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

Autor: J.M. López-Lozano, Mamoon A. Aldeyab, Nieves Gonzalo-Jiménez, Didier Hocquet, Arielle Beyaert, Timothy Lawes, David Farren, César Nebot, Jesús Rodríguez-Baño, Pilar Retamar, Xavier Bertrand, Ian M. Gould, Gábor Kardos, Geraldine Conlon-Bingham, Adina Fésűs, Michael G. Scott
Přispěvatelé: Hungarian Academy of Sciences, Wellcome Trust, Instituto de Salud Carlos III, Ministerio de Economía, Industria y Competitividad (España), Red Española de Investigación en Patología Infecciosa, European Commission, Laboratoire Chrono-environnement - CNRS - UBFC (UMR 6249) (LCE), Centre National de la Recherche Scientifique (CNRS)-Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC), Centre Hospitalier Régional Universitaire de Besançon (CHRU Besançon), MIRACL, University Hospital Virgen Macarena
Rok vydání: 2019
Předmět:
Microbiology (medical)
Acinetobacter baumannii
Methicillin-Resistant Staphylococcus aureus
medicine.medical_specialty
Time Factors
medicine.drug_class
Immunology
Antibiotics
Population
Drug resistance
medicine.disease_cause
Applied Microbiology and Biotechnology
Microbiology
03 medical and health sciences
Antimicrobial Stewardship
Antibiotic resistance
Bacterial Proteins
Environmental health
Epidemiology
Drug Resistance
Bacterial
Genetics
medicine
Escherichia coli
Humans
education
030304 developmental biology
0303 health sciences
education.field_of_study
biology
030306 microbiology
Pseudomonas aeruginosa
Incidence (epidemiology)
Gyógyszerészeti tudományok
Incidence
Cell Biology
Orvostudományok
Bacterial Infections
Models
Theoretical
biology.organism_classification
3. Good health
Anti-Bacterial Agents
Europe
[SDV.MP]Life Sciences [q-bio]/Microbiology and Parasitology
Zdroj: Digital.CSIC. Repositorio Institucional del CSIC
instname
Nature Microbiology
Nature Microbiology, Nature Publishing Group, 2019, 4 (7), pp.1160-1172. ⟨10.1038/s41564-019-0410-0⟩
ISSN: 2058-5276
DOI: 10.1038/s41564-019-0410-0⟩
Popis: 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.
G.K. was supported by a Bolyai Research Scholarship of the Hungarian Academy of Sciences. T.L. was supported by the Wellcome Trust. J.R.B. and P.R. received funding for research from Plan Nacional de I+D+i 2013–2016 and Instituto de Salud Carlos III, Subdirección General de Redes y Centros de Investigación Cooperativa, Ministerio de Economía, Industria y Competitividad, Spanish Network for Research in Infectious Diseases (RD16/0016/0001), co-financed by European Development Regional Fund ‘A way to achieve Europe’, Operative Programme Intelligent Growth 2014–2020.
Databáze: OpenAIRE
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