Antimicrobial Resistance Gap Analysis for Uropathogens in Nigeria: Pathogen Profiles, Resistance Burden, and Translational Microbiological Action Plans

Antimicrobial Resistance Gap Analysis for Uropathogens in Nigeria: Pathogen Profiles, Resistance Burden, and Translational Microbiological Action Plans | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Antimicrobial Resistance Gap Analysis for Uropathogens in Nigeria: Pathogen Profiles, Resistance Burden, and Translational Microbiological Action Plans View ORCID Profile Nooruldeen Saad , View ORCID Profile Jafar Eyad , View ORCID Profile Amjad B.A Banibella , View ORCID Profile Ramadhani Chambuso doi: https://doi.org/10.1101/2025.07.27.25332264 Nooruldeen Saad 1 Pathological Sciences Department, Division of Microbiology, College of Medicine, Ajman University , UAE Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Nooruldeen Saad Jafar Eyad 1 Pathological Sciences Department, Division of Microbiology, College of Medicine, Ajman University , UAE Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jafar Eyad Amjad B.A Banibella 1 Pathological Sciences Department, Division of Microbiology, College of Medicine, Ajman University , UAE Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Amjad B.A Banibella Ramadhani Chambuso 1 Pathological Sciences Department, Division of Microbiology, College of Medicine, Ajman University , UAE 2 Department of Global Health and Population, Harvard T. Chan School of Public Health, Harvard University , USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ramadhani Chambuso For correspondence: r.chambuso{at}ajman.ac.ae Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Background We investigated laboratory culture results for uropathogens with existing antimicrobial resistance (AMR) patterns to leverage novel microbiological gaps in Nigeria. Methods In a retrospective study, we analysed a total of 84,548 valid culture results from 26,630 patients across 25 public laboratories participated in the national AMR report. The WHO priority bacteria uropathogens with their AMR patterns for the key urinary tract infections (UTIs) antibiotic classes were assessed. A p-value of <0.05 was considered statistically significant. Results Urine contributed 6426 samples collected for culture. A total of 16 uropathogens were identified, Escherichia coli was the most frequently isolated organism each year. Among Gram-negatives, Klebsiella species and Escherichia coli showed alarming resistance to cephalosporins (80% each). Staphylococcus aureus demonstrated the highest resistance burden, with methicillin and folate pathway inhibitor resistance reaching 89% and 82%, respectively. In the gap analysis, cephalosporin susceptibility testing remained incomplete and in the mapping of six uropathogens and their recommended stewardship responses, E.coli was recommended to alert the monitoring of tetracycline and folate inhibitors. Conclusion High AMR across key drug classes for uropathogens like Klebsiella, Escherichia and Staphylococcus species suggests a narrowing window for effective treatment of UTIs in Nigeria. Download figure Open in new tab Introduction Urinary tract infections (UTIs) caused by uropathogens are among the most common bacterial infections globally, with over 400 million cases annually and significant recurrence rates.[ 1 ] In West Africa, the burden of UTIs is underestimated due to fragmented diagnostics and underreporting that UTIs may account for over 30% of hospital acquired infections.[ 2 ] In Nigeria, the most populated country in Africa, the local surveillance reports estimate tens of thousands of UTIs each year. UTIs contribute to antimicrobial overuse, hospital readmissions and increased burden of antimicrobial resistance (AMR). AMR has driven repeated empirical treatment failures, especially where diagnostics are limited and treatment faces huge drug resistance.[ 3 ] Previous studies have explored pathogen profiles and AMR trends, however a few have integrated uropathogens, drug classes AMR mapping with WHO priority pathogens for UTIs and the microbiological gaps. Most importantly, there are few national-level microbiology-driven gap analysis studies linking AMR data to actionable treatment policy recommendations in Nigeria. [ 3 , 4 ] AMR in uropathogens is fundamentally a microbiological phenomenon driven by selective pressure, genetic exchange, diagnostic limitations and therapeutic failure. WHO priority bacteria pathogens for UTIs such as Escherichia coli , Klebsiella pneumoniae , Pseudomonas aeruginosa and Enterococcus faecium are frequently the causative agents of UTIs, especially in healthcare-associated infections.[ 5 ] These organisms exhibit complex resistance mechanisms including extended-spectrum β-lactamase (ESBL) production, carbapenemase activity, and biofilm formation. For example E. coli , the most common uropathogen produces fimbriae with which it sticks to the wall of the urinary bladder to produce toxins that destroy bladder cells.[ 6 , 7 ] Also, Klebsiella species have a capsular protective coating layer that shields itself from the host immune system and enables the pathogen to be antibiotic-resistant, especially with beta-lactams.[ 8 , 9 ] The increasing resistance of uropathogens to first-line antimicrobial treatment compromises UTIs therapy and lead to prolonged infection control challenges. This promotes the spread of resistant strains and complicates the management of UTIs which is now a global health problem.[ 10 – 12 ] We hypothesize that specific uropathogens, particularly the WHO priority Gram-negatives have high AMR thresholds across the key UTIs antibiotic classes, yet this remains unlinked to treatment guidelines and health policy action plans for UTIs in Nigeria.[ 13 , 14 ] This gap between laboratory resistance data and empirical treatment practices reflects a critical microbiological disconnect that undermines the effectiveness of AMR stewardship in lower middle-income countries. In this gap analysis study, we analyzed existing national AMR report with laboratory culture results for uropathogens and the existing AMR patterns to leverage novel microbiological insights that can guide national-level evidence-based interventions for UTIs management in Nigeria. Methodology Study design A retrospective study of the national AMR surveillance report from Nigeria for the years 2016 to 2018, similar to a published study. [ 15 ] This published report was generated by the Mapping Antimicrobial Resistance and Antimicrobial Use Partnership (MAAP) consortium. It involved 25 sentinel laboratories across Nigeria with bacteriology testing capacity. This study adheres to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines.[ 16 ] Data sources and surveillance framework The original data were collected by Nigeria health authorities in collaboration with MAAP partners.[ 17 ] Surveillance activities were carried out across a national network of medical laboratories selected for their bacteriology testing capacity. A total of 25 laboratories contributed antimicrobial susceptibility testing (AST) data using WHONET software, a standardized microbiology data management tool.[ 18 ] Data collection involved trained field teams retrieving laboratory records from both paper-based and digital systems. Where feasible, these records were linked with hospital databases to include patient demographics and clinical metadata (e.g., age, sex, specimen type). The surveillance focused on human health and isolates were obtained from urine and other clinical specimens. The uropathogens reported from culture results include: Positive cultures with no specie identification (i) Klebsiella species (ii) Proteus species (iii) Pseudomonas species (iv) Enterococcus species (v) Staphylococcus species Positive cultures with specie identification (i) Klebsiella aerogenes (ii) Klebsiella oxytoca (iii) Klebsiella pneumoniae (iv) Escherichia coli (v) Escherichia fergusonii (vi) Proteus hauseri (vii) Proteus mirabilis (viii) Proteus vulgaris (ix) Pseudomonas aeruginosa (x) Pseudomonas fluorescen (xi) Pseudomonas stutzeri (xii) Enterococcus faecalis (xiii) Staphylococcus aureus Inclusion criteria and data Management Only isolates from the above bacterial groups with valid AST results were included in the analysis. In accordance with WHO Global Antimicrobial Resistance Surveillance System (GLASS) guidelines and Clinical and Laboratory Standards Institute (CLSI) M39-A4 recommendations, only the first isolate per patient per year was retained to avoid duplicate counting.[ 19 , 20 ] In cases where unique patient identifiers were not available, all isolates were retained and interpreted with appropriate caution. AST interpretations followed CLSI criteria, and where necessary, zone diameters or minimum inhibitory concentrations (MICs) were standardized using WHONET’s interpretive rules to ensure consistency across laboratories.[ 21 , 22 ] Data extraction from the report In this study, we manually extracted relevant pathogen-specific data from the national report, including: (i) Total number of participating laboratories (ii) Collected specimens per year (iii) Source of collected specimens (iv) Specimen types (v) Valid cultures (vi) Positive and negative cultures (vii) Species-level breakdown (e.g., E. coli, K. pneumoniae, P. aeruginosa , etc.) (viii) AST results for antibiotics tested (ix) Patient demographics (age and sex) These variables were compiled into a structured Microsoft Excel dataset with multiple spreadsheets for different variables ( Supplementary Datasets ) to enable consistent data analysis. Species were identified where possible, while others were recorded generically as Klebsiella spp., Escherichia spp., Proteus spp., Pseudomonas spp. , or Enterococcus spp ., according to how they appeared in the original report. AMR data were available for multiple antibiotics from antibiograms and figures for the analysis of year-wise resistance trends and identification of the most prevalent resistant antibiotic combinations. AST and quality control AST was conducted using both disk diffusion and MIC panels, depending on each laboratory’s capacity. The primary interpretive standard applied was CLSI criteria. Quality control strains were widely used for routine AST quality control. External Quality Assessment (EQA) was implemented across participating laboratories through a structured proficiency testing program coordinated by MAAP and the Fleming Fund Laboratories were encouraged to participate in periodic EQA rounds, and corrective actions were documented for laboratories with substandard performance.[ 23 , 24 ] However, details on the proportion of laboratories consistently participating in EQA or their inter-laboratory performance were not systematically reported. AMR calculation From the original report, AMR rates were derived from positive cultures with available AST results. AMR rates were calculated as the proportion of non-susceptible isolates (intermediate or resistant) relative to the total number of tested isolates within a single calendar year. Where: Non-susceptible isolates = Resistant + Intermediate Tested isolates = All isolates subjected to AST Statistical analysis Statistical analyses and figure visualizations were peformed using R version 4.3.2 and Python version 3.11. Missing data were about <1% and handled via complete-case analysis. We analysed valid culture results using descriptive statistics to compare culture positivity across demographic groups and years. Annual culture positivity rates were calculated with 95% Wald confidence intervals (CI). The heterogeneity across years was evaluated using Chi-square test. For categorical comparisons (e.g., sex, age group, year), the proportions for culture positivity were calculated using Pearson’s Chi-square test. A p-value of <0.05 was considered statistically significant.[ 25 , 26 ] Results Demographics and culture characteristics Among 84,548 valid cultures analysed, the majority were obtained from female patients (55.6%), who also exhibited a significantly higher culture positivity rate compared to males (35.1% vs 28.3%, respectively ( p65 years. Although the largest number of cultures originated from adults aged 18–49 years, their positivity rate was moderate (31.8%) compared with older groups. Over the three-year period, culture positivity rose from 28.5% in 2016 to 34.6% in 2018, suggesting either improved diagnostic targeting or a true increase in uropathogens burden. View this table: View inline View popup Download powerpoint Table 1. Distribution of valid microbiological cultures by gender, age group, and year Diagnostic yield, specimen distribution and temporal trends of uropathogens across surveillance laboratories Across the 25 sentinel laboratories surveyed, marked heterogeneity was observed in the microbiological diagnostic yield and reporting completeness of cultures ( Fig 1a ). While most laboratories reported a predominance of negative cultures, a concerning proportion of positive cultures lacked antimicrobial susceptibility testing (AST), particularly at Murtala Muhammad, and Minna laboratories. Only a few centres consistently linked culture positivity with AST reporting. Among the top ten leading specimen types, urine was the most frequently processed specimen across all years, peaking in 2018 ( Fig. b) . A total of 16 uropathogens were identified between 2016 and 2018, Escherichia coli was the most frequently isolated organism each year ( Fig 1c ). Klebsiella species isolates rose from 922 in 2016 to 1245 in 2018. Among species-level identifications, Klebsiella pneumoniae peaked in 2017 (851 isolates) before declining in 2018. Other frequently reported pathogens included Pseudomonas aeruginosa , Enterococcus species and Proteus mirabilis , while Escherichia fergusonii , Pseudomonas stutzeri , and Proteus hauseri were rarely detected. Download figure Open in new tab Fig 1. Laboratory diagnostic yield, specimen distribution, and temporal trends. (a) Laboratory-level distribution of valid urine cultures categorized by diagnostic yield with inter-laboratory variability, several sites showing high proportions of culture-positive specimens but lacking AST data. (b) Bar chart showing the top 10 specimen types processed annually. Urine remained the most common collected specimen type across all years. (c) Heatmap of year-wise isolate counts for key uropathogens. Escherichia coli consistently ranked as the leading pathogen, with counts declining slightly over the three years. Yearly AMR patterns across drug classes and specific uropathogen species Given the clinical reliance on empiric therapy for uropathogens in low-resource settings, and to understand the AMR patterns across drug classes and bacterial species, we performed a comparative AMR gradient, highlighting the proportion of AMR by pathogen–drug class combinations for year 2016 ( Fig 2 ). Among Gram-negatives, Klebsiella spp . and Escherichia coli showed alarming resistance to cephalosporins (80% each). Proteus spp. also exhibited striking resistance to folate pathway inhibitors (91%), a concern for empirical treatment in urinary infections. Interestingly, there was no vancomycin resistance to any pathogens. Notably, Staphylococcus aureus and all other Staphylococcus species displayed resistance above 80% to methicillin, reinforcing MRSA prevalence. Download figure Open in new tab Fig 2. Resistance gradient of priority uropathogens across antimicrobial classes. As expected, extensive resistance to methicillin was observed. Proteus species showed 91% resistance to folate pathway inhibitors. Resistance to carbapenems, vancomycin and aminoglycosides remained the lowest across uropathogens. Red-outlined bubbles denote AMR ≥80%, and gradients reflect urgent therapeutic constraints for UTIs. Note : Red-bordered bubbles indicate resistance rates ≥80%. In 2017, Klebsiella aerogenes exhibited the highest resistance levels, with 91% to second-generation cephalosporins and 83% to quinolones, reflecting potent β-lactam and fluoroquinolone resistance ( Fig 3 ). Escherichia coli and Streptococcus pneumonia showed elevated resistance to multiple β-lactam classes, including tetracyclines (88% and 81%, respectively), indicating cross-resistance trends. Resistance among Klebsiella and Staphylococcus species remained moderate but widespread across all tested drug classes, suggesting intrinsic resistance.[ 27 – 29 ] Download figure Open in new tab Fig 3. Resistance gradient of priority uropathogens to diverse antimicrobial classes Klebsiella aerogenes showed the most alarming resistance, with 83% and 91% to quinolones and 2nd-generation cephalosporins, respectively. Escherichia coli also exhibited high resistance to tetracyclines, reaching 88% AMR in multiple classes. Interestingly, Pseudomonas aeruginosa displayed relatively lower AMR rates. Note : Black-bordered bubbles indicate resistance rates ≥80%. In 2018, Staphylococcus aureus demonstrated the highest resistance burden, with methicillin and folate pathway inhibitor resistance reaching 89% and 82%, respectively ( Fig 4 ). Pseudomonas species and Escherichia coli also showed striking resistance patterns, particularly to tetracyclines and folate pathway inhibitors (86% and 85%, respectively), highlighting ongoing challenges in treating Gram-negative infections. High resistance to first-generation cephalosporins among Staphylococcus species and Escherichia coli points to diminishing empirical coverage. Download figure Open in new tab Fig 4. AMR gradients across uropathogens and drug classes Staphylococcus aureus and Escherichia coli showed the highest resistance to folate pathway inhibitors (89% and 85%, respectively), to and Pseudomonas species demonstrated concerning resistance to tetracycline. Notably, Klebsiella species had 80% resistance to third-generation cephalosporins. Note : Red-bordered bubbles indicate resistance rates ≥80%. AMR analysis for uropathogens to address critical microbiology-related gaps Understanding AMR gaps in uropathogens is essential for guiding actionable stewardship interventions. We identified six critical microbiology-related gaps that compromise the clinical utility of antibiograms and local resistance mapping ( Fig 5 ). Of concern is the widespread absence of molecular AMR gene surveillance and routine ESBL gene panel testing which are both essential for detecting emerging resistance before phenotypic failure. Despite high prevalence, cephalosporin susceptibility testing remains incomplete. Furthermore, the lack of feedback loops to antimicrobial stewardship programme (ASP) teams and the continued reliance on non–data-guided empirical treatment protocols perpetuate inappropriate prescribing. Download figure Open in new tab Fig 5. Visualizing AMR gaps in the management of UTIs. Poor diagnostic capacity (e.g., lack of molecular AMR gene surveillance and absence of routine ESBL testing), surveillance deficits (e.g., no feedback loops to antimicrobial stewardship programs), and treatment risks (e.g., rising carbapenem resistance and empiric therapies unaligned with local resistance data) are highlighted. These gaps represent a critical barrier to effective UTIs management, delaying appropriate therapy, increasing resistance selection pressure and undermining infection control efforts. Addressing identified pathogen-specific challenges for uropathogens Understanding how laboratory evidence of AMR translates into actionable clinical policy is a critical gap in surveillance-response frameworks across low-resource settings. Despite increasing availability of pathogen-specific AMR data, translation into stewardship interventions remains fragmented, especially for uropathogens with multidrug resistance profiles. To address that, we performed a structured pathogen–recommendation linkage analysis, visually synthesizing resistance challenges for uropathogens and the targeted recommended policy responses to support more actionable UTIs guidelines ( Fig 6 ). This approach offers a novel visual policy–microbiology interface that highlights missed opportunities, redundancy, and priority action points within national AMR plans. A map linking six uropathogens and their recommended stewardship responses show that pathogens with the highest resistance burden such as Staphylococcus species (Folate pathway inhibitors 89%, Fig 4 ) were recommended to update the treatment guidelines and perform regional antibiogram alerts and E.coli was recommended to alert the monitoring of tetracycline and folate inhibitors. Recurrent linkages, such as multiplex PCR and species-level identification for Streptococcus and restrict for cephalosporins, fluoroquinolones and third-generation cephalosporins empirically for Klebsiella species reveal diagnostic infrastructure and AMR stewardship gaps. Download figure Open in new tab Fig 6. Pathogen-specific AMR recommendations for each uropathogen. Arcs dash lines connect each pathogen to its associated recommendation, with arc colour uniquely mapped to specific pathogen. E.coli was recommended to alert the monitoring of tetracycline and folate inhibitors while Pseudomonas species recommended to include guidance on excluding fluoroquinolones from empiric protocols and enhancing national surveillance, while Streptococcus species were recommended to enhance species identification and multiplex PCR. This mapping supports targeted AMR mitigation and informs evidence-based updates to UTIs treatment policies. Discussion To our knowledge, this is the largest multicentre AMR surveillance study analyzed over 84,000 valid clinical cultures collected from 2016 to 2018 across 25 laboratories in Nigeria and providing a comprehensive gap analysis assessment of uropathogen resistance patterns and microbiology-related gaps. Focusing on the cultures of uropathogens, we systematically mapped annual AMR trends, pathogen-specific burdens and diagnostic stewardship deficits. Also, we provided novel insights include the integration of resistance gradient visualizations, pathogen-to-policy linkage maps and critical microbiological gap analyses tool rarely used in side by side in tandem microbiology studies. Our findings are significant for global health microbiology as they expose the urgent need to align local diagnostic practices and AMR data to reduce inappropriate prescribing and improve UTIs outcomes. This study reveals critical diagnostic and therapeutic vulnerabilities in uropathogens management in Nigeria. High resistance rates to key UTIs antibiotics, particularly folate pathway inhibitors and cephalosporins, reflect both selective pressure from empirical prescribing and limited access to updated diagnostics.[ 30 , 31 ] The underutilization of AST and molecular tools compromises the interpretive power of positive cultures led to ineffective treatment decisions and delayed containment of resistant strains. In addition, the disconnect between laboratory performance, AMR burden and clinical policy as evidenced by the absence of actionable stewardship linkages in the gap analysis, highlights a systemic failure in translating laboratory evidence into UTIs treatment guideline updates, reinforcing the importance of integrated AMR surveillance to laboratory policy frameworks.[ 32 , 33 ] Findings in this study offer immediate translational potential for strengthening Nigeria’s microbiology-informed health systems response to AMR in uropathogens. By mapping specific pathogen–antibiotic resistance profiles to existing actionable stewardship and recommendations, our study lays the groundwork for evidence-based intervention to UTIs treatment guidelines and antibiogram development in LMICs. The identification of diagnostic gaps, such as incomplete AST and lack of molecular resistance surveillance, emphasizes the urgent priorities for capacity-building in clinical microbiology laboratories.[ 34 , 35 ] Embedding these insights into the national AMR action plans specifically for UTIs can enhance therapeutic accuracy, support rational antibiotic use, and ultimately reduce resistance selection pressure, aligning Nigeria’s response with global targets for microbiology-driven AMR containment. The high methicillin resistance among Staphylococcus species (>80%) observed in this study was higher than reported in previous UTIs-AMR reports from Nigeria.[ 36 , 37 ] However, these two studies lacked gap analysis perspective compared to our study which provides a comprehensive 3-year, multi-centre gap analysis perspective with pathogen-level AMR stewardship mapping. Also, this discrepancy could be possibly due to methodological differences or sampling biases. Regional variations in resistance rates may also reflect disparities in laboratory diagnostic capacities, antibiotic use policies or infection control implementation across settings. Focusing on a global health microbiology perspective, our findings significantly contribute to the understanding of AMR in uropathogens within high-burden regions for UTIs like Nigeria.[ 38 ] The alarmingly high resistance rates to frontline antibiotics mirrored trends reported in global surveillance networks like GLASS, emphasising the universal threat posed by resistant uropathogens.[ 39 , 40 ] However, our data uniquely provide granular, pathogen-specific resistance and stewardship gaps in a West African setting, a region often absent in global meta-analyses for uropathogens and AMR. The study’s structured pathogen–policy mapping approach offers a reproducible model for integrating microbiological evidence into AMR containment strategies in Africa in general, aligning with WHO’s One Health approach framework.[ 41 , 42 ] These findings not only fill a geographic data void but also provide actionable new insights to inform global AMR policy, diagnostics development, and stewardship interventions for UTIs tailored for low-resource settings.[ 43 ] The strengths of this study include: (i) Large sample size of total cultures analyzed, providing robust statistical power and generalizability for UTIs cultures. (ii) Gap analysis study to expose the hidden Pathogen–drug class resistance patterns, allowing actionable clinical interpretation. (iii) Inclusion of data from three consecutive years (2016–2018) enabled to analyse the trends of AMR evolution in uropathogens. (iv) Focused on WHO priority bacteria uropathogens, enhancing relevance to global microbiology and AMR surveillance goals. (v) Identification of diagnostic gaps highlighted critical microbiology weaknesses such as lack of ESBL testing and poor species-level identification of uropathogens. (vi) Visualization of laboratory/AMR policy with microbiological recommendations, introduce a novel visual mapping of uropathogen specific challenges and the suggested actionable solutions. (vii) The use of public reports to analyse exist and missing gaps to strengthen treatment outcomes in a global health perspective is innovative. The potential weaknesses of this study may include: (i) Our study did not correlate the AMR patterns with patient-level clinical outcomes such as treatment failure, complications or mortality. (ii) Absence of urine-specific culture stratification instead total cultures were used without exclusive disaggregation for confirmed urine specimens. (iii) No molecular diagnostics with genotypic AMR confirmation e.g., ESBL or carbapenemase gene detection. (iv) AST testing was not uniformly reported across all drug–pathogen pairs, we focused only on the WHO priority bacterial pathogens. (v) Several genera data (e.g., Streptococcus , Enterococcus ) lacked precise species identification, limiting all pathogen-specific interpretation. Despite these limitations, our study remains a critical contribution to global AMR surveillance by providing one of the largest multi-year laboratory-based datasets on uropathogens in Nigeria, offering valuable empirical evidence of resistance gradients and treatment failure threats. The scale, temporal scope, and organism-specific analysis bridge significant knowledge gaps in regional AMR epidemiology, particularly for WHO priority bacterial pathogens like Klebsiella , E. coli , and Pseudomonas . Our findings deliver actionable novel insights into antimicrobial policy revisions, reinforcing the urgency of laboratory strengthening, and create a foundation for more targeted stewardship interventions. This evidence base is pivotal for aligning Nigerian microbiology practices with global AMR containment strategies. Conclusions High AMR across key drug classes for uropathogens like K lebsiella, Escherichia coli and Staphylococcus species suggests a narrowing window for effective treatment of UTIs in Nigeria. Our data offer a timely, actionable microbiological framework to support diagnostic stewardship, inform empiric treatment updates and guide national AMR containment policies in this region. Translationally, these results emphasize the need for enhanced AMR surveillance, strengthening molecular diagnostics capacity and feedback systems for uropathogens within Nigeria’s microbiology infrastructure. Future perspectives include embedding these new insights into regional and global AMR strategies to strengthen UTIs management and antimicrobial stewardship. Data Availability Data is available as a Supplementary Material https://aslm.org/wp-content/uploads/2023/07/AMR_REPORT_NIGERIA.pdf?x89467 Declaration of Interest Statement ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☒ The author is an Editorial Board Member/Editor-in-Chief/Associate Editor/Guest Editor for this journal and was not involved in the editorial review or the decision to publish this article. ☒ The authors declare no financial interests/personal relationships which may be considered as potential competing interests. Funding Source This study received no specific grant from any funding agencies in the public, commercial or non-profit sectors. Ethics statement This study utilized publicly available, de-identified AMR surveillance data originally collected by the Nigerian Ministry of Health under the Fleming Fund Regional Grant (Phase 1, https://aslm.org/wp-content/uploads/2023/07/AMR_REPORT_NIGERIA.pdf?x89467 ). No individual-level data were accessed. Secondary data use complied with the Declaration of Helsinki and did not require additional ethical clearance. Acknowledgments We would like to thank and acknowledge the Mapping Antimicrobial Resistance and Antimicrobial Use Partnership (MAAP) consortium for enhancing global health research by facilitating the public access of all national AMR surveillance reports, including the Nigerian AMR report which was the source of our data collection. We also appreciate the contributions of Nigeria Federal Ministry of Health, the Fleming Fund Country Grant (Phase 1) team, national AMR coordinators, microbiologists, and data managers who ensured quality-assured reporting across surveillance sites in the report. References [1]. ↵ Barron M. Why Do Urinary Tract Infections (UTI) Recur? 2024 . American Society for Microbiology ; https://asm.org/articles/2024/may/why-urinary-tract-infections-uti-recur . [2]. ↵ Melariri H , Freercks R , van der Merwe E , Ham-Baloyi WT , Oyedele O , Murphy RA , et al. The burden of hospital-acquired infections (HAI) in sub-Saharan Africa: a systematic review and meta-analysis . eClinicalMedicine . 2024 ; 71 . doi: 10.1016/j.eclinm.2024.102571 . OpenUrl CrossRef [3]. ↵ NCDC . ANTIMICROBIAL RESISTANCE . In: Prevention NCfDCa , editor. Nigeria CDC 2023 . https://ncdc.gov.ng/diseases/factsheet/70#:∼:text=%C3%A2%E2%82%AC%C2%A2%20Picture%20of%20AMR%20we%20are%20getting,were%2020%2D30%25%20while%20ESBL%20rates%20were%2060%2D80%25. [4]. ↵ Ajani TA , Elikwu CJ , Nwadike VU , Tayo B , Shonekan OA , Okangba CC , et al. Urinary tract infections and antimicrobial susceptibility pattern among female students in a tertiary institution in southwest Nigeria - A cross sectional study . Journal of Clinical Sciences . 2020 ; 17 ( 4 ). https://journals.lww.com/jocs/fulltext/2020/17040/urinary_tract_infections_and_antimicrobial.9.aspx . [5]. ↵ Bayaba S , Founou RC , Tchouangueu FT , Dimani BD , Mafo LD , Nkengkana OA , et al. High prevalence of multidrug resistant and extended-spectrum β-lactamase-producing Escherichia coli and Klebsiella pneumoniae isolated from urinary tract infections in the West region, Cameroon . BMC infectious diseases . 2025 ; 25 ( 1 ): 115 . doi: 10.1186/s12879-025-10483-8 . OpenUrl CrossRef PubMed [6]. ↵ Shrestha R , Khanal S , Poudel P , Khadayat K , Ghaju S , Bhandari A , et al. Extended spectrum β-lactamase producing uropathogenic Escherichia coli and the correlation of biofilm with antibiotics resistance in Nepal . Ann Clin Microbiol Antimicrob . 2019 ; 18 ( 1 ): 42 . doi: 10.1186/s12941-019-0340-y . OpenUrl CrossRef PubMed [7]. ↵ Nasrollahian S , Graham JP , Halaji M . A review of the mechanisms that confer antibiotic resistance in pathotypes of E. coli . Front Cell Infect Microbiol . 2024 ; 14 : 1387497 . doi: 10.3389/fcimb.2024.1387497 . OpenUrl CrossRef [8]. ↵ Liu X , Xu Q , Yang X , Heng H , Yang C , Yang G , et al. Capsular polysaccharide enables Klebsiella pneumoniae to evade phagocytosis by blocking host-bacteria interactions . mBio . 2025 ; 16 ( 3 ): e0383824 . doi: 10.1128/mbio.03838-24 . OpenUrl CrossRef PubMed [9]. ↵ Xu L , Li J , Wu W , Wu X , Ren J . Klebsiella pneumoniae capsular polysaccharide: Mechanism in regulation of synthesis, virulence, and pathogenicity . Virulence . 2024 ; 15 ( 1 ): 2439509 . doi: 10.1080/21505594.2024.2439509 . OpenUrl CrossRef PubMed [10]. ↵ Mancuso G , Midiri A , Gerace E , Marra M , Zummo S , Biondo C . Urinary Tract Infections: The Current Scenario and Future Prospects . Pathogens . 2023 ; 12 ( 4 ). doi: 10.3390/pathogens12040623 . OpenUrl CrossRef PubMed [11]. A. Abdalla B , A. Anwar K , S. Fakhraldden S , Sardar Saida B , Bapir R , M. Ahmed S , et al. Decoding urinary tract infections: pathogen profiles and antimicrobial resistance in a cross-sectional study . African Journal of Urology . 2025 ; 31 ( 1 ): 37 . doi: 10.1186/s12301-025-00508-8 . OpenUrl CrossRef [12]. ↵ Zhou Y , Zhou Z , Zheng L , Gong Z , Li Y , Jin Y , et al. Urinary Tract Infections Caused by Uropathogenic Escherichia coli: Mechanisms of Infection and Treatment Options . International journal of molecular sciences . 2023 ; 24 ( 13 ). doi: 10.3390/ijms241310537 . OpenUrl CrossRef [13]. ↵ Carlotta N , Nitin M , Dave B , Bhaskar Kumar S. Gram negative bacteria related urinary tract infections: spectrum of antimicrobial resistance over 9 years in a University tertiary referral Hospital . Ther Adv Infect Dis . 2024 ; 11 : 20499361241228342 . doi: 10.1177/20499361241228342 . OpenUrl CrossRef PubMed [14]. ↵ Nedbal C , Mahobia N , Browning D , Somani BK . Variability in Profiles and Prevalences of Gram-Negative Bacteria in Urinary Tract Infections: A Population-Based Analysis . J Clin Med . 2024 ; 13 ( 17 ). doi: 10.3390/jcm13175311 . OpenUrl CrossRef [15]. ↵ Osena G , Kapoor G , Kalanxhi E , Ouassa T , Shumba E , Brar S , et al. Antimicrobial resistance in Africa: A retrospective analysis of data from 14 countries, 2016–2019 . PLOS Medicine . 2025 ; 22 ( 6 ): e1004638 . doi: 10.1371/journal.pmed.1004638 . OpenUrl CrossRef [16]. ↵ Skrivankova VW , Richmond RC , Woolf BAR , Yarmolinsky J , Davies NM , Swanson SA , et al. Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization: The STROBE-MR Statement . JAMA : the journal of the American Medical Association . 2021 ; 326 ( 16 ): 1614 – 21 . doi: 10.1001/jama.2021.18236 . OpenUrl CrossRef PubMed [17]. ↵ MAAP . ANTIMICROBIAL RESISTANCE SURVEILLANCE GUIDANCE FOR THE AFRICAN REGION . 2024 . https://aslm.org/wp-content/uploads/2024/10/Antimicrobial-Resistance-Surveillance-Guidance.pdf . [18]. ↵ Dikkatwar M , Vaghasiya J . Development and application of WHONET software in hospital antibiogram . Clinical Epidemiology and Global Health . 2023 ; 24 : 101424 . doi: 10.1016/j.cegh.2023.101424 . OpenUrl CrossRef [19]. ↵ Pierce VM , Mathers AJ . Setting Antimicrobial Susceptibility Testing Breakpoints: A Primer for Pediatric Infectious Diseases Specialists on the Clinical and Laboratory Standards Institute Approach . J Pediatric Infect Dis Soc . 2022 ; 11 ( 2 ): 73 – 80 . doi: 10.1093/jpids/piab106 . OpenUrl CrossRef PubMed [20]. ↵ WHO . Global antimicrobial resistance and use surveillance system (GLASS) report: 2022 . 2022 . https://www.who.int/publications/i/item/9789240062702 . [21]. ↵ Banibella ABA , Saad N , Eyad J , Chambuso R . Laboratory Epidemiology of Salmonella Infections and Multi-Drug Resistance Profiles in Nigeria: Barriers, Challenges and Proposed Solutions . medRxiv . 2025 10.1101/2025.07.11.25331365:2025.07.11.25331365. doi: 10.1101/2025.07.11.25331365 . OpenUrl Abstract / FREE Full Text [22]. ↵ Kowalska-Krochmal B , Dudek-Wicher R . The Minimum Inhibitory Concentration of Antibiotics: Methods, Interpretation, Clinical Relevance . Pathogens . 2021 ; 10 ( 2 ). doi: 10.3390/pathogens10020165 . OpenUrl CrossRef PubMed [23]. ↵ Kostyanev T , Al-Mir H , Prifti K , Rasmussen LB , Abegaz FA , Teixeira Dos Santos P , et al. External quality assurance (EQA) network in South and South-East Asia: experience and results from an international EQA programme in One Health sector reference laboratories . J Antimicrob Chemother . 2025 ; 80 ( 4 ): 1037 – 46 . doi: 10.1093/jac/dkaf032 . OpenUrl CrossRef PubMed [24]. ↵ Osena G , Kapoor G , Kalanxhi E , Ouassa T , Shumba E , Brar S , et al. Antimicrobial resistance in Africa: A retrospective analysis of data from 14 countries, 2016-2019 . PLoS Med . 2025 ; 22 ( 6 ): e1004638 . doi: 10.1371/journal.pmed.1004638 . OpenUrl CrossRef [25]. ↵ Vetter TR . Descriptive Statistics: Reporting the Answers to the 5 Basic Questions of Who, What, Why, When, Where, and a Sixth, So What? Anesthesia and analgesia . 2017 ; 125 ( 5 ): 1797 – 802 . doi: 10.1213/ane.0000000000002471 . OpenUrl CrossRef PubMed [26]. ↵ Erdoğan S , Gülhan OT . Alternative Confidence Interval Methods Used in the Diagnostic Accuracy Studies . Comput Math Methods Med . 2016 ; 2016 : 7141050 . doi: 10.1155/2016/7141050 . OpenUrl CrossRef PubMed [27]. ↵ Ballén V , Gabasa Y , Ratia C , Ortega R , Tejero M , Soto S . Antibiotic Resistance and Virulence Profiles of Klebsiella pneumoniae Strains Isolated From Diwerent Clinical Sources . Frontiers in Cellular and Infection Microbiology . 2021 ;Volume 11 - 2021. doi: 10.3389/fcimb.2021.738223 . OpenUrl CrossRef [28]. Gebremeskel L , Teklu T , Kasahun GG , Tuem KB . Antimicrobial resistance pattern of Klebsiella isolated from various clinical samples in Ethiopia: a systematic review and meta-analysis . BMC infectious diseases . 2023 ; 23 ( 1 ): 643 . doi: 10.1186/s12879-023-08633-x . OpenUrl CrossRef PubMed [29]. ↵ Barman S , Kurnaz LB , Leighton R , Hossain MW , Decho AW , Tang C . Intrinsic antimicrobial resistance: Molecular biomaterials to combat microbial biofilms and bacterial persisters . Biomaterials . 2024 ; 311 : 122690 . doi: 10.1016/j.biomaterials.2024.122690 . OpenUrl CrossRef PubMed [30]. ↵ Mareș C , Petca RC , Popescu RI , Petca A , Geavlete BF , Jinga V. Uropathogens’ Antibiotic Resistance Evolution in a Female Population: A Sequential Multi-Year Comparative Analysis . Antibiotics (Basel) . 2023 ; 12 ( 6 ). doi: 10.3390/antibiotics12060948 . OpenUrl CrossRef [31]. ↵ Benea A , Turaiche M , Rosca O , Hogea E , Suba MI , Varga NI , et al. Comparative Assessment of Lower Urinary Tract Infections in Hospitalized Adults from Western Romania: A Retrospective Cohort with Microbiological Analysis . Microorganisms . 2025 ; 13 ( 5 ). doi: 10.3390/microorganisms13051130 . OpenUrl CrossRef [32]. ↵ Rodriguez B , Prinzi AM , Hill BK , Tibbetts R , Salazar H , McAdams D , et al. Uniting disciplines against antimicrobial resistance (AMR): highlights from a multidisciplinary inaugural AMR summit . Antimicrob Steward Healthc Epidemiol . 2025 ; 5 ( 1 ): e149 . doi: 10.1017/ash.2025.10039 . OpenUrl CrossRef [33]. ↵ Mao S , Soputhy C , Lay S , Jacobs J , Ku GM , Chau D , et al. The barriers and facilitators of implementing a national laboratory-based AMR surveillance system in Cambodia: key informants’ perspectives and assessments of microbiology laboratories . Frontiers in Public Health . 2023 ;Volume 11 - 2023. doi: 10.3389/fpubh.2023.1332423 . OpenUrl CrossRef PubMed [34]. ↵ Miller JM , Binnicker MJ , Campbell S , Carroll KC , Chapin KC , Gilligan PH , et al. A Guide to Utilization of the Microbiology Laboratory for Diagnosis of Infectious Diseases: 2018 Update by the Infectious Diseases Society of America and the American Society for Microbiology . Clinical infectious diseases : an owicial publication of the Infectious Diseases Society of America . 2018 ; 67 ( 6 ): e1 – e94 . doi: 10.1093/cid/ciy381 . OpenUrl CrossRef PubMed [35]. ↵ Miller JM , Binnicker MJ , Campbell S , Carroll KC , Chapin KC , Gonzalez MD , et al. Guide to Utilization of the Microbiology Laboratory for Diagnosis of Infectious Diseases: 2024 Update by the Infectious Diseases Society of America (IDSA) and the American Society for Microbiology (ASM) . Clinical infectious diseases : an owicial publication of the Infectious Diseases Society of America . 2024 10.1093/cid/ciae104. doi: 10.1093/cid/ciae104 . OpenUrl CrossRef PubMed [36]. ↵ Ezeh CK , Eze CN , Dibua MEU , Emencheta SC . A meta-analysis on the prevalence of resistance of Staphylococcus aureus to diwerent antibiotics in Nigeria . Antimicrob Resist Infect Control . 2023 ; 12 ( 1 ): 40 . doi: 10.1186/s13756-023-01243-x . OpenUrl CrossRef PubMed [37]. ↵ Ike B , Ugwu MC , Ikegbunam MN , Nwobodo D , Ejikeugwu C , Gugu T , et al. Prevalence, Antibiogram and Molecular Characterization of Comunity-Acquired Methicillin-Resistant Staphylococcus Aureus in AWKA, Anambra Nigeria . Open Microbiol J . 2016 ; 10 : 211 – 21 . doi: 10.2174/1874285801610010211 . OpenUrl CrossRef PubMed [38]. ↵ Ogunleye OO , Oyawole MR , Odunuga PT , Kalejaye F , Yinka-Ogunleye AF , Olalekan A , et al. A multicentre point prevalence study of antibiotics utilization in hospitalized patients in an urban secondary and a tertiary healthcare facilities in Nigeria: findings and implications . Expert Review of Anti-infective Therapy . 2022 ; 20 ( 2 ): 297 – 306 . doi: 10.1080/14787210.2021.1941870 . OpenUrl CrossRef PubMed [39]. ↵ Ajulo S , Awosile B . Global antimicrobial resistance and use surveillance system (GLASS 2022): Investigating the relationship between antimicrobial resistance and antimicrobial consumption data across the participating countries . PloS one . 2024 ; 19 ( 2 ): e0297921 . doi: 10.1371/journal.pone.0297921 . OpenUrl CrossRef PubMed [40]. ↵ Abdullah S , Rahman SU , Muhammad F , Mohsin M . Association between antimicrobial consumption and resistance rate of Escherichia coli in hospital settings . J Appl Microbiol . 2023 ; 134 ( 1 ). doi: 10.1093/jambio/lxac003 . OpenUrl CrossRef [41]. ↵ Gulumbe BH , Haruna UA , Almazan J , Ibrahim IH , Faggo AA , Bazata AY . Combating the menace of antimicrobial resistance in Africa: a review on stewardship, surveillance and diagnostic strategies . Biol Proced Online . 2022 ; 24 ( 1 ): 19 . doi: 10.1186/s12575-022-00182-y . OpenUrl CrossRef PubMed [42]. ↵ Cella E , Giovanetti M , Benedetti F , Scarpa F , Johnston C , Borsetti A , et al. Joining Forces against Antibiotic Resistance: The One Health Solution . Pathogens . 2023 ; 12 ( 9 ). doi: 10.3390/pathogens12091074 . OpenUrl CrossRef [43]. ↵ Luong Nguyen T , Didier W , Mats M , Peter Søgaard J . ’When global health meets global goals’: assessing the alignment between antimicrobial resistance and sustainable development policies in 10 African and Asian countries . BMJ Global Health . 2025 ; 10 ( 3 ): e017837 . doi: 10.1136/bmjgh-2024-017837 . OpenUrl Abstract / FREE Full Text View the discussion thread. Back to top Previous Next Posted July 27, 2025. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. 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