Antimicrobial resistance in Africa: A retrospective analysis of data from 14 countries, 2016–2019

Background Antimicrobial resistance (AMR) is a major global health issue that exacerbates the burden of infectious diseases and healthcare costs. However, the scarcity of national-level AMR data in African countries hampers our understanding of its scale and contributing factors in the region. To gain insights into AMR prevalence in Africa, we collected and analyzed retrospective AMR data from 14 countries. Methods and findings We estimated bacterial AMR prevalence, defined as the proportion of resistant human isolates tested from antimicrobial susceptibility (AST) data collected retrospectively for 2016–2019 from 205 laboratories across 14 African countries. We generated 95% confidence intervals (CIs) for aggregated AMR estimates to account for data quality disparities across countries; the median data quality score was 73.1%, ranging from 56.4% to 80.8%. We assessed 819,584 culture records covering 9,266 pathogen–drug combinations, of which 187,832 (22.9%) were positive cultures with AST results. The most frequently cultured specimens were urine (32.0%) and purulent samples (28.1%), and the most frequently isolated pathogens were Escherichia coli (22.2%) and Staphylococcus aureus (15.0%). Aggregated AMR estimates did not change significantly across the years studied (p>0.337); however, there were significant variations in AMR prevalence estimates in culture-positive samples across countries, regions, patient departments (inpatient/outpatient), and specimen sources (p<0.05). Male sex (adjusted odds ratio [aOR] 1.15; 95% CI [1.09,1.21]; p<0.0001), ages above 65 (aOR 1.28; 95% CI [1.16–1.41]; p<0.0001), and inpatient department (aOR 1.24; 95% CI [1.13–1.35]; p<0.0001) were associated with higher AMR prevalence among culture-positive samples. The lack of routine testing, as reflected in the low data volume from most contributing laboratories, and the absence of patient clinical information, represent significant limitations of this study. Conclusion Analysis of the largest retrospective AMR dataset in Africa indicates high variability in AMR prevalence across countries, coupled with differences in AMR testing capacities, data quality, and AMR estimates. Gaps in AST practices and inadequate digital infrastructures for data collection and reporting represent barriers to estimating the true AMR burden in the region. These barriers warrant large-scale investments to expand healthcare access and strengthen bacteriology laboratory capacities.