Laboratory Epidemiology of Salmonella Infections and Multi-Drug Resistance Profiles in Nigeria: Barriers, Challenges and Proposed Solutions

Laboratory Epidemiology of Salmonella Infections and Multi-Drug Resistance Profiles in Nigeria: Barriers, Challenges and Proposed Solutions | 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 Laboratory Epidemiology of Salmonella Infections and Multi-Drug Resistance Profiles in Nigeria: Barriers, Challenges and Proposed Solutions View ORCID Profile Amjad B.A Banibella , View ORCID Profile Nooruldeen Saad , View ORCID Profile Jafar Eyad , View ORCID Profile Ramadhani Chambuso doi: https://doi.org/10.1101/2025.07.11.25331365 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 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 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 National surveillance data on Salmonella antimicrobial resistance (AMR) and associated laboratory challenges are limited. There is a critical need to identify and address the barriers and challenges to strengthen the national AMR surveillance and policy guide targeted interventions for Salmonella infections in different geographical areas. We investigated multi-laboratory culture records to quantify AMR profiles for Salmonella infections, diagnostic gaps, and challenges in Nigeria. Methods Using a retrospective study, we analysed a total of 84,548 culture results from 26,630 patients across 25 public laboratories participated in the AMR surveillance report from Nigeria. Salmonella species and stool culture positivity rates were compared throughout the 3 years period. Stool sampling gaps were quantified and Salmonella species AMR for key antibiotic classes were assessed. Chi-square test and Wald risk ratios (RR) were used for statistical analysis, a p-value of <0.05 was considered statistically significant. Results Out of 84,548 culture results, a total of 621 Salmonella species were isolated with Salmonella typhi being the most commonly reported species. Stool samples represented only 3% of all collected specimens, yet Salmonella species culture positivity escalated from 64% to 97% (2016 to 2017; RR 1.51, 95% CI 1.37–1.65, p<0.001 ). AMR remained entrenched: trimethoprim– sulfamethoxazole ≥90%, fluoroquinolones ≥69%, and nalidixic acid 91%; cephalosporin resistance climbed from 60% to 88%. We identified a limited stool collection compared to other samples, which impacted identification of Salmonella infections in an endemic area like Nigeria. The key barriers were limited laboratory data integration and lack of One Health surveillance which amplified Salmonella infections AMR threat. Conclusion Limited stool culture and escalating multi-drug resistance jeopardise the empirical therapy for Salmonella infections. Our study offers immediate, scalable interventions to strengthen One-Health Salmonella infections AMR control in Nigeria. Highlights Exceptionally high Salmonella positivity rate in underutilized stool samples. >75% resistance to core antibiotics, fluoroquinolones and cephalosporins threatens empirical Salmonella infections treatment. Limited species-level reporting on Salmonella cultures and low One Health Salmonella surveillance. Novel framework links microbiological gaps to targeted antimicrobial resistance (AMR) containment strategies for Salmonella infections. Significant contribution This work provides a rare, multi-site analysis of Salmonella diagnostic patterns, resistance profiles, and laboratory performance, revealing critical microbiological gaps and underutilized diagnostics potential. It contributes a novel, evidence-driven recommendations framework that integrates species-level data, AMR surveillance, and stewardship strategies, advancing precision microbiology for Salmonella infection control in resource-limited settings. Download figure Open in new tab Introduction Salmonella infections remain as the leading cause of foodborne illnesses globally, with 93.8 million cases of gastroenteritis and approximately 155,000 deaths each year, of which around 80 million cases (85%) are attributed to contaminated food particularly poultry, eggs, pork, and dairy products. 1 – 4 Invasive Salmonella infections present a particular severe form of disease causing roughly 510,000 cases and emerging as a major public health concern in 2021, with the highest disease incidence and disability-adjusted life years (DALYs) concentrated in Sub-Saharan Africa and among infants under one year of age. 5 – 7 The rise of antimicrobial resistance (AMR) in Salmonella infections has become a critical global health concern, with a particularly severe health impact in endemic West African regions like Nigeria. 8 – 11 Previous studies in Salmonella infections show that the whole-genome sequencing for Salmonella infections have revealed a high prevalence of resistance genes. 12 , 13 These resistant genes were among Salmonella isolates from poultry, with fluoroquinolone resistance nearing 70% and widespread detection of extended-spectrum β-lactamase (ESBL) genes. 8 , 11 , 14 , 15 Despite increasing reports of multidrug-resistant Salmonella infections in Nigeria, evidence remains limited on how the gaps in laboratory diagnostic practices can influence the accuracy and completeness of AMR detection and reporting. 16 – 18 Specifically, microbiological gaps such as stool sample collection and culture for Salmonella infections, AMR testing protocols, and specific Salmonella infections policy alignment with the laboratory practices. 19 , 20 There is a critical need to identify and address the barriers and challenges to strengthen the national AMR surveillance and guide targeted interventions for Salmonella infections in different geographical areas. 21 – 24 There are two primary species of concern: Salmonella enterica and Salmonella bongori , with S. enterica being responsible for most human infections. S. enterica is further classified into over 2,500 serotypes, among which Salmonella Typhi is the most notable due to its role in causing typhoid fever, a severe systemic illness often found in areas with inadequate sanitation. 25 – 27 Salmonella is a genus of rod-shaped, Gram-negative bacteria that is a major cause of foodborne illnesses. It belongs to the family Enterobacteriaceae and is primarily known for causing gastrointestinal infections, commonly referred to as salmonellosis. 28 , 29 Salmonella infections drug resistance to frontline treatments such as azithromycin has reached as high as 99.4%, raising concern over treatment options for invasive infections. 30 – 36 This include Salmonella ’s ability to form biofilms, exchange resistance genes through horizontal gene transfer, and withstand environmental stresses. 37 , 38 In this study, we investigated on multi-laboratory culture records to quantify AMR profiles for Salmonella infections, microbiological gaps including stool culture, challenges and suggested for data-driven practical solutions. Methodology Ethics statement This study utilized publicly available, de-identified AMR surveillance data originally collected by the Nigerian Ministry of Health under the Fleming Fund Reginal 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. Study design We conducted a retrospective study of the national AMR surveillance report of Nigeria for the years 2016 to 2018, similar to a published study. 39 This report, published in 2022, was generated through the Mapping Antimicrobial Resistance and Antimicrobial Use Partnership (MAAP) and involved 25 sentinel laboratories across Nigeria with bacteriology testing capacity. The study adheres to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines. 40 Data sources and surveillance framework The original data were collected by the Nigerian health authorities in collaboration with MAAP partners. 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. 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). Inclusion criteria and data management Only Salmonella isolated 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. 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. Data extraction from the report In this study, we manually extracted relevant Salmonella -specific data from the national report, including; (i) Total number of participated laboratories (ii) Collected specimens per year (iii) Source of collected specimens (iv) Specimen type (v) Valid cultures (vi) Positive and negative cultures. (vii) Species-level breakdown ( Salmonella Typhi , S. Paratyphi , S. Enterica , and Salmonella spp.) (viii) AST results for antibiotics tested (ix) Patient demographics (age and sex) These variables were compiled into a structured Microsoft Excel dataset for a convenient data analysis. Among the Salmonella isolates, specific species were identified whenever possible, while others were recorded generically as Salmonella spp. according to how they were recorded in the original report. AMR data were available for multiple antibiotics, allowing the analysis of year-wise AMR trends and identification of the most prevalent resistant antibiotic combinations. AST and quality control AST was conducted using both disk diffusion and minimum inhibitory concentration (MIC) panels, depending on the laboratory’s capacity. The primary interpretive standard applied was the CLSI criteria. Quality control (QC) strains, were widely used across labs for routine QC of AST procedures. External quality assessment (EQA) was implemented across participating laboratories via a structured proficiency testing program, coordinated with the support of MAAP and the Fleming Fund. Laboratories were encouraged to participate in periodic EQA rounds and corrective actions were documented for laboratories with poor performance. However, details on the proportion of laboratories consistently participating in EQA or specific inter-laboratory performance scores were not systematically reported. AMR calculation From the original report, AMR rates in 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: AMR = Antimicrobial Resistance Non-susceptible isolates = Resistant + Intermediate Tested isolates = All isolates subjected to AST Statistical analysis Statistical analyses were conducted using R version 4.3.2. Missing data were <1% and handled via complete-case analysis. We analysed valid culture results using descriptive statistics to compare culture positivity across demographic groups and years. For stool samples, annual Salmonella positivity rates were calculated with 95% Wald confidence intervals (CI), and pairwise risk ratios (RR) with 95% CI were derived using 2016 as the reference year. The Wald log method was employed to estimate RRs, and heterogeneity across years was evaluated using χ² tests. For categorical comparisons (e.g., sex, age group, year), proportions of positive versus negative cultures were assessed using Pearson’s Chi-square test, with statistical significance defined as p < 0.05 . Results Demographics of the total cultures and the characterization of the stool culture samples Out of 84,548 valid cultures analysed, 35.1% of female samples yielded positive results compared to 28.3% in males ( p < 0.0001 ). Age-stratified analysis revealed that positivity rates increased with age, peaking at 40 % in individuals above 65 years, however, 42.5% of all culture samples had unknown age. Over time, a significant upward trend in positivity was observed from 28.5% in 2016 to 33.0% in 2017 and 30.7 in 2018 ( p < 0.0001 ), suggesting improved diagnostic yield. Importantly, AST was performed for over 95% of positive cultures, ensuring robust downstream resistance profiling ( Table 1 ). View this table: View inline View popup Download powerpoint Table 1. Demographics of the total cultures analayzed in the study population In order to quantify the annual Salmonella positivity rates in stool cultures and assess the temporal trends in Salmonella detection from stool samples, we analysed whether laboratory yield improved over time and if outbreak signals were emerging, or if diagnostic protocols required were strengthening. We revealing a substantial and statistically significant rise in Salmonella infections positivity rate from 64.2% in 2016 to over 89% in 2018 (39% higher, p < 0.001 , Table 2 ). View this table: View inline View popup Download powerpoint Table 2. Characterization of stool samples for each year and the Salmonella yield Collected specimens distribution, culture positivity rate, and Salmonella spp profile Further analyses of collected specimen patterns and laboratory performance revealed critical microbiological insights for Salmonella surveillance. Fig 1a shows that while stool specimens were consistently submitted across all years, other diagnostically relevant fluids such as bile and peritoneal aspirates remained underutilized, suggesting sampling blind spots in cases of invasive Salmonella infections. In Fig 1b , laboratory-level culture positivity ranged widely from 6.9% to 67.5%, indicating stark disparities in diagnostic performance that may compromise pathogen recovery, particularly in under-resourced sites. Despite high Salmonella yield, stool samples ranked only sixth among all specimen types ( Fig 1c ), reinforcing the disconnect between clinical practice and the need for stool-based diagnostics. Fig 1d identifies a sharp rise in S. typhi positive cultures in 2017 (n = 44), consistent with a potential outbreak, although species-level resolution for S. enterica and S. paratyphi remained minimal. Blood specimen submissions peaked in 2017 ( Fig 1e ) , coinciding with the rise in typhoid cases in the same year, however, stool for culture did not appear among the top five specimen types, further underscoring diagnostic underutilization. Collectively, these findings point to critical gaps in laboratory practices, specimen prioritization, and species-level identification that must be addressed to enhance Salmonella detection and response across endemic regions, like Nigeria. Download figure Open in new tab Fig 1. Patterns of specimen distribution, culture positivity, and Salmonella spp. profile. ( a ) Distribution of specimen types by year showing all specimens collected. ( b ) Laboratory-wise culture positivity showing wide variability (6.9%–67.5%. ( c ) Top ten clinical specimen types collected, showing stool samples were the sixth most collected ( d ) Yearly Salmonella species distribution shows a notable spike in S. typhi in 2017. ( e ) Trend analysis of the five most collected specimen types reveals a sharp rise and fall in blood sample collection in 2017 whereas stool remained excluded from leading collected specimen categories throughout. AMR surveillance in all clinical isolates and the culture for Salmonella Species The AMR landscape showed a deepening crisis in the treatment of enteric infections. The overall resistance burden across the full clinical dataset shows that several key antibiotic classes such as aminopenicillins, third-generation cephalosporins, and folate pathway inhibitors exhibited resistance rates exceeding 70% ( Fig 2a ). This cross-pathogen resistance profile points to intense selection pressure and uncontained antimicrobial misuse within the system. In contrast, when we focused specifically on Salmonella species AMR, we uncovered a worrying temporal escalation. Between 2016 and 2018, resistance to fluoroquinolones rose sharply from 47% to 69%, while quinolone and first-generation cephalosporin resistance reached 91% and 88%, respectively, by 2018 ( Fig 2b ). Notably, even in 2016, Salmonella isolates already showed near-complete resistance to folate pathway inhibitors (98%) and concerning levels to third-generation cephalosporins (60%) ( Fig 2b ). Download figure Open in new tab Fig 2. AMR surveillance in all clinical isolates and Salmonella Species. ( a ) Presents the overall antimicrobial resistance profile across all clinical pathogens in the dataset, revealing highest resistance rates against folate pathway inhibitors, aminopenicillins, and third-generation cephalosporins. ( b ) Shows specifically on Salmonella species on the marked temporal rise in resistance: fluoroquinolone resistance increased from 47% in 2017 to 69% in 2018 Critical microbiological and surveillance gaps undermining effective Salmonella AMR control To better contextualize the critical microbiological challenges surrounding Salmonella spp. AMR, we summarized key microbiological and surveillance gaps identified. This synthesis analysed six interlinked gaps that, taken together, impede effective detection, treatment, and containment of drug-resistant Salmonella strains. These gaps span between both microbiological and system-level domains, including resistance to key antibiotic classes, under-prioritization of stool sampling, and lack of integrated One Health surveillance ( Fig 3 ). We highlighted what remains neglected in Salmonella AMR response efforts across clinical and public health settings. The observed gaps include: (i) Fluoroquinolone (FQ) resistance (46 – 75 %) Resistance to fluoroquinolones span from 46 % to 75 %, indicating that nearly half to three-quarters of Salmonella isolates tested were non-susceptible to this drug class. (ii) Invasive non-Typhoidal Salmonella (iNTS) in co-Infections A distinct category flags invasive NTS associated with co-infective presentations. This was recorded as a discrete surveillance element, separating iNTS through bloodstream diagnosis from the broader pool of gastrointestinal cases. (iii) Surveillance data gaps There was observed surveillance data gap confirming incomplete or missing laboratory information, specifically, deficiencies in detailed isolate characterisation (e.g., species, serovar, and susceptibility metadata). (iv) Trimethoprim–Sulfamethoxazole (TMP-SMX) resistance (> 90 %) Resistance to TMP-SMX surpasses the 90 % threshold, signifying that fewer than one in ten Salmonella isolates remained susceptible to this long-standing drug. (v) Absence of integrated One-Health surveillance The “One-Health surveillance” was recorded as a gap, denoting that coordinated data capture across human, animal, and environmental sources was insufficiently documented for Salmonella . (vi) Stool sample collection for Salmonella culture and AST Stool sampling was explicitly not in top 5 most collected samples for culture, confirming that, despite its relevance for enteric pathogens, stool was collected less frequently than at least five other specimen categories across the study years ( Fig 3 ). Download figure Open in new tab Fig 3. Analysis of microbiological and surveillance gaps undermining Salmonella AMR control. Six distinct gaps identified for Salmonella spp . Fluoroquinolone resistance was recorded within a range of 46–75%. Invasive non-typhoidal Salmonella (iNTS) associated with co-infections was identified as a separate category of concern. Surveillance data gaps were evident, particularly in species-level reporting. Trimethoprim-sulfamethoxazole (TMP-SMX) resistance exceeded 90%. Limited evidence of integrated One Health surveillance across human, animal, and environmental sectors was observed. Stool sample collection did not appear among the top five most collected specimen types. Microbiological recommendations for strengthening Salmonella AMR control To translate the observed microbiological gaps into actionable priorities, a targeted framework of laboratory-informed recommendations was developed ( Fig 4 ) . This approach systematically maps each challenge ranging from AMR and underdiagnosis to surveillance fragmentation against practical interventions rooted in microbiological best practices. These recommendations aim to improve Salmonella spp. detection, guide treatment decisions, and enhance cross-sectoral AMR surveillance capacity, particularly in high-burden and resource-limited settings. The recommendations are as follows: (i) Integrate stool sample collection into AMR protocols. Routine stool sampling must be embedded into national AMR surveillance protocols, particularly for suspected Salmonella infections. Stool specimen is essential for identifying gastrointestinal Salmonella strains, yet underutilization, previously demonstrated by its absence from the top five specimen types, undermines pathogen recovery. Integration into diagnostic algorithms will improve case detection, enable species-level typing, and ensure more representative resistance profiling. (ii) Enforce stewardship Laws to stop off-label fluoroquinolone use in poultry. Widespread off-label use of fluoroquinolones in poultry contributes significantly to the development of resistant Salmonella strains, which may be transmitted to humans via food or environmental routes. Enforcing antimicrobial stewardship regulations targeting veterinary sectors is crucial to reduce selection pressure and prevent further resistance amplification in zoonotic reservoirs. (iii) Remove TMP-SMX from empiric Salmonella protocols and revise national guidelines. Given the AMR of >90% observed for TMP-SMX, national treatment guidelines should be updated to exclude it from empirical therapy for suspected Salmonella cases. This change is critical to prevent ineffective treatment, mitigate resistance escalation, and redirect clinicians toward evidence-based, susceptibility-guided Salmonella treatment. (iv) Operationalize national AMR surveillance across human, veterinary, and poultry sectors. To capture the full epidemiological context of Salmonella transmission, surveillance must be extended beyond clinical settings to include veterinary and poultry sectors. This recommendation aligns with a One Health framework, ensuring coordinated sampling, data integration, and resistance monitoring across reservoirs that perpetuate resistant Salmonella strains. (v) Develop WHONET-linked Salmonella module covering foodborne diseases AMR. A dedicated Salmonella module within the WHONET platform would enable centralized, standardized tracking of resistance trends in foodborne disease isolates. This module would allow microbiologists to monitor susceptibility shifts in real time, detect emerging resistance phenotypes, and correlate food source data with clinical outcomes. (vi) Integrate iNTS surveillance in national sentinel disease sites. iNTS infections often associated with bloodstream infections in immunocompromised individuals require dedicated monitoring within sentinel sites. Integration of iNTS surveillance into national systems will enable early detection, facilitate targeted intervention, and support the development of diagnostics and vaccines specific to this under-recognized but clinically significant form of Salmonella infections ( Fig 4 ) . Download figure Open in new tab Fig 4. Microbiological challenges and strategic recommendations to strengthen Salmonella AMR control This conceptual framework analysed form the data to illustrate potential nine microbiological and surveillance challenges surrounding Salmonella antimicrobial resistance (red circles), each linked to targeted, actionable recommendations (green squares). This synthesis bridges clinical microbiology, AMR policy, and pathogen ecology, offering a roadmap for strengthening diagnostic prioritization and containment strategies in endemic settings. Discussion We analysed a total of 84,548 valid clinical cultures from Nigeria collected from 2016 to 2018. The specimen isolates distribution for Salmonella infections, detection patterns, AMR profiles, and surveillance gaps were systematically analysed across multiple diagnostic laboratories. We revealed several novel insights: despite the highest Salmonella culture positivity, stool samples remained significantly underutilized; species-level identification was largely absent or under reported; and fluoroquinolone resistance in Salmonella rose sharply, surpassing 69% by 2018. We also identified significant inter-laboratory variation in culture positivity (6.9%–67.5%) and extremely high TMP-SMX resistance (>90%), both previously underreported in regional AMR surveillance. Importantly, we developed a novel microbiological-driven gap analysis and recommendations framework linking diagnostic gaps to targeted interventions, including WHONET integration, empirical therapy revision, and One Health surveillance. This translational approach offers a scalable model for enhancing clinical microbiology for Salmonella control and AMR containment in low-resource, high-burden settings like Nigeria. 41 – 46 These findings underscore a critical disruption in the microbiological ecosystem of Salmonella spp., where diagnostic underperformance and limited AMR evolution are converging. The consistent lack of species-level identification hampers the understanding of circulating Salmonella lineages, masking shifts in virulence and resistance phenotypes that are essential for guiding outbreak responses, vaccine targeting, and phage therapy design. 47 – 50 In addition, the rise of multidrug-resistant Salmonella compromises the utility of both oral and parenteral treatment regimens. This may narrowing therapeutic windows and increase reliance on last-resort agents. This reflects the limited spread of mobile Salmonella genetic elements and clonal expansion of resistant strains, which, if unaddressed, could establish persistent reservoirs across clinical, foodborne, and environmental niches. 35 , 51 Previous literature on Salmonella infections in Nigeria has largely focused on outbreak descriptions, bloodstream infections, or molecular typing in isolated settings, with limited integration of diagnostic workflow data, stool specimen utilization, and real-time AMR patterns. 52 – 59 Our study bridges that gap by revealing the operational disconnect between high-yield diagnostics such as stool cultures and their actual underutilization in routine practice. While previous reports have documented rising fluoroquinolone resistance, our study provides robust temporal and cross-laboratory evidence of its progression, supported by context-specific resistance thresholds across antibiotic classes. 60 Furthermore, the striking absence of species-level Salmonella identification aligns with known challenges in LMIC microbiology but has rarely been quantified across systems. This work adds value by providing the first structured linkage between microbiological failure points and practical, lab-driven interventions, thereby elevating the role of microbiologists from passive reporters to active architects of AMR response and surveillance reform. 61 We exposed hidden microbiological failures in Salmonella infection detection and resistance containment gaps that traditional surveillance studies often overlook. By triangulating specimen practices, pathogen recovery rates, and Salmonella -specific resistance data, we reveal how stool under-sampling, poor species resolution, and inconsistent laboratory performance collectively distort the microbiological landscape of enteric fever caused by Salmonella infections. 62 Unlike previous studies limited to single-centre or outbreak reports, this work unifies fragmented data into an actionable framework, making visible the structural and diagnostic blind spots that perpetuate Salmonella misdiagnosis and AMR progression. 63 This integrative approach provides microbiologists with a blueprint for reforming diagnostic priorities and surveillance architecture in Salmonella endemic regions. 64 , 65 We translated microbiological data into pragmatic interventions that are both context-sensitive and scalable. By aligning laboratory findings with system level diagnostics and policy gaps, our gap analysis study enables targeted improvements such as integrating stool sampling into AMR protocols, eliminating ineffective antibiotics from empirical regimens, and operationalizing One Health surveillance platforms. 66 – 68 Our recommendations are practical and data oriented, derived directly from field-level diagnostic perspectives, ensuring high feasibility and translational relevance. Importantly, the new framework proposed in this study serves not only as a diagnostic audit tool but also as a decision-making scaffold for health ministries, AMR committees, and laboratory networks seeking to enhance Salmonella control through sustainable microbiology-led interventions for resource-limited countries. 11 , 69 , 70 Key strengths of this study include: (i) First to link diagnostic performance with Salmonella treatment gaps by establishing a unique correlation between underdiagnosis, resistance evolution, and policy inaction in endemic contexts. (ii) Large multi-year, multi-site dataset from Salmonella infection endemic region. Analysis of 84,548 valid cultures across multiple diagnostic centres over three years provides robust, generalizable findings. (iii) Microbiologically-focused gap analysis study which identified a novel framework for diagnostic and surveillance blind spots specific to Salmonella infections, including stool specimen prioritization, species identification, and AMR trends. (iv) Time-trend AMR profiling which shown the escalating Salmonella resistance to fluoroquinolones and cephalosporins in a temporal, multi-centre context from a LMIC setting. (v) Actionable laboratory-based recommendations focused on microbiology and directly translated findings into implementable solutions such as empirical treatment revisions, WHONET integration, and One Health coordination in real world. (vi) New systems-level diagnostic insights which integrated specimen workflow data with microbiological outcomes specific for Salmonella infections, exposing variability in stool culture positivity and diagnostic capacity for each year. (vii) Translational relevance to public health by providing microbiological evidence to inform Salmonella infections AMR combating strategies, health ministries, and laboratory networks seeking to enhance Salmonella infections surveillance and treatment policies in limited-resource countries. (viii) Scalable and reproducible methodology which offers a replicable model for other LMICs to evaluate microbiology systems and align laboratory practices with global AMR control strategies for Salmonella infections. Limitations include; (i) MDR profiles were inferred from phenotypic data without confirmatory genotypic or plasmid-based analysis. (ii) Most Salmonella isolates were not identified to species or serovar level, restricting lineage-specific interpretations and outbreak mapping. (iii) Missing patient-level clinical data (e.g., HIV status, antibiotic exposure, comorbidities) limited the ability to link resistance with clinical outcomes. (iv) Inter-laboratory differences in methods and quality assurance may have influenced Salmonella culture positivity and AMR reporting, introducing bias. (v) Despite identifying One Health gaps, the study did not include data from animal or environmental sources, limiting source attribution. (vi) Findings may not fully capture the recent national trends in Nigeria, especially in rural or informal health settings not represented in the dataset or recent years. (vii) Potential underreporting of negative cultures could skew specimen trends for Salmonella . Despite these limitations, our study provides valuable microbiology-focused analysis of Salmonella infections diagnostics and resistance trends in a West-African context. Its strength lies in transforming routine laboratory data into actionable insights and revealing systemic microbiological blind spots previously unreported. The translational framework and scalable recommendations make it a timely and impactful contribution to global AMR response efforts in new microbiological perspectives. Conclusions This study successfully identified critical weaknesses in stool specimen collection, species-level identification for Salmonella infections, and AST that compromise the detection, reporting, and control of Salmonella infections in endemic settings. For Salmonella infections, enhancing stool culture prioritization, enforcing species-level identification, and integrating real-time resistance surveillance (e.g., WHONET-linked modules) are urgent and feasible actions. By linking these diagnostic and surveillance failures with escalating resistance to first-line and second-line antibiotics, our study fills a major gap in contextualizing laboratory performance within the broader AMR crisis for Salmonella infections. Future work should build on this framework to expand One Health AMR monitoring, incorporate molecular diagnostics, and strengthen laboratory networks to translate microbiological data into early warning and containment systems for Salmonella infections and other enteric pathogens. Data Availability All data produced are available online. https://aslm.org/wp-content/uploads/2023/07/AMR_REPORT_NIGERIA.pdf?x89467 Acknowledgments We would like to thank and acknowledge the Mapping Antimicrobial Resistance and Antimicrobial Use Partnership (MAAP) consortium by 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. 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