Methicillin-resistant Staphylococcus aureus (MRSA) strains have spread in Saudi Arabia, increasing morbidity, mortality, and financial burdens. Recent studies have suggested the phenotyping methods typically used to classify MRSA as either health care MRSA (HA-MRSA) or community-associated MRSA (CA-MRSA) cases are unreliable, because they lack concordance with the results of genotyping. Yet the expense associated with genotyping precludes its use in the Saudi Aramco population in Saudi Arabia. The absence of a standardized and affordable method to classify MRSA into CA-MRSA and HA-MRSA has been a challenge for infection control programs in Saudi Arabia. The objective of this quantitative, secondary data analysis was to determine the most reliable phenotyping approach to strain identification using John Hopkins Aramco hospital data. The ecological and antibiotics selection pressure theories framed this research. The results of concordance, and sensitivity and specificity tests, suggested hospital admission profiles and susceptibility pattern were the most reliable phenotypic predictors of genotype-based classifications. Multiple logistic regression for susceptibility pattern (OR = 15.47, p < .001) and hospital admission profile (OR = 2.87, p = .008) confirmed those results, whereas all other variables were not found to be statistically significant. These results can be used to clarify the epidemiological and molecular factors that affect the transition of MRSA from health care facilities to the Saudi Aramco community. Implications for positive social change include faster and more reliable classification of MRSA to aid in disease surveillance and the selection of appropriate treatments to reduce MRSA-related morbidity and mortality.
| Identifer | oai:union.ndltd.org:waldenu.edu/oai:scholarworks.waldenu.edu:dissertations-2668 |
| Date | 01 January 2015 |
| Creators | Bazzi, Ali M. |
| Publisher | ScholarWorks |
| Source Sets | Walden University |
| Language | Arabic |
| Detected Language | English |
| Type | text |
| Source | Walden Dissertations and Doctoral Studies |
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