ijerph-19-04758

ijerph-19-04758

Int. J. Environ. Res. Public Health 2022, 19(8), 4758; doi:10.3390/ijerph19084758

Article

International Travel as a Risk Factor for Carriage of Extended-Spectrum β-Lactamase-Producing Escherichia coli in a Large Sample of European Individuals—The AWARE Study

Daloha Rodríguez-Molina 1,2,3,*,†Orcid, Fanny Berglund 4,5, Hetty Blaak ⁶, Carl-Fredrik Flach 4,5Orcid, Merel Kemper ⁶, Luminita Marutescu 7,8, Gratiela Pircalabioru Gradisteanu 7,8, Marcela Popa 7,8, Beate Spießberger 9,10,11, Laura Wengenroth ¹Orcid, Mariana Carmen Chifiriuc 7,8Orcid, D. G. Joakim Larsson 4,5Orcid, Dennis Nowak 1,12Orcid, Katja Radon ¹, Ana Maria de Roda Husman ⁶, Andreas Wieser 9,10,11 and Heike Schmitt ⁶

¹

Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich, 80336 Munich, Germany

²

Institute for Medical Information Processing, Biometry and Epidemiology—IBE, LMU Munich, 81377 Munich, Germany

³

Pettenkofer School of Public Health, 81377 Munich, Germany

⁴

Department of Infectious Diseases, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, 40530 Gothenburg, Sweden

⁵

Centre for Antibiotic Resistance Research (CARe), University of Gothenburg, 40530 Gothenburg, Sweden

⁶

Centre of Infectious Disease Control, National Institute for Public Health and the Environment, 3721 MA Bilthoven, The Netherlands

⁷

Department of Microbiology and Immunology, Faculty of Biology, University of Bucharest and the Academy of Romanian Scientists, 050657 Bucharest, Romania

⁸

Earth, Environmental and Life Sciences Section, Research Institute of the University of Bucharest, University of Bucharest, 030018 Bucharest, Romania

⁹

German Centre for Infection Research (DZIF), Partner Site Munich, 80336 Munich, Germany

¹⁰

Max von Pettenkofer Institute, Faculty of Medicine, LMU Munich, 81377 Munich, Germany

¹¹

Department of Infectious Diseases and Tropical Medicine, LMU University Hospital Munich, 80802 Munich, Germany

¹²

Comprehensive Pneumology Center Munich (CPC-M), German Center for Lung Research (DZL), 80336 Munich, Germany

*

Correspondence: daloha.rodriguez_molina@med.uni-muenchen.de; Tel.: +49-(89)-4400-52358; Fax: +49-(89)-4400-54954

†

Current address: Occupational and Environmental Epidemiology and NetTeaching Unit, Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich, Ziemssenstr. 5, 80336 Munich, Germany.

Academic Editor: Lucinda J. Bessa

Received: 11 March 2022 / Accepted: 13 April 2022 / Published: 14 April 2022

Abstract

:

Antibiotic resistance (AR) is currently a major threat to global health, calling for a One Health approach to be properly understood, monitored, tackled, and managed. Potential risk factors for AR are often studied in specific high-risk populations, but are still poorly understood in the general population. Our aim was to explore, describe, and characterize potential risk factors for carriage of Extended-Spectrum Beta-Lactamase-resistant Escherichia coli (ESBL-EC) in a large sample of European individuals aged between 16 and 67 years recruited from the general population in Southern Germany, the Netherlands, and Romania. Questionnaire and stool sample collection for this cross-sectional study took place from September 2018 to March 2020. Selected cultures of participants’ stool samples were analyzed for detection of ESBL-EC. A total of 1183 participants were included in the analyses: 333 from Germany, 689 from the Netherlands, and 161 from Romania. Travels to Northern Africa (adjusted Odds Ratio, aOR 4.03, 95% Confidence Interval, CI 1.67–9.68), Sub-Saharan Africa (aOR 4.60, 95% CI 1.60–13.26), and Asia (aOR 4.08, 95% CI 1.97–8.43) were identified as independent risk factors for carriage of ESBL-EC. Therefore, travel to these regions should continue to be routinely asked about by clinical practitioners as possible risk factors when considering antibiotic therapy.

Keywords:

antibiotic resistance; antimicrobial resistance; risk factors; ESBL E. coli; travels

1. Introduction

Extended-spectrum β-lactamases (ESBLs) are plasmid-mediated enzymes that inactivate β-lactam antibiotics, posing a significant therapeutic challenge in the treatment of both hospital and community-acquired infections [1]. Infections with ESBL-producing E. coli (ESBL-EC) often require therapy with last-resort antibiotics, increasing both the risk of resistance and the associated healthcare costs [2,3]. Resistance to last resort antibiotics further limits treatment options and is associated with prolonged hospital stays and increased mortality [4]. An increase in the prevalence of ESBL-EC, in both community and healthcare settings, is now observed worldwide: the current global prevalence of healthy individuals with ESBL-EC from 2003 to 2018 is estimated to be 16.5%; having increased from 2.6% in 2003–2005 to 21.1% in 2015–2018 [5]. In 2019, we estimated the prevalence of these bacteria in the general population of three European countries, and we found it to be 13% in Romania, 8% in Germany, and 6% in the Netherlands [6]. For comparison, the current prevalence in Europe is 6% [5].

The development and spread of antibiotic resistance (AR) is correlated with the use of antibiotics in the healthcare sector and in the agriculture and husbandry sectors [1,3,7,8]. Antibiotic therapy is also a risk factor for carriage of AR by individuals. Other potential risk factors include: travels to high-risk areas for AR [2,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28], consumption of food contaminated with AR bacteria [29,30], a poorer health status that leads individuals into being treated with antibiotics or at healthcare facilities increasing their exposure to AR bacteria [23,26], and occupation where the workplace might potentially increase exposure to antibiotics or AR bacteria, such as working at animal markets, dairy facilities, farms, slaughterhouses, wastewater treatment plants, and healthcare facilities [31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46]. However, most of the studies examining potential risk factors focus on high-risk populations, such as travelers [10,12,16,20,22,26,27,47], healthcare workers and patients [40,45,46,48,49,50], swimmers [51,52,53], farmers [33,34,35,36,38,39,41,43,44], and slaughterhouse workers [32], and often use small, convenient samples of, e.g., students [2,18,19,23]. However, risk factors for AR in the general population have not yet been sufficiently investigated. This is of great importance for developing preventive measures and antibiotic therapy policies.

As part of the larger AWARE study [6,54], this study aimed to explore, describe, and characterize potential risk factors for carriage of ESBL-EC in a large sample of European individuals recruited from the general population in three countries with a different prevalence of AR, i.e., Germany, the Netherlands, and Romania.

2. Materials and Methods

2.1. Study Design and Participants

The study population comes from participants enrolled in the large trans-European cross-sectional AWARE study (Antibiotic Resistance in Wastewater: Transmission Risks for Employees and Residents around Wastewater Treatment Plants). The full methodology of this project has been previously described [6,54]. The subset of the data used in these analyses corresponds to individuals from the general population living more than 1000 m away from a local WWTP, and, thus, not exposed to potential AR bacteria coming from such facilities. Data collection took place from September 2018 to March 2020 in Southern Germany, the Netherlands, and Romania. Having age between 16 and 67 years was an inclusion criterion.

In Southern Germany, we recruited participants using households as the unit of recruitment. We obtained household participant information from local civil registries. Invitation letters were mailed to all individuals older than 16 years of age within the household. For locations where we could not obtain participant information through the civil registries, invitation letters were dropped in household mailboxes by members of the study team. Aids in recruitment included two reminder letters, articles about the project in the local newspaper, recruitment campaigns via Facebook, and a raffle of shopping vouchers worth EUR 1500 in total for participants who completed the study. In the Netherlands, the offices of general practitioners served as recruitment points. We used ArcGis™ [55] to identify all postal addresses in a 500-m radius from 22 different General Practitioners’ (GP) practices and then we randomly retrieved contact information for 200–500 households per GP practice using the Dutch Personal Records Database. The invitation to participate was addressed to all members of these households aged over the age of 16 (conform the conditions for General Data Protection Regulation data use). All participants who completed the study received a shopping voucher worth EUR 20. In Romania, we identified participant households and invited participants through door-to-door visits.

Ethics approval was obtained from the Ethics Committee of the University of Munich (LMU) (Project-No. 17-734) and the Research Ethics Committee of the University of Bucharest (Registration-No. 164/05.12.2017). The ethics board in the Netherlands exempted this study for ethical approval under the Dutch Medical Research Involving Human Subjects Act (WMO; Committee: Medisch Ethische Toetsingscommissie, number of confirmation: 19-001/C). All subjects gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki of 1975, revised in 2013.

2.2. Variables of Interest

Potential Risk Factors

Participants were asked to complete an online questionnaire [54] containing questions about socio-demographic characteristics, including date of birth (used to operationalize age in years), sex (female, male), educational level (according to the national educational system), and country of residence (Germany, the Netherlands, or Romania). The questionnaire also included questions about potential risk factors for carriage of ESBL-EC in the past year, such as: job history; hospital and farm visits (no, yes); contact with animals (no, yes); contact with patients or human tissues at work (never, rarely, sometimes, often, always); use of antibiotics and antacids (no, yes, do not know); self-reported health status (poor, fair, good, very good, excellent); self-reported frequency of diarrhea (never, rarely, sometimes, often, always); surgeries (no, yes); and international travel to Europe, Asia, North Africa, Sub-Saharan Africa, North America, Central America or Mexico, South America, and Australia or Oceania (never, once, 2–3 times, more than 3 times, do not know). The details on how these variables were chosen have been previously published [54].

Educational level was explored using national educational system levels and then dichotomized into low (pre-primary education to lower secondary education) or high educational level (upper secondary education to Doctoral or equivalent) according to the Standard Classification of Education (ISCED) [56,57,58]. Variables using a frequency scale with five levels were reduced to two levels in the case of frequency of diarrhea (never, rarely, or sometimes/often or always) and of self-reported health status (good, very good or excellent/fair or poor), and in the case of patient contact and of work with human tissues into three levels (never/rarely or sometimes/often or always). In questions including a “do not know” option (antibiotics and antacid intake, travels to Europe), this option was coded into the “no” category considering that the proportion of participants choosing this option was very low (3.1% for antibiotic intake, 2.9% for antacid intake, 0.1% for travels to Europe). We show descriptive counts for international travel variables as we collected the questionnaire data, i.e., using the following frequency scale for travel in the past 12 months: “never”, “once”, “2–3 times”, “more than 3 times”, “do not know”. For inferential analysis using regression models, these variables were collapsed into two levels: “never” and “at least once”. For the regression models, travels to Central and South America were collapsed into one variable. Additionally, we constructed a travel score considering travel to Asia, North Africa, Sub-Saharan Africa, Central America or Mexico, South America, and the European countries Italy, Bulgaria, Greece, and Slovenia as high-risk areas for AR. The travel score adds one point for travelling once, two points for travelling 2–3 times, and 3 points for travelling more than 3 times to any of these areas in the past year, while “never” was translated into zero points.

2.3. Outcome of Interest

In the Netherlands, all recruited participants were asked to provide a stool sample using a stool sample kit. In Germany and Romania, only participants who completed the online questionnaire were asked to provide a stool sample. After sampling, stool samples were kept refrigerated, transported in cooling boxes (2 °C to 8 °C), and processed within 24 h. Samples were inoculated directly into TBX (only in the Netherlands and Romania) or MacConkey (in Germany) agar plates (for E. coli), and on ChromID® ESBL (for ESBL-EC) and incubated at 36 °C ± 1 °C for 24–48 h. In case of positive results for ESBL-EC, 2 separate isolates per sample were collected from the selective ESBL plate for antibiotic resistance phenotype confirmation, and identification using MALDI-TOF MS (Matrix Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry). ESBL confirmatory tests were performed using cefotaxime and ceftazidime disks, alone and combined with clavulanate, following guidelines from the Clinical Laboratory Standards Institute (CLSI) [59]. The test was considered positive for strains showing a 5 mm increase in zone diameter in the presence of clavulanate. Stool sample results were coded binarily as positive or negative and included in the analyses.

2.4. Statistical Analyses

We used a Mann–Whitney test for observing differences in non-normally distributed numerical variables (age and travel score) and the Fisher’s exact test for differences in proportions (all the other variables). Variable selection was performed using a combination of bivariate analysis results (p-value ≤ 0.2) and expert opinion. We regressed carriage of ESBL-EC on a set of potential risk factors using two logistic regression models. The first model included sociodemographic variables (age, sex, educational level, and country of residence), frequency of diarrhea, antibiotics use, and travel score. The second model was similar to the first one, except that, instead of the travel score, it included each geographical area as we assessed them in the questionnaire, with “Central America or Mexico” and “South America” collapsed into one variable. We report both crude and adjusted estimates for both models. Missing values were handled by multiple imputation where the missing mechanism was missing at random (MAR) or missing completely at random (MCAR). MAR means that the probability of the data being missing is not due to unobserved data, conditional on the data that were collected. MAR is the second-best scenario for multiple imputation after MCAR, which occurs when the probability of the data being missing does not depend on the observed or unobserved data, and is, thus, the best scenario for multiple imputation [60]. Multiple imputation diagnostic tables can be found in the Supplementary Materials (Supplementary Tables S1 and S2). Inverse probability of sample weights was used to adjust for non-response by country [61,62]. We present model results in odds ratios (OR) with the corresponding 95% confidence intervals (CI). All analyses were performed in R version 4.1.0 [63].

3. Results

3.1. Study Population

In Germany, we invited 3153 residents (response 11%), while in the Netherlands we contacted 13,918 identified individuals by postal service, of which 10,448 were eligible by age (response 6%), and in Romania we invited 280 residents (response 54%). A total of 1183 participants were included in the analyses: 333 from Germany, 689 from the Netherlands, and 161 from Romania. The average prevalence of ESBL-EC carriage across the three countries was 7.5%, which corresponds to 8.4% in Germany, 6.1% in the Netherlands, and 12.6% in Romania. A total of 109 participants (95 in Germany, 3 in the Netherlands, and 11 in Romania) did not hand in a stool sample or had non-valid stool samples (9.2%). The large proportion of missing stool samples in Germany stems from having a short window for sample collection and transportation in this location, with which many participants failed to hand in the sample. This, however, did not happen in the Netherlands or Romania where samples were to be brought to GP practices within a 500-m distance from people’s homes collected by door-to-door visits.

The majority of participants in the overall sample were women (59.4%), middle-aged (median age 48 years, IQR 35–59), and highly educated (66.5%). Most participants reported no major risk factors for AR in the past year: no hospital visits neither as patient (92.9%), nor as professional (96.5%) or visitor (97.9%), no patient contact (73.6%), no use of antibiotics (76.1%) or antacids (77.2%), no surgeries (95.5%), no or infrequent diarrhea (94.2%), no work with human tissues (75.4%), no work with animals (96.5%), no work at a farm (99.0%), no work at a slaughterhouse (99.8%), no work with manure (97.0%), no farm visits (89.3%), and no animal contact (has no horses: 97.0%, has no dogs: 77.2%, has no cats: 75.7%). Additionally, most participants reported a health status from good to excellent (86.5%). Although a little more than two thirds of the study population reported travelling within Europe at least once in the past year (71.7%), they rarely traveled outside of the European continent: Australia or Oceania (1.0%), Central America (2.0%), South America (1.9%), Sub-Saharan Africa (2.4%), North America (3.6%), Northern Africa (4.2%), or Asia (7.2%). The proportion of population characteristics for individuals with a positive stool sample for ESBL-EC were similar as for the whole study population (Table 1 and Table 2).

3.2. Risk Factors for ESBL-EC Carriage

Descriptive analyses including data from all study centers showed that ESBL-EC positive participants had higher education and were less likely to have a dog as a pet (Table 1). Furthermore, they were more likely to have had traveled at least once in the past year to Sub-Saharan Africa, Northern Africa, Asia, or North America according to bivariate analyses.

Country-specific analyses showed that travels to Northern Africa were associated with ESBL-EC carriage in the German sub-population, while an association was identified in the Dutch sub-population for traveling to Northern Africa, Sub-Saharan Africa, or Asia. In the Romanian subpopulation, high educational level, not having a dog as a pet, and working with human tissues were factors associated with ESBL-EC carriage. The travel score for travel to geographical areas with a known high-risk for AR, was significantly higher in the overall and Dutch ESBL-EC positive populations (p-value 0.02 and 0.001, respectively), compared to participants without ESBL-EC carriage (Table 2).

Confirming descriptive and bivariate results, self-reported travel to North Africa, Sub-Saharan Africa, and Asia at least once in the past year were identified as independent risk factors for ESBL-EC carriage in our study population, both in crude and adjusted models (Figure 1). A summary of the adjusted estimates for travel to different geographical areas can be seen in Figure 2.

On average, participants were about four times more likely to be carriers of ESBL-EC after travelling at least once in the past year to Northern Africa (adjusted OR 4.03, 95% CI 1.67–9.68), Sub-Saharan Africa (adjusted OR 4.60, 95% CI 1.60–13.26), and Asia (adjusted OR 4.08, 95% CI 1.97–8.43, Supplementary Table S4), compared with no travels to these regions. Although participants were twice as likely to be ESBL-EC carriers after traveling to North America, we could only identify a statistically significant association in the crude model (OR crude 2.79, 95% CI 1.17–6.67 vs. OR adjusted 2.40, 95% CI 0.94–6.09). The model including the travel score confirms these findings (Figure 1, Supplementary Table S3): Participants were 28% more likely to be ESBL-EC carriers when their travel score increased by one point, i.e., when they traveled at least once to any of the pre-specified high-risk areas for AR (adjusted OR 1.28, 95% CI 1.01–1.64, Supplementary Table S3).

4. Discussion

In this study, we found that destination for travels made during the past year is an important personal risk factor for carriage of ESBL-EC in the general population, especially North Africa, Sub-Saharan Africa, Asia, and—to some extent—North America. Other studies in risk populations have found similar results: some of these studies indicate that the prevalence of ESBL-EC acquisition is worryingly high in visitors returning from India, China and Southeast Asia, Middle East, Northern Africa, and Central and South America [64,65]. For European residents, travel outside of Europe was identified as a major travel risk factor [17]. A 2017 prospective study performed on Dutch travelers (n = 2001) found out that 34.3% of participants who were ESBL negative before travel, became positive for ESBL-EC during their travels, with the highest number being among participants travelling to Southern Asia [13].

We also found some differences in the country-specific travel patterns. By having collected a large sample size in The Netherlands, we were able to identify that this sub-population is at higher risk of ESBL-EC carriage when travelling to North Africa, Sub-Saharan Africa, and Asia within the past year. These results are comparable to those of a recently published large cross-sectional study of the Dutch general population, which identified traveling to Africa and Asia as independent risk factors for ESBL-EC carriage [66]. We found similar patterns in our German study population, where participants are at higher risk of ESBL-EC carriage after travels to Northern Africa and North America within the past year. Given that the national estimated prevalence of ESBL-EC causing urinary tract infections in the U.S. is 15.7%, ranging from 10.6% in the West North Central states to as high as 29.6% in the Mid-Atlantic states [67], our finding that travelers to North America were also at increased risk is not surprising. Conversely, in Romania, although the prevalence is already high, we found that the travel frequency is lower, therefore limiting our ability to analyze the effect of travel on ESBL-EC carriage in this subpopulation. Most of the Romanian participants reported not having travelled internationally at all within the past year. These findings suggest that the role of travel is country or context dependent.

The sewage surveillance data regarding the AR are in line with the estimated global burden of this threat. Current estimates indicate that the presence of AR genes found in the sewage is alarmingly at the highest level in Africa followed by Asia [68]. Models from sewage surveillance data show that the predicted clinical resistance to aminopenicillin, fluoroquinolones, and third generation cephalosporins are also at the highest resistance levels in Africa, followed by Asia [69]. These results from sewage surveillance data are in line with estimated global burden of disease from AR. The percentage of resistant isolates and the estimated death rate from AR E. coli have been reported to be at the highest in South Asia, followed by Sub-Saharan Africa [70]. Even though there have been some efforts in starting and maintaining clinical and sewage surveillance of AR bacteria in some countries of Africa and Asia [71], data on AR in these areas are still lacking to a large extent [70]. Some of these efforts include stewardship and surveillance programs in Ethiopia [72] and Ghana [73], or more generally in the African [74,75] and Asian regions [76,77,78]. The World Health Organization Global Antimicrobial Resistance and Use Surveillance System (WHO-GLASS) Report in 2021 states that out of 47 African countries, territories, and areas, only half (23/47, 49%) are enrolled in GLASS and only a third (15/47, 32%) reported information from the national surveillance system to GLASS [79]. The South East Asia region provides a better outlook: out of 11 countries, territories, and areas in South East Asia, all of them are enrolled in GLASS, and nine of them (81%) reported information from the national surveillance system [79]. However, some of the challenges to these programs include bias in sampling and data collection in these areas, which leads to gaps in knowledge about the AR situation at the global level.

Our findings have implications for clinical practice. Asking patients about their travel history in the past year might help clinicians in their decision-making process for choosing specific antibiotic protocols as the first-, second-, or third-line of treatment. Further, the use of a travel score, such as the one we have constructed, might be a straightforward way of quantifying the degree of risk due to travel. However, our travel score is still far from ready to be used in clinical practice in its current form. On the one hand, it does not include other details about the travel experience, such as reason for travel, length of stay, or place of residence within the visited location. It might be that individuals who travel abroad for business reasons are exposed to a very different set of environmental factors than those who travel to visit friends or family, partly because their consumption patterns might be different. Additionally, closer interactions with locals might increase the risk of direct or indirect exposure to AR bacteria such as ESBL-EC when sharing toilets with friends or family members, as opposed to staying at a hotel with private toilet facilities and frequent cleaning and disinfection.

According to our data, no other risk factor explored besides travels posed an effect on carriage of ESBL-EC. Antibiotics use is a risk factor for AR commonly mentioned in the literature [23,26]. We believe that one of the reasons why we were not able to estimate an effect for antibiotics use in our study is that, although these effects are relatively easy to identify in high-risk populations such as travelers, farmers, slaughterhouse workers, healthcare providers, or patients, the sample size needed to detect an effect in the general population would be considerably higher. Another potential reason is that the effect of antibiotics use on AR might not be detectable more than 6 months after travel. A recent study by Bunt et al. [66] in 4177 Dutch participants from the general population (four times the size of our study) showed a positive effect of antibiotics use for ESBL-EC carriage up to 6 months before study participation, but not at 6 to 12 months, nor more than a year before participation.

The main strength of our study is that, to our knowledge, this is the first international study across several countries that confirms travel risks for AR in the general population. Whereas many previously published studies have indeed reported travel as a risk factor for ESBL-EC carriage, our study was performed on a large sample stemming from the general population. These are generally healthy, working adults that were recruited without considering any specific high-risk factor for AR. Yet, we have found that travel is a risk factor for carriage of ESBL-EC, have characterized high-risk geographical areas for travels, and have estimated the magnitude of the effect of travelling to these areas. Additionally, although the study population was enrolled as part of the large trans-European cross-sectional AWARE study, it was assumed that individuals from the general population living more than 1000 m away from a local WWTP were not exposed to potential AR bacteria coming from such facilities. Therefore, we have a relatively large sample of participants drawn from the general population in Southern Germany, the Netherlands, and Romania. In contrast, other similar studies explored risk factors in large sample sizes from only one country [66], in specific high-risk populations, such as farmers [33,34,35,36,38,39,41,43,44] and slaughterhouse workers [32], healthcare workers and patients [40,45,46,48,49,50], or travelers [8,10,14,18,20,25,26,46], or in convenient samples of students [2,18,19,23]. Further, when exploring frequency of travel, we considered all areas of the globe, and did not limit ourselves to low-and-middle income countries or other areas that would have been otherwise considered a priori as high-risk areas for AR.

Some of the limitations of our study include a low response, especially in Germany and in the Netherlands, and a high proportion of missing values, especially in Germany, which lead to a relatively low statistical power for some potential risk factors and might limit the representativeness of our sample. We have used analytical tools, such as inverse probability of sampling weights, based on the response and multiple imputation to address these issues. Our potential risk factors were assessed by a questionnaire instead of by direct measurement or by cross-referencing with medical data, which might lead to recall bias and, thus, misclassification based on the risk factors. If this was the case, we would be erring on the conservative side by underestimating potential effects. Further, our sample might not be exempt from selection effects as our population was relatively young and highly educated. Age and socio-economic status (SES) might also play a role in our estimation of results from travel variables because we might assume that younger people travel more often and to different regions of the globe than older people, or because people of a higher SES might have the financial resources and freedom to travel more often than people of lower SES. In our study, we have included age and educational level (as a proxy for SES) in our regression models, thus adjusting for these potential confounders.

5. Conclusions

In our study, we have identified travel to Northern Africa, Sub-Saharan Africa, Asia, and—to some extent—North America as independent risk factors for ESBL-EC carriage in a large sample of European individuals residing in Southern Germany, the Netherlands, and Romania. With our data, we were not able to identify other potential risk factors for carriage of ESBL-EC frequently mentioned in the literature such as the use of antibiotics within the past year, probably because the sample size needed to detect such effects in the general population would have to be at least about four times as large as ours. Further, we have developed a travel score that, although it needs refining to include information, such as reason for travel, length of stay, or place of residence, could be developed as a valuable tool in clinical practice when dealing with patients in need of an empirical treatment protocol with antibiotics. Questions about travel to Africa and Asia should continue to be routinely asked in clinical practice, as these travels are risk factors when considering antibiotic therapy.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijerph19084758/s1, Table S1: Multiple imputation diagnostics—Traditional (unweighted) logistic regression models, complete cases vs. imputed for model with personal travel score, AWARE Study, 2022; Table S2: Multiple imputation diagnostics—Traditional (unweighted) logistic regression models, complete cases vs. imputed for model with individual travel areas, AWARE Study, 2022; Table S3: Models comparing risk factors for ESBL-producing E. coli in stool samples, with personal travel score, AWARE Study, 2022; Table S4: Models comparing risk factors for ESBL-producing E. coli in stool samples, with individual travel areas, AWARE Study, 2022.

Author Contributions

Study conception and design: M.C.C., D.G.J.L., K.R., D.N., A.W., A.M.d.R.H. and H.S. Fieldwork and data collection: D.R.-M., H.B., M.P., M.K., L.M., G.P.G. and L.W. Microbiology: H.B., M.P., M.K., L.M., G.P.G., M.C.C., B.S., A.W., A.M.d.R.H. and H.S. Data cleaning and analysis: D.R.-M., H.B. and M.P. Interpretation of the data: D.R.-M., F.B., H.B., C.-F.F., L.W., D.G.J.L., K.R., A.M.d.R.H. and H.S. Drafting of the manuscript: D.R.-M. All authors have read and agreed to the published version of the manuscript.

Funding

AWARE (Antibiotic Resistance in Wastewater: Transmission Risks for Employees and Residents around Wastewater Treatment Plants) is supported by the European Commission (JPI-EC-AMR ERA-Net Cofund grant no 681055), the Bundesministerum für Bildung und Forschung, DLR Projektträger (01KI1708), UEFISCDI project ERANET-JPI-EC-AMR-AWARE-WWTP No. 26/2017, the Netherlands Organisation for Health Research and Development, The Hague, the Netherlands (ZonMw, grant 547001007), and the Swedish Research Council VR Grant No. 2016-06512.

Institutional Review Board Statement

Ethics approval was obtained from the Ethics Committee of the University of Munich (LMU) (Project-No. 17-734) and the Research Ethics Committee of the University of Bucharest (Registration-No. 164/05.12.2017). The ethics board in the Netherlands exempted this study for ethical approval under the Dutch Medical Research Involving Human Subjects Act (WMO; Committee: Medisch Ethische Toetsingscommissie, number of confirmation: 19-001/C).

Informed Consent Statement

All subjects gave their informed consent for inclusion before they participated in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy reasons.

Acknowledgments

AWARE (Antibiotic Resistance in Wastewater: Transmission Risks for Employees and Residents around Wastewater Treatment Plants) is supported by the European Commission (JPI-EC-AMR ERA-Net Cofund grant no 681055), the Bundesministerum für Bildung und Forschung, DLR Projektträger (01KI1708), UEFISCDI project ERANET-JPI-EC-AMR-AWARE-WWTP No. 26/2017, the Netherlands Organisation for Health Research and Development, The Hague, the Netherlands (ZonMw, grant 547001007), and the Swedish Research Council VR Grant No. 2016-06512.

Conflicts of Interest

The authors declare no conflict of interest.

References

Subramaniam, G.; Girish, M. Antibiotic Resistance—A Cause for Reemergence of Infections. Indian J. Pediatr. 2020, 87, 937–944. [Google Scholar] [CrossRef]

Kamenshchikova, A.; Wolffs, P.F.G.; Hoebe, C.J.P.A.; Penders, J.; Park, H.Y.; Kambale, M.S.; Horstman, K. Combining stool and stories. Exploring antimicrobial resistance among a longitudinal cohort of international health students. BMC Infect. Dis. 2021, 21, 1008. [Google Scholar] [CrossRef]

Polianciuc, S.I.; Gurzău, A.E.; Kiss, B.; Ştefan, M.G.; Loghin, F. Antibiotics in the environment: Causes and consequences. Med. Pharm. Rep. 2020, 93, 231–240. [Google Scholar] [CrossRef]

Budhram, D.R.; Mac, S.; Bielecki, J.M.; Patel, S.N.; Sander, B. Health outcomes attributable to carbapenemase-producing Enterobacteriaceae infections: A systematic review and meta-analysis. Infect. Control Hosp. Epidemiol. 2020, 41, 37–43. [Google Scholar] [CrossRef]

Bezabih, Y.M.; Sabiiti, W.; Alamneh, E.; Bezabih, A.; Peterson, G.M.; Bezabhe, W.M.; Roujeinikova, A. The global prevalence and trend of human intestinal carriage of ESBL-producing Escherichia coli in the community. J. Antimicrob. Chemother. 2021, 76, 22–29. [Google Scholar] [CrossRef]

Rodríguez-Molina, D.; Berglund, F.; Blaak, H.; Flach, C.-F.; Kemper, M.; Marutescu, L.; Gradisteanu, G.P.; Popa, M.; Spießberger, B.; Weinmann, T.; et al. Carriage of ESBL-producing Enterobacterales in wastewater treatment plant workers and surrounding residents—The AWARE Study. Eur. J. Clin. Microbiol. Infect. Dis. 2021. [Google Scholar] [CrossRef]

Larsson, D.G.J.; Andremont, A.; Bengtsson-Palme, J.; Brandt, K.K.; de Roda Husman, A.M.; Fagerstedt, P.; Fick, J.; Flach, C.-F.; Gaze, W.H.; Kuroda, M.; et al. Critical knowledge gaps and research needs related to the environmental dimensions of antibiotic resistance. Environ. Int. 2018, 117, 132–138. [Google Scholar] [CrossRef]

Iwu, C.D.; Korsten, L.; Okoh, A.I. The incidence of antibiotic resistance within and beyond the agricultural ecosystem: A concern for public health. MicrobiologyOpen 2020, 9, e1035. [Google Scholar] [CrossRef]

Arcilla, M.S.; Van Hattem, J.M.; Bootsma, M.C.; Van Genderen, P.J.; Goorhuis, A.; Schultsz, C.; E Stobberingh, E.; A Verbrugh, H.; De Jong, M.D.; Melles, D.C.; et al. The Carriage of Multiresistant Bacteria after Travel (COMBAT) prospective cohort study: Methodology and design. BMC Public Health 2014, 14, 410. [Google Scholar] [CrossRef]

Kantele, A.; Lääveri, T.; Mero, S.; Vilkman, K.; Pakkanen, S.; Ollgren, J.; Antikainen, J.; Kirveskari, J. Antimicrobials Increase Travelers’ Risk of Colonization by Extended-Spectrum Betalactamase-Producing Enterobacteriaceae. Clin. Infect. Dis. 2015, 60, 837–846. [Google Scholar] [CrossRef]

Ruppé, E.; Armand-Lefèvre, L.; Estellat, C.; Consigny, P.-H.; El Mniai, A.; Boussadia, Y.; Goujon, C.; Ralaimazava, P.; Campa, P.; Girard, P.-M.; et al. High Rate of Acquisition but Short Duration of Carriage of Multidrug-Resistant Enterobacteriaceae after Travel to the Tropics. Clin. Infect. Dis. 2015, 61, 593–600. [Google Scholar] [CrossRef]

van Hattem, J.M.; Arcilla, M.S.; Bootsma, M.C.; van Genderen, P.J.; Goorhuis, A.; Grobusch, M.P.; Molhoek, N.; Lashof, A.M.O.; Schultsz, C.; E Stobberingh, E.; et al. Prolonged carriage and potential onward transmission of carbapenemase-producing Enterobacteriaceae in Dutch travelers. Future Microbiol. 2016, 11, 857–864. [Google Scholar] [CrossRef]

Arcilla, M.S.; van Hattem, J.M.; Haverkate, M.R.; Bootsma, M.C.J.; van Genderen, P.J.J.; Goorhuis, A.; Grobusch, M.P.; Lashof, A.M.O.; Molhoek, N.; Schultsz, C.; et al. Import and spread of extended-spectrum β-lactamase-producing Enterobacteriaceae by international travellers (COMBAT study): A prospective, multicentre cohort study. Lancet Infect. Dis. 2017, 17, 78–85. [Google Scholar] [CrossRef]

Woerther, P.-L.; Andremont, A.; Kantele, A. Travel-acquired ESBL-producing Enterobacteriaceae: Impact of colonization at individual and community level. J. Travel Med. 2017, 24 (Suppl. 1), S29–S34. [Google Scholar] [CrossRef]

Lorme, F.; Maataoui, N.; Rondinaud, E.; Esposito-Farèse, M.; Clermont, O.; Ruppe, E.; Arlet, G.; Genel, N.; Matheron, S.; Andremont, A.; et al. Acquisition of plasmid-mediated cephalosporinase producing Enterobacteriaceae after a travel to the tropics. PLoS ONE 2018, 13, e0206909. [Google Scholar] [CrossRef]

Vilkman, K.; Lääveri, T.; Pakkanen, S.H.; Kantele, A. Stand-by antibiotics encourage unwarranted use of antibiotics for travelers’ diarrhea: A prospective study. Travel Med. Infect. Dis. 2019, 27, 64–71. [Google Scholar] [CrossRef]

Arcilla, M.S.; Van Hattem, J.M.; Bootsma, M.C.; van Genderen, P.J.; Goorhuis, A.; Grobusch, M.P.; Klaassen, C.H.; Lashof, A.M.O.; Schultsz, C.; Stobberingh, E.E.; et al. Prevalence and risk factors for carriage of ESBL-producing Enterobacteriaceae in a population of Dutch travellers: A cross-sectional study. Travel Med. Infect. Dis. 2020, 33, 101547. [Google Scholar] [CrossRef]

Dao, T.L.; Canard, N.; Hoang, V.T.; Ly, T.D.A.; Drali, T.; Ninove, L.; Fenollar, F.; Raoult, D.; Parola, P.; Marty, P.; et al. Risk factors for symptoms of infection and microbial carriage among French medical students abroad. Int. J. Infect. Dis. 2020, 100, 104–111. [Google Scholar] [CrossRef]

Dao, T.L.; Hoang, V.T.; Ly, T.D.A.; Magmoun, A.; Canard, N.; Drali, T.; Fenollar, F.; Ninove, L.; Raoult, D.; Parola, P.; et al. Infectious disease symptoms and microbial carriage among French medical students travelling abroad: A prospective study. Travel Med. Infect. Dis. 2020, 34, 101548. [Google Scholar] [CrossRef]

Mellon, G.; Turbett, S.E.; Worby, C.; Oliver, E.; Walker, A.T.; Walters, M.; Kelly, P.; Leung, D.; Knouse, M.; Hagmann, S.; et al. Acquisition of Antibiotic-Resistant Bacteria by U.S. International Travelers. N. Engl. J. Med. 2020, 382, 1372–1374. [Google Scholar] [CrossRef]

Meurs, L.; Lempp, F.S.; Lippmann, N.; Trawinski, H.; Rodloff, A.C.; Eckardt, M.; Klingeberg, A.; Eckmanns, T.; Walter, J.; Lübbert, C.; et al. Intestinal colonization with extended-spectrum β-lactamase producing Enterobacterales (ESBL-PE) during long distance travel: A cohort study in a German travel clinic (2016–2017). Travel Med. Infect. Dis. 2020, 33, 101521. [Google Scholar] [CrossRef]

Worby, C.J.; Earl, A.M.; Turbett, S.E.; Becker, M.; Rao, S.R.; Oliver, E.; Walker, A.T.; Walters, M.; Kelly, P.; Leung, D.T.; et al. Acquisition and Long-term Carriage of Multidrug-Resistant Organisms in US International Travelers. Open Forum Infect. Dis. 2020, 7, ofaa543. [Google Scholar] [CrossRef]

Dao, T.L.; Hoang, V.T.; Magmoun, A.; Ly, T.D.A.; Baron, S.A.; Hadjadj, L.; Canard, N.; Drali, T.; Gouriet, F.; Raoult, D.; et al. Acquisition of multidrug-resistant bacteria and colistin resistance genes in French medical students on internships abroad. Travel Med. Infect. Dis. 2021, 39, 101940. [Google Scholar] [CrossRef]

Kantele, A.; Lääveri, T. Extended-spectrum beta-lactamase-producing strains among diarrhoeagenic Escherichia coli—Prospective traveller study with literature review. J. Travel Med. 2021, 29, taab042. [Google Scholar] [CrossRef]

Lääveri, T.; Antikainen, J.; Mero, S.; Pakkanen, S.H.; Kirveskari, J.; Roivainen, M.; Kantele, A. Bacterial, viral and parasitic pathogens analysed by qPCR: Findings from a prospective study of travellers’ diarrhoea. Travel Med. Infect. Dis. 2021, 40, 101957. [Google Scholar] [CrossRef]

Sridhar, S.; Turbett, S.E.; Harris, J.B.; LaRocque, R.C. Antimicrobial-resistant bacteria in international travelers. Curr. Opin. Infect. Dis. 2021, 34, 423–431. [Google Scholar] [CrossRef]

Tufic-Garutti, S.d.S.; Ramalho, J.V.A.R.; Longo, L.G.d.A.; de Oliveira, G.C.; Rocha, G.T.; Vilar, L.C.; da Costa, M.D.; Picão, R.C.; de Carvalho Girão, V.B.; Santoro-Lopes, G.; et al. Acquisition of antimicrobial resistance determinants in Enterobacterales by international travelers from a large urban setting in Brazil. Travel Med. Infect. Dis. 2021, 41, 102028. [Google Scholar] [CrossRef]

Turunen, K.A.; Kantele, A.; Professor of Infectious Diseases. Revisiting travellers’ diarrhoea justifying antibiotic treatment: Prospective study. J. Travel Med. 2021, 28, taaa237. [Google Scholar] [CrossRef]

Mulder, M.; Jong, J.K.-D.; Goessens, W.; de Visser, H.; Ikram, M.A.; Verbon, A.; Stricker, B. Diet as a risk factor for antimicrobial resistance in community-acquired urinary tract infections in a middle-aged and elderly population: A case–control study. Clin. Microbiol. Infect. 2019, 25, 613–619. [Google Scholar] [CrossRef]

Mughini-Gras, L.; Dorado-García, A.; Van Duijkeren, E.; van Bunt, G.; van den Dierikx, C.M.; Bonten, M.J.M.; Bootsma, M.C.J.; Schmitt, H.; Hald, T.; Evers, E.G.; et al. Attributable sources of community-acquired carriage of Escherichia coli containing β-lactam antibiotic resistance genes: A population-based modelling study. Lancet Planet. Health 2019, 3, e357–e369. [Google Scholar] [CrossRef]

Sasaki, Y.; Kakizawa, H.; Baba, Y.; Ito, T.; Haremaki, Y.; Yonemichi, M.; Ikeda, T.; Kuroda, M.; Ohya, K.; Hara-Kudo, Y.; et al. Antimicrobial Resistance in Salmonella Isolated from Food Workers and Chicken Products in Japan. Antibiotics 2021, 10, 1541. [Google Scholar] [CrossRef] [PubMed]

Van Gompel, L.; Dohmen, W.; Luiken, R.E.C.; Bouwknegt, M.; Heres, L.; Van Heijnsbergen, E.; Jongerius-Gortemaker, B.G.M.; Scherpenisse, P.; Greve, G.D.; Tersteeg-Zijderveld, M.H.G.; et al. Occupational Exposure and Carriage of Antimicrobial Resistance Genes (tetW, ermB) in Pig Slaughterhouse Workers. Ann. Work Expo. Health 2020, 64, 125–137. [Google Scholar] [CrossRef] [PubMed]

Wang, Y.; Lyu, N.; Liu, F.; Liu, W.J.; Bi, Y.; Zhang, Z.; Ma, S.; Cao, J.; Song, X.; Wang, A.; et al. More diversified antibiotic resistance genes in chickens and workers of the live poultry markets. Environ Int. 2021, 153, 106534. [Google Scholar] [CrossRef] [PubMed]

Talukder, S.; Hasan, M.; Mandal, A.K.; Tasmim, S.T.; Parvin, M.S.; Ali, Y.; Nahar, A.; Islam, Z.; Islam, T. Epidemiology and antimicrobial resistance profiles of Salmonella in chickens, sewage, and workers of broiler farms in selected areas of Bangladesh. J. Infect. Dev. Ctries 2021, 15, 1155–1166. [Google Scholar] [CrossRef]

Momoh, A.H.; Kwaga, J.K.P.; Bello, M.; Sackey, A.K.B.; Larsen, A.R. Antibiotic resistance and molecular characteristics of Staphylococcus aureus isolated from backyard-raised pigs and pig workers. Trop. Anim. Health Prod. 2018, 50, 1565–1571. [Google Scholar] [CrossRef]

Elhariri, M.; Elhelw, R.; Selim, S.; Ibrahim, M.; Hamza, D.; Hamza, E. Virulence and Antibiotic Resistance Patterns of Extended-Spectrum Beta-Lactamase-Producing Salmonella enterica serovar Heidelberg Isolated from Broiler Chickens and Poultry Workers: A Potential Hazard. Foodborne Pathog. Dis. 2020, 17, 373–381. [Google Scholar] [CrossRef]

Zieliński, W.; Korzeniewska, E.; Harnisz, M.; Drzymała, J.; Felis, E.; Bajkacz, S. Wastewater treatment plants as a reservoir of integrase and antibiotic resistance genes—An epidemiological threat to workers and environment. Environ Int. 2021, 156, 106641. [Google Scholar] [CrossRef]

Tamta, S.; Kumar, O.R.V.; Singh, S.V.; Pruthvishree, B.S.; Karthikeyan, R.; Rupner, R.; Sinha, D.K.; Singh, B.R. Antimicrobial resistance pattern of extended-spectrum β-lactamase-producing Escherichia coli isolated from fecal samples of piglets and pig farm workers of selected organized farms of India. Vet World 2020, 13, 360–363. [Google Scholar] [CrossRef]

Ding, D.; Zhu, J.; Gao, Y.; Yang, F.; Ma, Y.; Cheng, X.; Li, J.; Dong, P.; Yang, H.; Chen, S. Effect of cattle farm exposure on oropharyngeal and gut microbial communities and antibiotic resistance genes in workers. Sci. Total Environ. 2022, 806, 150685. [Google Scholar] [CrossRef]

Ymaña, B.; Luque, N.; Ruiz, J.; Pons, M.J. Worrying levels of antimicrobial resistance in Gram-negative bacteria isolated from cell phones and uniforms of Peruvian intensive care unit workers. Trans. R. Soc. Trop. Med. Hyg. 2022, trab186. [Google Scholar] [CrossRef]

Chanchaithong, P.; Perreten, V.; Am-In, N.; Lugsomya, K.; Tummaruk, P.; Prapasarakul, N. Molecular Characterization and Antimicrobial Resistance of Livestock-Associated Methicillin-Resistant Staphylococcus aureus Isolates from Pigs and Swine Workers in Central Thailand. Microb. Drug Resist. 2019, 25, 1382–1389. [Google Scholar] [CrossRef]

Xu, H.; Zhang, W.; Guo, C.; Xiong, H.; Chen, X.; Jiao, X.; Su, J.; Mao, L.; Zhao, Z.; Li, Q. Prevalence, Serotypes, and Antimicrobial Resistance Profiles among Salmonella Isolated from Food Catering Workers in Nantong, China. Foodborne Pathog. Dis. 2019, 16, 346–351. [Google Scholar] [CrossRef] [PubMed]

Tahoun, A.B.M.B.; Abou Elez, R.M.M.; Abdelfatah, E.N.; Elsohaby, I.; El-Gedawy, A.A.; Elmoslemany, A.M. Listeria monocytogenes in raw milk, milking equipment and dairy workers: Molecular characterization and antimicrobial resistance patterns. J. Glob. Antimicrob. Resist. 2017, 10, 264–270. [Google Scholar] [CrossRef] [PubMed]

Sun, J.; Huang, T.; Chen, C.; Cao, T.-T.; Cheng, K.; Liao, X.-P.; Liu, Y.-H. Comparison of Fecal Microbial Composition and Antibiotic Resistance Genes from Swine, Farm Workers and the Surrounding Villagers. Sci. Rep. 2017, 7, 4965. [Google Scholar] [CrossRef] [PubMed]

Singh, S.; Malhotra, R.; Grover, P.; Bansal, R.; Galhotra, S.; Kaur, R.; Jindal, N. Antimicrobial resistance profile of Methicillin-resistant Staphylococcus aureus colonizing the anterior nares of health-care workers and outpatients attending the remotely located tertiary care hospital of North India. J. Lab. Physicians 2017, 9, 317–321. [Google Scholar] [CrossRef]

Wang, H.-P.; Zhang, H.-J.; Liu, J.; Dong, Q.; Duan, S.; Ge, J.-Q.; Wang, Z.-H.; Zhang, Z. Antimicrobial resistance of 3 types of gram-negative bacteria isolated from hospital surfaces and the hands of health care workers. Am. J. Infect. Control 2017, 45, E143–E147. [Google Scholar] [CrossRef] [PubMed]

Paltansing, S.; Vlot, J.A.; Kraakman, M.E.M.; Mesman, R.; Bruijning, M.L.; Bernards, A.T.; Visser, L.G.; Veldkamp, K.E. Extended-spectrum β-lactamase-producing enterobacteriaceae among travelers from the Netherlands. Emerg. Infect. Dis. 2013, 19, 1206–1213. [Google Scholar] [CrossRef]

Moirongo, R.M.; Lorenz, E.; Ntinginya, N.E.; Dekker, D.; Fernandes, J.; Held, J.; Lamshöft, M.; Schaumburg, F.; Mangu, C.; Sudi, L.; et al. Regional Variation of Extended-Spectrum β-Lactamase (ESBL)-Producing Enterobacterales, Fluoroquinolone-Resistant Salmonella enterica and Methicillin-Resistant Staphylococcus aureus among Febrile Patients in Sub-Saharan Africa. Front. Microbiol. 2020, 11, 567235. Available online: https://www.frontiersin.org/article/10.3389/fmicb.2020.567235 (accessed on 16 February 2022). [CrossRef]

Gashaw, M.; Berhane, M.; Bekele, S.; Kibru, G.; Teshager, L.; Yilma, Y.; Ahmed, Y.; Fentahun, N.; Assefa, H.; Wieser, A.; et al. Emergence of high drug resistant bacterial isolates from patients with health care associated infections at Jimma University medical center: A cross sectional study. Antimicrob. Resist. Infect Control. 2018, 7, 138. [Google Scholar] [CrossRef]

Tham, J.; Odenholt, I.; Walder, M.; Andersson, L.; Melander, E. Risk factors for infections with extended-spectrum β-lactamase-producing Escherichia coli in a county of Southern Sweden. Infect. Drug Resist. 2013, 6, 93–97. [Google Scholar] [CrossRef]

Leonard, A.F.; Zhang, L.; Balfour, A.J.; Garside, R.; Hawkey, P.M.; Murray, A.K.; Ukoumunne, O.C.; Gaze, W.H. Exposure to and colonisation by antibiotic-resistant E. coli in UK coastal water users: Environmental surveillance, exposure assessment, and epidemiological study (Beach Bum Survey). Environ. Int. 2018, 114, 326–333. [Google Scholar] [CrossRef] [PubMed]

Schijven, J.F.; Blaak, H.; Schets, F.M.; de Roda Husman, A.M. Fate of Extended-Spectrum β-Lactamase-Producing Escherichia coli from Faecal Sources in Surface Water and Probability of Human Exposure through Swimming. Environ. Sci. Technol. 2015, 49, 11825–11833. [Google Scholar] [CrossRef] [PubMed]

Dorado-García, A.; Smid, J.H.; van Pelt, W.; Bonten, M.J.M.; Fluit, A.C.; van den Bunt, G.; Wagenaar, J.A.; Hordijk, J.; Dierikx, C.M.; Veldman, K.T.; et al. Molecular relatedness of ESBL/AmpC-producing Escherichia coli from humans, animals, food and the environment: A pooled analysis. J. Antimicrob. Chemother. 2018, 73, 339–347. [Google Scholar] [CrossRef] [PubMed]

Wengenroth, L.; Berglund, F.; Blaak, H.; Chifiriuc, M.; Flach, C.-F.; Pircalabioru, G.; Larsson, D.; Marutescu, L.; van Passel, M.; Popa, M.; et al. Antibiotic Resistance in Wastewater Treatment Plants and Transmission Risks for Employees and Residents: The Concept of the AWARE Study. Antibiotics 2021, 10, 478. [Google Scholar] [CrossRef]

ESRI. ArcGIS Desktop: Release 10; Environmental Systems Research Institute: Redlands, CA, USA, 2011. [Google Scholar]

BMBF TD des. ISCED 2011—BMBF Datenportal. Datenportal des Bundesministeriums für Bildung und Forschung—BMBF. Available online: https://www.datenportal.bmbf.de/portal/de/glossary.html (accessed on 12 April 2022).

Luijkx, R.; de Heus, M. The educational system of the Netherlands. In The International Standard Classification of Education (ISCED-97) An Evaluation of Content and Criterion Validity for 15 European Countries; Mannheimer Zentrum für Europäische Sozialforschung: Mannheim, Germany, 2008; pp. 47–75. [Google Scholar]

Clasificarea Internațională Standard a Educației—ISCED. 2018. Available online: https://www.parintiicerschimbare.ro/clasificarea-internationala-standard-a-educatiei/ (accessed on 12 April 2022).

CLSI. Performance Standards for Antimicrobial Susceptibility Testing, 28th ed.; Clinical and Laboratory Standards Institute: Wayne, PA, USA, 2018. [Google Scholar]

White, I.R.; Royston, P.; Wood, A.M. Multiple imputation using chained equations: Issues and guidance for practice. Stat. Med. 2011, 30, 377–399. [Google Scholar] [CrossRef]

Haneuse, S.; Schildcrout, J.; Crane, P.; Sonnen, J.; Breitner, J.; Larson, E. Adjustment for selection bias in observational studies with application to the analysis of autopsy data. Neuroepidemiology 2009, 32, 229–239. [Google Scholar] [CrossRef]

Cole, S.R.; Stuart, E.A. Generalizing Evidence From Randomized Clinical Trials to Target Populations: The ACTG 320 Trial. Am. J. Epidemiol. 2010, 172, 107–115. [Google Scholar] [CrossRef]

R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2021; Available online: https://www.R-project.org/ (accessed on 12 April 2022).

Frost, I.; Van Boeckel, T.P.; Pires, J.; Craig, J.; Laxminarayan, R. Global geographic trends in antimicrobial resistance: The role of international travel. J. Travel Med. 2019, 26, taz036. [Google Scholar] [CrossRef]

Bengtsson-Palme, J.; Angelin, M.; Huss, M.; Kjellqvist, S.; Kristiansson, E.; Palmgren, H.; Larsson, D.G.J.; Johansson, A. The Human Gut Microbiome as a Transporter of Antibiotic Resistance Genes between Continents. Antimicrob. Agents Chemother. 2015, 59, 6551–6560. [Google Scholar] [CrossRef]

van den Bunt, G.; van Pelt, W.; Hidalgo, L.; Scharringa, J.; de Greeff, S.C.; Schürch, A.C.; Mughini-Gras, L.; Bonten, M.J.M.; Fluit, A.C. Prevalence, risk factors and genetic characterisation of extended-spectrum beta-lactamase and carbapenemase-producing Enterobacteriaceae (ESBL-E and CPE): A community-based cross-sectional study, the Netherlands, 2014 to 2016. Eurosurveillance 2019, 24, 1800594. [Google Scholar] [CrossRef]

Critchley, I.A.; Cotroneo, N.; Pucci, M.J.; Mendes, R. The burden of antimicrobial resistance among urinary tract isolates of Escherichia coli in the United States in 2017. PLoS ONE 2019, 14, e0220265. [Google Scholar] [CrossRef] [PubMed]

Hendriksen, R.S.; Munk, P.; Njage, P.; Van Bunnik, B.; McNally, L.; Lukjancenko, O.; Röder, T.; Nieuwenhuijse, D.; Pedersen, S.K.; Kjeldgaard, J.; et al. Global monitoring of antimicrobial resistance based on metagenomics analyses of urban sewage. Nat. Commun. 2019, 10, 1124. [Google Scholar] [CrossRef] [PubMed]

Karkman, A.; Berglund, F.; Flach, C.-F.; Kristiansson, E.; Larsson, D.G.J. Predicting clinical resistance prevalence using sewage metagenomic data. Commun. Biol. 2020, 3, 711. [Google Scholar] [CrossRef] [PubMed]

Murray, C.J.; Ikuta, K.S.; Sharara, F.; Swetschinski, L.; Aguilar, G.R.; Gray, A.; Han, C.; Bisignano, C.; Rao, P.; Wool, E.; et al. Global burden of bacterial antimicrobial resistance in 2019: A systematic analysis. Lancet 2022, 399, 629–655. [Google Scholar] [CrossRef]

Musoke, D.; Namata, C.; Lubega, G.B.; Niyongabo, F.; Gonza, J.; Chidziwisano, K.; Nalinya, S.; Nuwematsiko, R.; Morse, T. The role of Environmental Health in preventing antimicrobial resistance in low- and middle-income countries. Environ. Health Prev. Med. 2021, 26, 100. [Google Scholar] [CrossRef]

Ibrahim, R.A.; Teshal, A.M.; Dinku, S.F.; Abera, N.A.; Negeri, A.A.; Desta, F.G.; Seyum, E.T.; Gemeda, A.W.; Keficho, W.M. Antimicrobial resistance surveillance in Ethiopia: Implementation experiences and lessons learned. Afr. J. Lab. Med. 2018, 7, 4. [Google Scholar] [CrossRef]

Opintan, J.A. Leveraging donor support to develop a national antimicrobial resistance policy and action plan: Ghana’s success story. Afr. J. Lab. Med. 2018, 7, 1–4. [Google Scholar] [CrossRef]

Varma, J.K.; Oppong-Otoo, J.; Ondoa, P.; Perovic, O.; Park, B.J.; Laxminarayan, R.; Peeling, R.W.; Schultsz, C.; Li, H.; Ihekweazu, C.; et al. Africa Centres for Disease Control and Prevention’s framework for antimicrobial resistance control in Africa. Afr. J. Lab. Med. 2018, 7, 4. [Google Scholar] [CrossRef]

Elton, L.; Thomason, M.J.; Tembo, J.; Velavan, T.P.; Pallerla, S.R.; Arruda, L.B.; Vairo, F.; Montaldo, C.; Ntoumi, F.; Hamid, M.M.A.; et al. Antimicrobial resistance preparedness in sub-Saharan African countries. Antimicrob. Resist. Infect. Control 2020, 9, 145. [Google Scholar] [CrossRef]

Gandra, S.; Alvarez-Uria, G.; Turner, P.; Joshi, J.; Limmathurotsakul, D.; van Doorn, H.R. Antimicrobial Resistance Surveillance in Low- and Middle-Income Countries: Progress and Challenges in Eight South Asian and Southeast Asian Countries. Clin. Microbiol. Rev. 2022, 33, 33. Available online: https://journals.asm.org/doi/abs/10.1128/CMR.00048-19 (accessed on 16 February 2022). [CrossRef]

Yam, E.L.Y.; Hsu, L.Y.; Yap, E.P.-H.; Yeo, T.W.; Lee, V.; Schlundt, J.; Lwin, M.O.; Limmathurotsakul, D.; Jit, M.; Dedon, P.; et al. Antimicrobial Resistance in the Asia Pacific region: A meeting report. Antimicrob. Resist. Infect. Control 2019, 8, 202. [Google Scholar] [CrossRef] [PubMed]

Kakkar, M.; Chatterjee, P.; Chauhan, A.S.; Grace, D.; Lindahl, J.; Beeche, A.; Jing, F.; Chotinan, S. Antimicrobial resistance in South East Asia: Time to ask the right questions. Glob. Health Action 2018, 11, 1483637. [Google Scholar] [CrossRef] [PubMed]

World Health Organization. Global Antimicrobial Resistance and Use Surveillance System (GLASS) Report: 2021; World Health Organization: Geneva, Switzerland, 2021; Available online: https://www.who.int/publications-detail-redirect/9789240027336 (accessed on 2 March 2022).

Figure 1. Risk factor analysis for carriage of ESBL-producing E. coli in stool samples.

Figure 1. Risk factor analysis for carriage of ESBL-producing E. coli in stool samples.

Figure 2. Travel areas as risk factors for ESBL-EC carriage (adjusted OR). Note: The European spot in South America corresponds to French Guiana.

Figure 2. Travel areas as risk factors for ESBL-EC carriage (adjusted OR). Note: The European spot in South America corresponds to French Guiana.

Table 1. Categorical descriptive characteristics of ESBL-producing E. coli carriers by country, n = 1183.

Table 1. Categorical descriptive characteristics of ESBL-producing E. coli carriers by country, n = 1183.

| Overall, n = 1074 | Germany, n = 238 | The Netherlands, n = 686 | Romania, n = 150
Missing Values for Stool Samples, n | 109 | 95 | 3 | 11
Variable | Missing | Level | ESBL_EC+, n (%) | p | ESBL_EC+, n (%) | p | ESBL_EC+, n (%) | p | ESBL_EC+, n (%) | p
ESBL-EC positives |  |  | 81 (8) |  | 20 (8) |  | 42 (6) |  | 19 (13) |
Sex | 4 | Female | 47 (7) | 0.814 | 12 (9) | 1.000 | 25 (6) | 0.871 | 19 (13) | 1.000
|  | Male | 34 (8) |  | 8 (8) |  | 17 (6) |  | 9 (13) |
Highest educational level obtained a | 2 | Low | 19 (5) | 0.050 | 6 (10) | 0.602 | 13 (5) | 0.196 | 0 (0) | 0.217
|  | High | 62 (9) |  | 14 (8) |  | 29 (7) |  | 19 (14) |
Work with animals in the past year | 35 | No | 75 (7) | 0.752 | 18 (8) | 1.000 | 40 (6) | 0.659 | 17 (12) | 0.555
|  | Yes | 3 (8) |  | 0 (0) |  | 2 (8) |  | 1 (17) |
Work at a farm in the past year | 25 | No | 77 (7) | 0.197 | 18 (8) | 1.000 | 40 (6) | 0.104 | 19 (13) | NA
|  | Yes | 2 (18) |  | 0 (0) |  | 2 (22) |  | --- (---) |
Work at a slaughterhouse in the past year | 20 | No | 79 (7) | 1.000 | 18 (8) | NA | 42 (6) | 1.000 | 19 (13) | 1.000
|  | Yes | 0 (0) |  | --- (---) |  | 0 (0) |  | 0 (0) |
Work with manure in the past year | 22 | No | 76 (7) | 1.000 | 18 (8) | 1.000 | 41 (6) | 1.000 | 17 (12) | 0.482
|  | Yes | 2 (6) |  | 0 (0) |  | 1 (5) |  | 1 (20) |
Patient contact or work with human tissues in the past year b | 20 | No | 52 (7) | 1.000 | 12 (8) | 1.000 | 29 (7) | 0.738 | 11 (10) | 0.133
|  | Yes | 26 (7) |  | 6 (7) |  | 13 (6) |  | 7 (21) |
Patient contact in the past year | 20 | Never | 58 (7) | 0.481 | 13 (8) | 0.552 | 31 (6) | 0.872 | 14 (12) | 0.672
|  | Rarely or sometimes | 11 (9) |  | 3 (12) |  | 5 (7) |  | 3 (18) |
|  | Often or always | 9 (6) |  | 2 (5) |  | 6 (5) |  | 1 (12) |
Work with human tissues in the past year | 16 | Never | 57 (7) | 0.704 | 13 (8) | 1.000 | 32 (6) | 0.928 | 12 (10) | 0.097
|  | Rarely or sometimes | 13 (9) |  | 3 (8) |  | 6 (7) |  | 4 (25) |
|  | Often or always | 8 (7) |  | 2 (7) |  | 4 (5) |  | 2 (20) |
Hospital visits as a patient in the past year | 0 | No | 76 (8) | 0.672 | 16 (8) | 0.517 | 41 (6) | 0.507 | 19 (13) | 0.597
|  | Yes | 5 (6) |  | 4 (11) |  | 1 (2) |  | 0 (0) |
Hospital visits as a professional in the past year | 0 | No | 78 (8) | 0.761 | 19 (9) | 1.000 | 41 (6) | 1.000 | 18 (12) | 0.336
|  | Yes | 3 (8) |  | 1 (5) |  | 1 (7) |  | 1 (33) |
Hospital visits as a visitor in the past year | 0 | No | 79 (8) | 0.690 | 18 (8) | 0.169 | 42 (6) | 1.000 | 19 (13) | 1.000
|  | Yes | 2 (9) |  | 2 (22) |  | 0 (0) |  | 0 (0) |
Farm visits in the past year | 4 | No | 71 (7) | 0.578 | 17 (9) | 0.773 | 35 (6) | 0.097 | 19 (13) | 0.596
|  | Yes | 10 (9) |  | 3 (7) |  | 7 (11) |  | 0 (0) |
Owning horses in the past year | 139 | No | 77 (8) | 0.722 | 20 (10) | 0.605 | 40 (7) | 1.000 | 17 (15) | 1.000
|  | Yes | 1 (4) |  | 0 (0) |  | 1 (7) |  | 0 (0) |
Having dogs as pets in the past year | 70 | No | 70 (9) | 0.011 | 20 (11) | 0.084 | 34 (7) | 0.267 | 16 (16) | 0.156
|  | Yes | 9 (4) |  | 0 (0) |  | 7 (4) |  | 2 (6) |
Having cats as pets in the past year | 75 | No | 65 (9) | 0.130 | 17 (10) | 0.418 | 34 (7) | 0.348 | 14 (15) | 0.558
|  | Yes | 13 (5) |  | 3 (5) |  | 7 (5) |  | 3 (9) |
Use of antibiotics in the past year | 0 | No | 60 (7) | 0.685 | 13 (8) | 1.000 | 36 (6) | 0.544 | 11 (11) | 0.289
|  | Yes | 21 (8) |  | 7 (8) |  | 6 (5) |  | 8 (17) |
Use of antacids in the past year | 2 | No | 64 (8) | 0.783 | 12 (7) | 0.297 | 36 (7) | 0.253 | 16 (13) | 1.000
|  | Yes | 17 (7) |  | 8 (12) |  | 6 (4) |  | 3 (11) |
Surgeries in the past year | 1 | No | 80 (8) | 0.255 | 19 (9) | 1.000 | 42 (6) | 0.403 | 19 (13) | 1.000
|  | Yes | 1 (2) |  | 1 (6) |  | 0 (0) |  | 0 (0) |
Self-reported frequency of diarrhea in the past year | 4 | Never, rarely or sometimes | 74 (7) | 0.223 | 17 (8) | 0.069 | 38 (6) | 0.347 | 19 (13) | 1.000
|  | Often or always | 7 (11) |  | 3 (25) |  | 4 (9) |  | 0 (0) |
Self-reported health status in the past year | 5 | Good, very good or excellent | 69 (7) | 0.734 | 18 (8) | 0.365 | 34 (6) | 0.523 | 17 (13) | 1.000
|  | Fair or poor | 12 (8) |  | 2 (13) |  | 8 (7) |  | 2 (11) |
Travel to high-risk areas for AR in the past year c | 8 | No | 36 (6) | 0.012 | 6 (6) | 0.336 | 17 (4) | 0.004 | 13 (13) | 0.791
|  | Yes | 42 (10) |  | 13 (10) |  | 24 (10) |  | 5 (10) |
Travels to Europe in the past year | 5 | Never | 27 (9) | 0.498 | 5 (12) | 0.378 | 11 (6) | 0.718 | 11 (13) | 0.498
|  | Once | 18 (8) |  | 1 (2) |  | 12 (7) |  | 5 (18) |
|  | 2–3 times | 22 (6) |  | 8 (8) |  | 12 (5) |  | 2 (8) |
|  | More than 3 times | 12 (7) |  | 5 (10) |  | 7 (7) |  | 0 (0) |
Travels to Bulgaria, Greece, Italy, or Slovenia in the past year | 7 | No | 59 (8) | 0.514 | 10 (8) | 1.000 | 34 (6) | 0.561 | 15 (15) | 0.182
|  | Yes | 19 (6) |  | 9 (8) |  | 7 (5) |  | 3 (6) |
Travels to Sub-Saharan Africa in the past year | 5 | Never | 73 (7) | 0.010 | 19 (8) | 1.000 | 36 (5) | 0.002 | 18 (12) | NA
|  | Once | 4 (19) |  | 0 (0) |  | 4 (22) |  | --- (---) |
|  | 2–3 times | 1 (33) |  | --- (---) |  | 1 (33) |  | --- (---) |
|  | More than 3 times | 1 (50) |  | --- (---) |  | 1 (50) |  | --- (---) |
Travels to Northern Africa in the past year | 6 | Never | 69 (7) | 0.001 | 17 (7) | 0.013 | 35 (5) | 0.019 | 17 (12) | 0.324
|  | Once | 8 (20) |  | 2 (25) |  | 5 (17) |  | 1 (33) |
|  | 2–3 times | 2 (50) |  | 1 (100) |  | 1 (33) |  | --- (---) |
|  | More than 3 times | 0 (0) |  | --- (---) |  | 0 (0) |  | --- (---) |
Travels to Asia in the past year | 4 | Never | 63 (6) | <0.001 | 15 (7) | 0.116 | 31 (5) | <0.001 | 17 (12) | 0.408
|  | Once | 13 (20) |  | 3 (16) |  | 9 (22) |  | 1 (25) |
|  | 2–3 times | 2 (18) |  | 1 (25) |  | 1 (14) |  | --- (---) |
|  | More than 3 times | 1 (50) |  | --- (---) |  | 1 (50) |  | --- (---) |
Travels to North America in the past year | 4 | Never | 73 (7) | 0.036 | 17 (8) | 0.041 | 38 (6) | 0.146 | 18 (12) | NA
|  | Once | 5 (17) |  | 2 (20) |  | 3 (16) |  | --- (---) |
|  | 2–3 times | 2 (25) |  | 1 (50) |  | 1 (17) |  | --- (---) |
|  | More than 3 times | 0 (0) |  | --- (---) |  | 0 (0) |  | --- (---) |
Travels to Central America or Mexico in the past year | 6 | Never | 78 (7) | 0.190 | 19 (8) | 1.000 | 41 (6) | 0.171 | 18 (12) | NA
|  | Once | 0 (0) |  | 0 (0) |  | 0 (0) |  | --- (---) |
|  | 2–3 times | 0 (0) |  | 0 (0) |  | --- (---) |  | --- (---) |
|  | More than 3 times | 1 (50) |  | --- (---) |  | 1 (50) |  | --- (---) |
Travels to South America in the past year | 6 | Never | 77 (7) | 0.149 | 18 (8) | 0.287 | 41 (6) | 0.126 | 18 (12) | NA
|  | Once | 1 (6) |  | 1 (25) |  | 0 (0) |  | --- (---) |
|  | 2–3 times | 0 (0) |  | --- (---) |  | 0 (0) |  | --- (---) |
|  | More than 3 times | 1 (100) |  | --- (---) |  | 1 (100) |  | --- (---) |
Travels to Australia or Oceania in the past year | 6 | Never | 78 (7) | 0.572 | 19 (8) | 0.465 | 41 (6) | 1.000 | 18 (12) | NA
|  | Once | 1 (10) |  | 1 (17) |  | 0 (0) |  | --- (---) |
|  | 2–3 times | 0 (0) |  | 0 (0) |  | --- (---) |  | --- (---) |
|  | More than 3 times | --- (---) |  | --- (---) |  | --- (---) |  | --- (---) |

Notes: a Educational level according to the International Standard Classification of Education (ISCED): Low = ISCED 0–2 (Pre-primary education to Lower secondary education), High = ISCED ≥ 3 (Upper secondary education to Doctoral or equivalent). b Work with human tissues in the past year: Includes self-reported contact with human tissues (e.g., blood, urine, sputum, feces, vomit, saliva, or primary cell lines). c Travels to high-risk areas for AR in the past year: Includes travels to North Africa, Sub-Saharan Africa, Asia, Central and South America, as well as the European countries Italy, Greece, Bulgaria and Slovenia. ESBL_EC+: Positive stool sample for Extended-Spectrum Beta-Lactamase-Producing E. coli; AR: Antibiotic Resistance. Bold highlighting means statistically significant at the p ≤ 0.05 level. Shown are the number of ESBL-EC carriers per variable and the percentage of ESBL-EC carriers relative to the total participants within the same level of that variable.

Table 2. Numerical descriptive characteristics of ESBL-producing E. coli carriers by country.

Table 2. Numerical descriptive characteristics of ESBL-producing E. coli carriers by country.

| Overall, n = 1074 | Germany, n = 238 | The Netherlands, n = 686 | Romania, n = 151
Missing Values for Stool Samples, n | 109 | 95 | 3 | 11
Variable | Missings | ESBL_EC+ | ESBL_EC− | p | ESBL_EC+ | ESBL_EC− | p | ESBL_EC+ | ESBL_EC− | p | ESBL_EC+ | ESBL_EC− | p
n |  | 81 | 993 |  | 20 | 218 |  | 42 | 644 |  | 19 | 131 |
Age, years (median [IQR]) | 0 | 47 [34, 57] | 51 [37, 60] | 0.172 | 38 [31, 50] | 49 [36, 58] | 0.146 | 55 [42, 61] | 54 [39, 61] | 0.964 | 39 [34, 44] | 40 [33, 50] | 0.739
Travel score (mean ± SD, median [min, max]) a | 12 | 0.86 ± 1.60, 1 [0, 13] | 0.46 ± 0.79, 0 [0, 15] | 0.020 | 1 ± 0.94, 1 [0, 3] | 0.64 ± 0.74, 1 [0, 6] | 0.081 | 1.05 ± 2.07, 1 [0, 13] | 0.42 ± 0.84, 0 [0, 15] | 0.001 | 0.28 ± 0.46, 0 [0, 1] | 0.38 ± 0.56, 0 [0, 3] | 0.533

Notes: a Travel score was constructed based on frequency of personal travels to high risk areas for antibiotic resistance in the past year: Includes travels to North Africa, Sub-Saharan Africa, Asia, Central and South America, as well as the European countries Italy, Greece, Bulgaria, and Slovenia. The score is the sum of: zero points for not travelling to these areas in the past year, one point for travelling once to these areas in the past year, two points for travelling to these areas two or three times in the past year, and three points for travelling to these areas more than three times in the past year. Test used for bivariate hypothesis testing: Mann–Whitney test. ESBL_EC+: Positive stool sample for Extended-Spectrum Beta-Lactamase-Producing E. coli; ESBL_EC−: Negative stool sample for Extended-Spectrum Beta-Lactamase-Producing E. coli. IQR: Inter-quartile range. Bold highlighting means statistically significant at the p ≤ 0.05 level.

| Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.