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Analysis of temporal trends in potential COVID-19 cases reported through NHS Pathways England

The National Health Service (NHS) Pathways triage system collates data on enquiries to 111 and 999 services in England. Since the 18th of March 2020, these data have been made publically available for potential COVID-19 symptoms self-reported by members of the public. Trends in such reports over tim...

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Autores principales: Leclerc, Quentin J., Nightingale, Emily S., Abbott, Sam, Jombart, Thibaut
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8007605/
https://www.ncbi.nlm.nih.gov/pubmed/33782427
http://dx.doi.org/10.1038/s41598-021-86266-3
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author Leclerc, Quentin J.
Nightingale, Emily S.
Abbott, Sam
Jombart, Thibaut
author_facet Leclerc, Quentin J.
Nightingale, Emily S.
Abbott, Sam
Jombart, Thibaut
author_sort Leclerc, Quentin J.
collection PubMed
description The National Health Service (NHS) Pathways triage system collates data on enquiries to 111 and 999 services in England. Since the 18th of March 2020, these data have been made publically available for potential COVID-19 symptoms self-reported by members of the public. Trends in such reports over time are likely to reflect behaviour of the ongoing epidemic within the wider community, potentially capturing valuable information across a broader severity profile of cases than hospital admission data. We present a fully reproducible analysis of temporal trends in NHS Pathways reports until 14th May 2020, nationally and regionally, and demonstrate that rates of growth/decline and effective reproduction number estimated from these data may be useful in monitoring transmission. This is a particularly pressing issue as lockdown restrictions begin to be lifted and evidence of disease resurgence must be constantly reassessed. We further assess the correlation between NHS Pathways reports and a publicly available NHS dataset of COVID-19-associated deaths in England, finding that enquiries to 111/999 were strongly associated with daily deaths reported 16 days later. Our results highlight the potential of NHS Pathways as the basis of an early warning system. However, this dataset relies on self-reported symptoms, which are at risk of being severely biased. Further detailed work is therefore necessary to investigate potential behavioural issues which might otherwise explain our conclusions.
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spelling pubmed-80076052021-03-30 Analysis of temporal trends in potential COVID-19 cases reported through NHS Pathways England Leclerc, Quentin J. Nightingale, Emily S. Abbott, Sam Jombart, Thibaut Sci Rep Article The National Health Service (NHS) Pathways triage system collates data on enquiries to 111 and 999 services in England. Since the 18th of March 2020, these data have been made publically available for potential COVID-19 symptoms self-reported by members of the public. Trends in such reports over time are likely to reflect behaviour of the ongoing epidemic within the wider community, potentially capturing valuable information across a broader severity profile of cases than hospital admission data. We present a fully reproducible analysis of temporal trends in NHS Pathways reports until 14th May 2020, nationally and regionally, and demonstrate that rates of growth/decline and effective reproduction number estimated from these data may be useful in monitoring transmission. This is a particularly pressing issue as lockdown restrictions begin to be lifted and evidence of disease resurgence must be constantly reassessed. We further assess the correlation between NHS Pathways reports and a publicly available NHS dataset of COVID-19-associated deaths in England, finding that enquiries to 111/999 were strongly associated with daily deaths reported 16 days later. Our results highlight the potential of NHS Pathways as the basis of an early warning system. However, this dataset relies on self-reported symptoms, which are at risk of being severely biased. Further detailed work is therefore necessary to investigate potential behavioural issues which might otherwise explain our conclusions. Nature Publishing Group UK 2021-03-29 /pmc/articles/PMC8007605/ /pubmed/33782427 http://dx.doi.org/10.1038/s41598-021-86266-3 Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Leclerc, Quentin J.
Nightingale, Emily S.
Abbott, Sam
Jombart, Thibaut
Analysis of temporal trends in potential COVID-19 cases reported through NHS Pathways England
title Analysis of temporal trends in potential COVID-19 cases reported through NHS Pathways England
title_full Analysis of temporal trends in potential COVID-19 cases reported through NHS Pathways England
title_fullStr Analysis of temporal trends in potential COVID-19 cases reported through NHS Pathways England
title_full_unstemmed Analysis of temporal trends in potential COVID-19 cases reported through NHS Pathways England
title_short Analysis of temporal trends in potential COVID-19 cases reported through NHS Pathways England
title_sort analysis of temporal trends in potential covid-19 cases reported through nhs pathways england
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8007605/
https://www.ncbi.nlm.nih.gov/pubmed/33782427
http://dx.doi.org/10.1038/s41598-021-86266-3
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