Transmission visualizations of healthcare infection clusters: A scoping review

Mya B Brady; Helena M VonVille; Joseph F White; Elise M Martin; Nathan J Raabe; Julie M Slaughter; Graham M Snyder

doi:10.1017/ash.2022.237

Transmission visualizations of healthcare infection clusters: A scoping review

Antimicrob Steward Healthc Epidemiol. 2022 Jun 15;2(1):e92. doi: 10.1017/ash.2022.237. eCollection 2022.

Authors

Mya B Brady^{1

2}, Helena M VonVille³, Joseph F White⁴, Elise M Martin^{2

5}, Nathan J Raabe^{2

4}, Julie M Slaughter¹, Graham M Snyder^{1

2}

Affiliations

¹ Department of Infection Prevention and Control, UPMC Presbyterian-Shadyside, Pittsburgh, Pennsylvania.
² Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
³ University of Pittsburgh Health Sciences Library System, Pittsburgh, Pennsylvania.
⁴ Department of Epidemiology, University of Pittsburgh School of Public Health, Pittsburgh, Pennsylvania.
⁵ Veterans' Affairs Pittsburgh Healthcare System, Pittsburgh, Pennsylvania.

Abstract

Objective: To evaluate infectious pathogen transmission data visualizations in outbreak publications.

Design: Scoping review.

Methods: Medline was searched for outbreak investigations of infectious diseases within healthcare facilities that included ≥1 data visualization of transmission using data observable by an infection preventionist showing temporal and/or spatial relationships. Abstracted data included the nature of the cluster(s) (pathogen, scope of transmission, and individuals involved) and data visualization characteristics including visualization type, transmission elements, and software.

Results: From 1,957 articles retrieved, we analyzed 30 articles including 37 data visualizations. The median cluster size was 20.5 individuals (range, 7-1,963) and lasted a median of 214 days (range, 12-5,204). Among the data visualization types, 10 (27%) were floor-plan transmission maps, 6 (16%) were timelines, 11 (30%) were transmission networks, 3 (8%) were Gantt charts, 4 (11%) were cluster map, and 4 (11%) were other types. In addition, 26 data visualizations (70%) contained spatial elements, 26 (70%) included person type, and 19 (51%) contained time elements. None of the data visualizations contained contagious periods and only 2 (5%) contained symptom-onset date.

Conclusions: The data visualizations of healthcare-associated infectious disease outbreaks in the systematic review were diverse in type and visualization elements, though no data visualization contained all elements important to deriving hypotheses about transmission pathways. These findings aid in understanding the visualizing transmission pathways by describing essential elements of the data visualization and will inform the creation of a standardized mapping tool to aid in earlier initiation of interventions to prevent transmission.