Low prevalence of methicillin resistant as determined by an automated identification system in two private hospitals in Nairobi, Kenya: A cross sectional study

22Citations
Citations of this article
96Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

Background: Staphylococcus aureus (S.aureus) is a major cause of both healthcare and community acquired infections. In developing countries, manual phenotypic tests are the mainstay for the identification of staphylococci with the tube and slide coagulase tests being relied upon as confirmatory tests for. The subjectivity associated with interpretation of these tests may result in misidentification of coagulase negative staphylococci as. Given that antibiotic resistance is more prevalent in CONS, this may result in over estimation of methicillin resistant (MRSA) prevalence. Methods: A review of susceptibility data from all non-duplicate isolates generated between March 2011 and May 2013 by the Vitek-2 (bioMérieux) automated system was performed by the authors. The data was generated routinely from processed clinical specimens submitted to the microbiology laboratories for culture and sensitivity at the Aga Khan University Hospital and Gertrude's children's hospital both situated in Nairobi. Results: Antimicrobial susceptibility data from a total of 731 non-duplicate isolates was reviewed. Majority (79.2%) of the isolates were from pus swabs. Only 24 isolates were both cefoxitin and oxacillin resistant while 3 were resistant to oxacillin but susceptible to cefoxitin giving an overall MRSA prevalence of 3.7% (27/731). None of the isolates were resistant to mupirocin, linezolid, tigecycline, teicoplanin or vancomycin. Conclusion: The prevalence of MRSA in this study is much lower than what has been reported in most African countries. The significant change in antibiotic susceptibility compared to what has previously been reported in our hospital is most likely a consequence of the transition to an automated platform rather than a trend towards lower resistance rates.

Cite

CITATION STYLE

APA

Omuse, G., Kabera, B., & Revathi, G. (2015). Low prevalence of methicillin resistant as determined by an automated identification system in two private hospitals in Nairobi, Kenya: A cross sectional study. BMC Infectious Diseases, 14(1). https://doi.org/10.1186/s12879-014-0669-y

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free