Pre-pandemic psychiatric disorders and risk of COVID-19: a UK Biobank cohort analysis
Summary: Background: Psychiatric morbidities have been associated with a risk of severe infections through compromised immunity, health behaviours, or both. However, data are scarce on the association between multiple types of pre-pandemic psychiatric disorders and COVID-19. We aimed to assess the...
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Elsevier
2020-11-01
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Series: | The Lancet. Healthy Longevity |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2666756820300131 |
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Article |
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DOAJ |
language |
English |
format |
Article |
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DOAJ |
author |
Huazhen Yang, MS Wenwen Chen, MS Yao Hu, BS Yilong Chen, MS Yu Zeng, MS Yajing Sun, MS Zhiye Ying, MS Junhui He, MS Yuanyuan Qu, MA Donghao Lu, PhD Fang Fang, ProfPhD Unnur A Valdimarsdóttir, ProfPhD Huan Song, ProfPhD |
spellingShingle |
Huazhen Yang, MS Wenwen Chen, MS Yao Hu, BS Yilong Chen, MS Yu Zeng, MS Yajing Sun, MS Zhiye Ying, MS Junhui He, MS Yuanyuan Qu, MA Donghao Lu, PhD Fang Fang, ProfPhD Unnur A Valdimarsdóttir, ProfPhD Huan Song, ProfPhD Pre-pandemic psychiatric disorders and risk of COVID-19: a UK Biobank cohort analysis The Lancet. Healthy Longevity |
author_facet |
Huazhen Yang, MS Wenwen Chen, MS Yao Hu, BS Yilong Chen, MS Yu Zeng, MS Yajing Sun, MS Zhiye Ying, MS Junhui He, MS Yuanyuan Qu, MA Donghao Lu, PhD Fang Fang, ProfPhD Unnur A Valdimarsdóttir, ProfPhD Huan Song, ProfPhD |
author_sort |
Huazhen Yang, MS |
title |
Pre-pandemic psychiatric disorders and risk of COVID-19: a UK Biobank cohort analysis |
title_short |
Pre-pandemic psychiatric disorders and risk of COVID-19: a UK Biobank cohort analysis |
title_full |
Pre-pandemic psychiatric disorders and risk of COVID-19: a UK Biobank cohort analysis |
title_fullStr |
Pre-pandemic psychiatric disorders and risk of COVID-19: a UK Biobank cohort analysis |
title_full_unstemmed |
Pre-pandemic psychiatric disorders and risk of COVID-19: a UK Biobank cohort analysis |
title_sort |
pre-pandemic psychiatric disorders and risk of covid-19: a uk biobank cohort analysis |
publisher |
Elsevier |
series |
The Lancet. Healthy Longevity |
issn |
2666-7568 |
publishDate |
2020-11-01 |
description |
Summary: Background: Psychiatric morbidities have been associated with a risk of severe infections through compromised immunity, health behaviours, or both. However, data are scarce on the association between multiple types of pre-pandemic psychiatric disorders and COVID-19. We aimed to assess the association between pre-pandemic psychiatric disorders and the subsequent risk of COVID-19 using UK Biobank. Methods: For this cohort analysis, we included participants from UK Biobank who were registered in England and excluded individuals who died before Jan 31, 2020, (the start of the COVID-19 outbreak in the UK) or had withdrawn from UK Biobank. Participants diagnosed with a psychiatric disorder before Jan 31 were included in the group of individuals with pre-pandemic psychiatric disorders, whereas participants without a diagnosis before the outbreak were included in the group of individuals without pre-pandemic psychiatric disorders. We used the Public Health England dataset, UK Biobank hospital data, and death registers to collect data on COVID-19 cases. To examine the relationship between pre-pandemic psychiatric disorders and susceptibility to COVID-19, we used logistic regression models to estimate odds ratios (ORs), controlling for multiple confounders and somatic comorbidities. Key outcomes were all COVID-19, COVID-19 specifically diagnosed in inpatient care, and COVID-19-related deaths. ORs were also estimated separately for each psychiatric disorder and on the basis of the number of pre-pandemic psychiatric disorders. As a positive disease control, we repeated analyses for hospitalisation for other infections. Findings: We included 421 014 UK Biobank participants in our study and assessed their COVID-19 status between Jan 31 and July 26, 2020. 50 809 participants were diagnosed with psychiatric disorders before the outbreak, while 370 205 participants had no psychiatric disorders. The mean age at outbreak was 67·80 years (SD 8·12). We observed an elevated risk of COVID-19 among individuals with pre-pandemic psychiatric disorders compared with that of individuals without such conditions. The fully adjusted ORs were 1·44 (95% CI 1·28–1·62) for All COVID-19 cases, 1·55 (1·34–1·78) for Inpatient COVID-19 cases, and 2·03 (1·59–2·59) for COVID-19-related deaths. We observed excess risk, defined as risk that increased with the number of pre-pandemic psychiatric disorders, across all diagnostic categories of pre-pandemic psychiatric disorders. We also observed an association between psychiatric disorders and elevated risk of hospitalisation due to other infections (OR 1·74, 95% CI 1·58–1·93). Interpretation: Our findings suggest that pre-existing psychiatric disorders are associated with an increased risk of COVID-19. These findings underscore the need for surveillance of and care for populations with pre-existing psychiatric disorders during the COVID-19 pandemic. Funding: National Natural Science Foundation of China. |
url |
http://www.sciencedirect.com/science/article/pii/S2666756820300131 |
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doaj-9ac01d0403a049cbb7aba2ee7bda3b6b2021-02-27T04:41:26ZengElsevierThe Lancet. Healthy Longevity2666-75682020-11-0112e69e79Pre-pandemic psychiatric disorders and risk of COVID-19: a UK Biobank cohort analysisHuazhen Yang, MS0Wenwen Chen, MS1Yao Hu, BS2Yilong Chen, MS3Yu Zeng, MS4Yajing Sun, MS5Zhiye Ying, MS6Junhui He, MS7Yuanyuan Qu, MA8Donghao Lu, PhD9Fang Fang, ProfPhD10Unnur A Valdimarsdóttir, ProfPhD11Huan Song, ProfPhD12West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China; Medical Big Data Center, Sichuan University, Chengdu, ChinaDivision of Nephrology, Kidney Research Institute, State Key Laboratory of Biotherapy and Cancer Centre, West China Hospital, Sichuan University, Chengdu, ChinaWest China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China; Medical Big Data Center, Sichuan University, Chengdu, ChinaWest China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China; Medical Big Data Center, Sichuan University, Chengdu, ChinaWest China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China; Medical Big Data Center, Sichuan University, Chengdu, ChinaWest China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China; Medical Big Data Center, Sichuan University, Chengdu, ChinaWest China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China; Medical Big Data Center, Sichuan University, Chengdu, ChinaDepartment of Anaesthesiology, West China Hospital, Sichuan University and the Research Units of West China, Chinese Academy of Medical Sciences, Chengdu, ChinaWest China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China; Medical Big Data Center, Sichuan University, Chengdu, ChinaClinical Research Centre for Breast Diseases, West China Hospital, Sichuan University, Chengdu, China; Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden; Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USAInstitute of Environmental Medicine, Karolinska Institute, Stockholm, SwedenDepartment of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden; Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA; Centre of Public Health Sciences, Faculty of Medicine, University of Iceland, Reykjavík, IcelandWest China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China; Medical Big Data Center, Sichuan University, Chengdu, China; Centre of Public Health Sciences, Faculty of Medicine, University of Iceland, Reykjavík, Iceland; Correspondence to: Prof Huan Song, West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu 610041, ChinaSummary: Background: Psychiatric morbidities have been associated with a risk of severe infections through compromised immunity, health behaviours, or both. However, data are scarce on the association between multiple types of pre-pandemic psychiatric disorders and COVID-19. We aimed to assess the association between pre-pandemic psychiatric disorders and the subsequent risk of COVID-19 using UK Biobank. Methods: For this cohort analysis, we included participants from UK Biobank who were registered in England and excluded individuals who died before Jan 31, 2020, (the start of the COVID-19 outbreak in the UK) or had withdrawn from UK Biobank. Participants diagnosed with a psychiatric disorder before Jan 31 were included in the group of individuals with pre-pandemic psychiatric disorders, whereas participants without a diagnosis before the outbreak were included in the group of individuals without pre-pandemic psychiatric disorders. We used the Public Health England dataset, UK Biobank hospital data, and death registers to collect data on COVID-19 cases. To examine the relationship between pre-pandemic psychiatric disorders and susceptibility to COVID-19, we used logistic regression models to estimate odds ratios (ORs), controlling for multiple confounders and somatic comorbidities. Key outcomes were all COVID-19, COVID-19 specifically diagnosed in inpatient care, and COVID-19-related deaths. ORs were also estimated separately for each psychiatric disorder and on the basis of the number of pre-pandemic psychiatric disorders. As a positive disease control, we repeated analyses for hospitalisation for other infections. Findings: We included 421 014 UK Biobank participants in our study and assessed their COVID-19 status between Jan 31 and July 26, 2020. 50 809 participants were diagnosed with psychiatric disorders before the outbreak, while 370 205 participants had no psychiatric disorders. The mean age at outbreak was 67·80 years (SD 8·12). We observed an elevated risk of COVID-19 among individuals with pre-pandemic psychiatric disorders compared with that of individuals without such conditions. The fully adjusted ORs were 1·44 (95% CI 1·28–1·62) for All COVID-19 cases, 1·55 (1·34–1·78) for Inpatient COVID-19 cases, and 2·03 (1·59–2·59) for COVID-19-related deaths. We observed excess risk, defined as risk that increased with the number of pre-pandemic psychiatric disorders, across all diagnostic categories of pre-pandemic psychiatric disorders. We also observed an association between psychiatric disorders and elevated risk of hospitalisation due to other infections (OR 1·74, 95% CI 1·58–1·93). Interpretation: Our findings suggest that pre-existing psychiatric disorders are associated with an increased risk of COVID-19. These findings underscore the need for surveillance of and care for populations with pre-existing psychiatric disorders during the COVID-19 pandemic. Funding: National Natural Science Foundation of China.http://www.sciencedirect.com/science/article/pii/S2666756820300131 |