An interactive data visualisation application to investigate nosocomial transmission of infections [version 1; peer review: 2 approved]

Catherine M. Smith*, David J. Allen, Sameena Nawaz, Zisis Kozlakidis, Eleni Nastouli, Andrew Hayward, Katherine N. Ward

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Background: Healthcare-associated infections represent a major threat to patient, staff and visitor safety. Identification of episodes that are likely to have resulted from nosocomial transmission has important implications for infection control. Routinely collected data on ward admissions and sample dates, combined with pathogen genomic information could provide useful insights. We describe a novel, open-source, application for visualising these data, and demonstrate its utility for investigating nosocomial transmission using a case study of a large outbreak of norovirus infection. Methods: We developed the application using Shiny, a web application framework for R. For the norovirus case study, cases were defined as patients who had a faecal sample collected at the hospital in a winter season that tested positive for norovirus. Patient demographics and ward admission dates were extracted from hospital systems. Detected norovirus strains were genotyped and further characterised through sequencing of the hypervariable P2 domain. The most commonly detected sub-strain was visualised using the interactive application. Results: There were 156 norovirus-positive specimens collected from 107 patients. The most commonly detected sub-strain affected 30 patients in five wards. We used the interactive application to produce three visualisations: a bar chart, a timeline, and a schematic ward plan highlighting plausible transmission links. Visualisations showed credible links between cases on the elderly care ward. Conclusions: Use of the interactive application provided insights into transmission in this large nosocomial outbreak of norovirus, highlighting where infection control practices worked well or could be improved. This is a flexible tool that could be used for investigation of any infection in any hospital by interactively changing parameters. Challenges include integration with hospital systems for extracting data. Prospective use of this application could inform better infection control in real time.

Original languageEnglish
Article number100
JournalWellcome Open Research
Volume4
DOIs
Publication statusPublished - 2019

Bibliographical note

Funding Information:
This publication presents independent research supported by the Health Innovation Challenge Fund T5-344 (ICONIC), a parallel funding partnership between the Department of Health and Wellcome Trust (ref. 098608). The views expressed in this publication are those of the author(s) and not necessarily those of the Department of Health or Wellcome Trust. D.J.A. is affiliated to the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Gastrointestinal Infections at University of Liverpool in partnership with Public Health England (PHE), in collaboration with University of East Anglia, University of Oxford and the Institute of Food Research. D.J.A. is based at The London School of Hygiene & Tropical Medicine, and PHE. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, the Department of Health or Public Health England. A.H. is a National Institute for Health Research (NIHR) Senior Investigator. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health and Social Care. The authors thank Dr Helen Fifer for assistance in collating clinical and demographic data from hospital records, Dr John Harris for advice on epidemiological analyses, and Prof Miren Iturriza-Gomara for advice and useful discussions on analysis of data.

Funding Information:
This publication presents independent research supported by the Health Innovation Challenge Fund T5-344 (ICONIC), a parallel funding partnership between the Department of Health and Wellcome Trust (ref. 098608). The views expressed in this publication are those of the author(s) and not necessarily those of the Department of Health or Wellcome Trust.

Funding Information:
Grant information: This publication presents independent research supported by the Health Innovation Challenge Fund T5-344 (ICONIC), a parallel funding partnership between the Department of Health and Wellcome Trust (ref. 098608). The views expressed in this publication are those of the author(s) and not necessarily those of the Department of Health or Wellcome Trust. D.J.A. is affiliated to the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Gastrointestinal Infections at University of Liverpool in partnership with Public Health England (PHE), in collaboration with University of East Anglia, University of Oxford and the Institute of Food Research. D.J.A. is based at The London School of Hygiene & Tropical Medicine, and PHE. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, the Department of Health or Public Health England. A.H. is a National Institute for Health Research (NIHR) Senior Investigator. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health and Social Care.

Publisher Copyright:
© 2019 Smith CM et al.

Keywords

  • Cross infection
  • Infection control
  • Norovirus
  • Outbreak
  • Software
  • Virus genomics

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