# Drug Resistance Patterns ofEscherichia coli in Ethiopia: A Meta-Analysis

Authors: Kald Beshir Tuem; Abadi Kahsu Gebre; Tesfay Mehari Atey; Helen Bitew; Ebrahim M. Yimer; Derbew Fikadu Berhe
Journal: BioMed Research International
 (2018)

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## Abstract

Background. Antimicrobial drug resistance is a global threat for treatment of infectious diseases and costs life and money and threatens health delivery system’s effectiveness. The resistance ofE. coli to frequently utilized antimicrobial drugs is becoming a major challenge in Ethiopia. However, there is no inclusive countrywide study. Therefore, this study intended to assess the prevalence ofE. coli resistance and antimicrobial-specific resistance pattern amongE. coli clinical isolates in Ethiopia.Methods. Articles were retrieved from PubMed, Embase, and grey literature from 2007 to 2017. The main outcome measures were overallE. coli and drug-specific resistance patterns. A random-effects model was used to determine pooled prevalence with 95% confidence interval (CI), using DerSimonian and Laird method. In addition, subgroup analysis was conducted to improve the outcome. The study bias was assessed by Begg’s funnel plot. This study was registered in PROSPERO as follows: PROSPERO 2017: CRD42017070106.Results. Of 164 articles retrieved, 35 articles were included. A total of 19,235 study samples participated in the studies and 2,635E. coli strains were isolated. Overall,E. coli antibacterial resistance was 45.38% (95% confidence interval (CI): 33.50 to 57.27). The resistance pattern ranges from 62.55% in Addis Ababa to 27.51% in Tigray region. The highest resistance ofE. coli reported was to ampicillin (83.81%) and amoxicillin (75.79%), whereas only 13.55% ofE. coli isolates showed resistance to nitrofurantoin.Conclusion.E. coli antimicrobial resistance remains high with disparities observed among regions. The bacterium was found to be highly resistant to aminopenicillins. The finding implies the need for effective prevention strategies for theE. coli drug resistance and calls for multifaceted approaches with full involvement of all stakeholders.


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## Body

## 1. Background

Escherichia coli (E. coli)is one of the most widespread bacteria throughout the world. Some strains ofE. coli can cause serious illness for humankind [1] including urinary tract infections [2–4], bloodstream infections [5], skin infection, otitis media [6, 7], and diarrhea [8].E. coli resistance to antimicrobials is creating trouble to the healthcare system worldwide [9, 10]. This complicates treatment outcomes, increases the cost of treatment, and limits the therapeutic options that contribute to the global spectra of a postantimicrobial age in which some of the most effective drugs lose their efficiency [11]. The bacterium is becoming highly resistant to conventionally used antibiotics (to both the newer and older medicines) as evidenced by many previous studies [12–16]. Adaptive resistance was supposed to be the main mechanism for the development of resistance including that to lethal doses of the antimicrobials [17].Antimicrobial resistance ofE. coli in developing countries including Ethiopia is reported to be one major reason for failure of treatment of infectious diseases [18]. A number of studies conducted in Ethiopia from various clinical settings show increments in the prevalence of antimicrobial resistance patterns ofE. coli [6, 19, 20]. However, there is no comprehensive and aggregated nationwide study to show the pattern of antimicrobial resistance inE. coli. Hence, the purpose of this meta-analysis was to sum up the available data and to establish the pooled prevalence and antimicrobial resistance ofE. coli in Ethiopia.


## 2. Methods

This study was conducted in a similar approach to Eshetie et al. (2016) [21] and according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) Checklist [22] (additional S1 file).


### 2.1. Study Selection

A systematic literature search was conducted in PubMed and Embase, and also manual search for articles potentially relevant to our study was identified. We built our search strategy by combining the three main arms (Table1):E. coli, drugs-related terms, and Ethiopia.Table 1
Searching strategies including search arm and terms used in the study.
Search arm Search terms 1 E. coli “Escherichia coli” OR “E. coli” 2 Drug and/or resistance “antibiotic resistance” OR “drug resistance” OR “drug resistance, microbial” OR “drug resistance, microbial” OR “antibacterial resistance” OR “antibiotic resistance” OR “antimicrobial resistance” 3 Ethiopia “Ethiopia” The word “OR” was used to combine search terms within each arm and the word “AND” was used to connect the three search arms (#1 AND #2 AND #3).Among the citations extracted, abstracts were reviewed to retrieve the clinical studies onE. coli colonization. Articles that were relevant, by title and abstract, were accessed in full text to determine those that provided sufficient information to be included in our meta-analysis. Finally, the references cited by each eligible study were screened to identify additional articles.


### 2.2. Inclusion and Exclusion Criteria

Studies included in this meta-analysis were those that had extractable data on the prevalence of drug resistance ofE. coli on a human in Ethiopian hospitals or research centers and were only published from 2007 to 2017 and were only in English language.


### 2.3. Outcome of Interest

The main outcome of interest was the prevalence of drug resistance or antimicrobial susceptibility ofE. coli among the totalE. coli clinical isolates. The prevalence was calculated by dividing the numbers of resistantE. coli isolates by the total number of clinically isolatedE. coli. As a secondary outcome of interest, we had also calculated the pooled resistance pattern ofE. coli isolates to specific antibiotics.


### 2.4. Data Extraction and Assessment of Quality of Study

Screening by title, abstract, and full text and data extraction were done independently by two authors (Kald Beshir Tuem and Abadi Kahsu Gebre) at each step and Derbew Fikadu Berhe was involved in consensus for discrepancies (if any) between the two authors (Kald Beshir Tuem and Abadi Kahsu Gebre). In cases of insufficient data, the authors reviewed the full text of the article for further information and clarification. The extracted data from each article were summarized into a spreadsheet. References and data for each study were carefully cross-checked to ensure that no overlapping data were present and to maintain the integrity of the meta-analysis. Information extracted from each paper was region, study area, study design, study population, culture specimens, number ofE. coli isolated, the average percentage of resistantE. coli, antimicrobial resistance rate ofE. coli, and references. With all the articles used in this study being cross-sectional, the score for the quality of the study was assessed using the modified Newcastle-Ottawa scale for the representativeness of sample, appropriateness of sample size, response rate, validity of method, strategy to control confounding factors, reliability of outcome determination, and appropriate statistical analyses. The quality score (Table 2) disagreements were resolved by consensus and a final agreed-upon rating was assigned to each study (S2 file) [58].Table 2
Summary of 35 studies reporting the prevalence and resistance pattern ofE. coliin different parts of Ethiopia.
References Region Study area Study period Study design Study population Culture specimens Study samples Number ofE. coli isolated Average %resistantE. coli NOS quality score out of 7 Abejew et al., 2014 [23] Amhara Dessie January to March 2012 RCS Test results of patients diagnosed with UTI Urine 2486 410 51.8 6 Kibret and Abera, 2014 [24] Dessie — R Patients Mid-stream morning urine 1404 203 53.2 6 Kibret and Abera, 2011 [25] Dessie — R Patients Urine, ear discharge, pus swab from wounds, and eye discharge 3,149 446 44.2 6 Azene and Beyene, 2011 [26] Dessie — R Patents with wound infections Wound swab 599 82 49.8 6 Alebachew et al., 2016 [27] Gondar March 1 to May 2, 2013 CS HIV patients Blood 100 01 44.4 7 Alemu et al., 2012 [28] Gondar March 22 to April 30, 2011 CS Pregnant women attending ANC Urine 385 19 29.2 7 Wondimeneh et al., 2014 [29] Gondar January to May 2013 CS Fistula patients “Clean-catch” mid-stream urine specimen 53 6 59.3 6 Wondimu et al., 2013 [30] Gondar December 2011 to June 2012 CS Blood donated from donors at blood bank blood 137 2 0.7 7 Eshetie et al., 2015 [31] Gondar February to May 2014 CS UTI-suspected patients Urine 442 112 48.0 4 Tiruneh et al., 2014 [32] Gondar September 1, 2011, to June 30, 2012 CS UTI-suspected patients Urine 284 120 68.0 4 Eshetie et al., 2016 [33] Gondar February to June 2014 CS Patients with UTI Urine 446 112 38.8 6 Mulu et al., 2017 [34] Debre Markos January 2015 R Patients Pus/swab from wound, urine, ear discharge, blood, stool, urethral or cervical discharge, nasal or throat swab, and CSF 575 39 31.5 6 Abera and Kibret, 2014 [35] West Gojjam November 2009 to February 2010 CS Adult patients who underwent trachomatous trichiasis surgery Conjunctival swabs 1413 20 26.5 5 Adugna et al., 2015 [36] Bahir Dar December 2011 to February 2012 CS Children below five years of age with acute diarrhea Stool sample 422 204 42.8 7 Demilie et al., 2012 [37] Bahir Dar October 2010 to January 2011 CS Pregnant women Clean-catch urine 367 16 41.7 4 Derbie et al., 2017 [38] Bahir Dar January 2015 R Patients Urine 446 72 55.3 5 Melaku et al., 2012 [39] Bahir Dar April to August 2010 CS Patients Urine 1254 33 87.8 6 Mulu et al., 2012 [40] Bahir Dar October 2010 to January 2011 CS Patients Wound swab and venous blood samples 294 8 53.1 6 Wondemagegn et al., 2015 [41] Bahir Dar May to November 2013 CS Women of reproductive age Vaginal swab 409 105 50.5 7  Dereje et al., 2017 [42] Central Ethiopia Addis Ababa February to May 2015 CS Fistula patients Clean-catch mid-stream urine 210 65 38.2 7 Dessie et al., 2016 [43] Addis Ababa October 2013 and March 2014 CS Surgical site infected patients Wound swab 107 24 68.4 6 Desta et al., 2016 [44] Addis Ababa December 2012 CS All age groups patients Fecal samples/swabs 267 235 83.0 7 Mamuye, 2016 [45] Addis Ababa August 2013 to January 2014 CS Outpatient and inpatient and pregnant women Mid-urine samples 424 53 52.2 5 Legese et al., 2017 [46] Addis Ababa January to March 2014 CS Septicemia and UTI-suspected patients Blood and urine 322 6 72.9 6  Ramos et al., 2014 [47] Oromia West Arsi July to December 2013 CS Leprosy patients Pus produced by ulcer 68 17 35.8 5 Derese et al., 2016 [48] Dire Dawa February 18, 2015, to March 25, 2015 CS Pregnant women Urine specimens 186 9 34.4 6 Beyene and Tsegaye, 2011 [49] Jimma April to June 2010 CS UTI cases patients Urine 228 7 27.1 5 Debalke et al., 2014 [50] Jimma September to December 2012 CS HIV/AIDS patients Urine 481 31 36.6 7 Mama et al., 2014 [51] Jimma May to September 2013 CS Patients with wound infection Wound swab 150 29 61.6 6 Mulualem, 2012 [52] Jimma February to March 2007 CS Inpatients and outpatients Urine, sputum, stool, and wound 359 67 45.7 6 Zenebe et al., 2011 [53] Jimma October 27, 2009, to March 26, 2010 CS Febrile patients Venous blood 260 4 25.0 6  Nigussie and Amsalu, 2017 [54] SNNP Hawassa June to October 2014 CS Diabetic patients Mid-stream urine 240 11 47.7 7 Amsalu et al., 2016 [55] Hawassa January 2012 to December 2014 RCS Patients registered at microbiology lab book Pus, ear discharge, nasal swab, urine, genital swab, CSF, and stool 510 35 64.0 7 Tadesse et al., 2014 [56] Hawassa March to September 2012 CS Pregnant women attending ANC Mid-stream urine 244 16 56.3 6  Wasihun et al., 2015 [57] Tigray Mekelle March to October 2014 PCS Febrile patients Venous blood 514 16 27.5 6  Total 19,235 2,635 ANC: antenatal clinic, CSF: cerebrospinal fluid, CS: cross-sectional, PCS: prospective cross-sectional, R: retrospective review of culture, RCS: retrospective cross-sectional, SNNP: Southern Nations, Nationalities, and Peoples, UTI: urinary tract infection, —: no data.


### 2.5. Quality Control

The quality of eligible studies was checked independently by two authors (Kald Beshir Tuem and Abadi Kahsu Gebre) using a set of predetermined criteria such as research design quality of paper, completeness of extractable information, and employed methods forE. coli isolation. The study bias was measured by Begg’s funnel plot [59]. This study was registered in PROSPERO as follows: PROSPERO 2017: CRD42017070106.


### 2.6. Data Analysis

A random-effects model was used to determine pooled prevalence, subgroup analysis, and 95% confidence interval (CI) by employing the approach of DerSimonian and Laird [60]. Variances and CIs were stabilized using Freeman-Tukey arc-sine methodology [61]; the reason is that using the standard approach of inverse variance method to calculate pooled prevalence does not work well in meta-analysis of single-arm study because, for studies with small or large prevalence, the inverse variance method causes the variance to become small and the calculated CI may be outside of the range [62]. Heterogeneity of study results was assessed using I2 test and significant heterogeneity was considered at p<0.10 and I2>50% [60, 63]. Statistical analyses were performed using Open Meta-Analyst (version 3.13) and Comprehensive Meta-Analysis (version 3.1). In addition, we performed subgroup analyses according to the region of the country and the mechanism of action of the tested drugs to improve the specificity of the assessment.


## 2.1. Study Selection

A systematic literature search was conducted in PubMed and Embase, and also manual search for articles potentially relevant to our study was identified. We built our search strategy by combining the three main arms (Table1):E. coli, drugs-related terms, and Ethiopia.Table 1
Searching strategies including search arm and terms used in the study.
Search arm Search terms 1 E. coli “Escherichia coli” OR “E. coli” 2 Drug and/or resistance “antibiotic resistance” OR “drug resistance” OR “drug resistance, microbial” OR “drug resistance, microbial” OR “antibacterial resistance” OR “antibiotic resistance” OR “antimicrobial resistance” 3 Ethiopia “Ethiopia” The word “OR” was used to combine search terms within each arm and the word “AND” was used to connect the three search arms (#1 AND #2 AND #3).Among the citations extracted, abstracts were reviewed to retrieve the clinical studies onE. coli colonization. Articles that were relevant, by title and abstract, were accessed in full text to determine those that provided sufficient information to be included in our meta-analysis. Finally, the references cited by each eligible study were screened to identify additional articles.


## 2.2. Inclusion and Exclusion Criteria

Studies included in this meta-analysis were those that had extractable data on the prevalence of drug resistance ofE. coli on a human in Ethiopian hospitals or research centers and were only published from 2007 to 2017 and were only in English language.


## 2.3. Outcome of Interest

The main outcome of interest was the prevalence of drug resistance or antimicrobial susceptibility ofE. coli among the totalE. coli clinical isolates. The prevalence was calculated by dividing the numbers of resistantE. coli isolates by the total number of clinically isolatedE. coli. As a secondary outcome of interest, we had also calculated the pooled resistance pattern ofE. coli isolates to specific antibiotics.


## 2.4. Data Extraction and Assessment of Quality of Study

Screening by title, abstract, and full text and data extraction were done independently by two authors (Kald Beshir Tuem and Abadi Kahsu Gebre) at each step and Derbew Fikadu Berhe was involved in consensus for discrepancies (if any) between the two authors (Kald Beshir Tuem and Abadi Kahsu Gebre). In cases of insufficient data, the authors reviewed the full text of the article for further information and clarification. The extracted data from each article were summarized into a spreadsheet. References and data for each study were carefully cross-checked to ensure that no overlapping data were present and to maintain the integrity of the meta-analysis. Information extracted from each paper was region, study area, study design, study population, culture specimens, number ofE. coli isolated, the average percentage of resistantE. coli, antimicrobial resistance rate ofE. coli, and references. With all the articles used in this study being cross-sectional, the score for the quality of the study was assessed using the modified Newcastle-Ottawa scale for the representativeness of sample, appropriateness of sample size, response rate, validity of method, strategy to control confounding factors, reliability of outcome determination, and appropriate statistical analyses. The quality score (Table 2) disagreements were resolved by consensus and a final agreed-upon rating was assigned to each study (S2 file) [58].Table 2
Summary of 35 studies reporting the prevalence and resistance pattern ofE. coliin different parts of Ethiopia.
References Region Study area Study period Study design Study population Culture specimens Study samples Number ofE. coli isolated Average %resistantE. coli NOS quality score out of 7 Abejew et al., 2014 [23] Amhara Dessie January to March 2012 RCS Test results of patients diagnosed with UTI Urine 2486 410 51.8 6 Kibret and Abera, 2014 [24] Dessie — R Patients Mid-stream morning urine 1404 203 53.2 6 Kibret and Abera, 2011 [25] Dessie — R Patients Urine, ear discharge, pus swab from wounds, and eye discharge 3,149 446 44.2 6 Azene and Beyene, 2011 [26] Dessie — R Patents with wound infections Wound swab 599 82 49.8 6 Alebachew et al., 2016 [27] Gondar March 1 to May 2, 2013 CS HIV patients Blood 100 01 44.4 7 Alemu et al., 2012 [28] Gondar March 22 to April 30, 2011 CS Pregnant women attending ANC Urine 385 19 29.2 7 Wondimeneh et al., 2014 [29] Gondar January to May 2013 CS Fistula patients “Clean-catch” mid-stream urine specimen 53 6 59.3 6 Wondimu et al., 2013 [30] Gondar December 2011 to June 2012 CS Blood donated from donors at blood bank blood 137 2 0.7 7 Eshetie et al., 2015 [31] Gondar February to May 2014 CS UTI-suspected patients Urine 442 112 48.0 4 Tiruneh et al., 2014 [32] Gondar September 1, 2011, to June 30, 2012 CS UTI-suspected patients Urine 284 120 68.0 4 Eshetie et al., 2016 [33] Gondar February to June 2014 CS Patients with UTI Urine 446 112 38.8 6 Mulu et al., 2017 [34] Debre Markos January 2015 R Patients Pus/swab from wound, urine, ear discharge, blood, stool, urethral or cervical discharge, nasal or throat swab, and CSF 575 39 31.5 6 Abera and Kibret, 2014 [35] West Gojjam November 2009 to February 2010 CS Adult patients who underwent trachomatous trichiasis surgery Conjunctival swabs 1413 20 26.5 5 Adugna et al., 2015 [36] Bahir Dar December 2011 to February 2012 CS Children below five years of age with acute diarrhea Stool sample 422 204 42.8 7 Demilie et al., 2012 [37] Bahir Dar October 2010 to January 2011 CS Pregnant women Clean-catch urine 367 16 41.7 4 Derbie et al., 2017 [38] Bahir Dar January 2015 R Patients Urine 446 72 55.3 5 Melaku et al., 2012 [39] Bahir Dar April to August 2010 CS Patients Urine 1254 33 87.8 6 Mulu et al., 2012 [40] Bahir Dar October 2010 to January 2011 CS Patients Wound swab and venous blood samples 294 8 53.1 6 Wondemagegn et al., 2015 [41] Bahir Dar May to November 2013 CS Women of reproductive age Vaginal swab 409 105 50.5 7  Dereje et al., 2017 [42] Central Ethiopia Addis Ababa February to May 2015 CS Fistula patients Clean-catch mid-stream urine 210 65 38.2 7 Dessie et al., 2016 [43] Addis Ababa October 2013 and March 2014 CS Surgical site infected patients Wound swab 107 24 68.4 6 Desta et al., 2016 [44] Addis Ababa December 2012 CS All age groups patients Fecal samples/swabs 267 235 83.0 7 Mamuye, 2016 [45] Addis Ababa August 2013 to January 2014 CS Outpatient and inpatient and pregnant women Mid-urine samples 424 53 52.2 5 Legese et al., 2017 [46] Addis Ababa January to March 2014 CS Septicemia and UTI-suspected patients Blood and urine 322 6 72.9 6  Ramos et al., 2014 [47] Oromia West Arsi July to December 2013 CS Leprosy patients Pus produced by ulcer 68 17 35.8 5 Derese et al., 2016 [48] Dire Dawa February 18, 2015, to March 25, 2015 CS Pregnant women Urine specimens 186 9 34.4 6 Beyene and Tsegaye, 2011 [49] Jimma April to June 2010 CS UTI cases patients Urine 228 7 27.1 5 Debalke et al., 2014 [50] Jimma September to December 2012 CS HIV/AIDS patients Urine 481 31 36.6 7 Mama et al., 2014 [51] Jimma May to September 2013 CS Patients with wound infection Wound swab 150 29 61.6 6 Mulualem, 2012 [52] Jimma February to March 2007 CS Inpatients and outpatients Urine, sputum, stool, and wound 359 67 45.7 6 Zenebe et al., 2011 [53] Jimma October 27, 2009, to March 26, 2010 CS Febrile patients Venous blood 260 4 25.0 6  Nigussie and Amsalu, 2017 [54] SNNP Hawassa June to October 2014 CS Diabetic patients Mid-stream urine 240 11 47.7 7 Amsalu et al., 2016 [55] Hawassa January 2012 to December 2014 RCS Patients registered at microbiology lab book Pus, ear discharge, nasal swab, urine, genital swab, CSF, and stool 510 35 64.0 7 Tadesse et al., 2014 [56] Hawassa March to September 2012 CS Pregnant women attending ANC Mid-stream urine 244 16 56.3 6  Wasihun et al., 2015 [57] Tigray Mekelle March to October 2014 PCS Febrile patients Venous blood 514 16 27.5 6  Total 19,235 2,635 ANC: antenatal clinic, CSF: cerebrospinal fluid, CS: cross-sectional, PCS: prospective cross-sectional, R: retrospective review of culture, RCS: retrospective cross-sectional, SNNP: Southern Nations, Nationalities, and Peoples, UTI: urinary tract infection, —: no data.


## 2.5. Quality Control

The quality of eligible studies was checked independently by two authors (Kald Beshir Tuem and Abadi Kahsu Gebre) using a set of predetermined criteria such as research design quality of paper, completeness of extractable information, and employed methods forE. coli isolation. The study bias was measured by Begg’s funnel plot [59]. This study was registered in PROSPERO as follows: PROSPERO 2017: CRD42017070106.


## 2.6. Data Analysis

A random-effects model was used to determine pooled prevalence, subgroup analysis, and 95% confidence interval (CI) by employing the approach of DerSimonian and Laird [60]. Variances and CIs were stabilized using Freeman-Tukey arc-sine methodology [61]; the reason is that using the standard approach of inverse variance method to calculate pooled prevalence does not work well in meta-analysis of single-arm study because, for studies with small or large prevalence, the inverse variance method causes the variance to become small and the calculated CI may be outside of the range [62]. Heterogeneity of study results was assessed using I2 test and significant heterogeneity was considered at p<0.10 and I2>50% [60, 63]. Statistical analyses were performed using Open Meta-Analyst (version 3.13) and Comprehensive Meta-Analysis (version 3.1). In addition, we performed subgroup analyses according to the region of the country and the mechanism of action of the tested drugs to improve the specificity of the assessment.


## 3. Results

From Embase, PubMed, and manual searching, we found 164 potentially relevant studies, of which 35 were included for analysis (Figure1).Figure 1
Flowchart shows selected articles for meta-analysis.The study design of all included articles (35) was cross-sectional study. In these studies, a total of 19,235 study samples participated, from which 2,635E. coli strains were isolated. The published studies were from four regions in Ethiopia (Table 2) which include the federal capital city of Ethiopia, Addis Ababa. No report was obtained from other regions in the country (Afar, Benishangul-Gumuz, Gambella, and Somali). Most of the studies indicated that various specimens had been utilized for screening ofE. coli; particularly multisite swabbing was performed from different parts of the body, including skin, nasal, eye, ear, urethra, throat, vagina, or genital area (Table 2), and other biological fluids like blood, urine, pus, stool, and cerebrospinal fluid (CSF) were taken for test. A total of 2,635E. coli strains were isolated from these various sites. The lowest and highest proportions ofE. coli resistance were reported, respectively, from Bahir Dar (55.20%) and Mekelle (27.50%) cities. The average prevalence ofE. coli resistance was also noted in different regions of Ethiopia; Addis Ababa region was ranked first (62.55%, 95% CI: 38.28–6.83%), followed by Southern Nations, Nationalities, and Peoples of Ethiopia (58.14%, 95% CI: 48.69–67.58%), Amhara (47.83%, 95% CI: 39.77–55.89%), and Oromia (42.86%, 95% CI: 32.77–52.95%), whereas relatively low magnitude ofE. coli resistance was reported from Tigray region (27.51%, 95% CI: 16.14–38.88%) (Figures 2 and 3).Figure 2
Proportion ofE. coli resistance in different regions of Ethiopia, 2007–2017. Values in parenthesis indicated 95% CI ofE. coli resistance in different regions of Ethiopia.Figure 3
Subgroup analysis ofE. coli antibacterial resistance according to regions of Ethiopia.Subgroup analyses (Figure3) were carried out based on the region (Addis Ababa, Amhara, Oromia, SNNP, and Tigray) and the mechanism of action of the drugs (cell wall synthesis inhibitors, protein synthesis inhibitors, DNA synthesis inhibitors, and antimetabolites). A paper-based analysis in our study showed that the overallE. coli resistance in Ethiopia was 48.87% (95% CI: 42.17–55.57%) with highest prevalence in the capital city, Addis Ababa, 62.55% (95% CI: 38.28–86.83%) (Figure 3).As presented in Figure4 and Table 3, the pooled prevalence ofE. coli resistance was 45.38% (95% CI: 33.50–57.27%), and high resistance rates were observed to ampicillin, 83.81% (95% CI: 76.95–90.67%), amoxicillin, 75.79% (95% CI: 64.26–87.32%), tetracycline, 67.18% (95% CI: 58.89–75.47%), trimethoprim-sulfamethoxazole, 57.47% (95% CI: 58.89–75.47%), and cephalothin, 56.69% (95% CI: 33.74–79.64%). A relatively low level of nitrofurantoin resistance was observed, 13.55% (95% CI: 5.83–21.27%).Table 3
Percentage of pooled antibacterial resistance rates ofE. coli in Ethiopia, 2007–2017.
Studies Antibacterial AML AMP AMC KF CRO TE DO C E CN K CIP NA F NOR SXT Abejew et al., 2014 [23] 84.6 80.0 --- 42.3 46.9 82.2 64.5 49.0 94.4 34.0 20.0 28.3 13.3 10.4 --- 75.8 Abera and Kibret, 2014 [35] --- --- --- --- 40 25 --- 35 --- 40 --- 4 --- --- --- 15 Adugna et al., 2015 [36] --- 86.8 47.5 --- --- 76 --- 36.2 --- 37.2 --- 6.9 --- --- 9.3 --- Alebachew et al., 2016 [27] --- 100 100 --- 0 100 --- 0 --- 0 --- 0 --- 0 --- 100 Alemu et al., 2012 [28] 100 100 36.8 --- 0 52.6 --- 0 --- 5.3 --- 0 --- --- 0 26.3 Beyene and Tsegaye, 2011 [49] 100 100 --- --- 0 28.6 --- 0 --- 0 --- 14.3 0 0 --- 28.6 Debalke et al., 2014 [50] --- 88 --- --- 4 --- --- 0 --- --- --- --- 52 0 16 96 Demilie et al., 2012 [37] 75 81.7 37.5 --- --- 68.8 --- 37.5 --- 31.2 18.8 18.8 43.8 6.3 25 56.2 Derbie et al., 2017 [38] --- 89.1 78.6 --- --- 66.1 --- --- --- 27.6 --- 64.4 --- 25 27 64.5 Dereje et al., 2017 [42] --- --- 21.6 --- 24.6 --- --- 32.2 --- 53.8 --- 56.9 --- 40 --- --- Derese et al., 2016 [48] 77.8 77.8 --- --- 0 66.7 --- 11.1 --- 0 --- 11.1 44.4 44.4 --- 11.1 Dessie et al., 2016 [43] --- 95.8 70.8 --- 83.3 83.3 --- 25 --- 54.2 --- 66.7 --- --- --- --- Desta et al., 2016 [44] --- --- 93 98 --- --- --- --- --- 63 --- 78 --- --- --- --- Eshetie et al., 2015 [31] --- 92.9 60.7 --- 17.9 53.6 --- 64.3 --- 57.1 --- 10.7 25 --- --- 50 Kibret and Abera, 2014 [24] 87.3 --- --- 56.9 33.9 80.1 61 37.4 93.7 24.5 --- 34.1 --- 3.8 --- 72.1 Kibret and Abera, 2011 [25] 86 --- --- 59.5 37.4 72.4 --- 35.3 89.4 13 --- 19.9 --- 3.6 6.5 62.9 Azene and Beyene, 2011 [26] 85.0 --- --- 64.7 66.7 71.4 33.3 36.1 51.9 14.4 --- 7.7 --- --- --- 67.1 Mama et al., 2014 [51] --- 100 --- 100 62 79 44.8 65.5 --- 51.7 --- 34 41 --- 44.8 55 Mamuye, 2016 [45] 75.5 79.2 --- 32.1 45.3 83.0 71.7 30.2 --- 22.6 --- 54.7 73.6 20.8 67.9 22.6 Melaku et al., 2012 [39] 85.7 100 --- --- --- 81.6 --- 83.7 --- --- --- --- --- --- --- --- Mulu et al., 2017 [34] 25 33.3 --- --- 3.3 75 75 13 0 69.6 --- 18.2 --- --- 23.1 11.1 Mulualem et al., 2012 [52] 86 86 70.1 --- 9 73.1 --- 35.8 --- 3 --- 20.9 --- --- 16.4 56.7 Mulu et al., 2012 [40] 90 78 --- --- 55.6 66.7 66.7 0 --- 44.4 44.4 44.4 66.7 22.2 44.4 67 Nigussie and Amsalu, 2017 [54] --- 100 36.4 --- 63.6 --- --- --- --- 72.7 --- 18.2 --- 0 9.1 81.8 Ramos et al., 2014 [47] 64.7 70.6 5.9 --- 5.9 41.2 29.4 35.3 41.2 11.8 --- 29.4 --- --- --- 58.8 Wasihun et al.2015 [57] --- --- 6.7 --- 60 --- 40 --- --- 13.3 --- 6.7 --- 26.7 60 6.7 Wondimeneh et al., 2014 [29] 50 66.7 --- --- 50 100 --- 16.7 --- 66.7 --- 50 --- --- 66.7 66.7 Wondimu et al., 2013 [30] --- 33.3 0 --- 0 0 0 0 0 0 --- 33.3 --- --- --- 0 Zenebe et al., 2011 [53] 0 100 --- 0 0 75 --- 100 --- 75 --- 0 0 --- --- 100 Amsalu1 et al., 2016 [54] --- 100 26.1 --- 45.7 --- --- 64.3 --- 60.6 --- 56.2 --- --- 68.8 90 Tadesse et al., 2014 [56] --- 68.8 --- --- --- --- --- --- --- 43.8 --- --- --- --- 18.8 81.2 Tiruneh et al., 2014 [32] 87.5 83.3 --- --- --- 79.2 --- 58.3 --- 44.2 --- 46.7 --- --- --- 76.7 Legese et al., 2017 [46] 100 --- 100 --- --- 83.3 --- 66.7 --- 66.7 --- --- --- 0 66.7 100 Eshetie et al., 2016 [33] --- 98.2 42 --- --- 43.8 --- 54.5 --- 52.7 --- 0.9 17 --- --- 64.3 Wondemagegn et al., 2015 [41] 80 73.3 --- --- --- 73.3 --- --- --- 26.7 --- 20 --- --- 20 60 AMC: amoxicillin-clavulanic acid, AML: amoxicillin, AMP: ampicillin, C: chloramphenicol, CIP: ciprofloxacin, CN: gentamicin, CRO: ceftriaxone, DO: doxycycline, E: erythromycin, F: nitrofurantoin, K: kanamycin, KF: cephalothin, NA: nalidixic acid, NOR: norfloxacin, SXT: trimethoprim/sulfamethoxazole, TE: tetracycline, ---: not done.Figure 4
Forest plot of the pooled percentage and confidence interval ofE. coli resistance to antibacterial drugs in Ethiopia from 2007 to 2017.Comparing the prevalence ofE. coli resistance among the antibacterial drugs, subgroup analysis (Figure 5) revealed that the cell wall synthesis inhibitors account for the greatest resistance percentage, 59.37% (95% CI: 36.21–82.53%), and DNA synthesis inhibitors account for the lowest resistance percentage, 26.14% (95% CI: 33.50–57.27%).Figure 5
Subgroup analysis of pooled percentage and confidence interval ofE. coli resistance to antibacterial drugs according to drug mechanism of action.There was a high level of heterogeneity by random model methods (I2=97.89%; p<0.01). Hence, the included studies have been conducted in different study settings, study periods, and study populations, which could have an effect on the heterogeneity of the included studies. The symmetry of funnel plot showed small study bias, which yielded insignificant effect.


## 4. Discussion

Antibiotic resistance continues to be a major global challenge in the management of bacterial infection. The trouble behind antibiotic resistance is highly marked in undeveloped or developing countries, including Ethiopia, where infectious diseases are highly prevalent [64]. Factors responsible for an increase in rates of antimicrobial resistance include misuse/overuse of antibiotics by healthcare professionals and general public and inadequate surveillance systems due to lack of reliable microbiological techniques leading to the inappropriate prescription of antibiotics [33]. Antimicrobial resistance inE. coli has increased worldwide and its susceptibility patterns show substantial variation in different geographical locations [5]. To date, the overall epidemiology and burden of multidrug resistance (MDR) bacteria have not been fully understood, especially in resource-limited countries including Ethiopia [64, 65]. To the best of our knowledge, this is the first meta-analysis study conducted to determine the pooled prevalence ofE. coli prevalence and resistance in Ethiopia. Our result revealed thatE. coli strains displayed diverse resistance patterns, with percentages varying slightly based on sample type and geographical distribution. Based on the tested antimicrobials, the overallE. coli resistance in Ethiopia was nearly 50% (45.38% (95% CI: 33.50–57.27%)).Developing countries have comparatively higher risk factors associated with MDR strains than the developed ones [64, 66]. Resistance to antibacterial agents is a normal evolutionary process for microorganisms, but it is highly aggravated by continuous deployment of antimicrobial drugs in treating infections [67, 68]. It is claimed that more than half of drugs are prescribed, sold, or dispensed without following standard protocols, and the situation is more pronounced in developing countries including Ethiopia [69]. Sosa et al. (2010) reported that antibiotic usage in most of the low-income countries is generally unregulated, which is a prime factor for the occurrence of resistant bacterial strains [64]. This implies that antibiotics are being used widely and inappropriately in resource-limited countries including Ethiopia. This may lead to an increase in the occurrence of drug-resistive bacterial strains such asE. coli.In our study, the regional prevalence ofE. coli resistance was estimated, and the subgroup analysis showed that the highest prevalence ofE. coli resistance (62.55%) was noted in Addis Ababa city, which was almost two times higher than Tigray region (27.51%). The observed variation might be due to differences in study location, hospital setup, and antimicrobial utilization.Subgroup analysis also showed thatE. coli strains exhibited higher resistance with cell wall inhibitors, specifically aminopenicillins (ampicillin and amoxicillin), followed by protein synthesis inhibitors, mainly to tetracycline, and lesser resistance prevalence to nitrofurantoin. In line with our data, globally,E. coli strains were reported to be highly resistant to the above-mentioned antibiotics, mainly to aminopenicillins [70, 71]. Săndulescu (2016) reported thatE. coli showed low resistance to nitrofurantoin, which is in line with the present finding.Moreover, our finding indicated the higher magnitude ofE. coli resistance. This may imply the need for intervention in prescribing and using antibacterial againstE. coli infections. Interventional strategies may include creating public awareness, maintaining hand hygiene, applying infection prevention protocols, and maintaining environmental sanitation, which are encouraged for preventing infection. In addition to these, promoting health education, maintaining continuous professional educations, and advocating rational prescribing habits are evidently effective in the minimization of the unwanted use of antibiotics, which in turn decrease selective pressure of resistant strains.


## 5. Conclusion

In this meta-analysis, the pooledE. coli resistance is considerably high.E. coli strains were highly resistant to ampicillin but showed lesser resistance to nitrofurantoin. Adopting safety protocols and implementing proper antibiotic prescription policies could be potential interventional strategies to address the emerging resistance ofE. coli.



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*Source: 4536905-2018-05-06.xml*