Differential occupational risks to healthcare workers from SARS-CoV-2 observed during a prospective observational study
- University of Oxford, United Kingdom
- Oxford University Hospitals, United Kingdom
- Nuffield Department of Medicine, University of Oxford, United Kingdom
- Oxford University Hospitals NHS Foundation Trust, United Kingdom
- NIHR Oxford Biomedical Research Centre, University of Oxford, United Kingdom
- Oxford University, United Kingdom
- Target Discovery Institute, University of Oxford, United Kingdom
Abstract
We conducted voluntary Covid-19 testing programmes for symptomatic and asymptomatic staff at a UK teaching hospital using naso-/oro-pharyngeal PCR testing and immunoassays for IgG antibodies. 1128/10,034(11.2%) staff had evidence of Covid-19 at some time. Using questionnaire data provided on potential risk-factors, staff with a confirmed household contact were at greatest risk (adjusted odds ratio [aOR] 4.82 [95%CI 3.45-6.72]). Higher rates of Covid-19 were seen in staff working in Covid-19-facing areas (22.6% vs. 8.6% elsewhere) (aOR 2.47 [1.99-3.08]). Controlling for Covid-19-facing status, risks were heterogenous across the hospital, with higher rates in acute medicine (1.52 [1.07-2.16]) and sporadic outbreaks in areas with few or no Covid-19 patients. Covid-19 intensive care unit staff were relatively protected (0.44 [0.28-0.69]), likely by a bundle of PPE-related measures. Positive results were more likely in Black (1.66 [1.25-2.21]) and Asian (1.51 [1.28-1.77]) staff, independent of role or working location, and in porters and cleaners (2.06 [1.34-3.15]).
Data availability
The data studied are available from the Infections in Oxfordshire Research Database (https://oxfordbrc.nihr.ac.uk/research-themes-overview/antimicrobial-resistance-and-modernising-microbiology/infections-in-oxfordshire-research-database-iord/), subject to an application and research proposal meeting the ethical and governance requirements of the Database. For further details on how to apply for access to the data and for a research proposal template please email iord@ndm.ox.ac.uk.
Article and author information
Author details
Bernadette C Young
Philip George Drennan
Alexander J Mentzer
Funding
UK Government: Department of Health and Social Care
- David W Eyre
- Sheila F Lumley
- Denise O'Donnell
- Mark Campbell
- Elizabeth Sims
- Elaine Lawson
- Fiona Warren
- Tim James
- Stuart Cox
- Alison Howarth
- George Doherty
- Stephanie B Hatch
- James Kavanagh
- Kevin K Chau
- Philip W Fowler
- Jeremy Swann
- Denis Volk
- Fan Yang-Turner
- Nicole Stoesser
- Philippa C Matthews
- Maria Dudareva
- Timothy Davies
- Robert H Shaw
- Leon Peto
- Louise O Downs
- Alexander Vogt
- Ali Amini
- Bernadette C Young
- Philip George Drennan
- Alexander J Mentzer
- Donal T Skelly
- Fredrik Karpe
- Matt J Neville
- Monique Andersson
- Andrew J Brent
- Nicola Jones
- Lucas Martins Ferreira
- Thomas Christott
- Brian D Marsden
- Sarah Hoosdally
- Richard Cornall
- Derrick W Crook
- David I Stuart
- Gavin Screaton
- Timothy EA Peto
- Bruno Holthof
- Anne-Marie O'Donnell
- Daniel Ebner
- Christopher P Conlon
- Katie Jeffery
- Timothy M Walker
Wellcome Trust Clinical Research Training Fellow (216417/Z/19/Z)
- Ali Amini
NIHR Clinical Lecturer
- Bernadette C Young
Structural Genomics Consortium
- Lucas Martins Ferreira
- Thomas Christott
- Brian D Marsden
Kennedy Trust for Rheumatology Research
- Brian D Marsden
Wellcome Trust Senior Investigator
- Gavin Screaton
Schmidt Foundation
- Gavin Screaton
Wellcome Trust Career Development Fellow (214560/Z/18/Z)
- Timothy M Walker
National Institute of Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance (HPRU-2012-10041)
- David W Eyre
- Sheila F Lumley
- Denise O'Donnell
- Mark Campbell
- Elizabeth Sims
- Elaine Lawson
- Fiona Warren
- Tim James
- Stuart Cox
- Alison Howarth
- George Doherty
- Stephanie B Hatch
- James Kavanagh
- Kevin K Chau
- Philip W Fowler
- Jeremy Swann
- Denis Volk
- Fan Yang-Turner
- Nicole Stoesser
- Philippa C Matthews
- Maria Dudareva
- Timothy Davies
- Robert H Shaw
- Leon Peto
- Louise O Downs
- Alexander Vogt
- Ali Amini
- Bernadette C Young
- Philip George Drennan
- Alexander J Mentzer
- Donal T Skelly
- Fredrik Karpe
- Matt J Neville
- Monique Andersson
- Andrew J Brent
- Nicola Jones
- Lucas Martins Ferreira
- Thomas Christott
- Brian D Marsden
- Sarah Hoosdally
- Richard Cornall
- Derrick W Crook
- David I Stuart
- Gavin Screaton
- Timothy EA Peto
- Bruno Holthof
- Anne-Marie O'Donnell
- Daniel Ebner
- Christopher P Conlon
- Katie Jeffery
- Timothy M Walker
Robertson Foundation
- David W Eyre
NIHR Oxford BRC Senior Fellow
- David W Eyre
- Philippa C Matthews
Wellcome Trust Clinical Research Fellow
- Sheila F Lumley
Medical Research Council (MR/N00065X/1)
- David I Stuart
Wellcome Trust Intermediate Fellowship (110110/Z/15/Z)
- Philippa C Matthews
NIHR Doctoral Research Fellow
- Maria Dudareva
Medical Research Foundation (MRF-145-004-TPG-AVISO)
- Kevin K Chau
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Marc Lipsitch, Harvard TH Chan School of Public Health, United States
Ethics
Human subjects: All asymptomatic staff data collection and testing were part of enhanced hospital infection prevention and control measures instituted by the UK Department of Health and Social Care (DHSC). Deidentified data from staff testing and patients were obtained from the Infections in Oxfordshire Research Database (IORD) which has generic Research Ethics Committee, Health Research Authority and Confidentiality Advisory Group approvals (19/SC/0403, ECC5-017(A)/2009). De-identified patient data extracted included admission and discharge dates, ward location and positive Covid-19 test results.
Version history
- Received: July 2, 2020
- Accepted: August 18, 2020
- Accepted Manuscript published: August 21, 2020 (version 1)
- Version of Record published: September 11, 2020 (version 2)
Copyright
© 2020, Eyre 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.
Metrics
-
- 5,260
- views
-
- 636
- downloads
-
- 178
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Differential occupational risks to healthcare workers from SARS-CoV-2 observed during a prospective observational study
eLife 9:e60675.
https://doi.org/10.7554/eLife.60675
Further reading
-
- Epidemiology and Global Health
We discuss 12 misperceptions, misstatements, or mistakes concerning the use of covariates in observational or nonrandomized research. Additionally, we offer advice to help investigators, editors, reviewers, and readers make more informed decisions about conducting and interpreting research where the influence of covariates may be at issue. We primarily address misperceptions in the context of statistical management of the covariates through various forms of modeling, although we also emphasize design and model or variable selection. Other approaches to addressing the effects of covariates, including matching, have logical extensions from what we discuss here but are not dwelled upon heavily. The misperceptions, misstatements, or mistakes we discuss include accurate representation of covariates, effects of measurement error, overreliance on covariate categorization, underestimation of power loss when controlling for covariates, misinterpretation of significance in statistical models, and misconceptions about confounding variables, selecting on a collider, and p value interpretations in covariate-inclusive analyses. This condensed overview serves to correct common errors and improve research quality in general and in nutrition research specifically.
-
- Ecology
- Epidemiology and Global Health
Zoonotic disease dynamics in wildlife hosts are rarely quantified at macroecological scales due to the lack of systematic surveys. Non-human primates (NHPs) host Plasmodium knowlesi, a zoonotic malaria of public health concern and the main barrier to malaria elimination in Southeast Asia. Understanding of regional P. knowlesi infection dynamics in wildlife is limited. Here, we systematically assemble reports of NHP P. knowlesi and investigate geographic determinants of prevalence in reservoir species. Meta-analysis of 6322 NHPs from 148 sites reveals that prevalence is heterogeneous across Southeast Asia, with low overall prevalence and high estimates for Malaysian Borneo. We find that regions exhibiting higher prevalence in NHPs overlap with human infection hotspots. In wildlife and humans, parasite transmission is linked to land conversion and fragmentation. By assembling remote sensing data and fitting statistical models to prevalence at multiple spatial scales, we identify novel relationships between P. knowlesi in NHPs and forest fragmentation. This suggests that higher prevalence may be contingent on habitat complexity, which would begin to explain observed geographic variation in parasite burden. These findings address critical gaps in understanding regional P. knowlesi epidemiology and indicate that prevalence in simian reservoirs may be a key spatial driver of human spillover risk.
