Household Wastewater as a Sentinel for Community-Level Antimicrobial Resistance: A Cross-Sectional Study in Gombe, Nigeria

Household Wastewater as a Sentinel for Community-Level Antimicrobial Resistance: A Cross-Sectional Study in Gombe, Nigeria | 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 Article Household Wastewater as a Sentinel for Community-Level Antimicrobial Resistance: A Cross-Sectional Study in Gombe, Nigeria Zeenatuddeen Muhammad¹, Muhammad Tukur Adamu¹, Lawal Garba¹, Tawfiq Abdullahi Umar¹, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7620506/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Background Antimicrobial resistance (AMR) represents a critical global health challenge, with projections suggesting 10 million annual deaths by 2050. Environmental transmission routes, particularly through wastewater, remain understudied despite their significant role in resistance development and spread. This study investigated household wastewater as a sentinel for community-level AMR patterns in Gombe, Nigeria. Methods A cross-sectional study was conducted between December 2024 and February 2025, collecting 320 household wastewater samples across seven districts in Gombe using multistage sampling techniques. Bacterial isolation followed standard conventional methods, with identification through morphological characteristics, gram staining, and biochemical tests. Antibiotic susceptibility testing was performed using the disk diffusion method. Extended-spectrum beta-lactamase (ESBL) production was confirmed using double-disc synergy tests, and PCR detected key resistance genes in selected isolates. Results Microbiological analysis yielded 402 bacterial isolates, with 81% classified as multidrug-resistant (MDR). MDR prevalence across districts ranged from 60.3% to 95.9% (p < 0.01). Gram-negative bacteria predominated, with Escherichia coli (32.7%), Klebsiella pneumoniae (19.2%), and Pseudomonas aeruginosa (11.2%) being the most common. ESBL production was detected in 54% of tested isolates. MDR isolates demonstrated resistance to approximately 8 antibiotics (median), while non-MDR isolates showed resistance to only 1–2 antibiotics. Molecular analysis revealed a high prevalence of clinically significant resistance genes, with blaCTX-M detected in 100% of tested isolates. Conclusion This study demonstrates household wastewater's value as a community-level antimicrobial resistance indicator. The high prevalence of MDR bacteria (81%) highlights significant environmental reservoirs that could contribute to community AMR transmission. Wastewater-based epidemiology can serve as a cost-effective complement to traditional clinical surveillance, especially in resource-limited settings. Health sciences/Diseases Earth and environmental sciences/Environmental sciences Biological sciences/Microbiology Antimicrobial resistance Wastewater surveillance Environmental stewardship Multidrug resistance Extended-spectrum beta-lactamase Carbapanemase Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Antimicrobial resistance (AMR) has emerged as one of the most pressing global health challenges of the 21st century, with conservative estimates projecting 10 million annual deaths by 2050 if current trends continue unchecked( 1 ). This crisis transcends traditional healthcare boundaries, threatening the foundation of modern medicine and jeopardizing routine medical procedures that depend on effective antimicrobial therapy( 2 ). While clinical antimicrobial stewardship programs have received substantial attention and resources, environmental transmission routes of antimicrobial resistance, particularly through wastewater systems, remain significantly understudied despite mounting evidence of their critical role in resistance development, maintenance, and dissemination( 3 , 4 ). This knowledge gap is particularly pronounced in low- and middle-income countries (LMICs) such as Nigeria, where inadequate wastewater treatment infrastructure and poor sanitation systems create ideal conditions for the proliferation and spread of resistant organisms( 5 , 6 ). Conventional AMR surveillance strategies have traditionally focused on clinical isolates obtained from healthcare settings, creating significant blind spots in our understanding of community-level resistance dynamics and environmental reservoirs( 7 , 8 ). The One Health framework, which recognizes AMR as an interconnected phenomenon spanning human, animal, and environmental domains, emphasizes the critical importance of environmental surveillance( 9 ). However, environmental monitoring remains substantially underutilized as both a surveillance tool and stewardship intervention( 8 , 10 ). Household wastewater represents a complex microbial ecosystem containing human excreta, pharmaceutical residues, personal care products, and organic waste materials. This mixture serves as an ideal reservoir for resistant bacteria and provides optimal conditions for horizontal gene transfer events that facilitate the spread of resistance determinants( 11 ). When improperly managed or inadequately treated, household wastewater introduces resistant organisms directly into environmental ecosystems, where they can interact with indigenous microbial communities and facilitate the exchange of genetic material across diverse microbiomes( 12 , 13 ). The concept of wastewater-based epidemiology has gained significant traction in recent years, particularly following its successful application in COVID-19 surveillance programs worldwide( 14 , 15 ). This approach offers several advantages over traditional clinical surveillance methods, including the ability to capture population-level trends, detect asymptomatic infections, and provide early warning signals for emerging health threats( 16 , 17 ). In the context of AMR, wastewater surveillance offers unique opportunities to monitor community-level resistance patterns, track the effectiveness of stewardship interventions, and identify environmental hotspots of resistance development. Nigeria, Africa's most populous country with over 200 million inhabitants, faces significant challenges in antimicrobial stewardship and resistance monitoring. The country's healthcare system is characterized by limited laboratory capacity, inadequate surveillance infrastructure, and widespread availability of antimicrobials without prescription( 18 ). Additionally, poor sanitation infrastructure and limited wastewater treatment capacity create ideal conditions for environmental AMR amplification and dissemination. This study addresses the critical knowledge gap regarding environmental AMR surveillance in Nigeria by investigating household wastewater as a sentinel system for community-level antimicrobial resistance patterns in Gombe. Gombe, located in northeastern Nigeria, represents a typical Nigerian state with mixed urban and rural populations, limited healthcare infrastructure, and challenges in waste management that are representative of broader Nigerian contexts. The primary objectives of this research were threefold: first, to quantify the prevalence of multidrug-resistant bacteria in household wastewater across different districts in Gombe; second, to characterize the resistance profiles of isolated organisms through phenotypic and molecular analytical methods; and third, to evaluate the potential of wastewater surveillance as a practical tool for AMR monitoring and stewardship that could extend antimicrobial stewardship programs beyond traditional clinical settings. 2. Materials and Methods 2.1 Study Design and Setting This cross-sectional study was conducted between December 2024 and February 2025 in Gombe State, northeastern Nigeria. The study focused on households within the 11 wards of Gombe Local Government Area (LGA), which comprises a total of 46,112 households distributed across various districts(19). Gombe LGA was selected as the study site due to its representative demographic profile, a mix of urban and rural communities, and the typical wastewater management challenges it shares with other parts of Nigeria. A multi-stage random sampling technique was employed to select specific communities and households, ensuring representation of different socio-economic and geographic characteristics. Each household served as a unit for examining wastewater samples, associated practices, and potential public health impacts. Seven districts were randomly selected from the state's administrative divisions, representing both urban and rural settings. Within each district, Proportional sampling was used to ensure that each district's representation in the sample is proportional to its population size. Two wards were randomly selected from each of the seven districts by balloting and simple random sampling technique was applied to select the number of households in each ward to be included in the study. This minimizes selection bias and ensures geographical representativeness. 2.2 Sample Size Determination To determine the required sample size for this research, Cochran's formula was applied. This formula is widely used in prevalence studies to ensure that the sample is large enough to provide statistically valid results(20,21). The formula is as follows: Where: Z is the Z-score corresponding to the desired confidence level (1.96 for 95% confidence). P is the estimated prevalence of MDR bacteria in wastewater, which in this case is 75% (0.75) (Yusuf et al., 2023). 1−P1 - P1−P represents the proportion without the phenomenon, calculated as 0.25. e is the margin of error, set at 5% (0.05). Substituting these values into the formula: This results in an initial sample size of 288 households. To account for potential non-response, a 10% non-response rate was included. The adjusted sample size was calculated as follows: 2.3 Ethical Considerations Ethical approval for this study was obtained from the Gombe State Environmental Protection Agency before sample collection (approval number: ES/GOSEPA/ADM/S/38/V.I). The study protocol adhered to international guidelines for environmental research and ensured that all sampling activities were conducted in compliance with local regulations and community consent procedures. 2.4 Sample Collection A total of 320 household wastewater samples were collected across seven districts over the three-month study period. Sample collection was standardized to minimize temporal variation, with collections conducted during consistent periods (early morning hours) to capture similar household activities. 20ml of wastewater sample was collected from each household’s wastewater into wide-mouthed sterile plastic containers with screw cap tops and corked tightly. The containers were labeled with date, time and sites of collection, and transported to the laboratory within 4 hours of collection using appropriate cold chain procedures to maintain sample integrity. 2.5 Bacterial Isolation and Identification Bacterial isolation was performed using standard conventional microbiological methods as described by Cheesbrough (2009)(22). First, the water samples were incubated in sterile peptone water overnight and the enriched samples were then streaked on to differential selective media namely, Eosin Methylene blue agar and MacConkey agar. After 24 hours of incubation, colonies were sub cultured onto freshly prepared solidified nutrient agar and incubated for 24 h at 37°C to get pure and distinct colonies. This was repeated several times until satisfactory pure isolates were obtained. The bacterial isolates were tentatively identified using morphological characteristics, cellular and biochemical tests. Morphological characteristics were observed for each bacterial colony after 24 h of growth. The appearance of each colony of each isolate on the media was noted and the characteristics observed include cell shape, elevation, edge, consistency colony surface and pigmentation. To confirm the cellular morphology and classification of the isolates, the Gram staining technique, with 100X optical microscopy visualization, was used to determine the shape, arrangement and classification of isolates. Biochemical tests carried out include motility, indole, urease, citrate utilization, oxidase, methyl red, and lactose fermentation tests. The results were compared with Bergey’s Manual of Determinative Bacteriology(23) to ensure accurate species identification. 2.6.0 Antimicrobial Susceptibility Testing Antibiotic susceptibility testing was performed on Mueller-Hinton agar (MHA) plates using the disk diffusion method (Kirby-Bauer technique) according to guidelines established by the National Committee for Clinical Laboratory Standards Institute (CLSI) 2024(24). A standardized panel of antimicrobial agents was selected to represent major antibiotic classes commonly used in clinical practice and available in the Nigerian healthcare system. Bacterial suspensions equivalent to 0.5 McFarland standard were prepared and evenly distributed on MHA plates. The commercially available gram negative antibiotic discs containing the following antibiotics: cefotaxime (CTX, 25 µg), cefuroxime (CXM, 30 µg), gentamicin (GEN, 10 µg), ceftriaxone (CRO, 45 µg), imipenem (IMP, 10 µg), ampiclox (ACX, 10 µg), ofloxacin (OFX, 5 µg), amoxicillin clavulanate (AUG, 30 µg), cefepime (ZEM, 5 µg), nitrofurantoin (NF, 300 µg), nalidixic acid (NA, 30 µg) and levofloxacin (LEV, 5 µg) (www.celtechproducts.com) were aseptically placed on the surfaces of the sensitivity agar plates with a sterile forceps and incubated at 37°C for 18 hours. Zones of inhibition after incubation were observed and the interpretation was made using susceptibility breakpoints of CLSI (2024). The diameters of the zone of inhibition around the discs were measured to the nearest millimeter using a meter rule and the isolates were classified as sensitive, intermediate or resistant. 2.6.1 Multidrug Resistance Definition and Analysis Multidrug resistance (MDR) was defined according to international consensus criteria as resistance to at least one agent in three or more antimicrobial categories(25,26). This definition ensures consistency with global surveillance programs and facilitates comparison with other studies. The Multiple Antibiotic Resistance (MAR) index was calculated for each isolate using the formula: MAR index = Number of antibiotics to which the isolate is resistant / Total number of antibiotics tested(27). MAR index values greater than 0.2 indicate high-risk sources of contamination and significant antimicrobial pressure. 2.7 Phenotypic Detection of Extended-Spectrum Beta-Lactamase (ESBL) ESBL production was investigated using the double-disc synergy test (DDST), following the European Committee on Antimicrobial Susceptibility Testing (EUCAST) 2006 guidelines(28). This method involves placing disks containing ceftriaxone, ceftazidime, and cefotaxime at a standardized distance from an amoxicillin-clavulanate disk. The presence of ESBL is indicated by the expansion of the inhibition zone around beta-lactam antibiotics toward the amoxicillin-clavulanate disk, creating a characteristic "keyhole" appearance. Quality control was maintained using standard reference strains: Escherichia coli ATCC 25922 (ESBL-negative control) and Klebsiella pneumoniae ATCC 700603 (ESBL-positive control). Suspected carbapenemase-resistant Pseudomonas aeruginosa isolates were selected based on preliminary identification using standard biochemical tests and confirmed by antimicrobial susceptibility testing (AST), which revealed resistance to carbapenems such as imipenem. 2.8 Molecular Analysis Polymerase Chain Reaction (PCR) was employed to detect key antimicrobial resistance genes in selected isolates representing three species ( E. coli, Klebsiella pneumoniae and Pseudomonas aeruginosa ) and resistance phenotypes. DNA extraction was performed using standard protocols, followed by PCR amplification targeting clinically significant resistance genes(29). The molecular analysis focused on extended spectrum beta-lactamase genes including blaTEM, blaSHV, blaCTX-M, and carbapenemase genes including blaKPC, blaVIM, and blaNDM. These genes were selected based on their clinical significance and prevalence in previous studies from similar geographical regions. PCR conditions were optimized for each gene target, with appropriate positive and negative controls. Amplification products were analyzed using gel electrophoresis, and gene presence was confirmed based on expected amplicon sizes. 2.9 Statistical Analysis Statistical analysis was performed using R Statistics version 4.4.0. Descriptive statistics were calculated for all variables, including frequencies, percentages, medians, and interquartile ranges as appropriate for data distribution. Comparative analyses between groups (MDR vs. non-MDR isolates, different districts, different species) were performed using appropriate statistical tests. Chi-square tests were used for categorical variables, while Mann-Whitney U test (Wilcoxon rank-sum test) or Kruskal Walis test were employed for continuous variables depending on data distribution and number of groups compared. Correlation analyses were performed to examine relationships between variables such as the number of resistant antibiotics and MAR index values. Statistical significance was set at p < 0.05 for all analyses, and confidence intervals were calculated where appropriate. 3. Results 3.1 Overall Bacterial Isolation and Identification The analysis of 320 household wastewater samples collected across seven districts in Gombe State yielded a total of 402 bacterial isolates, representing an average of 1.26 isolates per sample. 3.2 Species Distribution Among the identified isolates, Escherichia coli emerged as the most prevalent species, accounting for 32.7% (131/402) of all bacteria isolated. Klebsiella pneumoniae represented the second most common species at 19.2% (77/402), followed by Pseudomonas aeruginosa at 11.2% (45/402). Other significant species included Salmonella spp . at 11% (44/402), Klebsiella oxytoca at 6.5% (26/402), Enterobacter spp. at 5.5% (22/402), Proteus mirabilis at 5% (20/402), Proteus vulgaris at 4.2% (17/402), Shigella spp . at 4% (16/402) while Serratia marcescens represented less than 1% of the total isolates (3/402). 3.3 Multidrug Resistance Prevalence The analysis revealed a concerning prevalence of MDR among wastewater isolates. Of the 402 bacterial isolates analyzed, 326 (81.1%) were classified as MDR according to the established criteria of resistance to at least one agent in three or more antimicrobial categories. Also, widespread multi-drug resistance across all bacterial species was examined, with resistance rates consistently exceeding 60%. While Serratia marcescens showed complete resistance and Proteus mirabilis demonstrated the highest rates among larger sample sizes, the overall pattern indicated uniformly high MDR prevalence throughout the bacterial population. There was no significant difference in resistance patterns between species (p=0.13). 3.4 District-Level Variation in MDR Prevalence Significant geographical variation in MDR prevalence was observed across the study region, with a notable 24.7 percentage point difference between the highest and lowest performing districts. Statistical analysis confirmed highly significant inter-district differences (p less than 0.01). 3.5 Extended-Spectrum Beta-Lactamase (ESBL) Production ESBL testing of 235 selected isolates from three key Enterobacteriaceae species revealed substantial enzymatic resistance capacity, with over half demonstrating this clinically significant mechanism. The observed inter-species variation in ESBL production rates highlights differential resistance strategies among these closely related bacterial populations, with certain species showing greater propensity for this particular resistance mechanism. 3.6 Resistance Metrics Comparison Antimicrobial susceptibility testing revealed a concerning pattern of widespread resistance across the antibiotic panel, with resistance rates exceeding 40% for most agents tested. The highest resistance was observed against commonly used antibiotics, with several agents showing resistance rates approaching 60%. A notable gradient in resistance patterns emerged, with newer or more specialized antimicrobials demonstrating substantially lower resistance rates. Statistical comparison between MDR and non-MDR isolates revealed profound differences in resistance burden, with MDR bacteria demonstrating approximately four-fold greater antibiotic resistance capacity. A strong positive correlation (r = 0.99) was observed between the number of resistant antibiotics and MAR index values. 3.7 Molecular Resistance Gene Detection PCR-based molecular analysis confirmed the genetic foundation of observed resistance phenotypes, revealing universal or near-universal distribution of extended-spectrum beta-lactamase genes among tested isolates. Carbapenemase gene detection, particularly represents a critical finding indicating potential resistance to last-resort antibiotics. Table 1: Antimicrobial Resistance Genes Detected by PCR Resistance Gene Number Tested Present (n, %) Absent (n, %) bla_CTX-M 10 10 (100%) 0 (0%) bla_SHV 10 6 (60%) 4 (40%) bla_TEM 10 9 (90%) 1 (10%) bla_NDM 5 0 (0%) 5 (100%) bla_KPC 5 1 (20%) 4 (80%) bla_VIM 5 3 (60%) 2 (40%) 4. Discussion The bacterial diversity observed in household wastewater systems reflects the characteristic microbiological composition of domestic effluents, consistent with findings from similar environmental studies( 30 , 31 ). The predominance of enteric gram-negative bacteria, particularly E. coli , aligns with established understanding of wastewater microbiology, where these organisms naturally dominate due to their fecal origin and environmental persistence( 32 ). The predominance of gram-negative bacteria represents the higher end of documented ranges internationally, which is particularly alarming given the limited therapeutic options available for treating infections caused by these organisms. The absence of significant differences in multi-drug resistance patterns between bacterial species suggests that antimicrobial resistance has become widespread across diverse bacterial populations, consistent with current understanding of resistance genes transfer between different bacterial species in environmental settings( 33 , 34 ). The overall MDR prevalence represents one of the highest rates reported in environmental surveillance studies from sub-Saharan Africa( 35 ), substantially exceeding the 70% MDR rates documented in clinical settings from some west African countries( 36 ). The significant geographical variation in MDR prevalence observed between districts supports the established role of environmental factors in shaping antimicrobial resistance patterns. This variation likely reflects differences in local antibiotic usage patterns, healthcare infrastructure, sanitation practices, and demographic factors that influence resistance selection pressure( 35 ). The nearly 35-percentage-point difference between districts, while all maintained concerning levels above 60%, suggests that local interventions could have measurable impacts on community-level resistance patterns. When contextualized within the broader sub-Saharan African AMR landscape, these findings take on heightened significance. The region already bears the highest global mortality burden from AMR, with 27.3 deaths per 100,000 attributed to antimicrobial resistance in 2019, and western sub-saharan Africa experiencing death rates exceeding 100 per 100,000 individuals( 36 ). The environmental burden of resistance demonstrated suggests that traditional clinical surveillance significantly underestimates the true scope of the AMR problem in Nigerian communities. The detection of ESBL production in 54% of tested isolates represents a critical finding that aligns with documented rates from both clinical and environmental surveillance programs in similar settings( 37 – 39 ). When compared to regional studies from Burkina Faso healthcare center wastewater( 40 ) and South African wastewater treatment plants( 41 ), the prevalence rates observed in Gombe confirm a severe environmental burden of ESBL-producing bacteria. The clinical implications are profound, as ESBL-producing bacteria are associated with increased mortality, prolonged hospitalization, and elevated healthcare costs( 42 , 43 ). In resource-limited settings like Nigeria, where third-generation cephalosporins serve as first-line therapy for many serious infections( 44 ), this environmental prevalence suggests widespread therapeutic challenges. The molecular analysis provides crucial insights into the genetic basis of resistance. The universal presence of blaCTX-M genes exceeds prevalence rates documented in most international wastewater surveillance studies and is particularly concerning given that this gene family has become the predominant ESBL type globally and is associated with rapid horizontal transfer between bacterial species( 45 ). Most alarming is the detection of carbapenemase genes, particularly blaVIM in P. aeruginosa isolates. Given that carbapenems are considered last-resort antibiotics for treating multidrug-resistant infections( 46 , 47 ), the environmental circulation of these resistance mechanisms suggests that carbapenem resistance may be more widespread in Nigerian communities than clinical surveillance indicates. This study demonstrates the feasibility and exceptional value of wastewater-based epidemiology for AMR surveillance in resource-limited settings, contributing to the growing international evidence base supporting this approach( 48 ). The methodology aligns with successful surveillance studies in Taiwan( 49 ), Nigeria( 50 ), and Niger( 51 ), while providing novel insights specific to household-level surveillance in sub-Saharan Africa. The strong correlation between resistance gene detection and phenotypic resistance patterns validates the biological relevance of wastewater surveillance findings and positions it as an early warning system for clinically relevant resistance patterns( 7 , 52 , 53 ). The findings strongly support the One Health approach to AMR control by demonstrating clear linkages between environmental and clinical resistance patterns. The household wastewater environment emerges as a critical interface between human, animal, and environmental antimicrobial resistance, facilitating the exchange of resistance determinants across different bacterial populations. Study limitations include the cross-sectional design that provides only a snapshot of resistance patterns, focus on culturable bacteria which may underestimate true diversity, and limited molecular analysis to selected isolates and specific resistance genes. The study did not investigate the viability or infectivity of resistant bacteria in wastewater, which would be important for assessing direct transmission risks. Conclusion These findings demonstrate that household wastewater systems serve as significant reservoirs of antimicrobial resistance, with complex interactions between bacterial communities, environmental factors, and human activities driving the emergence and maintenance of resistance phenotypes. This study provides some of the most comprehensive environmental AMR surveillance data available from sub-saharan Africa, documenting resistance burdens that exceed most internationally reported rates and highlighting the critical importance of environmental surveillance in understanding community-level AMR patterns. The molecular characterization provides novel insights into resistance gene circulation in African communities and establishes wastewater surveillance as a feasible and valuable tool for AMR monitoring in resource-limited settings. The district-level variation documented offers hope that targeted interventions could have measurable impacts on community resistance patterns, while the overall burden emphasizes the need for coordinated, multi-sectoral responses to address what appears to be one of the most severe environmental AMR burdens documented globally. These findings contribute significantly to the global understanding of environmental AMR and provide crucial baseline data for monitoring the effectiveness of future intervention strategies in one of the world's most affected regions. Abbreviations AMR Antimicrobial Resistance MDR Multidrug-Resistant ESBL Extended-Spectrum Beta-Lactamase PCR Polymerase Chain Reaction CLSI Clinical Laboratory Standards Institute EUCAST European Committee on Antimicrobial Susceptibility Testing LGA Local Government Area MAR Multiple Antibiotic Resistance LMICs Low-and Middle-Income Countries AST Antimicrobial Susceptibility Testing DDST Double Disk Synergy Test Declarations Ethics approval and consent to participate This study was approved by the Gombe State Environmental Protection Agency (GOSEPA), Nigeria. The ethical approval reference number is ES/GOSEPA/ADM/S/38/V.I . All procedures involving environmental sampling were conducted in accordance with national and international guidelines for environmental health research. Although no human participants or biological tissues were directly involved in the study, verbal informed consent was obtained from the heads of households before wastewater sample collection. Participants were informed about the purpose of the study, assured of confidentiality, and their voluntary participation was respected. The need for written consent was waived by the ethics committee due to the non-invasive nature of environmental sampling and absence of identifiable human data. Consent for publication Not applicable. This manuscript does not contain data from any individual person, including individual details, images, or videos. Availability of data and materials The datasets generated and/or analysed during the current study are not publicly available due to privacy and ethical restrictions but are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or non-profit sectors. Authors’ contributions ZM conducted the sample collection, laboratory and data analysis, and jointly conceptualized the research with MTA, LG, and TAU. MTA, LG, and TAU also contributed to the study design and methodology. MKU critically reviewed and revised the manuscript. SSA and IY provided technical advice and support throughout the study. All authors read and approved the final manuscript. Acknowledgements The authors wish to acknowledge the support of the staff of the Gombe State Environmental Protection Agency (GOSEPA) for granting ethical approval and facilitating community access during the fieldwork. We also thank the participating households for their cooperation and the technical staff of the Microbiology Laboratory at Gombe State University for their assistance with sample processing and bacterial identification. References Tang KWK, Millar BC, Moore JE. Antimicrobial Resistance (AMR). Br J Biomed Sci [Internet]. 2023 Jun 28 [cited 2025 Jun 18];80:11387. Available from: https://www.frontierspartnerships.org/articles/10.3389/bjbs.2023.11387/full Alabi ED, Rabiu AG, Adesoji AT. A review of antimicrobial resistance challenges in Nigeria: The need for a one health approach. One Health [Internet]. 2025 Jun [cited 2025 Jun 18];20:101053. Available from: https://linkinghub.elsevier.com/retrieve/pii/S2352771425000898 Kaiser RA, Taing L, Bhatia H. 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Supplementary Files ZeenatuddeendataLab.xlsx floatimage5.png Plate 1: Detection of ESBL Production in E. coli and Klebsiella spp Using the Double Disk Synergy Test (DDST) floatimage6.png Plate 2: AST Plate of Pseudomonas aeruginosa Showing Resistance to Imipenem Cite Share Download PDF Status: Published Journal Publication published 02 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 14 Oct, 2025 Reviews received at journal 14 Oct, 2025 Reviewers agreed at journal 26 Sep, 2025 Reviews received at journal 25 Sep, 2025 Reviewers agreed at journal 24 Sep, 2025 Reviewers invited by journal 24 Sep, 2025 Editor invited by journal 18 Sep, 2025 Editor assigned by journal 17 Sep, 2025 Submission checks completed at journal 16 Sep, 2025 First submitted to journal 15 Sep, 2025 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. 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1","display":"","copyAsset":false,"role":"figure","size":292238,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Frequency and Percentage Distribution of Bacterial Isolates from Household Wastewater Samples\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7620506/v1/60768b68b2b8454407c61372.jpeg"},{"id":92963354,"identity":"3aad864c-95fa-49d6-9b3f-802281196b58","added_by":"auto","created_at":"2025-10-07 15:19:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":28272,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpecie-specific MDR Prevalence Across Ten Bacterial Isolates\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7620506/v1/c30c1de1ea59b6491a20384e.png"},{"id":92962758,"identity":"0324dde5-1dd5-430f-ab6d-05f136e1e352","added_by":"auto","created_at":"2025-10-07 15:11:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":70418,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of MDR Isolates Across Seven Districts in Gombe\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-7620506/v1/b5fd8d95175c31b4fbb54c43.png"},{"id":92963355,"identity":"e5bc3f59-841e-4593-976d-9aa268d29c1e","added_by":"auto","created_at":"2025-10-07 15:19:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":35484,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhenotypic Detection of ESBL Production in Some Isolates\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-7620506/v1/bf7e611386f20e8245c0a461.png"},{"id":92962745,"identity":"d338c464-c3bf-46aa-b0cd-4e9071699fce","added_by":"auto","created_at":"2025-10-07 15:11:49","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":30597,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBacterial Resistance Rates to Twelve Commonly Used Antibiotics\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-7620506/v1/310ca13ad76e1bfa2220f73d.png"},{"id":92964421,"identity":"15d45b19-2408-49aa-831e-68f9dc32c410","added_by":"auto","created_at":"2025-10-07 15:27:49","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":23509,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of Multiple MAR Index and Resistant Antibiotic Count in MDR versus Non-MDR Bacterial Isolates\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-7620506/v1/5903d4c3cdafd40eeae5bda9.png"},{"id":102234175,"identity":"454b33de-0b87-45dc-94c5-d2817dd0e225","added_by":"auto","created_at":"2026-02-09 16:07:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1592238,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7620506/v1/51a81d26-27c5-49d6-b938-e3d047fdf576.pdf"},{"id":92963357,"identity":"64cc9f3c-f08c-4cf5-876b-e8cc6d91bef0","added_by":"auto","created_at":"2025-10-07 15:19:49","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":65354,"visible":true,"origin":"","legend":"","description":"","filename":"ZeenatuddeendataLab.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7620506/v1/6c521de087bcb61045ab1cff.xlsx"},{"id":92962785,"identity":"65f7b9e7-03b4-4986-ab83-aa071ea6369c","added_by":"auto","created_at":"2025-10-07 15:11:50","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":217100,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePlate 1: Detection of ESBL Production in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eE. coli \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eand \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eKlebsiella spp \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eUsing the Double Disk Synergy Test (DDST)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7620506/v1/e46de7d8789fe24f808978f1.png"},{"id":92962766,"identity":"440abe63-acf4-49d6-8975-2bc733118702","added_by":"auto","created_at":"2025-10-07 15:11:49","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":515638,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePlate 2: AST Plate of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePseudomonas aeruginosa\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e Showing Resistance to Imipenem\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7620506/v1/4fcea85d46fcc856a67b8074.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Household Wastewater as a Sentinel for Community-Level Antimicrobial Resistance: A Cross-Sectional Study in Gombe, Nigeria","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAntimicrobial resistance (AMR) has emerged as one of the most pressing global health challenges of the 21st century, with conservative estimates projecting 10\u0026nbsp;million annual deaths by 2050 if current trends continue unchecked(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). This crisis transcends traditional healthcare boundaries, threatening the foundation of modern medicine and jeopardizing routine medical procedures that depend on effective antimicrobial therapy(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhile clinical antimicrobial stewardship programs have received substantial attention and resources, environmental transmission routes of antimicrobial resistance, particularly through wastewater systems, remain significantly understudied despite mounting evidence of their critical role in resistance development, maintenance, and dissemination(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). This knowledge gap is particularly pronounced in low- and middle-income countries (LMICs) such as Nigeria, where inadequate wastewater treatment infrastructure and poor sanitation systems create ideal conditions for the proliferation and spread of resistant organisms(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eConventional AMR surveillance strategies have traditionally focused on clinical isolates obtained from healthcare settings, creating significant blind spots in our understanding of community-level resistance dynamics and environmental reservoirs(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The One Health framework, which recognizes AMR as an interconnected phenomenon spanning human, animal, and environmental domains, emphasizes the critical importance of environmental surveillance(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). However, environmental monitoring remains substantially underutilized as both a surveillance tool and stewardship intervention(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHousehold wastewater represents a complex microbial ecosystem containing human excreta, pharmaceutical residues, personal care products, and organic waste materials. This mixture serves as an ideal reservoir for resistant bacteria and provides optimal conditions for horizontal gene transfer events that facilitate the spread of resistance determinants(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). When improperly managed or inadequately treated, household wastewater introduces resistant organisms directly into environmental ecosystems, where they can interact with indigenous microbial communities and facilitate the exchange of genetic material across diverse microbiomes(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe concept of wastewater-based epidemiology has gained significant traction in recent years, particularly following its successful application in COVID-19 surveillance programs worldwide(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). This approach offers several advantages over traditional clinical surveillance methods, including the ability to capture population-level trends, detect asymptomatic infections, and provide early warning signals for emerging health threats(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). In the context of AMR, wastewater surveillance offers unique opportunities to monitor community-level resistance patterns, track the effectiveness of stewardship interventions, and identify environmental hotspots of resistance development.\u003c/p\u003e\u003cp\u003eNigeria, Africa's most populous country with over 200\u0026nbsp;million inhabitants, faces significant challenges in antimicrobial stewardship and resistance monitoring. The country's healthcare system is characterized by limited laboratory capacity, inadequate surveillance infrastructure, and widespread availability of antimicrobials without prescription(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Additionally, poor sanitation infrastructure and limited wastewater treatment capacity create ideal conditions for environmental AMR amplification and dissemination.\u003c/p\u003e\u003cp\u003eThis study addresses the critical knowledge gap regarding environmental AMR surveillance in Nigeria by investigating household wastewater as a sentinel system for community-level antimicrobial resistance patterns in Gombe. Gombe, located in northeastern Nigeria, represents a typical Nigerian state with mixed urban and rural populations, limited healthcare infrastructure, and challenges in waste management that are representative of broader Nigerian contexts.\u003c/p\u003e\u003cp\u003eThe primary objectives of this research were threefold: first, to quantify the prevalence of multidrug-resistant bacteria in household wastewater across different districts in Gombe; second, to characterize the resistance profiles of isolated organisms through phenotypic and molecular analytical methods; and third, to evaluate the potential of wastewater surveillance as a practical tool for AMR monitoring and stewardship that could extend antimicrobial stewardship programs beyond traditional clinical settings.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Study Design and Setting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis cross-sectional study was conducted between December 2024 and February 2025 in Gombe State, northeastern Nigeria. The study focused on households within the 11 wards of Gombe Local Government Area (LGA), which comprises a total of 46,112 households distributed across various districts(19). Gombe LGA was selected as the study site due to its representative demographic profile, a mix of urban and rural communities, and the typical wastewater management challenges it shares with other parts of Nigeria. A multi-stage random sampling technique was employed to select specific communities and households, ensuring representation of different socio-economic and geographic characteristics. Each household served as a unit for examining wastewater samples, associated practices, and potential public health impacts. Seven districts were randomly selected from the state\u0026apos;s administrative divisions, representing both urban and rural settings. Within each district, Proportional sampling was used to ensure that each district\u0026apos;s representation in the sample is proportional to its population size. Two wards were randomly selected from each of the seven districts by balloting and simple random sampling technique was applied to\u0026nbsp;select the number of households in each ward\u0026nbsp;to be included in the study. This\u0026nbsp;minimizes selection bias and ensures geographical representativeness.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Sample Size Determination\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo determine the required sample size for this research, Cochran\u0026apos;s formula was applied. This formula is widely used in prevalence studies to ensure that the sample is large enough to provide statistically valid results(20,21). The formula is as follows:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1759849360.png\" width=\"439\" height=\"72\"\u003e\u003c/p\u003e\n\u003cp\u003eWhere:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eZ is the Z-score corresponding to the desired confidence level (1.96 for 95% confidence).\u003c/li\u003e\n \u003cli\u003eP is the estimated prevalence of MDR bacteria in wastewater, which in this case is 75% (0.75) (Yusuf et al., 2023).\u003c/li\u003e\n \u003cli\u003e1\u0026minus;P1 - P1\u0026minus;P represents the proportion without the phenomenon, calculated as 0.25.\u003c/li\u003e\n \u003cli\u003ee is the margin of error, set at 5% (0.05).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSubstituting these values into the formula:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1759849376.png\" width=\"435\" height=\"99\"\u003e\u003c/p\u003e\n\u003cp\u003eThis results in an initial sample size of 288 households.\u003c/p\u003e\n\u003cp\u003eTo account for potential non-response, a 10% non-response rate was included. The adjusted sample size was calculated as follows:\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003cimg src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1759849420.png\" width=\"631\" height=\"184\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Ethical Considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for this study was obtained from the Gombe State Environmental Protection Agency before sample collection (approval number: ES/GOSEPA/ADM/S/38/V.I). The study protocol adhered to international guidelines for environmental research and ensured that all sampling activities were conducted in compliance with local regulations and community consent procedures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Sample Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 320 household wastewater samples were collected across seven districts over the three-month study period. Sample collection was standardized to minimize temporal variation, with collections conducted during consistent periods (early morning hours) to capture similar household activities. 20ml of wastewater sample was collected from each household\u0026rsquo;s wastewater into wide-mouthed sterile plastic containers with screw cap tops and corked tightly. The containers were labeled with date, time and sites of collection, and transported to the laboratory within 4 hours of collection using appropriate cold chain procedures to maintain sample integrity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Bacterial Isolation and Identification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBacterial isolation was performed using standard conventional microbiological methods as described by Cheesbrough (2009)(22). First, the water samples were incubated in sterile peptone water overnight and the enriched samples were then streaked on to differential selective media namely, Eosin Methylene blue agar and MacConkey agar. After 24 hours of incubation, colonies were sub cultured onto freshly prepared solidified nutrient agar and incubated for 24 h at 37\u0026deg;C to get pure and distinct colonies. This was repeated several times until satisfactory pure isolates were obtained.\u003c/p\u003e\n\u003cp\u003eThe bacterial isolates were tentatively identified using morphological characteristics, cellular and biochemical tests. Morphological characteristics were observed for each bacterial colony after 24 h of growth. The appearance of each colony of each isolate on the media was noted and the characteristics observed include cell shape, elevation, edge, consistency colony surface and pigmentation. To confirm the cellular morphology and classification of the isolates, the Gram staining technique, with 100X optical microscopy visualization, was used to determine the shape, arrangement and classification of isolates. \u0026nbsp;Biochemical tests carried out include motility, indole, urease, citrate utilization, oxidase, methyl red, and lactose fermentation tests. The results were compared with Bergey\u0026rsquo;s Manual of Determinative Bacteriology(23) to ensure accurate species identification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6.0 Antimicrobial Susceptibility Testing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAntibiotic susceptibility testing was performed on Mueller-Hinton agar (MHA) plates using the disk diffusion method (Kirby-Bauer technique) according to guidelines established by the National Committee for Clinical Laboratory Standards Institute (CLSI) 2024(24). A standardized panel of antimicrobial agents was selected to represent major antibiotic classes commonly used in clinical practice and available in the Nigerian healthcare system.\u003c/p\u003e\n\u003cp\u003e\u003cdel cite=\"mailto:Zeenatuddeen%20Muhammad\" datetime=\"2025-07-08T12:49\"\u003e\u0026nbsp;\u003c/del\u003e\u003c/p\u003e\n\u003cp\u003eBacterial suspensions equivalent to 0.5 McFarland standard were prepared and evenly distributed on MHA plates. The commercially available gram negative antibiotic discs containing the following antibiotics: cefotaxime (CTX, 25 \u0026micro;g), cefuroxime (CXM, 30 \u0026micro;g), gentamicin (GEN, 10 \u0026micro;g), ceftriaxone (CRO, 45 \u0026micro;g), imipenem (IMP, 10 \u0026micro;g), ampiclox (ACX, 10 \u0026micro;g), ofloxacin (OFX, 5 \u0026micro;g), amoxicillin clavulanate (AUG, 30 \u0026micro;g), cefepime (ZEM, 5 \u0026micro;g), nitrofurantoin (NF, 300 \u0026micro;g), nalidixic acid (NA, 30 \u0026micro;g) and levofloxacin (LEV, 5 \u0026micro;g) (www.celtechproducts.com) were aseptically placed on the surfaces of the sensitivity agar plates with a sterile forceps and incubated at 37\u0026deg;C for 18 hours. Zones of inhibition after incubation were observed and the interpretation was made using susceptibility breakpoints of CLSI (2024). The diameters of the zone of inhibition around the discs were measured to the nearest millimeter using a meter rule and the isolates were classified as sensitive, intermediate or resistant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6.1 Multidrug Resistance Definition and Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultidrug resistance (MDR) was defined according to international consensus criteria as resistance to at least one agent in three or more antimicrobial categories(25,26). This definition ensures consistency with global surveillance programs and facilitates comparison with other studies.\u003c/p\u003e\n\u003cp\u003eThe Multiple Antibiotic Resistance (MAR) index was calculated for each isolate using the formula: MAR index = Number of antibiotics to which the isolate is resistant / Total number of antibiotics tested(27). MAR index values greater than 0.2 indicate high-risk sources of contamination and significant antimicrobial pressure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 Phenotypic Detection of Extended-Spectrum Beta-Lactamase (ESBL)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eESBL production was investigated using the double-disc synergy test (DDST), following the European Committee on Antimicrobial Susceptibility Testing (EUCAST) 2006 guidelines(28). This method involves placing disks containing ceftriaxone, ceftazidime, and cefotaxime at a standardized distance from an amoxicillin-clavulanate disk. The presence of ESBL is indicated by the expansion of the inhibition zone around beta-lactam antibiotics toward the amoxicillin-clavulanate disk, creating a characteristic \u0026quot;keyhole\u0026quot; appearance.\u003c/p\u003e\n\u003cp\u003eQuality control was maintained using standard reference strains: \u003cem\u003eEscherichia coli\u003c/em\u003e ATCC 25922 (ESBL-negative control) and \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e ATCC 700603 (ESBL-positive control).\u003c/p\u003e\n\u003cp\u003eSuspected carbapenemase-resistant \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e isolates were selected based on preliminary identification using standard biochemical tests and confirmed by antimicrobial susceptibility testing (AST), which revealed resistance to carbapenems such as imipenem.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.8 Molecular Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePolymerase Chain Reaction (PCR) was employed to detect key antimicrobial resistance genes in selected isolates representing three species (\u003cem\u003eE. coli, Klebsiella pneumoniae and Pseudomonas aeruginosa\u003c/em\u003e) and resistance phenotypes. DNA extraction was performed using standard protocols, followed by PCR amplification targeting clinically significant resistance genes(29).\u003c/p\u003e\n\u003cp\u003eThe molecular analysis focused on extended spectrum beta-lactamase genes including \u003cem\u003eblaTEM,\u003c/em\u003e \u003cem\u003eblaSHV, blaCTX-M,\u003c/em\u003e and carbapenemase genes including \u003cem\u003eblaKPC, blaVIM,\u003c/em\u003e and \u003cem\u003eblaNDM.\u003c/em\u003e These genes were selected based on their clinical significance and prevalence in previous studies from similar geographical regions.\u003c/p\u003e\n\u003cp\u003ePCR conditions were optimized for each gene target, with appropriate positive and negative controls. Amplification products were analyzed using gel electrophoresis, and gene presence was confirmed based on expected amplicon sizes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.9 Statistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analysis was performed using R Statistics version 4.4.0. Descriptive statistics were calculated for all variables, including frequencies, percentages, medians, and interquartile ranges as appropriate for data distribution.\u003c/p\u003e\n\u003cp\u003eComparative analyses between groups (MDR vs. non-MDR isolates, different districts, different species) were performed using appropriate statistical tests. Chi-square tests were used for categorical variables, while Mann-Whitney U test (Wilcoxon rank-sum test) or Kruskal Walis test were employed for continuous variables depending on data distribution and number of groups compared.\u003c/p\u003e\n\u003cp\u003eCorrelation analyses were performed to examine relationships between variables such as the number of resistant antibiotics and MAR index values. Statistical significance was set at p \u0026lt; 0.05 for all analyses, and confidence intervals were calculated where appropriate.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Overall Bacterial Isolation and Identification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis of 320 household wastewater samples collected across seven districts in Gombe State yielded a total of 402 bacterial isolates, representing an average of 1.26 isolates per sample.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Species Distribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the identified isolates, \u003cem\u003eEscherichia coli\u003c/em\u003e emerged as the most prevalent species, accounting for 32.7% (131/402) of all bacteria isolated. \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e represented the second most common species at 19.2% (77/402), followed by \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e at 11.2% (45/402). Other significant species included \u003cem\u003eSalmonella spp\u003c/em\u003e. at 11% (44/402), \u003cem\u003eKlebsiella oxytoca\u003c/em\u003e at 6.5% (26/402), \u003cem\u003eEnterobacter spp.\u003c/em\u003e at 5.5% (22/402), \u003cem\u003eProteus mirabilis\u003c/em\u003e at 5% (20/402), \u003cem\u003eProteus vulgaris\u003c/em\u003e at 4.2% (17/402), \u003cem\u003eShigella spp\u003c/em\u003e. at 4% (16/402) while \u003cem\u003eSerratia marcescens\u003c/em\u003e represented less than 1% of the total isolates (3/402).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Multidrug Resistance Prevalence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis revealed a concerning prevalence of MDR among wastewater isolates. Of the 402 bacterial isolates analyzed, 326 (81.1%) were classified as MDR according to the established criteria of resistance to at least one agent in three or more antimicrobial categories.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlso, widespread multi-drug resistance across all bacterial species was examined, with resistance rates consistently exceeding 60%. While \u003cem\u003eSerratia marcescens\u003c/em\u003e showed complete resistance and \u003cem\u003eProteus mirabilis\u003c/em\u003e demonstrated the highest rates among larger sample sizes, the overall pattern indicated uniformly high MDR prevalence throughout the bacterial population. There was no significant difference in resistance patterns between species (p=0.13).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 District-Level Variation in MDR Prevalence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSignificant geographical variation in MDR prevalence was observed across the study region, with a notable 24.7 percentage point difference between the highest and lowest performing districts. Statistical analysis confirmed highly significant inter-district differences (p less than 0.01).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Extended-Spectrum Beta-Lactamase (ESBL) Production\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eESBL testing of 235 selected isolates from three key Enterobacteriaceae species revealed substantial enzymatic resistance capacity, with over half demonstrating this clinically significant mechanism. The observed inter-species variation in ESBL production rates highlights differential resistance strategies among these closely related bacterial populations, with certain species showing greater propensity for this particular resistance mechanism.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Resistance Metrics Comparison\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAntimicrobial susceptibility testing revealed a concerning pattern of widespread resistance across the antibiotic panel, with resistance rates exceeding 40% for most agents tested. The highest resistance was observed against commonly used antibiotics, with several agents showing resistance rates approaching 60%. A notable gradient in resistance patterns emerged, with newer or more specialized antimicrobials demonstrating substantially lower resistance rates.\u003c/p\u003e\n\u003cp\u003eStatistical comparison between MDR and non-MDR isolates revealed profound differences in resistance burden, with MDR bacteria demonstrating approximately four-fold greater antibiotic resistance capacity. A strong positive correlation (r = 0.99) was observed between the number of resistant antibiotics and MAR index values.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.7 Molecular Resistance Gene Detection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePCR-based molecular analysis confirmed the genetic foundation of observed resistance phenotypes, revealing universal or near-universal distribution of extended-spectrum beta-lactamase genes among tested isolates. Carbapenemase gene detection, particularly represents a critical finding indicating potential resistance to last-resort antibiotics.\u003c/p\u003e\n\u003cp\u003eTable 1: Antimicrobial Resistance Genes Detected by PCR\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"485\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResistance Gene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber Tested\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePresent (n, %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbsent (n, %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cem\u003ebla_CTX-M\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e10 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 137px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cem\u003ebla_SHV\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e6 (60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 137px;\"\u003e\n \u003cp\u003e4 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cem\u003ebla_TEM\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e9 (90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 137px;\"\u003e\n \u003cp\u003e1 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cem\u003ebla_NDM\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 137px;\"\u003e\n \u003cp\u003e5 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cem\u003ebla_KPC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e1 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 137px;\"\u003e\n \u003cp\u003e4 (80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cem\u003ebla_VIM\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e3 (60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 137px;\"\u003e\n \u003cp\u003e2 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe bacterial diversity observed in household wastewater systems reflects the characteristic microbiological composition of domestic effluents, consistent with findings from similar environmental studies(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). The predominance of enteric gram-negative bacteria, particularly \u003cem\u003eE. coli\u003c/em\u003e, aligns with established understanding of wastewater microbiology, where these organisms naturally dominate due to their fecal origin and environmental persistence(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). The predominance of gram-negative bacteria represents the higher end of documented ranges internationally, which is particularly alarming given the limited therapeutic options available for treating infections caused by these organisms.\u003c/p\u003e\u003cp\u003eThe absence of significant differences in multi-drug resistance patterns between bacterial species suggests that antimicrobial resistance has become widespread across diverse bacterial populations, consistent with current understanding of resistance genes transfer between different bacterial species in environmental settings(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). The overall MDR prevalence represents one of the highest rates reported in environmental surveillance studies from sub-Saharan Africa(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), substantially exceeding the 70% MDR rates documented in clinical settings from some west African countries(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe significant geographical variation in MDR prevalence observed between districts supports the established role of environmental factors in shaping antimicrobial resistance patterns. This variation likely reflects differences in local antibiotic usage patterns, healthcare infrastructure, sanitation practices, and demographic factors that influence resistance selection pressure(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). The nearly 35-percentage-point difference between districts, while all maintained concerning levels above 60%, suggests that local interventions could have measurable impacts on community-level resistance patterns.\u003c/p\u003e\u003cp\u003eWhen contextualized within the broader sub-Saharan African AMR landscape, these findings take on heightened significance. The region already bears the highest global mortality burden from AMR, with 27.3 deaths per 100,000 attributed to antimicrobial resistance in 2019, and western sub-saharan Africa experiencing death rates exceeding 100 per 100,000 individuals(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). The environmental burden of resistance demonstrated suggests that traditional clinical surveillance significantly underestimates the true scope of the AMR problem in Nigerian communities.\u003c/p\u003e\u003cp\u003eThe detection of ESBL production in 54% of tested isolates represents a critical finding that aligns with documented rates from both clinical and environmental surveillance programs in similar settings(\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). When compared to regional studies from Burkina Faso healthcare center wastewater(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e) and South African wastewater treatment plants(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e), the prevalence rates observed in Gombe confirm a severe environmental burden of ESBL-producing bacteria. The clinical implications are profound, as ESBL-producing bacteria are associated with increased mortality, prolonged hospitalization, and elevated healthcare costs(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). In resource-limited settings like Nigeria, where third-generation cephalosporins serve as first-line therapy for many serious infections(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e), this environmental prevalence suggests widespread therapeutic challenges.\u003c/p\u003e\u003cp\u003eThe molecular analysis provides crucial insights into the genetic basis of resistance. The universal presence of \u003cem\u003eblaCTX-M\u003c/em\u003e genes exceeds prevalence rates documented in most international wastewater surveillance studies and is particularly concerning given that this gene family has become the predominant ESBL type globally and is associated with rapid horizontal transfer between bacterial species(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Most alarming is the detection of carbapenemase genes, particularly \u003cem\u003eblaVIM\u003c/em\u003e in \u003cem\u003eP. aeruginosa\u003c/em\u003e isolates. Given that carbapenems are considered last-resort antibiotics for treating multidrug-resistant infections(\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e), the environmental circulation of these resistance mechanisms suggests that carbapenem resistance may be more widespread in Nigerian communities than clinical surveillance indicates.\u003c/p\u003e\u003cp\u003eThis study demonstrates the feasibility and exceptional value of wastewater-based epidemiology for AMR surveillance in resource-limited settings, contributing to the growing international evidence base supporting this approach(\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). The methodology aligns with successful surveillance studies in Taiwan(\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e), Nigeria(\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e), and Niger(\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e), while providing novel insights specific to household-level surveillance in sub-Saharan Africa. The strong correlation between resistance gene detection and phenotypic resistance patterns validates the biological relevance of wastewater surveillance findings and positions it as an early warning system for clinically relevant resistance patterns(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe findings strongly support the One Health approach to AMR control by demonstrating clear linkages between environmental and clinical resistance patterns. The household wastewater environment emerges as a critical interface between human, animal, and environmental antimicrobial resistance, facilitating the exchange of resistance determinants across different bacterial populations.\u003c/p\u003e\u003cp\u003eStudy limitations include the cross-sectional design that provides only a snapshot of resistance patterns, focus on culturable bacteria which may underestimate true diversity, and limited molecular analysis to selected isolates and specific resistance genes. The study did not investigate the viability or infectivity of resistant bacteria in wastewater, which would be important for assessing direct transmission risks.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThese findings demonstrate that household wastewater systems serve as significant reservoirs of antimicrobial resistance, with complex interactions between bacterial communities, environmental factors, and human activities driving the emergence and maintenance of resistance phenotypes. This study provides some of the most comprehensive environmental AMR surveillance data available from sub-saharan Africa, documenting resistance burdens that exceed most internationally reported rates and highlighting the critical importance of environmental surveillance in understanding community-level AMR patterns.\u003c/p\u003e\u003cp\u003eThe molecular characterization provides novel insights into resistance gene circulation in African communities and establishes wastewater surveillance as a feasible and valuable tool for AMR monitoring in resource-limited settings. The district-level variation documented offers hope that targeted interventions could have measurable impacts on community resistance patterns, while the overall burden emphasizes the need for coordinated, multi-sectoral responses to address what appears to be one of the most severe environmental AMR burdens documented globally.\u003c/p\u003e\u003cp\u003eThese findings contribute significantly to the global understanding of environmental AMR and provide crucial baseline data for monitoring the effectiveness of future intervention strategies in one of the world's most affected regions.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAMR\u0026nbsp; \u0026nbsp;Antimicrobial Resistance\u003c/p\u003e\n\u003cp\u003eMDR\u0026nbsp; \u0026nbsp;Multidrug-Resistant\u003c/p\u003e\n\u003cp\u003eESBL\u0026nbsp;\u0026nbsp;Extended-Spectrum Beta-Lactamase\u003c/p\u003e\n\u003cp\u003ePCR\u0026nbsp; \u0026nbsp;Polymerase Chain Reaction\u003c/p\u003e\n\u003cp\u003eCLSI\u0026nbsp; \u0026nbsp;Clinical Laboratory Standards Institute\u003c/p\u003e\n\u003cp\u003eEUCAST \u0026nbsp; European Committee on Antimicrobial Susceptibility Testing\u003c/p\u003e\n\u003cp\u003eLGA\u0026nbsp; \u0026nbsp;\u0026nbsp;Local Government Area\u003c/p\u003e\n\u003cp\u003eMAR\u0026nbsp; \u0026nbsp;Multiple Antibiotic Resistance\u003c/p\u003e\n\u003cp\u003eLMICs\u0026nbsp;\u0026nbsp; Low-and Middle-Income Countries\u003c/p\u003e\n\u003cp\u003eAST\u0026nbsp; \u0026nbsp;\u0026nbsp;Antimicrobial Susceptibility Testing\u003c/p\u003e\n\u003cp\u003eDDST \u0026nbsp; Double Disk Synergy Test\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Gombe State Environmental Protection Agency (GOSEPA), Nigeria. The ethical approval reference number is \u003cstrong\u003eES/GOSEPA/ADM/S/38/V.I\u003c/strong\u003e. All procedures involving environmental sampling were conducted in accordance with national and international guidelines for environmental health research. Although no human participants or biological tissues were directly involved in the study, verbal informed consent was obtained from the heads of households before wastewater sample collection. Participants were informed about the purpose of the study, assured of confidentiality, and their voluntary participation was respected. The need for written consent was waived by the ethics committee due to the non-invasive nature of environmental sampling and absence of identifiable human data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This manuscript does not contain data from any individual person, including individual details, images, or videos.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available due to privacy and ethical restrictions but are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or non-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZM conducted the sample collection, laboratory and data analysis, and jointly conceptualized the research with MTA, LG, and TAU. MTA, LG, and TAU also contributed to the study design and methodology. MKU critically reviewed and revised the manuscript. SSA and IY provided technical advice and support throughout the study. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to acknowledge the support of the staff of the Gombe State Environmental Protection Agency (GOSEPA) for granting ethical approval and facilitating community access during the fieldwork. We also thank the participating households for their cooperation and the technical staff of the Microbiology Laboratory at Gombe State University for their assistance with sample processing and bacterial identification.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eTang KWK, Millar BC, Moore JE. Antimicrobial Resistance (AMR). Br J Biomed Sci [Internet]. 2023 Jun 28 [cited 2025 Jun 18];80:11387. 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Available from: https://link.springer.com/10.1007/s11356-021-13170-8\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Plate","content":"\u003cp\u003ePlate 1 and 2 are available in the Supplementary Files section.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Antimicrobial resistance, Wastewater surveillance, Environmental stewardship, Multidrug resistance, Extended-spectrum beta-lactamase, Carbapanemase","lastPublishedDoi":"10.21203/rs.3.rs-7620506/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7620506/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eAntimicrobial resistance (AMR) represents a critical global health challenge, with projections suggesting 10\u0026nbsp;million annual deaths by 2050. Environmental transmission routes, particularly through wastewater, remain understudied despite their significant role in resistance development and spread. This study investigated household wastewater as a sentinel for community-level AMR patterns in Gombe, Nigeria.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA cross-sectional study was conducted between December 2024 and February 2025, collecting 320 household wastewater samples across seven districts in Gombe using multistage sampling techniques. Bacterial isolation followed standard conventional methods, with identification through morphological characteristics, gram staining, and biochemical tests. Antibiotic susceptibility testing was performed using the disk diffusion method. Extended-spectrum beta-lactamase (ESBL) production was confirmed using double-disc synergy tests, and PCR detected key resistance genes in selected isolates.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eMicrobiological analysis yielded 402 bacterial isolates, with 81% classified as multidrug-resistant (MDR). MDR prevalence across districts ranged from 60.3% to 95.9% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Gram-negative bacteria predominated, with \u003cem\u003eEscherichia coli\u003c/em\u003e (32.7%), \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e (19.2%), and \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e (11.2%) being the most common. ESBL production was detected in 54% of tested isolates. MDR isolates demonstrated resistance to approximately 8 antibiotics (median), while non-MDR isolates showed resistance to only 1\u0026ndash;2 antibiotics. Molecular analysis revealed a high prevalence of clinically significant resistance genes, with \u003cem\u003eblaCTX-M\u003c/em\u003e detected in 100% of tested isolates.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study demonstrates household wastewater's value as a community-level antimicrobial resistance indicator. The high prevalence of MDR bacteria (81%) highlights significant environmental reservoirs that could contribute to community AMR transmission. Wastewater-based epidemiology can serve as a cost-effective complement to traditional clinical surveillance, especially in resource-limited settings.\u003c/p\u003e","manuscriptTitle":"Household Wastewater as a Sentinel for Community-Level Antimicrobial Resistance: A Cross-Sectional Study in Gombe, Nigeria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-07 15:11:42","doi":"10.21203/rs.3.rs-7620506/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-14T11:13:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-14T10:48:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"53713043789286543419155899425400633279","date":"2025-09-26T10:36:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-25T08:41:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"98512700020675022177930328786863180553","date":"2025-09-24T18:08:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-24T13:12:58+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-19T02:11:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-17T06:57:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-16T05:11:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-15T12:15:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"83c638f9-93cb-4ef6-b40a-3dbd79a9e608","owner":[],"postedDate":"October 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":55768504,"name":"Health sciences/Diseases"},{"id":55768505,"name":"Earth and environmental sciences/Environmental sciences"},{"id":55768506,"name":"Biological sciences/Microbiology"}],"tags":[],"updatedAt":"2026-02-09T16:03:24+00:00","versionOfRecord":{"articleIdentity":"rs-7620506","link":"https://doi.org/10.1038/s41598-025-29778-6","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-02-02 15:59:16","publishedOnDateReadable":"February 2nd, 2026"},"versionCreatedAt":"2025-10-07 15:11:42","video":"","vorDoi":"10.1038/s41598-025-29778-6","vorDoiUrl":"https://doi.org/10.1038/s41598-025-29778-6","workflowStages":[]},"version":"v1","identity":"rs-7620506","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7620506","identity":"rs-7620506","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}