RT Journal Article
T1 A nonlinear time-series analysis approach to identify thresholds in associations between population antibiotic use and rates of resistance.
A1 Lopez-Lozano, Jose-Maria
A1 Lawes, Timothy
A1 Nebot, Cesar
A1 Beyaert, Arielle
A1 Bertrand, Xavier
A1 Hocquet, Didier
A1 Aldeyab, Mamoon
A1 Scott, Michael
A1 Conlon-Bingham, Geraldine
A1 Farren, David
A1 Kardos, Gabor
A1 Fesus, Adina
A1 Rodriguez-Baño, Jesus
A1 Retamar, Pilar
A1 Gonzalo-Jimenez, Nieves
A1 Gould, Ian M
K1 Risk factors
K1 Epidemiology
K1 Computational models
K1 Antimicrobial resistance
K1 Bacterial infection
AB 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.
PB Nature Publishing Group
YR 2019
FD 2019-07
LK http://hdl.handle.net/10668/13800
UL http://hdl.handle.net/10668/13800
LA en
NO López-Lozano JM, Lawes T, Nebot C, Beyaert A, Bertrand X, Hocquet D, et al. A nonlinear time-series analysis approach to identify thresholds in associations between population antibiotic use and rates of resistance. Nat Microbiol. 2019 Jul;4(7):1160-1172.
DS RISalud
RD Apr 5, 2025