Rapid feedback on hospital onset SARS-CoV-2 infections combining epidemiological and sequencing data
- University College London, United Kingdom
- University of Glasgow, United Kingdom
- The University of Sheffield, United Kingdom
- Sheffield Teaching Hospitals NHS Foundation Trust, United Kingdom
- London School of Hygiene and Tropical Medicine, United Kingdom
- NHS Greater Glasgow and Clyde, United Kingdom
- MRC-University of Glasgow, United Kingdom
Abstract
Background: Rapid identification and investigation of healthcare-associated infections (HCAIs) is important for suppression of SARS-CoV-2, but the infection source for hospital onset COVID-19 infections (HOCIs) cannot always be readily identified based only on epidemiological data. Viral sequencing data provides additional information regarding potential transmission clusters, but the low mutation rate of SARS-CoV-2 can make interpretation using standard phylogenetic methods difficult.
Methods: We developed a novel statistical method and sequence reporting tool (SRT) that combines epidemiological and sequence data in order to provide a rapid assessment of the probability of HCAI among HOCI cases (defined as first positive test >48 hours following admission) and to identify infections that could plausibly constitute outbreak events. The method is designed for prospective use, but was validated using retrospective datasets from hospitals in Glasgow and Sheffield collected February-May 2020.
Results: We analysed data from 326 HOCIs. Among HOCIs with time-from-admission >8 days the SRT algorithm identified close sequence matches from the same ward for 160/244 (65.6%) and in the remainder 68/84 (81.0%) had at least one similar sequence elsewhere in the hospital, resulting in high estimated probabilities of within-ward and within-hospital transmission. For HOCIs with time-from-admission 3-7 days, the SRT probability of healthcare acquisition was >0.5 in 33/82 (40.2%).
Conclusions: The methodology developed can provide rapid feedback on HOCIs that could be useful for infection prevention and control teams, and warrants further prospective evaluation. The integration of epidemiological and sequence data is important given the low mutation rate of SARS-CoV-2 and its variable incubation period.
Funding: COG-UK HOCI funded by COG-UK consortium, supported by funding from UK Research and Innovation, National Institute of Health Research and Wellcome Sanger Institute.
Data availability
The sequence data analysed are included within publicly available datasets (https://www.cogconsortium.uk/data/), and a list of the relevant sequence identification codes is provided (Supplementary File 1). Due to data governance restrictions related to individual patient data linked to genetic sequences it is not possible to publicly share the associated meta-data. Requests for access to the data can be made by submission of a research proposal to the COG-UK Steering Committee (contact@cogconsortium.uk).
Article and author information
Author details
Benjamin B Lindsey
Funding
COG-UK consortium
- Oliver Stirrup
COG-UK consortium
- Judith Breuer
The funders (MRC, NIHR and Wellcome) had no role in study design, data collection and interpretation, or the decision to submit the work for publication. Approval for publication was required from the Steering Group of the COG-UK consortium, which has funded the COG-UK HOCI study from their overarching project grant.
Ethics
Human subjects: Research Ethics for COG-UK Consortium and research undertaken under its auspices was granted by the PHE Research Ethics and Governance group as part of the emergency response to COVID-19 (24 April 2020, REF: R&D NR0195) and by the relevant Scottish biorepository authorities (16/WS/0207NHS and 10/S1402/33). This was a retrospective analysis on fully anonymized data, the collection of which did not involve any active research intervention. Consent therefore was neither required nor requested from individual patients.
Copyright
© 2021, Stirrup et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Rapid feedback on hospital onset SARS-CoV-2 infections combining epidemiological and sequencing data
eLife 10:e65828.
https://doi.org/10.7554/eLife.65828
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