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...

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ObjectivesTo explore temporal associations between planned antibiotic stewardship and infection control interventions and the molecular epidemiology of methicillin-resistant Staphylococcus aureus (MRSA).DesignRetrospective ecological study and time-series analysis integrating typing data from the Scottish MRSA reference laboratory.SettingRegional hospital and primary care in a Scottish Health Board.ParticipantsGeneral adult (N=1 051 993) or intensive care (18 235) admissions and primary care registrations (460 000 inhabitants) between January 1997 and December 2012.InterventionsHand-hygiene campaign; MRSA admission screening; antibiotic stewardship limiting use of macrolides and ‘4Cs’ (cephalosporins, coamoxiclav, clindamycin and fluoroquinolones).Outcome measuresPrevalence density of MRSA clonal complexes CC22, CC30 and CC5/Other in hospital (isolates/1000 occupied bed days, OBDs) and community (isolates/10 000 inhabitant-days).Results67% of all clinical MRSA isolates (10 707/15 947) were typed. Regional MRSA population structure was dominated by hospital epidemic strains CC30, CC22 and CC45. Following declines in overall MRSA prevalence density, CC5 and other strains of community origin became increasingly important. Reductions in use of ‘4Cs’ and macrolides anticipated declines in sublineages with higher levels of associated resistances. In multivariate time-series models (R2=0.63–0.94) introduction of the hand-hygiene campaign, reductions in mean length of stay (when >4 days) and bed occupancy (when >74 to 78%) predicted declines in CC22 and CC30, but not CC5/other strains. Lower importation pressures, expanded MRSA admission screening, and reductions in macrolide and third generation cephalosporin use (thresholds for association: 135–141, and 48–81 defined daily doses/1000 OBDs, respectively) were followed by declines in all clonal complexes. Strain-specific associations with fluoroquinolones and clindamycin reflected resistance phenotypes of clonal complexes.ConclusionsInfection control measures and changes in population antibiotic use were important predictors of MRSA strain dynamics in our region. Strategies to control MRSA should consider thresholds for effects and strain-specific impacts.

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BackgroundTo estimate the impact of antimicrobials stewardship in the temporary evolution of the Density Incidence of Pseudomonas aeruginosa resistant to carbapenems (DI_PaCRE).MethodsIn Modena Policlinico, a tertiary care hospital, from September 2014, universal rectal screening and antibiotic stewardship (ABS) focusing on carbapenem sparing strategy were introduced. We used the statistical approach already described (A nonlinear time-series analysis approach to identify thresholds in associations between population antibiotic use and rates of resistance. López-Lozano JM, et al. Nat Microbiol. 2019 Apr 8. doi: 10.1038/s41564-019–0410-0) in order to adjust a non-linear model (Multivariate Adaptive Regression Splines) looking for the identification and estimation of thresholds in the influence of the use of Carbapenems on the DI_PaCRE.ResultsWe identified a threshold at 5.65 DDD/100 bed-days of Carbapenems (CarbUse). Indeed, above this threshold, for every increase of one DDD/100 bed-days, the DI_PaCRE increased by 0.15 new cases by 1000 bed-days. On the basis of our analyses, considering a typical course of 7 days of treatment, an average CarbUse of 6.5 DDD/100 bed-days in 2012 and 2013, for an average of 15000 monthly bed-days, means around 139 treated patients. In order to avoid the emergence of resistance, the threshold was equivalent to a maximum number of 121 patients to be treated. Below this threshold, i.e in 2018, CarbUse including 66 patients treated did not select antimicrobial resistance.ConclusionPaCRE could be controlled by reducing the use of carbapenems below minimum thresholds of 6.5 DDD/100 bed-days. In order to avoid the onset of resistance to carbapenems in Pseudomonas aeruginosa, less than 121 patients per month should receive this class of drug. This finding could provide innovative approach to guide antimicrobial stewardship strategy focused on limit the spread of PaCRE in hospital populations. Disclosures All authors: No reported disclosures.

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