Monitoring Extended-spectrum-β-lactamases-producing Klebsiella pneumoniae: Do Surface Water Phenotypic Patterns Reflect Community and Clinical Resistance?

Monitoring Extended-spectrum-β-lactamases-producing Klebsiella pneumoniae: Do Surface Water Phenotypic Patterns Reflect Community and Clinical Resistance? | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Monitoring Extended-spectrum-β-lactamases-producing Klebsiella pneumoniae: Do Surface Water Phenotypic Patterns Reflect Community and Clinical Resistance? Etando Ayukafangha, Joshua Mbanga, Sabiha Y. Essack, Akebe Luther King Abia This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9523007/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose ESBL- Klebsiella pneumoniae (ESBL-Kp) prevalence and resistance profiles were evaluated to determine if Surface water (SW) is an Alternative Antimicrobial-resistance Monitoring System (AlARMS) for nearby community (CM) and clinical (CS) settings. Method Over eight months, 462 (SW), 1,167 (CM), and 244 (CS) K. pneumoniae isolates were screened for ESBL production; antimicrobial susceptibility testing was conducted using VITEK 2. Results ESBL-Kp prevalence varied ( p 0.05) to amoxicillin-clavulanate, piperacillin-tazobactam, cephalosporins, carbapenems, amikacin, and tigecycline, but differed ( p 0.05) in resistance to cephalosporins, amikacin, gentamicin, and trimethoprim-sulphamethoxazole, but differed for amoxicillin-clavulanic acid, piperacillin-tazobactam, ciprofloxacin, and tigecycline. A non-MDR pattern was common to all sources, with more SW-CM (63.3%) than SW-CS (3.3%) overlap. MDR pattern overlapped was seen in SW-CM but not in SW-CS. Conclusion Surface water isolates did not fully mirror clinical or community phenotypes; nevertheless, shared clinically relevant patterns suggest transmission potential, necessitating refined molecular insights for AlARMS evaluation. General Microbiology Applied & Industrial Microbiology Infectious Diseases One-Health Early warning systems aquatic ecosystems antimicrobial resistance public health Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Study Highlights Samples from surface water, community residents, and the hospital all carried ESBL-Kp. Resistance varied for “Access” drugs, while “Reserve” remained consistent across sites. Multidrug resistance is widespread, peaking in clinics and lowest in surface water. Surface water antibiogram overlapped more with community than clinical isolates. Phenotypic AMR in surface water did not mirror community- or clinical-level AMR. 1.0 INTRODUCTION The World Health Organisation (WHO) considers infections caused by multidrug-resistant (MDR) bacteria as a significant public health concern. MDR is a type of antimicrobial resistance (AMR) in which microbes, such as bacteria, are resistant to multiple antibiotics from three or more classes (WHO, 2023 ). The AMR crisis has intensified dramatically in recent decades, fuelled primarily by the misuse and overuse of antibiotics. This resistance surge has significantly broadened the range and number of bacterial pathogens that defy treatment, accounting for up to 4.71 million deaths associated with bacterial AMR, including 1.14 million attributable deaths as of 2021 (Naghavi et al., 2024 ). As a result, regulatory bodies like the WHO published a list of priority bacterial pathogens for research and development of new antibiotics in 2017, and later updated it in 2024. Extended-spectrum β-lactamase-producing and carbapenem-resistant Enterobacterales, including Klebsiella pneumoniae ( K. pneumoniae ), are among the critical pathogens on the list (WHO 2017 , WHO 2024 ). K. pneumoniae is a Gram-negative bacterium of the Enterobacterales family, found in the environment (soil, surface water), on mammalian mucus membranes and in humans, where it colonises the upper part of the nasopharynx and gastrointestinal tract (Choby et al., 2020 ). K. pneumoniae possesses intrinsic resistance to ampicillin via the SHV-1 β-lactamase; however, it has acquired multidrug resistance through various mechanisms. Beyond producing extended-spectrum β-lactamases (ESBLs) and carbapenemases that degrade β-lactams, it employs aminoglycoside-modifying enzymes, mutations in the DNA gyrase genes for fluoroquinolone resistance, and plasmid-mediated mcr genes that confer resistance to colistin. Furthermore, active efflux pumps and decreased membrane permeability often render these strains resistant to nearly all available antibiotic classes (Han et al., 2025 ; Li et al., 2023 ). The emergence of ESBL-Kp resistance represents a serious public health concern in both healthcare (Founou et al., 2019 ) and community settings (Olaitan et al., 2025 ). Due to its opportunistic nature, current evidence suggests it has a wider ecological distribution and greater AMR diversity than other Gram-negative opportunists, suggesting its role in disseminating resistance between humans and their surrounding environments (Hooban et al., 2020 ; Wyres & Holt, 2018 ). The presence of ESBL-Kp has been reported across various niches, including the environment (Kagambèga et al., 2024 ), community residents (Mahomed et al., 2014 ) and clinical settings (Founou et al., 2019 ; Starzyk-Łuszcz et al., 2017 ). These studies highlighted the need for integrated surveillance that links human, animal, and environmental health to manage the risks of exposure and transmission effectively. AMR surveillance in bacteria, including K. pneumoniae , has relied on conventional laboratory detection methods, which have been noted to underestimate or overestimate AMR depending on the representativeness and robustness of the surveillance programme (Sugianli et al., 2020 ). In Low- and Middle-Income Countries (LMICs), the underutilisation of microbiological testing for both patient management and surveillance is largely driven by high costs, limited laboratory infrastructure, and a lack of standardised reporting(Lim et al., 2021 ) This surveillance gap is particularly pronounced in environmental and community settings, where the absence of routine monitoring obscures the transmission pathways of resistant strains. With the existence of resistant bacteria in the environment and the potential usefulness of the environment in monitoring AMR (Kagambèga et al., 2024 ; Rolbiecki et al., 2025 ), there has been a surge in exploring environmental elements like wastewater treatment plants (WWTPs) and hospital influent and effluent, and surface water for monitoring of AMR. The presence of resistant bacteria and genes in the environment could result from inappropriate disposal of faecal waste, community-level animal and agricultural activities, and sewage system effluents (Mills & Lee, 2019 ; Nashwan et al., 2024 ). These contamination sources may differ by community type. Informal settlements, also called slums, are unplanned, improvised human settlements that are not surveyed as residential areas and are characterised primarily by informal structures (Ooi & Phua, 2007 ). These settlements are usually overcrowded, with infrastructure deficits, including inadequate access to clean water, sanitation (including toilet facilities), and poor drainage systems, thereby increasing residents’ vulnerability to health risks and making the settlements prime hotspots for AMR (Nadimpalli et al., 2020 ; Nkonki-Mandleni et al., 2021 ). In addition to agricultural activities and animal husbandry, challenges such as poor drainage outlets significantly contribute to the discharge of human and household waste, including drug-resistant bacteria, into rivers (Thakur & Onwubu, 2024 ). Up to 4000 informal settlements exist in South Africa, with over 2 million households, and over 400 of these settlements are in the province of KwaZulu-Natal (Gamede, 2024 ; Housing Development Agency, 2021 ). This study determined the prevalence and AMR profiles of ESBL-producing K. pneumoniae isolated from surface water, apparently healthy residents of an informal settlement and a proximate healthcare facility, to determine similarities and differences, and to ascertain whether AMR surveillance in surface water in proximity to informal settlements is a potential proxy for AMR circulating in the community. 2.0 METHODS 2.1 Ethical Approval and Consent to Participate This study was approved by the Biomedical Research Ethics Committee of the University of KwaZulu-Natal (Ref: BREC/00003640/2021), the Provincial Health Research Ethics Committee of the KwaZulu-Natal Department of Health Ref: KZ_202203_023), and the National Health Laboratory Service (NHLS) Ref: PR2225862). All informal settlement residents were recruited after providing explicit, written, and voluntary informed consent. 2.2 Study Design and Study Area This study was a longitudinal prevalence study undertaken in the uMgungundlovu District Municipality (UMDM). UMDM is a Category C municipality with its seat in Pietermaritzburg, in the KwaZulu-Natal Province of South Africa, and comprises seven local municipalities, including Msunduzi, where the sampling was conducted. UMDM provides water and wastewater services to six municipalities, serving over 1.2 million inhabitants (UMDM, 2023 )Despite several environmental water resources, including six significant rivers, 19.6% of the population is not included in the “regional/local water scheme”, 5.3% still obtain water directly from rivers, 2.5% from springs and 1.4% from water vendors (uMgungundlovu District Municipality, 2023). Three per cent of the population has no form of toilet facilities (Development Bank of Southern Africa, 2025 ). About 63.4% of the district’s population lives below the poverty line; 45.6% have no source of income, and 17.8% earn less than R400 per month and reside in informal settlements (Cooperative Governance and Traditional Affairs, 2023). Consenting participants for this study were recruited from Ward 22, one of the largest settlements in the Msunduzi municipality, to provide stool samples. Residents of the Ward 22 informal settlement residing in six different areas, namely: Seven Ox, Emadamini, Fedsem, Lay-Centre, Units 3, and Tehuise, provided stool samples. Surface water samples were collected from the Kwapata River, which discharges into the Msunduzi River, a major tributary of the uMngeni River, which travels across the district. The Medical Microbiology service of the NHLS in UMDM provided the clinical isolates from proximate healthcare facilities, primarily the regional and tertiary hospitals Fig. 1 . 2.3 Sample Collection and Processing 2.3.1 Stool Sampling from apparently healthy individuals : We collected stool samples monthly for eight months (May 2023 to Dec 2023) after obtaining voluntary, written, informed consent. Participants were trained on the proper use of camping toilets and other materials, as well as the faeces collection procedure (Supplementary Material/S1). Demographic information, such as age and gender, was collected, and participants were resupplied with personal protective equipment (PPE) and stool sampling containers for each sampling date. The received samples were immediately transported in a cooler box at 4°C–10°C to the laboratory and processed for bacterial identification. 2.3.2 Surface water Sampling : Within the same period of stool sampling, surface water samples were collected from the Kwapata River, which drains into the Msunduzi River, a major tributary of the uMngeni River flowing across the district. Duplicate water samples were collected every 2 weeks from the two sampling points mentioned above near areas of high anthropogenic activity, for a total of 80 samples in the study. Grab water sampling was used to collect 500 mL of water using a plastic bottle placed in a free-flowing, upstream direction, 10–5 cm below the water surface (Musselman, 2012 ). The collected samples were placed in a cooler box (4°C–10°C) and transported to the Antimicrobial Research Unit (ARU) at the University of KwaZulu-Natal for same-day processing. 2.3.3 Clinical isolate sampling : Positive bacteriological culture plates of specimen types: stool, urine, blood cultures, cerebrospinal fluid, pus and other sterile fluids from hospitalised adult patients (> 18 years) suspected to have a clinical infection were received weekly in cooler boxes (4°C–10°C) from the NHLS. A Microsoft Excel spreadsheet listed the samples, with patient demographic information and diagnostic details (clinical diagnosis, specimen type, bacteria isolated, ESBL screening, and antimicrobial susceptibility testing results). The database was sorted for putative Klebsiella pneumoniae and ESBL + K. pneumoniae positive plates using ID codes from the database, and the colonies from the positive culture were purified and molecularly confirmed. 2.4 Bacteria Isolation and Phenotypic Identification. The duplicate surface water samples per collection point were mixed, diluted (10 − 1 , 10 − 2 , 10 − 3 ) and plated on Simmons Citrate Agar (SCA) (Oxoid™, Basingstoke, United Kingdom) and Inositol (I) (Sigma-Aldrich, St. Louis, Missouri, USA) for 44 hours at 42℃ (Bobis Camacho et al., 2024 ). The SCAI medium is not commercially available and was prepared in-house using previously described protocols (Rodrigues, 2020 ). The medium’s pH was sometimes below 7.2 after preparation and was therefore adjusted to 7.2 using 30% NaOH before pouring into petri dishes. Putative K. pneumoniae that appeared as large, yellow, and round colonies were further sub-cultured onto SCAI agar plates to obtain pure, distinct colonies after 42–44 hours of incubation. K. pneumoniae from the faecal samples from healthy individuals were isolated at a private pathology laboratory in Durban, South Africa, using the SCAI media following protocols for Klebsiella isolation from human and animal faecal material as described previously (Brisse et al., 2019 ). Clinical isolates, initially grown on MacConkey agar, were obtained according to the standard operating protocol of the microbiology unit at the NHLS, uMgungundlovu. All pure isolates were stored in tryptic soy broth (TSB) (Sigma-Aldrich, Missouri, United States of America (USA) in cryobank tubes with 20% glycerol (Associated Chemical Enterprises, Johannesburg, South Africa) for molecular confirmation. 2.5 DNA Extraction and Molecular Confirmation of Klebsiella pneumoniae The pure colonies of presumptive K. pneumoniae isolates were subjected to DNA extraction using the standard boiling method as previously described (Tiemtoré et al., 2022 ), and real-time PCR was used for molecular confirmation targeting the phoE gene for K. pneumoniae detection. Briefly, the 10 µL reaction mixture contained 5 uL of Luna® universal qPCR master mix (New England Biolabs, Ipswich, MA, USA), 1µL nuclease-free water (ThermoFisher Scientific, Waltham, MA, USA), 0.5 µL of forward primer, 0.5 µL of reverse primer, and 3 µL DNA. The PCR amplification of the phoE gene was performed using the following thermal cycling conditions: an initial denaturation at 94°C for 2 min, followed by 35 cycles of denaturation at 94°C for 30 s, annealing at 50°C for 1 min, and extension at 72°C for 30 s. A final extension was conducted at 72°C for 5 min to ensure complete synthesis of the amplicons. The universal primers of the K. pneumoniae housekeeping gene (F:5′-GTTTTCCCAGTCACGACGTTGTA-3′; R:5′-TTGTGAGCGGATAACAATTTC-3′) were used for sequencing. K. pneumoniae ATCC 35657 was used as a positive control (Bassetti et al., 2023 ). 2.6 ESBL-producing Klebsiella pneumoniae Detection and Antibiotic Susceptibility Testing (AST) Surface water and human stool isolates were first screened and selected for presumptive ESBL producers using CHROMID® ESBL Agar (bioMérieux, Marcy l’étoile, France). Subsequent confirmation of ESBL production was performed by a private pathology laboratory in Durban, South Africa, using the combination disk method. The Isolates were tested with cefotaxime (30 µg) and ceftazidime (30 µg) disks, both alone and in combination with clavulanic acid (10 µg). A ≥ 5 mm increase in the zone of inhibition diameter for either antimicrobial agent tested in combination with clavulanic acid, compared to the agent tested alone, was interpreted as a positive result for ESBL production(CLSI, 2021 ). Confirmed ESBL-producing K pneumoniae isolates were subjected to AST on the VITEK® 2 system (bioMérieux, North Carolina, USA) using the VITEK® 2 AST-N256 card for Gram-negative bacteria as per the manufacturer’s instructions. Clinical isolates underwent AST at the NHLS using the VITEK® 2 Compact System. To detect ESBL production, the system utilised its Advanced Expert System™ (AES), which analyses the computed Minimum Inhibitory Concentration (MIC) of third-generation cephalosporins to detect putative ESBL-producers (Young et al., 2019 ). K. pneumoniae (ATCC-700603) was used as a control for all samples AST results were interpreted as susceptible, intermediate, or resistant according to the standards and breakpoints defined by the Clinical and Laboratory Standards Institute (CLSI) CLSI, 2021 ). Seventeen (17) antibiotics representing seven antibiotic classes were tested on isolates recovered from stool and surface water samples. Clinical isolates were tested against antibiotics from eight antibiotic classes by the NHLS. The antibiotics assessed included β-lactams: amoxicillin/clavulanic acid (AMC), piperacillin/tazobactam (TZP), cefoxitin (FOX), cefuroxime (CXM), cefotaxime (CTX), ceftazidime (CAZ), cefepime (FEP), imipenem (IMP), doripenem (DOR), meropenem (MEM), and ertapenem (ERT); aminoglycosides: amikacin (AMK), gentamicin (GEN), and tobramycin (TOB); fluoroquinolone: ciprofloxacin (CIP); sulfonamide: trimethoprim-sulphamethoxazole (SXT); and glycylcycline: tigecycline (TGC). Notably, cefoxitin (FOX) and nitrofurantoin were excluded from testing on surface water and community (stool) isolates, whereas tobramycin (TOB) and doripenem (DOR) were not included in the clinical isolate testing panel based on NHLS and private laboratory testing protocols. Isolates were classified as MDR if they demonstrated resistance to at least one antibiotic in three or more distinct antibiotic classes (Magiorakos et al. 2012). Statistical Analysis Data were compiled in Microsoft Excel 365 and analysed using R version 4.5.1 within RStudio 2025.05.1 ( https://cran.rstudio.com/ ). The prevalence of ESBL-producing K. pneumoniae was calculated as the proportion of positive isolates within each sampling source. Differences in prevalence and resistance profiles among sources were evaluated using chi-squared tests, while pairwise Fisher’s exact tests with Benjamini–Hochberg false discovery rate (FDR) correction were applied for within-source comparisons. Effect sizes, including risk ratios and risk differences, with 95% confidence intervals (CIs), were estimated. Antimicrobial resistance profiles were visualised using hierarchical clustering heatmaps(Ain et al., 2025 ) generated with the ggplot2 and pheatmap packages(Wickham, 2011 ). Variations in MDR proportions were assessed using chi-squared tests. Overlap and similarity between antibiogram profiles were explored using Venn diagrams (Khan et al., 2019) constructed with the VennDiagram package, while pattern variation was assessed using permutational multivariate analysis of variance (PERMANOVA) from the vegan package in R (Oksanen et al., 2025 ). The Shannon diversity index was employed to quantify the complexity and evenness of resistance profiles across sources. All statistical analyses were performed at a 95% confidence level. 3.0 RESULTS 3.1 Detection of ESBL-producing K. pneumoniae ( ESBL-Kp) The study analysed 4,212 samples for K. pneumoniae over eight months (1,978 stool samples from healthy informal residents, 2,154 clinical cultures from healthcare settings, and 80 surface water samples) (Fig. 2 ). The overall chi-square test confirmed significant variation in ESBL prevalence between the three sources (χ² = 29.9, df = 2, p < 0.00001. ESBL-Kp prevalence was lowest among healthy residents (6.3%, 95% CI 5.1–7.9), higher in surface water (13.0%, 95% CI 10.2–16.4), and highest in clinical isolates (15.2%, 95% CI 11.2–20.2). Pairwise comparisons showed that ESBL-Kp in healthy informal residents differed significantly from both surface water (RR 0.45, RD − 8.2%, p < 0.001) and clinical isolates (RR 0.38, RD − 11.1%, p < 0.001). 3.2 Antibiotic susceptibility profiles There was noticeable variability in the percentage resistance to some antibiotics across sources. The last line antibiotics (amikacin, ertapenem, imipenem, and meropenem) consistently showed a low resistance rate (0-1.7%) across all three sources. Percentage resistance varied significantly across the three sources ( p < 0.001) for amoxicillin/clavulanic acid, piperacillin/tazobactam, gentamicin, ciprofloxacin, and trimethoprim-sulfamethoxazole (SXT). For amoxicillin/clavulanic acid and piperacillin/tazobactam, resistance was notably high (96%) in isolates from both surface water and healthy individuals. However, resistance to these agents was substantially lower among clinical isolates (45.9% for amoxicillin/clavulanic acid and 48.6% for piperacillin/tazobactam). Conversely, clinical isolates displayed significantly ( p < 0.0000) higher resistance to gentamicin, ciprofloxacin, and trimethoprim-sulfamethoxazole (GEN:89.2%, CIP:56.8%, SXT: 97.3%) than community (GEN:28.4%, CIP:21.6%, SXT: 52.7%) and surface water (GEN:25%, CIP:10%, SXT: 26.7%), respectively. Consistent with the presence of ESBL-producing isolates, resistance was significantly high across all sources to β-lactams, specifically second, third-, and fourth-generation cephalosporins (cefuroxime, cefotaxime, ceftazidime, cefepime). Conversely, resistance to carbapenems (ertapenem, imipenem, and meropenem) was low or absent across all sources (p > 0.05), as shown in Table 1 . Pairwise comparisons (chi-square statistics - X 2 ) between isolates from surface water and healthy community residents showed statistically similar resistance profiles ( p > 0.05 ) for β-lactams (amoxicillin/clavulanic acid, piperacillin/tazobactam, cephalosporins, carbapenems), amikacin, and tigecycline. Conversely, resistance to gentamicin, tobramycin, ciprofloxacin, and trimethoprim-sulfamethoxazole differed significantly between these two sources ( p 0.05). However, resistance differed significantly ( p < 0.001) for amoxicillin/clavulanic acid, piperacillin/tazobactam, ciprofloxacin, and tigecycline. Table 1 Antibiotic Resistance Profiles of ESBL-producing K. pneumoniae Antibiotics Isolate source Tricycle Analysis Pairwise Analysis Surface Water Healthy Individuals Clinical X 2 – p -value R (all 3 sources) Pairwise Surface Water and Healthy Residents Pairwise Surface Water & Clinical Amoxicillin clavulanic acid 100% (60) 98.6% (73), 45.9% (17) p 0.05* p < 0.0000 piperacillin/ tazobactam 96.7% (58) 97.3% (72) 48.6% (18), p 0.05* p 0.4366* Cefotaxime 100% (60) 98.6% (73), 100% (37) p = 0.5172* p = 0.999* N/A a Ceftazidime 95% (57) 97.3% (72) 100% (37) p = 0.3594* p = 0.3156* P = 0.4366* Cefepime 100% (60) 93.2% (69) 97.3% (36) p = 0.1024* p = 0.111* P = 0.8062* Doripenem 0% (0) 1.4% (1) NU = 100% (37) -- p > 0.05 N/A b Ertapenem 1.7% (1) 4.1% (3) 0% (0) p = 0.3758 p 0.0207 p > 0.05 Imipenem 0% (0) 0% (0) 0% (0) -- N/A a N/A a Meropenem 1.7% (1) 0% (0) 0% (0) p = 0.3944* N/A b N/A b Amikacin 0% (0) 0% (0) 0% (0) -- P = 0.5709* P = 0.8062* Gentamicin 25% (15) 28.4% (21) 89.2% (33) p < 0.0000 p < 0.0000 P = 0.2033* Tobramycin 78.3% (47) 28.4% (21), NU = 100% (37) -- p < 0.0000 N/A b Ciprofloxacin 10% (6) 21.6% (16) 56.8% (21) p < 0.0000 P = 0.0081 P = 0.0000 trimethoprim-sulfamethoxazole 26.7% (16) 52.7% (39) 97.3% (36) p < 0.0000 p < 0.0000 P = 0.9784* nitrofurantoin NU = 100% (60) NU = 100% (74) 40.5% (15) --- N/A b N/A b Tigecycline 15% (9) 39.2% (29) 2.7% (1) p < 0.0000 P = 0.6034* p = 0.001 ND = Not Determined ( Antibiotic Not Used on isolates source), N /A a = Chi square not calculated due to complete or no Resistance between sources, N/A b = Chi square not calculated due to absence of phenotypes in a source because antibiotic was not used. p-values in bold were significant. We performed hierarchical clustering of antibiotic resistance percentages using a heatmap (Fig. 2 ) to visualise similarities and differences in resistance profiles across isolate sources. β-lactams, excluding carbapenems, generally clustered closely across the three sources. In particular, surface water isolates and isolates from the community clustered at higher resistance (> 95%) for piperacillin/tazobactam, amoxicillin/clavulanic acid, cefuroxime, cefotaxime, ceftazidime, and cefepime compared to clinical isolates, which differed (resistance < 50%) for piperacillin/tazobactam and amoxicillin/clavulanic acid. All three sources did not show variation at low levels of carbapenem and amikacin resistance (dark-green rows, Fig. 3 ). Resistance to ciprofloxacin, gentamicin, and trimethoprim-sulfamethoxazole showed a close resemblance at low to moderate levels in both surface water and community isolates, but differed among clinical isolates. In general, surface water isolates displayed a better mirroring of resistance to community isolates than to clinical isolates. 3.4 Antibiograms and Multidrug Resistance (MDR) of isolates There were 31 antibiograms (Fig. 4 ) from all isolates across the three sources; 18 of the patterns were MDR. A complete list of all MDR and non-MDR patterns per source is available (Supplementary Material/S2). While some antibiogram patterns were shared between sources, the majority of the patterns were seen in community isolates (total = 18, MDR;10, non-MDR;8), followed by clinical isolates (total = 11, MDR; 5, non-MDR; 6), and surface water isolates (total = 11, MDR; 5, non-MDR; 6). We used the Shannon Diversity test to assess the complexity and evenness of patterns across sources (Supplementary Material/S3). Healthy individuals exhibited the highest overall antibiogram pattern diversity (H′ = 2.4916, 95% CI: 2.1367 − 2.5675; Evenness J = 0.8620), followed by clinical isolates (H′ = 1.7729, 95% CI: 1.2356 − 1.9453; Evenness J = 0.7394), then surface water (H′ = 1.5067, 95% CI: 1.0752 − 1.7334; Evenness J = 0.6283). The most common MDR antibiogram for surface water was AMC-TZP-CXM-CTX-CAZ-FEP-GEN-CIP-SXT-TGC (6 isolates, 10.0%, MDR), while the most common MDR antibiogram for clinical isolates was AMC-TZP-CXM-CTX-CAZ-FEP-GEN-CIP-SXT (11 isolates, 29.7%, MDR) and for healthy residents was AMC-TZP-CXM-CTX-CAZ-FEP-GEN-SXT (9 isolates, 12.2%, MDR). The most common non-MDR antibiogram was AMC-TZP-CXM-CTX-CAZ-FEP (surface water; 36 isolates, 60.0%, non-MDR), AMC-TZP-CXM-CTX-CAZ-FEP-SXT (clinical; 1 isolate, 2.7%, non-MDR) and AMC-TZP-CXM-CTX-CAZ-FEP-TGC (healthy residents; 10 isolates, 13.5%, non-MDR). Among these patterns, MDR was evident in 94.59% of clinical isolates, 41.89% of community isolates, and 25% of surface water isolates ( p < 0.0001) (Fig. 4 ). Pairwise analysis of MDR prevalence showed that clinical isolates had higher MDR prevalence than surface water and community isolates ( p < 0.000001 ) . 3.5 Triangulation of antibiogram patterns and MDR phenotypes. Venn diagram analysis revealed that resistance patterns were largely source-specific (Fig. 5 ). Clinical isolates harboured the most unique MDR patterns (n = 7), while healthy residents possessed the highest number of unique non-MDR profiles (n = 6). Overall, MDR antibiograms were more prevalent in clinical isolates, whereas non-MDR patterns were more prevalent in healthy residents. The overall composition of resistance patterns varied significantly by source (R² = 0.1223, F = 11.7088, p = 0.0001). Stratification by MDR status revealed source-specific differences remained significant for both MDR (R² = 0.1332, F = 5.9907, p = 0.0001) and non-MDR patterns (R² = 0.1270, F = 6.3259, p = 0.0001), In both groups, the source accounted for approximately 12–13% of the observed compositional variance. Four MDR patterns were shared by isolates from surface water and healthy community residents (Jaccard index = 0.3636, p < 0.0001), two from community and clinical isolates (Jaccard index = 0.1176, p < 0.0001) and none by surface water and clinical isolates. No unique MDR pattern was shared across all three sources. For non-MDR patterns, one pattern was shared by all three sources: one by surface water and community isolates (Jaccard index = 0.1667, p < 0.0001), and none between surface water and the clinical isolates (Jaccard index = 0.1429, p < 0.0001). Details of patterns unique to and shared by sources, as per the Venn analysis, are available in Supplementary Material/S4/TableS4/C. 4.0 Discussion Klebsiella pneumoniae remains one of the common ESBL-producing Enterobacterales that cause drug-resistant infections in both hospital and community settings, resulting in high morbidity, mortality and medical expenses (Olaitan et al., 2025 ). Traditionally, AMR surveillance relies heavily on clinical and hospital-based sampling and data, which provide critical insights into resistance trends in patient populations. However, these systems are often limited by underreporting, unequal laboratory capacity, and their inability to capture resistance circulating silently in the broader community or environmental reservoirs (Frost et al., 2021 ; Kusuma et al., 2025 ). With the potential seen in recent studies (Kagambèga et al., 2024 ; Rolbiecki et al., 2025 ) on the role of environment in AMR surveillance, this study explored phenotypic AMR profiles of ESBL-Kp isolated from surface water running through an informal settlement to see if resistance reflects what is seen among healthy community residents and the proximate clinical setting. We detected ESBL-Kp with significantly different resistance prevalence and profiles in the three sources. Isolates were multidrug resistant, peaking in clinical isolates and least evident in surface water. Resistance levels differed per antibiotic per source. Surface water isolates shared MDR and non-MDR antibiograms more frequently with community isolates than with clinical ones. Patterns were more unique to respective sources, and surface water did not generally reflect phenotypic resistance in human sources 4.1 Detection of ESBL-producing K. pneumoniae ESBL-Kp was detected in all three isolate sources, with the lowest prevalence among healthy residents (6.3%), through surface water (13.0%), and the highest prevalence in clinical isolates (15.2%). According to a 2025 meta-analysis including 119 eligible studies from 25 countries from the Sub-Saharan African subregions (Olaitan et al., 2025 ), the pooled prevalence of ESBL-Kp in South Africa was reported to be 23.3% (95% CI: 6.5–45.8), which is comparable to the ESBL-Kp prevalence observed in our study (ranging from 6.3% to 15.3%) across all three settings. Although our findings are slightly lower than the point estimate, they fall entirely within the wide confidence interval, indicating that they are not statistically distinct from the national average. Compared with clinical isolates, the prevalence of ESBL-producers in surface water isolates in our study was significantly lower, consistent with a study in Ethiopia (Abera et al., 2016) that compared clinical and water isolates and found significantly higher ESBL-Kp in clinical isolates (69.94%) than in water isolates (33.3%). Clinical environments concentrate vulnerable and infected patient populations and are sites of high antimicrobial selective pressure, leading to the persistence and dissemination of resistant bacteria. Conversely, aquatic environments are typically more open, subject to dilution, and exposed to environmental factors that generally limit bacterial survival (Ferreira et al., 2023 ; Freeman et al., 2018 ).Thus, the prevalence of bacteria such as ESBL-Kp is likely to be higher in clinical settings than in aquatic environments, as seen in our study. Interestingly, we detected higher ESBL-Kp in surface (13.0%) water than among healthy informal settlement residents (6.3%). Similar findings have been reported in a Malawian study, which sampled human stool from residents of a rural community and their surrounding river water and reported up to 50% prevalence of ESBL-Kp in river water compared to 17% in residents(Cocker et al., 2023 ). Another study by Yasmeen and colleagues ( 2023 ) in Punjab, Pakistan, sampled ESBL-Kp from several niches, including healthy individuals and water samples and observed higher rates of resistant isolates in water (6.7%) than among healthy individuals (3.0%). These findings may suggest that the surrounding aquatic environment is receiving ESBL-Kp contamination from unidentified sources, or that apparently healthy people are less colonised by the bacteria. Although we initially assumed that surface water contamination in the informal settlement was primarily of human origin, as per literature (Cocker et al., 2023 ; Martak et al., 2022 ), other studies have highlighted that the presence of ESBL-Kp is not uncommon in surface waters near sources like hospital wastewater treatment plants and sites where there are animal husbandry and agricultural activities (Cocker et al., 2023 ; Lepuschitz et al., 2019 ; Yasmeen et al., 2023 ) For instance, previous studies conducted within the same locality identified Klebsiella spp. and other ESBL-producing Enterobacterales in hospital effluents and wastewater treatment plants (Gumede et al., 2021 ; King et al., 2020 ). Although our study did not explore the potential links between sewage systems and the release of bacteria into surrounding aquatic environments, studies worldwide have documented that poorly maintained wastewater treatment plants can disseminate resistant bacteria, contributing to higher levels of contamination in surface waters than carriage among community residents (Buriánková et al., 2021 ; Freeman et al., 2018 ; Ojer-Usoz et al., 2014 ). These studies confirmed the dissemination of resistant bacteria, directly or indirectly, to surrounding aquatic environments, including surface water, making surface water a potential reservoir for ESBL-Kp and other organisms, thereby necessitating further studies to elucidate bacterial transmission routes into the aquatic environments proximate to this community. Taken all together, we confirmed the presence of ESBL-Kp across all three sources and observed that the prevalence in surface water isolates differed significantly ( p < 0.05) from that in community and clinical isolates. Notwithstanding its relatively low prevalence, this finding highlights its potential as a transmission reservoir for community residents of the settlement. 4.2 Antibiotic susceptibility profiles of ESBL-producing K. pneumoniae The percentage of isolates resistant to an antibiotic may vary widely depending on the source of isolates and local selection pressure. In clinical settings, hospitals drive and maintain high-level resistance through sustained antibiotic use and dense human contact; communities maintain moderate, transient resistance through limited exposure; and surface waters dilute bacterial populations, showing varying phenotypic resistance at different levels for different sources (Husna et al., 2023 ). However, due to interactions among sources, isolates may exhibit similar resistance patterns to some antibiotics. We observed that resistance to amoxicillin-clavulanic acid and piperacillin/tazobactam in surface water isolates was similar to community isolates but differed from clinical isolates, consistent with the report by Yasmeen et al. ( 2023 ), who observed a 100% resistance rate of ESBL-Kp against amoxicillin-clavulanic acid and piperacillin/ tazobactam in both surface water isolates and community isolates. In South Africa, penicillin-inhibitor combinations such as amoxicillin-clavulanic acid are among the most frequently prescribed antibiotics in outpatient and primary healthcare settings (Alabi & Essack, 2022 ). While cephalosporins exert direct selective pressure on ESBL phenotypes, widespread community use of amoxicillin-clavulanate likely contributes through co-selection. Because ESBL genes are frequently co-located on multidrug-resistance plasmids alongside genes conferring resistance to inhibitor combinations, frequent AMC exposure can indirectly maintain and propagate ESBL-Kp lineages within the population. The extensive use of antibiotics leads to the discharge of significant quantities of parent compounds and metabolites into sewage systems via human excretion. Because conventional wastewater treatment is often inadequate, these antibiotic residues frequently reach surface waters(Ncube et al., 2021 ). Consequently, both the community gut microbiota and environmental niches are exposed to comparable selective pressures, which may result in similar resistance profiles. Tigecycline resistance in community residents was 39%, compared with 15% in surface water ( p > 0.05). Our findings contradicted those of Yasmeen et al. ( 2023 ), a One Health study in Pakistan, which did not detect tigecycline resistance among water and human isolates, likely due to differential antibiotic selection pressure, as tigecycline use and prescribing policies in human and animal health differ between South Africa and Pakistan. An in vitro study conducted at a tertiary care hospital in Pakistan reported universal susceptibility to tigecycline among nosocomial isolates and explicitly noted minimal prior clinical exposure to the drug at the institution at the time of sampling(Shakoor et al., 2010 ). Tigecycline in our study area remains reserved as a parenteral, reserve antibiotic for severe, complicated infections (Brink et al., 2010 ; Cassir et al., 2014 ). Resistance (> 10%) in K. pneumoniae has, however, been recently documented in inpatients and hospital effluent samples (Masalane et al., 2025a). While tigecycline usage is restricted to hospital settings (Cassir et al., 2014 ), 1st generation glycylcyclines like tetracyclines are widely used and less controlled in non-clinical settings like food animal production (Mupfunya et al., 2021 ), with evidence of decreased susceptibility to tigecycline following overexposure of bacteria to tetracyclines mediated through efflux pumps (Nasralddin et al., 2025 ). Also, the dissemination of tetracycline-resistant bacteria into the environment following animal husbandry introduces both the resistant bacteria and tetracycline antibiotic residues (Ramessar et al., 2025 ). These residues exert selective pressure in the surrounding aquatic environment, potentially co-selecting for tigecycline resistance (due to shared resistance mechanisms) within the aquatic bacterial population and subsequently within the community following environmental interactions (Mupfunya et al., 2021 ; Ramessar et al., 2025 ) Clinical isolates also showed similar resistance proportions to gentamicin and trimethoprim-sulphamethoxazole ( p > 0.05), suggesting no significant difference from surface water isolates. This observation is consistent with a One Health study by Craddock et al. ( 2025 ), which investigated ESBL-Kp among Bedouin communities in Israel. Craddock’s study found that resistance to these specific drugs was also statistically similar ( p > 0.05) between clinical urine isolates (GEN: 27.3%, SXT: 77.3%) and environmental water isolates (GEN: 35.3%, SXT: 88.2%). In South Africa, gentamicin and trimethoprim-sulfamethoxazole are common antibiotics used to treat uncomplicated UTIs, with the latter further used to prevent opportunistic infections in HIV-infected persons (Fourie et al., 2021 ; Goodlet et al., 2018 ). Recent local studies have reported over 50% gentamicin and trimethoprim-sulfamethoxazole resistance in patients and hospital effluent(Bassetti et al., 2023 ; Masalane et al., 2025), as well as 0–3% for gentamicin and 0–30% for sulfamethoxazole in urban water influenced by anthropogenic activities (Tucker et al., 2022 ). In South Africa, Masalane and colleagues observed that K. pneumoniae clinical isolates were fully resistant to gentamicin and trimethoprim-sulphamethoxazole, whereas isolates recovered from hospital effluent remained completely susceptible. Within the environmental context, Tucker’s study of the urban water cycle in Stellenbosch reported gentamicin resistance reaching up to 3% and sulfamethoxazole resistance up to 30% in river water and wastewater treatment plants situated near informal settlements. These findings suggest the potential release of antibiotic-resistant bacteria into surrounding aquatic environments, alongside contributions from other human activities. However, high-resolution genomic analyses to detect shared antimicrobial resistance genes are relevant to establish whether ESBL-K. pneumoniae and related ESBL determinants are exchanged between clinical settings and the aquatic environment. Overall, surface water isolates closely mirrored community isolates for amoxicillin-clavulanic acid and piperacillin/tazobactam, cephalosporins, and tigecycline, while among clinical isolates and surface water, there was resemblance in cephalosporin, gentamicin, and trimethoprim-sulphamethoxazole resistance. Resistance to amikacin, imipenem, meropenem, and ertapenem was generally absent or very low across all sources. 4.3 Antibiograms and Multidrug Resistance (MDR) of ESBL K. pneumoniae isolates Antibiogram patterns were utilised as a phenotypic screening tool to identify the potential transmission of isolates across different sources. Matches between these patterns provided phenotypic evidence of relatedness, suggesting the dissemination of ESBL-Kp between clinical, community, and environmental sources. MDR was significantly lower in surface water (25%) compared to healthy residents (41.89%) and clinical isolates (94.59%). While integrated studies evaluating all three reservoirs simultaneously are scarce, our findings align with individual reports worldwide. In Thailand,Chaisaeng et al., ( 2024 ) assessed 507 non-duplicate clinical isolates from a clinical setting and reported an ESBL-Kp MDR rate of 100%. Similarly, healthy students at the Dilla University student cafeteria in Ethiopia, revealed faecal carriage ESBL-Kp MDR rate of 37.5%(Diriba et al., 2020 ) and in the environment, a study profiling antibiotic resistance in K. pneumoniae isolates from Mula-Mutha River (Pune, India)(Kaur et al., 2025 ) showed that 15.3% of the MDR isolates harboured ESBL activity. A study of the peri-urban area of Burkina-Faso assessed ESBL-producing Enterobacterales, including ESBL-Kp, using a One Health approach in healthy cattle farmers and their environment and reported higher MDR rates in farmers’ stools (36.2%) than in water (27.8%) (Soma et al., 2024 ). Although specific percentages vary by studies and regions, these data reinforce a consistent global trend: MDR burden peaks in clinical settings and remains comparatively lower in aquatic environments. Notably, our observations and those of other authors projected lower MDR in the surface water environment than in human populations. Although some authors have reported MDR rates as high as 95% in water sources (Davidova-Gerzova et al., 2024), MDR strains exhibit lower persistence in aquatic environments. This has previously been attributed to factors such as dilution effects, environmental stressors, and other ecological variables that limit bacterial persistence in aquatic systems (Larsson & Flach, 2022 ), further emphasising the differences in MDR rates between sources. The consistent detection of MDR ESBL-Kp in surface water indicates that these aquatic systems serve as both a reservoir of antibiotic-resistant bacteria and a conduit for their spread. The overlapping resistance profiles among healthy residents, clinical patients, and the environment illustrate an interconnected resistance cycle. This suggests that AMR dynamics in the community are not isolated; instead, there is a continuous exchange of resistant strains between human populations and their surrounding water sources. Within informal settlements characterised by poor infrastructure and insufficient water, sanitation, and hygiene services, direct interaction between residents and polluted surface water increases exposure risks. This cycle facilitates the ongoing circulation of ESBL-resistant bacteria (Erb et al., 2018 ; Kim et al., 2025 ; Nadimpalli et al., 2020 ) and remains a significant public health threat. Our results further showed that, among the circulating MDR isolates, several antibiogram patterns overlapped between sources. These shared phenotypic profiles highlight a degree of resistance similarity between clinical, community, and environmental ESBL-Ec populations. Although there were significant differences in non-MDR pattern composition across sources ( p < 0.0001), surface water isolates shared more patterns with healthy residents (n = 38, 63.3%) than they did with clinical isolates (n = 2, 3.3%; Jaccard index = 0.1667). Similarly, surface water isolates (14 isolates, 23.3%) shared four MDR patterns with community isolates, while no overlap was observed with clinical isolates (Jaccard index = 0.3636, p < 0.0001). There was significant correlation in resistance patterns between sources ( p < 0.0001); despite modest overlap in specific profiles. However, the Jaccard similarity coefficients for non-MDR (0.1667) and MDR strains (0.3636) indicate that, while the distribution of resistance is not random, the majority of resistance phenotypes remain source-specific, suggesting that, while common selective pressures may exist, the community and surface water reservoirs maintain distinct resistance signatures. Previous research has demonstrated that human interaction with the environment, whether through direct use of surface waters for domestic activities, animal watering, and irrigation, or indirectly through household wastewater discharge and natural hydrological processes, can facilitate the exchange of antibiotic-resistant bacteria and their associated genetic elements (Craddock et al., 2025 ; Kim et al., 2025 ). Similarly, inadequate maintenance and inefficiencies in community and hospital wastewater treatment systems (Department of Water and Sanitation, 2023 ; Hounmanou et al., 2024 ; Zagui et al., 2025 ) have been linked to the persistence and spread of resistant ESBL-producing Enterobacterales within interconnected ecosystems (Gumede et al., 2021 ; Zagui et al., 2025 ). The presence of resistant bacteria, including MDR strains observed in this study, represents a critical public health concern. Therefore, high-resolution genomic insight on isolates displaying comparable resistance profiles is warranted to confirm the presence of shared resistance genes and mobile genetic elements within these settings, thereby elucidating the potential of surface water as a tool for AMR surveillance in the community and clinical setting while exploring routes and mechanisms driving resistance dissemination across environmental and human interfaces. 5.0 Conclusion Our study confirmed the presence of ESBL-Kp in surface water, healthy community residents, and clinical isolates, with statistically significant differences in prevalence among these sources. Surface water isolates showed resistance rates similar to community isolates for amoxicillin–clavulanic acid, piperacillin–tazobactam, cephalosporins, and tigecycline, whereas clinical isolates predominantly exhibited resistance to cephalosporins, gentamicin, and trimethoprim–sulfamethoxazole. Multidrug resistance profiles varied between sources. Surface water isolates shared MDR and non-MDR antibiograms more frequently with community isolates than with clinical ones. Although surface water isolates exhibited resistance to clinically important antibiotics, as observed in both community and clinical isolates, their overall prevalence and MDR rates remained significantly lower, indicating limited reflection of the full breadth of resistance observed in community and clinical settings. Declarations Declaration of competing interest s : S.Y.E. is a chairperson of the Global Respiratory Partnership and a member of the Global Hygiene Council, both supported by unrestricted educational grants from Reckitt (Pty.) Ltd., UK. All other authors have no competing interests. Funding This work was supported by the Wellcome Trust (Grant No.: 228172/Z/23/Z), the South African Research Chairs Initiative of the Department of Science and Technology and National Research Foundation of South Africa (Grant No. 98342) and the South African Medical Research Council (SAMRC) Self-Initiated Research Grant. The funders had no role in the design of the study, the collection, analysis, or interpretation of data, the writing of the manuscript, or the decision to publish the results. Any opinion, finding, conclusion or recommendation expressed in this material is that of the authors. References Ain, N. U., Elton, L., Sadouki, Z., McHugh, T. D., & Riaz, S. (2025). Exploring New Delhi Metallo Beta Lactamases in Klebsiella pneumoniae and Escherichia coli : genotypic vs. phenotypic insights. Annals of Clinical Microbiology and Antimicrobials 2025 24:1 , 24 (1), 12-. https://doi.org/10.1186/S12941-025-00775-X Alabi, M. E., & Essack, S. Y. (2022). 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BMC Infect Dis , 25 (1), 843-. https://doi.org/10.1186/S12879-025-11276-9 Ooi, G. L., & Phua, K. H. (2007). Urbanization and Slum Formation. Journal of Urban Health , 84 (Suppl 1), 27–34. https://doi.org/10.1007/s11524-007-9167-5 Ramessar, K., Egbewale, O. S., Kumar, A., & Olaniran, A. O. (2025). Prevalence and removal of antibiotic-resistant bacteria and genes in treated effluents of four wastewater treatment plants and receiving surface waters. Journal of Water Process Engineering , 77 , 108432. https://doi.org/10.1016/J.JWPE.2025.108432 Roca, I., Akova, M., Baquero, F., Carlet, J., Cavaleri, M., Coenen, S., Cohen, J., Findlay, D., Gyssens, I., Heure, O. E., Kahlmeter, G., Kruse, H., Laxminarayan, R., Liébana, E., López-Cerero, L., MacGowan, A., Martins, M., Rodríguez-Baño, J., Rolain, J.-M., … Vila, J. (2015). The global threat of antimicrobial resistance: science for intervention. New Microbes and New Infections , 6 , 22–29. https://doi.org/10.1016/j.nmni.2015.02.007 Rodrigues, C. (2020, January 3). Isolation of Klebsiella strains from water samples . protocols.io. MedVetKlebs Consortium. https://doi.org/dx.doi.org/10.17504/protocols.io.baxuifnw Rolbiecki, D., Kiedrzyńska, E., Czatzkowska, M., Kiedrzyński, M., Korzeniewska, E., & Harnisz, M. (2025). Global dissemination of Klebsiella pneumoniae in surface waters: Genomic insights into drug resistance, virulence, and clinical relevance. Drug Resistance Updates , 79 , 101204. https://doi.org/10.1016/j.drup.2025.101204 Shakoor, S., Khan, E., Zafar, A., & Hasan, R. (2010). In vitro Activity of Tigecycline and Other Tetracyclines against Carbapenem-Resistant Acinetobacter Species: Report from a Tertiary Care Centre in Karachi, Pakistan. Chemotherapy , 56 (3), 184–189. https://doi.org/10.1159/000316328 Soma, D., Bonkoungou, I. J. O., Garba, Z., Diarra, F. B. J., Somda, N. S., Nikiema, M. E. M., Bako, E., Sore, S., Sawadogo, N., Barro, N., & Haukka, K. (2024). Extended-Spectrum Beta-Lactamase-Producing and Multidrug-Resistant Escherichia coli and Klebsiella spp. from the Human–Animal–Environment Interface on Cattle Farms in Burkina Faso. Microbiology Research , 15 (4), 2286–2297. https://doi.org/10.3390/MICROBIOLRES15040153 Starzyk-Łuszcz, K., Zielonka, T. M., Jakubik, J., & Życińska, K. (2017). Mortality Due to Nosocomial Infection with Klebsiella pneumoniae ESBL+. Advances in Experimental Medicine and Biology , 1022 , 19–26. https://doi.org/10.1007/5584_2017_38 Sugianli, A. K., Ginting, F., Lia Kusumawati, R., Parwati, I., de Jong, M. D., van Leth, F., & Schultsz, C. (2020). Laboratory-based versus population-based surveillance of antimicrobial resistance to inform empirical treatment for suspected urinary tract infection in Indonesia. PLOS ONE , 15 (3), e0230489. https://doi.org/10.1371/JOURNAL.PONE.0230489 Thakur, R., & Onwubu, S. C. (2024). Household waste management behaviour amongst residents in an informal settlement in Durban, South Africa. Journal of Environmental Management , 349 , 119521. https://doi.org/10.1016/J.JENVMAN.2023.119521 Tiemtoré, R. Y. W., Dabiré, A. M., Ouermi, D., Sougué, S., Benao, S., & Simporé, J. (2022). Isolation and Identification of Escherichia coli and Klebsiella pneumoniae Strains Resistant to the Oxyimino-Cephalosporins and the Monobactam by Production of GES Type Extended Spectrum Bêta-Lactamase (ESBL) at Saint Camille Hospital Center in Ouagadougou, Burkina Faso. Infection and Drug Resistance , 15 , 3191–3204. https://doi.org/10.2147/IDR.S360945 Tucker, K., Mageiros, L., Carstens, A., Bröcker, L., Archer, E., Smith, K., Mourkas, E., Pascoe, B., Nel, D., Meric, G., Sheppard, S. K., Kasprzyk-Hordern, B., Botes, M., Feil, E. J., & Wolfaardt, G. (2022). Spatiotemporal Investigation of Antibiotic Resistance in the Urban Water Cycle Influenced by Environmental and Anthropogenic Activity. Microbiology Spectrum , 10 (5), e02473-22. https://doi.org/10.1128/spectrum.02473-22 UMDM. (2023). uMgungundlovu District Municipality: Annual Report - 2023/2024 Financial Year . https://umdm.gov.za/ WHO. (2017). Global Priority List of Antibiotic-Resistant Bacteria to Guide Research, Discovery, and Development of New Antibiotics. In WHO publishes list of bacteria for which new antibiotics are urgently needed . https://www.who.int/news/item/27-02-2017-who-publishes-list-of-bacteria-for-which-new-antibiotics-are-urgently-needed WHO. (2023, November 21). Antimicrobial resistance . https://www.who.int/news-room/fact-sheets/detail/antimicrobial-resistance WHO. (2024). WHO bacterial priority pathogens list, 2024: Bacterial pathogens of public health importance to guide research, development and strategies to prevent and control antimicrobial resistance. Bacterial Pathogens of Public Health Importance to Guide Research, Development and Strategies to Prevent and Control Antimicrobial Resistance , 72. https://www.who.int/publications/i/item/9789240093461 Wickham, H. (2011). ggplot2. Wiley Interdisciplinary Reviews: Computational Statistics , 3 (2), 180–185. https://doi.org/10.1002/WICS.147;PAGE:STRING:ARTICLE/CHAPTER Wyres, K. L., & Holt, K. E. (2018). Klebsiella pneumoniae as a key trafficker of drug resistance genes from environmental to clinically important bacteria. Current Opinion in Microbiology , 45 , 131–139. https://doi.org/10.1016/J.MIB.2018.04.004 Yasmeen, N., Aslam, B., Fang, L. X., Baloch, Z., & Liu, Y. (2023). Occurrence of extended- spectrum β-lactamase harboring K. pneumoniae in various sources: a one health perspective. Front. Cell. Infect. Microbiol , 13 , 1103319. https://doi.org/10.3389/FCIMB.2023.1103319 Young, A. L., Nicol, M. P., Moodley, C., & Bamford, C. M. (2019). The accuracy of extended-spectrum beta-lactamase detection in Escherichia coli and Klebsiella pneumoniae in South African laboratories using the Vitek 2 Gram-negative susceptibility card AST-N255. Southern African Journal of Infectious Diseases , 34 (1), 114. https://doi.org/10.4102/SAJID.V34I1.114 Zagui, G. S., Moreira, N. C., Ferreira, J. C., Agnesini, M. V., Barth, P. O., Barth, A. L., Costa Darini, A. L., Andrade, L. N., & Segura-Muñoz, S. I. (2025). Municipal sewage as a pathway for multidrug-resistant KPC-producing Klebsiella pneumoniae from hospital effluent to urban stream: challenges for wastewater management. International Journal of Hygiene and Environmental Health , 269 , 114640. https://doi.org/10.1016/J.IJHEH.2025.114640 Additional Declarations The authors declare no competing interests. Supplementary Files ESBLKpSupplementaryMaterial2.xlsx ESBLKpPrevalenceGraphicalabstract.jpg Graphical Abstract Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9523007","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":629344944,"identity":"34190bba-7328-45ac-b1ed-b3e16480ef18","order_by":0,"name":"Etando Ayukafangha","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYJAC5h8GEnb8IFZCATHq2ZgZmBkqLJIlG0BaDIjWcqaCccMBEI8YLfzy/Qc/F7ZJMBufX5344YEBgzy/2AH8WiTbmJmlZ7ZJ8JndeLtZAugww5mzE/BrMTjGzCDBC7TF7MbZDSAtCQa3CWixP8bM/AOohXHzjLObfxClxYCNmU2a54wE4wb+3m3E2SJxLNnMckaFRLLEDd5tFgkGEoT9wt988PGNDwZ1dvz9Zzff/FFhI88vTUALkn1glRLEKgfbd4AU1aNgFIyCUTCSAAAMCj/BZVv+NQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-5678-4121","institution":"University of KwaZulu-Natal, Durban, South Africa","correspondingAuthor":true,"prefix":"","firstName":"Etando","middleName":"","lastName":"Ayukafangha","suffix":""},{"id":629344945,"identity":"a8d7a2c5-8462-4691-bd4a-97ef9608b6b4","order_by":1,"name":"Joshua Mbanga","email":"","orcid":"https://orcid.org/0000-0001-6592-2338","institution":"National University of Science and Technology, Bulawayo, Zimbabwe","correspondingAuthor":false,"prefix":"","firstName":"Joshua","middleName":"","lastName":"Mbanga","suffix":""},{"id":629344946,"identity":"4f19cdff-9af3-47c3-b5f0-2ba28ffaf072","order_by":2,"name":"Sabiha Y. Essack","email":"","orcid":"https://orcid.org/0000-0003-3357-2761","institution":"University of KwaZulu-Natal, Durban, South Africa","correspondingAuthor":false,"prefix":"","firstName":"Sabiha","middleName":"Y.","lastName":"Essack","suffix":""},{"id":629344947,"identity":"f5a0c897-4442-437b-819f-c20c52de95e0","order_by":3,"name":"Akebe Luther King Abia","email":"","orcid":"https://orcid.org/0000-0002-5194-2810","institution":"University of KwaZulu-Natal, Durban, South Africa","correspondingAuthor":false,"prefix":"","firstName":"Akebe","middleName":"Luther King","lastName":"Abia","suffix":""}],"badges":[],"createdAt":"2026-04-25 06:54:00","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9523007/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9523007/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107966175,"identity":"fd4cac84-ee75-47a0-818e-f40a8bb10092","added_by":"auto","created_at":"2026-04-28 05:48:06","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3282918,"visible":true,"origin":"","legend":"\u003cp\u003eInformal settlement ward 22 in the Umgungundlovu District Municipality and healthcare facility\u003c/p\u003e","description":"","filename":"Figure1SiteMap.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9523007/v1/188fd9139fa966db472906e4.jpg"},{"id":107966178,"identity":"aeeec0d5-f24d-4b2a-9487-11ef915041d6","added_by":"auto","created_at":"2026-04-28 05:48:06","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1069739,"visible":true,"origin":"","legend":"\u003cp\u003eSamples and isolates processed and Prevalence of ESBL-producing\u003cstrong\u003e \u003c/strong\u003e\u003cem\u003eK. pneumoniae.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure2HierarchicalHeatmap.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9523007/v1/61cfc7782a6e8b5bc744f931.jpg"},{"id":108007452,"identity":"7c1c35cf-e351-4d48-8b3c-a941397abf64","added_by":"auto","created_at":"2026-04-28 13:00:07","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":666804,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of antibiotic-resistant profile by isolate sources. Columns represent the sources of isolates, and rows represent antibiotics. Red blocks indicate the highest percentage of resistance, while green blocks indicate the lowest. The percentage resistance of isolates from surface water more closely resembles that from healthy individuals than that from clinical sources.\u003c/p\u003e","description":"","filename":"Figure3HierarchicalHeatmap.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9523007/v1/37da38ace4fef8a71e528561.jpg"},{"id":107966179,"identity":"d4480d41-82bc-4e1e-a4db-3e0caa7bca55","added_by":"auto","created_at":"2026-04-28 05:48:06","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":365140,"visible":true,"origin":"","legend":"\u003cp\u003eAntibiograms of ESBL-producing \u003cem\u003eK. pneumoniae\u003c/em\u003e across sources\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure4Antibiograms.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9523007/v1/560a78c753ceff69cd02de8d.jpg"},{"id":108007222,"identity":"42fc6ec9-3507-4ae0-a757-aa708b411c50","added_by":"auto","created_at":"2026-04-28 12:59:00","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":180807,"visible":true,"origin":"","legend":"\u003cp\u003eVenn diagram showing overlap of MDR (Left) and non-MDR (Right) patterns of ESBL K. pneumoniae isolates across sources: The central intersection of the three circles represents the antibiotic resistance patterns that are common to all three sources: Clinical (light-green), Healthy Residents (Pink), and Surface Water (sky-blue). The side intersections represent patterns shared by any two sources, and the larger, non-overlapping parts of each circle represent the antibiotic resistance patterns that are unique to each source.\u003c/p\u003e","description":"","filename":"Figure5MDRandNonMDRVennDiagrams.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9523007/v1/296ea6943aa370e644c14a46.jpg"},{"id":108008986,"identity":"7a9c0653-85dc-49b2-ae52-38cd17d3edbe","added_by":"auto","created_at":"2026-04-28 13:08:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6143274,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9523007/v1/f1cc64c5-19fa-4e13-be73-5579b70784bd.pdf"},{"id":107966176,"identity":"378bac1a-db37-480c-ab2f-3f490758861a","added_by":"auto","created_at":"2026-04-28 05:48:06","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1422511,"visible":true,"origin":"","legend":"","description":"","filename":"ESBLKpSupplementaryMaterial2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9523007/v1/ce61e66ea38235fd15242a69.xlsx"},{"id":108007415,"identity":"658bfe17-ed0e-471f-b371-491ea2b9f4ec","added_by":"auto","created_at":"2026-04-28 12:59:54","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":843010,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical Abstract\u003c/p\u003e","description":"","filename":"ESBLKpPrevalenceGraphicalabstract.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9523007/v1/34657ebcecb99182bf55abb9.jpg"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eMonitoring Extended-spectrum-β-lactamases-producing Klebsiella pneumoniae: Do Surface Water Phenotypic Patterns Reflect Community and Clinical Resistance?\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Study Highlights","content":"\u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eSamples from surface water, community residents, and the hospital all carried ESBL-Kp.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eResistance varied for \u0026ldquo;Access\u0026rdquo; drugs, while \u0026ldquo;Reserve\u0026rdquo; remained consistent across sites.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMultidrug resistance is widespread, peaking in clinics and lowest in surface water.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSurface water antibiogram overlapped more with community than clinical isolates.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePhenotypic AMR in surface water did not mirror community- or clinical-level AMR.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e"},{"header":"1.0 INTRODUCTION","content":"\u003cp\u003eThe World Health Organisation (WHO) considers infections caused by multidrug-resistant (MDR) bacteria as a significant public health concern. MDR is a type of antimicrobial resistance (AMR) in which microbes, such as bacteria, are resistant to multiple antibiotics from three or more classes (WHO, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The AMR crisis has intensified dramatically in recent decades, fuelled primarily by the misuse and overuse of antibiotics. This resistance surge has significantly broadened the range and number of bacterial pathogens that defy treatment, accounting for up to 4.71\u0026nbsp;million deaths associated with bacterial AMR, including 1.14\u0026nbsp;million attributable deaths as of 2021 (Naghavi et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). As a result, regulatory bodies like the WHO published a list of priority bacterial pathogens for research and development of new antibiotics in 2017, and later updated it in 2024. Extended-spectrum β-lactamase-producing and carbapenem-resistant Enterobacterales, including \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e (\u003cem\u003eK. pneumoniae\u003c/em\u003e), are among the critical pathogens on the list (WHO \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, WHO \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eK. pneumoniae\u003c/em\u003e is a Gram-negative bacterium of the Enterobacterales family, found in the environment (soil, surface water), on mammalian mucus membranes and in humans, where it colonises the upper part of the nasopharynx and gastrointestinal tract (Choby et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). \u003cem\u003eK. pneumoniae\u003c/em\u003e possesses intrinsic resistance to ampicillin via the SHV-1 β-lactamase; however, it has acquired multidrug resistance through various mechanisms. Beyond producing extended-spectrum β-lactamases (ESBLs) and carbapenemases that degrade β-lactams, it employs aminoglycoside-modifying enzymes, mutations in the DNA gyrase genes for fluoroquinolone resistance, and plasmid-mediated mcr genes that confer resistance to colistin. Furthermore, active efflux pumps and decreased membrane permeability often render these strains resistant to nearly all available antibiotic classes (Han et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe emergence of ESBL-Kp resistance represents a serious public health concern in both healthcare (Founou et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and community settings (Olaitan et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Due to its opportunistic nature, current evidence suggests it has a wider ecological distribution and greater AMR diversity than other Gram-negative opportunists, suggesting its role in disseminating resistance between humans and their surrounding environments (Hooban et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wyres \u0026amp; Holt, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The presence of ESBL-Kp has been reported across various niches, including the environment (Kagamb\u0026egrave;ga et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), community residents (Mahomed et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and clinical settings (Founou et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Starzyk-Łuszcz et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These studies highlighted the need for integrated surveillance that links human, animal, and environmental health to manage the risks of exposure and transmission effectively.\u003c/p\u003e \u003cp\u003eAMR surveillance in bacteria, including \u003cem\u003eK. pneumoniae\u003c/em\u003e, has relied on conventional laboratory detection methods, which have been noted to underestimate or overestimate AMR depending on the representativeness and robustness of the surveillance programme (Sugianli et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In Low- and Middle-Income Countries (LMICs), the underutilisation of microbiological testing for both patient management and surveillance is largely driven by high costs, limited laboratory infrastructure, and a lack of standardised reporting(Lim et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) This surveillance gap is particularly pronounced in environmental and community settings, where the absence of routine monitoring obscures the transmission pathways of resistant strains. With the existence of resistant bacteria in the environment and the potential usefulness of the environment in monitoring AMR (Kagamb\u0026egrave;ga et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Rolbiecki et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), there has been a surge in exploring environmental elements like wastewater treatment plants (WWTPs) and hospital influent and effluent, and surface water for monitoring of AMR.\u003c/p\u003e \u003cp\u003eThe presence of resistant bacteria and genes in the environment could result from inappropriate disposal of faecal waste, community-level animal and agricultural activities, and sewage system effluents (Mills \u0026amp; Lee, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Nashwan et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These contamination sources may differ by community type.\u003c/p\u003e \u003cp\u003eInformal settlements, also called slums, are unplanned, improvised human settlements that are not surveyed as residential areas and are characterised primarily by informal structures (Ooi \u0026amp; Phua, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). These settlements are usually overcrowded, with infrastructure deficits, including inadequate access to clean water, sanitation (including toilet facilities), and poor drainage systems, thereby increasing residents\u0026rsquo; vulnerability to health risks and making the settlements prime hotspots for AMR (Nadimpalli et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Nkonki-Mandleni et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In addition to agricultural activities and animal husbandry, challenges such as poor drainage outlets significantly contribute to the discharge of human and household waste, including drug-resistant bacteria, into rivers (Thakur \u0026amp; Onwubu, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Up to 4000 informal settlements exist in South Africa, with over 2\u0026nbsp;million households, and over 400 of these settlements are in the province of KwaZulu-Natal (Gamede, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Housing Development Agency, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study determined the prevalence and AMR profiles of ESBL-producing \u003cem\u003eK. pneumoniae\u003c/em\u003e isolated from surface water, apparently healthy residents of an informal settlement and a proximate healthcare facility, to determine similarities and differences, and to ascertain whether AMR surveillance in surface water in proximity to informal settlements is a potential proxy for AMR circulating in the community.\u003c/p\u003e"},{"header":"2.0 METHODS","content":"\u003cp\u003e \u003cstrong\u003e2.1 Ethical Approval and Consent to Participate\u003c/strong\u003e \u003cp\u003e This study was approved by the Biomedical Research Ethics Committee of the University of KwaZulu-Natal (Ref: BREC/00003640/2021), the Provincial Health Research Ethics Committee of the KwaZulu-Natal Department of Health Ref: KZ_202203_023), and the National Health Laboratory Service (NHLS) Ref: PR2225862). All informal settlement residents were recruited after providing explicit, written, and voluntary informed consent.\u003c/p\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Study Design and Study Area\u003c/h2\u003e \u003cp\u003eThis study was a longitudinal prevalence study undertaken in the uMgungundlovu District Municipality (UMDM). UMDM is a Category C municipality with its seat in Pietermaritzburg, in the KwaZulu-Natal Province of South Africa, and comprises seven local municipalities, including Msunduzi, where the sampling was conducted. UMDM provides water and wastewater services to six municipalities, serving over 1.2\u0026nbsp;million inhabitants (UMDM, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)Despite several environmental water resources, including six significant rivers, 19.6% of the population is not included in the \u0026ldquo;regional/local water scheme\u0026rdquo;, 5.3% still obtain water directly from rivers, 2.5% from springs and 1.4% from water vendors (uMgungundlovu District Municipality, 2023). Three per cent of the population has no form of toilet facilities (Development Bank of Southern Africa, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). About 63.4% of the district\u0026rsquo;s population lives below the poverty line; 45.6% have no source of income, and 17.8% earn less than R400 per month and reside in informal settlements (Cooperative Governance and Traditional Affairs, 2023).\u003c/p\u003e \u003cp\u003e Consenting participants for this study were recruited from Ward 22, one of the largest settlements in the Msunduzi municipality, to provide stool samples. Residents of the Ward 22 informal settlement residing in six different areas, namely: Seven Ox, Emadamini, Fedsem, Lay-Centre, Units 3, and Tehuise, provided stool samples. Surface water samples were collected from the Kwapata River, which discharges into the Msunduzi River, a major tributary of the uMngeni River, which travels across the district. The Medical Microbiology service of the NHLS in UMDM provided the clinical isolates from proximate healthcare facilities, primarily the regional and tertiary hospitals Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Sample Collection and Processing\u003c/h2\u003e \u003cp\u003e \u003cb\u003e2.3.1 Stool Sampling from apparently healthy individuals\u003c/b\u003e: We collected stool samples monthly for eight months (May 2023 to Dec 2023) after obtaining voluntary, written, informed consent. Participants were trained on the proper use of camping toilets and other materials, as well as the faeces collection procedure (Supplementary Material/S1). Demographic information, such as age and gender, was collected, and participants were resupplied with personal protective equipment (PPE) and stool sampling containers for each sampling date. The received samples were immediately transported in a cooler box at 4\u0026deg;C\u0026ndash;10\u0026deg;C to the laboratory and processed for bacterial identification.\u003c/p\u003e\u003cp\u003e \u003cb\u003e2.3.2 Surface water Sampling\u003c/b\u003e: Within the same period of stool sampling, surface water samples were collected from the Kwapata River, which drains into the Msunduzi River, a major tributary of the uMngeni River flowing across the district. Duplicate water samples were collected every 2 weeks from the two sampling points mentioned above near areas of high anthropogenic activity, for a total of 80 samples in the study. Grab water sampling was used to collect 500 mL of water using a plastic bottle placed in a free-flowing, upstream direction, 10\u0026ndash;5 cm below the water surface (Musselman, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The collected samples were placed in a cooler box (4\u0026deg;C\u0026ndash;10\u0026deg;C) and transported to the Antimicrobial Research Unit (ARU) at the University of KwaZulu-Natal for same-day processing.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.3.3 Clinical isolate sampling\u003c/b\u003e: Positive bacteriological culture plates of specimen types: stool, urine, blood cultures, cerebrospinal fluid, pus and other sterile fluids from hospitalised adult patients (\u0026gt;\u0026thinsp;18 years) suspected to have a clinical infection were received weekly in cooler boxes (4\u0026deg;C\u0026ndash;10\u0026deg;C) from the NHLS. A Microsoft Excel spreadsheet listed the samples, with patient demographic information and diagnostic details (clinical diagnosis, specimen type, bacteria isolated, ESBL screening, and antimicrobial susceptibility testing results). The database was sorted for putative \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e and ESBL\u0026thinsp;+\u0026thinsp;\u003cem\u003eK. pneumoniae\u003c/em\u003e positive plates using ID codes from the database, and the colonies from the positive culture were purified and molecularly confirmed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Bacteria Isolation and Phenotypic Identification.\u003c/h2\u003e \u003cp\u003eThe duplicate surface water samples per collection point were mixed, diluted (10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e, 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e ) and plated on Simmons Citrate Agar (SCA) (Oxoid\u0026trade;, Basingstoke, United Kingdom) and Inositol (I) (Sigma-Aldrich, St. Louis, Missouri, USA) for 44 hours at 42℃ (Bobis Camacho et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The SCAI medium is not commercially available and was prepared in-house using previously described protocols (Rodrigues, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The medium\u0026rsquo;s pH was sometimes below 7.2 after preparation and was therefore adjusted to 7.2 using 30% NaOH before pouring into petri dishes. Putative \u003cem\u003eK. pneumoniae\u003c/em\u003e that appeared as large, yellow, and round colonies were further sub-cultured onto SCAI agar plates to obtain pure, distinct colonies after 42\u0026ndash;44 hours of incubation.\u003c/p\u003e \u003cp\u003e \u003cem\u003eK. pneumoniae\u003c/em\u003e from the faecal samples from healthy individuals were isolated at a private pathology laboratory in Durban, South Africa, using the SCAI media following protocols for \u003cem\u003eKlebsiella\u003c/em\u003e isolation from human and animal faecal material as described previously (Brisse et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Clinical isolates, initially grown on MacConkey agar, were obtained according to the standard operating protocol of the microbiology unit at the NHLS, uMgungundlovu.\u003c/p\u003e \u003cp\u003eAll pure isolates were stored in tryptic soy broth (TSB) (Sigma-Aldrich, Missouri, United States of America (USA) in cryobank tubes with 20% glycerol (Associated Chemical Enterprises, Johannesburg, South Africa) for molecular confirmation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.5 DNA Extraction and Molecular Confirmation of \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe pure colonies of presumptive \u003cem\u003eK. pneumoniae\u003c/em\u003e isolates were subjected to DNA extraction using the standard boiling method as previously described (Tiemtor\u0026eacute; et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and real-time PCR was used for molecular confirmation targeting the \u003cem\u003ephoE\u003c/em\u003e gene for \u003cem\u003eK. pneumoniae\u003c/em\u003e detection. Briefly, the 10 \u0026micro;L reaction mixture contained 5 uL of Luna\u0026reg; universal qPCR master mix (New England Biolabs, Ipswich, MA, USA), 1\u0026micro;L nuclease-free water (ThermoFisher Scientific, Waltham, MA, USA), 0.5 \u0026micro;L of forward primer, 0.5 \u0026micro;L of reverse primer, and 3 \u0026micro;L DNA. The PCR amplification of the \u003cem\u003ephoE\u003c/em\u003e gene was performed using the following thermal cycling conditions: an initial denaturation at 94\u0026deg;C for 2 min, followed by 35 cycles of denaturation at 94\u0026deg;C for 30 s, annealing at 50\u0026deg;C for 1 min, and extension at 72\u0026deg;C for 30 s. A final extension was conducted at 72\u0026deg;C for 5 min to ensure complete synthesis of the amplicons. The universal primers of the \u003cem\u003eK. pneumoniae\u003c/em\u003e housekeeping gene (F:5\u0026prime;-GTTTTCCCAGTCACGACGTTGTA-3\u0026prime;; R:5\u0026prime;-TTGTGAGCGGATAACAATTTC-3\u0026prime;) were used for sequencing. \u003cem\u003eK. pneumoniae\u003c/em\u003e ATCC 35657 was used as a positive control (Bassetti et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.6 ESBL-producing\u003c/b\u003e \u003cb\u003eKlebsiella pneumoniae\u003c/b\u003e \u003cb\u003eDetection and Antibiotic Susceptibility Testing (AST)\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eSurface water and human stool isolates were first screened and selected for presumptive ESBL producers using CHROMID\u0026reg; ESBL Agar (bioM\u0026eacute;rieux, Marcy l\u0026rsquo;\u0026eacute;toile, France). Subsequent confirmation of ESBL production was performed by a private pathology laboratory in Durban, South Africa, using the combination disk method. The Isolates were tested with cefotaxime (30 \u0026micro;g) and ceftazidime (30 \u0026micro;g) disks, both alone and in combination with clavulanic acid (10 \u0026micro;g). A\u0026thinsp;\u0026ge;\u0026thinsp;5 mm increase in the zone of inhibition diameter for either antimicrobial agent tested in combination with clavulanic acid, compared to the agent tested alone, was interpreted as a positive result for ESBL production(CLSI, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Confirmed ESBL-producing \u003cem\u003eK pneumoniae\u003c/em\u003e isolates were subjected to AST on the VITEK\u0026reg; 2 system (bioM\u0026eacute;rieux, North Carolina, USA) using the VITEK\u0026reg; 2 AST-N256 card for Gram-negative bacteria as per the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e \u003cp\u003eClinical isolates underwent AST at the NHLS using the VITEK\u0026reg; 2 Compact System. To detect ESBL production, the system utilised its Advanced Expert System\u0026trade; (AES), which analyses the computed Minimum Inhibitory Concentration (MIC) of third-generation cephalosporins to detect putative ESBL-producers (Young et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). \u003cem\u003eK. pneumoniae\u003c/em\u003e (ATCC-700603) was used as a control for all samples\u003c/p\u003e \u003cp\u003eAST results were interpreted as susceptible, intermediate, or resistant according to the standards and breakpoints defined by the Clinical and Laboratory Standards Institute (CLSI) CLSI, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Seventeen (17) antibiotics representing seven antibiotic classes were tested on isolates recovered from stool and surface water samples. Clinical isolates were tested against antibiotics from eight antibiotic classes by the NHLS. The antibiotics assessed included β-lactams: amoxicillin/clavulanic acid (AMC), piperacillin/tazobactam (TZP), cefoxitin (FOX), cefuroxime (CXM), cefotaxime (CTX), ceftazidime (CAZ), cefepime (FEP), imipenem (IMP), doripenem (DOR), meropenem (MEM), and ertapenem (ERT); aminoglycosides: amikacin (AMK), gentamicin (GEN), and tobramycin (TOB); fluoroquinolone: ciprofloxacin (CIP); sulfonamide: trimethoprim-sulphamethoxazole (SXT); and glycylcycline: tigecycline (TGC). Notably, cefoxitin (FOX) and nitrofurantoin were excluded from testing on surface water and community (stool) isolates, whereas tobramycin (TOB) and doripenem (DOR) were not included in the clinical isolate testing panel based on NHLS and private laboratory testing protocols. Isolates were classified as MDR if they demonstrated resistance to at least one antibiotic in three or more distinct antibiotic classes (Magiorakos et al. 2012).\u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistical Analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eData were compiled in Microsoft Excel 365 and analysed using R version 4.5.1 within RStudio 2025.05.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.rstudio.com/\u003c/span\u003e\u003cspan address=\"https://cran.rstudio.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The prevalence of ESBL-producing \u003cem\u003eK. pneumoniae\u003c/em\u003e was calculated as the proportion of positive isolates within each sampling source. Differences in prevalence and resistance profiles among sources were evaluated using chi-squared tests, while pairwise Fisher\u0026rsquo;s exact tests with Benjamini\u0026ndash;Hochberg false discovery rate (FDR) correction were applied for within-source comparisons. Effect sizes, including risk ratios and risk differences, with 95% confidence intervals (CIs), were estimated. Antimicrobial resistance profiles were visualised using hierarchical clustering heatmaps(Ain et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) generated with the ggplot2 and pheatmap packages(Wickham, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Variations in MDR proportions were assessed using chi-squared tests. Overlap and similarity between antibiogram profiles were explored using Venn diagrams (Khan et al., 2019) constructed with the VennDiagram package, while pattern variation was assessed using permutational multivariate analysis of variance (PERMANOVA) from the vegan package in R (Oksanen et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The Shannon diversity index was employed to quantify the complexity and evenness of resistance profiles across sources. All statistical analyses were performed at a 95% confidence level.\u003c/p\u003e \u003c/div\u003e"},{"header":"3.0 RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.1 Detection of ESBL-producing\u003c/b\u003e \u003cb\u003eK. pneumoniae\u003c/b\u003e \u003cb\u003e(\u003c/b\u003eESBL-Kp)\u003c/h2\u003e \u003cp\u003eThe study analysed 4,212 samples for \u003cem\u003eK. pneumoniae\u003c/em\u003e over eight months (1,978 stool samples from healthy informal residents, 2,154 clinical cultures from healthcare settings, and 80 surface water samples) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The overall chi-square test confirmed significant variation in ESBL prevalence between the three sources (χ\u0026sup2; = 29.9, df\u0026thinsp;=\u0026thinsp;2, p\u0026thinsp;\u0026lt;\u0026thinsp;0.00001. ESBL-Kp prevalence was lowest among healthy residents (6.3%, 95% CI 5.1\u0026ndash;7.9), higher in surface water (13.0%, 95% CI 10.2\u0026ndash;16.4), and highest in clinical isolates (15.2%, 95% CI 11.2\u0026ndash;20.2). Pairwise comparisons showed that ESBL-Kp in healthy informal residents differed significantly from both surface water (RR 0.45, RD\u0026thinsp;\u0026minus;\u0026thinsp;8.2%, \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001) and clinical isolates (RR 0.38, RD\u0026thinsp;\u0026minus;\u0026thinsp;11.1%, \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Antibiotic susceptibility profiles\u003c/h2\u003e \u003cp\u003eThere was noticeable variability in the percentage resistance to some antibiotics across sources. The last line antibiotics (amikacin, ertapenem, imipenem, and meropenem) consistently showed a low resistance rate (0-1.7%) across all three sources.\u003c/p\u003e \u003cp\u003ePercentage resistance varied significantly across the three sources (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for amoxicillin/clavulanic acid, piperacillin/tazobactam, gentamicin, ciprofloxacin, and trimethoprim-sulfamethoxazole (SXT). For amoxicillin/clavulanic acid and piperacillin/tazobactam, resistance was notably high (96%) in isolates from both surface water and healthy individuals. However, resistance to these agents was substantially lower among clinical isolates (45.9% for amoxicillin/clavulanic acid and 48.6% for piperacillin/tazobactam). Conversely, clinical isolates displayed significantly (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0000) higher resistance to gentamicin, ciprofloxacin, and trimethoprim-sulfamethoxazole (GEN:89.2%, CIP:56.8%, SXT: 97.3%) than community (GEN:28.4%, CIP:21.6%, SXT: 52.7%) and surface water (GEN:25%, CIP:10%, SXT: 26.7%), respectively.\u003c/p\u003e \u003cp\u003eConsistent with the presence of ESBL-producing isolates, resistance was significantly high across all sources to β-lactams, specifically second, third-, and fourth-generation cephalosporins (cefuroxime, cefotaxime, ceftazidime, cefepime). Conversely, resistance to carbapenems (ertapenem, imipenem, and meropenem) was low or absent across all sources (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Pairwise comparisons (chi-square statistics - \u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e) between isolates from surface water and healthy community residents showed statistically similar resistance profiles (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003cem\u003e)\u003c/em\u003e for β-lactams (amoxicillin/clavulanic acid, piperacillin/tazobactam, cephalosporins, carbapenems), amikacin, and tigecycline. Conversely, resistance to gentamicin, tobramycin, ciprofloxacin, and trimethoprim-sulfamethoxazole differed significantly between these two sources (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For isolates from surface water and clinical settings, resistance to cephalosporins, amikacin, gentamicin, and trimethoprim-sulfamethoxazole was statistically similar (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, resistance differed significantly (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001) for amoxicillin/clavulanic acid, piperacillin/tazobactam, ciprofloxacin, and tigecycline.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eAntibiotic Resistance Profiles of ESBL-producing\u003c/b\u003e \u003cb\u003eK. pneumoniae\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAntibiotics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eIsolate source\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTricycle Analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ePairwise Analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSurface\u003c/p\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHealthy\u003c/p\u003e \u003cp\u003eIndividuals\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eClinical\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e \u0026ndash; \u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003cp\u003eR (all 3 sources)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePairwise Surface Water and Healthy Residents\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePairwise Surface Water\u003c/p\u003e \u003cp\u003e\u0026amp;\u003c/p\u003e \u003cp\u003eClinical\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmoxicillin clavulanic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100% (60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.6% (73),\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.9% (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.0000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epiperacillin/ tazobactam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96.7% (58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.3% (72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.6% (18),\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.0000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecefoxitin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNU\u0026thinsp;=\u0026thinsp;100% (60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNU\u0026thinsp;=\u0026thinsp;100% (74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.8% (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN/A\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN/A\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCefuroxime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95% (57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.6% (73),\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100% (37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.2163*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.4692*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.4366*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCefotaxime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100% (60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.6% (73),\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100% (37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.5172*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.999*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN/A\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCeftazidime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95% (57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.3% (72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100% (37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.3594*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.3156*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u0026thinsp;=\u0026thinsp;0.4366*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCefepime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100% (60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93.2% (69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97.3% (36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.1024*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.111*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u0026thinsp;=\u0026thinsp;0.8062*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoripenem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0% (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4% (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNU\u0026thinsp;=\u0026thinsp;100% (37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN/A\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eErtapenem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.7% (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.1% (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.3758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e \u003cb\u003e0.0207\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImipenem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0% (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0% (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN/A\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN/A\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeropenem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.7% (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0% (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.3944*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN/A\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN/A\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmikacin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0% (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0% (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u0026thinsp;=\u0026thinsp;0.5709*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u0026thinsp;=\u0026thinsp;0.8062*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGentamicin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25% (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.4% (21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89.2% (33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.0000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.0000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u0026thinsp;=\u0026thinsp;0.2033*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTobramycin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78.3% (47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.4% (21),\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNU\u0026thinsp;=\u0026thinsp;100% (37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.0000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN/A\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCiprofloxacin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10% (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.6% (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.8% (21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.0000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eP\u0026thinsp;=\u0026thinsp;0.0081\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eP\u0026thinsp;=\u003c/b\u003e\u0026thinsp;\u003cb\u003e0.0000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etrimethoprim-sulfamethoxazole\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.7% (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.7% (39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97.3% (36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.0000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.0000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u0026thinsp;=\u0026thinsp;0.9784*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enitrofurantoin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNU\u0026thinsp;=\u0026thinsp;100% (60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNU\u0026thinsp;=\u0026thinsp;100% (74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.5% (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN/A\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN/A\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTigecycline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15% (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.2% (29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.7% (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.0000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u0026thinsp;=\u0026thinsp;0.6034*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eND\u0026thinsp;=\u0026thinsp;Not Determined \u003cb\u003e(\u003c/b\u003eAntibiotic Not Used on isolates source), \u003cb\u003eN\u003c/b\u003e/A\u003csup\u003ea\u003c/sup\u003e = Chi square not calculated due to complete or no Resistance between sources, N/A\u003csup\u003eb =\u003c/sup\u003e Chi square not calculated due to absence of phenotypes in a source because antibiotic was not used. p-values in bold were significant.\u003c/p\u003e \u003cp\u003eWe performed hierarchical clustering of antibiotic resistance percentages using a heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) to visualise similarities and differences in resistance profiles across isolate sources. β-lactams, excluding carbapenems, generally clustered closely across the three sources. In particular, surface water isolates and isolates from the community clustered at higher resistance (\u0026gt;\u0026thinsp;95%) for piperacillin/tazobactam, amoxicillin/clavulanic acid, cefuroxime, cefotaxime, ceftazidime, and cefepime compared to clinical isolates, which differed (resistance\u0026thinsp;\u0026lt;\u0026thinsp;50%) for piperacillin/tazobactam and amoxicillin/clavulanic acid. All three sources did not show variation at low levels of carbapenem and amikacin resistance (dark-green rows, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Resistance to ciprofloxacin, gentamicin, and trimethoprim-sulfamethoxazole showed a close resemblance at low to moderate levels in both surface water and community isolates, but differed among clinical isolates. In general, surface water isolates displayed a better mirroring of resistance to community isolates than to clinical isolates.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Antibiograms and Multidrug Resistance (MDR) of isolates\u003c/h2\u003e \u003cp\u003eThere were 31 antibiograms (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) from all isolates across the three sources; 18 of the patterns were MDR. A complete list of all MDR and non-MDR patterns per source is available (Supplementary Material/S2). While some antibiogram patterns were shared between sources, the majority of the patterns were seen in community isolates (total\u0026thinsp;=\u0026thinsp;18, MDR;10, non-MDR;8), followed by clinical isolates (total\u0026thinsp;=\u0026thinsp;11, MDR; 5, non-MDR; 6), and surface water isolates (total\u0026thinsp;=\u0026thinsp;11, MDR; 5, non-MDR; 6). We used the Shannon Diversity test to assess the complexity and evenness of patterns across sources (Supplementary Material/S3). Healthy individuals exhibited the highest overall antibiogram pattern diversity (H\u0026prime; = 2.4916, 95% CI: 2.1367\u0026thinsp;\u0026minus;\u0026thinsp;2.5675; Evenness J\u0026thinsp;=\u0026thinsp;0.8620), followed by clinical isolates (H\u0026prime; = 1.7729, 95% CI: 1.2356\u0026thinsp;\u0026minus;\u0026thinsp;1.9453; Evenness J\u0026thinsp;=\u0026thinsp;0.7394), then surface water (H\u0026prime; = 1.5067, 95% CI: 1.0752\u0026thinsp;\u0026minus;\u0026thinsp;1.7334; Evenness J\u0026thinsp;=\u0026thinsp;0.6283).\u003c/p\u003e \u003cp\u003eThe most common MDR antibiogram for surface water was AMC-TZP-CXM-CTX-CAZ-FEP-GEN-CIP-SXT-TGC (6 isolates, 10.0%, MDR), while the most common MDR antibiogram for clinical isolates was AMC-TZP-CXM-CTX-CAZ-FEP-GEN-CIP-SXT (11 isolates, 29.7%, MDR) and for healthy residents was AMC-TZP-CXM-CTX-CAZ-FEP-GEN-SXT (9 isolates, 12.2%, MDR). The most common non-MDR antibiogram was AMC-TZP-CXM-CTX-CAZ-FEP (surface water; 36 isolates, 60.0%, non-MDR), AMC-TZP-CXM-CTX-CAZ-FEP-SXT (clinical; 1 isolate, 2.7%, non-MDR) and AMC-TZP-CXM-CTX-CAZ-FEP-TGC (healthy residents; 10 isolates, 13.5%, non-MDR).\u003c/p\u003e \u003cp\u003eAmong these patterns, MDR was evident in 94.59% of clinical isolates, 41.89% of community isolates, and 25% of surface water isolates (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Pairwise analysis of MDR prevalence showed that clinical isolates had higher MDR prevalence than surface water and community isolates (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.000001\u003cem\u003e)\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.5 Triangulation of antibiogram patterns and MDR phenotypes.\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eVenn diagram analysis revealed that resistance patterns were largely source-specific (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Clinical isolates harboured the most unique MDR patterns (n\u0026thinsp;=\u0026thinsp;7), while healthy residents possessed the highest number of unique non-MDR profiles (n\u0026thinsp;=\u0026thinsp;6). Overall, MDR antibiograms were more prevalent in clinical isolates, whereas non-MDR patterns were more prevalent in healthy residents. The overall composition of resistance patterns varied significantly by source (R\u0026sup2; = 0.1223, F\u0026thinsp;=\u0026thinsp;11.7088, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0001). Stratification by MDR status revealed source-specific differences remained significant for both MDR (R\u0026sup2; = 0.1332, F\u0026thinsp;=\u0026thinsp;5.9907, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0001) and non-MDR patterns (R\u0026sup2; = 0.1270, F\u0026thinsp;=\u0026thinsp;6.3259, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0001), In both groups, the source accounted for approximately 12\u0026ndash;13% of the observed compositional variance.\u003c/p\u003e \u003cp\u003eFour MDR patterns were shared by isolates from surface water and healthy community residents (Jaccard index\u0026thinsp;=\u0026thinsp;0.3636, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), two from community and clinical isolates (Jaccard index\u0026thinsp;=\u0026thinsp;0.1176, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and none by surface water and clinical isolates. No unique MDR pattern was shared across all three sources. For non-MDR patterns, one pattern was shared by all three sources: one by surface water and community isolates (Jaccard index\u0026thinsp;=\u0026thinsp;0.1667, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and none between surface water and the clinical isolates (Jaccard index\u0026thinsp;=\u0026thinsp;0.1429, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Details of patterns unique to and shared by sources, as per the Venn analysis, are available in Supplementary Material/S4/TableS4/C.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4.0 Discussion","content":"\u003cp\u003e \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e remains one of the common ESBL-producing Enterobacterales that cause drug-resistant infections in both hospital and community settings, resulting in high morbidity, mortality and medical expenses (Olaitan et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Traditionally, AMR surveillance relies heavily on clinical and hospital-based sampling and data, which provide critical insights into resistance trends in patient populations. However, these systems are often limited by underreporting, unequal laboratory capacity, and their inability to capture resistance circulating silently in the broader community or environmental reservoirs (Frost et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kusuma et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). With the potential seen in recent studies (Kagamb\u0026egrave;ga et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Rolbiecki et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) on the role of environment in AMR surveillance, this study explored phenotypic AMR profiles of ESBL-Kp isolated from surface water running through an informal settlement to see if resistance reflects what is seen among healthy community residents and the proximate clinical setting.\u003c/p\u003e \u003cp\u003eWe detected ESBL-Kp with significantly different resistance prevalence and profiles in the three sources. Isolates were multidrug resistant, peaking in clinical isolates and least evident in surface water. Resistance levels differed per antibiotic per source. Surface water isolates shared MDR and non-MDR antibiograms more frequently with community isolates than with clinical ones. Patterns were more unique to respective sources, and surface water did not generally reflect phenotypic resistance in human sources\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Detection of ESBL-producing \u003cem\u003eK. pneumoniae\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eESBL-Kp was detected in all three isolate sources, with the lowest prevalence among healthy residents (6.3%), through surface water (13.0%), and the highest prevalence in clinical isolates (15.2%). According to a 2025 meta-analysis including 119 eligible studies from 25 countries from the Sub-Saharan African subregions (Olaitan et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), the pooled prevalence of ESBL-Kp in South Africa was reported to be 23.3% (95% CI: 6.5\u0026ndash;45.8), which is comparable to the ESBL-Kp prevalence observed in our study (ranging from 6.3% to 15.3%) across all three settings. Although our findings are slightly lower than the point estimate, they fall entirely within the wide confidence interval, indicating that they are not statistically distinct from the national average.\u003c/p\u003e \u003cp\u003eCompared with clinical isolates, the prevalence of ESBL-producers in surface water isolates in our study was significantly lower, consistent with a study in Ethiopia (Abera et al., 2016) that compared clinical and water isolates and found significantly higher ESBL-Kp in clinical isolates (69.94%) than in water isolates (33.3%). Clinical environments concentrate vulnerable and infected patient populations and are sites of high antimicrobial selective pressure, leading to the persistence and dissemination of resistant bacteria. Conversely, aquatic environments are typically more open, subject to dilution, and exposed to environmental factors that generally limit bacterial survival (Ferreira et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Freeman et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).Thus, the prevalence of bacteria such as ESBL-Kp is likely to be higher in clinical settings than in aquatic environments, as seen in our study.\u003c/p\u003e \u003cp\u003eInterestingly, we detected higher ESBL-Kp in surface (13.0%) water than among healthy informal settlement residents (6.3%). Similar findings have been reported in a Malawian study, which sampled human stool from residents of a rural community and their surrounding river water and reported up to 50% prevalence of ESBL-Kp in river water compared to 17% in residents(Cocker et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Another study by Yasmeen and colleagues (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) in Punjab, Pakistan, sampled ESBL-Kp from several niches, including healthy individuals and water samples and observed higher rates of resistant isolates in water (6.7%) than among healthy individuals (3.0%). These findings may suggest that the surrounding aquatic environment is receiving ESBL-Kp contamination from unidentified sources, or that apparently healthy people are less colonised by the bacteria. Although we initially assumed that surface water contamination in the informal settlement was primarily of human origin, as per literature (Cocker et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Martak et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), other studies have highlighted that the presence of ESBL-Kp is not uncommon in surface waters near sources like hospital wastewater treatment plants and sites where there are animal husbandry and agricultural activities (Cocker et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lepuschitz et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yasmeen et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) For instance, previous studies conducted within the same locality identified \u003cem\u003eKlebsiella\u003c/em\u003e spp. and other ESBL-producing Enterobacterales in hospital effluents and wastewater treatment plants (Gumede et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; King et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Although our study did not explore the potential links between sewage systems and the release of bacteria into surrounding aquatic environments, studies worldwide have documented that poorly maintained wastewater treatment plants can disseminate resistant bacteria, contributing to higher levels of contamination in surface waters than carriage among community residents (Buri\u0026aacute;nkov\u0026aacute; et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Freeman et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ojer-Usoz et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). These studies confirmed the dissemination of resistant bacteria, directly or indirectly, to surrounding aquatic environments, including surface water, making surface water a potential reservoir for ESBL-Kp and other organisms, thereby necessitating further studies to elucidate bacterial transmission routes into the aquatic environments proximate to this community.\u003c/p\u003e \u003cp\u003eTaken all together, we confirmed the presence of ESBL-Kp across all three sources and observed that the prevalence in surface water isolates differed significantly (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) from that in community and clinical isolates. Notwithstanding its relatively low prevalence, this finding highlights its potential as a transmission reservoir for community residents of the settlement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Antibiotic susceptibility profiles of ESBL-producing \u003cem\u003eK. pneumoniae\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe percentage of isolates resistant to an antibiotic may vary widely depending on the source of isolates and local selection pressure. In clinical settings, hospitals drive and maintain high-level resistance through sustained antibiotic use and dense human contact; communities maintain moderate, transient resistance through limited exposure; and surface waters dilute bacterial populations, showing varying phenotypic resistance at different levels for different sources (Husna et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, due to interactions among sources, isolates may exhibit similar resistance patterns to some antibiotics. We observed that resistance to amoxicillin-clavulanic acid and piperacillin/tazobactam in surface water isolates was similar to community isolates but differed from clinical isolates, consistent with the report by Yasmeen et al. (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who observed a 100% resistance rate of ESBL-Kp against amoxicillin-clavulanic acid and piperacillin/ tazobactam in both surface water isolates and community isolates. In South Africa, penicillin-inhibitor combinations such as amoxicillin-clavulanic acid are among the most frequently prescribed antibiotics in outpatient and primary healthcare settings (Alabi \u0026amp; Essack, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). While cephalosporins exert direct selective pressure on ESBL phenotypes, widespread community use of amoxicillin-clavulanate likely contributes through co-selection. Because ESBL genes are frequently co-located on multidrug-resistance plasmids alongside genes conferring resistance to inhibitor combinations, frequent AMC exposure can indirectly maintain and propagate ESBL-Kp lineages within the population. The extensive use of antibiotics leads to the discharge of significant quantities of parent compounds and metabolites into sewage systems via human excretion. Because conventional wastewater treatment is often inadequate, these antibiotic residues frequently reach surface waters(Ncube et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Consequently, both the community gut microbiota and environmental niches are exposed to comparable selective pressures, which may result in similar resistance profiles.\u003c/p\u003e \u003cp\u003eTigecycline resistance in community residents was 39%, compared with 15% in surface water (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Our findings contradicted those of Yasmeen et al. (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), a One Health study in Pakistan, which did not detect tigecycline resistance among water and human isolates, likely due to differential antibiotic selection pressure, as tigecycline use and prescribing policies in human and animal health differ between South Africa and Pakistan. An in vitro study conducted at a tertiary care hospital in Pakistan reported universal susceptibility to tigecycline among nosocomial isolates and explicitly noted minimal prior clinical exposure to the drug at the institution at the time of sampling(Shakoor et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Tigecycline in our study area remains reserved as a parenteral, reserve antibiotic for severe, complicated infections (Brink et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Cassir et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Resistance (\u0026gt;\u0026thinsp;10%) in \u003cem\u003eK. pneumoniae\u003c/em\u003e has, however, been recently documented in inpatients and hospital effluent samples (Masalane et al., 2025a). While tigecycline usage is restricted to hospital settings (Cassir et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), 1st generation glycylcyclines like tetracyclines are widely used and less controlled in non-clinical settings like food animal production (Mupfunya et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), with evidence of decreased susceptibility to tigecycline following overexposure of bacteria to tetracyclines mediated through efflux pumps (Nasralddin et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Also, the dissemination of tetracycline-resistant bacteria into the environment following animal husbandry introduces both the resistant bacteria and tetracycline antibiotic residues (Ramessar et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These residues exert selective pressure in the surrounding aquatic environment, potentially co-selecting for tigecycline resistance (due to shared resistance mechanisms) within the aquatic bacterial population and subsequently within the community following environmental interactions (Mupfunya et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ramessar et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eClinical isolates also showed similar resistance proportions to gentamicin and trimethoprim-sulphamethoxazole (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting no significant difference from surface water isolates. This observation is consistent with a One Health study by Craddock et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), which investigated ESBL-Kp among Bedouin communities in Israel. Craddock\u0026rsquo;s study found that resistance to these specific drugs was also statistically similar (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) between clinical urine isolates (GEN: 27.3%, SXT: 77.3%) and environmental water isolates (GEN: 35.3%, SXT: 88.2%). In South Africa, gentamicin and trimethoprim-sulfamethoxazole are common antibiotics used to treat uncomplicated UTIs, with the latter further used to prevent opportunistic infections in HIV-infected persons (Fourie et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Goodlet et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Recent local studies have reported over 50% gentamicin and trimethoprim-sulfamethoxazole resistance in patients and hospital effluent(Bassetti et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Masalane et al., 2025), as well as 0\u0026ndash;3% for gentamicin and 0\u0026ndash;30% for sulfamethoxazole in urban water influenced by anthropogenic activities (Tucker et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In South Africa, Masalane and colleagues observed that \u003cem\u003eK. pneumoniae\u003c/em\u003e clinical isolates were fully resistant to gentamicin and trimethoprim-sulphamethoxazole, whereas isolates recovered from hospital effluent remained completely susceptible. Within the environmental context, Tucker\u0026rsquo;s study of the urban water cycle in Stellenbosch reported gentamicin resistance reaching up to 3% and sulfamethoxazole resistance up to 30% in river water and wastewater treatment plants situated near informal settlements. These findings suggest the potential release of antibiotic-resistant bacteria into surrounding aquatic environments, alongside contributions from other human activities. However, high-resolution genomic analyses to detect shared antimicrobial resistance genes are relevant to establish whether ESBL-K. pneumoniae and related ESBL determinants are exchanged between clinical settings and the aquatic environment.\u003c/p\u003e \u003cp\u003eOverall, surface water isolates closely mirrored community isolates for amoxicillin-clavulanic acid and piperacillin/tazobactam, cephalosporins, and tigecycline, while among clinical isolates and surface water, there was resemblance in cephalosporin, gentamicin, and trimethoprim-sulphamethoxazole resistance. Resistance to amikacin, imipenem, meropenem, and ertapenem was generally absent or very low across all sources.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Antibiograms and Multidrug Resistance (MDR) of ESBL \u003cem\u003eK. pneumoniae\u003c/em\u003e isolates\u003c/h2\u003e \u003cp\u003eAntibiogram patterns were utilised as a phenotypic screening tool to identify the potential transmission of isolates across different sources. Matches between these patterns provided phenotypic evidence of relatedness, suggesting the dissemination of ESBL-Kp between clinical, community, and environmental sources. MDR was significantly lower in surface water (25%) compared to healthy residents (41.89%) and clinical isolates (94.59%). While integrated studies evaluating all three reservoirs simultaneously are scarce, our findings align with individual reports worldwide. In Thailand,Chaisaeng et al., (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) assessed 507 non-duplicate clinical isolates from a clinical setting and reported an ESBL-Kp MDR rate of 100%. Similarly, healthy students at the Dilla University student cafeteria in Ethiopia, revealed faecal carriage ESBL-Kp MDR rate of 37.5%(Diriba et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and in the environment, a study profiling antibiotic resistance in \u003cem\u003eK. pneumoniae\u003c/em\u003e isolates from Mula-Mutha River (Pune, India)(Kaur et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) showed that 15.3% of the MDR isolates harboured ESBL activity. A study of the peri-urban area of Burkina-Faso assessed ESBL-producing Enterobacterales, including ESBL-Kp, using a One Health approach in healthy cattle farmers and their environment and reported higher MDR rates in farmers\u0026rsquo; stools (36.2%) than in water (27.8%) (Soma et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough specific percentages vary by studies and regions, these data reinforce a consistent global trend: MDR burden peaks in clinical settings and remains comparatively lower in aquatic environments.\u003c/p\u003e \u003cp\u003eNotably, our observations and those of other authors projected lower MDR in the surface water environment than in human populations. Although some authors have reported MDR rates as high as 95% in water sources (Davidova-Gerzova et al., 2024), MDR strains exhibit lower persistence in aquatic environments. This has previously been attributed to factors such as dilution effects, environmental stressors, and other ecological variables that limit bacterial persistence in aquatic systems (Larsson \u0026amp; Flach, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), further emphasising the differences in MDR rates between sources. The consistent detection of MDR ESBL-Kp in surface water indicates that these aquatic systems serve as both a reservoir of antibiotic-resistant bacteria and a conduit for their spread. The overlapping resistance profiles among healthy residents, clinical patients, and the environment illustrate an interconnected resistance cycle. This suggests that AMR dynamics in the community are not isolated; instead, there is a continuous exchange of resistant strains between human populations and their surrounding water sources. Within informal settlements characterised by poor infrastructure and insufficient water, sanitation, and hygiene services, direct interaction between residents and polluted surface water increases exposure risks. This cycle facilitates the ongoing circulation of ESBL-resistant bacteria (Erb et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kim et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Nadimpalli et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and remains a significant public health threat.\u003c/p\u003e \u003cp\u003eOur results further showed that, among the circulating MDR isolates, several antibiogram patterns overlapped between sources. These shared phenotypic profiles highlight a degree of resistance similarity between clinical, community, and environmental ESBL-Ec populations. Although there were significant differences in non-MDR pattern composition across sources (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), surface water isolates shared more patterns with healthy residents (n\u0026thinsp;=\u0026thinsp;38, 63.3%) than they did with clinical isolates (n\u0026thinsp;=\u0026thinsp;2, 3.3%; Jaccard index\u0026thinsp;=\u0026thinsp;0.1667). Similarly, surface water isolates (14 isolates, 23.3%) shared four MDR patterns with community isolates, while no overlap was observed with clinical isolates (Jaccard index\u0026thinsp;=\u0026thinsp;0.3636, \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.0001). There was significant correlation in resistance patterns between sources (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001); despite modest overlap in specific profiles. However, the Jaccard similarity coefficients for non-MDR (0.1667) and MDR strains (0.3636) indicate that, while the distribution of resistance is not random, the majority of resistance phenotypes remain source-specific, suggesting that, while common selective pressures may exist, the community and surface water reservoirs maintain distinct resistance signatures. Previous research has demonstrated that human interaction with the environment, whether through direct use of surface waters for domestic activities, animal watering, and irrigation, or indirectly through household wastewater discharge and natural hydrological processes, can facilitate the exchange of antibiotic-resistant bacteria and their associated genetic elements (Craddock et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Kim et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Similarly, inadequate maintenance and inefficiencies in community and hospital wastewater treatment systems (Department of Water and Sanitation, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hounmanou et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zagui et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) have been linked to the persistence and spread of resistant ESBL-producing Enterobacterales within interconnected ecosystems (Gumede et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zagui et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The presence of resistant bacteria, including MDR strains observed in this study, represents a critical public health concern. Therefore, high-resolution genomic insight on isolates displaying comparable resistance profiles is warranted to confirm the presence of shared resistance genes and mobile genetic elements within these settings, thereby elucidating the potential of surface water as a tool for AMR surveillance in the community and clinical setting while exploring routes and mechanisms driving resistance dissemination across environmental and human interfaces.\u003c/p\u003e \u003c/div\u003e"},{"header":"5.0 Conclusion","content":"\u003cp\u003eOur study confirmed the presence of ESBL-Kp in surface water, healthy community residents, and clinical isolates, with statistically significant differences in prevalence among these sources. Surface water isolates showed resistance rates similar to community isolates for amoxicillin\u0026ndash;clavulanic acid, piperacillin\u0026ndash;tazobactam, cephalosporins, and tigecycline, whereas clinical isolates predominantly exhibited resistance to cephalosporins, gentamicin, and trimethoprim\u0026ndash;sulfamethoxazole. Multidrug resistance profiles varied between sources. Surface water isolates shared MDR and non-MDR antibiograms more frequently with community isolates than with clinical ones. Although surface water isolates exhibited resistance to clinically important antibiotics, as observed in both community and clinical isolates, their overall prevalence and MDR rates remained significantly lower, indicating limited reflection of the full breadth of resistance observed in community and clinical settings.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eDeclaration of competing interest\u003c/h2\u003e \u003cp\u003e \u003cb\u003es\u003c/b\u003e: S.Y.E. is a chairperson of the Global Respiratory Partnership and a member of the Global Hygiene Council, both supported by unrestricted educational grants from Reckitt (Pty.) Ltd., UK. All other authors have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the Wellcome Trust (Grant No.: 228172/Z/23/Z), the South African Research Chairs Initiative of the Department of Science and Technology and National Research Foundation of South Africa (Grant No. 98342) and the South African Medical Research Council (SAMRC) Self-Initiated Research Grant. The funders had no role in the design of the study, the collection, analysis, or interpretation of data, the writing of the manuscript, or the decision to publish the results. Any opinion, finding, conclusion or recommendation expressed in this material is that of the authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAin, N. U., Elton, L., Sadouki, Z., McHugh, T. D., \u0026amp; Riaz, S. (2025). 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Municipal sewage as a pathway for multidrug-resistant KPC-producing \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e from hospital effluent to urban stream: challenges for wastewater management. \u003cem\u003eInternational Journal of Hygiene and Environmental Health\u003c/em\u003e, \u003cem\u003e269\u003c/em\u003e, 114640. https://doi.org/10.1016/J.IJHEH.2025.114640\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"e0108306-5e34-4a03-a1bd-f9fecd54d647","identifier":"10.13039/100010269","name":"Wellcome Trust","awardNumber":"228172/Z/23/Z","order_by":0},{"identity":"33af8712-4788-48ba-8de4-c5923f2e9a93","identifier":"10.13039/501100001322","name":"South African Medical Research Council","awardNumber":"*","order_by":1}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Wellcome Trust","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"One-Health, Early warning systems, aquatic ecosystems, antimicrobial resistance, public health","lastPublishedDoi":"10.21203/rs.3.rs-9523007/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9523007/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eESBL-\u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e (ESBL-Kp) prevalence and resistance profiles were evaluated to determine if Surface water (SW) is an Alternative Antimicrobial-resistance Monitoring System (AlARMS) for nearby community (CM) and clinical (CS) settings.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eOver eight months, 462 (SW), 1,167 (CM), and 244 (CS) \u003cem\u003eK. pneumoniae\u003c/em\u003e isolates were screened for ESBL production; antimicrobial susceptibility testing was conducted using VITEK 2.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eESBL-Kp prevalence varied (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) among SW (14.9%), CM (6.8%), and CS (17.9%); multidrug resistance (MDR) was highest (94.6%) in CS and lowest in SW (25%). SW and CM isolates showed similar resistance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) to amoxicillin-clavulanate, piperacillin-tazobactam, cephalosporins, carbapenems, amikacin, and tigecycline, but differed (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for gentamicin, tobramycin, ciprofloxacin, and trimethoprim-sulphamethoxazole. SW and CS were similar (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) in resistance to cephalosporins, amikacin, gentamicin, and trimethoprim-sulphamethoxazole, but differed for amoxicillin-clavulanic acid, piperacillin-tazobactam, ciprofloxacin, and tigecycline. A non-MDR pattern was common to all sources, with more SW-CM (63.3%) than SW-CS (3.3%) overlap. MDR pattern overlapped was seen in SW-CM but not in SW-CS.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eSurface water isolates did not fully mirror clinical or community phenotypes; nevertheless, shared clinically relevant patterns suggest transmission potential, necessitating refined molecular insights for AlARMS evaluation.\u003c/p\u003e","manuscriptTitle":"Monitoring Extended-spectrum-β-lactamases-producing Klebsiella pneumoniae: Do Surface Water Phenotypic Patterns Reflect Community and Clinical Resistance?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-28 05:48:02","doi":"10.21203/rs.3.rs-9523007/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"074cc930-defc-4867-a214-12b95a1c1a31","owner":[],"postedDate":"April 28th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":66991438,"name":"General Microbiology"},{"id":66991439,"name":"Applied \u0026 Industrial Microbiology"},{"id":66991440,"name":"Infectious Diseases"}],"tags":[],"updatedAt":"2026-04-28T05:48:02+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-28 05:48:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9523007","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9523007","identity":"rs-9523007","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}