{"paper_id":"2d8ec836-bbd2-496d-8274-9a054cedadc2","body_text":"Epidemiology of bloodstream infections caused by extended-spectrum cephalosporin-resistant Escherichia coli and Klebsiella pneumoniae in Switzerland, 2015-2022: secular trends and association with the COVID-19 pandemic | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Epidemiology of bloodstream infections caused by extended-spectrum cephalosporin-resistant Escherichia coli and Klebsiella pneumoniae in Switzerland, 2015-2022: secular trends and association with the COVID-19 pandemic Lauro Damonti, Michael Gasser, Kronenberg Andreas, Niccolò Buetti This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3869934/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Jun, 2024 Read the published version in Journal of Hospital Infection → Version 1 posted You are reading this latest preprint version Abstract Purpose : The association between the COVID-19 pandemic and the incidence of invasive infections caused by multidrug-resistant organisms remains a topic of debate. The aim of this study was to analyse the national incidence rates of bloodstream infections (BSI) caused by Escherichia coli (EC) and Klebsiella pneumoniae (KP) with extended-spectrum cephalosporin-resistance (ESCR) in two distinct regions in Switzerland, each exhibiting varying antimicrobial resistance patterns and that were impacted differently by the pandemic. Methods : We analysed data of positive blood cultures prospectively collected by the nationwide surveillance system (ANRESIS) from January 1, 2015, to August 31, 2022. To explore the potential relationship between COVID-19 patient occupancy and ESCR incidence rates, we conducted an in-depth analysis over the two-year pandemic period from April 1, 2020, to March 30, 2022. We employed Quasi-Poisson and logistic regression analyses to investigate these associations. Results : During the study period, a total of 40997 EC-BSI and 8537 KP-BSI episodes were collected and reported to ANRESIS by the participating hospitals. ESCR was observed in 11% (n=4313) of E. coli and 8% (n=664) of K. pneumoniae , respectively. A significant reduction in ESCR-EC BSI incidence occurred during the pandemic in the region with the highest COVID-19 incidence. Conversely, ESCR-KP BSI incidence initially fell considerably and then increased during the pandemic in both regions; however, this effect was not statistically significant. Conclusion : In the early phase of the COVID-19 pandemic, a decrease in ESCR rates was observed, particularly in ESCR-EC BSI within the most heavily impacted region. COVID-19 bloodstream infections extended-spectrum cephalosporin-resistance Escherichia coli Klebsiella pneumoniae Figures Figure 1 Figure 2 Introduction The pandemic of COVID-19 has affected healthcare systems worldwide, significantly influencing various aspects of infection control and prevention. This impact includes antimicrobial prescribing and consumption [ 1 , 2 ] as well as healthcare-associated infections (HAIs) [ 3 ], including bloodstream infections (HA-BSI) [ 4 ]. The effect of COVID-19 on antimicrobial resistance (AMR), a leading cause of mortality [ 5 ], has been also subject of study, with heterogeneous findings [ 6 – 8 ]. Recently, a report has explored the role of multidrug-resistant organisms (MDRO) in causing invasive infections like bloodstream infections (BSI) during the pandemic, resulting in conflicting outcomes [ 9 ]. Overall, our understanding of the pandemic's influence on clinically relevant infections, such as BSI, caused by MDRO, remains limited, with most studies focusing on the impact of the initial pandemic wave and investigations into national and long-term trends being rare. Despite efforts to overcome AMR, rates of extended-spectrum cephalosporin-resistant (ESCR, i.e. : with resistance to third- and fourth generation cephalosporins) and extended spectrum beta lactamase (ESBL) producing Enterobacteriales are increasing across several world regions in both the healthcare and the community setting [ 10 , 11 ]. The WHO lists these MDRO as critical priority pathogens for research and development of new antibiotics [ 12 ]. Escherichia coli and Klebsiella pneumoniae are the two most common and clinically relevant Enterobacterales and the main drivers of these trends. In this study, our primary objective was to analyse the long-term national AMR trends of BSI caused by ESCR Escherichia coli (ESCR-EC) and Klebsiella pneumoniae (ESCR-KP) in Switzerland, and identify any deviations during the pandemic. Given the distinct AMR patterns observed across various linguistic regions [ 13 ] and considering the diverse impact of the pandemic on these regions, we examined trends at a regional level, using the regions as proxy for the pandemic's burden. Subsequently, in the second part of the study (objective 2), we investigated the reasons for the observed results using more detailed and comprehensive models, aiming to better understand the patterns during the pandemic. Specifically, we wanted to investigate if there was an association between regional monthly COVID-19 patient occupancy rates and the endpoints. Methods Study setting and design In this study, we retrospectively analysed national epidemiological and microbiological data on positive blood cultures (i.e., bloodstream infections [BSI]) prospectively collected by the Swiss Centre for Antibiotic Resistance (ANRESIS) and COVID-19 related hospital occupancy data provided by the Swiss Federal Office of Public Health (FOPH). Hypotheses The hypotheses of our study were that i) the COVID-19 pandemic may be associated with changes in the epidemiology of ESCR-KP and ESCR-EC bloodstream infections in Switzerland and these changes may vary over time in two distinct Swiss regions, which had diverse pre-pandemic antimicrobial resistance patterns and were differentially affected by the pandemic; ii) calculated regional monthly COVID-19 patient occupancy rates may be associated with the incidence of ESCR-KP and ESCR-EC BSI and extended-spectrum cephalosporin resistance rates. Data sources, collection, and definitions Microbiological surveillance data : ANRESIS regularly receives information on all positive blood cultures from over 30 Swiss microbiology laboratories, some of them collecting data from multiple hospitals. Hospitals are distributed across the country and represented 66% of annual hospitalization days in 2015 and 90% in 2020. Species identification and antimicrobial susceptibility testing are based on tests performed in the local laboratories, which apply either European Committee on Antimicrobial Susceptibility Testing (EUCAST, https://eucast.org ) or Clinical and Laboratory Standards Institute (CLSI, https://clsi.org ) guidelines. For this study, we collected data on blood cultures positive for Escherichia coli and Klebsiella pneumoniae . In case of multiple positive blood cultures only the first blood culture per microorganism, patient and calendar year was considered. For consistency over the study period we included for the current analyses only data from those Swiss hospitals which were included in the ANRESIS surveillance in the first year of the study ( i.e. , January 1, 2015 for the first objective and April 1, 2020 for the second objective) and that sent information during the entire study period. Variables collected and baseline data : Variables routinely collected by ANRESIS and used in part in aggregated forms for this study include the age group ( i.e. , 0–2,3–15,16–45, 46–65 and > 65 years old), sex (male, female), year, month as well as the exact date of detection of the episode, detected microorganism species, hospital type (non-university versus university), department (i.e. intensive care unit (ICU) versus non-ICU) and region of residency (as defined below). Extended-spectrum cephalosporin-resistant Escherichia coli (ESCR-EC) and Klebsiella pneumoniae (ESCR-KP) were defined as resistant to at least one of all third- or fourth-generation cephalosporin tested. Study periods : To investigate the association between the COVID-19 pandemic and the occurrence of ESCR-EC and ESCR-KP, we analysed two distinct periods: the pre-pandemic phase spanning from January 1, 2015, to February 29, 2020, and the pandemic phase from March 1, 2020 to August 31, 2022. Furthermore, to assess the association between COVID-19 patient occupancy and the incidence of BSI as well as the resistance rates, we specifically examined the two-year period from April 1, 2020, to March 30, 2022, as data collection by the FOPH commenced on March 30, 2020. Regions : The association between the COVID-19 pandemic and the level and the changes of ESCR-KP and ESCR-EC incidence rates were analysed in two distinct linguistic and sociocultural regions of Switzerland, which experienced varying effects from different pandemic waves. The first region (Latin-languages-speaking region, hereinafter referred to as 'Latin region'), includes the Italian-speaking and French-speaking parts of Switzerland. This region was significantly affected by the first wave of COVID-19. The second region, the German-speaking region (hereinafter referred to as 'German region'), was largely spared from the first wave. Subsequently, the pandemic had a more homogeneous impact on the country as a whole. Denominators : Incidence rates were calculated using corrected population data as denominator. Data were provided by the Swiss Federal Statistical Office (FSO), are freely available on their website ( https://www.bfs.admin.ch/bfs/en/home/statistics/population.html ), and were corrected for the population covered by ANRESIS. For the years 2021 and 2022 a corrected reference scenario from the FSO was used [ 14 ]. COVID-19 occupancy rates (percent of beds occupied by COVID-19 patients) were calculated for each geographic region on a monthly (quasi-Poisson regression) and daily (for the logistic regression) basis. Data on Swiss hospital’s occupancy regarding patients with and without COVID-19 were freely available from the Swiss Federal Office of Public Health (FOPH) ( https://www.covid19.admin.ch ). Statistical analyses The statistical analysis included two steps. First, to investigate the association between the COVID-19 pandemic and the incidence rates of ESCR-KP and ESCR-EC bloodstream infections in the German and Latin regions, a Quasi-Poisson regression was performed. Two separate models were built for each linguistic region to examine potential association of COVID-19 on the levels (model A) and the changes (model B) of ESCR-KP and ESCR-EC incidence rates. In these models, the COVID-19 pandemic was coded as a binary variable as described in Bernal et al. [ 15 ]. A harmonic wave with a 12-month period was included in this model to test for seasonality and the coverage corrected population, as defined above, was included as offset. F-Tests were performed for each predictor. Since four models were fitted for each pathogen, the P-values were Bonferroni corrected. Besides the models which were built for statistical inference testing additional comprehensive exploratory quasi-Poisson models were developed ( i.e. , backward reduction was applied) to explore the potential influence of higher-level interactions on the AMR count. [ 16 ] Second, to evaluate if the incidence of ESCR-KP and ESCR-EC would be affected by the occupancy of hospital beds by COVID-19 patients during the pandemic months (from April 1st, 2020, to March 30th, 2022), quasi-Poisson models were fitted, similarly to the first analysis. However, instead of using a binary variable for the pandemic, the COVID-19 occupancy rate was included in these models. Moreover, in order investigate if the ESCR-KP and ESCR-EC resistance rates were affected by COVID-19 patient occupancy rates logistic regression models were fitted. These models also included hospital (type, department) and patient (age group, sex) specific predictors. Likelihood-ratio tests were used to test for significance. All analyses were performed with R (version 4.2.2). P-values < 0.05 were considered statistically significant. The analysis is in compliance with the STROBE guidelines for observational studies [ 17 ]. Results During the study period, a total of 49,534 blood culture positive for E. coli or K. pneumoniae were collected and reported to ANRESIS by the participating hospitals, the majority of these episodes (83%) being caused by E. coli . ESCR was observed in 11% (n = 4313) of E. coli and 8% (n = 664) of K. pneumoniae , respectively (see Supplementary Material (SM) Fig. 1 ). Baseline characteristics of the patients with BSI are provided in Table 1 . The population in the German part was 5'905'544 (3'865'769 coverage corrected) and 2'421'582 (1'682'273 coverage corrected) in the Latin part in 2015 and 6'275'027 (4'107'633 coverage corrected) in the German part and 2'560'028 (1'778'451 coverage corrected) in the Latin part in 2022. Table 1. Baseline characteristics of the included patients (period 01.01.2015 - 31.08.2022) Baseline characteristics E.coli ESCR N = 4,313 E.coli ESCS N = 36,684 K. pneumoniae ESCR N = 664 K. pneumoniae ESCS N = 7873 Female gender 2,050 (45%) 20,641 (54%) 227 (32%) 3,173 (38%) Age (median, IQR) 70 (60, 80) 75 (60, 80) 70 (55, 75) 70 (60, 80) Latin Region 1728 (40%) 12418 (34%) 305 (46%) 2603 (38%) Departement ICU 226 (5.2%) 1,254 (3.4%) 73 (11%) 526 (6.7%) interdisciplinary 877 (20%) 8,909 (24%) 92 (14%) 1,684 (21%) medicine 705 (16%) 5,462 (15%) 148 (22%) 1,456 (18%) surgery 441 (10%) 2,300 (6.3%) 103 (16%) 751 (9.5%) other 231 (5.4%) 1,708 (4.7%) 44 (6.6%) 378 (4.8%) outpatient 1,833 (42%) 17,051 (46%) 204 (31%) 3,078 (39%) Legend. ICU: intensive care unit. IQR: Interquartile range. ESCR: extended-spectrum cephalosporin-resistant. ESCS Extended-spectrum cephalosporin-sensitive In the second part of the analysis including only data from the pandemic period, we investigated the association of the hospital occupancy resulting from COVID-19 patients with incidence rates and percentages of ESCR-KP and ESCR-EC. This analysis was based on a total of collected 13’875 positive blood cultures (see SM Fig. 1 and, for the baseline characteristics of the included patients, SM Table 1 ). Similar to the previous analysis, ESCR was observed in 10% (n = 1173) of E. coli and 8% (n = 199) of K. pneumoniae. E. coli and ESCR-EC The main findings are illustrated and summarized in Fig. 1 and Table 2 . Overall, the incidence rates of ESCR-EC were higher in the Latin region compared to the German region. These rates displayed an overall upward trend throughout the entire study period, although this increase did not reach statistical significance following Bonferroni correction. However, a noteworthy and statistically significant decrease of ESCR-EC incidence was observed for the pandemic period in the Latin region (p < 0.001). Conversely, in the German region, there were no significant alterations in both the level and the slope of ESCR-EC incidence rates. Seasonality was not significant in either linguistic region for both the models. The drop in the Latin region during the pandemic can also be observed in the fit of the exploratory model (Fig. 1 and SM Table 2 ). Table 2: Changes in the ESCR-EC BSI incidence rates in the German and Latin Regions Model A: Investigating if the level of ESCR-EC BSI incidence rates changes during the COVID-19 pandemic German region Latin region Estimate 95% CI P-value* Estimate 95% CI P-value* COVID-19 -0.11 -0.59 -0.23 0.68 -0.41 -0.59 -0.23 <0.001 Time ¥ 0.00 0.00 0.01 0.19 0.00 0.00 0.01 0.06 Seasonality + Term 1 -0.05 -0.09 0.05 0.49 -0.02 -0.09 0.05 1 Term 2 0.03 -0.08 0.06 -0.01 -0.08 0.06 Model B: Investigating if the ESCR-EC BSI slope of the incidence rates changes during the COVID-19 pandemic Increase/decrease during COVID-19 § COVID-19 -0.12 -0.29 0.05 1 -0.52 -0.75 -0.30 0.36 Time 0.00 0.00 0.01 0.00 0.00 0.01 Interaction 0.00 -0.01 0.01 0.01 0.00 0.02 Seasonality + Term 1 -0.05 -0.11 0.00 0.50 -0.02 -0.09 0.05 1 Term 2 0.03 -0.03 0.09 -0.01 -0.08 0.06 In Quasi-Poisson-regression models it was tested if there is an effect of the COVID-19 pandemic on the level (Model A) and slope (Model B) of AMR incidences in different linguistic regions in Switzerland. In addition, it should be investigated which model fits the data better. Legend * Bonferroni correction was applied (multiplication of p-values by 4 as for each region models were fitted with and without interaction). ¥ The study month was included as a numeric predictor. + Seasonality: Amplitudes of a sine (Term 1) and a cosine (Term 2) component were estimated. The wavelength was set to 12 months. § The COVID-19-time-interaction was fitted to detect a change in the slope during the COVID-19 pandemic. Residual deviance and degrees of freedom are shown below to illustrate the goodness of fit of different models: Model A, German Residual deviance 82.6 on 87 degrees of freedom Model A, Latin Residual deviance, 88.0 on 87 degrees of freedom Model B, German Residual deviance, 82.6 on 86 degrees of freedom Model B, Latin Residual deviance, 85.1 on 86 degrees of freedom When considering the models containing data from the pandemic only, increasing incidences rates were confirmed but a dependence from the COVID-19 patient occupancy rates was not observed (SM Table 3 ). Similarly, the logistic regression model revealed no significant association between COVID-19 occupancy rates and resistance rates. Nevertheless, this model did identify significant predictors for the resistance rates, including time, linguistic region, hospital type, and sex. These same predictors persisted in the final exploratory model, as detailed in (SM Table 4). Table 3 Changes in the ESCR-KP BSI incidence rates in the German and Latin Regions Model A: Investigating if the level of ESCR-KP BSI incidence rates changes during the COVID-19 pandemic German region Latin region Estimate 95% CI P-value* Estimate 95% CI P-value* COVID-19 -0.24 -0.63 0.15 1 -0.15 -0.58 0.29 1 Time ¥ 0.01 0.01 0.02 0.007 0.00 0.00 0.01 1 Seasonality + Term 1 -0.16 -0.32 0.00 0.55 -0.07 -0.24 0.10 1 Term 2 0.08 -0.07 0.24 -0.02 -0.20 0.15 Model B: Investigating if the ESCR-KP BSI slope of the incidence rates changes during the COVID-19 pandemic Increase/decrease during COVID-19 § COVID-19 -0.51 -0.98 -0.05 0.14 -0.46 -0.75 -0.30 0.12 Time 0.01 0.00 0.02 0.01 0.00 0.01 Interaction 0.03 0.00 0.05 0.03 0.00 0.02 Seasonality + Term 1 -0.16 -0.32 -0.01 0.4 -0.07 -0.09 0.05 1 Term 2 0.10 -0.06 0.25 -0.01 -0.08 0.06 In Quasi-Poisson-regression models it was tested if there is an effect of the COVID-19 pandemic on the level (Model A) and slope (Model B) of AMR incidences in different linguistic regions in Switzerland. In addition, it should be investigated which model fits the data better. Legend * Bonferroni correction was applied (multiplication of p-values by 4 as for each region models were fitted with and without interaction). ¥ The study month was included as a numeric predictor. + Seasonality: Amplitudes of a sine (Term 1) and a cosine (Term 2) component were estimated. The wavelength was set to 12 months. § The COVID-19-time-interaction was fitted to detect a change in the slope during the COVID-19 pandemic. Residual deviance and degrees of freedom are shown below to illustrate the goodness of fit of different models: Model A, German Residual deviance 108.7 on 87 degrees of freedom Model A, Latin Residual deviance 104.3 on 87 degrees of freedom Model B, German Residual deviance 103.3 on 86 degrees of freedom Model B, Latin Residual deviance 98.7 on 86 degrees of freedom K. pneumoniae and ESCR-KP The main findings are depicted and summarized in Fig. 2 and Table 3 Throughout the entire study period, the incidence rates of ESCR-KP were consistently higher in the Latin region when compared to the German region. Over time, these rates showed an upward trend in both regions, with a statistically significant long-term increase observed only in the German region. A more noticeable increase was observed in both linguistic regions during the COVID-19 pandemic; however, this change in slope did not retain statistical significance after applying Bonferroni correction. Seasonal variations were not found to be statistically significant in either linguistic region. The exploratory model incorporating data from both regions indicated region- and pandemic-specific increases (Fig. 2 and SM Table 2 ). When focusing on models containing data solely from the pandemic period, a significant increase in ESCR-KP rates during this time frame was observed, but this increase did not appear to be dependent on the COVID-19 patient occupancy rates, with time and linguistic region being the only predictors in the final exploratory model (SM Table 5). No significant association was observed between the COVID-19 occupancy and the resistance rates in the logistic regression model; in contrast, the predictors time, patient age, hospital type ( i.e. , university vs . non-university hospital) and department ( i.e. , ICU vs . non-ICU) were significant and retained their significance in the final model of our exploratory analysis, which initially included seasonality and higher-order interactions (SM Table 6). Discussion In this comprehensive nationwide study, we have identified a reduction in ESCR-EC rates during the COVID-19 pandemic, with the most notable decline occurring in the region most severely affected by the pandemic. Conversely, an initial non-significant decrease in ESCR-KP BSI incidence was followed by an upward trajectory over time, eventually reverting to and even surpassing pre-pandemic levels. Overall, knowledge on the influence of the pandemic on clinically relevant infections caused by MDR is limited. Data on ESCR-EC and ESCR-KP associated BSI are scarce, conflicting and mostly limited to the first wave of the pandemic [ 18 – 21 ]. A French multicentre study found a significant increase in the rate of ESCR Enterobacterales bloodstream infections, including ESBL-producing K. pneumoniae , during March-April 2020 compared to 2019, accompanied by higher blood culture sampling and antibiotic usage [ 21 ]. In a tertiary hospital in Rome, Italy, there was no overall difference in the incidence of ESCR Enterobacterales BSI between the COVID-19 period and a pre-COVID period [ 18 ]. A study conducted in a reference center in Jakarta found similar results, with no increase in the frequency of ESCR-KP and ESCR-EC between 2019 and 2020 [ 19 ]. In contrast, a significant decrease was found in the rate of various infections (including BSI) caused by ESBL-producing Enterobacterales during the first and second quarter of 2020 in an American study [ 22 ]. Noteworthy, these data need to be interpreted with caution, due to the limited reporting and the fact that more studies are likely to be published in this area in the near future. This reflects conflicting findings from studies on the impact of COVID-19 on AMR ( i.e. , not necessarily invasive infections). In a recent systematic review [ 6 ] of twelve studies, it was observed that ESBL-producing E. coli and K. pneumoniae infections decreased during the COVID-19 pandemic (in contrast with the previous upward trend) while a meta-analysis [ 7 ] pooling several resistant Gram-negative organisms did not find a significant effect. Even more recently, a Canadian study investigating the ESBL rates in urine cultures, showed decreased rates for E. coli in both the community and long-term-care facilities, but increasing rates for ESBL K. pneumoniae rates in the latter [ 23 ]. Collectively, these observations, along with our own, underscore the intricate interplay between the COVID-19 pandemic and AMR: as far as our knowledge extends no study has addressed this matter by accounting for the impact of the entire pandemic on distinct regions differently hit by the pandemic within an entire country. Furthermore, our findings highlighted how the significance of local circumstances identified in our studies may elucidate the divergent findings reported previously. The reason behind the significant decrease in ESCR-EC, a predominantly community-acquired pathogen [ 24 ], can likely be attributed to the introduction of community preventive measures and the restrictions imposed by the federal government (culminating in the national lockdown on March 16, 2020). These measures led to a reduction in travel, movement, and overall interpersonal contacts, subsequently resulting in a decrease in the transmission of pathogens, and, secondarily, to the observed reduction in the use of antibiotics [ 25 , 26 ], the main driver of MDR. Conversely, the rise of ESCR-KP, primarily a pathogen acquired within healthcare settings, might be linked to the heightened usage of antibiotics in inpatients [ 21 , 25 ], to the occurrence of hospital outbreaks, as reported in several studies [ 27 , 28 ], and to a challenges such as patients overflow and understaffing. Notably, our in-depth analysis did not reveal any discernible association between the increase in hospital occupancy due to COVID-19 patients and the incidence or the resistance rates of the bacteria included. Conversely, university hospitals and ICUs (where the usage of broad-spectrum antibiotics is more prominent and where outbreaks were most often described) were found to be predictors for ESCR-KP. Nevertheless, more nuanced and challenging-to-attribute factors might have contributed. For instance, in the latter phase of the study was characterized by a substantial upsurge in migration from regions characterized by higher rates of AMR [ 29 ]. Our study has several limitations. Clinical individual patient data, including baseline comorbidities, reasons for hospital admission, source of BSI and presence of risk factors ( e.g. , indwelling urinary catheters), COVID-19 status at individual level and antibiotic exposure information, were not available in the datasets used for our study. Secondly, information on the site of acquisition of BSI (community vs hospital-acquired) was incomplete and an analysis was not possible. Roughly, 40% of the blood cultures were collected from outpatients, with a substantial majority likely being obtained in emergency rooms from patients who were subsequently hospitalized. Thirdly, if the burden of hospitalized COVID-19 patients does not seem to significantly impact the outcome, we could only hypothesize the reasons behind the presented results, and we might not have been able to account for certain relevant confounders, such as migration patterns and government-imposed measures/restrictions in response to the pandemic. A more intricate model featuring additional predictors and, perhaps, shorter time intervals could have better represented the data, yet such complexity might elevate the risk of overfitting. Switzerland is a country with relatively low incidences, especially concerning ESCR-KP. It is conceivable that higher incidences would have resulted in more statistical power. One of the key strengths of our study is the comprehensive nationwide data collection, spanning five years before and during the COVID-19 pandemic. This extensive dataset includes information from all university and tertiary hospitals, enabling us to stratify the data according to different linguistic and sociocultural regions. This is particularly valuable due to the country's heterogeneity, as different regions were impacted differently by the pandemic. Another strength is the statistical methodological approach used here. The separation of inference testing models from exploratory models allowed hypotheses to be tested without a bias that might be introduced during model development, while still providing evidence of further complex relationships that were not considered when the original hypotheses were formulated. Such evidence may then lead to new hypotheses that can be tested with new independent data (e.g. from other countries). Conclusions In the early phase of the COVID-19 pandemic, a decrease in ESCR rates was observed, particularly in ESCR-EC BSI within the most heavily impacted region. Declarations Ethical statement : This study was based on national surveillance data submitted to the Swiss Centre for Antibiotic Resistance ANRESIS. Because of the anonymous nature of the data, neither ethical approval nor written informed consent from patients was required Funding statement : This work is financially supported by the Swiss Federal Office of Public Health and the Institute for Infectious Diseases of the University of Bern, Switzerland. NB received a post.doc Mobility grant from the Swiss National Science Foundation (grant number: P4P4PM_194449) in 2021. Data availability : All data can be made available upon request to the corresponding author Competing Interests : none Authors’ contributions : Study conception: MG, LD, NB Data collection: AK Data processing, modelling and statistical analyses: MG and LD Data interpretation: MG, LD, NB and AK Drafting the manuscript: MG and LD Approving the final version: MG, LD, NB and AK References Russell, C.D., et al., Co-infections, secondary infections, and antimicrobial use in patients hospitalised with COVID-19 during the first pandemic wave from the ISARIC WHO CCP-UK study: a multicentre, prospective cohort study. Lancet Microbe, 2021. 2 (8): p. e354-e365. Langford, B.J., et al., Antibiotic prescribing in patients with COVID-19: rapid review and meta-analysis. Clin Microbiol Infect, 2021. 27 (4): p. 520-531. Baker, M.A., et al., The Impact of COVID-19 on Healthcare-Associated Infections. Clin Infect Dis, 2021. 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Hasan, M.R., et al., Trends in the Rates of Extended-Spectrum-beta-Lactamase-Producing Enterobacterales Isolated from Urine Cultures during the COVID-19 Pandemic in Ontario, Canada. Microbiol Spectr, 2023. 11 (1): p. e0312422. Day, M.J., et al., Extended-spectrum beta-lactamase-producing Escherichia coli in human-derived and foodchain-derived samples from England, Wales, and Scotland: an epidemiological surveillance and typing study. Lancet Infect Dis, 2019. 19 (12): p. 1325-1335. Friedli, O., et al., Impact of the COVID-19 Pandemic on Inpatient Antibiotic Consumption in Switzerland. Antibiotics (Basel), 2022. 11 (6). Mamun, A.A., et al., Community Antibiotic Use at the Population Level During the SARS-CoV-2 Pandemic in British Columbia, Canada. Open Forum Infect Dis, 2021. 8 (6): p. ofab185. Emeraud, C., et al., Outbreak of CTX-M-15 Extended-Spectrum beta-Lactamase-Producing Klebsiella pneumoniae ST394 in a French Intensive Care Unit Dedicated to COVID-19. Pathogens, 2021. 10 (11). Falcone, M., et al., Spread of hypervirulent multidrug-resistant ST147 Klebsiella pneumoniae in patients with severe COVID-19: an observational study from Italy, 2020-21. J Antimicrob Chemother, 2022. 77 (4): p. 1140-1145. Schultze, T., et al., Molecular surveillance of multidrug-resistant Gram-negative bacteria in Ukrainian patients, Germany, March to June 2022. Euro Surveill, 2023. 28 (1). Additional Declarations No competing interests reported. Supplementary Files anresisarmsmfinal.docx Cite Share Download PDF Status: Published Journal Publication published 01 Jun, 2024 Read the published version in Journal of Hospital Infection → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-3869934\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":267683078,\"identity\":\"b6a467fa-4c34-48bf-95b8-796c06f10523\",\"order_by\":0,\"name\":\"Lauro Damonti\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYBACPgjFDKESKiRAnAYGxgbcWtiQtDA2JJyRANMkaGFsYyBCC/sZsw8fGKzlddt7jz94OM8iT769sYHh5w48WnhyjGfOYEg33HbmXGJD4jaJYoMzBxsYe8/gc1iOMTMPw2HGbTdyDEFaEjdIJDYwQ1yIQwv/G7AWe4iWORKJ8+c/JKBFAmJLIkRLA9CKG4yEtDwrZpxhkJ687cwZwxkJx4AOO5PYcLAXjxZ+/uTNDB8qrG23He8x+Pijpi5xfvvhgw9+4tECAQZo/AOENIyCUTAKRsEowA8AgtBRw6WlgJ8AAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"University Hospital of Bern\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Lauro\",\"middleName\":\"\",\"lastName\":\"Damonti\",\"suffix\":\"\"},{\"id\":267683079,\"identity\":\"6f3f656c-605b-4d05-aa2c-8ea16e864abe\",\"order_by\":1,\"name\":\"Michael Gasser\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Bern\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Michael\",\"middleName\":\"\",\"lastName\":\"Gasser\",\"suffix\":\"\"},{\"id\":267683080,\"identity\":\"ebbb7d15-71d6-413a-9378-7737a0fd57a2\",\"order_by\":2,\"name\":\"Kronenberg Andreas\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Bern\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Kronenberg\",\"middleName\":\"\",\"lastName\":\"Andreas\",\"suffix\":\"\"},{\"id\":267683081,\"identity\":\"2f7b566a-d6e3-47f7-b34a-9f0afb2f4c1d\",\"order_by\":3,\"name\":\"Niccolò Buetti\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Geneva Hospitals, WHO Collaborating Center\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Niccolò\",\"middleName\":\"\",\"lastName\":\"Buetti\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-01-16 13:29:08\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-3869934/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-3869934/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1016/j.jhin.2024.05.013\",\"type\":\"published\",\"date\":\"2024-06-01T12:14:54+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":49828693,\"identity\":\"c69f109d-4dc2-484d-9b4d-22449eb29c34\",\"added_by\":\"auto\",\"created_at\":\"2024-01-18 16:04:53\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":44326,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eESCR-EC incidence in the two different linguistic regions of Switzerland and estimates from the exploratory quasi-Poisson model (see SM for more information). The dashed line shows a counterfactual scenario in which the COVID-19 pandemic had not occurred. The pandemic phase is highlighted in grey.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3869934/v1/baac6b79d2d6ea5cf8283eb8.png\"},{\"id\":49828694,\"identity\":\"4d2d7d46-93df-4ef1-9f1e-48e5f331c479\",\"added_by\":\"auto\",\"created_at\":\"2024-01-18 16:04:53\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":45681,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eESCR-KP incidence in the two different linguistic regions of Switzerland and estimates from the exploratory quasi-Poisson model (see SM for more information). The dashed line shows a counterfactual scenario in which the COVID-19 pandemic had not occurred. The pandemic phase is highlighted in grey.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3869934/v1/70150f73cca3be241369e7e1.png\"},{\"id\":57694666,\"identity\":\"fd4effcf-7719-4751-ba46-d9629b57a60d\",\"added_by\":\"auto\",\"created_at\":\"2024-06-04 12:14:58\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":798895,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3869934/v1/3828d9a2-28ed-443d-895c-abc211aaea59.pdf\"},{\"id\":49828692,\"identity\":\"f43e3573-f5c5-423a-8c70-126ed732fb21\",\"added_by\":\"auto\",\"created_at\":\"2024-01-18 16:04:53\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":97277,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"anresisarmsmfinal.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3869934/v1/bb0062bb76916897ff4ff3b7.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Epidemiology of bloodstream infections caused by extended-spectrum cephalosporin-resistant Escherichia coli and Klebsiella pneumoniae in Switzerland, 2015-2022: secular trends and association with the COVID-19 pandemic\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eThe pandemic of COVID-19 has affected healthcare systems worldwide, significantly influencing various aspects of infection control and prevention. This impact includes antimicrobial prescribing and consumption [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e] as well as healthcare-associated infections (HAIs) [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e], including bloodstream infections (HA-BSI) [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. The effect of COVID-19 on antimicrobial resistance (AMR), a leading cause of mortality [\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e], has been also subject of study, with heterogeneous findings [\\u003cspan additionalcitationids=\\\"CR7\\\" citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]. Recently, a report has explored the role of multidrug-resistant organisms (MDRO) in causing invasive infections like bloodstream infections (BSI) during the pandemic, resulting in conflicting outcomes [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. Overall, our understanding of the pandemic's influence on clinically relevant infections, such as BSI, caused by MDRO, remains limited, with most studies focusing on the impact of the initial pandemic wave and investigations into national and long-term trends being rare.\\u003c/p\\u003e \\u003cp\\u003eDespite efforts to overcome AMR, rates of extended-spectrum cephalosporin-resistant (ESCR, \\u003cem\\u003ei.e.\\u003c/em\\u003e: with resistance to third- and fourth generation cephalosporins) and extended spectrum beta lactamase (ESBL) producing Enterobacteriales are increasing across several world regions in both the healthcare and the community setting [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]. The WHO lists these MDRO as critical priority pathogens for research and development of new antibiotics [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]. \\u003cem\\u003eEscherichia coli\\u003c/em\\u003e and \\u003cem\\u003eKlebsiella pneumoniae\\u003c/em\\u003e are the two most common and clinically relevant Enterobacterales and the main drivers of these trends.\\u003c/p\\u003e \\u003cp\\u003eIn this study, our primary objective was to analyse the long-term national AMR trends of BSI caused by ESCR \\u003cem\\u003eEscherichia coli\\u003c/em\\u003e (ESCR-EC) and \\u003cem\\u003eKlebsiella pneumoniae\\u003c/em\\u003e (ESCR-KP) in Switzerland, and identify any deviations during the pandemic. Given the distinct AMR patterns observed across various linguistic regions [\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e] and considering the diverse impact of the pandemic on these regions, we examined trends at a regional level, using the regions as proxy for the pandemic's burden. Subsequently, in the second part of the study (objective 2), we investigated the reasons for the observed results using more detailed and comprehensive models, aiming to better understand the patterns during the pandemic. Specifically, we wanted to investigate if there was an association between regional monthly COVID-19 patient occupancy rates and the endpoints.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStudy setting and design\\u003c/h2\\u003e \\u003cp\\u003eIn this study, we retrospectively analysed national epidemiological and microbiological data on positive blood cultures (i.e., bloodstream infections [BSI]) prospectively collected by the Swiss Centre for Antibiotic Resistance (ANRESIS) and COVID-19 related hospital occupancy data provided by the Swiss Federal Office of Public Health (FOPH).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eHypotheses\\u003c/h2\\u003e \\u003cp\\u003eThe hypotheses of our study were that i) the COVID-19 pandemic may be associated with changes in the epidemiology of ESCR-KP and ESCR-EC bloodstream infections in Switzerland and these changes may vary over time in two distinct Swiss regions, which had diverse pre-pandemic antimicrobial resistance patterns and were differentially affected by the pandemic; ii) calculated regional monthly COVID-19 patient occupancy rates may be associated with the incidence of ESCR-KP and ESCR-EC BSI and extended-spectrum cephalosporin resistance rates.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eData sources, collection, and definitions\\u003c/h2\\u003e \\u003cp\\u003e\\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003eMicrobiological surveillance data\\u003c/span\\u003e: ANRESIS regularly receives information on all positive blood cultures from over 30 Swiss microbiology laboratories, some of them collecting data from multiple hospitals. Hospitals are distributed across the country and represented 66% of annual hospitalization days in 2015 and 90% in 2020. Species identification and antimicrobial susceptibility testing are based on tests performed in the local laboratories, which apply either European Committee on Antimicrobial Susceptibility Testing (EUCAST, \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://eucast.org\\u003c/span\\u003e\\u003cspan address=\\\"https://eucast.org\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) or Clinical and Laboratory Standards Institute (CLSI, \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://clsi.org\\u003c/span\\u003e\\u003cspan address=\\\"https://clsi.org\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) guidelines. For this study, we collected data on blood cultures positive for \\u003cem\\u003eEscherichia coli\\u003c/em\\u003e and \\u003cem\\u003eKlebsiella pneumoniae\\u003c/em\\u003e. In case of multiple positive blood cultures only the first blood culture per microorganism, patient and calendar year was considered. For consistency over the study period we included for the current analyses only data from those Swiss hospitals which were included in the ANRESIS surveillance in the first year of the study (\\u003cem\\u003ei.e.\\u003c/em\\u003e, January 1, 2015 for the first objective and April 1, 2020 for the second objective) and that sent information during the entire study period.\\u003c/p\\u003e \\u003cp\\u003e \\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003eVariables collected and baseline data\\u003c/span\\u003e: Variables routinely collected by ANRESIS and used in part in aggregated forms for this study include the age group (\\u003cem\\u003ei.e.\\u003c/em\\u003e, 0\\u0026ndash;2,3\\u0026ndash;15,16\\u0026ndash;45, 46\\u0026ndash;65 and \\u0026gt;\\u0026thinsp;65 years old), sex (male, female), year, month as well as the exact date of detection of the episode, detected microorganism species, hospital type (non-university versus university), department (i.e. intensive care unit (ICU) versus non-ICU) and region of residency (as defined below). Extended-spectrum cephalosporin-resistant \\u003cem\\u003eEscherichia coli\\u003c/em\\u003e (ESCR-EC) and \\u003cem\\u003eKlebsiella pneumoniae\\u003c/em\\u003e (ESCR-KP) were defined as resistant to at least one of all third- or fourth-generation cephalosporin tested.\\u003c/p\\u003e \\u003cp\\u003e \\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003eStudy periods\\u003c/span\\u003e: To investigate the association between the COVID-19 pandemic and the occurrence of ESCR-EC and ESCR-KP, we analysed two distinct periods: the pre-pandemic phase spanning from January 1, 2015, to February 29, 2020, and the pandemic phase from March 1, 2020 to August 31, 2022. Furthermore, to assess the association between COVID-19 patient occupancy and the incidence of BSI as well as the resistance rates, we specifically examined the two-year period from April 1, 2020, to March 30, 2022, as data collection by the FOPH commenced on March 30, 2020.\\u003c/p\\u003e \\u003cp\\u003e \\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003eRegions\\u003c/span\\u003e: The association between the COVID-19 pandemic and the level and the changes of ESCR-KP and ESCR-EC incidence rates were analysed in two distinct linguistic and sociocultural regions of Switzerland, which experienced varying effects from different pandemic waves. The first region (Latin-languages-speaking region, hereinafter referred to as 'Latin region'), includes the Italian-speaking and French-speaking parts of Switzerland. This region was significantly affected by the first wave of COVID-19. The second region, the German-speaking region (hereinafter referred to as 'German region'), was largely spared from the first wave. Subsequently, the pandemic had a more homogeneous impact on the country as a whole.\\u003c/p\\u003e \\u003cp\\u003e \\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003eDenominators\\u003c/span\\u003e: Incidence rates were calculated using corrected population data as denominator. Data were provided by the Swiss Federal Statistical Office (FSO), are freely available on their website (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.bfs.admin.ch/bfs/en/home/statistics/population.html\\u003c/span\\u003e\\u003cspan address=\\\"https://www.bfs.admin.ch/bfs/en/home/statistics/population.html\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e), and were corrected for the population covered by ANRESIS. For the years 2021 and 2022 a corrected reference scenario from the FSO was used [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. COVID-19 occupancy rates (percent of beds occupied by COVID-19 patients) were calculated for each geographic region on a monthly (quasi-Poisson regression) and daily (for the logistic regression) basis. Data on Swiss hospital\\u0026rsquo;s occupancy regarding patients with and without COVID-19 were freely available from the Swiss Federal Office of Public Health (FOPH) (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.covid19.admin.ch\\u003c/span\\u003e\\u003cspan address=\\\"https://www.covid19.admin.ch\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStatistical analyses\\u003c/h2\\u003e \\u003cp\\u003eThe statistical analysis included two steps. First, to investigate the association between the COVID-19 pandemic and the incidence rates of ESCR-KP and ESCR-EC bloodstream infections in the German and Latin regions, a Quasi-Poisson regression was performed. Two separate models were built for each linguistic region to examine potential association of COVID-19 on the levels (model A) and the changes (model B) of ESCR-KP and ESCR-EC incidence rates. In these models, the COVID-19 pandemic was coded as a binary variable as described in Bernal \\u003cem\\u003eet al.\\u003c/em\\u003e [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. A harmonic wave with a 12-month period was included in this model to test for seasonality and the coverage corrected population, as defined above, was included as offset. F-Tests were performed for each predictor. Since four models were fitted for each pathogen, the P-values were Bonferroni corrected. Besides the models which were built for statistical inference testing additional comprehensive exploratory quasi-Poisson models were developed (\\u003cem\\u003ei.e.\\u003c/em\\u003e, backward reduction was applied) to explore the potential influence of higher-level interactions on the AMR count. [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]\\u003c/p\\u003e \\u003cp\\u003eSecond, to evaluate if the incidence of ESCR-KP and ESCR-EC would be affected by the occupancy of hospital beds by COVID-19 patients during the pandemic months (from April 1st, 2020, to March 30th, 2022), quasi-Poisson models were fitted, similarly to the first analysis. However, instead of using a binary variable for the pandemic, the COVID-19 occupancy rate was included in these models. Moreover, in order investigate if the ESCR-KP and ESCR-EC resistance rates were affected by COVID-19 patient occupancy rates logistic regression models were fitted. These models also included hospital (type, department) and patient (age group, sex) specific predictors. Likelihood-ratio tests were used to test for significance.\\u003c/p\\u003e \\u003cp\\u003eAll analyses were performed with R (version 4.2.2). P-values\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 were considered statistically significant. The analysis is in compliance with the STROBE guidelines for observational studies [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eDuring the study period, a total of 49,534 blood culture positive for \\u003cem\\u003eE. coli\\u003c/em\\u003e or \\u003cem\\u003eK. pneumoniae\\u003c/em\\u003e were collected and reported to ANRESIS by the participating hospitals, the majority of these episodes (83%) being caused by \\u003cem\\u003eE. coli\\u003c/em\\u003e. ESCR was observed in 11% (n\\u0026thinsp;=\\u0026thinsp;4313) of \\u003cem\\u003eE. coli\\u003c/em\\u003e and 8% (n\\u0026thinsp;=\\u0026thinsp;664) of \\u003cem\\u003eK. pneumoniae\\u003c/em\\u003e, respectively (see Supplementary Material (SM) Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Baseline characteristics of the patients with BSI are provided in Table \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e. The population in the German part was 5\\u0026apos;905\\u0026apos;544 (3\\u0026apos;865\\u0026apos;769 coverage corrected) and 2\\u0026apos;421\\u0026apos;582 (1\\u0026apos;682\\u0026apos;273 coverage corrected) in the Latin part in 2015 and 6\\u0026apos;275\\u0026apos;027 (4\\u0026apos;107\\u0026apos;633 coverage corrected) in the German part and 2\\u0026apos;560\\u0026apos;028 (1\\u0026apos;778\\u0026apos;451 coverage corrected) in the Latin part in 2022.\\u003c/p\\u003e\\n\\u003cp\\u003eTable 1. Baseline characteristics of the included patients (period 01.01.2015 - 31.08.2022)\\u0026nbsp;\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"765\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"24.902216427640155%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eBaseline characteristics\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eE.coli\\u003c/em\\u003e\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026nbsp;ESCR \\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eN = 4,313\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eE.coli \\u0026nbsp;ESCS\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eN = 36,684\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eK. pneumoniae\\u003c/em\\u003e\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026nbsp;ESCR\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eN = 664\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eK. pneumoniae\\u003c/em\\u003e\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026nbsp; ESCS\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;N = 7873\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"24.902216427640155%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eFemale gender\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e2,050 (45%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e20,641 (54%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e227 (32%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e3,173 (38%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"24.902216427640155%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eAge (median, IQR)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e70 (60, 80)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e75 (60, 80)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e70 (55, 75)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e70 (60, 80)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"24.902216427640155%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eLatin Region\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e1728 (40%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e12418 (34%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e305 (46%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e2603 (38%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"24.902216427640155%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eDepartement\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"24.902216427640155%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eICU\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e226 (5.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e1,254 (3.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e73 (11%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e526 (6.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"24.902216427640155%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003einterdisciplinary\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e877 (20%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e8,909 (24%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e92 (14%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e1,684 (21%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"24.902216427640155%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003emedicine\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e705 (16%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e5,462 (15%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e148 (22%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e1,456 (18%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"24.902216427640155%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003esurgery\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e441 (10%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e2,300 (6.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e103 (16%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e751 (9.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"24.902216427640155%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eother\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e231 (5.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e1,708 (4.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e44 (6.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e378 (4.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"24.902216427640155%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eoutpatient\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e1,833 (42%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e17,051 (46%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e204 (31%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"18.77444589308996%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e3,078 (39%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003eLegend. ICU: intensive care unit. IQR: Interquartile range. ESCR: extended-spectrum cephalosporin-resistant. ESCS Extended-spectrum cephalosporin-sensitive\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eIn the second part of the analysis including only data from the pandemic period, we investigated the association of the hospital occupancy resulting from COVID-19 patients with incidence rates and percentages of ESCR-KP and ESCR-EC. This analysis was based on a total of collected 13\\u0026rsquo;875 positive blood cultures (see SM Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e and, for the baseline characteristics of the included patients, SM Table \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Similar to the previous analysis, ESCR was observed in 10% (n\\u0026thinsp;=\\u0026thinsp;1173) of \\u003cem\\u003eE. coli\\u003c/em\\u003e and 8% (n\\u0026thinsp;=\\u0026thinsp;199) of \\u003cem\\u003eK. pneumoniae.\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cspan type=\\\"ItalicUnderline\\\" class=\\\"ItalicUnderline\\\" name=\\\"Emphasis\\\"\\u003eE. coli\\u003c/span\\u003e \\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003eand ESCR-EC\\u003c/span\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe main findings are illustrated and summarized in Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e and Table \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e. Overall, the incidence rates of ESCR-EC were higher in the Latin region compared to the German region. These rates displayed an overall upward trend throughout the entire study period, although this increase did not reach statistical significance following Bonferroni correction. However, a noteworthy and statistically significant decrease of ESCR-EC incidence was observed for the pandemic period in the Latin region (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Conversely, in the German region, there were no significant alterations in both the level and the slope of ESCR-EC incidence rates. Seasonality was not significant in either linguistic region for both the models. The drop in the Latin region during the pandemic can also be observed in the fit of the exploratory model (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e and SM Table \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e\\n\\u003cp\\u003eTable 2: Changes in the ESCR-EC BSI incidence rates in the German and Latin Regions\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"869\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"100%\\\" colspan=\\\"10\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eModel A: Investigating if the level of ESCR-EC BSI incidence rates changes during\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026nbsp;the COVID-19 pandemic\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"26.006904487917147%\\\" colspan=\\\"2\\\" rowspan=\\\"2\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"36.59378596087457%\\\" colspan=\\\"4\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eGerman region\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"37.39930955120828%\\\" colspan=\\\"4\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eLatin region\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"12.441679626749611%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eEstimate\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"24.57231726283048%\\\" colspan=\\\"2\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e95% CI\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.441679626749611%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eP-value*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.530326594090202%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eEstimate\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"24.57231726283048%\\\" colspan=\\\"2\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e95% CI\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.441679626749611%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eP-value*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"26.006904487917147%\\\" colspan=\\\"2\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eCOVID-19\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.205983889528193%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-0.11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.090909090909092%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-0.59\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.090909090909092%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-0.23\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.205983889528193%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.68\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.01150747986191%\\\"\\u003e\\n \\u003cp\\u003e-0.41\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.090909090909092%\\\"\\u003e\\n \\u003cp\\u003e-0.59\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.090909090909092%\\\"\\u003e\\n \\u003cp\\u003e-0.23\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.205983889528193%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"26.006904487917147%\\\" colspan=\\\"2\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTime\\u003csup\\u003e\\u0026yen;\\u003c/sup\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.205983889528193%\\\"\\u003e\\n \\u003cp\\u003e0.00\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.090909090909092%\\\"\\u003e\\n \\u003cp\\u003e0.00\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.090909090909092%\\\"\\u003e\\n \\u003cp\\u003e0.01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.205983889528193%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.19\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.01150747986191%\\\"\\u003e\\n \\u003cp\\u003e0.00\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.090909090909092%\\\"\\u003e\\n \\u003cp\\u003e0.00\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.090909090909092%\\\"\\u003e\\n \\u003cp\\u003e0.01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.205983889528193%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.06\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"14.154200230149597%\\\" rowspan=\\\"2\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eSeasonality\\u003csup\\u003e+\\u003c/sup\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"11.852704257767549%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eTerm 1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.205983889528193%\\\"\\u003e\\n \\u003cp\\u003e-0.05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.090909090909092%\\\"\\u003e\\n \\u003cp\\u003e-0.09\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.090909090909092%\\\"\\u003e\\n \\u003cp\\u003e0.05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.205983889528193%\\\" rowspan=\\\"2\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.49\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.01150747986191%\\\"\\u003e\\n \\u003cp\\u003e-0.02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.090909090909092%\\\"\\u003e\\n \\u003cp\\u003e-0.09\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.090909090909092%\\\"\\u003e\\n \\u003cp\\u003e0.05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.205983889528193%\\\" rowspan=\\\"2\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e1\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"17.576791808873722%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eTerm 2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.651877133105803%\\\"\\u003e\\n \\u003cp\\u003e0.03\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.481228668941979%\\\"\\u003e\\n \\u003cp\\u003e-0.08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.481228668941979%\\\"\\u003e\\n \\u003cp\\u003e0.06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"14.84641638225256%\\\"\\u003e\\n \\u003cp\\u003e-0.01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.481228668941979%\\\"\\u003e\\n \\u003cp\\u003e-0.08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.481228668941979%\\\"\\u003e\\n \\u003cp\\u003e0.06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"100%\\\" colspan=\\\"10\\\" valign=\\\"top\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"100%\\\" colspan=\\\"10\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eModel B: \\u0026nbsp;Investigating if the ESCR-EC BSI slope of the incidence rates changes during\\u0026nbsp;\\u003c/strong\\u003e\\u003cstrong\\u003ethe COVID-19 pandemic\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"14.154200230149597%\\\" rowspan=\\\"3\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eIncrease/decrease during COVID-19\\u003csup\\u003e\\u0026sect;\\u003c/sup\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"11.852704257767549%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eCOVID-19\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.205983889528193%\\\"\\u003e\\n \\u003cp\\u003e-0.12\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.090909090909092%\\\"\\u003e\\n \\u003cp\\u003e-0.29\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.090909090909092%\\\"\\u003e\\n \\u003cp\\u003e0.05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.205983889528193%\\\" rowspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e1\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.01150747986191%\\\"\\u003e\\n \\u003cp\\u003e-0.52\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.090909090909092%\\\"\\u003e\\n \\u003cp\\u003e-0.75\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.090909090909092%\\\"\\u003e\\n \\u003cp\\u003e-0.30\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.205983889528193%\\\" rowspan=\\\"3\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.36\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"17.576791808873722%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eTime\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.651877133105803%\\\"\\u003e\\n \\u003cp\\u003e0.00\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.481228668941979%\\\"\\u003e\\n \\u003cp\\u003e0.00\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.481228668941979%\\\"\\u003e\\n \\u003cp\\u003e0.01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"14.84641638225256%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e0.00\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.481228668941979%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e0.00\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.481228668941979%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e0.01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"17.576791808873722%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eInteraction\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.651877133105803%\\\"\\u003e\\n \\u003cp\\u003e0.00\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.481228668941979%\\\"\\u003e\\n \\u003cp\\u003e-0.01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.481228668941979%\\\"\\u003e\\n \\u003cp\\u003e0.01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"14.84641638225256%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e0.01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.481228668941979%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e0.00\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.481228668941979%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e0.02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"14.154200230149597%\\\" rowspan=\\\"2\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eSeasonality\\u003csup\\u003e+\\u003c/sup\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"11.852704257767549%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eTerm 1\\u003c/p\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.205983889528193%\\\"\\u003e\\n \\u003cp\\u003e-0.05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.090909090909092%\\\"\\u003e\\n \\u003cp\\u003e-0.11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.090909090909092%\\\"\\u003e\\n \\u003cp\\u003e0.00\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.205983889528193%\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.50\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.01150747986191%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e-0.02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.090909090909092%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e-0.09\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.090909090909092%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e0.05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.205983889528193%\\\" rowspan=\\\"2\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e1\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"17.576791808873722%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eTerm 2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.651877133105803%\\\"\\u003e\\n \\u003cp\\u003e0.03\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.481228668941979%\\\"\\u003e\\n \\u003cp\\u003e-0.03\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.481228668941979%\\\"\\u003e\\n \\u003cp\\u003e0.09\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"14.84641638225256%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e-0.01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.481228668941979%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e-0.08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.481228668941979%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e0.06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003eIn Quasi-Poisson-regression models it was tested if there is an effect of the COVID-19 pandemic on the level (Model A) and slope (Model B) of AMR incidences in different linguistic regions in Switzerland. In addition, it should be investigated which model fits the data better.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eLegend\\u003c/p\\u003e\\n\\u003cp\\u003e* Bonferroni correction was applied (multiplication of p-values by 4 as for each region models were fitted with and without interaction).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003csup\\u003e\\u0026yen;\\u0026nbsp;\\u003c/sup\\u003eThe study month was included as a numeric predictor.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003csup\\u003e+\\u003c/sup\\u003e\\u003cstrong\\u003e\\u003csup\\u003e\\u0026nbsp;\\u003c/sup\\u003e\\u003c/strong\\u003eSeasonality: Amplitudes of a sine (Term 1) and a cosine (Term 2) component were estimated. The wavelength was set to 12 months.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003csup\\u003e\\u0026sect;\\u0026nbsp;\\u003c/sup\\u003eThe COVID-19-time-interaction was fitted to detect a change in the slope during the COVID-19 pandemic.\\u003c/p\\u003e\\n\\u003cp\\u003eResidual deviance and degrees of freedom are shown below to illustrate the goodness of fit of different models: \\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eModel A, German \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;\\u0026nbsp;Residual deviance 82.6 on 87 degrees of freedom\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eModel A, Latin \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;\\u0026nbsp;\\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;\\u0026nbsp;Residual deviance, 88.0 on 87 degrees of freedom\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eModel B, German \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;\\u0026nbsp;Residual deviance, 82.6 on 86 degrees of freedom\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eModel B, Latin \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; Residual deviance, 85.1 on 86 degrees of freedom\\u003c/p\\u003e\\n\\u003cp\\u003eWhen considering the models containing data from the pandemic only, increasing incidences rates were confirmed but a dependence from the COVID-19 patient occupancy rates was not observed (SM Table \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). Similarly, the logistic regression model revealed no significant association between COVID-19 occupancy rates and resistance rates. Nevertheless, this model did identify significant predictors for the resistance rates, including time, linguistic region, hospital type, and sex. These same predictors persisted in the final exploratory model, as detailed in (SM Table 4).\\u003c/p\\u003e\\n\\u003cdiv class=\\\"gridtable\\\"\\u003e\\n \\u003ctable id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003eChanges in the ESCR-KP BSI incidence rates in the German and Latin Regions\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"10\\\"\\u003e\\n \\u003cp\\u003eModel A: Investigating if the level of ESCR-KP BSI incidence rates changes during the COVID-19 pandemic\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" rowspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"4\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eGerman region\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"4\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eLatin region\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eEstimate\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e95% CI\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eP-value*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eEstimate\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e95% CI\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eP-value*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eCOVID-19\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.24\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.63\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.15\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e1\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.15\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.58\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.29\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e1\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eTime\\u003csup\\u003e\\u0026yen;\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.007\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.00\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.00\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e1\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eSeasonality\\u003csup\\u003e+\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTerm 1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.16\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.32\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.00\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.55\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.24\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e1\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTerm 2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.24\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.20\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.15\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"10\\\"\\u003e\\n \\u003cp\\u003eModel B: Investigating if the ESCR-KP BSI slope of the incidence rates changes during the COVID-19 pandemic\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003eIncrease/decrease during COVID-19\\u003csup\\u003e\\u0026sect;\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCOVID-19\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.51\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.98\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.14\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.46\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.75\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.30\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.12\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTime\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.00\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.00\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eInteraction\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.03\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.00\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.03\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.00\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eSeasonality\\u003csup\\u003e+\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTerm 1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.16\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.32\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.4\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.09\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e1\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTerm 2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.25\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003ctfoot\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"10\\\"\\u003eIn Quasi-Poisson-regression models it was tested if there is an effect of the COVID-19 pandemic on the level (Model A) and slope (Model B) of AMR incidences in different linguistic regions in Switzerland. In addition, it should be investigated which model fits the data better.\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"10\\\"\\u003eLegend\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"10\\\"\\u003e* Bonferroni correction was applied (multiplication of p-values by 4 as for each region models were fitted with and without interaction).\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"10\\\"\\u003e\\u003csup\\u003e\\u0026yen;\\u003c/sup\\u003e The study month was included as a numeric predictor.\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"10\\\"\\u003e\\u003csup\\u003e+\\u003c/sup\\u003e Seasonality: Amplitudes of a sine (Term 1) and a cosine (Term 2) component were estimated. The wavelength was set to 12 months.\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"10\\\"\\u003e\\u003csup\\u003e\\u0026sect;\\u003c/sup\\u003e The COVID-19-time-interaction was fitted to detect a change in the slope during the COVID-19 pandemic.\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"10\\\"\\u003eResidual deviance and degrees of freedom are shown below to illustrate the goodness of fit of different models:\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"10\\\"\\u003eModel A, German Residual deviance 108.7 on 87 degrees of freedom\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"10\\\"\\u003eModel A, Latin Residual deviance 104.3 on 87 degrees of freedom\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"10\\\"\\u003eModel B, German Residual deviance 103.3 on 86 degrees of freedom\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"10\\\"\\u003eModel B, Latin Residual deviance 98.7 on 86 degrees of freedom\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tfoot\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cp\\u003e\\u003cspan type=\\\"ItalicUnderline\\\" class=\\\"ItalicUnderline\\\" name=\\\"Emphasis\\\"\\u003eK. pneumoniae\\u003c/span\\u003e \\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003eand ESCR-KP\\u003c/span\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe main findings are depicted and summarized in Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e and Table \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e Throughout the entire study period, the incidence rates of ESCR-KP were consistently higher in the Latin region when compared to the German region. Over time, these rates showed an upward trend in both regions, with a statistically significant long-term increase observed only in the German region. A more noticeable increase was observed in both linguistic regions during the COVID-19 pandemic; however, this change in slope did not retain statistical significance after applying Bonferroni correction. Seasonal variations were not found to be statistically significant in either linguistic region. The exploratory model incorporating data from both regions indicated region- and pandemic-specific increases (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e and SM Table \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e\\n\\u003cp\\u003eWhen focusing on models containing data solely from the pandemic period, a significant increase in ESCR-KP rates during this time frame was observed, but this increase did not appear to be dependent on the COVID-19 patient occupancy rates, with time and linguistic region being the only predictors in the final exploratory model (SM Table\\u0026nbsp;5). No significant association was observed between the COVID-19 occupancy and the resistance rates in the logistic regression model; in contrast, the predictors time, patient age, hospital type (\\u003cem\\u003ei.e.\\u003c/em\\u003e, university \\u003cem\\u003evs\\u003c/em\\u003e. non-university hospital) and department (\\u003cem\\u003ei.e.\\u003c/em\\u003e, ICU \\u003cem\\u003evs\\u003c/em\\u003e. non-ICU) were significant and retained their significance in the final model of our exploratory analysis, which initially included seasonality and higher-order interactions (SM Table\\u0026nbsp;6).\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eIn this comprehensive nationwide study, we have identified a reduction in ESCR-EC rates during the COVID-19 pandemic, with the most notable decline occurring in the region most severely affected by the pandemic. Conversely, an initial non-significant decrease in ESCR-KP BSI incidence was followed by an upward trajectory over time, eventually reverting to and even surpassing pre-pandemic levels.\\u003c/p\\u003e \\u003cp\\u003eOverall, knowledge on the influence of the pandemic on clinically relevant infections caused by MDR is limited. Data on ESCR-EC and ESCR-KP associated BSI are scarce, conflicting and mostly limited to the first wave of the pandemic [\\u003cspan additionalcitationids=\\\"CR19 CR20\\\" citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. A French multicentre study found a significant increase in the rate of ESCR Enterobacterales bloodstream infections, including ESBL-producing \\u003cem\\u003eK. pneumoniae\\u003c/em\\u003e, during March-April 2020 compared to 2019, accompanied by higher blood culture sampling and antibiotic usage [\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. In a tertiary hospital in Rome, Italy, there was no overall difference in the incidence of ESCR Enterobacterales BSI between the COVID-19 period and a pre-COVID period [\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. A study conducted in a reference center in Jakarta found similar results, with no increase in the frequency of ESCR-KP and ESCR-EC between 2019 and 2020 [\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]. In contrast, a significant decrease was found in the rate of various infections (including BSI) caused by ESBL-producing Enterobacterales during the first and second quarter of 2020 in an American study [\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. Noteworthy, these data need to be interpreted with caution, due to the limited reporting and the fact that more studies are likely to be published in this area in the near future. This reflects conflicting findings from studies on the impact of COVID-19 on AMR (\\u003cem\\u003ei.e.\\u003c/em\\u003e, not necessarily invasive infections). In a recent systematic review [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e] of twelve studies, it was observed that ESBL-producing \\u003cem\\u003eE. coli\\u003c/em\\u003e and \\u003cem\\u003eK. pneumoniae\\u003c/em\\u003e infections decreased during the COVID-19 pandemic (in contrast with the previous upward trend) while a meta-analysis [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e] pooling several resistant Gram-negative organisms did not find a significant effect. Even more recently, a Canadian study investigating the ESBL rates in urine cultures, showed decreased rates for \\u003cem\\u003eE. coli\\u003c/em\\u003e in both the community and long-term-care facilities, but increasing rates for ESBL \\u003cem\\u003eK. pneumoniae\\u003c/em\\u003e rates in the latter [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eCollectively, these observations, along with our own, underscore the intricate interplay between the COVID-19 pandemic and AMR: as far as our knowledge extends no study has addressed this matter by accounting for the impact of the entire pandemic on distinct regions differently hit by the pandemic within an entire country. Furthermore, our findings highlighted how the significance of local circumstances identified in our studies may elucidate the divergent findings reported previously. The reason behind the significant decrease in ESCR-EC, a predominantly community-acquired pathogen [\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e], can likely be attributed to the introduction of community preventive measures and the restrictions imposed by the federal government (culminating in the national lockdown on March 16, 2020). These measures led to a reduction in travel, movement, and overall interpersonal contacts, subsequently resulting in a decrease in the transmission of pathogens, and, secondarily, to the observed reduction in the use of antibiotics [\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e], the main driver of MDR.\\u003c/p\\u003e \\u003cp\\u003eConversely, the rise of ESCR-KP, primarily a pathogen acquired within healthcare settings, might be linked to the heightened usage of antibiotics in inpatients [\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e], to the occurrence of hospital outbreaks, as reported in several studies [\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e], and to a challenges such as patients overflow and understaffing.\\u003c/p\\u003e \\u003cp\\u003eNotably, our in-depth analysis did not reveal any discernible association between the increase in hospital occupancy due to COVID-19 patients and the incidence or the resistance rates of the bacteria included. Conversely, university hospitals and ICUs (where the usage of broad-spectrum antibiotics is more prominent and where outbreaks were most often described) were found to be predictors for ESCR-KP. Nevertheless, more nuanced and challenging-to-attribute factors might have contributed. For instance, in the latter phase of the study was characterized by a substantial upsurge in migration from regions characterized by higher rates of AMR [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eOur study has several limitations. Clinical individual patient data, including baseline comorbidities, reasons for hospital admission, source of BSI and presence of risk factors (\\u003cem\\u003ee.g.\\u003c/em\\u003e, indwelling urinary catheters), COVID-19 status at individual level and antibiotic exposure information, were not available in the datasets used for our study. Secondly, information on the site of acquisition of BSI (community \\u003cem\\u003evs\\u003c/em\\u003e hospital-acquired) was incomplete and an analysis was not possible. Roughly, 40% of the blood cultures were collected from outpatients, with a substantial majority likely being obtained in emergency rooms from patients who were subsequently hospitalized. Thirdly, if the burden of hospitalized COVID-19 patients does not seem to significantly impact the outcome, we could only hypothesize the reasons behind the presented results, and we might not have been able to account for certain relevant confounders, such as migration patterns and government-imposed measures/restrictions in response to the pandemic. A more intricate model featuring additional predictors and, perhaps, shorter time intervals could have better represented the data, yet such complexity might elevate the risk of overfitting. Switzerland is a country with relatively low incidences, especially concerning ESCR-KP. It is conceivable that higher incidences would have resulted in more statistical power.\\u003c/p\\u003e \\u003cp\\u003eOne of the key strengths of our study is the comprehensive nationwide data collection, spanning five years before and during the COVID-19 pandemic. This extensive dataset includes information from all university and tertiary hospitals, enabling us to stratify the data according to different linguistic and sociocultural regions. This is particularly valuable due to the country's heterogeneity, as different regions were impacted differently by the pandemic. Another strength is the statistical methodological approach used here. The separation of inference testing models from exploratory models allowed hypotheses to be tested without a bias that might be introduced during model development, while still providing evidence of further complex relationships that were not considered when the original hypotheses were formulated. Such evidence may then lead to new hypotheses that can be tested with new independent data (e.g. from other countries).\\u003c/p\\u003e\"},{\"header\":\"Conclusions\",\"content\":\"\\u003cp\\u003eIn the early phase of the COVID-19 pandemic, a decrease in ESCR rates was observed, particularly in ESCR-EC BSI within the most heavily impacted region.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cu\\u003eEthical statement\\u003c/u\\u003e\\u003cstrong\\u003e:\\u0026nbsp;\\u003c/strong\\u003eThis study was based on national surveillance data submitted to the Swiss Centre for Antibiotic Resistance ANRESIS. Because of the anonymous nature of the data, neither ethical approval nor written informed consent from patients was required\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cu\\u003eFunding statement\\u003c/u\\u003e\\u003cstrong\\u003e:\\u003c/strong\\u003e This work is financially supported by the Swiss Federal Office of Public Health and the Institute for Infectious Diseases of the University of Bern, Switzerland. NB received a post.doc Mobility grant from the Swiss National Science Foundation (grant number: P4P4PM_194449) in 2021.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cu\\u003eData availability\\u003c/u\\u003e\\u003cstrong\\u003e:\\u0026nbsp;\\u003c/strong\\u003eAll data can be made available upon request to the corresponding author\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cu\\u003eCompeting Interests\\u003c/u\\u003e: none\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cu\\u003eAuthors’ contributions\\u003c/u\\u003e\\u003cstrong\\u003e:\\u003c/strong\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eStudy conception: MG, LD, NB\\u003c/p\\u003e\\n\\u003cp\\u003eData collection: AK\\u003c/p\\u003e\\n\\u003cp\\u003eData processing, modelling and statistical analyses: MG and LD\\u003c/p\\u003e\\n\\u003cp\\u003eData interpretation: MG, LD, NB and AK\\u003c/p\\u003e\\n\\u003cp\\u003eDrafting the manuscript: MG and LD\\u003c/p\\u003e\\n\\u003cp\\u003eApproving the final version: MG, LD, NB and AK\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n \\u003cli\\u003eRussell, C.D., et al., \\u003cem\\u003eCo-infections, secondary infections, and antimicrobial use in patients hospitalised with COVID-19 during the first pandemic wave from the ISARIC WHO CCP-UK study: a multicentre, prospective cohort study.\\u003c/em\\u003e Lancet Microbe, 2021. \\u003cstrong\\u003e2\\u003c/strong\\u003e(8): p. e354-e365.\\u003c/li\\u003e\\n \\u003cli\\u003eLangford, B.J., et al., \\u003cem\\u003eAntibiotic prescribing in patients with COVID-19: rapid review and meta-analysis.\\u003c/em\\u003e Clin Microbiol Infect, 2021. \\u003cstrong\\u003e27\\u003c/strong\\u003e(4): p. 520-531.\\u003c/li\\u003e\\n \\u003cli\\u003eBaker, M.A., et al., \\u003cem\\u003eThe Impact of COVID-19 on Healthcare-Associated Infections.\\u003c/em\\u003e Clin Infect Dis, 2021.\\u003c/li\\u003e\\n \\u003cli\\u003eBuetti, N., et al., \\u003cem\\u003eDifferent epidemiology of bloodstream infections in COVID-19 compared to non-COVID-19 critically ill patients: a descriptive analysis of the Eurobact II study.\\u003c/em\\u003e Crit Care, 2022. \\u003cstrong\\u003e26\\u003c/strong\\u003e(1): p. 319.\\u003c/li\\u003e\\n \\u003cli\\u003eAntimicrobial Resistance, C., \\u003cem\\u003eGlobal burden of bacterial antimicrobial resistance in 2019: a systematic analysis.\\u003c/em\\u003e Lancet, 2022. \\u003cstrong\\u003e399\\u003c/strong\\u003e(10325): p. 629-655.\\u003c/li\\u003e\\n \\u003cli\\u003eAbubakar, U., et al., \\u003cem\\u003eImpact of COVID-19 pandemic on multidrug resistant gram positive and gram negative pathogens: A systematic review.\\u003c/em\\u003e J Infect Public Health, 2023. \\u003cstrong\\u003e16\\u003c/strong\\u003e(3): p. 320-331.\\u003c/li\\u003e\\n \\u003cli\\u003eLangford, B.J., et al., \\u003cem\\u003eAntibiotic resistance associated with the COVID-19 pandemic: a systematic review and meta-analysis.\\u003c/em\\u003e Clin Microbiol Infect, 2022. \\u003cstrong\\u003e29\\u003c/strong\\u003e(3): p. 302-9.\\u003c/li\\u003e\\n \\u003cli\\u003eMonnet, D.L. and S. Harbarth, \\u003cem\\u003eWill coronavirus disease (COVID-19) have an impact on antimicrobial resistance?\\u003c/em\\u003e Euro Surveill, 2020. \\u003cstrong\\u003e25\\u003c/strong\\u003e(45).\\u003c/li\\u003e\\n \\u003cli\\u003eMicheli, G., et al., \\u003cem\\u003eThe Hidden Cost of COVID-19: Focus on Antimicrobial Resistance in Bloodstream Infections.\\u003c/em\\u003e Microorganisms, 2023. \\u003cstrong\\u003e11\\u003c/strong\\u003e(5).\\u003c/li\\u003e\\n \\u003cli\\u003eKarlowsky, J.A., et al., \\u003cem\\u003ePrevalence of ESBL non-CRE Escherichia coli and Klebsiella pneumoniae among clinical isolates collected by the SMART global surveillance programme from 2015 to 2019.\\u003c/em\\u003e Int J Antimicrob Agents, 2022. \\u003cstrong\\u003e59\\u003c/strong\\u003e(3): p. 106535.\\u003c/li\\u003e\\n \\u003cli\\u003eBezabih, Y.M., et al., \\u003cem\\u003eComparison of the global prevalence and trend of human intestinal carriage of ESBL-producing Escherichia coli between healthcare and community settings: a systematic review and meta-analysis.\\u003c/em\\u003e JAC Antimicrob Resist, 2022. \\u003cstrong\\u003e4\\u003c/strong\\u003e(3): p. dlac048.\\u003c/li\\u003e\\n \\u003cli\\u003eTacconelli, E., et al., \\u003cem\\u003eDiscovery, research, and development of new antibiotics: the WHO priority list of antibiotic-resistant bacteria and tuberculosis.\\u003c/em\\u003e Lancet Infect Dis, 2018. \\u003cstrong\\u003e18\\u003c/strong\\u003e(3): p. 318-327.\\u003c/li\\u003e\\n \\u003cli\\u003eRenggli, L., et al., \\u003cem\\u003eTemporal and structural patterns of extended-spectrum cephalosporin-resistant Klebsiella pneumoniae incidence in Swiss hospitals.\\u003c/em\\u003e J Hosp Infect, 2022. \\u003cstrong\\u003e120\\u003c/strong\\u003e: p. 36-42.\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cem\\u003ehttps://www.bfs.admin.ch/bfs/de/home/statistiken/bevoelkerung/zukuenftige-entwicklung/kantonale-szenarien.assetdetail.12107013.html\\u003c/em\\u003e. 12.12.2023].\\u003c/li\\u003e\\n \\u003cli\\u003eBernal, J.L., S. Cummins, and A. Gasparrini, \\u003cem\\u003eInterrupted time series regression for the evaluation of public health interventions: a tutorial.\\u003c/em\\u003e Int J Epidemiol, 2017. \\u003cstrong\\u003e46\\u003c/strong\\u003e(1): p. 348-355.\\u003c/li\\u003e\\n \\u003cli\\u003eHarrell, F.E., \\u003cem\\u003eRegression modeling strategies: with applications to linear models, logistic regression, and survival analysis\\u003c/em\\u003e. Vol. 608. 2001: Springer.\\u003c/li\\u003e\\n \\u003cli\\u003evon Elm, E., et al., \\u003cem\\u003eStrengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies.\\u003c/em\\u003e BMJ, 2007. \\u003cstrong\\u003e335\\u003c/strong\\u003e(7624): p. 806-8.\\u003c/li\\u003e\\n \\u003cli\\u003eSegala, F.V., et al., \\u003cem\\u003eIncidence of bloodstream infections due to multidrug-resistant pathogens in ordinary wards and intensive care units before and during the COVID-19 pandemic: a real-life, retrospective observational study.\\u003c/em\\u003e Infection, 2023: p. 1-9.\\u003c/li\\u003e\\n \\u003cli\\u003eSinto, R., et al., \\u003cem\\u003eBlood culture utilization and epidemiology of antimicrobial-resistant bloodstream infections before and during the COVID-19 pandemic in the Indonesian national referral hospital.\\u003c/em\\u003e Antimicrob Resist Infect Control, 2022. \\u003cstrong\\u003e11\\u003c/strong\\u003e(1): p. 73.\\u003c/li\\u003e\\n \\u003cli\\u003eDenny, S., et al., \\u003cem\\u003eBacteraemia variation during the COVID-19 pandemic; a multi-centre UK secondary care ecological analysis.\\u003c/em\\u003e BMC Infect Dis, 2021. \\u003cstrong\\u003e21\\u003c/strong\\u003e(1): p. 556.\\u003c/li\\u003e\\n \\u003cli\\u003eAmarsy, R., J. Robert, and V. Jarlier, \\u003cem\\u003e[Impact of the first year of the COVID-19 pandemic on the epidemiology of invasive infections (bacteremia) in the hospitals of the Assistance Publique-Hopitaux de Paris].\\u003c/em\\u003e Bull Acad Natl Med, 2023. \\u003cstrong\\u003e207\\u003c/strong\\u003e(2): p. 131-135.\\u003c/li\\u003e\\n \\u003cli\\u003eCole, J. and E. Barnard, \\u003cem\\u003eThe impact of the COVID-19 pandemic on healthcare acquired infections with multidrug resistant organisms.\\u003c/em\\u003e Am J Infect Control, 2021. \\u003cstrong\\u003e49\\u003c/strong\\u003e(5): p. 653-654.\\u003c/li\\u003e\\n \\u003cli\\u003eHasan, M.R., et al., \\u003cem\\u003eTrends in the Rates of Extended-Spectrum-beta-Lactamase-Producing Enterobacterales Isolated from Urine Cultures during the COVID-19 Pandemic in Ontario, Canada.\\u003c/em\\u003e Microbiol Spectr, 2023. \\u003cstrong\\u003e11\\u003c/strong\\u003e(1): p. e0312422.\\u003c/li\\u003e\\n \\u003cli\\u003eDay, M.J., et al., \\u003cem\\u003eExtended-spectrum beta-lactamase-producing Escherichia coli in human-derived and foodchain-derived samples from England, Wales, and Scotland: an epidemiological surveillance and typing study.\\u003c/em\\u003e Lancet Infect Dis, 2019. \\u003cstrong\\u003e19\\u003c/strong\\u003e(12): p. 1325-1335.\\u003c/li\\u003e\\n \\u003cli\\u003eFriedli, O., et al., \\u003cem\\u003eImpact of the COVID-19 Pandemic on Inpatient Antibiotic Consumption in Switzerland.\\u003c/em\\u003e Antibiotics (Basel), 2022. \\u003cstrong\\u003e11\\u003c/strong\\u003e(6).\\u003c/li\\u003e\\n \\u003cli\\u003eMamun, A.A., et al., \\u003cem\\u003eCommunity Antibiotic Use at the Population Level During the SARS-CoV-2 Pandemic in British Columbia, Canada.\\u003c/em\\u003e Open Forum Infect Dis, 2021. \\u003cstrong\\u003e8\\u003c/strong\\u003e(6): p. ofab185.\\u003c/li\\u003e\\n \\u003cli\\u003eEmeraud, C., et al., \\u003cem\\u003eOutbreak of CTX-M-15 Extended-Spectrum beta-Lactamase-Producing Klebsiella pneumoniae ST394 in a French Intensive Care Unit Dedicated to COVID-19.\\u003c/em\\u003e Pathogens, 2021. \\u003cstrong\\u003e10\\u003c/strong\\u003e(11).\\u003c/li\\u003e\\n \\u003cli\\u003eFalcone, M., et al., \\u003cem\\u003eSpread of hypervirulent multidrug-resistant ST147 Klebsiella pneumoniae in patients with severe COVID-19: an observational study from Italy, 2020-21.\\u003c/em\\u003e J Antimicrob Chemother, 2022. \\u003cstrong\\u003e77\\u003c/strong\\u003e(4): p. 1140-1145.\\u003c/li\\u003e\\n \\u003cli\\u003eSchultze, T., et al., \\u003cem\\u003eMolecular surveillance of multidrug-resistant Gram-negative bacteria in Ukrainian patients, Germany, March to June 2022.\\u003c/em\\u003e Euro Surveill, 2023. \\u003cstrong\\u003e28\\u003c/strong\\u003e(1).\\u003cstrong\\u003e\\u003c/strong\\u003e\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"COVID-19, bloodstream infections, extended-spectrum cephalosporin-resistance, Escherichia coli, Klebsiella pneumoniae\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-3869934/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-3869934/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cu\\u003ePurpose\\u003c/u\\u003e: The association between the COVID-19 pandemic and the incidence of invasive infections caused by multidrug-resistant organisms remains a topic of debate. The aim of this study was to analyse the national incidence rates of bloodstream infections (BSI) caused by \\u003cem\\u003eEscherichia coli\\u003c/em\\u003e (EC) and \\u003cem\\u003eKlebsiella pneumoniae\\u003c/em\\u003e (KP) with extended-spectrum cephalosporin-resistance (ESCR) in two distinct regions in Switzerland, each exhibiting varying antimicrobial resistance patterns and that were impacted differently by the pandemic.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cu\\u003eMethods\\u003c/u\\u003e: We analysed data of positive blood cultures prospectively collected by the nationwide surveillance system (ANRESIS) from January 1, 2015, to August 31, 2022. To explore the potential relationship between COVID-19 patient occupancy and ESCR incidence rates, we conducted an in-depth analysis over the two-year pandemic period from April 1, 2020, to March 30, 2022. We employed Quasi-Poisson and logistic regression analyses to investigate these associations.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cu\\u003eResults\\u003c/u\\u003e: During the study period, a total of 40997 EC-BSI and 8537 KP-BSI episodes were collected and reported to ANRESIS by the participating hospitals. ESCR was observed in 11% (n=4313) of \\u003cem\\u003eE. coli\\u003c/em\\u003e and 8% (n=664) of \\u003cem\\u003eK. pneumoniae\\u003c/em\\u003e, respectively. A significant reduction in ESCR-EC BSI incidence occurred during the pandemic in the region with the highest COVID-19 incidence. Conversely, ESCR-KP BSI incidence initially fell considerably and then increased during the pandemic in both regions; however, this effect was not statistically significant.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cu\\u003eConclusion\\u003c/u\\u003e: In the early phase of the COVID-19 pandemic, a decrease in ESCR rates was observed, particularly in ESCR-EC BSI within the most heavily impacted region.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Epidemiology of bloodstream infections caused by extended-spectrum cephalosporin-resistant Escherichia coli and Klebsiella pneumoniae in Switzerland, 2015-2022: secular trends and association with the COVID-19 pandemic\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-01-18 16:04:49\",\"doi\":\"10.21203/rs.3.rs-3869934/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"70c7d301-948b-47e2-a7df-30df2b20ba30\",\"owner\":[],\"postedDate\":\"January 18th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-06-04T12:14:54+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-3869934\",\"link\":\"https://doi.org/10.1016/j.jhin.2024.05.013\",\"journal\":{\"identity\":\"journal-of-hospital-infection\",\"isVorOnly\":true,\"title\":\"Journal of Hospital Infection\"},\"publishedOn\":\"2024-06-01 12:14:54\",\"publishedOnDateReadable\":\"June 1st, 2024\"},\"versionCreatedAt\":\"2024-01-18 16:04:49\",\"video\":\"\",\"vorDoi\":\"10.1016/j.jhin.2024.05.013\",\"vorDoiUrl\":\"https://doi.org/10.1016/j.jhin.2024.05.013\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-3869934\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-3869934\",\"identity\":\"rs-3869934\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}