Balancing access to antibiotics with control of antibiotic resistance is a global public health priority. Currently, antibiotic stewardship is informed by a 'use it and lose it' principle, in which population antibiotic use is linearly related to resistance rates. However, theoretical and mathematical models suggest use-resistance relationships are non-linear. One explanation is that resistance genes are commonly associated with 'fitness costs', impairing pathogen replication or transmissibility. 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: optimising control of resistance while avoiding over-restriction of antibiotics. We evaluated the generalisability 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 use of selected antibiotics and rates of carbapenem-resistant Acinetobacter baumannii (in Hungary), extended spectrum β-lactamase producing Escherichia coli (Spain), cefepime-resistant Escherichia 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 rationalising population antibiotic use and controlling resistance. Prospective intervention studies restricting antibiotic consumption are needed to validate Results Identifying non-linear temporal relationships: from experiment to applicationIn a Monte Carlo experiment we compared the ability of linear and non-linear time-series analysis (Multivariate Adaptive Regression Splines, MARS) to identify pre-defined relationships between simulated explanatory and outcome time-series (Supplementary Figure 1). Non-linear time-series analysis (NL-TSA) accurately identified both truly linear and nonlinear associations. However, linear time-series analysis provided biased estimations and overall poorer data-fit if relationships were non-linear. NL-TSA models applied to retrospective time-series data from five European study populations (examples 1-5), frequently identified minimum thresholds in antibiotic useresistance relationships, (figures 1-5 and Supplementary Table 1). 'Ceiling effects', in which further increases in explanatory variables did not affect resistance rates, were found at highlevels of use of some antibiotics and hand hygiene. Non-linearities in autoregression and population interaction terms further indicated the complexity of transmission dynamics within and between clinical populations. Example 1: Carbapenem-resistant Acinetobacter baumannii (Debrecen, Hungary) We examined ecological determinants of carbapenem-resistant A. baumannii (CRAb) in a tertiary hospital population in Debrecen, Hungary (figure 1). Betwee...

Background Increasing antibiotic resistance may reciprocally affect consumption and lead to use of broader-spectrum alternatives; a vicious cycle that may gradually limit therapeutic options. Our aim in this study was to demonstrate this vicious cycle in gram-negative bacteria and show the utility of vector autoregressive (VAR) models for time-series analysis in explanatory and dependent roles simultaneously. Methods Monthly drug consumption data in defined daily doses per 100 bed-days and incidence densities of gram-negative bacteria (Escherichia coli, Klebsiella spp., Pseudomonas aeruginosa, and Acinetobacter baumannii) resistant to cephalosporins or to carbapenems were analyzed using VAR models. These were compared to linear transfer models used earlier. Results In case of all gram-negative bacteria, cephalosporin consumption led to increasing cephalosporin resistance, which provoked carbapenem use and consequent carbapenem resistance and finally increased colistin consumption, exemplifying the vicious cycle. Different species were involved in different ways. For example, cephalosporin-resistant Klebsiella spp. provoked carbapenem use less than E. coli, and the association between carbapenem resistance of P. aeruginosa and colistin use was weaker than that of A. baumannii. Colistin use led to decreased carbapenem use and decreased carbapenem resistance of P. aeruginosa but not of A. baumannii. Conclusions VAR models allow analysis of consumption and resistance series in a bidirectional manner. The reconstructed resistance spiral involved cephalosporin use augmenting cephalosporin resistance primarily in E. coli. This led to increased carbapenem use, provoking spread of carbapenem-resistant A. baumannii and consequent colistin use. Emergence of panresistance is fueled by such antibiotic-resistance spirals.

The dominant carbapenem resistant Acinetobacter baumannii harboring blaOXA-23-like carbapenemase was replaced by blaOXA-40-like carriers in a Hungarian tertiary-care center with high meropenem but relatively low imipenem use. We hypothesized that alterations in antibiotic consumption may have contributed to this switch. Our workgroup previous study examined the relation between resistance spiral and the antibiotic consumption, and the results suggest that the antibiotic usage provoked the increasing resistance in case of A. baumannii. We aimed at measuring the activity of imipenem and meropenem to compare the selection pressure exerted by the different carbapenems in time-kill assays. Strain replacement was confirmed by whole genome sequencing, core-genome multilocus sequence typing (cgMLST), and resistome analysis. Based on results of the time-kill assays, we found a significant difference between two different sequence-types (STs) in case of meropenem, but not in case of imipenem susceptibility. The newly emerged ST636 and ST492 had increased resistance level against meropenem compared to the previously dominant ST2 and ST49. On the other hand, the imipenem and colistin resistance profiles were similar. These results suggest, that the uniform meropenem usage may have contributed to A. baumannii strain replacement in our setting.

We followed up the interplay between antibiotic use and resistance over time in a tertiary-care hospital in Hungary. Dynamic relationships between monthly time-series of antibiotic consumption data (defined daily doses per 100 bed-days) and of incidence densities of Gram-negative bacteria (Escherichia coli, Klebsiella spp., Pseudomonas aeruginosa, and Acinetobacter baumannii) resistant to cephalosporins or carbapenems were followed using vector autoregressive models sequentially built of time-series ending in 2015, 2016, 2017, 2018, and 2019. Relationships with Gram-negative bacteria as a group were fairly stable across years. At species level, association of cephalosporin use and cephalosporin resistance of E. coli was shown in 2015–2017, leading to increased carbapenem use in these years. Association of carbapenem use and carbapenem resistance, as well as of carbapenem resistance and colistin use in case of A. baumannii, were consistent throughout; associations in case of Klebsiella spp. were rarely found; associations in case of P. aeruginosa varied highly across years. This highlights the importance of temporal variations in the interplay between changes in selection pressure and occurrence of competing resistant species.

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