Abstract
Background
Escherichia coli (E. coli), a common member of the Enterobacteriaceae family, is a significant pathogen with increasing antimicrobial resistance (AMR), threatening both human and animal health. Despite its public health importance, data on its prevalence and resistance patterns in Ethiopia are scarce. The national AMR surveillance system is fragmented, with gaps in standardized monitoring and reporting. This systematic review and meta-analysis aimed to synthesize the prevalence and AMR patterns of E. coli isolates from human and animal sources in Ethiopia.
Method
Of 1,707 articles screened, 15 studies met the inclusion criteria. The pooled prevalence of E. coli was 28.8% (95% CI: 18.37–39.30, I² = 99.1% & p < 0.001). Begg’s and Egger’s tests were also used to look for publication bias. Antimicrobial resistance in humans was 55.5% (95% CI: 48.4–62.6), in animals 40.3% (95% CI: 28.8–51.8), and in combined sources 40.3% (95% CI: 25.50–55.20). High resistance was observed to piperacillin (92.3%), erythromycin (92.9%), and clindamycin (100%), although these extreme values should be interpreted cautiously due to potential methodological biases in antimicrobial susceptibility testing. Multidrug resistance was 80.7% among humans, 50.0% animals, and 76.0% combined isolates.
Results
Of 1,707 articles screened, 15 studies met the inclusion criteria. The pooled prevalence of E. coli was 28.8% (95% CI: 18.37–39.30, I2 = 99.1% & p < 0.001). Additionally, Begg’s and Egger’s tests were performed to assess publication bias. Antimicrobial resistance in humans was 55.5% (95% CI: 48.4–62.6), in animals 40.3% (95% CI: 28.8–51.8), and in combined sources 40.3% (95% CI: 25.50–55.20). High resistance was observed to piperacillin (92.3%), Erythromycin (92.9%), and clindamycin (100%), although these extreme values should be interpreted cautiously due to potential methodological biases in antimicrobial susceptibility testing. Multidrug resistance was observed in 80.7% of humans, 50.0% among animals, and 76.0% among combined isolates. Likely drivers include improper antibiotic use, food chain contamination, and limited surveillance.
Conclusions
This systematic review and meta-analysis displayed a substantial prevalence of E. coli and alarming antimicrobial and multidrug resistance in human and animal source samples in Ethiopia. The findings signal an urgent need for coordinated public health and veterinary interventions. Strengthening national antibiotic stewardship programs, expanding AMR surveillance networks, and promoting hygiene, sanitation, and rational drug use are crucial. Implementing these measures within a One Health perspective is vital to protect both human and animal health and to mitigate the growing AMR burden sustainably.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12879-025-12326-y.
Keywords: Escherichia coli, Prevalence, Antimicrobial resistance, Multi-drug resistance, Ethiopia
Introduction
Antimicrobial resistance (AMR) is an increasing global public health threat, mainly due to the spread of multidrug-resistant (MDR) bacteria, which challenge healthcare and overall development [1]. MDR is defined as the acquired ability of a microorganism to resist at least one agent in three or more antimicrobial classes [2, 3]. The use of antimicrobials in livestock, common since the 1950s in high-income countries, has significantly contributed to the emergence of MDR pathogens [1, 4]. Worldwide, about 73% of antibiotics are used in animal production, and this trend is rising due to increasing meat demand [4, 5]. Antimicrobials are used to treat and prevent infections and to promote growth, but their overuse, especially in low- and middle-income countries (LMICs), speeds up the development of AMR in both humans and animals [6, 7].
Escherichia coli is a ubiquitous gut bacterium that exists both as normal flora and in pathogenic forms, making it a key indicator for hygiene, failure of food safety, and AMR surveillance [7, 8]. Pathogenic strains of E. coli can cause urinary tract infections, diarrhea in children, cystitis, pyelonephritis, septicemia, meningitis, and other serious conditions [9, 10]. Certain strains also produce shiga toxins, which may result in enterohemorrhagic diarrhea and kidney failure [9].
Moreover, drug-resistant E. coli strains possess zoonotic potential, transmitting AMR genes between animals and humans via contaminated meat, water, and animal-source foods [9, 10]. This highlights the One Health importance of monitoring E. coli across human, animal, and environmental sources.
Bacterial antimicrobial resistance (AMR) was directly attributable to about 1.27 million deaths in 2019 and contributed to approximately 4.95 million deaths that year; E. coli is among the leading pathogens driving that burden [11]. Rising resistance in E. coli (including multidrug resistance) increases risks of treatment failure, complicated urinary tract infections, sepsis, longer hospital stays, and higher case fatality rates and is projected to cause many more deaths if unchecked [12]. Quantified global morbidity and mortality estimates for MDR E. coli in livestock are currently limited; existing work focuses on prevalence, economic impacts (production losses, treatment costs), and conceptual frameworks for measuring animal burden. Robust, comparable estimates for animal morbidities/mortalities are lacking [13]. AMR disproportionately affects LMICs, where inadequate infrastructure, limited awareness, and fragmented surveillance systems exacerbate its impact, leading to extended hospital stays, higher treatment costs, and premature deaths, thereby reducing overall productivity [14, 15]. Ethiopia faces such challenges, with poor integration across human, animal, and environmental health sectors, making localized data critical for guiding targeted interventions [15].
While beef and other animal-source foods are widely consumed in sub-Saharan Africa, including Ethiopia, there is a limited understanding of the prevalence, sources, and transmission pathways of antimicrobial-resistant bacteria between humans and animals [16–18]. Furthermore, Ethiopia’s AMR data are inconsistent, fragmented, and limited due to poor integration across human, animal, and environmental health sectors, limited surveillance, and inadequate monitoring systems [15]. The treatment of invasive E. coli infections is further complicated by MDR strains, limiting the effectiveness of conventional antimicrobials [4, 5].
The rationale for this systematic review and meta-analysis is to provide evidence on the prevalence and AMR patterns of E. coli sp. in human and animal populations. These findings aim to inform clinical decision-making, guide health policy, and support interventions across healthcare, veterinary, and environmental sectors. Furthermore, understanding the distribution of drug-resistant E. coli can empower healthcare settings to improve antimicrobial stewardship and reduce the spread of resistant strains within communities. Therefore, this systematic review and meta-analysis aims to generate recent evidence to contribute to targeted interventions in line with One Health.
Methods
Design and protocol
This systematic review and meta-analysis was aimed at systematically synthesizing the prevalence and AMR patterns of E. coli strains isolated from human and animal sources in Ethiopia, in accordance with PRSIMA-2020 [19]. The Population, Intervention, Comparison, and Outcome (PICO) framework was used to formulate the research question and guide the design of this systematic review and meta-analysis on antimicrobial resistance (AMR) in Escherichia coli among humans and animals in Ethiopia. The primary research question was: “What are the prevalence and AMR patterns of E. coli isolated from humans and animals in Ethiopia?”.
In this framework, the Population (P) included humans and animals in Ethiopia; the Intervention (I) referred to the use of specific antibiotics; the Comparison (C) involved differences in E. coli prevalence and AMR patterns across sample sources and geographic regions; and the Outcome (O) focused on the prevalence of E. coli strains and its associated AMR profiles.
Inclusion and exclusion criteria
Inclusion criteria
This meta-analysis included original studies conducted in Ethiopia, written in English, and published between 2019 and 2024. Both human and animal sources of samples were included regardless of gender and age. Furthermore, studies reported the prevalence and antimicrobial resistance patterns of E. coli isolates from human and animal source samples also included for analysis.
Exclusion criteria
Studies carried out in the specified period (2019–2024) were excluded. Likewise, studies reported E. coli prevalence and AMR patterns from non-targeted sources such as samples from water sources, solid surface swabs, and other environmental samples were excluded to reduce heterogeneity and bias.
Searching strategy
The search strategy was developed using MeSH terms and free-text keywords, and the final string applied in PubMed was (“E. coli” OR “Escherichia coli” OR “Enterobacteriaceae”) AND (“prevalence” OR “occurrence”) AND (“drug resist*” OR “antimicrob resist*” OR “antibiogram” OR “Microbial Sensitivity Tests”) AND (“animal specim*” OR “animal sources” OR “livestock” OR “cattle” OR “bovine” OR"human sources” OR “human specim*”) AND (“Ethiopia”). We also applied equivalent strings with appropriate syntax in other databases (Scopus, Web of Science, and Google Scholar). The search strategy was developed and refined in accordance with Population (P) Intervention (I) Comparison (C), and Output (O) (PICO), primarily focusing on the pertinent studies’ title and abstract search methodology approach with input from three independent reviewers (GMZ, TSA, and GMB). The discrepancies were resolved through consensus and arbitration by two additional reviewers (STM and SM). The electronic databases searched were PubMed, Scopus, Cochrane, and ScienceDirect. In addition, Google Scholar was used as a supplementary search tool to identify additional relevant studies, including gray literature such as thesis, reports, and preprints between July 1, 2019, and July 1, 2024, in the English language.
Quality assessment for included studies
S1). The methodological validity of included studies was assessed using the Joanna Briggs Institute (JBI) critical appraisal checklist for the analytical cross-sectional studies [20]. The checklist comprises eight criteria, which were independently evaluated by three reviewers. Each criterion was scored as 1 for (“yes”) or 0 for (“no,” “unclear,” or “not applicable”). Corresponding authors were contacted to clarify any unclear report of included study. The entire review process adhered to the PRISMA 2020 guidelines [19]. Studies scoring above 50% (≥ 4 out of 8) were included in the meta-analysis. Studies were graded based on the JBI checklist criteria, with the majority of included studies demonstrating high quality (>50%) and those scoring below 50% being excluded (Table S1).
Data extraction and analysis
Data were extracted from studies meeting the quality assessment criteria using a pre-designed Microsoft Excel sheet (Table S2). The extraction form captured key information, including author names, publication year, region, study area, study subject, sample type and, study setting, drug type, sample size; the number of cases, prevalence, and antimicrobial resistance patterns were described in supplementary files (Table S3). The pooled prevalence of E. coli isolates and their antimicrobial resistance patterns was estimated using a random-effects model in Stata version 16. Antimicrobial susceptibility testing was interpreted according to standardized guidelines from the Clinical and Laboratory Standards Institute (CLSI) to ensure reproducibility and comparability of resistance data. Both disk diffusion and minimum inhibitory concentration methods were accepted for determining antimicrobial resistance [21].
Publication bias and heterogeneity test
Publication bias and small study effects were examined using funnel plots and Egger’s weighted test [22]. To account for significant heterogeneity, data were analyzed using subgrouping based on study subjects. Forest plots displayed estimated pooled prevalence for each study with 95% confidence intervals and study weight. Heterogeneity was assessed using Higgins’s I² statistic, with I² values exceeding 50% indicating statistically significant heterogeneity [23]. Subgroup analysis was performed based on sample sources (human, animal, and both human and animal) to explore and potentially resolve substantial heterogeneity. In this study, the high heterogeneity (I² = 99.1%) indicated significant variability among included studies. To address this, we applied several statistical approaches, including a random-effects model, subgroup analyses, meta-regression (exploring covariates such as sample source), and sensitivity analysis [24]. A random-effects model was used for the overall meta-analysis.
Sensitivity analysis
A sensitivity analysis was conducted to identify factors disproportionately influencing the results including heterogeneity. The individual study had a negligible impact on the pooled estimate, indicating the robustness of the aggregated estimate. When examining the pooled E. coli prevalence by ignoring one study at a time, the results were consistent and accurate (Fig. 1).
Fig. 1.
Showing individual study impact on pooled estimates of prevalence of E.coli isolates
Results
Studies review process
A total of 1,707 articles were identified, and after screening through the PRISMA-guided selection process, 15 studies were included in the quantitative meta-analysis. These studies met our inclusion criteria and passed quality assessment using the JBI critical appraisal checklist for analytical cross-sectional studies. The primary reasons for the exclusion were irrelevant titles and abstracts (1,246), duplication (429), inaccessible full text (9), and incomplete records (2). The included studies examined human subjects [15, 25–31], animal subjects [32–36], and both [37, 38] and were conducted across Ethiopian regions such as Amhara, Beneshangul Gumuz, Oromia, Southern Ethiopia, and Addis Ababa (Fig. 2).
Fig. 2.
PRISMA flow chart describing screening protocols of studies for systematic review and meta-analysis
Publication bias findings
The Egger’s test yielded a statistically insignificant result (p = 0.150), suggesting no evidence of publication bias in the included studies (Table 1). Visual inspection of the funnel plot revealed a symmetrical distribution of studies within the triangular region, further supporting the absence of publication bias (Fig. 3).
Table 1.
Egger’s test
| Std_Eff | Coef. | Std. Err. | T | P > t | 95% CI |
|---|---|---|---|---|---|
| Slope | 1.24 | 1.24 | 1.00 | 0.34 | -1.44,3.93 |
| Bias | 0.71 | 0.47 | 1.52 | 1.5 | -0.30,1.73 |
Std Eff: Standard Effect; Coef: Coefficient: T: test Statistics; Std. Err: Standard Error; P: P- value of significance by assuming null zero value; CI: Confidence Interval
Fig. 3.
Funnel plot showing publication bias of included articles
Characteristics of included studies
A total of 15 studies conducted between 2019 and 2024 were included in the systematic review and meta-analysis. The studies were geographically distributed across five regions of Ethiopia, such as Amhara, Oromia, Benishangul-Gumuz, Addis Ababa, and Southern Ethiopia. Most studies (10 of 15) were based on human samples collected from healthcare settings, while others involved animal samples from abattoirs, farms, or food establishments. Two studies included both human and animal sources, reflecting a One Health approach.
Sample types mainly included stool, urine, and wound swabs from humans, and beef, carcass swabs, and fecal samples from animals. Culture and identification methods were typically performed using MacConkey agar, Eosin Methylene Blue (EMB) agar, and biochemical confirmation tests such as IMViC. For antimicrobial susceptibility testing (AST), the Kirby–Bauer disk diffusion method was the most commonly used, following the Clinical and Laboratory Standards Institute (CLSI) guidelines (Table 2).
Table 2.
Characteristics of included studies across regions in Ethiopia
| S/N | Author (Year) | Region | Study Area | Source | Study Setting | Sample Type | Sample Size | Positive Cases (n) | E. coli isolates prevalence (%) |
|---|---|---|---|---|---|---|---|---|---|
| 1 | Girma Z. et al.2022 | Amhara | Bahir Dar | Human | Health care | Urine/stool | 299 | 21 | 42.9 |
| 2 | Abu et al.2021 | Benishangul-Gumuz | Assosa | Human | Health care | Urine | 283 | 21 | 53.8 |
| 3 | Tadese et al., 2021 | Amhara | Debre Markos | Human | Health care | Urine | 514 | 36 | 15.0 |
| 4 | Ali et al., 2021 | Oromia | Jimma | Animal | Dairy farms | Fecal | 112 | 58 | 51.8 |
| 5 | Ayalneh et al., 2024 | Oromia | Shashemene | Human | Health care | Stool | 423 | 38 | 9.0 |
| 6 | Alebel et al., 2021 | Amhara | Bahir Dar | Human | Health care | Urine | 270 | 25 | 9.3 |
| 7 | F. Abunna et al., 2023 | Oromia | Bishoftu | Human & Animal | Community | Fecal/meat | 352 | 14 | 4.0 |
| 8 | Ayenew et al.,2021 | Amhara | Bahir Dar | Animal | Abattoir | Beef carcass swab | 280 | 25 | 8.9 |
| 9 | Belete et al., 2022 | Amhara | Bahir Dar | Human & Animal | Health care & community | Stool/fecal | 194 | 74 | 38.1 |
| 10 | Beyene et al., 2019 | Addis Ababa | Addis Ababa | Human | Health care | Blood/urine | 947 | 144 | 60.5 |
| 11 | Tonjo et al.,2022 | South Ethiopia | Arba Minch | Animal | Hotels & restaurants | Minced beef | 257 | 167 | 65.0 |
| 12 | Tarekegn et al., 2023 | Amhara | Awi Zone | Animal | Abattoir & retailer shops | Beef carcass | 248 | 22 | 8.9 |
| 13 | Tadese et al., 2021 | Oromia | Ambo | Animal | Abattoir & retailer shops | Meat swab | 197 | 46 | 23.4 |
| 14 | Sisay et al., 2024 | Amhara | Dessie | Human | Health care | Wound/urine | 384 | 41 | 22.8 |
| 15 | Ango et al., 2024 | Oromia | Jimma | Human | Community | Hand swab/stool | 234 | 48 | 21.3 |
Estimated pooled prevalence of E. coli
A meta-analysis of 15 studies reporting E. coli prevalence in human and animal samples in Ethiopia was conducted using a random-effects model. The pooled prevalence of E. coli isolates was calculated separately for human, animal, and combined sources using a random-effects model. Individual study estimates ranged widely, from 4.0% (95% CI: 1.95–6.05) in Bishoftu, Oromia [37] to 65.0% (95% CI: 59.17–70.83) in Arba Minch, Southern Ethiopia [36], respectively reflecting substantial variability across study settings. The overall pooled prevalence across all studies was 28.83% (95% CI: 18.37–39.30), based on a random-effects model, with significant heterogeneity observed (I² = 99.1%, p < 0.001), indicating considerable between-study variation likely due to differences in study populations, sampling procedures, and geographic locations. These findings highlight substantial inconsistencies in the outcome across the included studies, underscoring the need for context-specific interventions and further standardized investigations (Fig. 4).
Fig. 4.
Forest plot depicting pooled prevalence of E.coli isolates
Subgroup analysis revealed pooled prevalence was 29.28% (95% CI: 14.20-44.35) for E. coli sp. isolated from human sources [15, 25–31], 31.4% (95% CI: 10.97–51.83) from animal sources [32–36], and 20.89% (95% CI: -12.53-54.30) for combined sources [37, 38]. The negative confidence interval observed in the “Human & Animal” subgroup is a statistical artifact rather than a biological finding. The wide intervals reflect data instability due to the small number of studies and substantial heterogeneity. In the subgroup analysis by sample source showed higher prevalence in animal samples compared to human samples, likely reflecting animals as primary reservoirs with higher microbial loads at high-risk points such as abattoirs and retail meat. Human samples, often from the general population, exhibit lower or transient carriage due to hygiene practices and host defenses (Fig. 5).
Fig. 5.
Forest plot showing prevalence of E.coli isolates sub groups by study subjects
Regarding pooled prevalence of E. coli isolates, regional analysis disclosed varying prevalence rates such as Amhara 20.53% (95% CI: 12.22–28.84) [15, 26, 30, 31, 33, 35, 38], Oromia 21.06% (95% CI: 10.56–31.56) [27, 28, 32, 34, 37], Beneshangul Gumuz 53.8% (95% CI: 47.99–59.61) [25], Addis Ababa 60.5% (95% CI: 57.39–63.61) [29], and South Ethiopia 65% (95% CI: 59.17–70.83) [36] (Fig. 6).
Fig. 6.
Forest plot showing prevalence of E.coli isolates sub grouped by regions in Ethiopia
Estimated pooled prevalence of AMR pattern of E. coli
Among twenty-eight commonly prescribed antibiotics/drugs, the pooled prevalence of piperacillin resistance in E. coli isolates from human sources was 92.3% (95% CI: 75.9-108.7), followed by ampicillin at 86.4% (95% CI: 63.2-109.5) and cephazolin at 74.3% (95% CI: 71.5–77.1). In contrast, the lowest resistance estimates were for imipenem at 11.9% (95% CI: 4.2–19.5), followed by chloramphenicol at 12.5% (95% CI: 8.3–16.7) and azithromycin at 15.8% (95% CI: 12.3–19.3). The pooled prevalence of MDR was 80.7% (95% CI: 70.7–90.7). The overall estimated pooled prevalence for AMR was 55.5% (95% CI: 48.4–62.6) (Table 3).
Table 3.
AMR patterns of E. coli isolates from human sources against conventional prescribed antibiotics
| Authors | Drugs | PPE E.coli isolates AMR (%) | [95% CI] | Weight (%) | Heterogeneity (I2) | P-Value |
|---|---|---|---|---|---|---|
| (Girma Z et al., 2022) | AMP | 100.0 | (97.1-102.9) | 0.99 | 99.9% | P ≤ 0.001 |
| (Abu et al., 2021) | 9.5 | (6.1-12.9) | 0.99 | |||
| (Abebe et al., 2019) | 100.0 | (96.5-103.5) | 0.99 | |||
| (Ayalneh et al., 2024) | 100.0 | (95.1-104.9) | 0.99 | |||
| (Alebel et al., 2021) | 100.0 | (96.4-103.5) | 0.99 | |||
| (Beyene et al., 2019) | 100.0 | (97.3-102.7) | 0.99 | |||
| (Sisay et al., 2024) | 95.1 | (92.9-97.3) | 0.99 | |||
| Pooled ES | 86.4 | (63.2-109.5) | 6.94 | |||
| (Girma Z et al., 2022) | AMC | 95.0 | (92.8-97.6) | 0.99 | 99.5% | P ≤ 0.001 |
| (Abu et al., 2021) | 23.8 | (18.8-28.8) | 0.99 | |||
| (Abebe et al., 2019) | 41.7 | (37.5-45.9) | 0.99 | |||
| (Ayalneh et al., 2024) | 89.5 | (86.6-92.4) | 0.99 | |||
| (Alebel et al., 2021) | 60.0 | (54.2-65.8) | 0.99 | |||
| (Beyene et al., 2019) | 54.9 | (51.7-58.1) | 0.99 | |||
| (Sisay et al., 2024) | 46.3 | (41.3-51.3) | 0.99 | |||
| Pooled ES | 58.8 | (38.6-79.1) | 6.93 | |||
| (Girma Z et al., 2022) | MER | 4.8 | (2.4-7.2) | 0.99 | 99.9% | P ≤ 0.001 |
| (Abu et al., 2021) | 95.2 | (92.7-97.7) | 0.99 | |||
| (Ayalneh et al., 2024) | 31.6 | (27.2-36.0) | 0.99 | |||
| (Alebel et al., 2021) | 16.0 | (11.6-20.4) | 0.99 | |||
| Pooled ES | 36.9 | (-11.8-85.7) | 3.96 | |||
| (Girma Z et al., 2022) | PIP | 100.0 | (96.3-103.7) | 0.99 | .% | P =. |
| (Alebel et al., 2021) | 100.0 | (96.3-103.7) | 0.99 | |||
| (Beyene et al., 2019) | 77.1 | (74.4-79.8) | 0.99 | |||
| Pooled ES | 92.3 | (75.9-108.7) | 2.97 | |||
| (Girma Z et al., 2022) | NA | 42.9 | (37.3-48.5) | 0.99 | 99.2% | P ≤ 0.001 |
| (Abu et al., 2021) | 42.9 | (37.1-48.7) | 0.99 | |||
| (Abebe et al., 2019) | 83.3 | (80.1-86.5) | 0.99 | |||
| Pooled ES | 56.4 | (26.4-86.5) | 2.97 | |||
| (Girma Z et al., 2022) | NIT | 57.1 | (51.5-62.7) | 0.99 | .% | P =. |
| Pooled ES | 57.1 | (51.5-62.7) | 0.99 | |||
| (Girma Z et al., 2022) | CRO | 38.1 | (32.6-43.6) | 0.99 | 99.0% | P ≤ 0.001 |
| (Abu et al., 2021) | 71.4 | (66.1-76.7) | 0.99 | |||
| (Abebe et al., 2019) | 36.6 | (32.4-40.8) | 0.99 | |||
| (Ayalneh et al., 2024) | 31.6 | (27.2-36.0) | 0.99 | |||
| (Alebel et al., 2021) | 64.0 | (58.3-69.7) | 0.99 | |||
| (Beyene et al., 2019) | 69.4 | (66.5-72.3) | 0.99 | |||
| (Sisay et al., 2024) | 39.0 | (34.1-43.9) | 0.99 | |||
| (Ango et al., 2024) | 12.5 | (8.3-16.7) | 0.99 | |||
| Pooled ES | 45.3 | (29.8-60.8) | 7.91 | |||
| (Girma Z et al., 2022) | CAZ | 23.8 | (18.9-28.6) | 0.99 | 99.5% | P ≤ 0.001 |
| (Abu et al., 2021) | 81.0 | (76.4-85.6) | 0.99 | |||
| (Ayalneh et al., 2024) | 21.0 | (17.1-24.9) | 0.99 | |||
| (Alebel et al., 2021) | 68.0 | (62.4-73.6) | 0.99 | |||
| (Beyene et al., 2019) | 70.1 | (67.2-73.0) | 0.99 | |||
| (Ango et al., 2024) | 12.5 | (8.3-16.7) | 0.99 | |||
| Pooled ES | 46.1 | (21.8-70.3) | 5.94 | |||
| (Girma Z et al., 2022) | SXT | 74.4 | (69.5-79.3) | 0.99 | 99.1% | P ≤ 0.001 |
| (Abu et al., 2021) | 23.8 | (18.8-28.8) | 0.99 | |||
| (Beyene et al., 2019) | 61.0 | (56.8-65.2) | 0.99 | |||
| (Ayalneh et al., 2024) | 47.4 | (42.6-52.2) | 0.99 | |||
| (Alebel et al., 2021) | 60.0 | (54.2-65.8) | 0.99 | |||
| (Beyene et al., 2019) | 80.6 | (78.1-83.1) | 0.99 | |||
| (Sisay et al., 2024) | 87.8 | (84.5-91.1) | 0.99 | |||
| Pooled ES | 62.2 | (46.5-77.9) | 6.93 | |||
| (Girma Z et al., 2022) | GEN | 61.9 | (56.4-67.4) | 0.99 | 99.4% | P ≤ 0.001 |
| (Abu et al., 2021) | 47.6 | (41.8-53.4) | 0.99 | |||
| (Abebe et al., 2019) | 41.7 | (37.4-45.9) | 0.99 | |||
| (Ayalneh et al., 2024) | 5.3 | (3.2-7.4) | 0.99 | |||
| (Alebel et al., 2021) | 72.0 | (66.6-77.4) | 0.99 | |||
| (Beyene et al., 2019) | 38.2 | (35.1-41.3) | 0.99 | |||
| (Sisay et al., 2024) | 48.8 | (43.8-53.8) | 0.99 | |||
| Pooled ES | 45.0 | (25.5-64.5) | 6.93 | |||
| (Abu et al., 2021) | TOB | 100.0 | (96.9-103.1) | 0.99 | .% | P =. |
| (Beyene et al., 2019) | 39.6 | (36.5-42.7) | 0.99 | |||
| Pooled ES | 69.8 | (10.6-128.9) | 1.98 | |||
| (Abu et al., 2021) | AMK | 85.7 | (81.6-89.8) | 0.99 | 99.9% | P ≤ 0.001 |
| (Beyene et al., 2019) | 6.3 | (4.8-7.8) | 0.99 | |||
| Pooled ES | 45.9 | (-31.8-123.8) | 1.98 | |||
| (Abu et al., 2021) | CTX | 76.2 | (71.2-81.2) | 0.99 | 98.4% | P ≤ 0.001 |
| (Ayalneh et al., 2024) | 34.2 | (29.7-38.7) | 0.99 | |||
| (Alebel et al., 2021) | 60.0 | (54.2-65.8) | 0.99 | |||
| (Beyene et al., 2019) | 70.1 | (67.2-73.0) | 0.99 | |||
| (Sisay et al., 2024) | 43.9 | (38.9-48.9) | 0.99 | |||
| Pooled ES | 56.9 | (41.1-72.7) | 4.95 | |||
| (Abu et al., 2021) | CIP | 66.6 | (61.1-72.1) | 0.99 | 95.7% | P ≤ 0.001 |
| (Abebe et al., 2019) | 53.0 | (48.7-57.3) | 0.99 | |||
| (Alebel et al., 2021) | 40.0 | (34.2-45.8) | 0.99 | |||
| (Beyene et al., 2019) | 63.9 | (60.8-66.9) | 0.99 | |||
| (Sisay et al., 2024) | 43.9 | (38.9 -48.9) | 0.99 | |||
| Pooled ES | 53.6 | (43.8-63.4) | 4.95 | |||
| (Abu et al., 2021) | TET | 4.8 | (2.3-7.3) | 0.99 | 99.8% | P ≤ 0.001 |
| (Abebe et al., 2019) | 670 | (62.9-71.1) | 0.99 | |||
| (Ayalneh et al., 2024) | 57.9 | (53.2-62.6) | 0.99 | |||
| (Alebel et al., 2021) | 100.0 | (94.7-105.3) | 0.99 | |||
| (Beyene et al., 2019) | 83.3 | (80.9-85.7) | 0.99 | |||
| (Sisay et al., 2024) | 75.6 | (71.3-79.9) | 0.99 | |||
| (Ango et al., 2024) | 25.0 | (19.5-30.5) | 0.99 | |||
| Pooled ES | 59.1 | (30.1-88.0) | 6.93 | |||
| (Abebe et al., 2019) | NOR | 19.4 | (15.9-22.8) | 0.99 | 99.5% | P ≤ 0.001 |
| (Beyene et al., 2019) | 54.2 | (51.0-57.3) | 0.99 | |||
| Pooled ES | 36.8 | (2.7-70.9) | 1.63 | |||
| (Abebe et al., 2019) | AMO | 41.7 | (37.4-45.9) | 0.99 | .% | P =. |
| Pooled ES | 41.7 | (37.4-45.9) | 0.99 | |||
| (Abebe et al., 2019) | CHF | 44.4 | (40.1-48.7) | 0.99 | 99.6% | P ≤ 0.001 |
| (Ayalneh et al., 2024) | 5.3 | (3.2-7.4) | 0.99 | |||
| (Sisay et al., 2024) | 51.2 | (46.2-56.2) | 0.99 | |||
| Pooled ES | 33.6 | (1.5-65.7) | 2.97 | |||
| (Abebe et al., 2019) | DOX | 47.2 | (42.9-51.5) | 0.99 | .% | P =. |
| Pooled ES | 47.2 | (42.9-51.5) | 0.99 | |||
| (Ayalneh et al., 2024) | ETP | 31.6 | (27.2-36.0) | 0.99 | .% | P =. |
| Pooled ES | 31.6 | (27.2-36.0) | 0.99 | |||
| (Ayalneh et al., 2024) | IPM | 15.8 | (12.3-19.3) | 0.99 | 90.4% | P ≤ 0.001 |
| (Alebel et al., 2021) | 8.0 | (4.8-11.2) | 0.99 | |||
| Pooled ES | 11.9 | (4.2-19.5) | 1.98 | |||
| (Ayalneh et al., 2024) | CXM | 31.6 | (27.2-36.0) | 0.99 | 99.2% | P ≤ 0.001 |
| (Alebel et al., 2021) | 76.0 | (70.9 -81.1) | 0.99 | |||
| (Beyene et al., 2019) | 71.5 | (68.6-74.4) | 0.99 | |||
| Pooled ES | 59.7 | (33.4-85.9) | 2.97 | |||
| (Ayalneh et al., 2024) | AZM | 15.8 | (12.3-19.28) | 0.99 | .% | P =. |
| Pooled ES | 15.8 | (12.3 -19.3) | 0.99 | |||
| (Alebel et al., 2021) | F | 60.0 | 54.2-65.8) | 0.99 | 0.0% | P ≤ 0.768 |
| (Beyene et al., 2019) | 59.0 | (55.9-62.1) | 0.99 | |||
| Pooled ES | 59.2 | (56.5-61.9) | 1.98 | |||
| (Alebel et al., 2021) | CEP | 52.0 | (46.0-57.9) | 0.99 | .% | P =. |
| Pooled ES | 52.0 | 46.0-57.9) | 0.99 | |||
| (Beyene et al., 2019) | KZ | 74.3 | (71.5-77.1) | 0.99 | .% | P =. |
| Pooled ES | 74.3 | (71.5-77.1) | 0.99 | |||
| (Beyene et al., 2019) | PTZ | 17.4 | (14.9-19.8) | 0.99 | .% | P =. |
| Pooled ES | 17.4 | (14.9-19.8) | 0.99 | |||
| (Ango et al., 2024) | CHE | 12.5 | (8.3-16.7) | 0.99 | .% | P =. |
| Pooled ES | 12.5 | (8.3-16.7) | 0.99 | |||
| (Girma Z et al., 2022) | MDR | 76.2 | (71.4-81.0) | 0.99 | 99.8% | P ≤ 0.001 |
| (Abu et al., 2021) | 90.5 | (87.1-93.9) | 0.99 | |||
| (Ayalneh et al., 2024) | 84.2 | (80.7-87.7) | 0.99 | |||
| (Alebel et al., 2021) | 60.0 | (54.2-65.8) | 0.99 | |||
| (Beyene et al., 2019) | 99.3 | (98.8-99.8) | 0.99 | |||
| (Sisay et al., 2024) | 90.2 | (87.2-93.1) | 0.99 | |||
| (Ango et al., 2024) | 62.5 | (56.3-68.7) | 0.99 | |||
| Pooled ES | 80.7 | (70.7-90.7) | 6.93 | |||
| Over all pooled ES | 55.5 | (48.4-62.6) | 100.0 | 99.8% | P ≤ 0.001 |
PPE: Pooled prevalence estimation; ABR: antibiotic resistance; AMC: Amoxicillin clavulanic acid or Au: Augmentin, AMK: Amikacin, AMO: Amoxicillin, AMP: Ampicillin, AZM: Azithromycin, CAZ: Ceftazidime, CEP: Cefepime, CHF: Chloramphenicol CIP: Ciprofloxacin, CRO: Ceftriaxone, CTX: Cefotaxime, CXM: Cefuroxime, DOX: Doxycycline, ETP: Ertapenem, F: Nitrofurantoin, GEN: Gentamicin, IPM: Imipenem, KZ: Cephazolin, MDR: multi drug resistance, MER: Meropenem, NA: Nalidixic acid, NIT: Nitrofurantion, NOR: Norfloxacin, P: Penicillin, PIP: piperacillin, PTZ: Piperacillin-Tazobactem, SXT: Trimethoprim/sulfamethoxazole or Co: Co-trimoxazole, TET: Tetracycline, TOB: Tobramycin
In a pooled analysis of twenty antibiotics for animal sources, clindamycin resistance in E. coli isolates reached highest estimated pooled prevalence of 100.0% (95% CI: 95.3-104.7), followed by cefuroxime at 93.5% (95% CI: 90.1–96.9), neomycin at 74.1% (95% CI: 65.9–82.2), and amoxicillin at 73.4% (95% CI: 21.1-125.8). Conversely, the lowest resistance estimates were for Norfloxacin at 1.7% (95% CI: -0.7-4.1), Meropenem at 4.8% (95% CI: 2.2–7.4), and Cefotaxime at 8.6% (95% CI: 3.4–13.8). The pooled prevalence of MDR for E. coli isolates was found to be 49.9% (95% CI: 23.4–76.5, p < 0.001). The overall pooled prevalence of AMR for E. coli was 40.3% (95% CI: 28.8–51.8). In this pooled analysis, the exceptionally high resistance rates for certain antibiotics (e.g., clindamycin 100%) may reflect unregulated antibiotic use and cross-resistance in animal production systems. However, these results should be interpreted cautiously, as methodological variations in AST, small sample sizes, and potential reporting biases could have influenced the estimates (Table 4).
Table 4.
AMR patterns of E. coli isolates from animal sources against conventional prescribed antibiotics
| Studies (Authors) | Drugs | PPE E.coli isolates AMR (%) | [95% CI] | Weight (%) | Heterogeneity (I2) | P-value | |
|---|---|---|---|---|---|---|---|
| (Ali et al., 2021) | NEO | 74.1 | (65.9-82.2) | 3.01 | .% | P =. | |
| Pooled ES | 74.1 | (65.9-82.2) | 3.01 | ||||
| (Ali et al., 2021) | NOR | 1.7 | (-0.7-4.1) | 3.05 | .% | P =. | |
| Pooled ES | 1.7 | (-0.7-4.1) | 3.05 | ||||
| (Ali et al., 2021) | S | 8.6 | (3.4-13.8) | 3.04 | .% | P =. | |
| Pooled ES | 8.6 | (3.4-13.8) | 3.04 | ||||
| (Ali et al., 2021) | S3 | 37.9 | (28.9-46.9) | 3.00 | .% | P =. | |
| Pooled ES | 37.9 | (28.9-46.9) | 3.00 | ||||
| (Ali et al., 2021) | AMO | 46.6 | (37.4-55.8) | 3.00 | .% | P =. | |
| (Tadese et al., 2021) | 100.0 | (94.1-105.9) | 3.03 | ||||
| Pooled ES | 73.4 | (21.1-125.8) | 6.03 | ||||
| (Ali et al., 2021) | SXT | 34.5 | (25.7-43.3) | 3.00 | 95.3% | P ≤ 0.001 | |
| (Ayenew et al., 2021) | 12.0 | (8.2-15.8) | 3.04 | ||||
| Pooled ES | 22.9 | (0.8-44.9) | 6.04 | ||||
| (Ali et al., 2021) | CHF | 1.7 | (-0.7-4.1) | 3.05 | 99.9% | P≤0.001 | |
| (Tarekegn et al., 2023) | 81.8 | (76.9-86.6) | 3.04 | ||||
| Pooled ES | 41.722 | (-36.8-120.2) | 6.49 | ||||
| (Ali et al., 2021) | TET | 26.0 | (17.9-34.1) | 3.01 | 98.4% | P ≤ 0.001 | |
| (Tonjo et al., 2022) | 13.2 | (9.1-17.3) | 3.04 | ||||
| (Tarekegn et al., 2023) | 63.6 | (57.6-69.6) | 3.03 | ||||
| (Tadese et al., 2021) | 28.3 | (22.0-34.6) | 3.03 | ||||
| Pooled ES | 32.8 | (9.7-55.9) | 12.11 | ||||
| (Ali et al., 2021) | GEN | 3.4 | (0.1-6.7) | 3.05 | 94.5% | P ≤ 0.001 | |
| (Tonjo et al., 2022) | 3.0 | (0.9-5.1) | 3.05 | ||||
| (Tadese et al., 2021) | 21.7 | (15.9-27.5) | 3.03 | ||||
| Pooled ES | 8.9 | (0.4-17.4) | 9.13 | ||||
| (Ali et al., 2021) | CXT | 8.6 | (3.4-13.8) | 3.04 | .% | P =. | |
| Pooled ES | 8.6 | (3.4-13.8) | 3.04 | ||||
| (Tonjo et al., 2022) | Ce | 32.0 | (26.3-37.7) | 3.03 | .% | P =. | |
| Pooled ES | 32.0 | (26.3-37.7) | 3.03 | ||||
| (Ayenew et al., 2021) | CL | 100.0 | (95.3-104.7) | 3.04 | .% | P =. | |
| Pooled ES | 100.0 | (95.3-104.7) | 3.04 | ||||
| (Tonjo et al., 2022) | AMP | 59.9 | (53.9-65.9) | 3.03 | 96.8% | P ≤ 0.001 | |
| (Tarekegn et al., 2023) | 81.8 | (76.9-86.6) | 3.04 | ||||
| Pooled ES | 70.9 | (49.5-92.4) | 6.07 | ||||
| (Tonjo et al., 2022) | MER | 4.8 | (2.2-7.4) | 3.05 | .% | P =. | |
| Pooled ES | 4.8 | (2.2-7.4) | 3.05 | ||||
| (Tarekegn et al., 2023) | Co | 45.5 | (39.3-51.7) | 3.03 | .% | P =. | |
| Pooled ES | 45.5 | (39.3-51.7) | 3.03 | ||||
| (Tarekegn et al., 2023) | Na | 50.0 | (43.8-56.2) | 3.03 | .% | P =. | |
| Pooled ES | 50.0 | (43.8-56.2) | 3.03 | ||||
| (Tarekegn et al., 2023) | ERY | 27.3 | (21.8-32.8) | 3.03 | .% | P =. | |
| Pooled ES | 27.3 | (21.8-32.8) | 3.03 | ||||
| (Tadese et al., 2021) | AMC | 54.3 | (47.3-61.2) | 3.02 | .% | P =. | |
| Pooled ES | 54.300 | (47.3-61.3) | 3.02 | ||||
| (Tadese et al., 2021) | AMK | 15.2 | (10.2-20.2) | 3.04 | .% | P =. | |
| Pooled ES | 15.2 | (10.2-20.2) | 3.04 | ||||
| (Tadese et al., 2021) | CXM | 93.5 | (90.1-96.9) | 3.05 | .% | P =. | |
| Pooled ES | 93.5 | (90.1-96.9) | 3.05 | ||||
| (Ali et al., 2021) | MDR | 65.5 | (56.7-74.3) | 3.00 | 98.6% | P ≤ 0.001 | |
| (Tonjo et al., 2022) | 31.1 | (25.4-36.8) | 3.03 | ||||
| (Tarekegn et al., 2023) | 77.3 | (72.1-82.5) | 3.04 | ||||
| (Tadese et al., 2021) | 26.1 | (19.9-32.2) | 3.03 | ||||
| Pooled ES | 49.9 | (23.4-76.5) | 12.87 | ||||
| Over all pooled ES | 40.3 | 28.8-51.8 | 100.00 | 99.4% | P ≤ 0.001 | ||
PPE; Pooled prevalence estimation; ABR; antibiotic resistance; AMC; Amoxicillin clavulanic acid or Au; Augmentin, AMK; Amikacin, AMO; Amoxicillin, AMP; Ampicillin, Ce; Cefoxitin, CHF; Chloramphenicol, CL; Clindamycin, CTX; Cefotaxime, CXM; Cefuroxime, ERY; Erythromycin, GEN; Gentamicin, MDR; multi drug resistance, MER; Meropenem, Na; Nalidixic acid, NEO; Neomycin, NOR; Norfloxacin, S; Streptomycin, S3; Sulfonamides, SXT; Trimethoprim/sulfamethoxazole or Co; Co-trimoxazole
For the analysis of fourteen commonly prescribed antibiotics in both human and animal sources, the pooled estimate of erythromycin resistance in E. coli isolates was found to be 92.9% (95% CI: 90.2–95.6), followed by tetracycline at 71.1% (95% CI: 14.1-127.9) and amoxicillin at 64.9% (95% CI: 58.2–71.6). The MDR prevalence was recorded at 75.5% (95% CI: 55.1–95.9). The lowest resistance estimates were for nalidixic acid, kanamycin, and ceftazidime, each at 14.3% (95% CI: 10.6–17.9). The overall pooled estimate for this group was also 40.3% (95% CI: 25.5–55.2) (Table 5).
Table 5.
AMR patterns of E. coli isolates from human and animal sources against conventional prescribed antibiotics
| Studies (Authors) | Drugs | PPE E.coli isolates AMR (%) | [95% CI] | Weight (%) | Heterogeneity (I2) | P-value | |
|---|---|---|---|---|---|---|---|
| (F.Abunna et al., 2023) | AMP | 64.3 | (59.3-69.3) | 4.76 | 81.8% | P ≤ 0.002 | |
| (Belete et al., 2022) | 54.1 | (47.1-61.1) | 4.74 | ||||
| Pooled ES | 59.5 | (49.5-69.5) | 9.50 | ||||
| (F.Abunna et al., 2023) | NA | 14.3 | (10.6-17.9) | 4.78 | .% | P =. | |
| Pooled ES | 14.3 | (10.6-17.9) | 4.78 | ||||
| (F.Abunna et al., 2023) | KAN | 14.3 | (10.6-17.9) | 4.78 | .% | P =. | |
| Pooled ES | 14.3 | (10.6-17.9) | 4.78 | ||||
| (F.Abunna et al., 2023) | ERY | 92.9 | (90.2-95.6) | 4.78 | .% | P =. | |
| Pooled ES | 92.9 | (90.2-95.6) | 4.78 | ||||
| (F.Abunna et al., 2023) | CT | 14.3 | (10.6-17.9) | 4.78 | .% | P =. | |
| Pooled ES | 14.3 | (10.6-17.9) | 4.78 | ||||
| (F.Abunna et al., 2023) | CAZ | 14.3 | (10.6-17.9) | 4.78 | .% | P =. | |
| Pooled ES | 14.3 | (10.6-17.9) | 4.78 | ||||
| (F.Abunna et al., 2023) | CIP | 7.1 | (4.5-9.8) | 4.78 | 98.3% | P ≤ 0.001 | |
| (Belete et al., 2022) | 35.1 | (28.4-41.8) | 4.78 | ||||
| Pooled ES | 20.9 | (-6.5-48.3) | 9.53 | ||||
| (F.Abunna et al., 2023) | SXT | 14.3 | (10.6-17.9) | 5.02 | 89.2% | P = 0.002 | |
| (Belete et al., 2022) | 25.7 | (19.6-31.8) | 4.99 | ||||
| Pooled ES | 19.7 | (8.6-30.9) | 9.53 | ||||
| (Belete et al., 2022) | TET | 41.9 | (34.9-48.8) | 4.74 | .% | P =. | |
| (F.Abunna et al., 2023) | 100.0 | (95.1-104.9) | 4.77 | ||||
| Pooled ES | 71.0 | (14.1-127.9) | 9.51 | ||||
| (F.Abunna et al., 2023) | GEN | 7.1 | (4.4-9.8) | 4.78 | 99.4% | P ≤ 0.001 | |
| (Belete et al., 2022) | 56.8 | (49.8-63.8) | 4.74 | ||||
| Pooled ES | 31.8 | (-16.9-80.5) | 9.52 | ||||
| (Belete et al., 2022) | NOR | 17.6 | 12.2-22.9 | 4.74 | .% | P =. | |
| Pooled ES | 17.6 | (12.2-22.9) | 4.76 | ||||
| (Belete et al., 2022) | S3 | 41.9 | (34.8-49.0) | 4.74 | .% | P =. | |
| Pooled ES | 41.9 | (34.8-49.0) | 4.74 | ||||
| (Belete et al., 2022) | AMO | 64.9 | (58.2-71.6) | 4.74 | .% | P =. | |
| Pooled ES | 64.9 | (58.2-71.6) | 4.74 | ||||
| (Belete et al., 2022) | CHF | 16.2 | (11.0-21.4) | 4.76 | % | P =. | |
| Pooled ES | 16.2 | (11.0-21.4) | 4.76 | ||||
| (F.Abunna et al., 2023) | MDR | 85.7 | (82.0-89.4) | 4.78 | 96.5% | P ≤ 0.001 | |
| (Belete et al., 2022) | 64.9 | (58.2-71.6) | 4.74 | ||||
| Pooled ES | 75.5 | (55.1-95.9) | 9.52 | ||||
| Over all pooled ES | 40.3 | (25.5-55.2) | 100.0 | 99.6% | P ≤ 0.001 | ||
PPE; Pooled prevalence estimation; ABR; antibiotic resistance; AMO; Amoxicillin, AMP; Ampicillin, CAZ; Ceftazidime, CHF; Chloramphenicol CIP; Ciprofloxacin, CT; Colistin sulphate, ERY; Erythromycin, GEN; Gentamicin, KAN; Kanamycin, MDR; multi drug resistance, NA; Nalidixic acid, NOR; Norfloxacin, S3; Sulfonamides, SXT; Trimethoprim/sulfamethoxazole or Co; Co-trimoxazole, TET; Tetracycline
Discussion
E. coli strains are widespread bacteria that are increasingly developing AMR, posing a serious health risk to both humans and animals [39, 40]. E. coli can exist in the guts of humans and warm-blooded animals as normal flora. However, considerable species of E. coli are pathogenic, particularly for immunocompromised individuals as opportunistic infections [41]. This systematic review and meta-analysis is in line with One Health perspectives by addressing the widespread prevalence of E. coli sp., and its AMR pattern in both human and animals.
The pooled prevalence of E. coli isolates of both humans and animals was 28.83%, which was consistent with global reports to some extent (25–25.4%) [42], but higher than in Bangladesh (21%) [43], the overall prevalence reported in Africa (4.7%) [44], and in Ethiopia (25%) [45]. These differences may be attributed to variations in study period, sample sources, study areas, sample sizes, and hygiene and sanitation statuses [41, 44]. Additionally, environmental factors such as unsafe water sources, abattoir waste, and food chain contamination likely act as important reservoirs of E. coli and AMR genes.
The pooled prevalence of E. coli from human sources was 29.28%, which was slightly comparable with studies displayed globally (33.0%) [39], and in Ethiopia (29.16%) [46], but higher than reports in Bangladesh (17%) [43], Africa (2.8%) [44] and Ethiopia (19.79%) [47]. These discrepancies might be due to species-specific prevalence [47], the number of studies included [43, 47, 48], the scope of study settings [44, 48], and study periods [43, 44, 47, 48].
From animal sources, the pooled prevalence of E.coli isolates was 31.4%, consistent with global estimates (33.5%) [39], but lower than studies in India (50% and 63.2%) [48, 49] and higher than other reports in Ethiopia (15%) [50], Bangladesh (22%) [43], and Africa (5.4%) [44]. These differences may result from variations in the number of included studies [43, 48–50], study settings [44, 48], and study periods [43, 44, 47, 48]. Moreover, the highest pooled prevalence of E.coli sp., was observed in animal source samples, which implies due attention for animal health and safety while processing animal source foods [6, 7].
The pooled prevalence of E. coli sp. from combined human and animal source samples was 20.89%; however, there was insufficient data to compare findings and the possible contributing factors. In general, the high prevalence of E. coli strains indicates gaps in microbial safety practices, suggesting persistent E. coli causes public health risks and escalating antimicrobial resistance (AMR) challenges within the healthcare system [51].
Regarding AMR, the pooled prevalence in human-derived E. coli was 55.5%, higher than a prior meta-analysis in Ethiopia (45.38%); possibly due to differences in study setting (community or health care), sample sources, and types [52]. In animals, pooled AMR prevalence was 40.3%, lower than reported in poultry (76.96%) [53], but higher than the combined findings from foods, food handlers, animals, and environmental samples in Ethiopia (20%) [51]. These findings emphasize that E. coli AMR poses a significant threat to human compared to animals but not mean it is safe for animals. In conclusion, the AMR strains can spread among humans and animals with broad socio-economic implications and need urgent interventions such as information dissemination through affordable public channels [51, 53].
The pooled prevalence of multidrug resistance (MDR) E. coli sp. in human-source was 80.7%, higher than reported in other human-source samples (22%) [48], and supports a systematic review report in Ethiopia (79.17%) [46]. MDR prevalence in animal-source E. coli was 49.9%, lower than poultry studies (89.44%) [53], but higher than other mammals and aquatic mollusks [48, 54, 55]. The MDR of animal-source E.coli isolates (49.9%) was lower than human-source E.coli isolates (80.7%) implying that there was indiscriminate use of drugs. However, both findings showed that there was a possibility of significant treatment failure resulting in socio-economic complications.
Subgroup meta-analysis revealed significant regional variations in Ethiopia, with prevalence in South Ethiopia (65%), Addis Ababa (60.5%), Benishangul-Gumuz (53.8%), Amhara (20.5%), and Oromia (21.1%). These differences highlight the need for tailored interventions that consider local factors such as communities’ hygiene and sanitation coverage, and private and public health care facilities’ linkage to National Health Service protocols and guidelines.
Strengths and limitations
This systematic review and meta-analysis provide a comprehensive synthesis of Escherichia coli prevalence and antimicrobial resistance (AMR) patterns among human and animal populations in Ethiopia. By integrating a One Health perspective, it highlights the interconnected human–animal dimensions of AMR. However, most included studies were cross-sectional and relied solely on phenotypic susceptibility testing, which limits causal inference and restricts molecular insight into underlying resistance mechanisms. Additionally, this review lacks studies from some regions of Ethiopia, either due to the absence of published data or because available studies did not meet the inclusion criteria.
Conclusions
This meta-analysis demonstrates a substantial prevalence of E. coli and alarming antimicrobial and multidrug resistance levels in Ethiopia, with marked variation across regions and between human and animal sources. The findings signal an urgent need for coordinated public health and veterinary interventions to control AMR transmission. Strengthening national antibiotic stewardship programs, expanding AMR surveillance networks, and promoting hygiene, sanitation, and rational drug use are crucial. Implementing these measures within a One Health framework will be vital to protect both human and animal health and to mitigate the growing AMR burden in Ethiopia.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
We wish to acknowledge Mr. Desalegn Girma, who is a lecturer at Mizan-Tepi University, for his valuable contribution to the data search strategy design improvements, as well as with screening, acquiring, and reference literature management, and insightful contributions for the reviewing, improvement, and overall quality of the manuscript.
Author contributions
GMZ, TSA, and GMB contributed to study searching, data extraction, analysis, manuscript writing, and review. STM and SM participated in the final revision and review of the systematic review and meta-analysis manuscript.
Funding
Not applicable.
Data availability
The dataset containing all analyzed sources is available upon request to the corresponding authors.
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The dataset containing all analyzed sources is available upon request to the corresponding authors.