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Interactive data visualizations of antibiotic use and resistance in North America and Europe
Therapeutic antibiotic use in humans is a significant driver of antibiotic resistance. The seasonal effect of antibiotic use on antibiotic resistance has been poorly quantified because of lack of large-scale, spatially disaggregated time-series data on antibiotic use and resistance.
We used time-series analysis (Box–Jenkins) on US antibiotic usage from IMS Health and on antibiotic resistance from The Surveillance Network from 1999–2007 to estimate the effect of aminopenicillin, fluoroquinolone, trimethoprim/sulfamethoxazole, and tetracycline usage on resistance of Escherichia coli to drugs within these classes. We also quantified the effect of fluoroquinolone and macrolide/lincosamide usage on resistance of methicillin-resistant Staphylococcus aureus (MRSA) to ciprofloxacin and clindamycin (which has a similar mode of action to macrolides), respectively.
Prevalence of resistant Escherichia coli was significantly correlated with lagged (by 1 month) antibiotic prescriptions for aminopenicillins (0.22, P = .03) and fluoroquinolones (0.24, P = .02), which are highly prescribed, but was uncorrelated to antibiotic classes with lower prescription levels. Fluoroquinolone prescriptions were also significantly correlated with a 1-month lag with the prevalence of ciprofloxacin-resistant MRSA (0.23, P = .03).
Large-scale usage of antibiotics can generate seasonal patterns of resistance that fluctuate on a short time scale with changes in antibiotic retail sales, suggesting that use of antibiotics in the winter could have a significant effect on resistance. In addition, the strong correlation between community use of antibiotics and resistance isolated in the hospital indicates that restrictions imposed at the hospital level are unlikely to be effective unless coordinated with campaigns to reduce unnecessary antibiotic use at the community level.