S YS T E M AT I C R E V I E W Open Access © The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit ​h​t​​​​t​p​:​/​/​c​r​e​​a​​​t​i​ v​e​​c​​o​​m​​m​​o​n​s​.​o​r​g​/​l​i​c​e​n​s​e​s​/​b​y​-​n​c​-​n​d​/​4​.​0​/​​​​​.​​​ Somda et al. BMC Medical Genomics (2024) 17:267 https://doi.org/10.1186/s12920-024-02043-x BMC Medical Genomics *Correspondence: Eric S. Donkor esampane-donkor@ug.edu.gh 1Department of Medical Microbiology, University of Ghana Medical School, Korle Bu, P.O. Box KB 4236, Accra, Ghana Abstract Background  In Africa, the problem of carbapenem-resistant Enterobacteriaceae (CRE) is aggravated by many factors. This systematic review attempted to describe the current status of the molecular epidemiology of carbapenem resistance in West Africa (WA). Methods  Articles published from 16 West African countries on the molecular epidemiology of carbapenem resistance were reviewed. An extensive literature search was carried out in PubMed, Scopus, Web of Science, and African Journals Online (AJOL) using specific keywords. The meta-analysis and forest plots of major pathogens and carbapenem resistance genes were done using the Open Meta-Analyst, Task Order # 2 software. The data were analysed in binary random model effects by the DerSimonian-Laird method at a 95% confidence interval. Results  Of the 431 articles found in our initial search, 60 (13.92%) were considered suitable for inclusion. Only seven of the 16 West African countries formed part of the analysis, Nigeria (23/60), Ghana (19/60), Burkina Faso (7/60), Senegal (6/60), Benin (2/60), Mali (2/60), and Togo (1/60). Also, 80% (48/60) of the studies used clinical samples, 16.67% (10/60) used environmental samples, and 3.33% (2/60) used animal samples. The average prevalence was highest in Acinetobacter baumannii (18.6%; 95% CI = 14.0-24.6, I2 = 97.9%, p < 0.001), followed by Pseudomonas aeruginosa (6.5%; 95% CI = 3.1–13.4, I2 = 96.52%, p < 0.001), Klebsiella pneumoniae (5.8%; 95% CI = 4.2–7.9, I2 = 98.06%, p < 0.001) and Escherichia coli (4.1%; 95% CI = 2.2–7.7, I2 = 96.68%, p < 0.001). The average prevalence of the blaNDM gene was 10.6% (95% CI = 7.9–14.3, I2 = 98.2%, p < 0.001), followed by 3.9% (95% CI: 1.8–8.3, I2 = 96.73%, p < 0.001) for blaVIM and 3.1% (95% CI: 1.7–5.8, I2 = 91.69%, p < 0.001) for blaOXA-48. Conclusion  In West Africa, K. pneumoniae, E. coli, A. baumannii, and P. aeruginosa are the main CRE with blaNDM, blaVIM, and blaOXA-48 being the predominant carbapenem resistance genes. In view of these results, ongoing CRE surveillance combined with antimicrobial stewardship improved, laboratory detection methods, and adherence to infection control practices will be needed to control the spread of CRE. Keywords  West Africa, Molecular epidemiology, Carbapenem-resistance, Meta-analysis A systematic review and meta-analysis of carbapenem-resistant Enterobacteriaceae in West Africa Namwin Siourimè Somda1, Rabbi Nyarkoh1, Fleischer C. N. Kotey1, Patience B. Tetteh-Quarcoo1 and Eric S. Donkor1* http://creativecommons.org/licenses/by-nc-nd/4.0/ http://creativecommons.org/licenses/by-nc-nd/4.0/ http://crossmark.crossref.org/dialog/?doi=10.1186/s12920-024-02043-x&domain=pdf&date_stamp=2024-11-11 Page 2 of 18Somda et al. BMC Medical Genomics (2024) 17:267 Background Although a natural phenomenon in bacteria and other microorganisms, antimicrobial resistance (AMR) has become accelerated owing to excessive use, including abuse, of antimicrobials. Bacteria exhibit these traits via a multitude of mechanisms, the nature and effectiveness of which vary per the species involved and their origin [1]. Key among these mechanisms are mutations and acqui- sition of external genetic material. Especially unsettling is the fact that the magnitude of the AMR problem has become particularly multidimensionally precarious in the 21st Century [2]. According to predictive statistical models, an estimated 4.95  million deaths were associ- ated with bacterial AMR in 2019, 1.27  million of which were directly caused by bacterial AMR [2]. These enor- mous ramifications of AMR are considerably higher in low-income countries, with the conspicuous data gaps in most parts of these regions suggesting these appraisals to be worse than estimated [2, 3]. In the AMR landscape, carbapenems, which are often used as last-line drugs against bacterial infections, are now a major focus of keen interest, due to emergence and unbridled global spread of bacterial resistance against them [4]. This phenomenon is particularly notable in species of Gram-negative bacteria that naturally have relatively low transmembrane diffusion coefficients to β-lactams, such as Enterobacter cloacae, Pseudomo- nas aeruginosa, and Acinetobacter baumannii. It also remains much more common in hospital settings than in communities [5]. Most recent studies have shown that the global occurrence of carbapenem resistance (CR) among members of the Enterobacteriaceae (especially, Klebsiella pneumoniae, Escherichia coli, Enterobacter spp., and Citrobacter freundii), whether in livestock, the environment, or in hospital- and community-associated infections, impose a huge burden on the healthcare sys- tem [6–8]. More concerningly, as carbapenemase genes reside on mobile genetic elements (such as transposons and plasmids), they have a high potential for widespread transmission to a vast spectrum of bacterial genera. In addition, Enterobacteriaceae that harbour carbapene- mase-encoding genes can spread from person to person, making CRE a deadly threat within and outside health- care facilities [9]. In Africa, the problem of carbapenem-resistant Entero- bacteriaceae (CRE) is aggravated by factors such as the high rate of infections, poor diagnostic tools, sub-opti- mal disease surveillance systems, abuse of antibiotics, and dearth of CRE data [10]. In East Africa, high preva- lence of carbapenem resistance have been reported in clinical isolates of A. baumannii, P. aeruginosa, K. pneu- moniae, Proteus mirabilis, and E. coli [11]. In West Africa (WA), several studies have reported on the occurrence of CRE, but these rarely conducted detailed analyses on the molecular distribution and epidemiology of CRE in healthcare facilities. Besides, data on CRE differs among countries, are limited, and are not currently available in scientific publications for some countries. To identify data gaps and add to the existing knowledge to inform the scientific community and policymakers, we under- took a systematic review to assess the current status of the molecular epidemiology of carbapenem resistance in West Africa. The main objectives were to (i) evaluate the evolution of carbapenem resistance of bacteria isolated in WA over the years, (ii) determine the pathogens impli- cated the most, and (iii) identify the predominant car- bapenem resistance genes involved. Methods Study design This was a systematic review of the available studies on CRE conducted in 16 countries of West Africa, including Benin, Burkina Faso, Cape Verde, Ivory Coast, Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Maurita- nia, Niger, Nigeria, Senegal, Sierra Leone, and Togo. They included studies that were available in PubMed, Scopus, Web of Science, and African Journals Online (AJOL) databases, following a literature search using the key- words “carbapenem resistance, carbapenemase-pro- ducing, Metallo-β-lactamase, West Africa, molecular epidemiology, and the country name”. Only articles pub- lished in the English and French languages were included. Study eligibility criteria The results of the literature search were exported and compiled. Endnote was then used to remove duplicates and to catalogue, collate, and manage the publications. The included studies were available full-text research articles reporting on the prevalence of carbapenemase- producing bacteria isolated in West African countries, those reporting on the study population and the pheno- typic and molecular methods used to detect carbapenem resistance. The literature search was conducted from April 8, 2024 to May 20, 2024 and included all publica- tions before May 20, 2024. Data extraction Full texts of the screened publications were obtained from appropriate sources and the data were extracted in an MS Excel spreadsheet under multiple headings, such as study period, publication year, country, source of samples, number of isolates, number of carbapenem- resistant isolates, CR prevalence, bacteria, methods for CR gene detection, number of CR genes, and preva- lence of CR genes. Articles showing studies with incom- plete information related to AMR detection methods, only phenotypic methods, duplicate articles, abstracts, review articles, letters, short communications, posters, Page 3 of 18Somda et al. BMC Medical Genomics (2024) 17:267 conference proceedings, and studies outside WA coun- tries were excluded. Data analysis Initially, a total of 431 potential articles were identified. All titles and abstracts that were related to the study ques- tions were reviewed. Publications were further screened by reviewing their full details, and selected articles were further evaluated. Only those that met the inclusion cri- teria were included in this review (Fig. 1). The extracted data were used for descriptive statistics. Further analysis was carried out in multiple steps. Excel 2013 was used for data entry and some graphics. The meta-analysis and for- est plots of major pathogens and carbapenem resistance genes, as well as estimation of the country effect, were done using the Open Meta-Analyst, Task Order # 2 soft- ware (available at ​h​t​t​​p​s​:​/​​/​w​w​​w​.​​b​r​o​​w​n​.​e​​d​u​/​​a​c​​a​d​e​​m​i​c​s​​/​p​u​​ b​l​​i​c​-​h​e​a​l​t​h​/​r​e​s​e​a​r​c​h​/​e​v​i​d​e​n​c​e​-​b​a​s​e​d​-​m​e​d​i​c​i​n​e​/​r​e​s​e​a​r​c​h​-​ i​n​i​t​i​a​t​i​v​e​s​/​s​o​f​t​w​a​r​e​-​0​​​​​)​. The data were analysed in binary random model effects by the DerSimonian-Laird method at a 95% confidence interval. Individual models were used for analysis of each major pathogen. Inconsistency (or heterogeneity) across the studies estimated in the ran- dom-effects model was quantified using inverse variance index (I2). The I2 values at 25%, 50%, and 75% were con- sidered as low, moderate, and high heterogeneity, respec- tively [12]. Significance levels were set at p < 0.001. Results Systematic literature review and study A total of 60 articles presenting concrete information about the molecular epidemiology of CR in WA were included in the final analysis after screening 431 articles from different electronic databases (Fig. 1). The distribu- tion of the included articles was Benin (2/60), Burkina Faso (7/60), Ghana (19/60), Mali (2/60), Nigeria (23/60), Senegal (6/60), and Togo (1/60) (Table 1), which consti- tute seven out of 16 West African countries. No article, per the set inclusion criteria, was from Cote d’Ivoire, Cape Verde, Gambia, Guinea, Guinea Bissau, Liberia, Mauritania, Niger, and Sierra Leone. Distribution of the articles according to publication year The results showed a high diversity in terms of the pub- lication year. Only five studies were conducted between 2012 and 2017, and one (1.67%) in each of 2012, 2013, 2014, 2016, and 2017. The majority of the studies were done in 2023, followed by 20/60 (33.33%) in 2020. The years 2021 and 2022 recorded 9/60 (15.00%) publications each. Also, 5/60 (8.33%) articles were published in 2019 and 2/60 (3.33%) in 2018 (Table 1). Distribution of the articles according to sample origin Overall, 48/60 (80%) of the studies reviewed were on clinical samples, 10/60 (16.67%) were on environmental samples (four of which emanated from Nigeria [13–16] Fig. 1  Flow chart of systematic literature search, identification, screening, and article selection https://www.brown.edu/academics/public-health/research/evidence-based-medicine/research-initiatives/software-0 https://www.brown.edu/academics/public-health/research/evidence-based-medicine/research-initiatives/software-0 https://www.brown.edu/academics/public-health/research/evidence-based-medicine/research-initiatives/software-0 Page 4 of 18Somda et al. BMC Medical Genomics (2024) 17:267 Origin Numb of Isolates Numb of CR Methods Carba genes Numb carba genes Preva- lence of carba genes (%) CR organisms References Loca- tion Environment 256 45 PCR blaNDM-1 30 66.7 Klebsiella oxytoca, Klebsiella pneumoniae, Enterobacter aeroginosa, Enterobacter hormaechei, Enterobacter asburiae, Citrobacter freun- dii, Morganella morganii, E. coli, Proteus mirabilis, Enterobacter gergoviae, Klebsiella variicola [13] Nigeria Clinical 187 41 PCR blaNDM, blaOXA-48 3; 12 1.6; 6.4 E. coli [25] Nigeria Clinical 180 6 MALDI-TOF, Modified Hodge Test, RESIST-5 O.K.N.V.I, PCR blaNDM, bla- OXA-48, blaVIM 2; 2; 2 1.11 for each gene Escherichia coli, Pseu- domonas aeruginosa, Pseudomonas mendocina, Enterobacter cloacae, Aci- netobacter baumannii [26] Benin Environment 21 10 immu- no- chromato- graphic test O.K.N.V.I. RESIST-5 blaNDM, blaOXA-48 5; 6 23.81; 28.57 E. coli, Klebsiella pneumonia [17] Burkina Faso Environment 209 33 MALDI-TOF, PCR blaNDM, blaVIM, blaIMP, blaKPC, blaOXA-48 23; 8; 6; 15; 3 11.0; 3.83; 2.87; 7.18; 1.44 E. coli, Klebsiella pneumonia [18] Burkina Faso Clinical 473 25 immunochro- mato- graphic test O.K.N.V.I. RESIST-5 blaNDM, bla- OXA-48, blaVIM 21; 5; 1 4.44; 1.06; 0.21 E. coli, Klebsiella pneumonia [27] Burkina Faso Clinical 601 17 PCR, MLST blaNDM-1, blaOXA-58, bla0XA-181, blaVIM-2 9; 1; 6; 1 1.5; 0.17; 1.0; 0.17 Enterobacterales; A. baumannii; Pseudomonas aeruginosa [28] Burkina Faso Clinical 71 45 PCR, MALDI- TOF, WGS (using Illumina MiSeq), MLST blaNDM, blaOXA-48 27; 1 38.03; 1.41 Acinetobacter baylyi, Aci- netobacter indicus, Acineto- bacter pittii; Escherichia coli, Enterobacter bugandensis, Enterobacter cloacae, Esch- erichia, hermannii, Klebsi- ella pneumoniae, Leclercia, adecarboxylata, Pantoea agglomerans, Pseudomo- nas fluva, Pseudomonas stutzeri, Mixta calida [29] Ghana Clinical 62 2 PCR, WGS blaOXA-181 2 3.23 E. coli [30] Ghana Clinical 4 4 MALDI-TOF, sequencing (ONT and Illumina BlaDIM-1; blaIMP-1 4; 4 100 Pseudomonas [31] Ghana Clinical 3840 26 PCR blaNDM-1; blaVIM; blaOXA-48 16; 8; 2 0.42; 0.21; 0.052 P. aeruginosa, Acinetobacter species, E. coli, Pseudomo- nas putida, K. pneumoniae, Providencia stuartii, Shigella sonnei, [32] Ghana Clinical 36 4 PCR; MALDI- TOF; MLST; WGS (Illumina; ONT) blaOXA-23; blaOXA-51 2; 2 5.56; 5.56 Acinetobacter baumannii [33] Ghana Table 1  Review of West Africa-based carbapenem resistance studies Page 5 of 18Somda et al. BMC Medical Genomics (2024) 17:267 Origin Numb of Isolates Numb of CR Methods Carba genes Numb carba genes Preva- lence of carba genes (%) CR organisms References Loca- tion Clinical 45 22 MALDI-TOF; PFGE; MLST blaNDM-1, blaOXA-23, bla0XA-378, blaOXA-420 20; 20; 3; 2 44.44; 44.44; 6.67; 4.44 Acinetobacter baumannii [34] Ghana Clinical 230 13 PCR blaOXA-48; blaNDM-1 11; 13 5.65; 4.78 Proteus vulgaris, Proteus mirabilis, Citrobacter spp.; Klebsiella pneumoniae, E. coli [35] Ghana Clinical 144 5 PCR blaOXA-48; blaNDM 4; 1 2.78; 0.69 E. coli, Klebsiella pneumonia [36] Ghana Clinical 110 13 PCR blaNDM-1 3 2.73 Klebsiella pneumonia, Proteus mirabilis [37] Mali Clinical 240 27 MALDI-TOF; RT-PCR blaOXA-48; blaNDM-1 14; 13 5.83; 5.42 Escherichia coli, Klebsiella, pneumoniae, Enterobacter cloacae, Citrobacter freundii [38] Senegal Animal clinical 55 9 MALDI-TOF; PCR; RT-PCR; PFGE blaOXA-23 9 16.36 Acinetobacter baumannii [23] Senegal Clinical 53 15 PCR BlaNDM; blaVIM 13; 5 24.53; 9.43 E. coli [39] Burkina Faso Environment 183 18 PCR blaNDM-1, blaOXA-48 14; 4 7.65; 2.19 Escherichia coli, Klebsiella pneumoniae, Klebsiella oxytoca [19] Burkina Faso Clinical 29 29 WGS blaOXA-181 29 100 Klebsiella pneumoniae [40] Ghana Clinical 87 21 PCR blaNDM 21 24.14 Acinetobacter baumannii [41] Ghana Clinical 21 2 MALDI-TOF, MLST, PCR, WGS blaOXA − 181; blaNDM − 1 1; 1 4.76; 4.76 E. coli [42] Ghana Clinical 29 3 WGS, MLST, PCR blaOXA-48, blaOXA-181 1; 2 3.45; 6.90 K. quasipneumoniae, Klebsi- ella pneumonia, Enterobac- ter cloacae [43] Ghana Environment 36 25 WGS blaVIM, blaNDM-1, 25 69.44 Pseudomonas putida, Citrobacter werkmanii, [20] Ghana Clinical 382 2 PCR blaNDM-1 2 0.52 Klebsiella pneumoniae [44] Ghana Environment 174 61 PCR blaNDM-1 61 35.06 Acinetobacter spp., Citro- bacter freundii, Enterobac- ter spp., Escherichia coli, Bacillus spp., Klebsiella spp., Staphylococcus aureus, Staphylococcus epidermidis, Streptococcus agalactiae, Providencia spp., Pseudo- monas aeruginosa, Vibrio spp. [21] Ghana Clinical 181 5 PCR blaOXA-48, blaNDM-1, blaKPC 3; 2; 2 1.66; 1.10; 1.10 E. coli, K. pneumoniae, Acinetobacter baumannii, Providencia vermicola [45] Ghana Clinical 111 26 PCR, ERIC-PCR blaNDM-1, blaVIM-1; blaOXA-48 16; 8; 2 14.4; 7.2; 1.8 Acinetobacter baumannii, Pseudomonas aeruginosa, Escherichia coli; Klebsiella pneumonia, Pseudomonas putida; Providencia stuartii, Shigella sonnei [46] Ghana Clinical 50 1 PCR, MLST blaNDM-5 1 2.0 E. coli [47] Mali Animal 101 13 PCR, sequenc- ing (ONT) blaOXA-48; blaKPC-2 7; 6 6.93; 5,94 Klebsiella pneumoniae [48] Senegal Table 1  (continued) Page 6 of 18Somda et al. BMC Medical Genomics (2024) 17:267 Origin Numb of Isolates Numb of CR Methods Carba genes Numb carba genes Preva- lence of carba genes (%) CR organisms References Loca- tion Clinical 152 4 WGS, MLST, PCR blaNDM-5; blaOXA-181 1; 3 0.66; 1.97 Enterobacter cloacae, E. coli [49] Togo Clinical 67 12 PCR blaNDM, blaVIM, blaGES 4; 5; 2 5.97; 7.46; 2.98 Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa and Proteus spp. [50] Nigeria Clinical 66 57 MALDI-TOF, WGS(Illumina) blaNDM, blaOXA-23, blaOXA-66, blaOXA-98, blaOXA-58, blaVIM-5, blac- phA, blaOXA- 66, blaPOM-1 26; 6; 6; 3; 3; 4; 2; 1; 1 39.39; 9.1; 9.1; 4.55; 4.55; 6.1; 3.03; 1.52; 1.52 Acinetobacter baumannii, Aeromonas hydrophila, Pseudomonas otitidis, Provi- dencia rettgeri, Enterobacter cloacae, Escherichia coli, P. aeruginosa [51] Nigeria Clinical 306 21 PCR blaVIM, blaGES, blaNDM 9; 10; 2 2.94; 3.27; 0.65 E. coli, Pseudomonas spp., Proteus spp., Klebsiella spp. [52] Nigeria Clinical 119 30 Xpert1 Carba- R, RT-PCR blaNDM, blaVIM 26; 4 28.85; 3.36 Pseudomonas aeruginosa, Acinetobacter bauman- nii, Providencia rettgeri, Escherichia coli, Klebsiella pneumoniae, Enterobacter spp [53] Nigeria Environment 259 125 PCR, MALDI- TOF, WGS (using Illumina MiSeq) blaOXA-23; bla- OXA-40; blaNDM; blaPON, blaVIM 1; 2; 6; 2; 1 0.39; 0.72; 2.32; 0.72; 0.39 Klebsiella pneumoniae, Enterobacter cloacae, Pseudomonas otitidis, Acinetobacter baumannii, Aeromonas caviae, Citro- bacter freundii [14] Nigeria Animal 141 43 PCR blaIMP 43 30.50 E. coli, Pseudomonas aeruginosa, Klebsiella pneumonia [24] Nigeria Environment 65 61 PCR, WGS blaVIM-5 61 94.85 Pseudomonas, Stenotro- phomonas, Cupriavidus, Burkholderia, Pandoraea, Ralstonia [16] Nigeria Clinical 45 33 PCR, blaNDM-1, blaNDM-5, blaOXA-48, blaOXA-181 17; 12; 3; 1 37.78; 26.67; 6.67; 2.22 E. coli [54] Nigeria 306 161 PCR blaVIM, blaNDM, blaGES 9; 2; 10 2.94; 0.65; 3.27 Klebsiella spp., E. coli; Pseu- domonas spp. Proteus spp. [55] Nigeria Clinical 123 32 WGS (Il- lumina), MALDI-TOF blaNDM-1, blaVIM-2, blaVIM-5-like 32; 4; 11 26.02; 3.25; 8.94 Pseudomonas aeruginosa [56] Nigeria 34 34 WGS, PFGE blaOXA-51, bla- OXA-23, blaNDM, blaOXA-58, blaOXA-420 34; 17; 21; 15; 2; 2 100; 50; 61.76; 44.12; 5.88; 5.88 Acinetobacter baumannii [57] Nigeria Clinical 128 62 PCR; MLST blaVIM, bla- OXA-48, blaIMP, blaNDM, blaKPC 55; 37; 29; 22; 17 43.0; 28.9; 22.7; 17.2; 13.3 Klebsiella pneumonia [58] Nigeria Clinical 100 86 WGS balOXA-23; blaNDM-1; blaOXA-58 30; 24; 10 34.9; 27.9; 11.6 Acinetobacter baumannii [15] Nigeria Clinical 93 5 WGS; MLST blaNDM 5 5.38 Klebsiella quasipneumoniae subsp. similipneumoniae [59] Nigeria Table 1  (continued) Page 7 of 18Somda et al. BMC Medical Genomics (2024) 17:267 and three each from Burkina Faso [17–19] and Ghana [20–22]), and 2/60 (3.33%) from animal samples (one each from Senegal [23] and Nigeria [24]) (Table  1). The environmental samples included wastewater, soil, sedi- ment, effluent, and water sources. The animal samples were from cattle, poultry, termites, and chimpanzees. However, none of the studies was on food products. Regarding the studies involving clinical samples, 22/60 (36.67%) were on urine, 18/60 (30%) were on swabs (vaginal, wound, ear, eye, neonatal, and throat), 17/60 (28.33%) were on blood, 13/60 (21.67%) were on stool, and 11/60 (18.33%) were on pus. However, some clini- cal samples did not specify the type (Table  1). Among the genotypic methods used in detecting the carbape- nem resistance genes, PCR (standard PCR and RT-PCR) was the predominant (42/60; 70%), followed by whole- genome sequencing (20/60; 33.33%), MALDI-TOF (17/60; 28.33%), MLST (13/60; 21.67%), PFGE (3/60; 5%), immunochromatographic test O.K.N.V.I. RESIST-5 (3/60; 5%), and Xpert1 Carba-R (1/60; 1.67%) (Table 1). Prevalence of pathogens included Five types of bacteria were mainly prevalent in the 60 different articles studied. Klebsiella spp. was the most reported across the publications (n = 34; 56.67%) fol- lowed by E. coli (n = 27; 45%), Acinetobacter spp. (n = 20; 33.33%), Pseudomonas spp. (n = 17; 28.33%), and Entero- bacter spp. (n = 11; 18.33%). Some pathogens, such as Providencia spp. (n = 8; 13.33%), Citrobacter spp. (n = 10; 16.67%), Proteus spp. (n = 9; 15%), Aeromonas spp. (n = 3; 5%), Morganella spp. (n = 3; 5%), Shigella spp. (n = 2: 3.33%), and Vibrio spp. (n = 1; 1.67%), were less repre- sented. The pooled prevalence of Acinetobacter spp. in the samples was 42.17% (n = 20), followed by E. coli at 27.60% (n = 27), Klebsiella spp. at 25.20% (n = 34), Pseu- domonas spp. at 16.55% (n = 17), Proteus spp. at 7.94% (n = 4), Citrobacter spp. at 6.8% (n = 5), Providencia spp. at 6.36% (n = 7), Vibrio spp. at 4.02% (n = 7), Enterobacter spp. at 3.81% (n = 11), Morganella spp. at 2.34% (n = 1), Aeromonas spp. at 1.71% (n = 2), and Shigella spp. at 0.9% (n = 2). Origin Numb of Isolates Numb of CR Methods Carba genes Numb carba genes Preva- lence of carba genes (%) CR organisms References Loca- tion Environment 77 36 WGS blaNDM-1, blaOXA-23, bla0XA-58 24; 22; 7 31.2; 28.6; 9.1 Acinetaubacter baumannii [60] Nigeria Clinical 52 12 MALDI-TOF; WGS blaNDM; blaOXA395’ 9; 3 17.31; 5.77 E. coli, K. pneumoniae, and P. aeruginosa [61] Nigeria Clinical 200 5 PCR blaVIM 5 2.5 Pseudomonas aeruginosa [62] Nigeria Clinical 44 5 RT-PCR blaVIM 5 11.36 Pseudomonas spp. [63] Nigeria Clinical 8 3 PCR blaKPC, blaVIM, blaNDM 3; 3; 3 37.5; 37.5; 37.5 Klebsiella pneumonia [64] Nigeria Clinical 213 199 PCR blaNDM 199 93.4 Klebsiella pneumonia [65] Nigeria Clinical 29 26 PCR, MALDI blaOXA-51, bla- OXA-23, blaNDM 26; 26; 1 89.69; 89.69; 3.45 Acinetobacter baumannii [66] Senegal Clinical 28 6 PCR, MALDI-TOF blaOXA-48 6 21.43 Klebsiella pneumonia [67] Senegal Clinical 4 4 PCR, Sequencing blaOXA-181 4 100 Escherichia coli [68] Burkina Faso Clinical 218 9 WGS, MLST blaOXA-181, blaNDM-1; blaOXA-48 1; 2; 6 0.36; 0.71; 2.16 Klebsiella pneumoniae, Escherichia coli, Enterobac- ter cloacae, [69] Nigeria Clinical 3 3 MALDI-TOF; MLST blaOXA-23 3 100 Acinetobacter baumannii [70] Senegal Clinical 2 2 Sequencing (ONT) blaNDM-1 2 100 Klebsiella pneumonia, [71] Ghana Clinical 1 1 MALDI-TOF, WGS(Illumina) blaNDM-1, blaOXA-58, blaOXA-558 1; 1; 1 100 Acinetobacter baumannii [72] Benin Environment 121 23 WGS (Illu- mina); MLST blaOXA-181, blaNDM, blaOXA-48 16; 5; 2 13.22; 4.13; 1.65 Escherichia coli, Klebsiella pneumonia, Enterobacter cloacae [22] Ghana Numb = number, CR = carbapenem resistance, carba = carbapenem, % = percent Table 1  (continued) Page 8 of 18Somda et al. BMC Medical Genomics (2024) 17:267 Prevalence of CR bacteria in West Africa There was a high heterogeneity in the prevalence of car- bapenem-resistant bacteria isolated in the various stud- ies. The average prevalence was highest in A. baumannii (18.6%; 95% CI = 14.0–24.6, I2 = 97.9%, p < 0.001) (Fig. 2), followed by P. aeruginosa (6.5%; 95% CI = 3.1–13.4, I2 = 96.52%, p < 0.001) (Fig. 3), K. pneumoniae (5.8%; 95% CI = 4.2–7.9, I2 = 98.06%, p < 0.001) (Fig.  4), and E. coli (4.1%; 95% CI = 2.2–7.7, I2 = 96.68%, p < 0.001) (Fig.  5). However, CR was lowest in Providencia spp. (1.6%; 95% CI = 0.4–6.2, I2 = 93.23%, p < 0.001), P. mirabilis (3.8%), Vibrio spp. (3.45%), E. cloacae (2.4%; 95% CI = 1.5–3.8, I2 = 45.68%, p = 0.048), Morganella morganii (2.34%), Aeromonas spp. (1.71%), Citrobacter freundii (1.47%), and Shigella sonnei (0.45%). The highest pooled preva- lence of CR bacteria from the environment were in A. baumannii (16.92%), E. coli (10.15%), K. pneumoniae (7.67%), and P. aeruginosa (7.11%). Vibrio spp., M. morga- nii, and Aeromonas spp. were only found in environmen- tal samples, with average prevalence of 3.45%, 2.34%, and 1.71%, respectively. The pooled prevalence of E. cloacae, C. freundii, Providencia spp., and P. mirabilis were least in environmental and clinical samples, at 2.09%, 1.73%, 1.15%, and 0.78%, respectively. Shigella sonnei was not isolated in environmental samples. The pooled preva- lence of CR in the two studies on animal samples was highest in P. aeruginosa (11.35%), followed by K. pneu- moniae (8.79%), and then E. coli (8.51%). Prevalence of CR genetic determinants in West Africa The blaNDM gene is the most widespread CR gene in West Africa, evidenced by its detection in most of the studies (n = 46; 76.67%). The second most dominant genes were blaOXA-48 and blaVIM (n = 20; 33.33% each), fol- lowed by blaOXA-23 (n = 10; 16.67%). On the other hand, other genes like blaOXA-181 (n = 8; 13.33%), bla0XA-58 (n = 5; 8.33%), blaKPC (n = 5; 8.33%), blaIMP (n = 4; 6.67%), blaGES (n = 4; 6.67%), blaOXA-51 (n = 3; 5%) were least detected. The pooled prevalence of the blaNDM gene in West Africa, as calculated from the reviewed publica- tions (n = 46), was 10.6% (95% CI = 7.9–14.3, I2 = 98.2%, p < 0.001) (Fig.  6), comprising blaNDM-1 (30.39%) and blaNDM-5 (13.48%). The pooled prevalence of blaVIM and blaOXA-48, calculated from the reviewed publica- tions (n = 20), were 3.9% (95% CI = 1.8–8.3, I2 = 96.73%, p < 0.001) and 3.1% (95% CI = 1.7–5.8, I2 = 91.69%, p < 0.001) (Figs.  7 and 8), respectively. Also, the pooled prevalence of blaOXA-23 (n = 10) was 26.6% (95% CI = 16.1–44.0, I2 = 94.57%, p < 0.001); those of other genes were blaOXA-181 (9.4%; n = 8; 95% CI = 3.8–23.4, I2 = 96.52%, p < 0.001), blaOXA-58 (6.5%; n = 5; 95% Fig. 2  Forest plot with adjusted average prevalence of carbapenem-resistance of Acinetobacter baumannii in West Africa. Legend: Random Effects Mode (95% CI = 14.0–24.6, I2 = 97.9%, p < 0.001). X-axis is the proportion of the organism reported in individual studies as listed along the Y-axis, with the range of proportion in 95% confidence interval (CI) Page 9 of 18Somda et al. BMC Medical Genomics (2024) 17:267 CI = 1.6–26.7, I2 = 90.46%, p < 0.001), blaKPC (8.4%; n = 5; 95% CI = 4.0–17.8, I2 = 82.8%, p < 0.001) (Fig.  9A), and blaIMP (22.1%; n = 4; 95% CI = 8.8–55.3, I2 = 96.55%, p < 0.001) (Fig.  9B). In the environmental samples, the average prevalence were blaNDM (28.45%; n = 9), blaVIM (21.88%; n = 4), blaOXA-48 (4.89%; n = 4), bla0XA-181 (13.22%, n = 1), blaKPC (7.18%; n = 1), and blaIMP (3.83%; n = 1). The two studies on the animal samples had a pooled prevalence of 6.93% for blaOXA-48, 5.93% for blaKPC, and 30.5% for blaIMP [48] (Table 1). Distribution of CR genetic determinants by countries in West Africa The information on the prevalence of carbapenem resis- tance genes in West Africa varies considerably among countries and depends on the genes detected (Table  2). The highest pooled prevalence of blaNDM was in Nigeria (17.42%; 95% CI = 12.6–23.9, I2 = 97.87%, p < 0.001) and the lowest was in Togo (0.66%). In the case of blaVIM, the highest pooled prevalence was in Nigeria (5.8%) and the lowest was in Benin (0.87%). The prevalence of bla- OXA-48 ranged from 0.87% in Benin to 9.3% in Senegal. That of blaOXA-23 was 17.5% in Nigeria, 53.5% in Sen- egal, and 19.2% in Ghana. On the other hand, the preva- lence of blaOXA-181 was 100% in Burkina Faso, 14.3% in Ghana, 1.97% in Togo, and 1.29% in Nigeria. The bla- OXA-58 gene recorded a prevalence of 100% in Benin, 8.3% in Nigeria, and 0.17% in Burkina Faso. Those of blaIMP were 100%, 26.2%, and 3.83% in Ghana, Nigeria, and Burkina Faso, respectively. The prevalence of blaKPC in Nigeria, Burkina Faso, and Senegal were 20.7%, 7.18%, and 5.94%, respectively. The blaGES gene was only found in Nigeria (6.3%; 95% CI = 0.005–0.760, I2 = 98.33%, p < 0.001) (Table 2). Average prevalence of CR genetic determinants detected in the pathogens Nigeria The prevalence of blaNDM was highest in A. bauman- nii, followed by K. pneumoniae at 14.05%, P. aeruginosa at 8.48%, E. coli at 8.09%, and E. cloacae at 1.32%. The highest prevalence of blaVIM were seen in 10.28% of K. pneumoniae cases, 5.01% of P. aeruginosa cases, and 0.76% of E. coli cases. For K. pneumoniae, blaOXA-48 was detected in 14.68% of the samples, 4.5% in E. coli, and 1.83% in E. cloacae; blaOXA-23 and blaOXA-58 were only detected in 24.6% and 7.86% of A. bauman- nii, respectively. In A. baumannii, P. aeruginosa, K. pneumoniae, and E. coli, blaGES was prevalent at 4.76%, 2.91%, 0.76%, and 0.44%, respectively. On the other hand, the prevalence of blaIMP was 16.67% for K. pneumoniae, 11.35% for P. aeruginosa, and 9.93% for E. coli. The preva- lence of blaOXA-181 was 2.22% for E. coli and 0.36% for K. pneumoniae. However, blaKPC was only detected in 25.4% of K. pneumoniae. Ghana The highest pooled prevalence of blaNDM was 18.92% in K. pneumoniae, followed by 16.91% in A. baumannii, 8.69% in P. aeruginosa, 5.17% in E. cloacae, and 3.30% in Fig. 3  Forest plot with adjusted average prevalence of carbapenem-resistance of Pseudomonas aeruginosa in West Africa. Legend: Random Effects Mode (95% CI = 3.1–13.4, I2 = 96.52%, p < 0.001). X-axis is the proportion of the organism reported in individual studies as listed along the Y-axis, with the range of proportion in 95% confidence interval (CI) Page 10 of 18Somda et al. BMC Medical Genomics (2024) 17:267 E. coli. The highest pooled prevalence of blaOXA-48 was 4.78% in P. mirabilis, followed by 1.48% in E. coli, 1.41% in K. pneumoniae, and 0.98% in A. baumannii. In A. baumanni, blaVIM and blaIMP were 12.67% and 100% prevalent, respectively. Similarly, in K. pneumoniae and E. coli, blaOXA-181 was prevalent at 36.14% and 5.75%, respectively. Burkina Faso The pooled prevalence of the blaNDM gene was 9.42% in E. coli, 3.38% in K. pneumoniae, and 0.67% in A. bau- mannii. The blaVIM gene prevalence was 3.84% in E. coli and 0.22% in K. pneumoniae. In the case of blaOXA-48, the pooled prevalence was 6.86% in E. coli and 6.4% in K. pneumoniae. BlaKPC and blaIMP were prevalent at 0.48% and 1.19% in E. coli, and further at 2.87% and 0.48% in K. pneumoniae. The least prevalence of blaOXA-58 were in K. pneumoniae (1%) and A. baumannii (0.17%). However, in a study by Ouédraogo et al. [68], all the four E. coli isolated had the blaOXA-181 gene present. Senegal The pooled prevalence of blaNDM were 7.7%, 3.45%, and 3.4%, respectively, in K. pneumoniae, A. baumannii, and E. coli. For blaOXA-48, the highest pooled prevalence was in K. pneumoniae (11.34%) and the lowest was in E. coli (3.4%). BlaKPC and blaOXA-23 were prevalent at 5.94% and 68.66%, respectively, in K. pneumoniae and A. baumannii. Fig. 4  Forest plot with adjusted average prevalence of carbapenem-resistance of Klebsiella pneumoniae in West Africa. Legend: Random Effects Mode (95% CI = 4.2–7.9, I2 = 98.06%, p < 0.001). X-axis is the proportion of the organism reported in individual studies as listed along the Y-axis, with the range of proportion in 95% confidence interval (CI) Page 11 of 18Somda et al. BMC Medical Genomics (2024) 17:267 Discussion The aim of this systematic and meta-analysis, which focused on 16 West African countries, was to assess the evolution and molecular epidemiology of carbapenem resistance in West Africa. The findings showed wide- spread carbapenem-resistant bacteria and CR genetic determinants in West African countries. It also revealed Nigeria and Ghana as the countries with publication contributions on carbapenem-resistant bacteria in the region. A study in 2023 by Somda et al. on AMR among foodborne pathogenic bacteria in West Africa between 2010 and 2020 also made a similar observation regarding research efforts. Nigeria and Ghana were the predomi- nant countries publishing in the field. This was explained by the fact that those countries have more well-equipped universities and research centres than do the other WA countries, and their Governments prioritise dissemina- tion of scientific knowledge [73]. Most laboratories and health care centres in West Africa are not equipped with the necessary equipment and/or are unfamiliar with the significance of screening for carbapenem resistance genes and traits. According to Uthman and Uthman, researchers in African countries tend to publish their research articles (65%) in local journals that are not listed or indexed in international databases [74]. Out of the 60 publications reviewed for this study, nearly 78.33% covered the period between 2020 and 2023 and the majority were found in Nigeria and Ghana in 2023. This indicates, in part, an increased attention to the issues of carbapenem resistance of bacteria in this region in recent years, particularly their molecular epidemiol- ogy. The high number of studies from Nigeria and Ghana, could be primarily due to the availability of state-of- the-art laboratory equipment provided by international donor agencies and collaborative research. This systematic review revealed that carbapenem- resistant bacteria (CRB) are highly detected in clini- cal samples than in non-clinical ones. This is due to the observation that most of the published articles were on hospital-based studies (48/60), while just a few were environment (10/60) and animal-based (2/60). In recent years, the prevalence of CRB transmission around the world has risen, and further exacerbated by the increased Fig. 5  Forest plot with adjusted average prevalence of carbapenem-resistance of Escherichia coli in West Africa. Legend: Random Effects Mode (95% CI = 2.2–7.7, I2 = 96.68%, p < 0.001). X-axis is the proportion of the organism reported in individual studies as listed along the Y-axis, with the range of pro- portion in 95% confidence interval (CI) Page 12 of 18Somda et al. BMC Medical Genomics (2024) 17:267 antibiotic pressure associated with the COVID-19 pandemic [8]. Overall, the most prevalent CR bacte- ria across the West African region, based on our com- prehensive analysis, were A. baumannii (18.6%; 95% CI = 14.0–24.6; I2 = 97.9%, p < 0.001), P. aeruginosa (6.5%; 95% CI = 3.1–13.4; I2 = 96.52%, p < 0.001), K. pneumoniae (5.8%; 95% CI = 4.2–7.9; I2 = 98.06%, p < 0.001), and E. coli (4.1%; 95% CI = 2.2–7.7; I2 = 96.68%, p < 0.001). In East Africa, carbapenem resistance was more exhibited in A. baumannii (23%), P. aeruginosa (17%), K. pneumoniae (15%), P. mirabilis (14%), and E. coli (12%). Our results are consistent with the observation from East Africa by Fig. 6  Forest plot with adjusted average prevalence of blaNDM gene in West Africa. Legend: Random Effects Mode (95% CI = 7.9–14.3, I2 = 98.2%, p < 0.001). X-axis is the proportion of the blaNDM gene reported in individual studies as listed along the Y-axis, with the range of proportion in 95% con- fidence interval (CI) Page 13 of 18Somda et al. BMC Medical Genomics (2024) 17:267 Fig. 8  Forest plot with adjusted average prevalence of blaOXA-48 gene in West Africa. Legend: Random Effects Mode (95% CI = 1.7–5.8, I2 = 91.69%, p < 0.001). X-axis is the proportion of the blaOXA-48 gene reported in individual studies as listed along the Y-axis, with the range of proportion in 95% confidence interval (CI) Fig. 7  Forest plot with adjusted average prevalence of the blaVIM gene in West Africa. Legend: Random Effects Mode (95% CI = 1.8–8.3, I2 = 96.73%, p < 0.001). X-axis is the proportion of the blaVIM gene reported in individual studies as listed along the Y-axis, with the range of proportion in 95% confi- dence interval (CI) Page 14 of 18Somda et al. BMC Medical Genomics (2024) 17:267 Table 2  Average prevalence of CR genetic determinant by country in West Africa Countries blaNDM % (N) blaVIM % (N) bla- OXA-48% (N) bla- OXA-23% (N) bla- OXA-181% (N) bla- OXA-58% (N) blaIMP % (N) blaGES % (N) blaKPC % (N) bla- OXA-51% (N) Nigeria 17.42 (20) (I2 = 97.87%, p < 0.001) 5.8 (12) (I2 = 94.26%, p < 0.001) 7.9 (4) (I2 = 94.01%, p < 0.001) 19.2 (5) (I2 = 89.26%, p < 0.001) 1.29 (2) (I2 = 20.74%, p = 0.261) 8.3 (3) (I2 = 0%, p = 0.589) 26.6 (2) (I2 = 51.5%, p = 0.151) 6.3 (4) (I2 = 98.33%, p < 0.001) 20.7 (2) (I2 = 75.93%, p = 0.042) 100 (1) Ghana 9.0 (13) (I2 = 97.52%, p < 0.001) 4.8 (3) (I2 = 99.26%, p < 0.001) 1.4 (8) (I2 = 83.15%, p < 0.001) 17.5 (2) (I2 = 88.44%, p = 0.003) 14.3 (4) (I2 = 97.32%, p < 0.001) 0 100 (1) 0 0 5.56 (1) Burkina Faso 8.6 (6) (I2 = 92.89%, p < 0.001) 1.4 (4) (I2 = 86.77%, p < 0.001) 2.6 (4) (I2 = 80.48%, p = 0.002) 0 100 (1) 0.17 (1) 3.83 (1) 0 7.18 (1) 0 Senegal 5.2 (2) (I2 = 0%, p = 0.658) 0 9.3 (3) (I2 = 77.65%, p = 0.011) 53.5 (3) (I2 = 93.31%, p < 0.001) 0 0 0 0 5.94 (1) 89.69 (1) Benin 8.4 (2) (I2 = 96.66%, p < 0.001) 0.87 (1) 0.87 (1) 0 0 100 (1) 0 0 0 0 Mali 2.5 (2) (I2 = 0%, p = 0.786) 0 0 0 0 0 0 0 0 0 Togo 0.66 (1) 0 0 0 1.97 (1) 0 0 0 0 0 Legend: % = percent, (N) = Number of publications concerned, I2 = Inconsistency (or heterogeneity), p = p-value Fig. 9  Forest plot with adjusted average prevalence of blaKPC and blaIMP genes in West Africa. Legend: (A): Random Effects Mode (95% CI = 4.0–17.8, I2 = 82.8%, p < 0.001). X-axis is the proportion of the blaKPC gene reported in individual studies as listed along the Y-axis, with the range of proportion in 95% confidence interval (CI). (B): Random Effects Mode (95% CI = 8.8–55.3, I2 = 96.55%, p < 0.001). X-axis is the proportion of the blaIMP gene reported in individual studies as listed along the Y-axis, with the range of proportion in 95% confidence interval (CI) Page 15 of 18Somda et al. BMC Medical Genomics (2024) 17:267 Ssekatawa et al. [11] and those from other parts of the world [5, 75]. Indeed, our reports conform with world- wide reports acknowledging that the magnitude of CRB is similar to that of carbapenem-resistant Enterobacteria- ceae [76]. Contrary to this, Dossouvi et al., in their sys- tematic review, identified that the most reported CRB in West Africa were Escherichia spp. (26.1%), Klebsiella spp. (20.8%), Pseudomonas spp. (20%), and Acinetobacter spp. (19.2%) [77]. Additionally, in Nigeria, Tula et al. reported E. coli and Klebsiella as the most prevalent CRB [13]. An explanation for the observation could be the concurrent use of phenotypic and molecular carbapenem resistance detection techniques in their study. The distribution of CRB, such as E. coli, K. pneumoniae, P. aeruginosa, and A. baumannii, is general all over the world according to several studies [78–81]. Comparing results with those of other independent studies may be challenging due to dif- ferences in study design and population [82]. In addition, protective measures taken to reduce the risk of new virus transmission may simultaneously facilitate the spread of other drug-resistant bacteria [8]. In the healthcare setting, carbapenems are consid- ered the last resort for the treatment of patients, but an increasing trend of bacteria resistance is posing a big challenge. In this review, the prevalence of carbapenem resistance genetic determinants in WA was estimated as blaNDM (10.6%) blaVIM (3.9%), and balOXA-48 (3.1%). These prevalence are consistent with those reported in other studies in West Africa, Nigeria, and the USA [77, 83, 84]. They are, however, lower compared to those reported in other studies in East Africa (35.0%), India (30% and 43%), and South Africa (68%) [11, 85, 86]. Even though the prevalence differed from country to country, the most prevalent carbapenem resistance genes across WA were blaNDM, blaVIM, and blaOXA-48, similar to observations in other studies in West Africa [11, 13] and worldwide reports [5, 87–90]. Several studies show that the enzymes responsible for the hydrolysis of carbapen- ems frequently found and spread in the last decade are blaKPC, blaNDM, blaVIM, and blaOXA-48, which is in agreement with our results [91–93]. These studies also showed that the Enterobacterales harbouring these genes are mostly Klebsiella spp., E. coli, Pseudomonas spp., and Acinetobacter spp., which is consistent with our results. These bacteria, being responsible for most infections, are thus more prevalent in hospital settings and the envi- ronment. Carbapenems are widely used to combat these bacteria, which could explain the high resistance of these bacteria against these antibiotics. In this review, 10 studies conducted with environment- based samples were from Nigeria, Ghana, Burkina Faso, and Senegal, and two studies involved animal samples. In all these studies, A. baumannii, E. coli, K. pneu- moniae, and P. aeruginosa were mostly found. According to several studies, in Africa, the actual occurrence of environmental contamination by carbapenem-resistant bacteria is not well researched, although hospital envi- ronments tainted with CRB by infected patients are implicated as the main routes of transmission [11]. Nev- ertheless, the few studies that have been reported in other parts of the world have shown that blaNDM, blaKPC, blaVIM, and blaOXA-48 genes were mostly spreading [94, 95]. These obervations agree with our results indi- cating a prevalence of 28.45% for blaNDM, 21.88% for blaVIM, 4.89% for blaOXA-48, 13.22% for blaOXA-181, and 7.18% for blaKPC. The environment is considered to be the fastest route for the transmission and dissemina- tion of AMR genes. Antibiotics are employed in inten- sive livestock farms, for disease treatment of animals and animal growth promotion, which selects for resistant bacteria and results in presence of antibiotic residues in farming effluents. Therefore, the environment impacted by livestock farming has been regarded as a reservoir for resistant bacteria [94]. Likewise, some studies have reported on the presence of CRB in foods and aquatic products [96]. However, from the articles reviewed, no publication has reported on CRB in food or aquatic ori- gin in West Africa. In this systematic review, PCR and whole genome sequencing (WGS) constituted most methods used to identify carbapenem resistance genes. Our study had some limitations. First, the databases accessed for the publications are more international-ori- ented, which limits the scope of access since most African publications are in local databases. Another limitation is the non-existence of appropriate studies across some of the different countries in WA. In addition to this, there was a disproportion in the number of publications among the included countries, which may have introduced some bias. Also, the use of modern methods for the detection of CRBs was limited in some countries because they are scarce and expensive. Conclusion This review highlighted that in West Africa, Nigeria and Ghana are the countries which have the most publica- tions related to carbapenem-resistant bacteria. Further, the most prevalent CRBs were K. pneumoniae, E. coli, A. baumannii, and P. aeruginosa. Also, among the several types of carbapenem resistance genes detected, blaNDM, blaVIM, and blaOXA-48 were the most prevalent. It can be deduced that by employing the use of a robust molecular platform such as WGS, MALDI-TOF, multi- locus sequence typing (MLST), and phylogenetic analy- ses, all genetic determinants of carbapenem resistance in humans, the environment, and livestock could be iden- tified and documented. These methods could further deepen our understanding of CRB strains circulating in Page 16 of 18Somda et al. BMC Medical Genomics (2024) 17:267 West Africa. The findings of this study highlight the need to implement suitable and appropriate control strategies to reduce complications and prevent the dissemination of resistant bacteria in West Africa. Abbreviations CRE � Carbapenem-resistant Enterobacteriaceae CRB � Carbapenem Resistance Bacteria CR � Carbapenem resistance AJOL � African Journals Online WA � West Africa Acknowledgements The authors acknowledge and thank all the staff of the Department of Medical Microbiology, Medical School, University of Ghana. Author contributions Conception and design: SNS and ESD; development of data screening form: SNS; data screening: SNS and RN; data analysis and interpretation: SNS, RN, PBT-Q and FCNK; draft preparation and revisions: SNS, RN, FCNK, PBT-Q and ESD; funding acquisition: ESD. All the authors approved of the final version of the manuscript. Funding This review paper was supported by the Fogarty International Center of the National Institutes of Health through the Research and Capacity Building in Antimicrobial Resistance in West Africa (RECABAW) Training Programme hosted at the Department of Medical Microbiology, University of Ghana Medical School (Award Number: D43TW012487). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Data availability All data generated or analysed are included in this review. Declarations Ethical approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Received: 24 July 2024 / Accepted: 4 November 2024 References 1. WHO. World Health Organization. Report on surveillance of antibiotic con- sumption: 2016–2018 early implementation.Geneva 2018. 2. Wagenlehner FM, Dittmar F, Re. Global Burden of Bacterial Antimicrobial Resistance in 2019: a systematic analysis. Eur Urol. 2022:S0302–2838 (0322) 02614. 3. WHO: World Health Organization. Global Antimicrobial Resistance and Use Surveillance System (GLASS) Report2021 2021, vol. 2022. 4. Ma C-W, Ng KK-H, Yam BH-C, Ho P-L, Kao RY-T, Yang D. Rapid Broad spectrum detection of carbapenemases with a dual fluorogenic-colorimetric probe. J Am Chem Soc. 2021;143(18):6886–94. 5. Arowolo MT, Orababa OQ, Olaitan MO, Osibeluwo BV, Essiet UU, Batholomew OH, et al. Prevalence of carbapenem resistance in Acinetobacter baumannii and Pseudomonas aeruginosa in sub-saharan Africa: a systematic review and meta-analysis. PLoS ONE. 2023;18(11):e0287762. 6. Marimuthu K, Venkatachalam I, Koh V, Harbarth S, Perencevich E, Cherng BPZ, et al. Whole genome sequencing reveals hidden transmission of carbapene- mase-producing enterobacterales. Nat Commun. 2022;13(1):3052. 7. Mollenkopf DF, Stull JW, Mathys DA, Bowman AS, Feicht SM, Grooters SV, et al. Carbapenemase-producing Enterobacteriaceae recovered from the environ- ment of a swine farrow-to-finish operation in the United States. Antimicrob Agents Chemother. 2017;61(2). https://doi.org/10.1128/aac. 01298 – 01216. 8. Ma J, Song X, Li M, Yu Z, Cheng W, Yu Z, et al. Global spread of carbapenem- resistant Enterobacteriaceae: epidemiological features, resistance mecha- nisms, detection and therapy. Microbiol Res. 2023;266:127249. 9. Nordmann PNT, Poirel L. Propagation mondiale des entérobactéries produc- trices de carbapénémases. Emerg Infect Dis. 2011;17(10):1791–8. 10. Gulumbe BH, Ajibola O. Carbapenem Resistant Enterobacteriaceae in Africa. Institute for Research and Community Services Universitas Muhammadiyah Palangkaraya 2020, 3(2). 11. Ssekatawa K, Byarugaba DK, Wampande E, Ejobi F. A systematic review: the current status of carbapenem resistance in East Africa. BMC Res Notes. 2018;11:1–9. 12. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ. 2003;327(7414):557–60. 13. Tula M, Enabulele O, Ophori E, Okojie R, Joel F. Emergence of New Delhi metallo-β-lactamase-1 (NDM-1) producing enterobacterales from water sources: an impending public health challenge in Adamawa-North senatorial zone, Nigeria. Afr J Clin Exp Microbiol. 2023;24(3):258–65. 14. Le Terrier C, Masseron A, Uwaezuoke NS, Edwin CP, Ekuma AE, Olugbeminiyi F, et al. Wide spread of carbapenemase-producing bacterial isolates in a Nige- rian environment. J Glob Antimicrob Resist. 2020;21:321–3. 15. Odih EE, Sunmonu GT, Okeke IN, Dalsgaard A. NDM-1-and OXA-23-producing Acinetobacter baumannii in wastewater of a Nigerian hospital. Microbiol Spectr. 2023;11(6):e02381–02323. 16. Adelowo OO, Vollmers J, Mäusezahl I, Kaster A-K, Müller JA. Detection of the carbapenemase gene bla VIM-5 in members of the Pseudomonas putida group isolated from polluted Nigerian wetlands. Sci Rep. 2018;8(1):15116. 17. Garba Z, Bonkoungou IO, Millogo NO, Natama HM, Vokouma PA, Bonko MA, et al. Wastewater from healthcare centers in Burkina Faso is a source of ESBL, AmpC-β-lactamase and carbapenemase-producing Escherichia coli and Klebsiella pneumoniae. BMC Microbiol. 2023;23(1):351. 18. Kagambèga AB, Dembélé R, Bientz L, M’Zali F, Mayonnove L, Mohamed AH, et al. Detection and characterization of Carbapenemase-Producing Escherichia coli and Klebsiella pneumoniae from Hospital effluents of Ouagadougou, Burkina Faso. Antibiotics. 2023;12(10):1494. 19. Muhigwa M, Sanou S, Kantagba D, Ouangraoua S, Yehouenou CL, Michodigni F, et al. Characterization of extended-spectrum beta-lactamase and car- bapenemase genes in bacteria from environment in Burkina Faso. J Infect Dev Ctries. 2023;17(12):1714–21. 20. Delgado-Blas JF, Valenzuela Agüi C, Marin Rodriguez E, Serna C, Montero N, Saba CKS, et al. Dissemination routes of carbapenem and pan-aminoglyco- side resistance mechanisms in hospital and urban wastewater canalizations of Ghana. Msystems. 2022;7(1):e01019–01021. 21. Odonkor ST, Simpson SV, Morales Medina WR, Fahrenfeld N. Antibiotic-resis- tant bacteria and resistance genes in isolates from Ghanaian drinking water sources. J. Environ. Health. 2022, 2022. 22. Eger E, Homeier-Bachmann T, Adade E, Dreyer S, Heiden SE, Lübcke P, et al. Carbapenem-and cefiderocol-resistant enterobacterales in surface water in Kumasi, Ashanti Region, Ghana. JAC-Antimicrob Resist. 2024;6(2):dlae021. 23. Kempf M, Rolain J-M, Diatta G, Azza S, Samb B, Mediannikov O et al. Carbape- nem resistance and Acinetobacter baumannii in Senegal: the paradigm of a common phenomenon in natural reservoirs. PLoS ONE 17(12): e0277635. https:/​/doi.or​g/10.13​71/j​ournal.pone.0277635 24. Ejikeugwu C, Nworie O, Saki M, Al-Dahmoshi HO, Al-Khafaji NS, Ezeador C et al. Metallo-β-lactamase and AmpC genes in Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa isolates from abattoir and poultry origin in Nigeria. BMC Microbiol. 2021, 21:1–9. 25. Ajuba IM, Akujobi CN, Aghanya IN, Ushie SN, Okoro AE, Ofiaeli RO, et al. Emer- gence of New-Delhi metallo-betalactamase-1 and oxacillinase-48 positive Escherichia coli in South-Eastern Nigeria. Niger J Med. 2020;29(3):701–6. 26. Yehouenou CL, Soleimani R, Kpangon AA, Simon A, Dossou FM, Dalleur O. Carbapenem-resistant organisms isolated in surgical site infections in Benin: a public health problem. Trop Med Infect Dis. 2022;7(8):200. 27. Garba Z, Kaboré B, Bonkoungou IJ, Natama MH, Rouamba T, Haukka K, et al. Phenotypic detection of Carbapenemase and AmpC-β-Lactamase production among extended spectrum β-Lactamase (ESBL)-Producing Esch- erichia coli and Klebsiella spp. Isolated from clinical specimens. Antibiotics. 2023;13(1):31. https://doi.org/10.1128/aac https://doi.org/10.1371/journal.pone.0277635 Page 17 of 18Somda et al. BMC Medical Genomics (2024) 17:267 28. Sanou S, Ouedraogo AS, Aberkane S, Vendrell J, Ouchar O, Bouzimbi N, et al. Prevalence and molecular characterization of extended spectrum β-lactamase, plasmid-mediated quinolone resistance, and carbapenemase- producing gram-negative bacilli in Burkina Faso. Microb Drug Resist. 2021;27(1):18–24. 29. Acolatse JEE, Portal EA, Boostrom I, Akafity G, Dakroah MP, Chalker VJ, et al. Environmental surveillance of ESBL and carbapenemase-producing gram- negative bacteria in a Ghanaian Tertiary Hospital. Antimicrob Resist Infect Control. 2022;11(1):49. 30. Prah I, Ayibieke A, Mahazu S, Sassa CT, Hayashi T, Yamaoka S, et al. Emergence of oxacillinase-181 carbapenemase-producing diarrheagenic Escherichia coli in Ghana. Emerg Microbes Infect. 2021;10(1):865–73. 31. Janice J, Agyepong N, Owusu-Ofori A, Govinden U, Essack SY, Samuelsen Ø, et al. Carbapenem resistance determinants acquired through novel chromo- somal integrations in extensively drug-resistant Pseudomonas aeruginosa. Antimicrob Agents Chemother. 2021;65(7). https://doi.org/10.1128/aac. 00289 – 00221. 32. Codjoe FS, Donkor ES, Smith TJ, Miller K. Phenotypic and genotypic charac- terization of carbapenem-resistant gram-negative bacilli pathogens from hospitals in Ghana. Microb Drug Resist. 2019;25(10):1449–57. 33. Alafate A, Ayumi K, Masato S, Wakana S, Samiratu M, Isaac P et al. Prevalence and characterization of carbapenem-hydrolyzing class D β-lactamase- producing Acinetobacter isolates from Ghana. Front Microbiol 2021, 11. 34. Monnheimer M, Cooper P, Amegbletor HK, Pellio T, Groß U, Pfeifer Y, et al. High prevalence of carbapenemase-producing Acinetobacter baumannii in wound infections, Ghana, 2017/2018. Microorganisms. 2021;9(3):537. 35. Sampah J, Owusu-Frimpong I, Aboagye FT, Owusu-Ofori A. Prevalence of carbapenem-resistant and extended-spectrum beta-lactamase- producing Enterobacteriaceae in a teaching hospital in Ghana. PLoS ONE. 2023;18(10):e0274156. 36. Dwomoh FP, Kotey FC, Dayie NT, Osei M-M, Amoa-Owusu F, Bannah V, et al. Phenotypic and genotypic detection of carbapenemase-producing Escherichia coli and Klebsiella pneumoniae in Accra, Ghana. PLoS ONE. 2022;17(12):e0279715. 37. Kalambry AC, Potindji TMF, Guindo I, Kassogue A, Drame BSI, Togo S, et al. ESBL and carbapenemase-producing Enterobacteriaceae in infectious pleural effusions: current epidemiology at Hôpital Du Mali. Drug Target Insights. 2023;17:92. 38. Sarr H, Niang AA, Diop A, Mediannikov O, Zerrouki H, Diene SM, et al. The emergence of carbapenem-and colistin-resistant enterobacteria in Senegal. Pathogens. 2023;12(8):974. 39. Kaboré B, Ouédraogo HS, Zongo O, Ouédraogo GA, Tapsoba F, Bougma S et al. Emergence of New Delhi metallo-β-Lactamase (NDM) genes detected from clinical strains of Escherichia coli isolated in Ouagadougou, Burkina Faso. Int. J. Microbiol. 2023, 2023. 40. Labi A-K, Nielsen KL, Marvig RL, Bjerrum S, Enweronu-Laryea C, Bennedbæk M, et al. Oxacillinase-181 carbapenemase-producing Klebsiella pneumoniae in neonatal intensive care unit, Ghana, 2017–2019. Emerg Infect Dis. 2020;26(9):2235. 41. Olu-Taiwo MA, Opintan JA, Codjoe FS, Obeng Forson A. Metallo-beta-lacta- mase-producing Acinetobacter spp. from clinical isolates at a tertiary care hospital in Ghana. Biomed Res. Int. 2020, 2020. 42. Mahazu S, Prah I, Ayibieke A, Sato W, Hayashi T, Suzuki T, et al. Possible dissemination of Escherichia coli sequence type 410 closely related to B4/ H24RxC in Ghana. Front Microbiol. 2021;12:770130. 43. Mahazu S, Prah I, Ota Y, Hayashi T, Nukui Y, Suzuki M, et al. Klebsiella spe- cies and Enterobacter cloacae isolates harboring bla OXA-181 and bla OXA-48: Resistome, Fitness cost, and plasmid Stability. Microbiol Spectr. 2022;10(6):e03320–03322. 44. Obeng-Nkrumah N, Hansen DS, Awuah-Mensah G, Blankson NK, Frimodt- Møller N, Newman MJ, et al. High level of colonization with third-generation cephalosporin-resistant enterobacterales in African community settings. Ghana Diagn Microbiol Infect Dis. 2023;106(1):115918. 45. Owusu FA, Obeng-Nkrumah N, Gyinae E, Kodom S, Tagoe R, Tabi BKA, et al. Occurrence of Carbapenemases, extended-spectrum Beta-lactamases and AmpCs among Beta-lactamase-producing gram-negative Bacteria from clini- cal sources in Accra, Ghana. Antibiotics. 2023;12(6):1016. 46. Codjoe FS, Brown CA, Smith TJ, Miller K, Donkor ES. Genetic relatedness in carbapenem-resistant isolates from clinical specimens in Ghana using ERIC- PCR technique. PLoS ONE. 2019;14(9):e0222168. 47. Muggeo A, Maiga A, Maiga I, Brasme L, Dicko OA, de Champs C, et al. First description of IncX3 NDM-5-producing plasmid within Escherichia coli ST448 in Mali. J Med Microbiol. 2020;69(5):685–8. 48. Baron SA, Mediannikov O, Abdallah R, Kuete Yimagou E, Medkour H, Dubourg G, et al. Multidrug-resistant Klebsiella pneumoniae clones from wild chim- panzees and termites in Senegal. Antimicrob Agents Chemother. 2021;65(9). https://doi.org/10.1128/aac. 02557 – 02520. 49. Dossim S, Bonnin RA, Salou M, Tanga K, Godonou V, Dagnra AY, et al. Occur- rence of carbapenemase-producing Enterobacteriaceae in Togo, West Africa. Int J Antimicrob Agents. 2019;53(4):530–2. 50. Ogbolu D, Webber M. High-level and novel mechanisms of carbapenem resistance in Gram-negative bacteria from tertiary hospitals in Nigeria. Int J Antimicrob Agents. 2014;43(5):412–7. 51. Tickler IA, Shettima SA, Dela Cruz CM, Le VM, Dewell S, Sumner J, Tenover FC. Characterization of carbapenem-resistant gram-negative bacterial isolates from Nigeria by whole genome sequencing. Diagn Microbiol Infect Dis. 2021;101(1):115422. 52. Ogbolu DO, Piddock LJ, Webber MA. Opening Pandora’s box: high-level resistance to antibiotics of last resort in Gram-negative bacteria from Nigeria. J Glob Antimicrob Resist. 2020;21:211–7. 53. Shettima SA, Tickler IA, dela Cruz CM, Tenover FC. Characterisation of carbapenem-resistant Gram-negative organisms from clinical specimens in Yola. Nigeria J Glob Antimicrob Resist. 2020;21:42–5. 54. Medugu N, Tickler IA, Duru C, Egah R, James AO, Odili V, et al. Phenotypic and molecular characterization of beta-lactam resistant multidrug-resistant Enterobacterales isolated from patients attending six hospitals in Northern Nigeria. Sci Rep. 2023;13(1):10306. 55. Ogbolu DO, Alli OAT, Oluremi AS, Ogunjimi YT, Ojebode DI, Dada V, et al. Contribution of NDM and OXA-type carbapenemases to carbapenem resistance in clinical Acinetobacter baumannii from Nigeria. Infect Dis. 2020;52(9):644–50. 56. Olalekan A, Bader BK, Iwalokun B, Wolf S, Lalremruata A, Dike A, et al. High incidence of carbapenemase-producing Pseudomonas aeruginosa clinical isolates from Lagos, Nigeria. JAC-Antimicrob Resist. 2023;5(2):dlad038. 57. Odih EE, Irek EO, Obadare TO, Oaikhena AO, Afolayan AO, Underwood A, et al. Rectal colonization and nosocomial transmission of carbapenem-resistant Acinetobacter baumannii in an intensive care unit, Southwest Nigeria. Front Med. 2022;9:846051. 58. Odewale G, Jibola-Shittu MY, Ojurongbe O, Olowe RA, Olowe OA. Genotypic determination of Extended Spectrum β-Lactamases and carbapenemase production in clinical isolates of Klebsiella pneumoniae in Southwest Nigeria. Infect Dis Rep. 2023;15(3):339–53. 59. Brinkac LM, White R, D’Souza R, Nguyen K, Obaro SK, Fouts DE. Emergence of New Delhi metallo-β-lactamase (NDM-5) in Klebsiella quasipneumoniae from neonates in a Nigerian hospital. MSphere. 2019;4(2). ​h​t​t​​p​s​:​/​​/​d​o​​i​.​​o​r​g​/​1​0​.​1​1​2​8​/​ m​s​p​h​e​r​e​​​​​. 00685 – 00618. 60. Odih EE, Oaikhena AO, Underwood A, Hounmanou YMG, Oduyebo OO, Fadeyi A, et al. High genetic diversity of carbapenem-resistant Acinetobacter baumannii isolates recovered in Nigerian hospitals in 2016 to 2020. Msphere. 2023;8(3):e00098–00023. 61. Kawa DE, Tickler IA, Tenover FC, Shettima SA. Characterization of Beta- lactamase and Fluoroquinolone Resistance determinants in Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa isolates from a Tertiary Hospital in Yola, Nigeria. Trop med Infect. 2023;8(11):500. 62. Zubair K, Iregbu K. Resistance pattern and detection of metallo–beta–lac- tamase genes in clinical isolates of Pseudomonas aeruginosa in a central Nigeria tertiary hospital. Niger J Clin Pract. 2018;21(2):176–82. 63. Olowo-okere A, Ibrahim YKE, Ehinmidu JO, Mohammed Y, Nabti LZ, Olayinka BO. Emergence of VIM metallo-β-lactamase among carbapenem-resistant Pseudomonas species in northwest Nigeria. Gene Rep. 2020;21:100877. 64. Nkup J, Joseph S, Agabi Y, David V, Hashimu Z, Madubulum CC et al. Molecu- lar detection of carbapenem resistant Klebsiella pneumoniae isolated from clinical specimens in Jos, Nigeria. Microbes Infect. 2022. 65. Yakubu A, Anua SM, Suraiya S, Mazlan N. Preliminary screening of Bla NDM-1 gene of Carbapenem-resistant Klebsiella pneumoniae in clinical samples of patients at a Teaching Hospital. Malays J Med Health Sci. 2023, 19. 66. Lo G, Dieng A, Ba-Diallo A, Samb M, Tine A, Lo Ndiaye S, et al. Molecular Epidemiology of Carbapenem-resistant Acinetobacter baumannii isolates in a Senegalese University Teaching Hospital. J Adv Microbiol. 2022;22:73–82. 67. Ndiaye I, Bissoume Sambe B, Thiam F, Mouhamadou Moustapha B, Ousmane S, Cissé A, et al. Antibiotic Resistance and virulence factors of extended- spectrum beta-lactamase-producing Klebsiella Pneumoniae involved in https://doi.org/10.1128/aac https://doi.org/10.1128/aac https://doi.org/10.1128/msphere https://doi.org/10.1128/msphere Page 18 of 18Somda et al. BMC Medical Genomics (2024) 17:267 Healthcare-Associated infections in Dakar. Senegal Arch Microbiol Immunol. 2023;7(2):65–75. 68. Ouedraogo A-S, Dunyach-Remy C, Kissou A, Sanou S, Poda A, Kyelem CG, et al. High nasal carriage rate of Staphylococcus aureus containing panton- valentine leukocidin-and EDIN-encoding genes in community and hospital settings in Burkina Faso. Front Microbiol. 2016;7:1406. 69. Jesumirhewe C, Springer B, Lepuschitz S, Allerberger F, Ruppitsch W. Carbapenemase-producing enterobacteriaceae isolates from Edo State. Nigeria Antimicrob Agents Chemother. 2017;61(8). ​h​t​t​p​s​:​/​/​d​o​i​.​o​r​g​/​1​0​.​1​1​2​8​/​a​ a​c​​​​​. 00255 – 00217. 70. Diene SM, Fall B, Kempf M, Fenollar F, Sow K, Niang B, et al. Emergence of the OXA-23 carbapenemase-encoding gene in multidrug-resistant Acineto- bacter baumannii clinical isolates from the principal hospital of Dakar, Senegal. Int J Infect Dis. 2013;17(3):e209–10. 71. Ofosu-Appiah F, Acquah EE, Mohammed J, Sakyi Addo C, Agbodzi B, Ofosu DA et al. Klebsiella pneumoniae ST147 harboring bla NDM-1, multidrug resis- tance and hypervirulence plasmids. Microbiol Spectr. 2024:e03017–03023. 72. Yehouenou C, Bogaerts B, Vanneste K, Roosens NH, De Keersmaecker SC, Marchal K, et al. First detection of a plasmid-encoded New-Delhi metallo-beta-lactamase-1 (NDM-1) producing Acinetobacter baumannii using whole genome sequencing, isolated in a clinical setting in Benin. Ann clin Microbiol Antimicrob. 2021;20:1–7. 73. Somda N, Tankoano A, Métuor-Dabiré A, Kaboré D, Bonkoungou J, Kpoda D, et al. A systematic review and meta-analysis of antibiotic resistance of food- borne pathogenic bacteria in West Africa between 2010 and 2020. J Food Prot. 2023;86(3):100061. 74. Uthman OA, Uthman MB. Geography of Africa biomedical publications: an analysis of 1996–2005 PubMed papers. Afr J Food Agric Nutr Dev. 2008;8(2):1–11. 75. Ayobami O, Willrich N, Harder T, Okeke IN, Eckmanns T, Markwart R. The inci- dence and prevalence of hospital-acquired (carbapenem-resistant) Acineto- bacter baumannii in Europe, Eastern Mediterranean and Africa: a systematic review and meta-analysis. Emerg Microbes Infect. 2019;8(1):1747–59. 76. CDC. Facility guidance for control of carbapenem-resistant Enterobacteria- ceae (CRE). Atlanta: United States Department of Health and Human Services; 2015. 77. Dossouvi KM, Bakpatina-Batako KD. Carbapenem resistance in West Africa: a systematic review. Microbiol Indep Res J. 2024;11(1):25–56. 78. Sader HS, Carvalhaes CG, Arends SR, Castanheira M, Mendes RE. Aztreonam/ avibactam activity against clinical isolates of Enterobacterales collected in Europe, Asia and Latin America in 2019. J Antimicrob Chemother. 2021;76(3):659–66. 79. Mitgang EA, Hartley DM, Malchione MD, Koch M, Goodman JL. Review and mapping of carbapenem-resistant Enterobacteriaceae in Africa: using diverse data to inform surveillance gaps. Int J Antimicrob Agents. 2018;52(3):372–84. 80. Garza-González E, Bocanegra-Ibarias P, Bobadilla-del-Valle M, Ponce-de-León- Garduño LA, Esteban-Kenel V, Silva-Sánchez J, et al. Drug resistance pheno- types and genotypes in Mexico in representative gram-negative species: results from the infivar network. PLoS ONE. 2021;16(3):e0248614. 81. Zhou N, Cheng Z, Zhang X, Lv C, Guo C, Liu H, et al. Global antimicrobial resistance: a system-wide comprehensive investigation using the Global One Health Index. Infect Dis Poverty. 2022;11(1):92. 82. Sharifipour E, Shams S, Esmkhani M, Khodadadi J, Fotouhi-Ardakani R, Kooh- paei A, et al. Evaluation of bacterial co-infections of the respiratory tract in COVID-19 patients admitted to ICU. BMC Infect Dis. 2020;20:1–7. 83. Oduyebo O, Falayi O, Oshun P, Ettu A. Phenotypic determination of carbapen- emase producing Enterobacteriaceae isolates from clinical specimens at a tertiary hospital in Lagos. Nigeria Niger Postgrad Med J. 2015;22(4):223–7. 84. Cai B, Echols R, Magee G, Arjona Ferreira JC, Morgan G, Ariyasu M et al. Preva- lence of carbapenem-resistant gram-negative infections in the United States predominated by Acinetobacter baumannii and Pseudomonas aeruginosa. In: Open Forum Infect. Dis. 2017. Oxford University Press US: ofx176. 85. Mate PH, Devi KS, Devi KM, Damrolien S, Devi NL, Devi P. Prevalence of carbapenem resistance among Gram-negative bacteria in a tertiary care hospital in north-east India. IOSR J Dent Med Sci. 2014;13(12):56–60. 86. Singh-Moodley A, Perovic O. Antimicrobial susceptibility testing in predicting the presence of carbapenemase genes in Enterobacteriaceae in South Africa. BMC Infect Dis. 2016;16:1–10. 87. Awosile BB, Agbaje M, Adebowale O, Kehinde O, Omoshaba E. Beta-lacta- mase resistance genes in Enterobacteriaceae from Nigeria. Afr J Lab Med. 2022;11(1):1371. 88. El-Kholy A, El-Mahallawy HA, Elsharnouby N, Abdel Aziz M, Helmy AM, Kotb R. Landscape of multidrug-resistant gram-negative infections in Egypt: survey and literature review. Infect Drug Resist. 2021:1905–20. 89. Kazmierczak KMKJ, de Jonge BLM, Stone GG, Sahm DF. Epidemiology of car- bapenem resistance determinants identified in meropenem-nonsusceptible Enterobacterales collected as part of a global surveillance program, 2012 to 2017. Antimicrob Agents Chemother. 2021;65:e02000–02020. 90. Umair M, Walsh TR, Mohsin M. A systematic review and meta-analysis of car- bapenem resistance and its possible treatment options with focus on clinical Enterobacteriaceae: thirty years of development in Pakistan. Heliyon; 2024. 91. Bonomo RA, Burd EM, Conly J, Limbago BM, Poirel L, Segre JA, et al. Carbapenemase-producing organisms: a global scourge. Clin Infect Dis. 2018;66(8):1290–7. 92. De Angelis G, Grossi A, Menchinelli G, Boccia S, Sanguinetti M, Posteraro B. Rapid molecular tests for detection of antimicrobial resistance determinants in Gram-negative organisms from positive blood cultures: a systematic review and meta-analysis. Clin Microbiol Infect. 2020;26(3):271–80. 93. Puljko A, Barišić I, Rozman SD, Križanović S, Babić I, Jelić M, et al. Molecular epidemiology and mechanisms of carbapenem and colistin resistance in Klebsiella and other enterobacterales from treated wastewater in Croatia. Environ Int. 2024;185:108554. 94. Pereira AL, de Oliveira PM, Faria-Junior C, de Alves EG, Castro e Caldo Lima GR, da Costa Lamounier TA, et al. Environmental spreading of clinically relevant carbapenem-resistant gram-negative bacilli: the occurrence of Bla KPC-or- NDM strains relates to local hospital activities. BMC Microbiol. 2022;22:1–12. 95. Subirats J, Royo E, Balcázar JL, Borrego CM. Real-time PCR assays for the detection and quantification of carbapenemase genes (bla KPC, bla NDM, and Bla OXA-48) in environmental samples. Environ Sci Pollut. 2017;24:6710–4. 96. Ramírez-Castillo FY, Guerrero-Barrera AL, Avelar-González FJ. An overview of carbapenem-resistant organisms from food-producing animals, seafood, aquaculture, companion animals, and wildlife. Front Vet Sci. 2023;10:1158588. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. https://doi.org/10.1128/aac https://doi.org/10.1128/aac A systematic review and meta-analysis of carbapenem-resistant Enterobacteriaceae in West Africa Abstract Background Methods Study design Study eligibility criteria Data extraction Data analysis Results Systematic literature review and study Distribution of the articles according to publication year Distribution of the articles according to sample origin Prevalence of pathogens included Prevalence of CR bacteria in West Africa Prevalence of CR genetic determinants in West Africa Distribution of CR genetic determinants by countries in West Africa Average prevalence of CR genetic determinants detected in the pathogens Nigeria Ghana Burkina Faso Senegal Discussion Conclusion References