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A nonlinear time-series analysis approach to identify thresholds in associations between population antibiotic use and rates of resistance

2019; Nature Portfolio; Volume: 4; Issue: 7 Linguagem: Inglês

10.1038/s41564-019-0410-0

2058-5276

José-Marı́a López-Lozano, Timothy Lawes, César Nebot, Arielle Beyaert, Xavier Bertrand, Didier Hocquet, Mamoon A. Aldeyab, Michael Scott, Geraldine Conlon‐Bingham, David Farren, Gábor Kardos, Adina Fésüs, Jesús Rodríguez‐Baño, Pilar Retamar, Nieves Gonzalo-Jiménez, Ian M. Gould, José-Marı́a López-Lozano, Timothy Lawes, César Nebot, Arielle Beyaert, Xavier Bertrand, Didier Hocquet, Mamoon A. Aldeyab, Michael Scott, Geraldine Conlon‐Bingham, David Farren, Gábor Kardos, Adina Fésüs, Jesús Rodríguez‐Baño, Pilar Retamar, Nieves Gonzalo-Jiménez, Ian M. Gould, María Núñez‐Núñez, Ana Isabel Suárez, Michelle Thouverez, María Navarro-Cots, E Borrajo, Carlos Devesa García, Joan Grégori, Inmaculada González Cuello, Isabel Pacheco, María L. Cerón Loayza, Hajnalka Tóth,

Pesticide Residue Analysis and Safety

Balancing access to antibiotics with the control of antibiotic resistance is a global public health priority. At present, antibiotic stewardship is informed by a ‘use it and lose it’ principle, in which antibiotic use by the population is linearly related to resistance rates. However, theoretical and mathematical models suggest that use–resistance relationships are nonlinear. One explanation for this is that resistance genes are commonly associated with ‘fitness costs’ that impair the replication or transmissibility of the pathogen. Therefore, resistant genes and pathogens may only gain a survival advantage where antibiotic selection pressures exceed critical thresholds. These thresholds may provide quantitative targets for stewardship—optimizing the control of resistance while avoiding over-restriction of antibiotics. Here, we evaluated the generalizability of a nonlinear time-series analysis approach for identifying thresholds using historical prescribing and microbiological data from five populations in Europe. We identified minimum thresholds in temporal relationships between the use of selected antibiotics and incidence rates of carbapenem-resistant Acinetobacter baumannii (Hungary), extended-spectrum β-lactamase-producing Escherichia coli (Spain), cefepime-resistant E. coli (Spain), gentamicin-resistant Pseudomonas aeruginosa (France) and methicillin-resistant Staphylococcus aureus (Northern Ireland) in different epidemiological phases. Using routinely generated data, our approach can identify context-specific quantitative targets for rationalizing population antibiotic use and controlling resistance. Prospective intervention studies that restrict antibiotic consumption are needed to validate these thresholds. A nonlinear time-series analysis was used to identify antibiotic use thresholds that are associated with the emergence of antibiotic resistance.