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UK prevalence of underlying conditions which increase the risk of severe COVID-19 disease: a point prevalence study using electronic health records

BACKGROUND: Characterising the size and distribution of the population at risk of severe COVID-19 is vital for effective policy and planning. Older age, and underlying health conditions, are associated with higher risk of death from COVID-19. This study aimed to describe the population at risk of se...

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Autores principales: Walker, Jemma L., Grint, Daniel J., Strongman, Helen, Eggo, Rosalind M., Peppa, Maria, Minassian, Caroline, Mansfield, Kathryn E., Rentsch, Christopher T., Douglas, Ian J., Mathur, Rohini, Wong, Angel Y. S., Quint, Jennifer K., Andrews, Nick, Bernal, Jamie Lopez, Scott, J. Anthony, Ramsay, Mary, Smeeth, Liam, McDonald, Helen I.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7948667/
https://www.ncbi.nlm.nih.gov/pubmed/33706738
http://dx.doi.org/10.1186/s12889-021-10427-2
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author Walker, Jemma L.
Grint, Daniel J.
Strongman, Helen
Eggo, Rosalind M.
Peppa, Maria
Minassian, Caroline
Mansfield, Kathryn E.
Rentsch, Christopher T.
Douglas, Ian J.
Mathur, Rohini
Wong, Angel Y. S.
Quint, Jennifer K.
Andrews, Nick
Bernal, Jamie Lopez
Scott, J. Anthony
Ramsay, Mary
Smeeth, Liam
McDonald, Helen I.
author_facet Walker, Jemma L.
Grint, Daniel J.
Strongman, Helen
Eggo, Rosalind M.
Peppa, Maria
Minassian, Caroline
Mansfield, Kathryn E.
Rentsch, Christopher T.
Douglas, Ian J.
Mathur, Rohini
Wong, Angel Y. S.
Quint, Jennifer K.
Andrews, Nick
Bernal, Jamie Lopez
Scott, J. Anthony
Ramsay, Mary
Smeeth, Liam
McDonald, Helen I.
author_sort Walker, Jemma L.
collection PubMed
description BACKGROUND: Characterising the size and distribution of the population at risk of severe COVID-19 is vital for effective policy and planning. Older age, and underlying health conditions, are associated with higher risk of death from COVID-19. This study aimed to describe the population at risk of severe COVID-19 due to underlying health conditions across the United Kingdom. METHODS: We used anonymised electronic health records from the Clinical Practice Research Datalink GOLD to estimate the point prevalence on 5 March 2019 of the at-risk population following national guidance. Prevalence for any risk condition and for each individual condition is given overall and stratified by age and region with binomial exact confidence intervals. We repeated the analysis on 5 March 2014 for full regional representation and to describe prevalence of underlying health conditions in pregnancy. We additionally described the population of cancer survivors, and assessed the value of linked secondary care records for ascertaining COVID-19 at-risk status. RESULTS: On 5 March 2019, 24.4% of the UK population were at risk due to a record of at least one underlying health condition, including 8.3% of school-aged children, 19.6% of working-aged adults, and 66.2% of individuals aged 70 years or more. 7.1% of the population had multimorbidity. The size of the at-risk population was stable over time comparing 2014 to 2019, despite increases in chronic liver disease and diabetes and decreases in chronic kidney disease and current asthma. Separately, 1.6% of the population had a new diagnosis of cancer in the past 5 y. CONCLUSIONS: The population at risk of severe COVID-19 (defined as either aged ≥70 years, or younger with an underlying health condition) comprises 18.5 million individuals in the UK, including a considerable proportion of school-aged and working-aged individuals. Our national estimates broadly support the use of Global Burden of Disease modelled estimates in other countries. We provide age- and region- stratified prevalence for each condition to support effective modelling of public health interventions and planning of vaccine resource allocation. The high prevalence of health conditions among older age groups suggests that age-targeted vaccination strategies may efficiently target individuals at higher risk of severe COVID-19. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-021-10427-2.
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spelling pubmed-79486672021-03-11 UK prevalence of underlying conditions which increase the risk of severe COVID-19 disease: a point prevalence study using electronic health records Walker, Jemma L. Grint, Daniel J. Strongman, Helen Eggo, Rosalind M. Peppa, Maria Minassian, Caroline Mansfield, Kathryn E. Rentsch, Christopher T. Douglas, Ian J. Mathur, Rohini Wong, Angel Y. S. Quint, Jennifer K. Andrews, Nick Bernal, Jamie Lopez Scott, J. Anthony Ramsay, Mary Smeeth, Liam McDonald, Helen I. BMC Public Health Research Article BACKGROUND: Characterising the size and distribution of the population at risk of severe COVID-19 is vital for effective policy and planning. Older age, and underlying health conditions, are associated with higher risk of death from COVID-19. This study aimed to describe the population at risk of severe COVID-19 due to underlying health conditions across the United Kingdom. METHODS: We used anonymised electronic health records from the Clinical Practice Research Datalink GOLD to estimate the point prevalence on 5 March 2019 of the at-risk population following national guidance. Prevalence for any risk condition and for each individual condition is given overall and stratified by age and region with binomial exact confidence intervals. We repeated the analysis on 5 March 2014 for full regional representation and to describe prevalence of underlying health conditions in pregnancy. We additionally described the population of cancer survivors, and assessed the value of linked secondary care records for ascertaining COVID-19 at-risk status. RESULTS: On 5 March 2019, 24.4% of the UK population were at risk due to a record of at least one underlying health condition, including 8.3% of school-aged children, 19.6% of working-aged adults, and 66.2% of individuals aged 70 years or more. 7.1% of the population had multimorbidity. The size of the at-risk population was stable over time comparing 2014 to 2019, despite increases in chronic liver disease and diabetes and decreases in chronic kidney disease and current asthma. Separately, 1.6% of the population had a new diagnosis of cancer in the past 5 y. CONCLUSIONS: The population at risk of severe COVID-19 (defined as either aged ≥70 years, or younger with an underlying health condition) comprises 18.5 million individuals in the UK, including a considerable proportion of school-aged and working-aged individuals. Our national estimates broadly support the use of Global Burden of Disease modelled estimates in other countries. We provide age- and region- stratified prevalence for each condition to support effective modelling of public health interventions and planning of vaccine resource allocation. The high prevalence of health conditions among older age groups suggests that age-targeted vaccination strategies may efficiently target individuals at higher risk of severe COVID-19. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-021-10427-2. BioMed Central 2021-03-11 /pmc/articles/PMC7948667/ /pubmed/33706738 http://dx.doi.org/10.1186/s12889-021-10427-2 Text en © The Author(s) 2021 Open AccessThis 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/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Walker, Jemma L.
Grint, Daniel J.
Strongman, Helen
Eggo, Rosalind M.
Peppa, Maria
Minassian, Caroline
Mansfield, Kathryn E.
Rentsch, Christopher T.
Douglas, Ian J.
Mathur, Rohini
Wong, Angel Y. S.
Quint, Jennifer K.
Andrews, Nick
Bernal, Jamie Lopez
Scott, J. Anthony
Ramsay, Mary
Smeeth, Liam
McDonald, Helen I.
UK prevalence of underlying conditions which increase the risk of severe COVID-19 disease: a point prevalence study using electronic health records
title UK prevalence of underlying conditions which increase the risk of severe COVID-19 disease: a point prevalence study using electronic health records
title_full UK prevalence of underlying conditions which increase the risk of severe COVID-19 disease: a point prevalence study using electronic health records
title_fullStr UK prevalence of underlying conditions which increase the risk of severe COVID-19 disease: a point prevalence study using electronic health records
title_full_unstemmed UK prevalence of underlying conditions which increase the risk of severe COVID-19 disease: a point prevalence study using electronic health records
title_short UK prevalence of underlying conditions which increase the risk of severe COVID-19 disease: a point prevalence study using electronic health records
title_sort uk prevalence of underlying conditions which increase the risk of severe covid-19 disease: a point prevalence study using electronic health records
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7948667/
https://www.ncbi.nlm.nih.gov/pubmed/33706738
http://dx.doi.org/10.1186/s12889-021-10427-2
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