Network analysis of intra‐hospital transfers and hospital onset clostridium difficile infection. (19th October 2019)

• Record Type: Journal Article
• Authors: McHaney‐Lindstrom, Megan; Hebert, Courtney; Miller, Harvey; Moffatt‐Bruce, Susan; Root, Elisabeth
• Is Part Of: Health information and libraries journal. Volume 37:Number 1(2020:Mar.)
• Page Start: 26
• Page End: 34
• Publication Date: 2019-10-19
• Subjects: communicable diseases -- information science -- information systems -- public health; Medical libraries -- Periodicals; Public health libraries -- Periodicals; Life sciences libraries -- Periodicals; Medical libraries -- Great Britain -- Periodicals; Public health libraries -- Great Britain -- Periodicals; Information science -- Periodicals; 026.61094105
• DOI: 10.1111/hir.12274
• Languages: English
• ISSNs: 1471-1834

Abstract

Abstract: Objectives: To explore how social network analysis (SNA) can be used to analyse intra‐hospital patient networks of individuals with a hospital acquired infection (HAI) for further analysis in a geographical information systems (GIS) environment. Methods: A case and control study design was used to select 2008 patients. We retrieved locational data for the patients, which was then translated into a network with the SNA software and then GIS software. Overall metrics were calculated for the SNA based on three datasets and further analysed with a GIS. Results: The SNA analysis compared cases to control indicating significant differences in the overall structure of the networks. A GIS visual representation of these metrics was developed, showing spatial variation across the example hospital floor. Discussion: This study confirmed the importance that intra‐hospital patient networks play in the transmission of HAIs, highlighting opportunities for interventions utilising these data. Due to spatial variation differences, further research is necessary to confirm this is not a localised phenomenon, but instead a common situation occurring within many hospitals. Conclusion: Utilising SNA and GIS analysis in conjunction with one another provided a data‐rich environment in which the risk inherent in intra‐hospital transfer networks was quantified, visualised and interpreted for potential interventions.