Brady, Mya
(2021)
Transmission Visualizations of Healthcare Infection Clusters: A Pilot Scoping Review.
Master Essay, University of Pittsburgh.
Abstract
Background: Understanding transmission of COVID-19 in healthcare settings is critical to infection prevention and control efforts to mitigate its spread. Implementing interventions to interrupt transmission requires deriving hypothesized transmission pathways. Visualizations of transmission pathways can aid in hypothesis generation. SARS-CoV-2 provides a unique opportunity to determine ways in which data visualizations can be improved to aid in outbreaks due to presymptomatic/asymptomatic transmission, highly variable incubation period and transmission that can involve multiple individuals in healthcare facilities. The objective of this review was to conduct a scoping review of the current literature of transmission visualizations in the healthcare setting to describe the types and frequency of data elements used in these types of visualizations.
Methods: Medline (Ovid) was searched using a combination of MeSH terms, title, abstract and keywords developed in tandem with a University of Pittsburgh Graduate School of Public Health Health Sciences Librarian. Terms were cross-referenced with a set of known studies to ensure that the search would capture relevant articles. Article eligibility criteria was determined a priori. Inclusion criteria contained the following: published after 1985, written in English, peer-reviewed, healthcare facility infectious disease transmission, an infectious disease with ≥ 1 transmission event or infectious diseases with a National Healthcare safety Network (NHSN) definition, ≥ 1 data visualizations of transmission using data observable by an infection preventionist showing temporal and/or spatial relationships using patient health data. The articles were screened and selected using DistillerSR (Evidence Partners) reviewing software and the protocol was published on Open Science Forum (OSF) for transparency purposes.
Results: The initial search yielded 1,958 articles; 15% (299 articles) were used for this pilot review based on alphabetical order of author’s last name. Eleven articles were eligible for full review and 21 data visualizations were analyzed. Of the 21 data visualizations, all described either bacterial or viral transmission, almost all visualizations contained spatial data elements and patient data elements. None of the visualizations contained contagious periods.
Conclusion: The findings from this review support the need for a standardized data visualization tool to implement public health and infection prevention interventions sooner to interrupt transmission.
Share
| Citation/Export: |
|
| Social Networking: |
|
Details
| Item Type: |
Other Thesis, Dissertation, or Long Paper
(Master Essay)
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| Contributors: |
| Contribution | Contributors Name | Email | Pitt Username | ORCID  |
|---|
| Committee Chair | Glynn, Nancy | epidnwg@pitt.edu | epidnwg | UNSPECIFIED | | Committee Member | Snyder, Graham | snydergm3@upmc.edu | UNSPECIFIED | UNSPECIFIED | | Committee Member | Martin, Elise | martine6@upmc.edu | UNSPECIFIED | UNSPECIFIED | | Committee Member | Harrison, Lee | lharriso@edc.pitt.edu | lharriso | UNSPECIFIED | | Committee Member | Sharma, Ravi | rks1946@pitt.edu | rks1946 | UNSPECIFIED |
|
| Date: |
10 December 2021 |
| Date Type: |
Completion |
| Submission Date: |
10 December 2021 |
| Access Restriction: |
2 year -- Restrict access to University of Pittsburgh for a period of 2 years. |
| Number of Pages: |
47 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
School of Public Health > Epidemiology |
| Degree: |
MPH - Master of Public Health |
| Thesis Type: |
Master Essay |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
COVID-19, SARS-CoV-2, Nosocomial Infections, Healthcare Associated Infections, Data Visualizations |
| Date Deposited: |
06 Jan 2022 15:30 |
| Last Modified: |
06 Jan 2024 06:15 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/42054 |
Metrics
Monthly Views for the past 3 years
Plum Analytics
Actions (login required)
 |
View Item |
![[feed]](/images/feed-icon-32x32.png){kind=icon}