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Understanding the leading indicators of hospital admissions from COVID-19 across successive waves in the UK

Following the end of universal testing in the UK, hospital admissions are a key measure of COVID-19 pandemic pressure. Understanding leading indicators of admissions at the National Health Service (NHS) Trust, regional and national geographies help health services plan for ongoing pressures. We expl...

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Autores principales: Mellor, Jonathon, Overton, Christopher E, Fyles, Martyn, Chawner, Liam, Baxter, James, Baird, Tarrion, Ward, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cambridge University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10600913/
https://www.ncbi.nlm.nih.gov/pubmed/37664991
http://dx.doi.org/10.1017/S0950268823001449
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author Mellor, Jonathon
Overton, Christopher E
Fyles, Martyn
Chawner, Liam
Baxter, James
Baird, Tarrion
Ward, Thomas
author_facet Mellor, Jonathon
Overton, Christopher E
Fyles, Martyn
Chawner, Liam
Baxter, James
Baird, Tarrion
Ward, Thomas
author_sort Mellor, Jonathon
collection PubMed
description Following the end of universal testing in the UK, hospital admissions are a key measure of COVID-19 pandemic pressure. Understanding leading indicators of admissions at the National Health Service (NHS) Trust, regional and national geographies help health services plan for ongoing pressures. We explored the spatio-temporal relationships of leading indicators of hospitalisations across SARS-CoV-2 waves in England. This analysis includes an evaluation of internet search volumes from Google Trends, NHS triage calls and online queries, the NHS COVID-19 app, lateral flow devices (LFDs), and the ZOE app. Data sources were analysed for their feasibility as leading indicators using Granger causality, cross-correlation, and dynamic time warping at fine spatial scales. Google Trends and NHS triages consistently temporally led admissions in most locations, with lead times ranging from 5 to 20 days, whereas an inconsistent relationship was found for the ZOE app, NHS COVID-19 app, and LFD testing, which diminished with spatial resolution, showing cross-correlation of leads between –7 and 7 days. The results indicate that novel surveillance sources can be used effectively to understand the expected healthcare burden within hospital administrative areas though the temporal and spatial heterogeneity of these relationships is a key determinant of their operational public health utility.
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spelling pubmed-106009132023-10-27 Understanding the leading indicators of hospital admissions from COVID-19 across successive waves in the UK Mellor, Jonathon Overton, Christopher E Fyles, Martyn Chawner, Liam Baxter, James Baird, Tarrion Ward, Thomas Epidemiol Infect Original Paper Following the end of universal testing in the UK, hospital admissions are a key measure of COVID-19 pandemic pressure. Understanding leading indicators of admissions at the National Health Service (NHS) Trust, regional and national geographies help health services plan for ongoing pressures. We explored the spatio-temporal relationships of leading indicators of hospitalisations across SARS-CoV-2 waves in England. This analysis includes an evaluation of internet search volumes from Google Trends, NHS triage calls and online queries, the NHS COVID-19 app, lateral flow devices (LFDs), and the ZOE app. Data sources were analysed for their feasibility as leading indicators using Granger causality, cross-correlation, and dynamic time warping at fine spatial scales. Google Trends and NHS triages consistently temporally led admissions in most locations, with lead times ranging from 5 to 20 days, whereas an inconsistent relationship was found for the ZOE app, NHS COVID-19 app, and LFD testing, which diminished with spatial resolution, showing cross-correlation of leads between –7 and 7 days. The results indicate that novel surveillance sources can be used effectively to understand the expected healthcare burden within hospital administrative areas though the temporal and spatial heterogeneity of these relationships is a key determinant of their operational public health utility. Cambridge University Press 2023-09-04 /pmc/articles/PMC10600913/ /pubmed/37664991 http://dx.doi.org/10.1017/S0950268823001449 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
spellingShingle Original Paper
Mellor, Jonathon
Overton, Christopher E
Fyles, Martyn
Chawner, Liam
Baxter, James
Baird, Tarrion
Ward, Thomas
Understanding the leading indicators of hospital admissions from COVID-19 across successive waves in the UK
title Understanding the leading indicators of hospital admissions from COVID-19 across successive waves in the UK
title_full Understanding the leading indicators of hospital admissions from COVID-19 across successive waves in the UK
title_fullStr Understanding the leading indicators of hospital admissions from COVID-19 across successive waves in the UK
title_full_unstemmed Understanding the leading indicators of hospital admissions from COVID-19 across successive waves in the UK
title_short Understanding the leading indicators of hospital admissions from COVID-19 across successive waves in the UK
title_sort understanding the leading indicators of hospital admissions from covid-19 across successive waves in the uk
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10600913/
https://www.ncbi.nlm.nih.gov/pubmed/37664991
http://dx.doi.org/10.1017/S0950268823001449
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