Skip to main content
Download PDF

Drug resistance and extended-spectrum β-lactamase (ESBLs) - producing Enterobacteriaceae, Acinetobacter and Pseudomonas species from the views of one-health approach in Ethiopia: a systematic review and meta-analysis

One Health Outlook volume 5, Article number: 12 (2023) Cite this article

Abstract

Background

Although antimicrobial resistance (AMR) bacteria present a significant and ongoing public health challenge, its magnitude remains poorly understood, especially in many parts of the developing countries. Hence, this review was conducted to describe the current pooled prevalence of drug resistance, multidrug- resistance (MDR), and Extended-spectrum β-lactamase (ESBL)-producing Enterobacteriaceae, Acinetobacter, and Pseudomonas species in humans, the environment, and animals or food of animal origin in Ethiopia.

Methods

PubMed, Google Scholar, and other sources were searched for relevant articles as per the preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidelines. A critical appraisal for screening, eligibility, and inclusion in the meta-analysis was made based on the Joanna Briggs Institute’s (JBI) essential appraisal tools. The meta-analysis was done on Statistical Software Package (STATA) version 17.0.

Results

A total of 33 research articles were included in this systematic review and meta-analysis. Escherichia coli, Klebsiella species, Acinetobacter, and Pseudomonas species were the most frequently reported bacteria from two or more sources. More than 50% of Klebsiella species and 25% to 89% of Escherichia coli from two or more sources were resistant to all analysed antibiotics, except carbapenems. Fifty-five percent (55%) to 84% of Acinetobacter species and 33% to 79% of Pseudomonas species from human and environmental sources were resistant to all analyzed antibiotics. Carbapenem resistance was common in Acinetobacter and Pseudomonas species (38% to 64%) but uncommon in Enterobacteriaceae (19% to 44%). Acinetobacter species (92%), Klebsiella species (86%), and Pseudomonas species (79%) from human sources, and Proteus species (92%), and Acinetobacter species (83%), from environmental sources, were the common multidrug-resistant isolates. About 45% to 67% of E. coli, Klebsiella, Acinetobacter, and Pseudomonas species from human and environmental sources were ESBL producers.

Conclusion

Our review report concluded that there was a significant pooled prevalence of drug resistance, MDR, and ESBL-producing Enterobacteriaceae, Acinetobacter, and Pseudomonas species from two or more sources. Hence, our finding underlines the need for the implementation of integrated intervention approaches to address the gaps in reducing the emergence and spread of antibiotic- resistant bacteria.

Background

Antimicrobial resistance (AMR) remains a significant One- Health problem, affecting humans, animals, and the environment [1]. The infections caused by AMR bacteria are becoming more prevalent and can be difficult, and sometimes impossible to treat because the available drugs used to treat microbial infections have become less effective or ineffective. The AMR threat adds to the existing higher burden of bacterial infections, particularly in low- and middle-income settings in which there has been low access to adequate diagnostics, specifically at peripheral levels of the healthcare system. In addition to increased morbidity and mortality, resistant infections also add considerable costs to the healthcare system [1,2,3].

AMR gram-negative bacteria are the most frequently encountered bacterial isolates recovered from different clinical and non-clinical specimens [3]. The emergence of ESBL-producing and carbapenem-resistant gram-negative bacteria, particularly Klebsiella pneumoniae, Escherichia coli, Acinetobacter baumannii, and Pseudomonas aeruginosa, are a matter of national and international concern as they are an emerging cause of healthcare-associated infection (HAI) that pose a significant threat to human and animal health [4, 5]. The infections caused by these bacteria may not be treated with the available antibiotics due to high levels of resistance and are associated with poor treatment outcomes. Importantly, although there are existing knowledge gaps in understanding the transmission pathway of AMR bacteria, there are various routes for widespread transmission of resistance bacteria and genes between humans, animals and the surrounding environment [1, 6]. Resistant bacteria can spread across humans and animal communities, the food supply, healthcare facilities, and the environment, which increases the burden of resistance and antibiotic-resistant infections [6, 7].

Anyone of any age, in every country, can potentially be affected by the consequences of AMR. For instance, an estimated 4·95 million deaths were associated with bacterial AMR in 2019, and if not properly addressed, the numbers may be increase to 10 million per year by 2050 [8, 9]. The main factors exacerbating the issue of AMR in low-resource countries include limited access to quality antimicrobial drugs; antibiotics sold over the counter without prescriptions, or antibiotics used in feeding animals as prophylaxis or growth promoters. The issue of a lack of regulation and quality control of drugs, coupled with poor infection prevention and water, sanitation, and hygiene interventions, can accelerate the emergence and spread of drug-resistant microorganisms [10,11,12,13].

The ongoing public health threat of AMR bacteria was highlighted on the WHO list of critical-priorities for the need of new researches, discovery, and development of new antibiotics [14]. Ethiopia has also implemented the One Health approach to respond to the existing and emerging health security threats, including AMR [15]. However, poor integration among sectors, the institutionalization of One- Health as a good approach, limited research funds, and activities on One- Health are among the many challenges that need to be addressed. So far, no study has reported the current situation of AMR and ESBL-producing combinations in our country. Therefore, this systematic review and meta-analysis aimed to determine: I) the pooled prevalence of resistance to commonly prescribed broad-spectrum antibiotics; II) the pooled prevalence of MDR; and III) the pooled prevalence of ESBL-producing Enterobacteriaceae, Acinetobacter, and Pseudomonas species from humans, the environment, and animals, or food sources.

Main text

Data sources and search strategy

Objective and reproducible searches were made on PubMed and Google Scholar to find published articles related to our outcomes of interest. On PubMed, the following search string words were used: "drug resistance"[Mesh] OR "drug resistance, multiple, bacterial"[Mesh] OR "drug resistance, bacterial"[Mesh] OR "drug resistance, multiple"[Mesh] OR "drug resistance, microbial"[Mesh]) OR ("Enterobacteriaceae" [Mesh] OR "Enterobacteriaceae infections"[Mesh] OR "beta-lactamase, Enterobacteriaceae" [Supplementary concept]) OR ("Acinetobacter species"[Mesh] OR "Acinetobacter baumannii"[Mesh] OR "Acinetobacter infections"[Mesh] OR "beta-lactamase, Acinetobacter baumannii" [Supplementary concept] OR ("Pseudomonas species"[Mesh] OR "Pseudomonas infections"[Mesh] OR "Pseudomonas aeruginosa"[Mesh]) AND ("humans"[Mesh]) OR ("animals"[Mesh]) AND "human-animal interaction"[Mesh]) OR ("meat products"[Mesh]) OR ("poultry"[Mesh] OR "poultry products"[Mesh]) OR ("chicken"[Mesh]) OR ("cattle"[Mesh] OR "cattle diseases"[Mesh]) OR ("environment"[Mesh] OR "health facility environment"[Mesh]) AND ("Ethiopia"[Mesh]). The searching process was filtered by year of publication, from January 2014 to October 2022, and full-text research articles. Additionally, relevant studies were manually searched from the bibliographies of eligible studies and from other meta-analysis studies.

Selection and eligibility criteria

The systematic and comprehensive literature review methods were used to identify, select, and critically appraise relevant research and to collect and analyze data from the studies that are included in the review. Those research articles conducted in Ethiopia and published in English as research articles in the years 2014 to 2022, and those articles focusing on the reports of antimicrobial-resistant Enterobacteriaceae, Acinetobacter, and Pseudomonas species in humans, animals, or food of animal origin, and those that provided details on the number of studied isolates, are used as criteria for eligibility for the review. On the other side, those articles that did not provide full information on the outcomes of interest, provided data on gram positives only, conducted molecular investigations of AMR molecular markers only, were not freely accessible as a full text, and those reviewed articles on AMR were excluded. In order to guarantee the quality of studies, two independent reviewers were assigned to select the articles throughout each stage of the review (i.e., screening, eligibility, and inclusion in meta-analysis).

Article quality assessment

The article selection process was done based on the preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidelines [16] (Fig. 1). The quality assessment and enrollment of each article were made by two independently critical appraisers based on the Joanna Briggs Institute (JBI) critical appraisal tools [17] and the Cochrane Handbook for Systematic Reviews [18]. The criteria for quality assessment include: whether the research question is clear and adequate to the study; whether the study design used is appropriate to the set research question; whether descriptions of the setting, including periods of recruitment, and the sampling method are appropriate for the set research question and design; and whether the collected data was properly managed and analyzed. In addition, a comprehensive search strategy was made in order to reduce the impact of publication bias on the results of the review.

Fig. 1
figure 1

Flow diagram depicting the selection process of included articles

Full size image

Data extraction

An Excel database was designed for the purpose of extracting data from the included studies. The first author, publication year, study region or area, study or data collection period, study design, study subjects, type of sample, type and numbers of selected gram-negative bacteria, the number of isolates tested for antimicrobial resistance, the number of isolates reported as MDR, and if reported, the numbers of ESBL producers were extracted. Additionally, the investigation method (phenotypic or genotypic) was extracted. The data extraction process was done independently and in duplicate using piloting forms to ensure double-checking.

Data analysis

The total number of each bacterium species and the number of isolates tested for antimicrobial resistance from each source were extracted, and meta-analysis was done on STATA version 17. The pooled prevalence of AMR, MDR, and ESBL production for each bacterium was analyzed using the random-effects model. Cochran Q tests and the I2 statistic were used to analyze the heterogeneity of the studies, and significant variation was considered at p-values < 0.05 and I2 > 50% [19]. For the studies on the environment and food-producing animals, the meta-analysis was done if the outcome of interests was reported in at least three studies, whereas at least four studies were considered in the case of human sources. The pooled percentage for each reported resistant gram-negative species was then deduced from the total number of tested isolates. A categorical meta-analysis for each antibiotic resistance isolate was made based on their sources. Begg’s and Mazumdar rank correlation test was performed to assess the publication biases across the studies, and statistical significance was considered at a p-value < 0.05. Testing for publication bias and heterogeneity was carried out to check the extent of the variation in study outcomes between the included studies and whether the results of the studies were valid for systematic reviews and meta-analyses. Finally, the results were narrated in words and presented in figures and tables that were best suited for readers.

Results

General characteristics of the included studies

In this systematic review and meta-analysis, a total of 33 studies were included; of these, 14 were human studies, 11 were on environmental studies, and 8 were related to animals or foods of animal origin (Fig. 1). The included studies were published from 2014 to 2022, and 30 studies were done with a cross-sectional study design; two studies were retrospective and one was a cohort study. Based on the study area, half of human studies (50.0%) were from the Amhara region, 4 (36.4%) of the environmental studies were from southern Ethiopia, and 3 (37.5%) studies on animals or foods of animal origin were from Addis Ababa (Table 1).

Table 1 General characteristics of included studies (2014- 2022)
Full size table

Out of the 14 included studies on humans, 10 studies involved various clinical samples for the detection of drug-resistant bacteria from patients with multiple infections. Bloodstream infections (BSIs), urinary tract infections, nosocomial infections, and other conditions are commonly considered medical conditions from which drug-resistant bacteria isolates were reported. In studies involving animals or foods of animal origin, raw milk, meat or carcass swabs, animal feeds, and chicken droppings and caecum were the most frequently considered specimens in the detection of drug-resistant isolates. Swabs from hospital contact surfaces and mobile phones, indoor air, and waste/river water are the sources of samples for environmental studies. The detailed characteristics of the studies are presented below in Table 1.

In this review, Begg’s and Mazumdar rank correlation test showed that no significant effect of publication bias was observed among the included studies (p-value > 0.05). However, the variation in the study methodology, setups, study periods, and study populations could have an effect on the heterogeneity among the included studies.

The frequency of selected bacterial isolates

In this review, a total of 12 species of gram-negative bacteria were extracted; however, the meta-analysis was computed for eight gram-negative bacteria from studies in humans, the environment, and animals, or food of animal origin. Escherichia coli (n = 716), Klebsiella species (n = 543), Pseudomonas species (n = 401), and Acinetobacter species (n = 366) were the most frequently reported species from two or more sources (Fig. 2).

Fig. 2
figure 2

Type and frequency of bacteria isolated from humans, environment and from animals or food of animal origin

Full size image

The pooled prevalence of AMR for selected bacterial isolates

The pooled prevalence of AMR for each bacterium-antibiotic combination in each source was estimated using a random effect model. Accordingly, from isolates of humans, E. coli was reported to have a high proportion of pooled resistance to ampicillin (0.89; 95% CI: 0.81, 0.94), co-trimoxazole (0.83; 95% CI: 0.72, 0.91), ceftriaxone (0.79; 95% CI: 0.65, 0.88), ciprofloxacillin (0.77; 95% CI: 0.63, 0.87), and gentamycin (0.73; 95% CI: 0.56, 0.85). As E. coli, Klebsiella spp. showed a higher proportion of resistance to co-trimoxazole (0.82; 95% CI: 0.71, 0.90), ceftriaxone (0.80; 95% CI: 0.67, 0.88), ciprofloxacillin (0.73; 95% CI: 0.58, 0.85), and gentamycin (0.78; 95% CI: 0.65, 0.87), but relatively lower rates of resistance were observed to meropenem (0.38; 95% CI: 0.14, 0.70). However, a proportion of 0.64 (95% CI: 0.48, 0.78) Acinetobacter species and 0.55 (95% CI: 0.33, 0.74) Pseudomonas species was resistant to meropenem (Table 2).

Table 2 Estimated AMR gram-negative bacteria isolated from humans, animals/food, and the environment
Full size table

Among the isolates from the environmental sources, Klebsiella species accounted for the highest proportion of pooled resistance to ampicillin (0.82; 95% CI: 0.72, 0.89), amoxicillin-clavunilic acid (0.68; 95% CI: 0.54, 0.79), ceftriaxone (0.60; 95% CI: 0.44, 0.74), and co-trimoxazole (0.70; 95% CI: 0.57, 0.81). E. coli was also reported to have a high rate of pooled resistance to ampicillin (0.78; 95% CI: 0.67, 0.85), ceftriaxone (0.63; 95% CI: 0.50, 0.75), and co-trimoxazole (0.61; 95% CI: 0.48, 0.73). More than 70% of Acinetobacter species were resistant to most tested antibiotics, specifically ceftriaxone (0.81; 95% CI: 0.71, 0.88), co-trimoxazole (0.84; 95% CI: 0.75, 0.90), gentamycin (0.78; 95% CI: 0.67, 0.86), and ciprofloxacillin (0.74; 95% CI: 0.63, 0.83). A high proportion of resistance was also reported by Pseudomonas species to ceftriaxone (0.59; 95% CI: 0.44, 0.72), ciprofloxacillin (0.66; 95% CI: 0.53, 0.77), and co-trimoxazole (0.64; 95% CI: 0.50, 0.75). Resistance to meropenem was observed in 0.55 (95% CI: 0.38, 0.71) of Acinetobacter species, in 0.44 (95% CI: 0.25, 0.65) of Klebsiella spp., and in 0.38 (95% CI: 0.21, 0.58) of Pseudomonas species (Table 2).

Among isolates from animals or food of animal origin, the highest proportions of resistance to ampicillin (0.79; 95% CI: 0.68, 0.87), amoxicillin-clavunilic acid (0.50; 95% CI: 0.31, 0.69), and co-trimoxazole (0.51; 95% CI: 0.31, 0.71) were reported in E. coli. Salmonella species also showed the highest proportion of resistance to ampicillin (0.66; 95% CI: 0.52, 0.78), amoxicillin-clavunilic acid (0.61; 95% CI: 0.45, 0.74), and co-trimoxazole (0.63; 95% CI: 0.48, 0.76) (Table 2).

The pooled proportion of MDR bacterial isolates

In this review, the pooled prevalence of MDR for each bacterium was computed from the forest plots and was only calculated when the total number of isolates tested for multidrug resistance from the sectors was ≥ 50. Among human isolates, Acinetobacter species showed the highest pooled proportion of MDR (0.92; 95% CI: 0.75, 1.00), followed by Klebsiella species (0.86; 95% CI: 0.64, 0.98), and Pseudomonas species (0.79; 95% CI: 0.61, 0.93). Among the isolates from environmental studies, the highest proportion of MDR was found in Proteus species (0.94; 95% CI: 0.89, 0.97), Acinetobacter species (0.83; 95% CI: 0.45, 1.00), and Klebsiella species (0.70; 95% CI: 0.32, 0.98). In the case of isolates from animals or food of animal origin, E. coli and Salmonella species were reported with a pooled MDR of 0.36 (95% CI: 0.24, 0.50) and 0.29 (95% CI: 0.12, 0.42), respectively (Table 3).

Table 3 Estimated rate of MDR in gram-negative bacteria from humans, animals/food, and the environment
Full size table

The pooled prevalence of ESBL- production

In this review, the rate of ESBL production was also computed from the forest plots for each bacterium. Among human isolates, the highest proportion of ESBL production was recorded by Pseudomonas species (0.67; 95% CI: 0.55, 0.77), followed by Klebsiella species and E. coli each was (0.59; 95% CI: 0.46, 0.70) and Acinetobacter species (0.56; 95% CI: 0.44, 0.68). Among the isolates from environmental studies, the highest proportion of ESBL production was found in Acinetobacter species (0.66; 95% CI: 0.54, 0.76), Klebsiella species (0.62; 95% CI: 0.51, 0.72), and Pseudomonas species (0.48; 95% CI: 0.36, 0.61) (Table 4).

Table 4 Estimated ESBL-producers among gram-negative bacteria isolated from humans and the environment
Full size table

Discussion

This systematic review and meta-analysis was conducted to estimate drug- and multidrug-resistant bacteria from one-health perspective in Ethiopia. It also determined the prevalence of ESBL-producing gram-negative bacteria in human and environmental isolates. From human sources, more than 60% resistance was reported to commonly prescribed β-lactam antibiotics, ciprofloxacillin, gentamycin, and co-trimoxazole. In addition, the highest rates of MDR were found in Acinetobacter spp. (92%), followed by Klebsiella species (86%), and Pseudomonas species (79%). With some exceptions, almost consistent findings were reported in a review of findings in Ethiopia [53, 54], and in Cameroon [55], and East Africa [56]. Hence, this review suggests that, as infections caused by antibiotic- resistant bacteria are becoming more prevalent, serious concerns should be given to the use and choice of antibiotics for effective management of infections in Ethiopia.

Gram-negative bacteria use several mechanisms to develop resistance to antimicrobials. Mutations and recombination of genomic materials allow these bacteria to disseminate genes encoding for antimicrobial resistance within and across species [57]. Actions in the human and animal healthcare sectors are all considered to be contributing to the development of pathogen resistance to current available antimicrobials [57,58,59,60]. Frequent use of antibiotics may create favorable conditions for selective pressure, which leads to the further development of resistance. For instance, the production of β-lactamase that hydrolyzes the β-lactam ring is the most common resistance mechanism for these bacteria against β-lactam antibiotics. Gram-negative bacteria that produce ESBLs carry plasmid-encoded enzymes that can hydrolyze and confer resistance to a variety of β-lactam antibiotics, as well as fluoroquinolones, aminoglycosides, and trimethoprim-sulfamethoxazole [57, 61, 62].

In this review, above 50% of E. coli, Klebsiella, Pseudomonas, and Acinetobacter species from human sources were ESBL producers. The presence of bacteria in human and animal bodies as carriers may result in frequent exposure to antimicrobials used for treatment and prophylactic purposes [57, 59, 60, 62, 63]. There is no question that the widespread use, overuse, and misuse of antimicrobials have been associated with the explosion of antimicrobial resistance. A study confirmed that those who had exposure to third-generation cephalosporins, carbapenems, and fluoroquinolones had three-to-four times greater risk for infections with extended-spectrum β-lactamase-producing bacteria [64]. Therefore, updated and effective measures, such as antimicrobial stewardship which promotes the careful and responsible use of antimicrobials and prevents antimicrobial overuse and misuse in hospital and community settings, and infection prevention, are the most effective ways to reduce the spread and development of antimicrobial resistance and to protect patients from harms caused by unnecessary antibiotic use.

Antimicrobial susceptibility testing appeared to be inconsistent and low in animal, food, and environmental sources of isolates compared with humans. From environmental sources, E. coli, Klebsiella spp., and Acinetobacter spp. were recorded with more than 60% rates of AMR to ampicillin, amoxicillin-clavulanic acid, ceftriaxone, and co-trimoxazole. The rate of MDR was above 50% for five bacterial species. Mutation of bacterial genomes by different mechanisms, such as frequent antibiotic use or misuse in long-care facilities, may provide a selective advantage to the emergence of resistant variants [57, 59, 61]. For instance, in this review, 10 to 66% of the ESBL- production rate was found in environmental isolates, with the highest rates found in Acinetobacter (66%) and Klebsiella spp. (62%). Most of the included environmental studies were from hospital settings, specifically hospital surfaces, indoor air, and wastewater, suggesting a need for control of resistant gram-negative infections through a comprehensive approach, including detection and identification of resistant organisms and implementation of effective infection-control and prevention strategies in healthcare settings.

In isolates from animals or food of animal origin, the analysis for drug resistance was done only for E. coli and Salmonella species. Accordingly, greater than 50% of E. coli and Salmonella species were resistant to ampicillin, AMC, and co-trimoxazole, and the rate of MDR was 36% and 29%, respectively. A higher pooled estimate of antibiotic resistance (86%) and multidrug resistance (73%) was also reported in a review study in Africa [65]. Surface contamination with fecal matter, animal excreta, and water or soil sources may allow the transmission of drug-resistant bacterial populations to raw meat and carcasses, which could be transmitted to humans through consumption of animal products [66,67,68,69]. Additionally, the frequent contact between humans, dairy cattle, and poultry may also be a good opportunity for the bidirectional transmission of AMR bacteria such as E. coli [60, 69, 70]. Hence, the frequent contact with dairy cattle and poultry products as well as the habitual consumption of raw meat and milk may be contributing factors in the acquisition of resistance bacteria.

In general, in this review study, the prevalence of AMR, MDR, and ESBL-producing bacteria was higher in isolates from human samples as compared to other environmental and animal samples. However, some isolates from hospital environments showed comparable rates of AMR, MDR, and ESBL production. This may be indicated by the frequent exposures of humans to most antibiotics and the healthcare sectors, which can be contributing factors to the development of resistance and the possible transmission of antimicrobial- resistant bacteria from humans to the hospital environment and vice versa. Therefore, implementation of the integrated approaches, such as best regulation of the use of antibiotics, effective infection prevention, improving food safety, and preventing zoonotic disease infections, are important measures for the prevention and control of these complex AMR development and transmission cycles.

Conclusion

This review report consists of the most recent situation of AMR with commonly prescribed antibiotics from a one-health perspective in Ethiopia. The review indicated that the high pooled prevalence of antibiotic resistance, MDR, and ESBL-production was in Enterobacteriaceae, Acinetobacter, and Pseudomonas species isolated from humans, the environment, and animals or food of animal origin. Therefore, to address the gaps related to measures taken to reduce the emergence and spread of AMR bacteria in humans, animals, and the environment, it is time to implement a harmonized and multidisciplinary one-health approach.

Availability of data and materials

All the data generated and analyzed during this review are included in this published article in the form of the main tables, but on reasonable request, details of our analysis are available from the corresponding author.

Abbreviations

AMR:

Antimicrobial resistance

ESBL:

Extended-spectrum β-lactamase

MDR:

Multidrug-resistance

PRISMA:

Preferred reporting items for systematic reviews and meta-analysis

JBI:

Joanna Briggs Institute

STATA:

Statistical Software Package

References

  1. McEwen SA, Collignon PJ. Antimicrobial resistance: a one health perspective. Microbiol Spectr. 2018;6(2). https://0-doi-org.brum.beds.ac.uk/10.1128/microbiolspec.ARBA-0009-2017. PMID: 29600770.

  2. Cerceo E, Deitelzweig SB, Sherman BM, Amin AN. Multidrug-resistant gram-negative bacterial infections in the hospital setting: overview, implications for clinical practice, and emerging treatment options. Microb Drug Resist. 2016;22(5):412–31.

    Article  PubMed  CAS  Google Scholar 

  3. Kaye KS, Pogue JM. Infections caused by resistant gram-negative bacteria: epidemiology and management. Pharmacotherapy. 2015;35(10):949–62. https://0-doi-org.brum.beds.ac.uk/10.1002/phar.1636.

    Article  PubMed  CAS  Google Scholar 

  4. World Health Organization (2017) . Guidelines for the prevention and control of carbapenem-resistant Enterobacteriaceae, Acinetobacter baumannii and Pseudomonas aeruginosa in health care facilities. WHO. https://apps.who.int/iris/handle/10665/259462.

  5. Irek EO, Amupitan AA, Obadare TO, Aboderin AO. A systematic review of healthcare-associated infections in Africa: an antimicrobial resistance perspective. Afr J Lab Med. 2018;7(2):796.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Asaduzzaman M, Rodland EK, Mekonnen Z, et al. Understanding transmission pathways and integrated digital surveillance potential of antimicrobial resistance in Ethiopia in a One Health approach: a mixed-method study protocol. BMJ Open. 2022;12:e051022. https://0-doi-org.brum.beds.ac.uk/10.1136/bmjopen-2021-051022.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Ayukekbong JA, Ntemgwa M, Atabe AN. The threat of antimicrobial resistance in developing countries: causes and control strategies. Antimicrob Resist Infect Control. 2017;6:47.

    Article  PubMed  PubMed Central  Google Scholar 

  8. de Kraker MEA, Stewardson AJ, Harbarth S. Will 10 million people die a year due to antimicrobial resistance by 2050? PLoS Med. 2016;13(11):e1002184. https://0-doi-org.brum.beds.ac.uk/10.1371/journal.pmed.1002184.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Antimicrobial Resistance Collaborators. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet. 2022;399(10325):629–55. https://0-doi-org.brum.beds.ac.uk/10.1016/S0140-6736(21)02724-0.

    Article  Google Scholar 

  10. Ombelet S, Ronat JB, Walsh T, Yansouni CP, Cox J, Vlieghe E, et al. Clinical bacteriology in low-resource settings: today’s solutions. Lancet Infect Dis. 2018;18(8):e248–58.

    Article  PubMed  Google Scholar 

  11. Muhie OA. Antibiotic use and resistance pattern in Ethiopia: systematic review and meta- analysis. Int J Microbiol. 2019;2489063:8. https://0-doi-org.brum.beds.ac.uk/10.1155/2019/2489063.

    Article  Google Scholar 

  12. Seale AC, Hutchison C, Fernandes S, Stoesser N, et al. Supporting surveillance capacity for antimicrobial resistance: laboratory capacity strengthening for drug resistant infections in low- and middle-income countries. Wellcome Open Res. 2017;2:91. https://0-doi-org.brum.beds.ac.uk/10.12688/wellcomeopenres.12523.1.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Hazim C, Abubeker Ibrahim R, Westercamp M, Belete GA, et al. Establishment of a sentinel laboratory-based antimicrobial resistance surveillance network in Ethiopia. Health Secur. 2018;16(S1):S30–6. https://0-doi-org.brum.beds.ac.uk/10.1089/hs.2018.0052.

    Article  PubMed  Google Scholar 

  14. World Health Organization. Regional Office for Africa. (2021). Antimicrobial resistance in the WHO African Region: a systematic literature review. World Health Organization. Regional Office for Africa. https://apps.who.int/iris/handle/10665/349223. License: CC BY-NC-SA 3.0 IGO

  15. Erkyihun GA, Gari FR, Edao BM, et al. A review on one health approach in Ethiopia. One Health Outlook. 2022;4:8. https://0-doi-org.brum.beds.ac.uk/10.1186/s42522-022-00064-z.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. https://0-doi-org.brum.beds.ac.uk/10.1136/bmj.n71.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Aromataris E, Fernandez R, Godfrey C, Holly C, Kahlil H, Tungpunkom P. Summarizing systematic reviews: methodological development, conduct and reporting of an Umbrella review approach. Int J Evid Based Healthc. 2015;13(3):132–40.

    Article  PubMed  Google Scholar 

  18. Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors). Cochrane Handbook for Systematic Reviews of Interventions version 6.3 (updated February 2022). Cochrane. 2022. Available from; www.training.cochrane.org/handbook.

  19. Huedo-Medina TB, Sánchez-Meca J, Marín-Martínez F, Botella J. Assessing heterogeneity in meta-analysis: Q statistic or I 2 index? Psychol Methods. 2006;11(2):193–206. https://0-doi-org.brum.beds.ac.uk/10.1037/1082-989X.11.2.193.

    Article  PubMed  Google Scholar 

  20. Motbainor H, Bereded F, Mulu W. Multi-drug resistance of blood stream, urinary tract and surgical site nosocomial infections of Acinetobacter baumannii and Pseudomonas aeruginosa among patients hospitalized at Felegehiwot referral hospital, Northwest Ethiopia: a cross-sectional study. BMC Infect Dis. 2020;20:92. https://0-doi-org.brum.beds.ac.uk/10.1186/s12879-020-4811-8.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Abda EM, Adugna Z, Assefa A. Elevated level of imipenem-resistant gram-negative bacteria isolated from patients attending health centers in North Gondar. Ethiopia Infect Drug Resist. 2020;13:4509–17. https://0-doi-org.brum.beds.ac.uk/10.2147/IDR.S287700.

    Article  PubMed  CAS  Google Scholar 

  22. Moges F, Gizachew M, Dagnew M, Amare A, Sharew B, et al. Multidrug resistance and extended-spectrum beta-lactamase producing gram-negative bacteria from three referral hospitals of Amhara region, Ethiopia. Ann Clin Microbiol Antimicrob. 2021;20(1):16. https://0-doi-org.brum.beds.ac.uk/10.1186/s12941-021-00422-1.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Mulu W, Abera B, Yimer M, et al. Bacterial agents and antibiotic resistance profiles of infections from different sites that occurred among patients at Debre Markos referral hospital, Ethiopia: a cross-sectional study. BMC Res Notes. 2017;10:254. https://0-doi-org.brum.beds.ac.uk/10.1186/s13104-017-2584-y.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Admas A, Gelaw B, BelayTessema, Worku A, Melese A. Proportion of bacterial isolates, their antimicrobial susceptibility profile and factors associated with puerperal sepsis among post-partum/aborted women at a referral hospital in Bahir Dar, Northwest Ethiopia. Antimicrob Resist Infect Control. 2020;9(1):14. https://0-doi-org.brum.beds.ac.uk/10.1186/s13756-019-0676-2.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Mekonnen H, Seid A, Fenta GM, Gebrecherkos T. Antimicrobial resistance profiles and associated factors of Acinetobacter and Pseudomonas aeruginosa nosocomial infection among patients admitted at Dessie comprehensive specialized hospital, North-East Ethiopia. A cross-sectional study. PLoS ONE. 2021;16(11):e0257272. https://0-doi-org.brum.beds.ac.uk/10.1371/journal.pone.0257272.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Tilahun M, Gedefie A, Bisetegn H, Debash H. Emergence of high prevalence of extended-spectrum beta-lactamase and carbapenemase producing Acinetobacter Species and Pseudomonas aeruginosa among hospitalized patients at Dessie comprehensive specialized hospital. North-East Ethiopia Infect Drug Resist. 2022;15:895–911. https://0-doi-org.brum.beds.ac.uk/10.2147/IDR.S358116.

    Article  PubMed  CAS  Google Scholar 

  27. Solomon S, Akeju O, Odumade OA, Ambachew R, Gebreyohannes Z, Van Wickle K, et al. Prevalence and risk factors for antimicrobial resistance among newborns with gram-negative sepsis. PLoS ONE. 2021;16(8):e0255410. https://0-doi-org.brum.beds.ac.uk/10.1371/journal.pone.0255410.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Abdeta A, Bitew A, Fentaw S, Tsige E, Assefa D, Lejisa T, et al. Phenotypic characterization of carbapenem non-susceptible gram-negative bacilli isolated from clinical specimens. PLoS ONE. 2021;16(12):e0256556. https://0-doi-org.brum.beds.ac.uk/10.1371/journal.pone.0256556.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Bitew A. High prevalence of Multidrug- resistance and extended spectrum beta- lactamase production in non-fermenting gram-negative bacilli in Ethiopia. Infect Dis (Auckl). 2019;12:1178633719884951. https://0-doi-org.brum.beds.ac.uk/10.1177/1178633719884951.

    Article  PubMed  Google Scholar 

  30. Seman A, Mihret A, Sebre S, Awoke T, Yeshitela B, et al. Prevalence and molecular characterization of Extended Spectrum β-Lactamase and Carbapenemase Producing Enterobacteriaceae Isolates from Bloodstream Infection Suspected Patients in Addis Ababa, Ethiopia. Infect Drug Resist. 2022;15:1367–82. https://0-doi-org.brum.beds.ac.uk/10.2147/IDR.S349566.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Gashaw M, Berhane M, Bekele S, Kibru G, Teshager L, et al. Emergence of high drug resistant bacterial isolates from patients with health care associated infections at Jimma University medical center: a cross sectional study. Antimicrob Resist Infect Control. 2018;7:138. https://0-doi-org.brum.beds.ac.uk/10.1186/s13756-018-0431-0.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Tufa TB, Mackenzie CR, Orth HM, et al. Prevalence and characterization of antimicrobial resistance among gram-negative bacteria isolated from febrile hospitalized patients in central Ethiopia. Antimicrob Resist Infect Control. 2022;11:8. https://0-doi-org.brum.beds.ac.uk/10.1186/s13756-022-01053-7.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Ageru TA, Seid H, Abiso TL, et al. Burden of antibiotic resistance at Wolaita Sodo University comprehensive specialized hospital. BioMed Res Int. 2022;2022(7272024):10. https://0-doi-org.brum.beds.ac.uk/10.1155/2022/7272024.

    Article  CAS  Google Scholar 

  34. Yigrem C, Amare A, Eribo B. Prevalence of Carbapenem resistant Acinetobacter baumannii in leafy vegetable samples and clinical sources from Gondar Northwest Ethiopia. J Microbiol Res. 2021;11(1):8–20. https://0-doi-org.brum.beds.ac.uk/10.5923/j.microbiology.20211101.02.

    Article  Google Scholar 

  35. Moges F, Endris M, Belyhun Y, et al. Isolation and characterization of multiple drug resistance bacterial pathogens from waste water in hospital and non-hospital environments, Northwest Ethiopia. BMC Res Notes. 2014;7:215. https://0-doi-org.brum.beds.ac.uk/10.1186/1756-0500-7-215.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Firesbhat A, Tigabu A, Tegene B, et al. Bacterial profile of high-touch surfaces, leftover drugs and antiseptics together with their antimicrobial susceptibility patterns at University of Gondar comprehensive specialized hospital, Northwest Ethiopia. BMC Microbiol. 2021;21:309. https://0-doi-org.brum.beds.ac.uk/10.1186/s12866-021-02378-w.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Araya S, Desta K, Woldeamanuel Y. Extended-spectrum beta-lactamase-producing gram-negative bacteria on healthcare workers’ mobile phones: evidence from Tikur Anbessa Specialized Hospital, Addis Ababa, Ethiopia. Risk Manag Healthc Policy. 2021;14:283–91. https://0-doi-org.brum.beds.ac.uk/10.2147/RMHP.S291876.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Sebre S, Abegaz WE, Seman A, Awoke T, Desalegn Z, Mihret W, Mihret A, Abebe T. Bacterial profiles and antimicrobial susceptibility pattern of isolates from inanimate hospital environments at Tikur Anbessa specialized teaching Hospital, Addis Ababa. Ethiopia Infect Drug Resist. 2020;13:4439–48. https://0-doi-org.brum.beds.ac.uk/10.2147/IDR.S286293.

    Article  PubMed  CAS  Google Scholar 

  39. Belachew T, Mihret A, Legesse T, et al. High level of drug resistance by gram-negative bacteria from selected sewage polluted urban rivers in Addis Ababa, Ethiopia. BMC Res Notes. 2018;11:524. https://doi.org/10.1186/s13104-018-3622-0.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Kayta G, Manilal A, Tadesse D, Siraj M. Indoor air microbial load, antibiotic susceptibility profiles of bacteria, and associated factors in different wards of Arba Minch General Hospital, southern Ethiopia. PLoS ONE. 2022;17(7):e0271022. https://0-doi-org.brum.beds.ac.uk/10.1371/journal.pone.0271022.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Birru M, Mengistu M, Siraj M, Aklilu A, Boru K, et al. Magnitude, diversity, and antibiogram of bacteria isolated from patient-care equipment and inanimate objects of selected wards in Arba Minch general hospital. Southern Ethiopia Res Rep Trop Med. 2021;12:39–49. https://0-doi-org.brum.beds.ac.uk/10.2147/RRTM.S301215.

    Article  PubMed  Google Scholar 

  42. Hailemariam M, Worku M, Azerefegne E. Intensive care units and operating rooms bacterial load and antibiotic susceptibility pattern. J Surg. 2016;4(2):60–4. https://0-doi-org.brum.beds.ac.uk/10.11648/j.js.20160402.21.

    Article  Google Scholar 

  43. Solomon FB, Wadilo F, Tufa EG, Mitiku M. Extended spectrum and metalo beta-lactamase producing airborne Pseudomonas aeruginosa and Acinetobacter baumannii in restricted settings of a referral hospital: a neglected condition. Antimicrob Resist Infect Control. 2017;6:106. https://0-doi-org.brum.beds.ac.uk/10.1186/s13756-017-0266-0.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Darge A, Kahsay AG, Hailekiros H, Niguse S, Abdulkader M. Bacterial contamination and antimicrobial susceptibility patterns of intensive care units medical equipment and inanimate surfaces at Ayder Comprehensive Specialized Hospital, Mekelle, Northern Ethiopia. BMC Res Notes. 2019;12(1):621. https://0-doi-org.brum.beds.ac.uk/10.1186/s13104-019-4658-5.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Bushen A, Tekalign E, Abayneh M. Drug- and multidrug-resistance pattern of Enterobacteriaceae isolated from droppings of healthy chickens on a poultry farm in Southwest Ethiopia. Infect Drug Resist. 2021;14:2051–8. https://0-doi-org.brum.beds.ac.uk/10.2147/IDR.S312185.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Eyasu A, Moges F, Alemu A. Bacterial isolates from poultry litters and their antimicrobial susceptibility patterns in Gondar, Northwest Ethiopia. Int J Microbiol Res Rev. 2017;6:197–204.

    Google Scholar 

  47. Worku W, Desta M, Menjetta T. High prevalence and antimicrobial susceptibility pattern of salmonella species and extended-spectrum β-lactamase producing Escherichia coli from raw cattle meat at butcher houses in Hawassa city, Sidama regional state, Ethiopia. PLoS ONE. 2022;17(1):e0262308. https://0-doi-org.brum.beds.ac.uk/10.1371/journal.pone.0262308.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Tefera B, Tefera N, Tilki T, et al. Antimicrobial resistance status of selected bacteria isolated from animal source foods and feed in Ethiopia. Res Square. 2022. https://0-doi-org.brum.beds.ac.uk/10.21203/rs.3.rs-1470438/v1.

  49. Asfaw Ali D, Tadesse B, Ebabu A. Prevalence and antibiotic resistance pattern of Salmonella isolated from Caecal contents of exotic chicken in Debre Zeit and Modjo, Ethiopia. Int J Microbiol. 2020;2020(18):1910630. https://0-doi-org.brum.beds.ac.uk/10.1155/2020/1910630.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Ejo M, Garedew L, Alebachew Z, Worku W. Prevalence and antimicrobial resistance of Salmonella isolated from animal-origin food items in Gondar. Ethiopia Biomed Res Int. 2016;2016:4290506. https://0-doi-org.brum.beds.ac.uk/10.1155/2016/4290506.

    Article  PubMed  CAS  Google Scholar 

  51. Kebede A, Kemal J, Alemayehu H, Habte MS. Isolation, identification, and antibiotic susceptibility testing of Salmonella from slaughtered bovines and ovines in Addis Ababa Abattoir Enterprise, Ethiopia: a cross-sectional study. Int J Bacteriol. 2016;2016:3714785. https://0-doi-org.brum.beds.ac.uk/10.1155/2016/3714785.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. Bekele T, Zewde G, Tefera G, et al. Escherichia coli O157:H7 in raw meat in Addis Ababa, Ethiopia: prevalence at an abattoir and retailers and antimicrobial susceptibility. Food Contamination. 2014;1:4. https://0-doi-org.brum.beds.ac.uk/10.1186/s40550-014-0004-9.

    Article  Google Scholar 

  53. Tweldemedhin M, Muthupandian S, Gebremeskel TK, et al. Multidrug resistance from a one health perspective in Ethiopia: a systematic review and meta-analysis of literature (2015–2020). OneHealth. 2022;14:100390. https://0-doi-org.brum.beds.ac.uk/10.1016/j.onehlt.2022.100390.

    Article  CAS  Google Scholar 

  54. Alemayehu T. Prevalence of multidrug-resistant bacteria in Ethiopia: a systematic review and meta-analysis. J Glob Antimicrob Resist. 2021;26:133–9. https://0-doi-org.brum.beds.ac.uk/10.1016/j.jgar.2021.05.017.

    Article  PubMed  CAS  Google Scholar 

  55. Mouiche MMM, Moffo F, Akoachere JFTK, et al. Antimicrobial resistance from a one health perspective in Cameroon: a systematic review and meta-analysis. BMC Public Health. 2019;19:1135. https://0-doi-org.brum.beds.ac.uk/10.1186/s12889-019-7450-5.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Ampaire L, Muhindo A, Orikiriza P, Mwanga-Amumpaire J, Bebell L, Boum Y. A review of antimicrobial resistance in East Africa. Afr J Lab Med. 2016;5(1):1–6 (https://ajlmonline.org/index.php/ajlm/artcle/view/432).

    Article  Google Scholar 

  57. Morrison L, Zembower TR. Antimicrobial resistance. Gastrointest Endosc Clin N Am. 2020;30(4):619–35. https://0-doi-org.brum.beds.ac.uk/10.1016/j.giec.2020.06.004.

    Article  PubMed  Google Scholar 

  58. Holmes AH, Moore LSP, Sundsfjord A, Steinbakk M, Regmi S, Karkey A, et al. Understanding the mechanisms and drivers of antimicrobial resistance. Lancet. 2016;387(10014):176–87.

    Article  PubMed  CAS  Google Scholar 

  59. Christaki E, Marcou M, Tofarides A. Antimicrobial resistance in bacteria: mechanisms, evolution, and persistence. J Mol Evol. 2020;88(1):26–40. https://0-doi-org.brum.beds.ac.uk/10.1007/s00239-019-09914-3. (Epub 2019 Oct 28 PMID: 31659373).

    Article  PubMed  CAS  Google Scholar 

  60. Salinas L, Loayza F, Cárdenas P, Saraiva C, et al. Environmental spread of extended spectrum beta-lactamase (ESBL) producing Escherichia coli and ESBL genes among children and domestic animals in Ecuador. Environ Health Perspect. 2021;129(2):1–10. https://0-doi-org.brum.beds.ac.uk/10.1289/EHP7729.

    Article  Google Scholar 

  61. Holmes AH, et al. “Understanding the mechanisms and drivers of antimicrobial resistance”, (in eng). Lancet. 2016;387(10014):176–87.

    Article  PubMed  CAS  Google Scholar 

  62. Ayukekbong JA, Ntemgwa M, Atabe AN. The threat of antimicrobial resistance in developing countries: causes and control strategies. Antimicrob Resist Infect Control. 2017;6:47. https://0-doi-org.brum.beds.ac.uk/10.1186/s13756-017-0208-x.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Morosini MI, García-Castillo M, Coque TM, Valverde A, Novais A, Loza E, et al. Antibiotic co-resistance in ESBL-producing Enterobacteriaceae and in vitro activity of tigecycline. Antimicrob Agents Chemother. 2006;50(8):2695–9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  64. Hu, Y. J., Ogyu, A., Cowling, B. J., Fukudaa, K., & Panga, H. (2019). Available evidence of antibiotic resistance from extended-spectrum ß-lactamase-producing Enterobacteriaceae in pediatrics patients in 20 countries: A systematic review and meta-analysis. Bulletin of the World Health Organization. World Health Organization. https://0-doi-org.brum.beds.ac.uk/10.2471/BLT.18.225698

  65. Founou LL, Amoako DG, et al. Antibiotic resistance in food animals in Africa: a systematic review and meta-analysis. Microb Drug Resist. 2018;24(5):648–65. https://0-doi-org.brum.beds.ac.uk/10.1089/mdr.2017.0383.

    Article  PubMed  CAS  Google Scholar 

  66. Oloso NO, Fagbo S, Garbati M, Olonitola SO, et al. Antimicrobial resistance in food animals and the environment in Nigeria: a review. Int J Environ Res Public Health. 2018;15(6):1284. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph15061284.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  67. Bougnom PB, Piddock JL. Wastewater for urban agriculture. A significant factor in dissemination of antibiotic resistance. Environ Sci Technol. 2017;51:5863–4.

    Article  PubMed  CAS  Google Scholar 

  68. O’Neill J. Antimicrobials in agriculture and the environment: reducing unnecessary use and waste. The review on antimicrobial resistance. London. Wellcome Trust. 2015. Available from: https://amr-review.org/

  69. Cifuentes SG, Graham J, Loayza F, Saraiva C, et al. Evaluation of changes in the faecal resistome associated with children’s exposure to domestic animals and food animal production. J Glob Antimicrob Resist. 2022;31:212–5. https://0-doi-org.brum.beds.ac.uk/10.1016/j.jgar.2022.09.009.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  70. Rhouma M, Soufi L, Cenatus S, Archambault M, Butaye P. Current insights regarding the role of farm animals in the spread of antimicrobial resistance from a one health perspective. Vet Sci. 2022;9:480. https://0-doi-org.brum.beds.ac.uk/10.3390/vetsci9090480.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

The authors declare that, they did not receive any specific funding for this research.

Author information

Authors and Affiliations

  1. College of Medical and Health Science, Department of Medical Laboratory Sciences, Mizan-Tepi University, PO Box 260, Mizan-Aman, Ethiopia

    Mengistu Abayneh

  2. School of Medical Laboratory Sciences, Faculty of Health Sciences, Institute of Health, Jimma University, Jimma, Ethiopia

    Ahmed Zeynudin, Rahel Tamrat & Mulualem Tadesse

  3. Faculity of Public Health, Department of Health, Behavior and Society, Jimma University, Jimma, Ethiopia

    Abraham Tamirat

Authors
  1. Mengistu Abayneh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ahmed Zeynudin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rahel Tamrat
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mulualem Tadesse
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Abraham Tamirat
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

MA, AT, AZ, MT and RT were involved in the conception and design of the study, data extraction or acquisition of data or analysis and interpretation of data. MA and AT analyzed the data and drafted the manuscript. All authors read, revised and approved the final version manuscript.

Corresponding author

Correspondence to Mengistu Abayneh.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publications

Not applicable.

Competing interests

“The authors declare that they have no competing interest”.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, 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 changes were made. 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 http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abayneh, M., Zeynudin, A., Tamrat, R. et al. Drug resistance and extended-spectrum β-lactamase (ESBLs) - producing Enterobacteriaceae, Acinetobacter and Pseudomonas species from the views of one-health approach in Ethiopia: a systematic review and meta-analysis. One Health Outlook 5, 12 (2023). https://0-doi-org.brum.beds.ac.uk/10.1186/s42522-023-00088-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://0-doi-org.brum.beds.ac.uk/10.1186/s42522-023-00088-z

Keywords

One Health Outlook

ISSN: 2524-4655