Analysis of multiple bacterial species and antibiotic classes reveals large variation in the association between seasonal antibiotic use and resistance.

Understanding how antibiotic use drives resistance is crucial for guiding effective strategies to limit the spread of resistance, but the use-resistance relationship across pathogens and antibiotics remains unclear. We applied sinusoidal models to evaluate the seasonal use-resistance relationship across 3 species (Staphylococcus aureus, Escherichia coli, and Klebsiella pneumoniae) and 5 antibiotic classes (penicillins, macrolides, quinolones, tetracyclines, and nitrofurans) in Boston, Massachusetts. Outpatient use of all 5 classes and resistance in inpatient and outpatient isolates in 9 of 15 species-antibiotic combinations showed statistically significant amplitudes of seasonality (false discovery rate (FDR) < 0.05). While seasonal peaks in use varied by class, resistance in all 9 species-antibiotic combinations peaked in the winter and spring. The correlations between seasonal use and resistance thus varied widely, with resistance to all antibiotic classes being most positively correlated with use of the winter peaking classes (penicillins and macrolides). These findings challenge the simple model of antibiotic use independently selecting for resistance and suggest that stewardship strategies will not be equally effective across all species and antibiotics. Rather, seasonal selection for resistance across multiple antibiotic classes may be dominated by use of the most highly prescribed antibiotic classes, penicillins and macrolides.

Déclaration de conflit d'intérêts

I have read the journal’s policy and the authors of this manuscript have the following competing interests: YHG is on the scientific advisory board of Day Zero Diagnostics and has consulted for GlaxoSmithKline, Merck, and Quidel and received grant funding from Pfizer, Merck, US CDC, NIH/NIAID, the Smith Family Foundation, and Wellcome Trust. SK is an unpaid scientific advisor for PhAST Diagnostics and a member of a one-time scientific advisory board with GlaxoSmithKline, related to work on antibiotic susceptibility testing and prediction of antimicrobial resistance, respectively. SWO is employed by Biobot Analytics, Inc. ML received grants from US CDC, NIH/NIGMS, NIH/NIAID, UK National Institute for Health Research, and Pfizer, and consulting fees or honoraria from Merck, Sanofi Pasteur, Janssen, and Bristol Myers Squibb.