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  6. Geographic distribution of Staphylococcus spp. infections and antimicrobial resistance among dogs from Gauteng Province presented at a veterinary teaching hospital in South Africa.
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Geographic distribution of Staphylococcus spp. infections and antimicrobial resistance among dogs from Gauteng Province presented at a veterinary teaching hospital in South Africa.

Source Publication
Spatial and Spatio-temporal Epidemiology
Date Issued
February 1, 2019
Author(s)
Qekwana, Daniel Nenene
Oguttu, James Wabwire
Odoi, Agricola  
DOI
https://doi.org/10.1016/j.sste.2018.11.004
Permanent URI
https://trace.tennessee.edu/handle/20.500.14382/16334
Abstract

The objective of this study was to investigate spatial patterns of staphylococcal infections and resistance patterns of clinical isolates from dogs from Gauteng province in South Africa. Data from records of 1,497 dog clinical samples submitted to a veterinary teaching hospital between 2007 and 2012 were used in the study. Spatial empirical Bayesian smoothed risk maps were used to investigate spatial patterns of staphylococcal infections, antimicrobial resistance (AMR), and multidrug resistance (MDR). Moran’s I and spatial scan statistics were used to investigate spatial clusters at municipal and town spatial scales. Significant clusters of staphylococcal infections were identified at both the municipal (Relative Risk [RR]=1.71, p=0.003) and town (RR=1.65, p=0.039) scales. However, significant clusters of AMR (p=0.003) and MDR (p=0.007) were observed only at the town scale. Future larger studies will need to investigate local determinants of geographical distribution of the clusters so as to guide targeted control efforts.

Subjects

Moran's I

Spatial empirical Bay...

Staphylococcus

Antimicrobial resista...

Multi-drug resistance...

Spatial scan statisti...

Disciplines
Animal Diseases
Bacterial Infections and Mycoses
Disease Modeling
Submission Type
Post-print
File(s)
Thumbnail Image
Name

Qekwana_Canine_Spatial_Paper___Author_s_Post_print_Copy.pdf

Size

2.43 MB

Format

Adobe PDF

Checksum (MD5)

8e93890bbc7a4c28248196aab7196201


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