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Clinical academic research in the time of Corona: A simulation study in England and a call for action

OBJECTIVES: We aimed to model the impact of coronavirus (COVID-19) on the clinical academic response in England, and to provide recommendations for COVID-related research. DESIGN: A stochastic model to determine clinical academic capacity in England, incorporating the following key factors which aff...

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Autores principales: Banerjee, Amitava, Katsoulis, Michail, Lai, Alvina G., Pasea, Laura, Treibel, Thomas A., Manisty, Charlotte, Denaxas, Spiros, Quarta, Giovanni, Hemingway, Harry, Cavalcante, João L., Noursadeghi, Mahdad, Moon, James C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7425844/
https://www.ncbi.nlm.nih.gov/pubmed/32790708
http://dx.doi.org/10.1371/journal.pone.0237298
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author Banerjee, Amitava
Katsoulis, Michail
Lai, Alvina G.
Pasea, Laura
Treibel, Thomas A.
Manisty, Charlotte
Denaxas, Spiros
Quarta, Giovanni
Hemingway, Harry
Cavalcante, João L.
Noursadeghi, Mahdad
Moon, James C.
author_facet Banerjee, Amitava
Katsoulis, Michail
Lai, Alvina G.
Pasea, Laura
Treibel, Thomas A.
Manisty, Charlotte
Denaxas, Spiros
Quarta, Giovanni
Hemingway, Harry
Cavalcante, João L.
Noursadeghi, Mahdad
Moon, James C.
author_sort Banerjee, Amitava
collection PubMed
description OBJECTIVES: We aimed to model the impact of coronavirus (COVID-19) on the clinical academic response in England, and to provide recommendations for COVID-related research. DESIGN: A stochastic model to determine clinical academic capacity in England, incorporating the following key factors which affect the ability to conduct research in the COVID-19 climate: (i) infection growth rate and population infection rate (from UK COVID-19 statistics and WHO); (ii) strain on the healthcare system (from published model); and (iii) availability of clinical academic staff with appropriate skillsets affected by frontline clinical activity and sickness (from UK statistics). SETTING: Clinical academics in primary and secondary care in England. PARTICIPANTS: Equivalent of 3200 full-time clinical academics in England. INTERVENTIONS: Four policy approaches to COVID-19 with differing population infection rates: “Italy model” (6%), “mitigation” (10%), “relaxed mitigation” (40%) and “do-nothing” (80%) scenarios. Low and high strain on the health system (no clinical academics able to do research at 10% and 5% infection rate, respectively. MAIN OUTCOME MEASURES: Number of full-time clinical academics available to conduct clinical research during the pandemic in England. RESULTS: In the “Italy model”, “mitigation”, “relaxed mitigation” and “do-nothing” scenarios, from 5 March 2020 the duration (days) and peak infection rates (%) are 95(2.4%), 115(2.5%), 240(5.3%) and 240(16.7%) respectively. Near complete attrition of academia (87% reduction, <400 clinical academics) occurs 35 days after pandemic start for 11, 34, 62, 76 days respectively—with no clinical academics at all for 37 days in the “do-nothing” scenario. Restoration of normal academic workforce (80% of normal capacity) takes 11, 12, 30 and 26 weeks respectively. CONCLUSIONS: Pandemic COVID-19 crushes the science needed at system level. National policies mitigate, but the academic community needs to adapt. We highlight six key strategies: radical prioritisation (eg 3–4 research ideas per institution), deep resourcing, non-standard leadership (repurposing of key non-frontline teams), rationalisation (profoundly simple approaches), careful site selection (eg protected sites with large academic backup) and complete suspension of academic competition with collaborative approaches.
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spelling pubmed-74258442020-08-20 Clinical academic research in the time of Corona: A simulation study in England and a call for action Banerjee, Amitava Katsoulis, Michail Lai, Alvina G. Pasea, Laura Treibel, Thomas A. Manisty, Charlotte Denaxas, Spiros Quarta, Giovanni Hemingway, Harry Cavalcante, João L. Noursadeghi, Mahdad Moon, James C. PLoS One Research Article OBJECTIVES: We aimed to model the impact of coronavirus (COVID-19) on the clinical academic response in England, and to provide recommendations for COVID-related research. DESIGN: A stochastic model to determine clinical academic capacity in England, incorporating the following key factors which affect the ability to conduct research in the COVID-19 climate: (i) infection growth rate and population infection rate (from UK COVID-19 statistics and WHO); (ii) strain on the healthcare system (from published model); and (iii) availability of clinical academic staff with appropriate skillsets affected by frontline clinical activity and sickness (from UK statistics). SETTING: Clinical academics in primary and secondary care in England. PARTICIPANTS: Equivalent of 3200 full-time clinical academics in England. INTERVENTIONS: Four policy approaches to COVID-19 with differing population infection rates: “Italy model” (6%), “mitigation” (10%), “relaxed mitigation” (40%) and “do-nothing” (80%) scenarios. Low and high strain on the health system (no clinical academics able to do research at 10% and 5% infection rate, respectively. MAIN OUTCOME MEASURES: Number of full-time clinical academics available to conduct clinical research during the pandemic in England. RESULTS: In the “Italy model”, “mitigation”, “relaxed mitigation” and “do-nothing” scenarios, from 5 March 2020 the duration (days) and peak infection rates (%) are 95(2.4%), 115(2.5%), 240(5.3%) and 240(16.7%) respectively. Near complete attrition of academia (87% reduction, <400 clinical academics) occurs 35 days after pandemic start for 11, 34, 62, 76 days respectively—with no clinical academics at all for 37 days in the “do-nothing” scenario. Restoration of normal academic workforce (80% of normal capacity) takes 11, 12, 30 and 26 weeks respectively. CONCLUSIONS: Pandemic COVID-19 crushes the science needed at system level. National policies mitigate, but the academic community needs to adapt. We highlight six key strategies: radical prioritisation (eg 3–4 research ideas per institution), deep resourcing, non-standard leadership (repurposing of key non-frontline teams), rationalisation (profoundly simple approaches), careful site selection (eg protected sites with large academic backup) and complete suspension of academic competition with collaborative approaches. Public Library of Science 2020-08-13 /pmc/articles/PMC7425844/ /pubmed/32790708 http://dx.doi.org/10.1371/journal.pone.0237298 Text en © 2020 Banerjee et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Banerjee, Amitava
Katsoulis, Michail
Lai, Alvina G.
Pasea, Laura
Treibel, Thomas A.
Manisty, Charlotte
Denaxas, Spiros
Quarta, Giovanni
Hemingway, Harry
Cavalcante, João L.
Noursadeghi, Mahdad
Moon, James C.
Clinical academic research in the time of Corona: A simulation study in England and a call for action
title Clinical academic research in the time of Corona: A simulation study in England and a call for action
title_full Clinical academic research in the time of Corona: A simulation study in England and a call for action
title_fullStr Clinical academic research in the time of Corona: A simulation study in England and a call for action
title_full_unstemmed Clinical academic research in the time of Corona: A simulation study in England and a call for action
title_short Clinical academic research in the time of Corona: A simulation study in England and a call for action
title_sort clinical academic research in the time of corona: a simulation study in england and a call for action
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7425844/
https://www.ncbi.nlm.nih.gov/pubmed/32790708
http://dx.doi.org/10.1371/journal.pone.0237298
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