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Patterns of multimorbidity and risk of severe SARS-CoV-2 infection: an observational study in the U.K.
BACKGROUND: Pre-existing comorbidities have been linked to SARS-CoV-2 infection but evidence is sparse on the importance and pattern of multimorbidity (2 or more conditions) and severity of infection indicated by hospitalisation or mortality. We aimed to use a multimorbidity index developed specific...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8418288/ https://www.ncbi.nlm.nih.gov/pubmed/34481456 http://dx.doi.org/10.1186/s12879-021-06600-y |
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author | Chudasama, Yogini V. Zaccardi, Francesco Gillies, Clare L. Razieh, Cameron Yates, Thomas Kloecker, David E. Rowlands, Alex V. Davies, Melanie J. Islam, Nazrul Seidu, Samuel Forouhi, Nita G. Khunti, Kamlesh |
author_facet | Chudasama, Yogini V. Zaccardi, Francesco Gillies, Clare L. Razieh, Cameron Yates, Thomas Kloecker, David E. Rowlands, Alex V. Davies, Melanie J. Islam, Nazrul Seidu, Samuel Forouhi, Nita G. Khunti, Kamlesh |
author_sort | Chudasama, Yogini V. |
collection | PubMed |
description | BACKGROUND: Pre-existing comorbidities have been linked to SARS-CoV-2 infection but evidence is sparse on the importance and pattern of multimorbidity (2 or more conditions) and severity of infection indicated by hospitalisation or mortality. We aimed to use a multimorbidity index developed specifically for COVID-19 to investigate the association between multimorbidity and risk of severe SARS-CoV-2 infection. METHODS: We used data from the UK Biobank linked to laboratory confirmed test results for SARS-CoV-2 infection and mortality data from Public Health England between March 16 and July 26, 2020. By reviewing the current literature on COVID-19 we derived a multimorbidity index including: (1) angina; (2) asthma; (3) atrial fibrillation; (4) cancer; (5) chronic kidney disease; (6) chronic obstructive pulmonary disease; (7) diabetes mellitus; (8) heart failure; (9) hypertension; (10) myocardial infarction; (11) peripheral vascular disease; (12) stroke. Adjusted logistic regression models were used to assess the association between multimorbidity and risk of severe SARS-CoV-2 infection (hospitalisation/death). Potential effect modifiers of the association were assessed: age, sex, ethnicity, deprivation, smoking status, body mass index, air pollution, 25‐hydroxyvitamin D, cardiorespiratory fitness, high sensitivity C-reactive protein. RESULTS: Among 360,283 participants, the median age was 68 [range 48–85] years, most were White (94.5%), and 1706 had severe SARS-CoV-2 infection. The prevalence of multimorbidity was more than double in those with severe SARS-CoV-2 infection (25%) compared to those without (11%), and clusters of several multimorbidities were more common in those with severe SARS-CoV-2 infection. The most common clusters with severe SARS-CoV-2 infection were stroke with hypertension (79% of those with stroke had hypertension); diabetes and hypertension (72%); and chronic kidney disease and hypertension (68%). Multimorbidity was independently associated with a greater risk of severe SARS-CoV-2 infection (adjusted odds ratio 1.91 [95% confidence interval 1.70, 2.15] compared to no multimorbidity). The risk remained consistent across potential effect modifiers, except for greater risk among older age. The highest risk of severe infection was strongly evidenced in those with CKD and diabetes (4.93 [95% CI 3.36, 7.22]). CONCLUSION: The multimorbidity index may help identify individuals at higher risk for severe COVID-19 outcomes and provide guidance for tailoring effective treatment. |
format | Online Article Text |
id | pubmed-8418288 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-84182882021-09-07 Patterns of multimorbidity and risk of severe SARS-CoV-2 infection: an observational study in the U.K. Chudasama, Yogini V. Zaccardi, Francesco Gillies, Clare L. Razieh, Cameron Yates, Thomas Kloecker, David E. Rowlands, Alex V. Davies, Melanie J. Islam, Nazrul Seidu, Samuel Forouhi, Nita G. Khunti, Kamlesh BMC Infect Dis Research Article BACKGROUND: Pre-existing comorbidities have been linked to SARS-CoV-2 infection but evidence is sparse on the importance and pattern of multimorbidity (2 or more conditions) and severity of infection indicated by hospitalisation or mortality. We aimed to use a multimorbidity index developed specifically for COVID-19 to investigate the association between multimorbidity and risk of severe SARS-CoV-2 infection. METHODS: We used data from the UK Biobank linked to laboratory confirmed test results for SARS-CoV-2 infection and mortality data from Public Health England between March 16 and July 26, 2020. By reviewing the current literature on COVID-19 we derived a multimorbidity index including: (1) angina; (2) asthma; (3) atrial fibrillation; (4) cancer; (5) chronic kidney disease; (6) chronic obstructive pulmonary disease; (7) diabetes mellitus; (8) heart failure; (9) hypertension; (10) myocardial infarction; (11) peripheral vascular disease; (12) stroke. Adjusted logistic regression models were used to assess the association between multimorbidity and risk of severe SARS-CoV-2 infection (hospitalisation/death). Potential effect modifiers of the association were assessed: age, sex, ethnicity, deprivation, smoking status, body mass index, air pollution, 25‐hydroxyvitamin D, cardiorespiratory fitness, high sensitivity C-reactive protein. RESULTS: Among 360,283 participants, the median age was 68 [range 48–85] years, most were White (94.5%), and 1706 had severe SARS-CoV-2 infection. The prevalence of multimorbidity was more than double in those with severe SARS-CoV-2 infection (25%) compared to those without (11%), and clusters of several multimorbidities were more common in those with severe SARS-CoV-2 infection. The most common clusters with severe SARS-CoV-2 infection were stroke with hypertension (79% of those with stroke had hypertension); diabetes and hypertension (72%); and chronic kidney disease and hypertension (68%). Multimorbidity was independently associated with a greater risk of severe SARS-CoV-2 infection (adjusted odds ratio 1.91 [95% confidence interval 1.70, 2.15] compared to no multimorbidity). The risk remained consistent across potential effect modifiers, except for greater risk among older age. The highest risk of severe infection was strongly evidenced in those with CKD and diabetes (4.93 [95% CI 3.36, 7.22]). CONCLUSION: The multimorbidity index may help identify individuals at higher risk for severe COVID-19 outcomes and provide guidance for tailoring effective treatment. BioMed Central 2021-09-04 /pmc/articles/PMC8418288/ /pubmed/34481456 http://dx.doi.org/10.1186/s12879-021-06600-y Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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 Chudasama, Yogini V. Zaccardi, Francesco Gillies, Clare L. Razieh, Cameron Yates, Thomas Kloecker, David E. Rowlands, Alex V. Davies, Melanie J. Islam, Nazrul Seidu, Samuel Forouhi, Nita G. Khunti, Kamlesh Patterns of multimorbidity and risk of severe SARS-CoV-2 infection: an observational study in the U.K. |
title | Patterns of multimorbidity and risk of severe SARS-CoV-2 infection: an observational study in the U.K. |
title_full | Patterns of multimorbidity and risk of severe SARS-CoV-2 infection: an observational study in the U.K. |
title_fullStr | Patterns of multimorbidity and risk of severe SARS-CoV-2 infection: an observational study in the U.K. |
title_full_unstemmed | Patterns of multimorbidity and risk of severe SARS-CoV-2 infection: an observational study in the U.K. |
title_short | Patterns of multimorbidity and risk of severe SARS-CoV-2 infection: an observational study in the U.K. |
title_sort | patterns of multimorbidity and risk of severe sars-cov-2 infection: an observational study in the u.k. |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8418288/ https://www.ncbi.nlm.nih.gov/pubmed/34481456 http://dx.doi.org/10.1186/s12879-021-06600-y |
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