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Pre-pandemic psychiatric disorders and risk of COVID-19 : a UK Biobank cohort analysis

Pre-pandemic psychiatric disorders and risk of COVID-19 : a UK Biobank cohort analysis

Title: Pre-pandemic psychiatric disorders and risk of COVID-19 : a UK Biobank cohort analysis
Author: Yang, Huazhen
Chen, Wenwen
Hu, Yao
Chen, Yilong
Zeng, Yu
Sun, Yajing
Ying, Zhiye
He, Junhui
Qu, Yuanyuan
Lu, Donghao
... 3 more authors Show all authors
Date: 2020-11
Language: English
Scope: e69-e79
Department: Faculty of Medicine
Series: The Lancet Healthy Longevity; 1(2)
ISSN: 2666-7568
DOI: https://doi.org/10.1016/S2666-7568(20)30013-1
Subject: Geðsjúkdómar; COVID-19; Psychiatric disorders; COVID-19; Geriatrics and Gerontology; Family Practice; Psychiatry and Mental Health; Health (social science); SDG 3 - Good Health and Well-being
URI: https://hdl.handle.net/20.500.11815/3289

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Yang , H , Chen , W , Hu , Y , Chen , Y , Zeng , Y , Sun , Y , Ying , Z , He , J , Qu , Y , Lu , D , Fang , F , Valdimarsdóttir , U A & Song , H 2020 , ' Pre-pandemic psychiatric disorders and risk of COVID-19 : a UK Biobank cohort analysis ' , The Lancet Healthy Longevity , vol. 1 , no. 2 , pp. e69-e79 . https://doi.org/10.1016/S2666-7568(20)30013-1


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in England and excluded individuals who died before Jan 31, 2020, (the start of the COVID-19 outbreak in the UK) or had withdrawn from UK Biobank.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.


This work is supported by the National Natural Science Foundation of China (81971262 to HS), West China Hospital COVID-19 Epidemic Science and Technology Project (HX-2019-nCoV-014 to HS), Sichuan University Emergency Grant (2020scunCoVyingji10002 to HS), and EU Horizon 2020 Research and Innovation Action Grant (847776 to UAV and FF). We thank the team members involved in West China Biomedical Big Data Center for Disease Control and Prevention for their support. Publisher Copyright: © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license

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