Surveillance of antibiotic resistant Escherichia coli in human populations through urban wastewater in ten European countries

Journal Pre-proof Surveillance of antibiotic resistant Escherichia coli in human populations through urban wastewater in ten European countries Patricia M.C. Huijbers, D.G. Joakim Larsson, Carl-Fredrik Flach PII:

S0269-7491(19)36319-5

DOI:

https://doi.org/10.1016/j.envpol.2020.114200

Reference:

ENPO 114200

To appear in:

Environmental Pollution

Received Date: 28 October 2019 Revised Date:

11 February 2020

Accepted Date: 15 February 2020

Please cite this article as: Huijbers, P.M.C., Larsson, D.G.J., Flach, C.-F., Surveillance of antibiotic resistant Escherichia coli in human populations through urban wastewater in ten European countries, Environmental Pollution (2020), doi: https://doi.org/10.1016/j.envpol.2020.114200. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.

% Resistant clinical E. coli % Resistant wastewater E. coli

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COUNTRIES

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Surveillance of antibiotic resistant Escherichia coli in human populations through urban

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wastewater in ten European countries

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Patricia M.C. Huijbers1,2, D.G. Joakim Larsson1,2, Carl-Fredrik Flach1,2,*

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Centre for Antibiotic Resistance Research (CARe), University of Gothenburg, Gothenburg, Sweden

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2

Department of Infectious Diseases, Institute of Biomedicine, Sahlgrenska Academy, University of

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Gothenburg, Gothenburg, Sweden

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*Corresponding author. Tel +46 31 342 4655; E-Mail [email protected]; Address

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Guldhedsgatan 10, SE-431 46 Gothenburg, Sweden.

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Abstract

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Antibiotic resistance surveillance data is lacking in many parts of the world, limiting effective therapy

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and management of resistance development. Analysis of urban wastewater, which contains bacteria

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from thousands of individuals, opens up possibilities to generate informative surveillance data in a

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standardized and resource-efficient way. Here, we evaluate the relationship between antibiotic

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resistance prevalence in E. coli from wastewater and clinical samples by studying countries with

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different resistance situations as assessed by traditional clinical surveillance. Composite, influent

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wastewater samples were collected over 24 hours from treatment plants serving major cities in ten

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European countries. Using a broth screening method, resistance to six antibiotic classes was analyzed

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for 2507 E. coli isolates (n=247-252 per country). Resistance prevalence in wastewater E. coli was

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compared to that in clinical E. coli reported by the European Antibiotic Resistance Surveillance

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Network. Resistance prevalence was lower in wastewater than clinical E. coli but followed similar

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geographic trends. Significant relationships were found for resistance to aminopenicillins (R2=0.72,

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p=0.0019) and fluoroquinolones (R2=0.62, p=0.0072), but not for aminoglycosides (R2=0.13, p=0.31)

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and third-generation cephalosporins (R2=0.00, p=0.99) where regression analyses were based on

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considerably fewer resistant isolates. When all four antibiotic classes were taken into account, the

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relationship was strong (R2=0.85, p<.0001). Carbapenem resistance was rare in both wastewater and

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clinical isolates. Wastewater monitoring shows promise as method for generating surveillance data

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reflecting the clinical prevalence of antibiotic resistant bacteria. Such data may become especially

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valuable in regions where clinical surveillance is currently limited.

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Key words: surveillance; wastewater; antibiotic resistance; environment

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Two-line summary:

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Wastewater monitoring shows promise as method for generating surveillance data reflecting the

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clinical prevalence of antibiotic resistant bacteria.

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Introduction

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Antibiotic resistance has become a more and more apparent threat to the prevention and treatment of

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bacterial infections (WHO, 2014). An important tool for management of antibiotic resistance,

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including effective empirical treatment, is surveillance. Up to date information on local resistance

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patterns is essential for (initial) choice of antibiotic therapy when resistance data from individual cases

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are pending or unknown. There is, however, a general lack of national surveillance structures to

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provide an overview of the antibiotic resistance situation, no global consensus on methodology and

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data collection, and a limited capacity for timely information sharing (WHO, 2014). In response to

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this, the Global Antimicrobial Resistance Surveillance System (GLASS) was launched to provide a

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standardized approach to the collection, analysis and sharing of antibiotic resistance data (WHO,

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2018). Based on information provided by 68 countries enrolled in GLASS as of 2018, the WHO

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concludes that some countries still face substantial challenges in building their national surveillance

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systems and improvements are urgently needed (WHO, 2018).

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At full-strength, GLASS would be a powerful surveillance platform with global coverage.

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However, there is an urgent need for antibiotic resistance data now, especially in countries without

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longstanding surveillance networks (WHO, 2018; Williams et al., 2018). As sampling of individual

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patients requires considerable infrastructure and resources, it presents a barrier to the timely

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implementation of GLASS. Wastewater from the inlets of urban wastewater treatment plants contain

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fecal bacteria from thousands of individuals in the community connected to a wastewater system,

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opening up possibilities to generate antibiotic resistance data at the community level without the need

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to sample individuals. It would also avoid ethical challenges associated with screening of individuals

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from the general population, such as stigmatization of carriers of resistant bacteria (Rump et al., 2018).

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Wastewater has been used for the surveillance of poliovirus, and more recently for early warning of

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viral outbreaks and monitoring of salmonellosis (Hovi et al., 2012; Hellmèr et al., 2017; Yan et al.,

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2018). Antibiotic susceptibility testing of bacteria isolated from wastewater can provide information

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on antibiotic resistance in the community (Reinthaler et al., 2013; Kwak et al., 2015). In order for

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wastewater monitoring to be of value for the generation of surveillance data, it is necessary to

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investigate the relationship between the prevalence of antibiotic resistance in wastewater and clinical 3

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samples. Predicting the clinical resistance situation from the proportion of resistant bacteria in

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wastewater relies on a consistent, good correlation, not necessarily on a direct causal link between the

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two. Indeed, there is an apparent intermediary link that connects them - the proportion of resistant

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bacteria in feces. It is plausible that fecal resistance levels are directly, and rather strongly, linked to

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resistance in raw sewage as the latter is largely made up of fecal material. Still, one cannot exclude

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noise caused by e.g., differential survival of resistant/non-resistant strains in the sewers. On the other

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hand, the link between fecal resistance levels and resistant infections with enteric bacteria is not

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necessarily as strong. Although patients’ gut flora, including E. coli, is a common source of

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extraintestinal infections, those E. coli strains causing infections tend to be more resistant than random

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fecal E. coli (Dang et al., 2013; Nielsen et al., 2014). However, if the relative difference in resistance

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levels between fecal E. coli and those specific strains causing infections is stable, it could still be

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possible to predict the clinical resistance situation from wastewater analyses.

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Recently, Hutinel et al. (2019) showed strong and significant correlations (R2 values between

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0.82 and 0.95) between the proportion of resistant E. coli from wastewater samples and the proportion

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of resistant E. coli from clinical samples collected in Gothenburg, Sweden. An unexplored but critical

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aspect for the generation of surveillance data through wastewater monitoring is that there is a

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reasonably stable relationship in terms of resistance prevalence between wastewater and clinical

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isolates on a population level across geographical locations. If such a relationship can be established, it

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could in turn be used to predict clinical resistance prevalence based on wastewater analyses for regions

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where clinical resistance data is lacking. The aim of this study was, therefore, to evaluate the

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relationship between antibiotic resistance prevalence in Escherichia coli from wastewater and clinical

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samples in countries with different resistance situations and where clinical surveillance is well-

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developed.

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Methods

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Sample collection

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Ten large wastewater treatment plants in ten major European cities (population served >500,000)

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participated in this point prevalence study between December 2016 and December 2017. All treatment 4

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plants received domestic wastewater, with input from industries and hospitals. However, the

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proportions of these inputs are unknown. One-liter samples of untreated wastewater were collected

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from Mondays to Tuesdays or from Tuesdays to Wednesdays at the inlet of these wastewater

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treatment plants by automatic sampling equipment that allowed for subsampling over 24 hours.

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Information about each wastewater treatment plant was gathered by means of a questionnaire and

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summarized in Table 1. Samples were cooled during transport and processed approximately 12 hours

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after collection. Support for the collection of a single sample per treatment plant was provided by a

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previous study, which showed limited variation in proportions of resistant E. coli between eight

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influent samples from a single wastewater treatment plant collected over 18 months (Flach et al.,

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2018).

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Bacterial counts

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After vigorous shaking of the one-liter composite samples, 25 ml was taken and homogenized by

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vortexing with plating beads to break apart any material that could contain clusters of bacteria. Serial

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dilutions of this subsample were prepared in 0.85% sodium chloride. Aliquots of 100 µl from the 10-1,

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10-2 and 10-3 dilutions were plated on 10, 20 and 10 CHROMagar ECC™ plates (CHROMagar, Paris,

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France), respectively. In addition, aliquots of 100 µl of the original sample and 10-1 dilution were

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plated in triplicate on CHROMagar ECC™ plates containing cefpodoxime (2 µg/ml; Sigma-Aldrich,

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Steinheim, Germany) and cloxacillin (200 µg/ml; Sigma-Aldrich, Steinheim, Germany) to select for

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ESBL-producing E. coli. The latter antibiotic was added to inhibit the growth of AmpC-type beta-

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lactamase-producers. Aliquots of 100 µl from the 10-4 and 10-5 dilutions were plated on R2A agar to

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determine total heterotrophic bacterial counts. The CHROMagar ECC™ and R2A plates were

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incubated overnight (16-20h) at 37 oC and six days at room temperature, respectively.

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Identification and analysis of E. coli

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Presumptive E. coli isolates were identified on the CHROMagar ECC™ plates as blue colonies. In

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total, 252 blue colonies were picked per sample from the plates without antibiotics and included all

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blue colonies from the 10-3 dilution and nine to twelve blue colonies per plate from the 10-2 dilution for 5

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each country. Plates were incubated overnight at 37oC and isolates were subsequently stored at -20 oC

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in LB broth with 20% glycerol. All collected isolates were subjected to confirmatory species

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identification using MALDI-TOF MS (VITEK, Biomerieux, Marcy L’Étoile, France). This system is

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very accurate at identifying bacteria to species level (Bizzini et al., 2010) and is gradually becoming

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the clinical standard for species determination.

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Using a broth screening method, resistance to six antibiotics at clinical breakpoint

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concentrations were analyzed (EUCAST, 2016). These included four beta-lactams, one

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aminoglycoside, and one fluoroquinolone, which all have been in clinical use for decades. More

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specifically, ampicillin (8 mg/L; Mast Group Ltd., Merseyside, United Kingdom), ciprofloxacin (1

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mg/L; AppliChem, Darmstadt, Germany), cefotaxime (2 mg/L; Sigma-Aldrich, Steinheim, Germany),

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ceftazidime (4 mg/L; Sigma-Aldrich, Steinheim, Germany), gentamicin (4 mg/L; Sigma-Aldrich,

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Steinheim, Germany) and meropenem (8 mg/L; Sigma-Aldrich, Steinheim, Germany). These

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antibiotics represent the same classes as those analyzed for E. coli within the European Antibiotic

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Resistance Surveillance Network (EARS-net; ECDC, 2017a). Resistance to colistin (2 mg/L; Sigma-

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Aldrich, Steinheim, Germany) was investigated additionally due to its growing importance as a last-

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resort antibiotic. Stock solution concentrations were verified by broth microdilution according to

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EUCAST quality control guidelines using MIC determination for E. coli ATCC 25922 control strain

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(EUCAST, 2017a). For broth screening, stock solutions of each antibiotic were diluted in cation-

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adjusted Mueller-Hinton broth to breakpoint concentrations in 96-well plates. A plate containing broth

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without antibiotics was used as a positive growth control. Per plate, every other well was inoculated

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with E. coli from fresh, overnight cultures to a final concentration of about 5x105 CFU/mL. The

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remaining 48 wells served as contamination controls. After overnight incubation at 37 oC and 180 rpm,

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plates were visually inspected for growth in order to categorize them as resistant or susceptible. An

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isolate was considered resistant to third-generation cephalosporins if it was resistant to cefotaxime

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and/or ceftazidime. The prevalence of resistant E. coli in wastewater was compared to clinical

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resistance data reported to EARS-net. Resistance prevalence to aminopenicillins, fluoroquinolones,

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third-generation cephalosporins and aminoglycosides in invasive E. coli isolated from clinical samples

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of blood and cerebrospinal fluid, as well as the prevalence of isolates susceptible to all antibiotics, 6

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were extracted from the annual antimicrobial resistance surveillance report of the ECDC on antibiotic

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resistance for Denmark, Finland, Norway, Sweden, Belgium, France, Germany, Greece, Italy and

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Spain for 2016 (ECDC, 2017a). These countries have contributed a large number of clinical isolates

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(>1000) through EARS-net and represent European regions where resistance prevalence is known to

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differ.

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Statistical analysis

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The resistance prevalence for E. coli from wastewater and clinical samples and their 95% confidence

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interval were calculated based on the binomial probability function. A chi-square test of independence

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was performed to examine the relation between resistance prevalence and European region (north vs.

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west vs. south) for each antibiotic class. The relationship between the prevalence of resistance in

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wastewater and clinical samples was examined for each antibiotic class individually using simple

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linear regression (PROC REG). A generalized linear model including the variables ‘antibiotic class’ ,

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‘proportion of resistant wastewater E. coli’ and their interaction was used to assess homogeneity of the

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intercepts and regression slopes (PROC GLM). Proportions were arcsine square root transformed prior

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to regression analysis to improve normality of residuals and homogeneity of variances. All analyses

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were conducted using SAS 9.4 (SAS, 2017).

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Results

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Wastewater bacterial counts and isolation of E. coli

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The total number of bacteria in the sampled wastewater varied from 2.20x106 CFU/ml in France to

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3.20x107 CFU/ml in Greece (median 1.14x107 CFU/ml; Table 1) and the number of E. coli varied

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from 1.32x104 CFU/ml in Italy to 8.70x104 CFU/ml in Belgium (median 3.63x104 CFU/ml; Table 1).

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Of the 2519 isolates picked from CHROMagar ECCTM plates without antibiotics, 2507 were confirmed

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to be E. coli by MALDI-TOF MS (99.5%). Isolates that were not identified as E. coli (n=12) were

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Citrobacter spp. (n=7), Leclercia adercarboxylata (n=1), Burkholderia gladioli (n=1), Serratia

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liquefaciens (n=1), Providencia rettgeri (n=1) or unidentifiable (n=1). These isolates were excluded

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from subsequent analyses. 7

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Broth screening

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Due to a change in guidelines by EUCAST at the beginning of 2017 (EUCAST, 2017b), a subset of

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isolates (n=1245) were evaluated at breakpoints of 1 mg/L and 0.5 mg/L ciprofloxacin in parallel. The

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prevalence of resistance was 6.2% (95%CI 4.9-7.7) and 7.0% (95%CI 5.6-8.6) for 1 mg/L and 0.5

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mg/L ciprofloxacin, respectively, which was not significantly different (χ2(1, N=2490)=0.65; p=0.42).

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The percentage of isolates that was susceptible to all tested antibiotics varied between 60.3%

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(95%CI 53.9-66.5) in Spain and 79.8% (95%CI 74.3-84.5) in Finland (median 74.1%; Table 2). The

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prevalence of ampicillin resistance was higher than the prevalence of ciprofloxacin, cefotaxime and/or

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ceftazidime, or gentamicin resistance in all countries. In Sweden, Belgium, France, Greece, Italy and

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Spain the prevalence of ciprofloxacin resistance was significantly higher (p<0.05) than cefotaxime

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and/or ceftazidime or gentamicin resistance, while in Denmark, Finland, Norway and Germany there

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was no significant difference (p>0.05) in resistance to these three antibiotics (Table 2). Resistance to

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colistin was found in isolates from Denmark (n=2), Norway (n=2), Germany (n=1), France (n=3

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Greece (n=1) and Italy (n=1). Resistance to meropenem was found in a single E. coli isolate from

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Finland, which was also resistant to ampicillin, cefotaxime, ceftazidime and ciprofloxacin but not to

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gentamicin or colistin. The percentages of third-generation cephalosporin-resistant E. coli isolates

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obtained through broth screening with cefotaxime and ceftazidime were similar to the percentages of

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presumptive ESBL-producing E. coli obtained by selective plating on media with cefpodoxime and

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cloxacillin (Table 1).

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The prevalence of ampicillin resistance was significantly different between regions (χ2(2,

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N=2507)=28.26, p<0.0001) with 19.7% resistance in northern Europe (Finland, Denmark, Norway,

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Sweden), 26.4% resistance in western Europe (Germany, France, Belgium ) and 30.6% resistance in

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southern Europe (Greece, Italy, Spain). This also held true for ciprofloxacin (3.8%, 5.6%, 9.6%; χ2(2,

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N=2507)=26.15; p<0.0001) and gentamicin (1.9%, 1.3%, 3.3%; χ2(2, N=2507)=7.80; p=0.02) but not

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for cefotaxime and/or ceftazidime (3.0%, 3.3%, 2.9%; χ2(2, N=2507)=0.21, p=0.90).

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Comparison of percentages of resistant E. coli in wastewater and clinical samples 8

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Similar to wastewater samples, the prevalence of aminopenicillin resistance in E. coli from clinical

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samples was highest in all countries, followed by fluoroquinolone resistance. The prevalence of third-

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generation cephalosporin and aminoglycoside resistance were not significantly different (p>0.05) in

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Denmark, Norway, Greece and Spain but the latter resistance was lower than the former in the other

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six investigated countries (Table 2). Again similar to wastewater samples, the prevalence of

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aminopenicillin resistance in isolates from clinical samples was significantly different between regions

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(χ2(2, N=52036)=1127.96, p<0.0001) with 41.8% resistance in northern Europe (Finland, Denmark,

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Norway, Sweden), 53.4% resistance in western Europe (Germany, France, Belgium) and 64.1%

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resistance in southern Europe (Greece, Italy, Spain). This also held true for fluoroquinolone resistance

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(12.0%, 19.2%, 37.2%; χ2(2, N=65130)=3270.94; p<0.0001) and aminoglycoside resistance (6.1%,

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7.6%, 16.6%; χ2(2, N=64351)=1278.73; p<0.0001), and in contrast to isolates from wastewater, also

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for resistance to third-generation cephalosporins (7.1%, 11.3%, 21.5%; χ2(2, N=64841)= 1648.88;

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p<0.0001). For all these tested antibiotic classes, the prevalence of resistance was lower in isolates

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from wastewater than from clinical samples (Table 2). The prevalence of resistance to carbapenems in

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E. coli from clinical specimens was less than or equal to 0.1% in all studied countries, except Italy

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(0.3%) and Greece (0.9%). The rare clinical occurrence of carbapenem-resistance in all countries is

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also reflected by the infrequent occurrence of such isolates in wastewater samples.

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A simple linear regression was calculated to predict the prevalence of resistant E. coli from

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clinical samples based on the prevalence of resistant E. coli from wastewater samples for each

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antibiotic class. A significant regression equation was found for the aminopenicillins (F(1, 8)=20.45,

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p=0.0019) with an R2 of 0.72 and for the fluoroquinolones (F(1,8)=12.81, p=0.0072) with an R2 of

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0.62. A significant regression equation could not be detected for the third-generation cephalosporins

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(F(1,8)=0.00, p=0.99, R2=0.00) or the aminoglycosides (F(1,8)=1.15, p=0.31, R2=0.13; Figure 1).

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Slopes were not significantly different as indicated by a non-significant interaction term (p=0.35) and

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intercepts were also not significantly different as indicated by non-significance for the variable

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‘antibiotic class’ (p=0.75) when data for aminopenicillins, fluoroquinolones, third-generation

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cephalosporins and aminoglycosides was combined in the same model. A significant regression

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equation to predict the prevalence of resistant E. coli from clinical samples based on the prevalence of 9

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resistant E. coli from wastewater was found when data for all of these antibiotic classes was combined

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(F(1,38)=223.74, p=<.0001) with an R2 of 0.85.

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Discussion

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In the current study, significant relationships were shown between the proportion of resistant E. coli

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from wastewater samples and the proportion of resistant E. coli from clinical samples. Despite being

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based on a limited number of wastewater isolates from one time point and city within each of the ten

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countries, coefficients of determination were 0.62 and 0.72 for individual antibiotics and even reached

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0.85 when data for four antibiotic classes were combined. Other culture-based studies recognize that

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antibiotic resistance in wastewater could be a reflection of the situation in the human population

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(Reinthaler et al., 2013; Kwak et al., 2015; Hutinel et al., 2019), although none has encompassed a

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multinational comparison representing a broad range of resistance levels. Comparisons between

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wastewater- and patient-derived isolates in the studies by Reinthaler et al. (2013) and Kwak et al.

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(2015) were hampered, for example, by isolation of bacteria from a sample that has undergone

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wastewater treatment (i.e. E. coli from activated sewage sludge) or inconsistency between the

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breakpoints used to categorize E. coli as susceptible or resistant when isolated from either wastewater

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or clinical samples. In addition to culture-based approaches, it has been suggested that a metagenomics

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approach might be used as a tool for surveillance of antibiotic resistance (Su et al., 2017; Hendriksen

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et al., 2019). Although metagenomics can be used for inter-country comparisons of antibiotic gene

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abundances in both stool (Forslund et al., 2013) and wastewater samples (Hendriksen et al., 2019), it

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rarely allows detected genes to be linked to their bacterial hosts with confidence. This means that it is

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not possible to tell whether identified genes are hosted by a pathogen, or which pathogen. It is also, for

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the same reason, difficult to estimate co-resistance patterns. This hampers antimicrobial stewardship

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and guidance of empirical treatment based on data generated by gene counting alone. For the analysis

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of spatiotemporal trends and the effect of interventions regarding antibiotic resistance prevalence,

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wastewater monitoring based on metagenomics could, however, prove to be a valuable addition to

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current methods.

10

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The relationship between the prevalence of antibiotic resistant E. coli in wastewater and clinical

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samples appears stable when data for all antibiotic classes was combined. When analyzed for

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individual antibiotic classes, however, a relationship could not be detected for third-generation

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cephalosporins and aminoglycosides. This might be explained by lower prevalence and hence less

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precision in the regression analyses for these classes, and/or bias in wastewater and clinical data. One

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factor that might introduce bias in wastewater data involves the fluctuation of antibiotic resistance

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prevalence between wastewater samples taken at different time points. Three studies, where urban

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wastewater was collected and analyzed in a similar way to the current study, confirm that there is a

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certain amount of fluctuation between sampling occasions, for example prevalence of ciprofloxacin

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resistance among E. coli isolates ranged from 2 to 5% (n=154-628; Kwak et al., 2015), from 4 to 7%

271

(n=145-396; Flach et al., 2018) and from 3 to 7% (n=42-115; Hutinel et al., 2019). However, 95%

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confidence intervals of the mean resistance prevalence for the different antibiotics investigated

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(between ±0.5% and ±3.9%, Kwak et al., 2015; between ±0.7% and ±2.7%, Flach et al., 2018;

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between ±0.9% and ±2.8%, Hutinel et al., 2019) suggest that overall there is limited variation from

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one sampling occasion to the next and that this is the case for multiple antibiotics. Furthermore,

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Hendriksen et al. (2018) showed, based on metagenomics analyses of untreated sewage, that duplicate

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samples from separate days taken at eight different sites showed high within-site reproducibility,

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providing additional support for the approach taken here. Another factor which might introduce bias in

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wastewater data concerns the inadvertent sampling of several bacteria originating from the same

280

person, for example via small aggregates. Such bias could be revealed by studying the frequency of

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clones in the samples. Kwak et al. (2015) also acknowledged this challenge, but observed consistently

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high diversity, as measured by Simpson’s diversity index, among E. coli isolated from 17 urban

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wastewater samples taken at different time points during one year, suggesting a lack of common

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clones among isolates. However, a more systematic study of E. coli diversity in wastewater samples,

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including those from different sources (hospital vs. municipal) and obtained using different sampling

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methods (grab vs. composite), is warranted. Low population coverage might also result in biased

287

estimates of national prevalence due to geographical variations within countries (ECDC, 2017a;

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Martin et al., 2016). This is expected for wastewater data in the current study but is also likely for 11

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clinical data, as coverage can vary between 15% and 100% of the total population depending on the

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country (DANMAP, 2017; ECDC, 2017b). In the case of urban influent wastewater, Hendriksen et al.

291

(2019) showed that antibiotic resistance gene data from the same countries showed less variance

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across sites within countries than across sites between different countries. This suggests that a single

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sample taken from one large wastewater treatment plant can be representative for an entire country.

294

Sampling and laboratory routines were standardized for the collection of wastewater data, but in the

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clinical setting this is not always the case. Blood culture frequency and timing (e.g. before or after the

296

start of empirical treatment) might exhibit national or local variations. In terms of laboratory routines,

297

guidelines for clinical breakpoints, antibiotics used to represent each antibiotic class and the ability to

298

identify microorganisms and their associated susceptibility patterns may vary between laboratories

299

(ECDC, 2017a). The wastewater data presented here suffer less from such potential bias.

300

Besides the existence of bias in either wastewater or clinical data, the lack of relationship for

301

third-generation cephalosporins and aminoglycosides could also be explained by each country having

302

a similar resistance prevalence in the general population (which urban wastewater should represent

303

best), while in patient populations contributing invasive isolates there could be differences between

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countries in selection and transmission of resistant strains. Such differences in patient populations

305

could be due to differences in antibiotic use and infection control practices at hospitals (ECDC, 2018;

306

Hansen et al., 2015). This is especially plausible for third-generation cephalosporin resistance since

307

regional differences in prevalence were found among clinical (ECDC, 2017a) but not wastewater

308

isolates in the current study, which was confirmed both by broth screening with cefotaxime and

309

ceftazidime and selective plating with cefpodoxime (combined with cloxacillin). Selective plating

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enables the assessment of a much larger number of E. coli compared to broth screening, strengthening

311

the hypothesis that systematic differences between the human general population (providing the

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wastewater) and patient populations (providing the clinical data) play a role rather than low prevalence

313

and hence limited precision in our analysis. A meta-analysis of data collected from 66 studies using

314

selective plating methods lends further credibility to this hypothesis by showing that the pooled

315

prevalence of fecal carriage of ESBL-producing Enterobacteriaceae in apparently healthy individuals

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in central (3%, 95%CI 1-5), northern (4%, 95%CI 2-6) and southern Europe (6%, 95%CI 1-12) were 12

317

similar to each other (Karanika et al., 2016), in contrast to clinical surveillance data (ECDC, 2017a). It

318

should be noted that such fecal carriage data does not take into account the relative abundance of

319

ESBL-producing bacteria while this is the case with the methods employed in the current study. The

320

percentage of carriers would therefore define the upper limit of the percentage positive E. coli from

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wastewater, in other words if all E. coli of each carrier were ESBL-producers.

322

Despite the likely existence of various types of bias and potential differences in general and

323

patient populations as described above, a strong relationship between wastewater and clinical

324

antibiotic resistance prevalence was shown in the current study. This opens up the possibility of using

325

wastewater monitoring for the generation of surveillance data reflecting the clinical situation, which

326

could be especially valuable in countries where antibiotic resistance data is scarce or non-existent.

327

Suspected differences in the relationship between the human general and patient populations when

328

different countries are compared suggests that additional validation is necessary, especially if the goal

329

of surveillance is to provide data to support empirical treatment. A way forward here could be to take

330

samples of hospital wastewater, which theoretically resemble the patient population of that hospital

331

more closely than samples from a more downstream location where the hospital effluent is mixed and

332

diluted with wastewater from other parts of the community. The results of a parallel study conducted

333

over one year in Gothenburg (Sweden) support such an approach (Hutinel et al., 2019). Comparison of

334

regional, primary care data with urban wastewater data and hospitalized patient data with hospital

335

wastewater resulted in highly significant relationships between antibiotic resistance prevalences for

336

eleven antibiotics with R2 values between 0.82 and 0.95 (Hutinel et al., 2019). In terms of acceptable

337

R2 for implementation of wastewater monitoring, it can be argued that this depends on the surveillance

338

goal. For example, R2 probably needs to be higher for informing empirical treatment compared to

339

analysis of trends but an actual threshold will always be context dependent. When empirical antibiotic

340

treatment guidelines are formulated for different infections, resistance prevalence associated with the

341

causative pathogen(s) is taken into account. Specifically, a threshold is defined that, when exceeded,

342

leads to the rejection of a particular antibiotic for empirical treatment. This threshold is a trade-off

343

between the severity of the infection and risk for resistance development made by experts based on

344

epidemiological, microbiological and mathematical modeling studies. For example, in the 13

345

international clinical practice guidelines of the Infectious Diseases Society of America and the

346

European Society for Microbiology and Infectious Diseases, trimethoprim-sulfamethoxazole is no

347

longer recommended for empirical treatment if the local resistance prevalence of pathogens causing

348

uncomplicated urinary tract infections exceeds 20% (Gupta et al., 2011). Translation of wastewater

349

data into clinical thresholds cannot be determined based on the data presented in the current study

350

alone, but this is envisioned with additional validation.

351

The implementation of wastewater surveillance and start of data generation could be rapid since

352

sample collection and processing can be concentrated in time and should require considerably less

353

infrastructure and human and economic resources compared to sampling of individual patients. In

354

terms of laboratory capacity, data sharing and adherence to policy guidelines, wastewater surveillance

355

could benefit from the infrastructure being developed by many countries for participation in GLASS.

356

Moving wastewater surveillance from a European to a global scale requires some consideration, in

357

particular with respect to sampling. Sampling should be conducted at a point where E. coli from many

358

individuals comes together, meaning that the presence of a treatment facility is convenient but not

359

essential. Globally, 63% of urban populations had sewer connections (WHO/UNICEF, 2017), thus

360

providing opportunities for near world-wide sampling, including places where antibiotic resistance

361

data is scarce. Having said this, in some parts of the world, such as sub-Saharan Africa, central Asia

362

and southern Asia, on-site sanitation predominates (WHO/UNICEF, 2017), especially among rural

363

populations. Here, septic tanks or latrines could be good alternative sampling sites, although special

364

concern should be taken to avoid sampling of clones in matrixes with less mixing. If both sewer

365

connections and on-site sanitation are lacking, a third option may be to rely on sampling of, for

366

example, surface water in proximity to densely populated areas (Walsh et al., 2011; Ahammad et al.,

367

2014). Factors such as differential survival and dilution of human-associated E. coli in the

368

environment need to be considered to a greater extent for antibiotic resistance prevalence in the

369

contributing population in this case. Still, between 10% and 30% of urban populations in sub-Saharan

370

Africa, central Asia and southern Asia do use sewer connections, allowing for generation of data that

371

can provide an initial indication of the antibiotic resistance situation for regions where no information

372

is available at all. 14

373 374

Conclusion

375

In conclusion, significant relationships were shown between the proportion of resistant E. coli from

376

wastewater samples and the proportion of resistant E. coli from clinical samples. This suggests that

377

wastewater monitoring could be used to predict clinical resistance levels that in turn could help to

378

guide antibiotic use and resistance management.

379 380

Competing financial interests

381

The authors declare no conflict of interest.

382 383

Funding

384

The study was financially supported by the Swedish state under the agreement between the Swedish

385

government and the county councils, the ALF-agreement to DGJL (grant number ALFGBG-717901);

386

the Swedish Research Council Formas to CFF (219-2014-1575 and 2018-00833); the Centre for

387

Antibiotic Resistance Research at University of Gothenburg to CFF; the Swedish Research Council

388

VR to DGJL (2015-02492) and the Adlerbert Research Foundation to CFF and PMCH.

389 390

Acknowledgements

391

We would like to thank the managers and personnel at each of the ten wastewater treatment plants who

392

agreed to participate in this study for their kind cooperation and sampling assistance. We would also

393

like to thank Dr. S.D. Kotsakis and Dr. A. Bruno for facilitating contacts and providing laboratory

394

space, Ms. M. Genheden for her technical assistance and Prof. E. Kristiansson for his advice on

395

statistical procedures.

396

15

397

References

398

Ahammad, Z.S., Sreekrishnan, T.R., Hands, C.L., Knapp, C.W., Graham, D.W. 2014. Increased

399

waterborne blaNDM-1 resistance gene abundances associated with seasonal human

400

pilgrimages to the upper Ganges River. Environ. Sci. Technol. 48, 3014-3020,

401

https://doi.org/10.1021/es405348h.

402

Bizzini, A., Durussel, J., Bille, J., Greub, G., Prod’hom, G. 2010. Performance of matrix-assisted laser

403

desorption ionization-time of flight mass spectrometry for identification of bacterial

404

strains routinely isolated in a clinical microbiology laboratory. J. Clin. Microbiol. 48,

405

1549-1554, https://doi.org/10.1128/JCM.01794-09.

406

Dang, T.N.D., Zhang, L., Zollner, S., Srinivasan, U., Abbas, K., Marrs, C.F., Foxman, B. 2013.

407

Uropathogenic Escherichia coli are less likely than paired fecal E. coli to have CRISPR

408

loci. Infect. Genet. Evol. 19, 212-218, https://doi.org/10.1016/j.meegid.2013.07.017.

409

DANMAP. 2017. DANMAP 2016 – Use of antimicrobial agents and occurrence of antimicrobial

410

resistance in bacteria from food animals, food and humans in Denmark. Copenhagen,

411

Denmark: DANMAP. Available: https://www.danmap.org/downloads/reports.aspx

412

[accessed 27 January 2019].

413

ECDC (European Centre for Disease Prevention and Control). 2017a. Antimicrobial resistance

414

surveillance in Europe 2016. Annual Report of the European Antimicrobial Resistance

415

Surveillance Network (EARS-Net). Stockholm, Sweden: ECDC, 2017. Available:

416

https://ecdc.europa.eu/en/publications-data/antimicrobial-resistance-surveillance-

417

europe-2016 [accessed 27 January 2019].

418

ECDC (European Centre for Disease Prevention and Control). 2017b. ECDC country visit to Italy to

419

discuss antimicrobial resistance issues. Stockholm, Sweden: European Centre for

420

Disease Prevention and Control. Available: https://ecdc.europa.eu/en/publications-

421

data/ecdc-country-visit-italy-discuss-antimicrobial-resistance-issues [accessed 27

422

January 2019].

423 424

ECDC (European Centre for Disease Prevention and Control). 2018. Antimicrobial consumption – Annual epidemiological report for 2016. Stockholm, Sweden: European Centre for 16

425

Disease Prevention and Control. Available: https://ecdc.europa.eu/en/publications-

426

data/antimicrobial-consumption-annual-epidemiological-report-2016 [accessed 27

427

January 2019].

428

EUCAST (European Committee on Antimicrobial Susceptibility Testing). 2016. Breakpoint tables for

429

interpretation of MICs and zone diameters, Version 6.0. Available:

430

http://www.eucast.org [accessed 27 January 2019].

431

EUCAST (European Committee on Antimicrobial Susceptibility Testing). 2017a. Routine and

432

extended internal quality control for MIC determination and disk diffusion as

433

recommended by EUCAST, Version 7.0. Available: http://www.eucast.org [accessed

434

27 January 2019].

435

EUCAST (European Committee on Antimicrobial Susceptibility Testing). 2017b. Breakpoint tables

436

for interpretation of MICs and zone diameters, Version 7.0. Available:

437

http://www.eucast.org [accessed 27 January 2019].

438

Flach, C.F., Genheden, M., Fick, J., Larsson, D.G.J. 2018. A comprehensive screening of Escherichia

439

coli isolates from scandinavia's largest sewage treatment plant indicates no selection for

440

antibiotic resistance. Environ. Sci. Technol. 52, 11419-11428,

441

https://doi.org/10.1021/acs.est.8b03354.

442

Forslund, K., Sunagawa, S., Kultima, J.R., Mende, D.R., Arumugam, M., Typas, A., Bork, P. 2013.

443

Country-specific antibiotic use practices impact the human gut resistome. Genome. Res.

444

23, 1163-1169, https://doi.org/10.1101/gr.155465.113.

445

Gupta, K., Hooton, T.M., Naber, K.G., Wullt, B., Colgan, R., Miller, L.G., Moran, G.J., Nicolle, L.E.,

446

Rax, R., Schaeffer, A.J., Soper, D.E.. 2011. International clinical practice guidelines for

447

the treatment of acute uncomplicated cystitis and pyelonephritis in in women: A 2010

448

update by the Infectious Disease Society of America and the European Society for

449

Microbiology and Infectious Diseases. Clin. Infect. Dis. 52, e103,

450

https://doi.org/10.1093/cid/ciq257.

17

451

Hansen, S., Zingg, W., Ahmad, R., Kyratsis, Y., Behnke, M., Schwab, F., Pittet, D., Gastmeier, P.,

452

PROHIBIT study group. 2015. Organization of infection control in European hospitals.

453

J. Hosp. Infect. 91, 338-345, https://doi.org/10.1016/j.jhin.2015.07.011.

454

Hellmèr, M., Paxéus, N., Magnius, L., Enache, L., Arnholm, B., Johansson, A., Bergström, T., Norder

455

H. 2014. Detection of pathogenic viruses in sewage provided by early warnings of

456

Hepatitis A virus and Norovirus outbreaks. Appl. Environ. Microbiol. 80, 6771-6781,

457

https://doi.org/10.1128/AEM.01981-14.

458

Hendriksen, S.R., Munk, P., Njage, P., van Bunnik, B., McNally, L., Lukjancenko, O., Röder, T.,

459

Nieuwenhuijse, D., Pedersen, S.K., Kjeldgaard, J., Kaas, R.S., Clausen, P.T.L.C., Vogt,

460

J.K., Leekitcharoenphon, P., van de Schans, M.G.M., Zuidema, T., de Roda Husman,

461

A.M., Rasmussen, S., Petersen, B., Global Sewage Surveillance project consortium,

462

Amid, C., Cochrane, G., Sicheritz-Ponten, T., Schmitt, H., Alvarez, J.R.M., Aidara-

463

Kane, A., Pamp, S.J., Lund, O., Hald, T., Woolhouse, M., Koopmans, M.P., Vigre, H.,

464

Petersen, T.N., Aarestrup, F.M. 2019. Global monitoring of antimicrobial resistance

465

based on metagenomics analyses of urban sewage. Nat. Commun. 10, 1124,

466

https://doi.org/10.1038/s41467-019-08853-3.

467

Hovi, T., Schulman, L.M., Van der Avoort, H., Deshpande, J., Roivainen, M., De Gourville, E.M.

468

2012. Role of environmental poliovirus surveillance in global polio eradication and

469

beyond. Epidemiol. Infect. 140, 1-13, https://doi.org/10.1017/S095026881000316X.

470

Hutinel, M., Huijbers, P.M.C., Fick, J., Åhrén, C., Larsson, D.G.J., Flach, C.-F. 2019. Population-

471

based surveillance of anibitoic resistances in Escherichia coli through sewage analysis.

472

Euro. Surveill. 24, pii=1800497, https://doi.org/10.2807/1560-7917.

473

Karanika, S., Karantanos, T., Arvanitis, M., Grigoras, C., Mylonakis, E. 2016. Fecal colonization with

474

Extended-spectrum Beta-lactamase-producing Enterobacteriaceae and risk factors

475

among healthy individuals: A systematic review and meta-analysis. Clin. Infect. Dis.

476

63, 310-318, https://doi.org/10.1093/cid/ciw283.

477 478

Kwak, Y.K., Colque, P., Byfors, S., Giske, C.G., Möllby, R., Kühn, I. 2015. Surveillance of antimicrobial resistance among Escherichia coli in wastewater in Stockholm during 1 18

479

year: does it reflect the resistance trends in the society? Int. J. Antimicrob. Agents 45,

480

25-32, https://doi.org/10.1016/j.ijantimicag.2014.09.016.

481

Martin, D., Thibaut-Jovelin, S., Fougnot, S., Caillon, J., Gueudet, T., de Mouy, D., Grobost, F.,

482

Robert, J., pour le réseau Onerba-ville. 2016. Prévalence régionale de la production de

483

bêta-lactamase à spectre élargi et de la résistance aux antibiotiques au sein des souches

484

de Escherichia coli isolées d’infections urinaires en ville en 2013 en France. Bull.

485

Epidémiol. Hebd. 24-25, 414-418.

486

Nielsen, K.L., Dynesen, P., Larsen, P., Frimodt-Moller, N. 2014. Faecal Escherichia coli from patients

487

with E. coli urinary tract infection and healthy controls who have never had a urinary

488

tract infection. J. Med. Microbiol. 63, 582-589, https://doi.org/10.1099/jmm.0.068783-

489

0.

490

Reinthaler, F.F., Galler, H., Feierl, G., Haas, D., Leitner, E., Mascher, F., Melkes, A., Posch, J.,

491

Pertschy, B., Winter, I., Himmel, W., Marth, E., Zarfel, G. 2013. Resistance patterns of

492

Escherichia coli isolated from sewage sludge in comparison with those isolated from

493

human patients in 2000 and 2009. J. Water Health. 11, 13-20,

494

https://doi.org/10.2166/wh.2012.207.

495

Rump, B., Timen, A., Hulscher, M., Verweij, M. 2018. Ethics of infection control measures for

496

carriers of antimicrobial drug-resistant organisms. Emerg. Infect. Dis. 24, 1609-1616,

497

https://doi.org/10.3201/eid2409.171644.

498

SAS Version 9.4. 2017. Cary, NC, USA: SAS Institute, Inc.

499

Su, J.Q., An, X.L., Li, B., Chen, Q.L., Gillings, M.R., Chen, H., Gillings, M.R., Chen, H., Zhang, T.,

500

Zhu, YG. 2017. Metagenomics of urban sewage identifies an extensively shared

501

antibiotic resistome in China. Microbiome. 5, 84, https://doi.org/10.1186/s40168-017-

502

0298-y.

503

Walsh, T.R., Weeks, J., Livermore, D.M., Toleman, M.A. 2011. Dissemination of NDM-1 positive

504

bacteria in the New Delhi environment and its implications for human health: an

505

environmental point prevalence study. Lancet Infect. Dis. 11, 355-362,

506

https://doi.org/10.1016/S1473-3099(11)70059-7. 19

507

WHO (World Health Organisation). 2014. Antimicrobial resistance: global report on surveillance

508

2014. Geneva, Switzerland: World Health Organisation. Available:

509

http://www.who.int/drugresistance/documents/surveillancereport/en/ [accessed 27

510

January 2019].

511

WHO (World Health Organisation), UNICEF (United Nations Children’s Fund). 2017. Progress on

512

drinking water, sanitation and hygiene: 2017 update and SDG baselines. Geneva,

513

Switzerland: World Health Organisation. Available:

514

https://www.unicef.org/publications/ index_96611.html [accessed 27 January 2019].

515

WHO (World Health Organisation). 2018. Global antimicrobial resistance surveillance system

516

(GLASS) report: early implementation 2017-2018. Geneva, Switzerland: World Health

517

Organisation. Available: https://www.who.int/glass/resources/publications/early-

518

implementation-report-2017-2018/en/ [accessed 27 January 2019].

519

Williams, P.C.M., Isaacs, D., Berkley, J.A. 2018. Antimicrobial resistance among children in sub-

520

Saharan Africa. Lancet Infect. Dis. 18, e33-44, https://doi.org/10.1016/S1473-

521

3099(17)30467-X.

522

Yan, T., O’Brien, P., Shelton, J.M., Whelen, A.C., Pagaling, E. 2018. Municipal wastewater as a

523

microbial surveillance platform for enteric diseases: A case study for Salmonella and

524

salmonellosis. Environ. Sci. Technol. 52, 4869-4877,

525

https://doi.org/10.1021/acs.est.8b00163.

20

526

Table 1. Population, sampling and influent wastewater characteristics of ten participating European wastewater treatment plants (WWTPs).

Country

Total wastewater volume (m3/day)

Sampling

Concentration Concentration total bacteria E. coli (CFU/ml) (CFU/ml)

Growth on cefpodoximecloxacillin platesb (% of E. coli)

Cefotaxime/ ceftazidime resistantc (% of isolates)

Sampling day

Dry weather day

Datea

Mode

Interval (every)

1,200,000d

125,000

Unknown

2016-12-05

Timeproportional

18 min

1.14x107

4.90x104

2.2

3.6

Finland

800,000

239,000

236,000

2016-12-13

Flowproportional

19 min

1.63x107

5.50x104

1.6

3.6

Norway

601,000

Unknown

200,000

2016-12-20

Flowproportionale

10 min

1.49x107

1.70x104

3.1

2.0

Sweden

763,000

323,000

259,000

2017-03-28

Volumeproportional

1000 m3

7.50x106

1.77x104

1.4

2.8

Belgium

1,300,000

221,000

260,000

2017-05-31

Volumeproportional

1200 m3

1.18x107

8.70x104

1.2

2.4

France

800,000

240,000

240,000

2017-12-12

Flowproportional

10 min

2.20x106

2.35x104

1.6

3.2

Germany

839,000

132,000

105,000

2017-10-09

Volumeproportional

Variablef

9.70x106

2.00x104

5.2

4.4

3,300,000

745,000

670,000

2017-06-07

Volumeproportional

7500 m3

3.20x107

7.05x104

1.8

1.6

518,000

Unknown

151,000

2017-11-22

Flowproportional

15 min

5.70x106

1.32x104

2.8

3.2

2.0

4.1

Denmark

Greece Italy

TimeVariablef 1.13x107 5.10x104 proportional a Date indicates the end of collection of the 24h sample and the pick-up date. Exception: sample from Denmark was picked up on 2017-12-06. Spain

527

Estimated population served (N)

1,590,000

426,000

330,000

2017-02-08

528

b

The percentage of E. coli that grew on ECC plates supplemented with cefpodoxime and cloxacillin.

529

c

The percentage of E. coli isolates that were resistant to cefotaxime and/or ceftazidime by broth screening.

530

d

Total population served by three WWTPs. Influent from this population divided over WWTPs according to available capacity so no fixed allocation per WWTP.

531

e

Sampled influent includes wash water from the biological filters (approximately 10% of flow).

532

f

Variable volumes (Germany) and times (Spain) according to time of day, with more intensive sampling between 8.00 and 22.00.

21

533

Table 2. Total number of Escherichia coli tested (N), prevalence of completely susceptible isolates and prevalence of resistance to four different classes of

534

antibiotics, including 95% confidence intervals (95%CI), in urban wastewater and clinical samples in ten European countries. Aminopenicillinsa Country

Norway Sweden Belgium France

45.0 (44.0, 46.0)

4830

11.0 (10.0, 12.0)

4660

6.6 (6.0, 7.0)

4850

6.1 (5.0, 7.0)

250

19.6 (14.9, 25.1)

250

4.4 (2.2, 7.7)

250

3.6 (1.7, 6.7)

250

2.8 (1.1, 5.7)

250

Spain

535

N

Prevalence (95%CI)

N

Prevalence (95%CI)

N

Prevalence (95%CI)

N

Prevalence (95%CI)

78.8 (73.2, 83.7)

Hospital

2690

35.8 (34.0, 38.0)

4810

11.5 (11.0, 12.0)

4740

6.9 (6.0, 8.0)

4520

4.9 (4.0, 6.0)

2470

61.0 (59.0, 62.9)

wastewater

252

18.7 (14.0, 24.0)

252

2.4 (0.9, 5.1)

252

3.6 (1.7, 6.7)

252

2.0 (0.7, 4.6)

252

79.8 (74.3, 84.5)

Hospital

3620

42.9 (41.0, 45.0)

3610

10.9 (10.0, 12.0)

3620

5.6 (5.0, 6.0)

3610

5.5 (5.0, 6.0)

3610

55.0 (53.4, 56.6)

Wastewater

251

18.7 (14.1, 24.1)

251

3.2 (1.4, 6.2)

251

2.0 (0.7, 4.6)

251

2.0 (0.7, 4.6)

251

79.3 (73.7, 84.1)

Hospital

396b

34.1 (29.0, 39.0)b

6950

13.7 (13.0, 14.0)

6960

8.3 (8.0, 9.0)

6950

7.2 (7.0, 8.0)

-

-

Wastewater

251

21.9 (17.0, 27.6)

251

5.2 (2.8, 8.7)

251

2.8 (1.1, 5.7)

251

0.8 (0.1, 2.9)

251

76.1 (70.3, 81.2)

Hospital

3740

58.0 (56.0, 60.0)

3850

24.5 (23.0, 26.0)

3740

10.5 (10.0, 12.0)

3500

8.4 (8.0, 9.0)

3480

39.0 (37.4, 40.6)

Wastewater

252

26.6 (21.2, 32.5)

252

6.4 (3.7, 10.1)

252

2.4 (0.9, 5.1)

252

0.8 (0.1, 2.8)

252

72.2 (66.3, 77.7)

11200

57.2 (56.0, 58.0)

11300

16.7 (16.0, 17.0)

11300

11.2 (11.0, 12.0)

11100

7.9 (7.0, 8.0)

10900

41.0 (40.1, 41.9)

252

31.0 (25.3, 37.1)

252

5.2 (2.8, 8.7)

252

3.2 (1.4, 6.2)

252

0.8 (0.1, 2.8)

252

67.1 (60.9, 72.8)

Hospital

Germany Hospital

Italy

Aminoglycosidesa

4700

Wastewater

Greece

Third-generation cephalosporinsa

wastewater

Denmark Hospital Finland

Fluoroquinolonesa

Completely susceptible Prevalence N (95%CI) 4500 53.0 (51.5, 54.5)

Source

14600

49.3 (48.0, 50.0)

15800

19.7 (19.0, 20.0)

15800

11.5 (11.0, 12.0)

15600

7.1 (7.0, 8.0)

14400

48.0 (47.2, 48.8)

Wastewater

251

21.5 (16.6, 27.1)

251

5.2 (2.8, 8.7)

251

4.4 (2.2, 7.7)

251

2.4 (0.9, 5.1)

251

75.3 (69.5, 80.5)

Hospital

1170

56.9 (54.0, 60.0)

1300

32.1 (30.0, 35.0)

1300

17.6 (16.0, 20.0)

1300

16.8 (15.0, 19.0)

1170

40.0 (37.1, 42.8)

Wastewater

251

24.7 (19.5, 30.5)

251

8.4 (5.3, 12.5)

251

1.6 (0.4, 4.0)

251

2.0 (0.7, 4.6)

251

72.9 (67.0, 78.3)

Hospital

3110

66.9 (65.0, 69.0)

5950

43.3 (42.0, 45.0)

5940

29.8 (29.0, 31.0)

6080

19.0 (18.0, 20.0)

3090

29.0 (27.4, 30.6)

Wastewater

250

30.8 (25.1, 36.9)

250

7.2 (4.3, 11.1)

250

3.2 (1.4, 6.2)

250

2.4 (0.9, 5.2)

250

67.6 (61.4, 73.4)

Hospital

6790

64.1 (63.0, 65.0)

6790

32.8 (32.0, 34.0)

6800

15.0 (14.0, 16.0)

6800

14.5 (14.0, 15.0)

6770

32.0 (30.9, 33.1)

4.1 (2.0, 7.3)

247

5.7 (3.1, 9.3)

247

60.3 (53.9, 66.5)

Wastewater 247 36.4 (30.4, 42.8) 247 13.4 (9.4, 18.3) 247 Note: N, total number of isolates tested; 95%CI, 95% confidence interval; -, no data available.

536

a

537

fluoroquinolones, cefotaxime (2 mg/L) and ceftazidime (4 mg/L) for the third generation cephalosporins and gentamicin (4 mg/L) for the aminoglycosides.

For resistance profiling of E. coli from wastewater antibiotics were selected to represent each class of antibiotics: ampicillin (8 mg/L) for the aminopenicillins, ciprofloxacin (1 mg/L) for the

22

538

b

539

displayed for the hospital is from 2015.

Sweden substituted testing with ampicillin for testing with amoxicillin-clavulanic acid from 2015, therefore there is no data available for the hospital in 2016. The resistance percentage

23

70

Resistant clinical E. coli (%)

60

50

40

30

20

10

0 0

5

10

15 20 25 Resistant wastewater E. coli (%)

30

35

40

540 541

Figure 1. Percentage of antibiotic resistant E. coli from sampled wastewater compared to antibiotic resistant invasive E. coli reported by hospitals to the

542

European Antibiotic Resistance Surveillance Network (EARS-Net) over 2016 in ten European countries for aminopenicillins (diamond), fluoroquinolones

543

(triangle), third-generation cephalosporins (square) and aminoglycosides (circle).

24

Highlights •

Antibiotic resistance prevalence was lower in wastewater than clinical E. coli

•

Strong relationships exist between wastewater and clinical resistance prevalence

•

Wastewater monitoring could be used to predict antibiotic resistance in the clinic

Author statement

Patricia Huijbers: methodology, formal analysis, investigation, writing – original draft, visualization, project administration. Joakim Larsson: conceptualization, methodology, writing – review & editing, supervision, funding acquisition. Carl-Fredrik Flach: conceptualization, methodology, writing – review & editing, supervision, funding acquisition.

Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: