Pathogens. 2026 Jun 25;15(7):670. doi: 10.3390/pathogens15070670.
ABSTRACT
BACKGROUND: Antimicrobial resistance (AMR) is widely considered to be driven by antimicrobial consumption through within-host selection. However, whether this mechanism adequately explains population-level patterns of resistance in invasive infections remains uncertain. If antimicrobial use is the dominant determinant, resistance should be highest in demographic groups with the greatest exposure.
METHODS: We conducted a retrospective analysis of 44,792 community-onset Escherichia coli bloodstream infection episodes identified through national Australian surveillance data (2013-2024). Resistance prevalence across individual antimicrobials and composite multidrug resistance panels was analysed by age and sex. These data were compared with community antimicrobial dispensing derived from the Pharmaceutical Benefits Scheme. Mean resistance was modelled as a function of age and sex.
RESULTS: Antimicrobial use was substantially higher in females than males (~23% overall) and increased markedly with age, with individuals aged ≥80 years receiving approximately three times more antimicrobials than those aged 25-30 years. In contrast, resistance was consistently lower in females across most antimicrobials and composite measures. Resistance demonstrated an inverted U-shaped age distribution, peaking at 30-40 years before declining in older age groups. From early adulthood to older age, antimicrobial dispensing increased threefold, whereas mean resistance declined by approximately 20%. These patterns were consistent across antimicrobial classes, years, and jurisdictions.
CONCLUSIONS: These findings show that demographic patterns of antimicrobial resistance in community-onset E. coli bloodstream infections are not well explained by a simple population-level consumption model. These findings should be interpreted as important hypothesis-generating insights. Although antimicrobial exposure remains important for individual-level selection, the observed discordance between prescribing and resistance suggests that other factors, including differences in transmission pathways, healthcare contact, disease prevalence, community sanitation and socioeconomic circumstances may also significantly shape resistance patterns.
PMID:42514997 | PMC:PMC13415096 | DOI:10.3390/pathogens15070670