# Bacterial Profile among Patients with Suspected Bloodstream Infections in Ethiopia: A Systematic Review and Meta-Analysis

Authors: Birhan Alemnew; Habtamu Biazin; Asmamaw Demis; Melese Abate Reta
Journal: International Journal of Microbiology
 (2020)

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## Abstract

Introduction. The burden of bloodstream infections (BSIs) has been warranted in Ethiopia. Globally, the emergency and raised resistance rate of bacterial antimicrobial resistance is becoming a prominent problem, and it is difficult to treat patients having sepsis. In this review, we aimed to determine the pooled prevalence of bacterial isolates among presumptive patients with bloodstream infections in Ethiopia. Methods. A systematic search was performed from PubMed/MEDLINE, Scopus, HINARI, ScienceDirect, and Google Scholar electronic databases using PRISMA guidelines. The data analysis was carried out using STATATM version 14 after the records were cleaned and sorted out. Results. A total of 26 studies with 8,958 blood specimens and 2,382 culture-positive bacterial isolates were included for systematic review and meta-analysis. The meta-analysis derived a pooled culture-positive bacterial prevalence which was 25.78% (95% CI: 21.55–30.01%). The estimated pooled prevalence of Gram-positive and Gram-negative bacterial isolates was 15.50% (95% CI: 12.84–18.15%) and 10.48 % (95% CI: 8.32–12.63%), respectively. The two common Gram-positive bacteria isolated from patients suspected of BSIs were coagulase-negative Staphylococcus with a pooled prevalence of 5.75% (95% CI: 4.58–6.92%) and S. aureus 7.04 % (95% CI: 5.37–8.72%). Similarly, the common Gram-negative bacterial isolates and their estimated pooled prevalence were E. coli 1.69% (95% CI: 1.21–2.16%), Klebsiella species 7.04 % (95% CI: 5.37–8.72%), Pseudomonas species 0.39% (95% CI: 0.08–0.70%), Salmonella species 1.09% (95% CI: 0.79–1.38%), and Streptococcus pyogenes 0.88% (95% CI: 0.54–1.22%). Conclusion. The prevalence of bacterial isolates among presumptive patients suspected to BSIs in Ethiopia remains high. Furthermore, we found a remarkable variation in the pathogen distribution across the study setting.


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## Body

## 1. Background

Bloodstream infections are the leading cause of morbidity and mortality throughout the world [1]. Globally, around 200,000 cases of BSIs with mortality of rates ranging from 20 to 50% were reported annually [2]. In low- and middle-income countries including Ethiopia, BSIs are a major public health concern and cause illnesses and deaths in all groups of population [3], especially in immunocompromised individuals such as patients in an intensive care unit (ICU), elders, children [4], cancer patients [5, 6], neonates [7], and patients living with human immunodeficiency virus (HIV) [8].Bacterial bloodstream infections are defined as the presence of viable bacteria in the bloodstream that can elicit an immune response [9]. Bacteria may enter the bloodstream invasion, normally sterile parts of the body, in different ways. It is a serious, life-threatening infection that gets worse very quickly due to the spread of microorganisms and their toxins in blood [10]. Both Gram-negative and positive bacteria in a wide range of bacteria species cause BSIs [11]. As many previous studies highlighted, the common types of bacteria causing bloodstream infections are Gram-positive bacteria such as Staphylococcus aureus, coagulase-negative Staphylococci (CoNS), Streptococcus pyogenes, Streptococcus pneumoniae, Streptococcus agalactiae, and Enterococcus faecium and Gram-negative bacteria such as Escherichia coli, Pseudomonas aeruginosa, and Klebsiella species [4, 11, 12].Blood is normally a sterile site, in which a blood culture has a high positive predictive value and is a key laboratory diagnostic tool/or specimen for an accurate bacteremia diagnosis [13]. Furthermore, blood culture is considered a highly sensitive test to identify such bacterial isolates that can cause bloodstream infection; also, it is easy to perform [14]. Thus, rapid detection and identification of these possible bacterial pathogens in blood culture are very essential, and the determination of their antimicrobial resistance profile has a key role to diminish the impact of bacteria associated with bloodstream infections [15].Treatment of BSI is usually done by the timely administration of appropriate antimicrobial agents based on the sensitivity profile of the causative agents. However, due to the emergency and wide distribution of resistant bacteria to most of the currently prescribed antibiotics, it has become a serious health problem with many economic and social inferences all over the world [16].Clinical disease caused by multidrug-resistant bacteria prolongs the duration of illness, hospital stays, and healthcare-associated costs and makes patients lose protection to invasive procedures; besides, it lowers productivity and harms the global economy [17].On the contrary, bloodstream infections have an impact on dental prechemotherapy and transplant prescribed antimicrobial agents due to developing resistance; as a result, it is difficult to treat dental/oral health, transplantation, hematological diseases, and viral diseases such as coronavirus through reducing the immune systems and increasing the risk of morbidity and mortality due to underline BSIs [18–21].Extensive use and misuse of antibiotics in clinical, environmental, and agricultural areas, empirical treatment, and taking the drug without prescription are the major contributors to the emergence of antimicrobial resistance (AMR) and the development of different antibiotic-resistant gene mutations [22–24]. Additionally, the global movement of people and the extensive use of antibiotics as the last sort of drug for extended-spectrum antibiotics give rise to carbapenem resistance [24, 25]. Furthermore, prolonged hospital stay, presence of underline medical conditions, and invasive procedures contribute to the rise of AMR [26].The spread of drug resistance for many antibiotics makes the treatment options for bacterial bloodstream infections difficult. For this reason, in 2015, the World Health Organization (WHO) set strategies for detection, prevention, and control of AMR [27–29]. This strategy comprises improving awareness and understanding of AMR, performing surveillance and research to increase the knowledge and strengthen the evidence about AMR, improving the use of antimicrobial agents in human and animal health, and developing new drugs, vaccines, diagnostic tools, and other interventions in all countries [30, 31].Despite many efforts that have been undergoing, many bacterial pathogens have become resistant to the most common antibiotics and become a serious public health concern with economic and social implications. Globally, antimicrobial resistance is a major challenge, particularly in resource-limited countries including Ethiopia. Even though timely and appropriate use of antibiotics is the only way to treat bacterial bloodstream infection, there are no strict antibiotic stewardship practices and information showing the countrywide pooled prevalence of bloodstream infection-causing bacterial isolates and their antimicrobial resistance patterns, which may halt the resistance pattern. Therefore, this systematic review and meta-analysis aimed to determine pooled estimates of the prevalence of bacterial isolates causing bloodstream infections among presumptive patients of bloodstream infections in Ethiopia. Besides, this will help as a benchmark for developing antimicrobial stewardship programs and generating evidence-based selection of antimicrobial agents for proper treatment.


## 2. Methods

### 2.1. Protocol and Registration

We strictly follow the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) tool to report the findings of this systematic review and meta-analysis [32]. The completed PRISMA checklist is provided as a supplementary file (additional file: Table S1). The protocol for this study was submitted to the International Prospective Register of Systematic Reviews (PROSPERO) in February 2020 and was assigned the identification number (ID#149999).


### 2.2. Data Sources and Search Strategies

A systematic search was carried out using the following electronic databases: PubMed/Medline, HINARI, Scopus, ScienceDirect, and Google Scholar. Medical subject headings and related keywords were used extensively to search the appropriate articles from these databases using the following combinations of keywords: “bacterial isolates,” “bacterial pathogen,” “bacteria,” “bloodstream infection,” “bacteremia,” “sepsis,” “septicemia,” “antibiotic resistance pattern,” “antimicrobial resistance,” “antimicrobial susceptibility,” “antimicrobial susceptibility test,” and “Ethiopia.” These search words/phrases were further paired with each other or combined using “AND” and “OR” Boolean operators. Furthermore, reference lists of all included studies were screened to identify further potentially eligible studies and gray literature studies. Only those articles written in the English language and conducted in Ethiopia were considered.


### 2.3. Inclusion and Exclusion Criteria

Observational studies fulfilling the following criteria were included: (a) studies reporting bloodstream infections across all age groups and conducted in Ethiopia; (b) articles published in the English language and published from 2000 to 2020. Additionally, case reports, policy statements, reviews, and inaccessible full texts or those unable to receive from the corresponding author communicated through e-mail were excluded from the study.


### 2.4. Study Selection and Quality Assessment

All retrieved studies were exported into EndNote reference manager software version 8 (Thomson Reuters, London), and duplicated studies were removed. Four reviewers (BA, HB, AD, and MAR) independently screened the titles and abstracts, and full texts were reviewed to determine the eligibility of each study. Where there was disagreement, a decision was reached after discussion and consensus among all reviewers. On the contrary, the critical quality assessment checklist recommended by the “Joanna Briggs Institute (JBI)” was used to evaluate the quality of the included studies [33]. Two reviewers (BA and MAR) independently assessed the quality of the full-text articles. The discrepancy was resolved through discussion to reach on consensus and to include articles to the final analysis. The domain paper quality assessment criteria were clear inclusion criteria, details of study subjects, the study settings, reliable/valid measurements for exposure, outcome variables, and appropriate statistical analysis (additional file, Table S2). The cutoff point was declared after reviewing the relevant literature. Disagreements between the two authors were resolved by taking the mean score of the two authors’ evaluations.


### 2.5. Data Extraction

All articles included in the final analysis were reviewed by two authors independently using standardized data extraction tools prepared in the Microsoft Excel sheet. The following data were extracted from each original article using the data abstraction form: author’s name, year of publication, study region, study area, study design, study period, sample size, the prevalence of bacterial BSI, type of bacterial isolates, and prevalence of bacterial isolates.


### 2.6. Data Processing and Analysis

The extracted data using Microsoft Excel sheet 2016 were transferred into STATA version 14 software (Stata Corporation, College Station, Texas) for final analysis. Due to the presence of significant heterogeneity across studies, the random-effect model was applied to estimate the pooled effect size, odds ratios (ORs), and 95% confidence intervals (CIs) across studies. Subgroup analysis was conducted by sample size, year of publication, and study region. Heterogeneity of all included studies was assessed using theI2-statistical test. A p value of less than 0.05 was used to declare heterogeneity. Heterogeneity across the studies examined using the I2 statistics was categorized to 25%, 50%, and 75% which represent low, moderate, and high, respectively [34]. The source of heterogeneity was examined through sensitivity analysis and subgroup analysis. The presence of publication bias was evaluated using Egger’s regression test with a p value of less than 0.05 as a cutoff point to declare the presence of publication bias [29].


## 2.1. Protocol and Registration

We strictly follow the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) tool to report the findings of this systematic review and meta-analysis [32]. The completed PRISMA checklist is provided as a supplementary file (additional file: Table S1). The protocol for this study was submitted to the International Prospective Register of Systematic Reviews (PROSPERO) in February 2020 and was assigned the identification number (ID#149999).


## 2.2. Data Sources and Search Strategies

A systematic search was carried out using the following electronic databases: PubMed/Medline, HINARI, Scopus, ScienceDirect, and Google Scholar. Medical subject headings and related keywords were used extensively to search the appropriate articles from these databases using the following combinations of keywords: “bacterial isolates,” “bacterial pathogen,” “bacteria,” “bloodstream infection,” “bacteremia,” “sepsis,” “septicemia,” “antibiotic resistance pattern,” “antimicrobial resistance,” “antimicrobial susceptibility,” “antimicrobial susceptibility test,” and “Ethiopia.” These search words/phrases were further paired with each other or combined using “AND” and “OR” Boolean operators. Furthermore, reference lists of all included studies were screened to identify further potentially eligible studies and gray literature studies. Only those articles written in the English language and conducted in Ethiopia were considered.


## 2.3. Inclusion and Exclusion Criteria

Observational studies fulfilling the following criteria were included: (a) studies reporting bloodstream infections across all age groups and conducted in Ethiopia; (b) articles published in the English language and published from 2000 to 2020. Additionally, case reports, policy statements, reviews, and inaccessible full texts or those unable to receive from the corresponding author communicated through e-mail were excluded from the study.


## 2.4. Study Selection and Quality Assessment

All retrieved studies were exported into EndNote reference manager software version 8 (Thomson Reuters, London), and duplicated studies were removed. Four reviewers (BA, HB, AD, and MAR) independently screened the titles and abstracts, and full texts were reviewed to determine the eligibility of each study. Where there was disagreement, a decision was reached after discussion and consensus among all reviewers. On the contrary, the critical quality assessment checklist recommended by the “Joanna Briggs Institute (JBI)” was used to evaluate the quality of the included studies [33]. Two reviewers (BA and MAR) independently assessed the quality of the full-text articles. The discrepancy was resolved through discussion to reach on consensus and to include articles to the final analysis. The domain paper quality assessment criteria were clear inclusion criteria, details of study subjects, the study settings, reliable/valid measurements for exposure, outcome variables, and appropriate statistical analysis (additional file, Table S2). The cutoff point was declared after reviewing the relevant literature. Disagreements between the two authors were resolved by taking the mean score of the two authors’ evaluations.


## 2.5. Data Extraction

All articles included in the final analysis were reviewed by two authors independently using standardized data extraction tools prepared in the Microsoft Excel sheet. The following data were extracted from each original article using the data abstraction form: author’s name, year of publication, study region, study area, study design, study period, sample size, the prevalence of bacterial BSI, type of bacterial isolates, and prevalence of bacterial isolates.


## 2.6. Data Processing and Analysis

The extracted data using Microsoft Excel sheet 2016 were transferred into STATA version 14 software (Stata Corporation, College Station, Texas) for final analysis. Due to the presence of significant heterogeneity across studies, the random-effect model was applied to estimate the pooled effect size, odds ratios (ORs), and 95% confidence intervals (CIs) across studies. Subgroup analysis was conducted by sample size, year of publication, and study region. Heterogeneity of all included studies was assessed using theI2-statistical test. A p value of less than 0.05 was used to declare heterogeneity. Heterogeneity across the studies examined using the I2 statistics was categorized to 25%, 50%, and 75% which represent low, moderate, and high, respectively [34]. The source of heterogeneity was examined through sensitivity analysis and subgroup analysis. The presence of publication bias was evaluated using Egger’s regression test with a p value of less than 0.05 as a cutoff point to declare the presence of publication bias [29].


## 3. Results

### 3.1. Search Results

As illustrated in Figure1, we identified a total of 285 potentially relevant studies from electronic databases, and 39 articles were excluded due to duplication. After reviewing the titles and abstracts, 128 articles were excluded because they did not meet the objectives and the inclusion criteria of the review. Accordingly, 128 full-text articles were reviewed in-depth based on the preset inclusion criteria, of which 102 articles were excluded with reasons. Finally, 26 studies were considered and used for the final quantitative analysis (meta-analysis).Figure 1
PRISMA-adapted flow diagram showing the results of the search and reasons for the exclusion of articles [29].


### 3.2. Characteristics of Included Studies

As illustrated in Table1, all the included studies in the final quantitative analysis were observational, 20 were cross-sectional [7, 14, 16, 35, 36, 38, 39, 42–47, 49–53, 55] and 6 were retrospective [11, 16, 40, 41, 48, 52] by study design. The included studies were conducted in four regions (Amhara, Tigray, Oromia, and Southern Nations, Nationalities, and Peoples’ Region (SNNPR)) and from the two self-administrative cities (Addis Ababa and Dire Dawa). Of the 26 studies that fulfilled the review inclusion criteria [7, 11, 14, 16, 35, 36, 38–52, 55], eleven studies were conducted in Amhara region [7, 8, 11, 14, 36, 40, 44, 45, 52–54], nine in Addis Ababa [16, 41, 43, 46, 48–50, 55, 56], three in Oromia region [35, 38, 42], and a single study from Tigray [39], Dire Dawa [51], and SNNPR [47]. The sample size of individual studies ranged from 83 [53] to 856 [36].Table 1
Characteristics of included studies in this meta-analysis.
AuthorsYearRegionStudy areaHospital unitSpecimenStudy designStudy periodSample sizeNumber of isolates (n)Gram-positive bacterial isolates,n (%)Gram-negative bacterial isolates,n (%)Sorsa et al. [35]2019OromiaASRHNeonatal sepsisBloodCross-sectionalApril 2016–May 201730188 (29.24%)49 (16.28%)39 (12.96%)Abebaw Shiferaw et al. [36]2018AmharaUoGTHBacteremia-suspected patientsBloodRetrospectiveSeptember 2003–February 2013856174 (20.33%)112 (13.08%)62 (7.24%)Negussie et.al. [37]2015Addis AbabaTASH and YHSepticemia-suspected childrenBloodCross-sectionalOctober 2011–February 201220156 (27.86%)29 (14.43%)26 (12.94%)Abrha et al. [38]2011OromiaJURHMalnourished children admittedBloodCross-sectionalOctober 2009–May 201017035 (20.59%)24 (14.12%)11 (6.47%)Wasihun et al. [39]2015TigrayMKOutpatientsBloodCross-sectionalMarch–October 2014514144 (28.02%)104 (20.23%)40 (7.78%)Hailu et al. [14]2016AmharaBRHLCOutpatientsBloodCross-sectionalMarch 2013–January 2015561220 (39.22%)105 (18.72%)115 (20.50%)Moges et al. [40]2019AmharaFCSHBacteremia-suspected patientsBloodCross-sectionalDecember 2017–April 2018388207 (53.35%)61 (15.72%)146 (37.63%)Terfa Kitila et al. [41]2018Addis AbabaAARLBacteremia-suspected patientsBloodRetrospectiveJanuary 2015–December 2016500164 (32.8%)127 (25.4%)37 (7.40%)Dagnew et al. [11]2013AmharaUoGTHSepticemia-suspected patientsBloodRetrospectiveSeptember 2006–January 201239071 (18.21%)49 (12.56%)22 (5.64%)Seboxa et al. [16]2015Addis AbabaTASHSepticemia-suspected patientsBloodRetrospectiveAugust 2012–October 201329238 (13.04%)18 (6.16%)20 (6.85%)Tizazu et al. [42]2011OromiaJUSHSepticemia-suspected patientsBloodCross-sectionalOctober 2009–March 201026023 (8.85%)14 (5.39%)9 (3.46%)Eshetu et al. [43]2018Addis AbabaTASHSepticemia-suspected patientsBloodCross-sectionalSeptember 2016–October 201742264 (15.17%)29 (6.88%)35 (8.29%)Geyesus et al. [44]2017AmharaUoGTHUoGTH Suspected of neonatal sepsisBloodCross-sectionalSeptember 2015–May 2016251120 (47.81%)81 (32.27%)39 (15.54%)Jemal [8]2017AmharaFURHBSI in HIV-infected patientsBloodCross-sectionalDecember 2016384123 (32.03%)77 (20.05%)46 (12%)Alebachew et al. [45]2016AmharaUoGTHPatients infected with HIV and suspected of having sepsisBloodCross-sectionalMarch–May 201310031 (31%)26 (26%)5 (5%)Arega et al. [46]2017Addis AbabaTASHAdult febrile cancer patientsBloodCross-sectionalDecember 2011–June 201210713 (12.15%)--Gebrehiwot et al. [7]2012AmharaUoGTHClinically suspected neonatal sepsisBloodCross-sectionalJuly 2011–June 201218158 (32.04%)21 (11.60%)37 (20.44%)Alemayehu et al. [47]2019SNNPHUCSHPediatric patientsBloodCross-sectionalMarch–August 201629641 (13.85%)30 (10.14%)11 (3.72%)Adib and Worku [48]2012Addis AbabaTASHSuspected of neonatal sepsisBloodRetrospectiveSeptember 1, 2007–August 31, 2008578166 (28.72%)105 (18.17%)61 (10.55%)Mitiku et al. [49]2019Addis AbabaTASHFebrile pediatric patientsBloodCross-sectionalSeptember 2017–June 2018340127 (37.35%)63 (18.53%)74 (21.76%)Tsega et al. [50]2017Addis AbabaZMHSepticemia-suspected pediatric patientsBloodCross-sectionalJune 2016–March 2017309113 (36.57%)84 (27.18%)29 (9.39%)Demissie et al. [51]2019Dire DawaDCRHSepsis-suspected women attending the deliveryBloodCross-sectionalMay–July 30, 201944157 (12.9%)24 (5.44%)33 (7.48%)Hailu et al. [14]2016Addis AbabaTASHSepticemia-suspected patientsBloodCross-sectionalJanuary–March 201417723 (12.99%)12 (6.78%)11 (6.22%)Ali and Kebede [52]2008AmharaUoGTHFebrile patientsBloodRetrospectiveMarch 2001–April 2005472114 (24.15%)80 (16.95%)34 (7.20%)Endris et al. [53]2014AmharaUoGTHConfirmed VL patients suspected of sepsisBloodCross-sectionalFebruary 2012–May 20128316 (19.28%)12 (14.46%)4 (4.82%)Gebre-Egziabher et al. [54]2019AmharaUoGTHICU patients suspected of septicemiaBloodCross-sectionalFebruary–May 201838496 (25%)67 (17.45%)29 (7.55%)ATRH: Asella Teaching and Referral Hospital, UoGTH: University of Gondar Teaching Hospital, TASH: Tikur Anbessa Specialized Hospital, YH: Yekatit 12 Hospital, TUSH: Jimma University Specialized Hospital, MK: Mekelle Hospital, BRHRLC: Bahir Dar Regional Health Research Laboratory Center, FHCSH: Felege Hiwot Comprehensive Specialized Hospital, AARL: Addis Ababa Regional Laboratory, GUH: Gondar University Hospital, HUCSH: Hawassa University Comprehensive Specialized Hospital, ZMH: Zewuditu Memorial Hospital, and DCRH: Dil Chora Referral Hospital.


### 3.3. Culture-Positive Bacterial Profile among Patients with Suspected Bloodstream Infections in Ethiopia

In this meta-analysis, a total of 2,382 positive bacteria cultures obtained from 8,958 blood samples were included. The meta-analysis-derived pooled culture-positive bacterial prevalence from all blood samples was 25.78 % (95% CI: 21.55–30.01%) (Figure2). Pooled prevalence of Gram-positive and Gram-negative bacterial isolates was 15.50 % (95% CI: 12.84–18.15%) (Figure 3) and 10.48 % (95% CI: 8.32–12.63%) (Figure 4), respectively. The two common Gram-positive bacteria among patients suspected for bloodstream infections were recovered. The pooled prevalence of CoNS was 5.75 % (95% CI: 4.58–6.92%) (Figure 5). Similarly, the pooled estimated prevalence of S. aureus from these groups was 7.04 % (95% CI: 5.37–8.72%) (Figure 6). Likewise, we found five Gram-negative bacterial isolates among patients suspected for bloodstream infection in Ethiopia. The pooled estimates of Klebsiella species isolates were found to be 7.04 % (95% CI: 5.37–8.72%) (additional file: Figure S1 and Table 2) followed by E. coli 1.69% (95% CI: 1.21–2.16%) (additional file: Figure S2 and Table 2), Salmonella species 1.09% (95% CI: 0.79–1.38%) (additional file: Figure S3 and Table 2), S. pyogenes 0.88% (95% CI:0.54–1.22%) (additional file: Figure S4 and Table 2), and Pseudomonas species 0.39% (95% CI: 0.08–0.70%) (additional file: Figure S5 and Table 2).Figure 2
Forest plot showing the pooled prevalence of bacterial profile among patients with suspected bloodstream infections in Ethiopia.Figure 3
Forest plot showing the pooled prevalence of Gram-positive bacterial isolates among patients with suspected bloodstream infections in Ethiopia.Figure 4
Forest plot showing the pooled prevalence of Gram-negative bacterial isolates among patients with suspected bloodstream infections in Ethiopia.Figure 5
Forest plot showing the pooled prevalence CoNS bacterial isolates among patients with suspected bloodstream infections in Ethiopia.Figure 6
Forest plot showing the pooled prevalence ofS. aureus isolated among patients with suspected bloodstream infections in Ethiopia.Table 2
Results of each bacterial type isolated from bloodstream-infected patients.
Type of bacterial isolatesNo. of studiesTotal no. of culture-positive/from blood total specimensPooled prevalence rate (95% CI)I2 (p values)S.pyrogenes [36, 38, 39, 41, 42, 44, 45, 47]831/2,9470.88 (0.54, 1.22)0.0% (<0.638)Pseudomonas species [7, 11, 14, 36, 39–41, 43, 45, 48, 51]859/5,1080.39 (0.08, 0.70)0.0% (<0.885)Salmonella species [7, 11, 36–38, 40, 42, 48, 51, 52, 56]117/4,6281.09 (0.79, 1.38)51.1% (<0.021)E. coli [7, 11, 14, 35, 36, 38–45, 47, 48, 50–54, 56]21156/7,6341.69 (1.21, 2.16)65.9% (<0.001)Klebsiella species [7, 8, 11, 14, 35–45, 47, 48, 50–54, 56]23362/8,2194.30 (2.45, 6.16)91.0% (<0.001)


### 3.4. Heterogeneity and Publication Bias

The existence of heterogeneity and publication bias was assessed within the included studies. Consequently, there was considerable heterogeneity across twenty six included studies (I2 = 95.8%). Egger’s regression test for publication bias revealed marginally significant (p=0.044), which indicated the presence of publication bias (additional file: Figure S6). Moreover, heterogeneity across studies considered for Gram-positive isolates was I2 = 92.9% and Gram-negative isolates was I2 = 92.6%. The publication bias of Gram-positive and Gram-negative bacterial isolates was found to be p=0.001 and p=0.001, respectively, using Egger’s regression test.


### 3.5. Subgroup Analysis

Due to the presence of high heterogeneity across or within the included studies, we conducted subgroup analysis based on the study area (region), sample size, and year of publication to sort out the possible source of heterogeneity across the studies. However, the subgroup analysis result revealed that the source of heterogeneity was not due to the study region, sample size, and year of publication disparities (Table3 and Supplementary Figures S7–S9).Table 3
Subgroup pooled prevalence analysis of bacterial isolates among patients suspected of bloodstream infection in Ethiopia, 2020.
Subgroup byCharacteristicsNo. of studiesTotal sample sizePooled prevalence rate (95% CI)HeterogeneityI2 (p values)Study areaOromia [35, 38, 42]373119.45 (6.41, 32.48)95.5% (<0.001)Amhara [7, 8, 11, 14, 36, 40, 44, 45, 52–54]114,05031.11 (24.28, 37.95)95.8% (<0.001)Addis Ababa [16, 41, 43, 46, 48–50, 55, 56]92,92624.05 (17.27, 30.83)95.0% (<0.001)Others∗ [39, 47, 51]31,25118.23 (8.88, 27.58)99.0% (<0.001)Sample size>300 [8, 11, 35, 36, 39–41, 43, 48–52, 54]146,84026.76 (23.62, 33.90)96.0% (<0.001)≤300 [7, 14, 16, 37, 38, 42, 44–47, 53, 56]122,11821.53 (14.92, 28.14)94.1% (<0.001)Publication year2000–2014 [7, 11, 38, 42, 48, 52, 53]72,13421.59 (15.27, 27.90)82.9% (<0.001)2015–2020 [8, 14, 16, 35–41, 43–47, 49–51, 54]196,82427.31 (22.02, 32.60)96.4% (<0.001)Overall268,95825.78 (21.55, 30.01)95.8% (<0.001)∗Others: Tigray, Southern Nations, Nationalities, and Peoples’ Region, and Dire Dawa.


### 3.6. Sensitivity Analysis

The sensitivity analysis showed that the effect of individual studies on the pooled estimate was insignificant, suggesting the robustness of the aggregated estimate. Therefore, the pooled prevalence of bacterial isolates was steady and reliable when examined by neglecting one study at a time (additional file: Figure S10).


## 3.1. Search Results

As illustrated in Figure1, we identified a total of 285 potentially relevant studies from electronic databases, and 39 articles were excluded due to duplication. After reviewing the titles and abstracts, 128 articles were excluded because they did not meet the objectives and the inclusion criteria of the review. Accordingly, 128 full-text articles were reviewed in-depth based on the preset inclusion criteria, of which 102 articles were excluded with reasons. Finally, 26 studies were considered and used for the final quantitative analysis (meta-analysis).Figure 1
PRISMA-adapted flow diagram showing the results of the search and reasons for the exclusion of articles [29].


## 3.2. Characteristics of Included Studies

As illustrated in Table1, all the included studies in the final quantitative analysis were observational, 20 were cross-sectional [7, 14, 16, 35, 36, 38, 39, 42–47, 49–53, 55] and 6 were retrospective [11, 16, 40, 41, 48, 52] by study design. The included studies were conducted in four regions (Amhara, Tigray, Oromia, and Southern Nations, Nationalities, and Peoples’ Region (SNNPR)) and from the two self-administrative cities (Addis Ababa and Dire Dawa). Of the 26 studies that fulfilled the review inclusion criteria [7, 11, 14, 16, 35, 36, 38–52, 55], eleven studies were conducted in Amhara region [7, 8, 11, 14, 36, 40, 44, 45, 52–54], nine in Addis Ababa [16, 41, 43, 46, 48–50, 55, 56], three in Oromia region [35, 38, 42], and a single study from Tigray [39], Dire Dawa [51], and SNNPR [47]. The sample size of individual studies ranged from 83 [53] to 856 [36].Table 1
Characteristics of included studies in this meta-analysis.
AuthorsYearRegionStudy areaHospital unitSpecimenStudy designStudy periodSample sizeNumber of isolates (n)Gram-positive bacterial isolates,n (%)Gram-negative bacterial isolates,n (%)Sorsa et al. [35]2019OromiaASRHNeonatal sepsisBloodCross-sectionalApril 2016–May 201730188 (29.24%)49 (16.28%)39 (12.96%)Abebaw Shiferaw et al. [36]2018AmharaUoGTHBacteremia-suspected patientsBloodRetrospectiveSeptember 2003–February 2013856174 (20.33%)112 (13.08%)62 (7.24%)Negussie et.al. [37]2015Addis AbabaTASH and YHSepticemia-suspected childrenBloodCross-sectionalOctober 2011–February 201220156 (27.86%)29 (14.43%)26 (12.94%)Abrha et al. [38]2011OromiaJURHMalnourished children admittedBloodCross-sectionalOctober 2009–May 201017035 (20.59%)24 (14.12%)11 (6.47%)Wasihun et al. [39]2015TigrayMKOutpatientsBloodCross-sectionalMarch–October 2014514144 (28.02%)104 (20.23%)40 (7.78%)Hailu et al. [14]2016AmharaBRHLCOutpatientsBloodCross-sectionalMarch 2013–January 2015561220 (39.22%)105 (18.72%)115 (20.50%)Moges et al. [40]2019AmharaFCSHBacteremia-suspected patientsBloodCross-sectionalDecember 2017–April 2018388207 (53.35%)61 (15.72%)146 (37.63%)Terfa Kitila et al. [41]2018Addis AbabaAARLBacteremia-suspected patientsBloodRetrospectiveJanuary 2015–December 2016500164 (32.8%)127 (25.4%)37 (7.40%)Dagnew et al. [11]2013AmharaUoGTHSepticemia-suspected patientsBloodRetrospectiveSeptember 2006–January 201239071 (18.21%)49 (12.56%)22 (5.64%)Seboxa et al. [16]2015Addis AbabaTASHSepticemia-suspected patientsBloodRetrospectiveAugust 2012–October 201329238 (13.04%)18 (6.16%)20 (6.85%)Tizazu et al. [42]2011OromiaJUSHSepticemia-suspected patientsBloodCross-sectionalOctober 2009–March 201026023 (8.85%)14 (5.39%)9 (3.46%)Eshetu et al. [43]2018Addis AbabaTASHSepticemia-suspected patientsBloodCross-sectionalSeptember 2016–October 201742264 (15.17%)29 (6.88%)35 (8.29%)Geyesus et al. [44]2017AmharaUoGTHUoGTH Suspected of neonatal sepsisBloodCross-sectionalSeptember 2015–May 2016251120 (47.81%)81 (32.27%)39 (15.54%)Jemal [8]2017AmharaFURHBSI in HIV-infected patientsBloodCross-sectionalDecember 2016384123 (32.03%)77 (20.05%)46 (12%)Alebachew et al. [45]2016AmharaUoGTHPatients infected with HIV and suspected of having sepsisBloodCross-sectionalMarch–May 201310031 (31%)26 (26%)5 (5%)Arega et al. [46]2017Addis AbabaTASHAdult febrile cancer patientsBloodCross-sectionalDecember 2011–June 201210713 (12.15%)--Gebrehiwot et al. [7]2012AmharaUoGTHClinically suspected neonatal sepsisBloodCross-sectionalJuly 2011–June 201218158 (32.04%)21 (11.60%)37 (20.44%)Alemayehu et al. [47]2019SNNPHUCSHPediatric patientsBloodCross-sectionalMarch–August 201629641 (13.85%)30 (10.14%)11 (3.72%)Adib and Worku [48]2012Addis AbabaTASHSuspected of neonatal sepsisBloodRetrospectiveSeptember 1, 2007–August 31, 2008578166 (28.72%)105 (18.17%)61 (10.55%)Mitiku et al. [49]2019Addis AbabaTASHFebrile pediatric patientsBloodCross-sectionalSeptember 2017–June 2018340127 (37.35%)63 (18.53%)74 (21.76%)Tsega et al. [50]2017Addis AbabaZMHSepticemia-suspected pediatric patientsBloodCross-sectionalJune 2016–March 2017309113 (36.57%)84 (27.18%)29 (9.39%)Demissie et al. [51]2019Dire DawaDCRHSepsis-suspected women attending the deliveryBloodCross-sectionalMay–July 30, 201944157 (12.9%)24 (5.44%)33 (7.48%)Hailu et al. [14]2016Addis AbabaTASHSepticemia-suspected patientsBloodCross-sectionalJanuary–March 201417723 (12.99%)12 (6.78%)11 (6.22%)Ali and Kebede [52]2008AmharaUoGTHFebrile patientsBloodRetrospectiveMarch 2001–April 2005472114 (24.15%)80 (16.95%)34 (7.20%)Endris et al. [53]2014AmharaUoGTHConfirmed VL patients suspected of sepsisBloodCross-sectionalFebruary 2012–May 20128316 (19.28%)12 (14.46%)4 (4.82%)Gebre-Egziabher et al. [54]2019AmharaUoGTHICU patients suspected of septicemiaBloodCross-sectionalFebruary–May 201838496 (25%)67 (17.45%)29 (7.55%)ATRH: Asella Teaching and Referral Hospital, UoGTH: University of Gondar Teaching Hospital, TASH: Tikur Anbessa Specialized Hospital, YH: Yekatit 12 Hospital, TUSH: Jimma University Specialized Hospital, MK: Mekelle Hospital, BRHRLC: Bahir Dar Regional Health Research Laboratory Center, FHCSH: Felege Hiwot Comprehensive Specialized Hospital, AARL: Addis Ababa Regional Laboratory, GUH: Gondar University Hospital, HUCSH: Hawassa University Comprehensive Specialized Hospital, ZMH: Zewuditu Memorial Hospital, and DCRH: Dil Chora Referral Hospital.


## 3.3. Culture-Positive Bacterial Profile among Patients with Suspected Bloodstream Infections in Ethiopia

In this meta-analysis, a total of 2,382 positive bacteria cultures obtained from 8,958 blood samples were included. The meta-analysis-derived pooled culture-positive bacterial prevalence from all blood samples was 25.78 % (95% CI: 21.55–30.01%) (Figure2). Pooled prevalence of Gram-positive and Gram-negative bacterial isolates was 15.50 % (95% CI: 12.84–18.15%) (Figure 3) and 10.48 % (95% CI: 8.32–12.63%) (Figure 4), respectively. The two common Gram-positive bacteria among patients suspected for bloodstream infections were recovered. The pooled prevalence of CoNS was 5.75 % (95% CI: 4.58–6.92%) (Figure 5). Similarly, the pooled estimated prevalence of S. aureus from these groups was 7.04 % (95% CI: 5.37–8.72%) (Figure 6). Likewise, we found five Gram-negative bacterial isolates among patients suspected for bloodstream infection in Ethiopia. The pooled estimates of Klebsiella species isolates were found to be 7.04 % (95% CI: 5.37–8.72%) (additional file: Figure S1 and Table 2) followed by E. coli 1.69% (95% CI: 1.21–2.16%) (additional file: Figure S2 and Table 2), Salmonella species 1.09% (95% CI: 0.79–1.38%) (additional file: Figure S3 and Table 2), S. pyogenes 0.88% (95% CI:0.54–1.22%) (additional file: Figure S4 and Table 2), and Pseudomonas species 0.39% (95% CI: 0.08–0.70%) (additional file: Figure S5 and Table 2).Figure 2
Forest plot showing the pooled prevalence of bacterial profile among patients with suspected bloodstream infections in Ethiopia.Figure 3
Forest plot showing the pooled prevalence of Gram-positive bacterial isolates among patients with suspected bloodstream infections in Ethiopia.Figure 4
Forest plot showing the pooled prevalence of Gram-negative bacterial isolates among patients with suspected bloodstream infections in Ethiopia.Figure 5
Forest plot showing the pooled prevalence CoNS bacterial isolates among patients with suspected bloodstream infections in Ethiopia.Figure 6
Forest plot showing the pooled prevalence ofS. aureus isolated among patients with suspected bloodstream infections in Ethiopia.Table 2
Results of each bacterial type isolated from bloodstream-infected patients.
Type of bacterial isolatesNo. of studiesTotal no. of culture-positive/from blood total specimensPooled prevalence rate (95% CI)I2 (p values)S.pyrogenes [36, 38, 39, 41, 42, 44, 45, 47]831/2,9470.88 (0.54, 1.22)0.0% (<0.638)Pseudomonas species [7, 11, 14, 36, 39–41, 43, 45, 48, 51]859/5,1080.39 (0.08, 0.70)0.0% (<0.885)Salmonella species [7, 11, 36–38, 40, 42, 48, 51, 52, 56]117/4,6281.09 (0.79, 1.38)51.1% (<0.021)E. coli [7, 11, 14, 35, 36, 38–45, 47, 48, 50–54, 56]21156/7,6341.69 (1.21, 2.16)65.9% (<0.001)Klebsiella species [7, 8, 11, 14, 35–45, 47, 48, 50–54, 56]23362/8,2194.30 (2.45, 6.16)91.0% (<0.001)


## 3.4. Heterogeneity and Publication Bias

The existence of heterogeneity and publication bias was assessed within the included studies. Consequently, there was considerable heterogeneity across twenty six included studies (I2 = 95.8%). Egger’s regression test for publication bias revealed marginally significant (p=0.044), which indicated the presence of publication bias (additional file: Figure S6). Moreover, heterogeneity across studies considered for Gram-positive isolates was I2 = 92.9% and Gram-negative isolates was I2 = 92.6%. The publication bias of Gram-positive and Gram-negative bacterial isolates was found to be p=0.001 and p=0.001, respectively, using Egger’s regression test.


## 3.5. Subgroup Analysis

Due to the presence of high heterogeneity across or within the included studies, we conducted subgroup analysis based on the study area (region), sample size, and year of publication to sort out the possible source of heterogeneity across the studies. However, the subgroup analysis result revealed that the source of heterogeneity was not due to the study region, sample size, and year of publication disparities (Table3 and Supplementary Figures S7–S9).Table 3
Subgroup pooled prevalence analysis of bacterial isolates among patients suspected of bloodstream infection in Ethiopia, 2020.
Subgroup byCharacteristicsNo. of studiesTotal sample sizePooled prevalence rate (95% CI)HeterogeneityI2 (p values)Study areaOromia [35, 38, 42]373119.45 (6.41, 32.48)95.5% (<0.001)Amhara [7, 8, 11, 14, 36, 40, 44, 45, 52–54]114,05031.11 (24.28, 37.95)95.8% (<0.001)Addis Ababa [16, 41, 43, 46, 48–50, 55, 56]92,92624.05 (17.27, 30.83)95.0% (<0.001)Others∗ [39, 47, 51]31,25118.23 (8.88, 27.58)99.0% (<0.001)Sample size>300 [8, 11, 35, 36, 39–41, 43, 48–52, 54]146,84026.76 (23.62, 33.90)96.0% (<0.001)≤300 [7, 14, 16, 37, 38, 42, 44–47, 53, 56]122,11821.53 (14.92, 28.14)94.1% (<0.001)Publication year2000–2014 [7, 11, 38, 42, 48, 52, 53]72,13421.59 (15.27, 27.90)82.9% (<0.001)2015–2020 [8, 14, 16, 35–41, 43–47, 49–51, 54]196,82427.31 (22.02, 32.60)96.4% (<0.001)Overall268,95825.78 (21.55, 30.01)95.8% (<0.001)∗Others: Tigray, Southern Nations, Nationalities, and Peoples’ Region, and Dire Dawa.


## 3.6. Sensitivity Analysis

The sensitivity analysis showed that the effect of individual studies on the pooled estimate was insignificant, suggesting the robustness of the aggregated estimate. Therefore, the pooled prevalence of bacterial isolates was steady and reliable when examined by neglecting one study at a time (additional file: Figure S10).


## 4. Discussion

To our knowledge, the result of this review documented that the pooled prevalence of bacterial isolates causing bloodstream infections in Ethiopia remains high. The overall pooled prevalence of BSIs by bacterial isolates from blood cultures was 27.78%. The finding was relatively lower as compared to findings from meta-analysis done in West Africa in which the pooled prevalence of BSIs was 31.70% [57]. On the contrary, our finding was higher than a study done in low- and middle-income countries that the prevalence of bacterial isolates from community-acquired pediatric bloodstream infection was 19.1% [58]. Moreover, previous studies reported that bacterial positive blood culture ranged from 7 to 13.9% [59, 60]. Similarly, a systematic review done in Africa reported that the pooled prevalence of bacterial isolates from blood specimens among bloodstream infections was 17.4% [61]. Moreover, significant bloodstream infections and antibiotic resistance in the ICU were observed in North India, in which the blood culture positivity was estimated at 12% [62]. Bacterial isolates in a blood sample with a pooled prevalence of 7.4% were reported in Harare, Zimbabwe [63]. Furthermore, a previous systematic review and meta-analysis study done in Africa and Asia region indicated that the median prevalence of BSIs was 12.50% [64]. The possible explanation for the discrepancies might be due to the drug stewardship program, geographical location, epidemiological difference of the etiological agents, and nature of the patients.The current review revealed that the pooled prevalence of Gram-positive bacterial isolates was 15.50 %. A study conducted in resource-limited countries revealed that the prevalence of Gram-positive bacterial isolates in the blood culture was relatively lower, 6.2% [59].Similarly, in this meta-analysis, the pooled prevalence of Gram-negative bacterial isolates was 10.48 %. This finding was relatively similar to the previously conducted systematic review report which revealed that the pooled prevalence of Gram-negative bacterial isolates in blood specimens from children was 7.7% [59].Furthermore, the rapid rise of bacterial bloodstream infections and evidence of resistance to commonly prescribed antimicrobial agents have been a warranted public health problem in prevention and treatment of oral health/dental care transplantation process, cancer chemotherapy, hematological diseases, and others due to presensitized and existence of the resistance gene and suppressed host immune systems as a result of bacterial BSIs [18–21].


### 4.1. Limitations of the Study

Our review had many limitations. First, there was no documented history of antimicrobial therapy and history of antibiotic intake. Second, in this study, only the English language articles were considered for the analysis. Third, due to the lack of documented antibiogram data, we were unable to review and evaluate BSI causing bacteria multidrug resistance profile. Last, antimicrobial susceptibility standards and interpretive criteria change over time, which may have variations in interpretations and findings.


## 4.1. Limitations of the Study

Our review had many limitations. First, there was no documented history of antimicrobial therapy and history of antibiotic intake. Second, in this study, only the English language articles were considered for the analysis. Third, due to the lack of documented antibiogram data, we were unable to review and evaluate BSI causing bacteria multidrug resistance profile. Last, antimicrobial susceptibility standards and interpretive criteria change over time, which may have variations in interpretations and findings.


## 5. Conclusions

Generally, in this meta-analysis, we found a wide variety of bacterial isolates with the high pooled prevalence of both Gram-negative and Gram-positive bacteria, in particularS. aureus, CoNS, Klebsiella species, E. coli, S. pyogenes, Salmonella species, and Pseudomonas species. Therefore, strengthening of the tool to diagnose BSIs should be a routine practice to detect pathogenic bacteria in blood to select the appropriate/better antibiotics to treat the bacteria causing BSIs. Besides, an effort should be given to the resistance pattern, molecular genetics to detect specific resistance gene mutations associated with different antibiotics, and characterizations of its phylogenetic parameters for those commonly identified bacteria l isolates causing bloodstream infections.



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