Profiling of antibiotic resistance of bacterial species recovered from routine clinical isolates in Ethiopia - Robert-Jan ten Hove; Melaku Tesfaye; Witold Frederik ten Hove; Mesfin Nigussie (Annals of Clinical Microbiology and Antimicrobials, 2017)
Abstract Background With the alarming rise in antibiotic resistance in African countries, the need for a surveillance system in the region has become pressing. The rapid expansion of data networks makes it possible to set up healthcare applications that can be both cost-efficient and effective. Large data sets are available for assessment of current antibiotic resistance among Ethiopian patients. Based on the data-presentation, a practical approach is proposed on how diagnostic laboratories can participate remedial action against antibiotic resistance in Ethiopia. Methods In Addis Ababa (Ethiopia), raw data comprising bacterial species name, specimen type and antibiograms covering the period January 2014 to May 2015 was accessed from the laboratory information management system. Using R code, the data was read and fitted into data-frames and analyzed to assess antibiotic resistance in the Ethiopian patient population. Results Susceptibility to an antibiotic was tested with 14.983 cultures of 54 different bacterial species or subgroups, isolated from 16 types of specimen. Half of the cultures (n = 6444) showed resistance to an antibiotic. Resistance against penicillin was highest with, on average, 91.1% of 79 bacterial cultures showing resistance. Very high resistance rates were also observed for ampicillin, whereas resistance was lowest with cefoxitin. Conclusions Extraction and analysis of raw-data from the laboratory database is relatively simple and can provide valuable insight into the relationships between type of sample and drug-resistance in countries where such data is still scarce. With the largest number of antibiotic resistance tests described for Ethiopia, a tool is proposed for consistent data collection with specified core variables. Trends in antibiotic resistance can be revealed and treatment failures avoided when used as an easy accessible reference application for healthcare providers.