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

Abstract

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.

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Fig. 1: CrAB and antibiotic use.
Fig. 2: Extended-spectrum β-lactamase producing E. coli and antibiotic use in the hospital and community.
Fig. 3: Ec-FepR and antibiotic use.
Fig. 4: GRPa and antibiotic use.
Fig. 5: MRSA, hand hygiene and antibiotic use.

Data availability

The data that support the findings of this study are available from the corresponding author on reasonable request.

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Acknowledgements

We acknowledge D. L. Monnet, from the European Centre for Disease Prevention and Control for his continuous support, guidance and intellectual contributions to the THRESHOLDS project. J.-M.L.-L. acknowledges the continuous support of the management team of the Hospital Vega Baja, the technical support of C. Quiles (Cabosoft SL) in the development of TSA techniques in the field of antimicrobial resistance and the WebResist project (www.webresist.org), which provided a framework for the development of this study. 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.

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J.-M.L.-L., T.L., C.N., A.B. and I.M.G. proposed the original idea and designed the study. X.B., D.H., M.A., G.C.-B., M.S., D.F., G.K., J.R.B., P.R. and N.G.J. collated centre-specific data, situational analysis and hypotheses. J.-M.L.-L., T.L., C.N. and A.B. contributed to statistical analysis; C.N. and A.B. were the principal analysts. All authors discussed the results and commented on the manuscript.

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Correspondence to Timothy Lawes or Timothy Lawes.

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I.M.G. is in receipt of payments for consultancies and lectures from numerous pharmaceutical firms developing new antimicrobials. The other authors declare no competing interests.

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López-Lozano, J., Lawes, T., Nebot, C. et al. A nonlinear time-series analysis approach to identify thresholds in associations between population antibiotic use and rates of resistance. Nat Microbiol 4, 1160–1172 (2019). https://doi.org/10.1038/s41564-019-0410-0

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