancomycin among Gram-positive isolates. Notably, vancomycin showed 100% susceptibility. Multidrug resistance was highly prevalent, with 74.7% of isolates classified as MDR, indicating resistance to at least one agent in three different antibiotic classes. This high burden of MDR highlights a significant therapeutic challenge and underscores the role of antibiotic misuse and hospital-acquired infections in driving resistance. The findings of this study emphasize the need for routine antimicrobial susceptibility testing, strengthened infection prevention and control measures, and implementation of antimicrobial stewardship programs. Continuous surveillance and policy interventions are essential to mitigate the growing threat of antimicrobial resistance and improve clinical outcomes in post-caesarean SSIs.