| Background. Methicillin resistant Staphylococcus aureus (MRSA) is a bacterium known to cause a range of host illnesses from benign commensal carriage to systemic infection. MRSA primarily spreads through direct and indirect contact transmission pathways. Infection prevention strategies to control the spread of this organism are known to be effective, but are not consistently put into practice by healthcare providers. The role of the environment, as a reservoir for MRSA, was examined in this study through binary-outcome logistic modeling as well as through novel applications of GIS software using ESRI Corporation's ArcGIS(TM) suite. Methods. Linking several secondary datasets from the Calgary Health Region's (CHR) Departments of Planning and Design, Pharmacy, Nursing Integrated Systems, and Finance, along with laboratory and clinical data provided by the Canadian Nosocomial Infections Surveillance Program (CNISP) and the CHR Infection Prevention and Control department, clinical, spatial, and temporal data were merged to model the likelihood of healthcare-associated MRSA acquisition using logistic regression, as well as use these data, paired with the Pulsed Field Gel Electrophoresis (PFGE) to look at transmission patterns of MRSA in GIS. Results. The binary logistic model determined an increasing OR=1.45 (95% CI 1.27-1.64) for each 25 day shared environment score, OR=1.61 (95% CI 1.08-2.39) for every increment in average workload score, glycopeptide exposure OR=2.80 (95% CI 1.43-5.18), OR=1.22 (95% CI 1.06-1.38) for increases in year of admission, and OR=2.72 (95% CI 1.43-5.18) if patients were admitted to Unit 62. Spatial autocorrelation estimates failed to reject the null and from the available data, the Moran's I and Simpson's Index, there was no clear evidence to consider private rooms protective for MRSA as the dispersion of cases was heterogeneous with both of these measures. Conclusions. The inclusion of spatially-oriented variables contributes significant insight into the nature of disease transmission and complement traditional clinical risk factor analyses for MRSA. |
