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Prevalence of current patterns and predictive trends of multidrug-resistant Salmonella Typhi in Sudan

BACKGROUND: Enteric fever has persistence of great impact in Sudanese public health especially during rainy season when the causative agent Salmonella enterica serovar Typhi possesses pan endemic patterns in most regions of Sudan - Khartoum. OBJECTIVES: The present study aims to assess the recent st...

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Detalles Bibliográficos
Autores principales: Elshayeb, Ayman A., Ahmed, Abdelazim A., El Siddig, Marmar A., El Hussien, Adil A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5686854/
https://www.ncbi.nlm.nih.gov/pubmed/29137627
http://dx.doi.org/10.1186/s12941-017-0247-4
Descripción
Sumario:BACKGROUND: Enteric fever has persistence of great impact in Sudanese public health especially during rainy season when the causative agent Salmonella enterica serovar Typhi possesses pan endemic patterns in most regions of Sudan - Khartoum. OBJECTIVES: The present study aims to assess the recent state of antibiotics susceptibility of Salmonella Typhi with special concern to multidrug resistance strains and predict the emergence of new resistant patterns and outbreaks. METHODS: Salmonella Typhi strains were isolated and identified according to the guidelines of the International Standardization Organization and the World Health Organization. The antibiotics susceptibilities were tested using the recommendations of the Clinical Laboratories Standards Institute. Predictions of emerging resistant bacteria patterns and outbreaks in Sudan were done using logistic regression, forecasting linear equations and in silico simulations models. RESULTS: A total of 124 antibiotics resistant Salmonella Typhi strains categorized in 12 average groups were isolated, different patterns of resistance statistically calculated by (y = ax − b). Minimum bactericidal concentration’s predication of resistance was given the exponential trend (y = n e(x)) and the predictive coefficient R(2) > 0 < 1 are approximately alike. It was assumed that resistant bacteria occurred with a constant rate of antibiotic doses during the whole experimental period. Thus, the number of sensitive bacteria decreases at the same rate as resistant occur following term to the modified predictive model which solved computationally. CONCLUSION: This study assesses the prediction of multi-drug resistance among S. Typhi isolates by applying low cost materials and simple statistical methods suitable for the most frequently used antibiotics as typhoid empirical therapy. Therefore, bacterial surveillance systems should be implemented to present data on the aetiology and current antimicrobial drug resistance patterns of community-acquired agents causing outbreaks. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12941-017-0247-4) contains supplementary material, which is available to authorized users.