Abstract

Wastewater is considered a hotspot niche of multi-drug and pathogenic bacteria such as Enterobacteriaceae-producing extended-spectrum beta-lactamases (ESBL-E). Thus, the aim of this meta-analysis was to evaluate the prevalence of ESBL-E in different wastewater sources. Different databases (Medline, EMBASE, and Cochrane Library) were searched from inception to March 2021. Data were analyzed using random-effects modeling, and subgroup and meta-regression analyses were used to ascertain heterogeneity among the subgroups. Fifty-seven observational studies were selected, and the pooled prevalence of ESBL-E in wastewater was 24.81% (95% CI, 19.28–30.77). Escherichia coli had the highest ESBL prevalence. The blaCTX-M genes were the most prevalent in the selected studies (66.56%). The pooled prevalence of ESBL was significantly higher in reports from America (39.91%, 95% CI, 21.82–59.51) and reports studying hospital and untreated wastewaters (33.98%, 95% CI, 23.82–44.91 and 27.36%, 95% CI, 19.12–36.42). Overall, this meta-analysis showed that the prevalence of ESBL-E in wastewater is increasing over time and that hospital wastewater is the most important repository of ESBL-E. Therefore, there is a need for developing new sewage treatment systems that decrease the introduction of resistant bacteria and antibiotic residues.

HIGHLIGHTS

  • The global prevalence of Enterobacteriaceae-producing extended-spectrum beta-lactamases (ESBL-E) in wastewater was found to be 24.81%.

  • The pooled prevalence of ESBL-E was significantly higher in reports studying hospital wastewater.

  • The highest prevalence of ESBL-E was in America, and the lowest prevalence was in Europe.

  • Among ESBL genes, blaCTX-M genes had the highest prevalence, followed by blaTEM and blaSHV.

INTRODUCTION

Enterobacteriaceae are responsible for causing infections in humans and animals. They rely on their ability to resist antibiotics and on their virulence arsenal that facilitates their dissemination. The emergence of antimicrobial resistance in Enterobacteriaceae has become a significant concern to public health (Azuma & Hayashi 2020); it is believed that wastewater can contribute to propagating their antibiotic resistance (Meletis 2016). Wastewater is considered a hotspot niche of pathogenic bacteria and genetic exchange of genes encoding antibiotic resistance (Lepuschitz et al. 2019). Antibiotics released into wastewater increase the selective pressure on Enterobacteriaceae, thus causing antibiotic resistance to proliferate (Fouz et al. 2020). Moreover, wastewater treatments do not entirely eliminate the microbial contaminants; therefore, wastewater is mostly discharged to the receiving rivers enabling resistant Enterobacteriaceae and genes encoding antibiotic resistance to reach agricultural soils and water bodies used for domestic purposes (Korzeniewska & Harnisz 2013a). From there, these bacteria can disseminate into human and animal populations (Gatica & Cytryn 2013).

β-Lactams are one of the most commonly used antibiotics that are usually released in wastewater; these antibiotics can induce the production of β-lactamases in Enterobacteriaceae (Bonomo 2017). Among these β-lactamases, extended-spectrum beta-lactamases (ESBLs) are among the most widely spread resistance mechanisms (Teklu et al. 2019). They are a group of enzymes that can break down penicillins, β-lactamase inhibitors, and third- and fourth-generation cephalosporins and monobactams (Rahman et al. 2018). Most of them have been developed through spontaneous mutations of reduced spectrum β-lactamases (Reinthaler et al. 2010). Moreover, ESBLs are encoded by genes belonging mostly to three groups called blaCTX-M, blaTEM, and blaSHV (Pishtiwan & Khadija 2019). These genes are mostly located on conjugative plasmids, facilitating the horizontal gene transfer among bacterial groups (Jesumirhewe et al. 2020). Other less-studied types of ESBLs have also been found, including OXA (oxacillinase), VEB (Vietnamese extended-spectrum β-lactamase), PER (Pseudomonas extended-resistant), and GES (Guyana extended-spectrum β-lactamase) (Amirkamali et al. 2017).

The prevalence of ESBL-producing Enterobacteriaceae in wastewater varies with respect to geographical differences, wastewater source, and antimicrobial prescription patterns, most of the studies on ESBL-E in wastewater are limited to geographical areas such as Asia and Europe, and there has been no meta-analysis to the best of our knowledge that evaluated the prevalence of ESBL-E in different wastewater sources. Therefore, this meta-analysis aimed to determine the prevalence of ESBL-producing Enterobacteriaceae in different wastewater sources and analyze their influencing factors.

MATERIALS AND METHODS

Study and data collection

This meta-analysis was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Moher et al. 2009). A comprehensive literature search of PubMed/MEDLINE, EMBASE, and the Cochrane Library was performed until March 2021 to find potentially relevant articles. We also reviewed manually the references cited by the studies included to identify additional studies. The terms applied in electronic searches are listed in Supplementary Material, Table S1.

Inclusion and exclusion criteria

Studies were included if they met the following criteria: observational cross-sectional investigating the prevalence of ESBL-producing Enterobacteriaceae in wastewater. Exclusion criteria were included: (1) the absence of the total number of isolates, (2) the ESBL identification method is unclear, and (3) studies with the format of a congress abstract, a review article, or a book chapter.

Data extraction and quality assessment

Two authors independently screened the literature and extracted data, and the following variables were extracted: (1) authors' names, (2) location, (3) year of publication, (4) sample size, (5) the use of treatment or not, (6) prevalence of ESBL isolates, (7) species detected, (8) source of samples, (9) ESBL genes, and (10) ESBL identification methods. Since all the included studies were cross-sectional, each article's quality was assessed using Joanna Briggs Institute's quality assessment checklist (Munn et al. 2015) (Supplementary Material, Table S2).

Statistical analysis and data synthesis

STATA 16.0 (StataCorp LP, College Station, TX, USA) was used for the meta-analysis. The prevalence of ESBL-producing Enterobacteriaceae was estimated. The heterogeneity among studies was examined by the forest plot and the I2 heterogeneity test, in which 0–40, 50–60, 50–90, and 75–100% represented low, moderate, substantial, and considerable heterogeneity, respectively. Due to the high level of heterogeneity, the random-effects model was selected for analysis. Freeman–Tukey double arcsine transformation was used to avoid excluding studies where the prevalence of ESBL-E was close to 0% (Freeman & Tukey 1950; Nyaga et al. 2014). A subgroup analysis was performed, with a breakdown by the continents, use of treatment, year of publication, and the wastewater origin. A value of p ≤ 0.05 indicated statistical significance in the pooled effect. Meta-regression was used to ascertain heterogeneity among the subgroups. The selected independent factors are the wastewater origin (hospital wastewater, municipal wastewater, wastewater from rivers, hospital and municipal wastewaters and their receiving rivers, wastewater from other sources (farms and slaughterhouses)), continents (Africa, Europe, Asia, and America), the use of treatment (treated wastewater, untreated wastewater, and treated and untreated wastewater), and year of publication (2007–2015, 2016, 2017, 2018, 2019, and 2020). The number of studies from 2007 to 2015 is small. Therefore, they were put in the same category, so they will not be omitted by the software. The risk of publication bias across the studies was assessed by the funnel plot and the Egger test; the asymmetry of the funnel plot and statistical significance of Egger's regression test (p-value <0.05) were suggestive of publication bias (Egger et al. 1997). Sensitivity analysis was assessed to evaluate the robustness of the meta-analysis.

RESULTS

Eligible articles

The literature search process is shown in Figure 1. The database search yielded a total of 394 publications. After eliminating duplicates, 300 were selected to review their titles and abstracts, resulting in the exclusion of 214 publications; 86 studies were assessed then for eligibility by full-text reviewing based on the review's inclusion and exclusion criteria, and 29 were removed. Finally, 57 articles were ultimately included in the analysis.

Figure 1

PRISMA flow diagram of the selection process of the included studies.

Figure 1

PRISMA flow diagram of the selection process of the included studies.

Characteristics of the eligible studies

The main characteristics of the included studies are summarized in Table 1. The most used method for ESBL-E detection was the double-disk synergy method. In total, 28 countries from four continents studied ESBL-E prevalence in wastewater (Europe: 18, Asia: 18, Africa: 13, and America: 8). Among the 57 included studies, 17 studies examined hospital wastewater, 15 studies examined municipal wastewater, 13 studies examined hospital and municipal wastewaters and their receiving rivers, seven studies examined wastewater effluents in rivers, and five studies examined wastewater from other sources such as farms and slaughterhouses. Moreover, 23 studies examined the occurrence of ESBL-E in untreated wastewater, nine studies in treated wastewater, and 25 studies in untreated and treated wastewater. The selected studies were published between 2007 and 2020, 23.59% were carried out between the years 2007–2015 (n = 13), 21% were carried out in 2019 (n = 12), and 18.54% were carried in 2020 (n = 11).

Table 1

Characteristics of the included studies

ReferencesNo. of ESBL EnterobacteriaceaeWastewater originYearTreatmentMethods/guidelineCountry
Prado et al. (2007)  20 43 Hospital wastewater 2007 Treated and untreated CDT, CLSI Brazil 
Sabaté et al. (2008)  10 85 Wastewater from other sources (farms and slaughterhouses) 2008 Treated and untreated E-test, CLSI Spain 
Łuczkiewicz et al. (2010)  259 Urban wastewater 2010 Treated and untreated Phoenix Automated Microbiology System, CLSI Poland 
Chagas et al. (2011)  96 213 Hospital wastewater 2011 Treated DDST, CLSI Brazil 
Mokracka et al. (2012)  12 1,832 Urban wastewater 2012 Treated and untreated DDST, CLSI Poland 
Diallo et al. (2013)  1,248 Wastewater from other sources (farms and slaughterhouses) 2013 Treated E-test, CLSI France 
Korzeniewska & Harnisz (2013a)  150 310 Hospital wastewater 2013 Untreated CDT, CLSI Poland 
Korzeniewska & Harnisz (2013b)  34 246 Urban wastewater 2013 Treated and untreated CDT, CLSI Poland 
Bessa et al. (2014)  29 144 Hospital and municipal wastewaters and their receiving rivers 2014 Treated Disk approximation test, CLSI Portugal 
Chandran et al. (2014)  87 190 Hospital wastewater 2014 Untreated CDT India 
Čornejová et al. (2015)  27 104 Urban wastewater 2015 Treated and untreated Microdilution, CLSI Slovakia 
Kwak et al. (2015)  73 1,894 Hospital and municipal wastewaters and their receiving rivers 2015 Treated and untreated AREB microplates, CLSI Sweden 
Kotlarska et al. (2015)  774 Hospital and municipal wastewaters and their receiving rivers 2015 Treated and untreated DDST, EUCAST Poland 
De Oliveira & Van Der Sand (2016)  32 60 Hospital wastewater 2016 Treated and untreated  DDST, CLSI Brazil 
Drieux et al. (2016)  25 389 Hospital wastewater 2016 Untreated DDST, CA-SFM France 
Dropa et al. (2016)  200 Urban wastewater 2016 Untreated DDST, CLSI Brazil 
Egbule (2016)  96 Hospital wastewater 2016 Untreated DDST, CLSI Nigeria 
Lenart-Boroń (2016)  23 196 Wastewater-receiving rivers 2016 Untreated DDST, EUCAST Poland 
Maheshwari et al. (2016)  34 103 Hospital wastewater 2016 Untreated CDT, CLSI India 
Sultana et al. (2016)  30 166 Hospital wastewater 2016 Untreated DDST Bangladesh 
Caltagirone et al. (2017)  30 132 Hospital and municipal wastewaters and their receiving rivers 2017 Treated and untreated DDST, EUCAST Italy 
Conte et al. (2017)  55 152 Hospital and municipal wastewaters and their receiving rivers 2017 Treated and untreated VITEK-2 system and MALDI-TOF, CLSI Brazil 
Lien et al. (2017)  115 265 Hospital wastewater 2017 Treated and untreated CDT, CLSI Vietnam 
Obasi et al. (2017)  Urban wastewater 2017 Untreated Vitek 2, E-test strips, CLSI Nigeria 
Tafoukt et al. (2017)  20 Wastewater-receiving rivers 2017 Untreated DDST, CLSI Algeria 
Adelowo et al. (2018)  48 98 Hospital and municipal wastewaters and their receiving rivers 2018 Untreated DDST, CLSI Nigeria 
Daoud et al. (2018)  51 70 Hospital wastewater 2018 Untreated DDST, E-test ESBL strips, CLSI Lebanon 
Debabza et al. (2018)  143 254 Hospital and municipal wastewaters and their receiving rivers 2018 Treated and untreated DDST, CA-SFM Algeria 
Falodun et al. (2018)  30 189 Wastewater-receiving rivers 2018 Untreated DDST, CLSI Nigeria 
Flach et al. (2018)  89 4,028 Urban wastewater 2018 Treated and untreated DDST, EUCAST Sweden 
Park et al. (2018)  14 75 Wastewater from other sources (farms and slaughterhouses) 2018 Treated and untreated DDST, EUCAST South Korea 
Siddiqui et al. (2018)  175 506 Wastewater-receiving rivers 2018 Untreated PDCT, CLSI India 
Tokajian et al. (2018)  21 34 Hospital and municipal wastewaters and their receiving rivers 2018 Untreated DDST, CLSI Lebanon 
Vital et al. (2018)  27 147 Wastewater-receiving rivers 2018 Untreated DDST, CLSI Philippines 
Adekanmbi et al. (2019)  17 58 Hospital wastewater 2019 Untreated DDST, CLSI Nigeria 
Bartley et al. (2019)  20 40 Hospital and municipal wastewaters and their receiving rivers 2019 Treated and untreated  VITEK-2, CLSI Brazil 
Chaudhry et al. (2019)  17 112 Hospital wastewater 2019 Untreated DDST, CLSI Pakistan 
Falodun & Oladimeji (2019)  35 200 Hospital wastewater 2019 Treated and untreated DDST, CLSI Nigeria 
Haberecht et al. (2019)  47 70 Urban wastewater 2019 Treated and untreated CHROMagar ESBL USA 
Li et al. (2019)  50 70 Wastewater from other sources (farms and slaughterhouses) 2019 Treated and untreated DDST, CLSI China 
Mahato et al. (2019)  13 Hospital wastewater 2019 Treated DDST, CLSI Nepal 
Miyagi & Hirai (2019)  141 249 Hospital and municipal wastewaters and their receiving rivers 2019 Untreated DDST, CLSI Japan 
Paulshus et al. (2019)  314 3,123 Urban wastewater 2019 Untreated AREB microplates, CLSI Norway 
Raven et al. (2019)  192 388 Urban wastewater 2019 Treated and untreated Brilliance ESBL agar, Vitek UK 
Sghaier et al. (2019)  58 123 Urban wastewater 2019 Treated DDST, CLSI Tunisia 
Tesfaye et al. (2019)  54 Wastewater from other sources (farms and slaughterhouses) 2019 Untreated DDST, CLSI Ethiopia 
Adekanmbi et al. (2020)  12 33 Hospital wastewater 2020 Treated and untreated DDST, CLSI Nigeria 
Banjo et al. (2020)  12 23 Hospital wastewater 2020 Untreated DDST, CLSI Nigeria 
Khan et al. (2020)  35 61 Urban wastewater 2020 Treated CHROMagar ESBL, DDST, CLSI UAE 
King et al. (2020)  14 130 Hospital and municipal wastewaters and their receiving rivers 2020 Treated and untreated MASTDISCS D68C AmpC/ESBL kits, EUCAST South Africa 
Lenart Boroń et al. (2020)  50 Wastewater-receiving rivers 2020 Treated DDST, EUCAST Poland 
Saima et al. (2020)  10 Wastewater-receiving rivers 2020 Untreated DDST, CDT, CLSI Pakistan 
Smyth et al. (2020)  89 498 Urban wastewater 2020 Treated DDST, CLSI Ireland 
Surleac et al. (2020)  34 Hospital and municipal wastewaters and their receiving rivers 2020 Treated and untreated ChromID ESBL agar, CLSI Romania 
Urano et al. (2020)  64 Urban wastewater 2020 Treated and untreated DDST, CLSI Japan 
Urase et al. (2020)  13 264 Urban wastewater 2020 Treated DDST, CLSI Japan 
Zagui et al. (2020)  11 34 Hospital and municipal wastewaters and their receiving rivers 2020 Treated and untreated DDST, CLSI Brazil 
ReferencesNo. of ESBL EnterobacteriaceaeWastewater originYearTreatmentMethods/guidelineCountry
Prado et al. (2007)  20 43 Hospital wastewater 2007 Treated and untreated CDT, CLSI Brazil 
Sabaté et al. (2008)  10 85 Wastewater from other sources (farms and slaughterhouses) 2008 Treated and untreated E-test, CLSI Spain 
Łuczkiewicz et al. (2010)  259 Urban wastewater 2010 Treated and untreated Phoenix Automated Microbiology System, CLSI Poland 
Chagas et al. (2011)  96 213 Hospital wastewater 2011 Treated DDST, CLSI Brazil 
Mokracka et al. (2012)  12 1,832 Urban wastewater 2012 Treated and untreated DDST, CLSI Poland 
Diallo et al. (2013)  1,248 Wastewater from other sources (farms and slaughterhouses) 2013 Treated E-test, CLSI France 
Korzeniewska & Harnisz (2013a)  150 310 Hospital wastewater 2013 Untreated CDT, CLSI Poland 
Korzeniewska & Harnisz (2013b)  34 246 Urban wastewater 2013 Treated and untreated CDT, CLSI Poland 
Bessa et al. (2014)  29 144 Hospital and municipal wastewaters and their receiving rivers 2014 Treated Disk approximation test, CLSI Portugal 
Chandran et al. (2014)  87 190 Hospital wastewater 2014 Untreated CDT India 
Čornejová et al. (2015)  27 104 Urban wastewater 2015 Treated and untreated Microdilution, CLSI Slovakia 
Kwak et al. (2015)  73 1,894 Hospital and municipal wastewaters and their receiving rivers 2015 Treated and untreated AREB microplates, CLSI Sweden 
Kotlarska et al. (2015)  774 Hospital and municipal wastewaters and their receiving rivers 2015 Treated and untreated DDST, EUCAST Poland 
De Oliveira & Van Der Sand (2016)  32 60 Hospital wastewater 2016 Treated and untreated  DDST, CLSI Brazil 
Drieux et al. (2016)  25 389 Hospital wastewater 2016 Untreated DDST, CA-SFM France 
Dropa et al. (2016)  200 Urban wastewater 2016 Untreated DDST, CLSI Brazil 
Egbule (2016)  96 Hospital wastewater 2016 Untreated DDST, CLSI Nigeria 
Lenart-Boroń (2016)  23 196 Wastewater-receiving rivers 2016 Untreated DDST, EUCAST Poland 
Maheshwari et al. (2016)  34 103 Hospital wastewater 2016 Untreated CDT, CLSI India 
Sultana et al. (2016)  30 166 Hospital wastewater 2016 Untreated DDST Bangladesh 
Caltagirone et al. (2017)  30 132 Hospital and municipal wastewaters and their receiving rivers 2017 Treated and untreated DDST, EUCAST Italy 
Conte et al. (2017)  55 152 Hospital and municipal wastewaters and their receiving rivers 2017 Treated and untreated VITEK-2 system and MALDI-TOF, CLSI Brazil 
Lien et al. (2017)  115 265 Hospital wastewater 2017 Treated and untreated CDT, CLSI Vietnam 
Obasi et al. (2017)  Urban wastewater 2017 Untreated Vitek 2, E-test strips, CLSI Nigeria 
Tafoukt et al. (2017)  20 Wastewater-receiving rivers 2017 Untreated DDST, CLSI Algeria 
Adelowo et al. (2018)  48 98 Hospital and municipal wastewaters and their receiving rivers 2018 Untreated DDST, CLSI Nigeria 
Daoud et al. (2018)  51 70 Hospital wastewater 2018 Untreated DDST, E-test ESBL strips, CLSI Lebanon 
Debabza et al. (2018)  143 254 Hospital and municipal wastewaters and their receiving rivers 2018 Treated and untreated DDST, CA-SFM Algeria 
Falodun et al. (2018)  30 189 Wastewater-receiving rivers 2018 Untreated DDST, CLSI Nigeria 
Flach et al. (2018)  89 4,028 Urban wastewater 2018 Treated and untreated DDST, EUCAST Sweden 
Park et al. (2018)  14 75 Wastewater from other sources (farms and slaughterhouses) 2018 Treated and untreated DDST, EUCAST South Korea 
Siddiqui et al. (2018)  175 506 Wastewater-receiving rivers 2018 Untreated PDCT, CLSI India 
Tokajian et al. (2018)  21 34 Hospital and municipal wastewaters and their receiving rivers 2018 Untreated DDST, CLSI Lebanon 
Vital et al. (2018)  27 147 Wastewater-receiving rivers 2018 Untreated DDST, CLSI Philippines 
Adekanmbi et al. (2019)  17 58 Hospital wastewater 2019 Untreated DDST, CLSI Nigeria 
Bartley et al. (2019)  20 40 Hospital and municipal wastewaters and their receiving rivers 2019 Treated and untreated  VITEK-2, CLSI Brazil 
Chaudhry et al. (2019)  17 112 Hospital wastewater 2019 Untreated DDST, CLSI Pakistan 
Falodun & Oladimeji (2019)  35 200 Hospital wastewater 2019 Treated and untreated DDST, CLSI Nigeria 
Haberecht et al. (2019)  47 70 Urban wastewater 2019 Treated and untreated CHROMagar ESBL USA 
Li et al. (2019)  50 70 Wastewater from other sources (farms and slaughterhouses) 2019 Treated and untreated DDST, CLSI China 
Mahato et al. (2019)  13 Hospital wastewater 2019 Treated DDST, CLSI Nepal 
Miyagi & Hirai (2019)  141 249 Hospital and municipal wastewaters and their receiving rivers 2019 Untreated DDST, CLSI Japan 
Paulshus et al. (2019)  314 3,123 Urban wastewater 2019 Untreated AREB microplates, CLSI Norway 
Raven et al. (2019)  192 388 Urban wastewater 2019 Treated and untreated Brilliance ESBL agar, Vitek UK 
Sghaier et al. (2019)  58 123 Urban wastewater 2019 Treated DDST, CLSI Tunisia 
Tesfaye et al. (2019)  54 Wastewater from other sources (farms and slaughterhouses) 2019 Untreated DDST, CLSI Ethiopia 
Adekanmbi et al. (2020)  12 33 Hospital wastewater 2020 Treated and untreated DDST, CLSI Nigeria 
Banjo et al. (2020)  12 23 Hospital wastewater 2020 Untreated DDST, CLSI Nigeria 
Khan et al. (2020)  35 61 Urban wastewater 2020 Treated CHROMagar ESBL, DDST, CLSI UAE 
King et al. (2020)  14 130 Hospital and municipal wastewaters and their receiving rivers 2020 Treated and untreated MASTDISCS D68C AmpC/ESBL kits, EUCAST South Africa 
Lenart Boroń et al. (2020)  50 Wastewater-receiving rivers 2020 Treated DDST, EUCAST Poland 
Saima et al. (2020)  10 Wastewater-receiving rivers 2020 Untreated DDST, CDT, CLSI Pakistan 
Smyth et al. (2020)  89 498 Urban wastewater 2020 Treated DDST, CLSI Ireland 
Surleac et al. (2020)  34 Hospital and municipal wastewaters and their receiving rivers 2020 Treated and untreated ChromID ESBL agar, CLSI Romania 
Urano et al. (2020)  64 Urban wastewater 2020 Treated and untreated DDST, CLSI Japan 
Urase et al. (2020)  13 264 Urban wastewater 2020 Treated DDST, CLSI Japan 
Zagui et al. (2020)  11 34 Hospital and municipal wastewaters and their receiving rivers 2020 Treated and untreated DDST, CLSI Brazil 

CDT, combined disc diffusion method; CLSI, Clinical & Laboratory Standards Institute; DDST, The Double Disc Synergy Test; PDCT, Phenotypic disc confirmatory test; CA-SFM, Comité de l'Antibiogramme de la société Française de Microbiologie.

The most common producer of ESBL in wastewater was found to be Escherichia coli (51 studies), followed by Klebsiella (29 studies) and Enterobacter (18 studies). Moreover, the blaCTX-M gene was the most studied (30 studies), followed by the blaTEM and blaSHV genes (25 studies).

Pooled prevalence of ESBL-E

A total of 2,618 isolates of ESBL-E from 20,230 Enterobacteriaceae were found in the selected publications (Table 2), and the estimated prevalence varied widely across the studies ranging from 0.00% (95% CI, 0.00–7.13) to 85.71% (95% CI, 48.69–97.43) with substantial heterogeneity (χ2 = 4461.79, I2 = 98.74%, p < 0.001). The random-effect estimated pooled prevalence of ESBL-E was 24.81% (95% CI, 19.28, 30.77). Figure 2 shows the pooled prevalence of ESBL-E.

Table 2

Stratified pooled prevalence estimates of ESBL-producing Enterobacteriaceae in wastewater

SubgroupsNo. of studiesNo. of ESBLNo. of EnterobacteriaceaePooled estimate (%) of ESBL95% Confidence intervalHeterogeneity chi-squared (χ2)Heterogeneity test, I2 (%)Heterogeneity test, p-value
Continents
Europe 18 1,112 15,700 10.19 5.73, 15.72 1,645.83 98.97 <0.001 
Asia 18 830 2,433 33.95 24.05, 44.58 449.28 96.22 <0.001 
Africa 13 388 1,285 29.31 17.86, 42.17 250.07 95.20 <0.001 
America 288 812 39.91 21.82, 59.51 206.09 96.60 <0.001 
Overall 57 2,618 20,230 24.81 19.28, 30.77 4,461.79 98.74 <0.001 
Wastewater origin
Hospital wastewater 17 743 2,344 33.98 23.82, 44.91 442.77 96.39 <0.001 
Municipal wastewater 15 929 11,267 18.83 11.15, 27.88 1,410.51 99.01 <0.001 
Wastewater from rivers 259 1,118 13.67  5.36, 24.64 96.29 93.77 <0.001 
Hospital and municipal wastewaters and their receiving rivers 13 598 3,969  29.41 15.03, 46.19 1,123.05 98.93 <0.001 
Wastewater from other sources (farms and slaughterhouses) 89 1,532 18.43 1.04, 48.62 273.15 98.54 <0.001 
Overall 57 2,618 20,230 24.81 19.28, 30.77 4,461.79 98.74 <0.001 
The use of treatment
Treated wastewater 335 2,614 21.11 7.40, 39.17 649.12 98.77 <0.001 
Untreated wastewater 23 1,229 6,350 27.36 19.12, 36.42  956.04 97.70 <0.001 
Treated and untreated wastewater 25 1,054 11,266 23.93 15.87, 33.03 2,286.92 98.95 <0.001 
Overall 57 2,618 20,230 24.81 19.28, 30.77 4,461.79 98.74 <0.001 
Year of publication
2020 11 200 1,201 19.32 10.31, 30.17 144.56 93.08 <0.001 
2019 12 903 4,500 37.62 22.18, 54.43 766.42 98.56 <0.001 
2018 598 5,401 33.93 14.01, 57.34 1,191.61 99.33 <0.001 
2017 209 576 34.73 22.53, 47.96 28.30 85.86 <0.001 
2016 155 1,210 15.51 7.02, 26.45 126.54 95.26 <0.001 
2007–2015 13 553 7,342 15.08 7.17, 25.19 1,276.01 99.06 <0.001 
Overall 57 2,618 20,230 24.81 19.28, 30.77 4,461.79 98.74 <0.001 
SubgroupsNo. of studiesNo. of ESBLNo. of EnterobacteriaceaePooled estimate (%) of ESBL95% Confidence intervalHeterogeneity chi-squared (χ2)Heterogeneity test, I2 (%)Heterogeneity test, p-value
Continents
Europe 18 1,112 15,700 10.19 5.73, 15.72 1,645.83 98.97 <0.001 
Asia 18 830 2,433 33.95 24.05, 44.58 449.28 96.22 <0.001 
Africa 13 388 1,285 29.31 17.86, 42.17 250.07 95.20 <0.001 
America 288 812 39.91 21.82, 59.51 206.09 96.60 <0.001 
Overall 57 2,618 20,230 24.81 19.28, 30.77 4,461.79 98.74 <0.001 
Wastewater origin
Hospital wastewater 17 743 2,344 33.98 23.82, 44.91 442.77 96.39 <0.001 
Municipal wastewater 15 929 11,267 18.83 11.15, 27.88 1,410.51 99.01 <0.001 
Wastewater from rivers 259 1,118 13.67  5.36, 24.64 96.29 93.77 <0.001 
Hospital and municipal wastewaters and their receiving rivers 13 598 3,969  29.41 15.03, 46.19 1,123.05 98.93 <0.001 
Wastewater from other sources (farms and slaughterhouses) 89 1,532 18.43 1.04, 48.62 273.15 98.54 <0.001 
Overall 57 2,618 20,230 24.81 19.28, 30.77 4,461.79 98.74 <0.001 
The use of treatment
Treated wastewater 335 2,614 21.11 7.40, 39.17 649.12 98.77 <0.001 
Untreated wastewater 23 1,229 6,350 27.36 19.12, 36.42  956.04 97.70 <0.001 
Treated and untreated wastewater 25 1,054 11,266 23.93 15.87, 33.03 2,286.92 98.95 <0.001 
Overall 57 2,618 20,230 24.81 19.28, 30.77 4,461.79 98.74 <0.001 
Year of publication
2020 11 200 1,201 19.32 10.31, 30.17 144.56 93.08 <0.001 
2019 12 903 4,500 37.62 22.18, 54.43 766.42 98.56 <0.001 
2018 598 5,401 33.93 14.01, 57.34 1,191.61 99.33 <0.001 
2017 209 576 34.73 22.53, 47.96 28.30 85.86 <0.001 
2016 155 1,210 15.51 7.02, 26.45 126.54 95.26 <0.001 
2007–2015 13 553 7,342 15.08 7.17, 25.19 1,276.01 99.06 <0.001 
Overall 57 2,618 20,230 24.81 19.28, 30.77 4,461.79 98.74 <0.001 
Figure 2

Pooled prevalence of ESBL-producing Enterobacteriaceae in wastewater. Studies are sorted alphabetically, squares represent effect sizes of individual studies and the diamond indicates the estimated pooled effect size. CI, confidence interval.

Figure 2

Pooled prevalence of ESBL-producing Enterobacteriaceae in wastewater. Studies are sorted alphabetically, squares represent effect sizes of individual studies and the diamond indicates the estimated pooled effect size. CI, confidence interval.

Variables associated with ESBL-E prevalence

Subgroup meta-analysis results are presented in Table 2. The analysis by continent showed that the highest prevalence of ESBL-E was in America at 39.91%, and the lowest prevalence was in Europe at 10.19% (95% CI, 5.73–15.72). The pooled prevalence of ESBL was significantly higher (p < 0.001) in reports studying hospital wastewater (33.98%, 95% CI, 23.82–44.91) and, more specifically, in untreated wastewater (27.36%, 95% CI, 19.12–36.42). The number of reports on ESBL-E prevalence in wastewater increased over time, and the highest number of studies per year was reported in 2019 (n = 12). Forest plots of the ESBL-E prevalence among studies stratified by continents, wastewater sources, the year, and the use of treatment are outlined in Figures 36.

Figure 3

Pooled prevalence of ESBL-producing Enterobacteriaceae stratified by continents.

Figure 3

Pooled prevalence of ESBL-producing Enterobacteriaceae stratified by continents.

Figure 4

Pooled prevalence of ESBL-producing Enterobacteriaceae stratified by the year of publication.

Figure 4

Pooled prevalence of ESBL-producing Enterobacteriaceae stratified by the year of publication.

Figure 5

Pooled prevalence of ESBL-producing Enterobacteriaceae stratified by treatments.

Figure 5

Pooled prevalence of ESBL-producing Enterobacteriaceae stratified by treatments.

Figure 6

Pooled prevalence of ESBL-producing Enterobacteriaceae stratified by the wastewater origin.

Figure 6

Pooled prevalence of ESBL-producing Enterobacteriaceae stratified by the wastewater origin.

Among Enterobacteriaceae species, E. coli had the highest ESBL prevalence (15.02%, 95% CI, 11.12–19.36) (Table 3) followed by Klebsiella spp. (9.60%, 95% CI, 19.54 38.29) and Proteus spp. (3.80%, 95% CI, 1.61, 6.76). Moreover, among ESBL genes, the blaCTX-M gene had the highest prevalence (66.56%, 95% CI, 53.98–78.15), followed by blaTEM (49.88%, 95% CI, 35.01–64.76) and blaSHV (21.58%, 95% CI, 13.71–30.49).

Table 3

Meta-analysis of ESBL-E at the species level and ESBL-E gene prevalence in wastewaters

SubgroupsNo. of studiesNo. of ESBLNo. of EnterobacteriaceaePooled estimate (%) of ESBL95% Confidence intervalHeterogeneity χ2Heterogeneity test, I2 (%)Heterogeneity test, p-value
Enterobacteriaceae species
 E. coli 51 2,541 19,881 15.05 11.14, 19.41 2,902.78 98.28 <0.001 
Klebsiella 29 1,222 5,732 9.66 5.83, 14.25 665.17 95.79 <0.001 
Enterobacter 18 1,139 5,125 2.39 1.08, 4.09 149.25 88.61 <0.001 
Citrobacter 714 1,846 2.69 0.50, 6.23 87.10 91.96 <0.001 
Shigella 619 1,578 2.10 0.66, 4.19 28.58 79.01 <0.001 
Proteus 323 900 3.80 1.61, 6.76 18.02 72.26 <0.001 
Serratia 481 1,036 3.37 0.27, 8.89 48.36 91.73 <0.001 
Other species 12 785 2,064 3.07 1.04, 5.96 169.26 93.50 <0.001 
ESBL genesNo. of studiesNo. of ESBLNo. of EnterobacteriaceaePooled estimate (%) of ESBL genes95% Confidence intervalHeterogeneity χ2Heterogeneity test, I2 (%)Heterogeneity test, p-value
blaCTX-M 30 1,621 7,532 66.82% 52.57, 79.79 691.61 96.10 <0.001 
blaTEM 25 1,292 4,888 51.01% 35.52, 66.40 661.40  96.37 <0.001 
blaSHV 25 1,044 4,875 24.59% 15.42, 34.91 239.93 90.41 <0.001 
SubgroupsNo. of studiesNo. of ESBLNo. of EnterobacteriaceaePooled estimate (%) of ESBL95% Confidence intervalHeterogeneity χ2Heterogeneity test, I2 (%)Heterogeneity test, p-value
Enterobacteriaceae species
 E. coli 51 2,541 19,881 15.05 11.14, 19.41 2,902.78 98.28 <0.001 
Klebsiella 29 1,222 5,732 9.66 5.83, 14.25 665.17 95.79 <0.001 
Enterobacter 18 1,139 5,125 2.39 1.08, 4.09 149.25 88.61 <0.001 
Citrobacter 714 1,846 2.69 0.50, 6.23 87.10 91.96 <0.001 
Shigella 619 1,578 2.10 0.66, 4.19 28.58 79.01 <0.001 
Proteus 323 900 3.80 1.61, 6.76 18.02 72.26 <0.001 
Serratia 481 1,036 3.37 0.27, 8.89 48.36 91.73 <0.001 
Other species 12 785 2,064 3.07 1.04, 5.96 169.26 93.50 <0.001 
ESBL genesNo. of studiesNo. of ESBLNo. of EnterobacteriaceaePooled estimate (%) of ESBL genes95% Confidence intervalHeterogeneity χ2Heterogeneity test, I2 (%)Heterogeneity test, p-value
blaCTX-M 30 1,621 7,532 66.82% 52.57, 79.79 691.61 96.10 <0.001 
blaTEM 25 1,292 4,888 51.01% 35.52, 66.40 661.40  96.37 <0.001 
blaSHV 25 1,044 4,875 24.59% 15.42, 34.91 239.93 90.41 <0.001 

Meta-regression evaluating the effect of confounding factors on the prevalence of ESBL-E was performed, and the results are summarized in Table 4. In the univariable meta-regression, an association was found between ESBL prevalence and several factors at p ≤ 0.25. In the final multivariable meta-regression model, a positive association was found between ESBL-E carriage and studies reported in America (Coef. = 0.22, p = 0.01).

Table 4

Summary results of univariable and multivariable meta-regression of the effects of confounding factors on ESBL-E occurrence in wastewaters

Coef.b (95% CIc)SEp-valueCoef. (95% CI)SEp-value
ModeratorsUnivariable regression
Multivariable regression
Continents
 Europea – – – – – – 
 Asia 0.22 (0.08, 0.35) 0.06 0.002  0.14 ( − 0.01, 0.31) 0.08 0.078 
Africa 0.16 (0.007, 0.31) 0.07 0.040 0.06 ( − 0.11, 0.24) 0.09 0.507 
America 0.26 (0.08, 0.44) 0.09 0.003 0.22 (0.04, 0.39) 0.08 0.012 
Wastewater origin
Hospital wastewatera – – – – – – 
Municipal wastewater − 0.13 ( − 0.27, 0.016) 0.07 0.081 −0.11 ( − 0.27, 0.05) 0.08 0.183 
Wastewater effluents in rivers − 0.17 ( − 0.37, 0.017) 0.10 0.074 − 0.28 ( − 0.41, 0.008) 0.10 0.060 
Hospital and municipal wastewaters and their receiving rivers
Wastewater from other sources (farms and slaughterhouses) 
− 0.03 (−0.19, 0.11)
−0.13 (−0.34, 0.08) 
0.07
0.10 
0.650
0.222 
− 0.05 (−0.21, 0.11)
−0.14 (−0.36, 0.06) 
0.08
0.10 
0.557
0.163 
Treatments
Treated and untreated wastewatera – – – – – – 
Untreated wastewater 0.02 ( − 0.10, 0.14) 0.06 0.733 0.016 ( − 0.13, 0.16) 0.07 0.829 
Treated wastewater − 0.01 ( − 0.18, 0.14) 0.08 0.816 0.04 ( − 0.11, 0.20) 0.08 0.604 
Year of publication
2007–2015 – – – – – – 
2016 − 0.01 ( − 0.19, 0.16) 0.09 0.851 − 0.11 ( − 0.31, 0.09) 0.10 0.276 
2017 0.16 ( − 0.06, 0.38) 0.11 0.158 0.08 ( − 0.14, 0.31) 0.11 0.483 
2018 0.15 ( − 0.01, 0.32) 0.08 0.073 0.14 ( − 0.06, 0.35) 0.10 0.175 
2019 0.19 (0.03, 0.34) 0.08 0.018 0.13 ( − 0.03, 0.31) 0.08 0.122 
2020 0.02 ( − 0.14, 0.19) 0.08 0.784 − 0.01 ( − 0.20, 0.18) 0.09 0.911 
Coef.b (95% CIc)SEp-valueCoef. (95% CI)SEp-value
ModeratorsUnivariable regression
Multivariable regression
Continents
 Europea – – – – – – 
 Asia 0.22 (0.08, 0.35) 0.06 0.002  0.14 ( − 0.01, 0.31) 0.08 0.078 
Africa 0.16 (0.007, 0.31) 0.07 0.040 0.06 ( − 0.11, 0.24) 0.09 0.507 
America 0.26 (0.08, 0.44) 0.09 0.003 0.22 (0.04, 0.39) 0.08 0.012 
Wastewater origin
Hospital wastewatera – – – – – – 
Municipal wastewater − 0.13 ( − 0.27, 0.016) 0.07 0.081 −0.11 ( − 0.27, 0.05) 0.08 0.183 
Wastewater effluents in rivers − 0.17 ( − 0.37, 0.017) 0.10 0.074 − 0.28 ( − 0.41, 0.008) 0.10 0.060 
Hospital and municipal wastewaters and their receiving rivers
Wastewater from other sources (farms and slaughterhouses) 
− 0.03 (−0.19, 0.11)
−0.13 (−0.34, 0.08) 
0.07
0.10 
0.650
0.222 
− 0.05 (−0.21, 0.11)
−0.14 (−0.36, 0.06) 
0.08
0.10 
0.557
0.163 
Treatments
Treated and untreated wastewatera – – – – – – 
Untreated wastewater 0.02 ( − 0.10, 0.14) 0.06 0.733 0.016 ( − 0.13, 0.16) 0.07 0.829 
Treated wastewater − 0.01 ( − 0.18, 0.14) 0.08 0.816 0.04 ( − 0.11, 0.20) 0.08 0.604 
Year of publication
2007–2015 – – – – – – 
2016 − 0.01 ( − 0.19, 0.16) 0.09 0.851 − 0.11 ( − 0.31, 0.09) 0.10 0.276 
2017 0.16 ( − 0.06, 0.38) 0.11 0.158 0.08 ( − 0.14, 0.31) 0.11 0.483 
2018 0.15 ( − 0.01, 0.32) 0.08 0.073 0.14 ( − 0.06, 0.35) 0.10 0.175 
2019 0.19 (0.03, 0.34) 0.08 0.018 0.13 ( − 0.03, 0.31) 0.08 0.122 
2020 0.02 ( − 0.14, 0.19) 0.08 0.784 − 0.01 ( − 0.20, 0.18) 0.09 0.911 

aRef., reference category; Coef., regression coefficient; CI, confidence interval.

Analysis of publication bias and sensitivity

The publication bias among the included papers was evaluated with Egger's test, which showed some evidence of publication bias, with a bias co-efficient of 1.56 (p = 0.002). The funnel plot's shape was not symmetrical (Supplementary Material, Figure S1). Thus, there is a potential for publication bias. Moreover, the sensitivity analysis showed no individual study that influenced the overall meta-analysis estimate, which indicates the robustness of the results (Supplementary Material, Table S3).

DISCUSSION

Wastewater is an important environmental supplier of antibacterial-resistant bacteria and antimicrobial-resistance genes (Marti et al. 2013). In this study, the occurrence of ESBL-producing Enterobacteriaceae in different wastewater sources has been evaluated. There are some big differences between hospital, municipal, and animal wastewaters. Thus, we have chosen to do the subgroup analysis and study the prevalence of ESBL-E in each source separately. In total, the pooled prevalence of ESBL-E in wastewater was substantially important (24.81%), with the highest prevalence in untreated wastewater (27.36%). Untreated wastewater is loaded with different pollutants that can facilitate the proliferation of resistant bacteria (Preisner 2020). However, Du et al. (2015) suggested that biological treatments in wastewater treatment plants can also enhance the bacterial proliferation and genetic exchange.

Moreover, hospital wastewater had the highest prevalence of ESBL-E (33.98%). This could be due to the high usage of antibiotics in hospitals compared with public usage. Kümmerer (2009) found that the concentration of antibiotics in hospital effluents is 100 times higher than in municipal effluents. Moreover, the intensive use of antibiotics in hospitals could also contribute in the ESBL emergence (Debabza et al. 2018), which turns the hospital into a highly selective environment for ESBL-producing bacteria (Duong et al. 2008). Wastewater effluents in rivers had the lowest prevalence of ESBL-E. Wastewaters are usually treated before being released to waterways, which could explain the low prevalence of ESBL-E in rivers (Numberger et al. 2019).

Europe had the highest number of studies on ESBL-E and the lowest prevalence of ESBL-E. On the other hand, America (mainly South America because 7/8 of studies are in Brazil) and Asia had the highest prevalence of ESBL-E (39.91 and 33.95%, respectively), and according to the meta-regression results, studies reported in America are significantly associated with the presence of ESBL-E in wastewater. In developing countries, wastewater generated from farming, communities, and hospital effluents does not obtain appropriate treatment, and few functioning treatment facilities are available. This inadequate management can lead to environmental contamination with resistant bacteria such as ESBL-E into the water environment (Behnam et al. 2020; WHO 2018).

The highest prevalence of ESBL-E in wastewater was reported in studies undertaken in 2019. Gelband et al. (2015) analyzed data from 73 countries over the past 10 years and found that antibiotic use is growing steadily worldwide, which can explain, in part, the increase of ESBL-E in wastewater in the last 2 years. On the other hand, the biggest number of studies on ESBL-E in wastewater was also observed between the years 2019–2020; this could possibly mean that public awareness of the challenges related to the presence of resistant bacteria in wastewater is starting to rise lately.

The studies included in this meta-analysis reported that E. coli was the most isolated ESBL-producing Enterobacteriaceae (15.02%). These bacteria are indicators of fecal contamination, agents of several kinds of nosocomial and community-acquired infections, and are often associated with therapy failure when cephalosporins are used (Kola et al. 2007; Shakya et al. 2017). The high prevalence of these enzymes in E. coli is maybe due to the ability of E. coli to survive for long periods and multiply in wastewater without being affected with wastewater treatments, which leads to the selection and emergence of ESBL-producing E. coli (Jang et al. 2017). Moreover, the blaCTX-M genes were the most prevalent in the selected studies, accounting for a significant percentage of the ESBL genes detected (66.56%). This high prevalence of CTX-M-type ESBL producers could be due to the global spreading of clones with epidemic and pandemic potential, such as the extra-intestinal pathogenic E. coli (ExPEC) ST131 known for its ability to produce extended-spectrum β-lactamases, such as CTX-M-15 (Cantón et al. 2012; Tanaka et al. 2019).

These findings enhance our understanding of ESBL-producing Enterobacteriaceae in wastewater. However, some limitations need to be addressed; in some recent studies, ESBL-E isolates were directly isolated using selective media without isolating Enterobacteriaceae first, and these studies were not included in our study because the total number of isolates is absent. Moreover, most of the included studies on ESBL-E in treated wastewater did not report the type of treatment used, so we were obligated to put three categories (treated and untreated, treated, and untreated), and another potential limitation of the present study is that statistical results may have been influenced by publication bias based on the visual evaluation of the funnel plot and the Egger test results; this is maybe due to the inherent bias toward reporting positive results; despite our effort to use search strategies that enhance the reporting of negative results, we have only found one study (the study of Lenart-Boroń et al. (2020)) that reported negative data.

CONCLUDING REMARKS

Based on the 57 studies analyzed in this meta-analysis, we concluded that the prevalence of Enterobacteriaceae-producing ESBL in wastewater is increasing over time and that hospital wastewater is the most important repository of ESBL. This study also revealed a high prevalence of the blaCTX-M genes in Enterobacteriaceae isolated from wastewater, which is an alarming indicator of the global spreading of epidemic resistance plasmids.

Hence, these results highlight the need to develop effective strategies and measures adapted for removing ESBL-producing bacteria in wastewater and preventing the dissemination and transmission of antibiotic resistance from wastewater to different aquatic systems. Moreover, further research must focus on developing new sewage treatment systems that decrease the introduction of resistant bacteria and antibiotic residues.

FUNDING

This work received no external funding.

CONFLICT OF INTEREST

The authors declare no conflict of interest.

AUTHORS’ CONTRIBUTIONS

NZ: conceptualization, writing and editing; SB and NS: investigation and writing.

DATA AVAILABILITY STATEMENT

All relevant data are included in the paper or its Supplementary Information.

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