Prevalence of Vancomycin resistant enterococci (VRE) in Ethiopia: a systematic review and meta-analysis

Background The emergence of Vancomycin resistant enterococci (VRE) poses a major public health problem since it was first reported. Although the rising rates of VRE infections are being reported elsewhere in the worldwide; there is limited national pooled data in Ethiopia. Therefore, this study was aimed to estimate the pooled prevalence of VRE and antimicrobial resistance profiles of enterococci in Ethiopia. Methods Literature search was done at PubMed, EMBASE, Google scholar, African Journals online (AJOL) and Addis Ababa University repository following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. Both published and unpublished studies reporting the prevalence of VRE until June 30, 2019 were included. Data were extracted using Microsoft Excel and copied to Comprehensive Meta-analysis (CMA 2.0) for analysis. Pooled estimate of VRE was computed using the random effects model and the 95% CIs. The level of heterogeneity was assessed using Cochran’s Q and I2 tests. Publication bias was checked by visual inspection of funnel plots and Begg’s and/or Egger’s test. Results Twenty studies fulfilled the eligibility criteria and found with relevant data. A total of 831 enterococci and 71 VRE isolates were included in the analysis. The pooled prevalence of VRE was 14.8% (95% CI; 8.7–24.3; I2 = 74.05%; P <  0.001). Compared to vancomycin resistance, enterococci had higher rate of resistance to Penicillin (60.7%), Amoxicillin (56.5%), Doxycycline (55.1%) and Tetracycline (53.7%). Relatively low rate of resistance was found for Daptomycin and Linezolid with a pooled estimate of 3.2% (95% CI, 0.5–19.7%) and 9.9% (95% CI, 2.8–29.0%); respectively. The overall pooled multidrug resistance (MDR) rate of enterococci was 60.0% (95% CI, 42.9–75.0%). Conclusion The prevalence of VRE and drug resistant enterococci are on the rise in Ethiopia. Enterococcal isolates showed resistance to one or more of the commonly prescribed drugs in different or the same drug lines. Multidrug resistant (MDR) enterococci were also found. Although the rates were low, the emergence of resistance to Daptomycin and Linezolid is an alarm for searching new ways for the treatment and control of VRE infections. Adherence to antimicrobial stewardship, comprehensive testing and ongoing monitoring of VRE infections in the health care settings are required.


Introduction
The Joanna Briggs Institute (JBI) is an international, membership based research and development organization within the Faculty of Health Sciences at the University of Adelaide. The Institute specializes in promoting and supporting evidence-based healthcare by providing access to resources for professionals in nursing, midwifery, medicine, and allied health. With over 80 collaborating centres and entities, servicing over 90 countries, the Institute is a recognized global leader in evidence-based healthcare.

JBI Systematic Reviews
The core of evidence synthesis is the systematic review of literature of a particular intervention, condition or issue. The systematic review is essentially an analysis of the available literature (that is, evidence) and a judgment of the effectiveness or otherwise of a practice, involving a series of complex steps. The JBI takes a particular view on what counts as evidence and the methods utilized to synthesize those different types of evidence. In line with this broader view of evidence, the Institute has developed theories, methodologies and rigorous processes for the critical appraisal and synthesis of these diverse forms of evidence in order to aid in clinical decision-making in health care. There now exists JBI guidance for conducting reviews of effectiveness research, qualitative research, prevalence/incidence, etiology/risk, economic evaluations, text/opinion, diagnostic test accuracy, mixed-methods, umbrella reviews and scoping reviews. Further information regarding JBI systematic reviews can be found in the JBI Reviewer's Manual on our website.

JBI Critical Appraisal Tools
All systematic reviews incorporate a process of critique or appraisal of the research evidence. The purpose of this appraisal is to assess the methodological quality of a study and to determine the extent to which a study has addressed the possibility of bias in its design, conduct and analysis. All papers selected for inclusion in the systematic review (that is -those that meet the inclusion criteria described in the protocol) need to be subjected to rigorous appraisal by two critical appraisers. The results of this appraisal can then be used to inform synthesis and interpretation of the results of the study. JBI Critical appraisal tools have been developed by the JBI and collaborators and approved by the JBI Scientific Committee following extensive peer review. Although designed for use in systematic reviews, JBI critical appraisal tools can also be used when creating Critically Appraised Topics (CAT), in journal clubs and as an educational tool. This question relies upon knowledge of the broader characteristics of the population of interest and the geographical area. If the study is of women with breast cancer, knowledge of at least the characteristics, demographics and medical history is needed. The term "target population" should not be taken to infer every individual from everywhere or with similar disease or exposure characteristics. Instead, give consideration to specific population characteristics in the study, including age range, gender, morbidities, medications, and other potentially influential factors. For example, a sample frame may not be appropriate to address the target population if a certain group has been used (such as those working for one organisation, or one profession) and the results then inferred to the target population (i.e. working adults). A sample frame may be appropriate when it includes almost all the members of the target population (i.e. a census, or a complete list of participants or complete registry data).

Were study participants recruited in an appropriate way?
Studies may report random sampling from a population, and the methods section should report how sampling was performed. Random probabilistic sampling from a defined subset of the population (sample frame) should be employed in most cases, however, random probabilistic sampling is not needed when everyone in the sampling frame will be included/ analysed. For example, reporting on all the data from a good census is appropriate as a good census will identify everybody. When using cluster sampling, such as a random sample of villages within a region, the methods need to be clearly stated as the precision of the final prevalence estimate incorporates the clustering effect. Convenience samples, such as a street survey or interviewing lots of people at a public gatherings are not considered to provide a representative sample of the base population.

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Critical Appraisal Checklist for Prevalence Studies 5 3. Was the sample size adequate?
The larger the sample, the narrower will be the confidence interval around the prevalence estimate, making the results more precise. An adequate sample size is important to ensure good precision of the final estimate. Ideally we are looking for evidence that the authors conducted a sample size calculation to determine an adequate sample size. This will estimate how many subjects are needed to produce a reliable estimate of the measure(s) of interest. For conditions with a low prevalence, a larger sample size is needed. Also consider sample sizes for subgroup (or characteristics) analyses, and whether these are appropriate. Sometimes, the study will be large enough (as in large national surveys) whereby a sample size calculation is not required. In these cases, sample size can be considered adequate.
When there is no sample size calculation and it is not a large national survey, the reviewers may consider conducting their own sample size analysis using the following formula: (Naing et al. 2006, Daniel 1999 n= Z2P(1-P) d2 Where: n= sample size Z = Z statistic for a level of confidence P = Expected prevalence or proportion (in proportion of one; if 20%, P = 0.2) d = precision (in proportion of one; if 5%, d=0.05)