Network Analysis of Methicillin-Resistant Staphylococcus aureus Spread in a Large Tertiary Care Facility

Title: Network Analysis of Methicillin-Resistant Staphylococcus aureus Spread in a Large Tertiary Care Facility
Authors: Moldovan, Ioana Doina
Date: 2017
Abstract: Methicillin-resistant Staphylococcus aureus (MRSA) is an antibiotic-resistant bacterium of epidemiologic importance in Canadian healthcare facilities. The contact between MRSA colonized or infected patients with other patients, healthcare workers (HCWs) and/or the healthcare environment can result in MRSA transmission and healthcare-associated MRSA (HA-MRSA) infections in hospitals. These HA-MRSA infections are linked with increased length of hospital stay, economic burden, morbidity and mortality. Although infection prevention and control programs initiated in 2009 in Canada and other developed countries (e.g., UK, France, Belgium, Denmark, etc.) have been relatively successful in reducing the rate of HA-MRSA infections, they continue to pose a threat to patients, especially to the more vulnerable in long term care and geriatric institutions. Historically, MRSA was a problem mainly in hospital settings but after mid-1990s new strains of MRSA have been identified among people without healthcare-related risks and have been classified as community-associated MRSA (CA-MRSA). Furthermore, the distinction between HA-MRSA and CA-MRSA strains is gradually waning due to both the introduction of HA-MRSA in communities, and the emergence of CA-MRSA strains in hospitals. The purpose of this thesis was to explore the feasibility of constructing healthcare networks to evaluate the role of healthcare providers (e.g., physicians) and places (e.g., patient rooms) in the transmission of MRSA in a large tertiary care facility. Method of investigation: a secondary data case-control study, using individual characteristics and network structure measures, conducted at The Ottawa Hospital (TOH) between April 1st, 2013 and March 31th, 2014. Results: It was feasible to build social networks in a large tertiary care facility based on electronic medical records data. The networks' size (represented by the number of vertices and lines) increased during the outbreak period (period 1) compared to the pre-outbreak period (period 0) for both groups and at all three TOH campuses. The calculated median degree centrality showed significant increase in value for both study groups during period 1 compared to period 0 for two of the TOH campuses (Civic and General). There was no significant difference between the median degree centrality calculated for each study group at the Heart Institute when compared for the two reference periods. The median degree centrality of the MRSA case group for period 0 showed no significant difference when compared to the same measure determined for the control group for all three TOH campuses. However, the median degree centrality calculated for period 1 was significantly increased for the control group compared to the MRSA case group for two TOH campuses (Civic and General) but showed no significant difference between the two groups from the Heart Institute. In addition, there was a correlation between the two network measures (degree centrality and eigenvector centrality) calculated to determine the most influential person or place in the MRSA case group networks. However, there was no correlation between the two network’s measures calculated for physicians included in MRSA case group networks. Conclusions: It is feasible to use social network analysis as an epidemiologic analysis tool to characterize the MRSA transmission in a hospital setting. The network's visible changes between the groups and reference periods were reflected by the network measures and supported also by known hospital patient movements after the outbreak onset. Furthermore, we were able to identify potential source cases and places just prior of the outbreak start. Unfortunately, we were not able to show the role of healthcare workers in MRSA transmission in a hospital setting due to limitations in data collection and network measure chosen (eigenvector centrality). Further research is required to confirm these study findings.
CollectionThèses, 2011 - // Theses, 2011 -