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OpenSAFELY: factors associated with COVID-19-related hospital death in the linked electronic health records of 17 million adult NHS patients
Open accessPreprint — published as 10.1038/s41586-020-2521-4 · Paper page
Abstract
Background: Establishing who is at risk from a novel rapidly arising cause of death, and why, requires a new approach to epidemiological research with very large datasets and timely data. Working on behalf of NHS England we therefore set out to deliver a secure and pseudonymised analytics platform inside the data centre of a major primary care electronic health records vendor establishing coverage across detailed primary care records for a substantial proportion of all patients in England. The following results are preliminary. Data sources: Primary care electronic health records managed by the electronic health record vendor TPP, pseudonymously linked to patient-level data from the COVID-19 Patient Notification System (CPNS) for death of hospital inpatients with confirmed COVID-19, using the new OpenSAFELY platform. Population: 17,425,445 adults. Time period: 1st Feb 2020 to 25th April 2020. Primary outcome: Death in hospital among people with confirmed COVID-19. Methods: Cohort study analysed by Cox-regression to generate hazard ratios: age and sex adjusted, and multiply adjusted for co-variates selected prospectively on the basis of clinical interest and prior findings. Results: There were 5683 deaths attributed to COVID-19. In summary after full adjustment, death from COVID-19 was strongly associated with: being male (hazard ratio 1.99, 95%CI 1.88-2.10); older age and deprivation (both with a strong gradient); uncontrolled diabetes (HR 2.36 95% CI 2.18-2.56); severe asthma (HR 1.25 CI 1.08-1.44); and various other prior medical conditions. Compared to people with ethnicity recorded as white, black people were at higher risk of death, with only partial attenuation in hazard ratios from the fully adjusted model (age-sex adjusted HR 2.17 95% CI 1.84-2.57; fully adjusted HR 1.71 95% CI 1.44-2.02); with similar findings for Asian people (age-sex adjusted HR 1.95 95% CI 1.73-2.18; fully adjusted HR 1.62 95% CI 1.431.82). Conclusions: We have quantified a range of clinical risk factors for death from COVID-19, some of which were not previously well characterised, in the largest cohort study conducted by any country to date. People from Asian and black groups are at markedly increased risk of in-hospital death from COVID-19, and contrary to some prior speculation this is only partially attributable to pre-existing clinical risk factors or deprivation; further research into the drivers of this association is therefore urgently required. Deprivation is also a major risk factor with, again, little of the excess risk explained by co-morbidity or other risk factors. The findings for clinical risk factors are concordant with policies in the UK for protecting those at highest risk. Our OpenSAFELY platform is rapidly adding further NHS patients’ records; we will update and extend these results regularly.
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“However, most associations observed between baseline characteristics and mortality or intubation are in line with prior epidemiological studies [17,19].”
From Association between antidepressant use and reduced risk of intubation or death in hospitalized patients with COVID-19: results from an observational study (2021) · cites this paper as [17]
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“A considerably higher infection and fatality rate was reported in the USA among African Americans [1], in the UK among Black and Asian minorities [2], and among refugee immigrant groups in Sweden [3].”
Resources availability and COVID-19 mortality among US counties
Epane, J., Zengul, F., Ramamonjiarivelo, Z., et al. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2023.1098571 · Cited by 10
“Several studies have indicated that older patients (7–11), male patients (7, 9, 11), Black patients (8, 11), Asian or Pacific Islander patients (11), American Indian or Alaska native patients (8), and patients of Hispanic ethnicity (8, 12) are more likely to die of COVID-19, compared with their younger, female, and Wh…”
BackgroundInfection with SARS‐CoV‐2 among children with asthma: evidence from Global Asthma Network
Chiang, C.-Y., Ellwood, P., Ellwood, E., et al. (2021) · Pediatric Allergy and Immunology · doi:10.1111/pai.13709 · Cited by 10
Comparison of COVID-19 outcomes among shielded and non-shielded populations
Jani, B., Ho, F. K. W., Lowe, D. J., Traynor, J. P., MacBride‐Stewart, S., Mark, P. B., Mair, F. S., & Pell, J. P. (2021) · Scientific Reports · doi:10.1038/s41598-021-94630-6 · Cited by 24
Couzi, L., Manook, M., Caillard, S., Epailly, É., Barrou, B., Anglicheau, D., Büchler, M., Mussot, S., Dumortier, J., Thaunat, O., et al. (2021) · Néphrologie & Thérapeutique · doi:10.1016/j.nephro.2020.12.004 · Cited by 11
Focused action is required to protect ethnic minority populations from COVID-19 post-lockdown.
Khunti, K., Routen, A., Patel, K., et al. (2020) · The British journal of general practice : the journal of the Royal College of General Practitioners · doi:10.3399/bjgp21x714581 · Cited by 10
“Background Since the association between COVID-19 and ethnicity was first noted in April (1), numerous large-scale national datasets have been analysed (2-6).”
BackgroundStabilizing Cellular Barriers: Raising the Shields Against COVID-19
Hanchard, J., Capó-Vélez, C. M., Deusch, K., Lidington, D., & Bolz, S.-S. (2020) · Frontiers in Endocrinology · doi:10.3389/fendo.2020.583006 · Cited by 10
Los imperativos éticos de la pandemia de COVID-19: Un análisis desde la ética de los datos
Arriagada-Bruneau, G., Gilthorpe, M. S., & Müller, V. C. (2020) · Veritas · doi:10.4067/s0718-92732020000200013 · Cited by 13
“However when it was presented on pre-print (Collaborative et al., 2020), it received a lot of criticism by epidemi-ologists, particularly those working with causal inference.”
BackgroundHigh variation expected in the pace and burden of SARS-CoV-2 outbreaks across sub-Saharan AfricaPreprint
Rice, B. L., Annapragada, A., Baker, R., Bruijning, M., Dotse-Gborgbortsi, W., Mensah, K., Miller, I. F., Motaze, N. V., Raherinandrasana, A., Rajeev, M., et al. (2020) · medRxiv · doi:10.1101/2020.07.23.20161208 · Cited by 10
Roadmap to resuming care for liver diseases after coronavirus disease‐2019
Kapuria, D., Bollipo, S. J., Rabiee, A., Yakov, G. B., Kumar, G., Siau, K., Lee, H. W., Congly, S. E., Turnés, J., Dhanasekaran, R., et al. (2020) · Journal of Gastroenterology and Hepatology · doi:10.1111/jgh.15178 · Cited by 12
Obesity, walking pace and risk of severe COVID-19: Analysis of UK Biobank
Yates, T., Razieh, C., Zaccardi, F., et al. (2020) · medRxiv · doi:10.1101/2020.07.10.20150003 · Cited by 10
“Large routine database studies have recently reported the association of obesity with COVID19 outcomes (10), with mechanisms hypothesised to include restricted pulmonary function and chronic inflammation (2), with adipose tissue further suggested as an important vial reservoir (11).”
BackgroundResultAsthma and COVID-19: What do we know?
Sarıoğlu, N. (2020) · Turkish Journal of Tuberculosis & Thorax · doi:10.5578/tt.69775 · Cited by 10
Prioritising action on diabetes during COVID-19.
Erzse, A., Fraser, H., Levitt, N. S., et al. (2020) · South African medical journal = Suid-Afrikaanse tydskrif vir geneeskunde · doi:10.7196/samj.2020.v110i8.14961 · Cited by 10
Capps, B. (2020) · Cambridge Quarterly of Healthcare Ethics · doi:10.1017/s0963180120000456 · Cited by 18
“, AI) and tools (open source software), even if used ethically now, will become resources able to be used beyond the purposes, and protections offered by, public health.(105) Despite trust in some legal safeguards,(106) OS has also become an opportunity for capitalist platforms to provide many essential services based…”
BackgroundCOVID-19 :Determinants of Hospitalization, ICU and Death among 20,293 reported cases in Portugal
Peixoto, V. R., Vieira, A., Aguiar, P., et al. (2020) · medRxiv · doi:10.1101/2020.05.29.20115824 · Cited by 10
Youha, S. A., Doi, S. A. R., Jamal, M. H., et al. (2020) · medRxiv · doi:10.1101/2020.05.21.20108639 · Cited by 10
Fenton, N. (2020) · doi:10.48550/arxiv.2005.08608 · Cited by 10
“, 2020) highlights how ‘collider bias’ in statistical studies of COVID19 – such as the large-scale study (Collaborative et al., 2020) – may undermine our understanding of the disease risk.”
BackgroundResultInfluentialLassau, N., Ammari, S., Chouzenoux, E., et al. (2020) · medRxiv · doi:10.1101/2020.05.14.20101972 · Cited by 10
Strain, W., Jankowski, J., Davies, A., et al. (2020) · medRxiv · doi:10.1101/2020.05.05.20091967 · Cited by 10
COVID‐19: diabetes and death. A call to action
Hillson, R. (2020) · Practical diabetes · doi:10.1002/pdi.2271 · Cited by 10
Kelleni, M. T. (2021) · International Journal of Pulmonary & Respiratory Sciences · doi:10.19080/ijoprs.2021.05.555658 · Cited by 10
Risk of mortality among inpatients with COVID‐19 and type 2 diabetes: National data from Kuwait
Al‐Ozairi, E., Brown, R., Hamdan, Y., Al-Abdullah, L., Voase, N., Kandari, J. A., Alsaeed, D., Ozairi, A. A., Hasan, A., Al‐Mulla, F., et al. (2021) · Endocrinology Diabetes & Metabolism · doi:10.1002/edm2.287 · Cited by 18
Herrmann, M. L., Hahn, J.-M., Walter-Frank, B., Bollinger, D. M., Schmauder, K., Schnauder, G., Bitzer, M., Malek, N. P., Eschweiler, G. W., & Göpel, S. (2021) · PLoS ONE · doi:10.1371/journal.pone.0253154 · Cited by 10
“Large Chinese [6] and British [7] cohort studies of hospitalized COVID-19-patients examined age, comorbidities, and their influence on mortality without addressing functional outcome of survivors.”
BackgroundImpact of Smoking on Women During the Covid-19 Pandemic
Mihaltan, F. D., Râjnoveanu, A. G., & Rajnoveanu, R.-M. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.584061 · Cited by 9
COVID-19 from the interdisciplinary standpoint. Round table
Мамедов, М. Н., Rodionova, Y. V., Yavelov, I. S., Смирнова, М. И., Dudinskaya, Е. N., & Potievskaya, V. I. (2021) · CARDIOVASCULAR THERAPY AND PREVENTION · doi:10.15829/1728-8800-2021-2849 · Cited by 11
Optimizing SARS-CoV-2 vaccination strategies in France: Results from a stochastic agent-based modelPreprint
Hoertel, N., Blachier, M., Limosin, F., Rico, M. S., Blanco, C., Olfson, M., Luchini, S., Schwarzinger, M., & Leleu, H. (2021) · medRxiv · doi:10.1101/2021.01.17.21249970 · Cited by 8
Abbott, S., Parretti, H. M., Hazlehurst, J., & Tahrani, A. A. (2020) · Journal of Human Nutrition and Dietetics · doi:10.1111/jhn.12850 · Cited by 10
A Physician's Guide for Workers’ Return to Work During COVID-19 Pandemic
Baptista, M. C., Burton, W. N., Pawlecki, B., & Pransky, G. (2020) · Journal of Occupational and Environmental Medicine · doi:10.1097/jom.0000000000002118 · Cited by 13
“COPD has consistently been identified in several studies as an independent riskfactorforsevereformsofCOVID-19.”
InfluentialAre telephone consultations here to stay in rheumatology?
Raizada, S., Cleaton, N., Bateman, J., et al. (2020) · Rheumatology Advances in Practice · doi:10.1093/rap/rkaa071 · Cited by 9
Vassall, A., Sweeney, S., Barasa, E., et al. (2020) · Wellcome Open Research · doi:10.12688/wellcomeopenres.16380.1 · Cited by 9
“Case fatality risks for C19 are also dependent on local health services and the existing disease burden/co-morbidities, which vary substantially by setting and may also be correlated with economic status 68,69 .”
Metadata
- Authors
- Collaborative, T. O., Williamson, E., Walker, A. J., Bhaskaran, K., Bacon, S., Bates, C., Morton, C. E., Curtis, H. J., Mehrkar, A., Evans, D., Inglesby, P., Cockburn, J., McDonald, H. I., MacKenna, B., Tomlinson, L., Douglas, I. J., Rentsch, C. T., Mathur, R., Wong, A., Grieve, R., Harrison, D., Forbes, H., Schultze, A., Croker, R., Parry, J., Hester, F., Harper, S., Perera, R., Evans, S., Smeeth, L., & Goldacre, B.
- Affiliations
- London School of Hygiene & Tropical Medicine (GB)
- University of Oxford (GB)
- National Institute for Health and Care Research (GB)
- Corresponding author
- Goldacre, Ben
- ORCID
- Williamson, Elizabeth
- Walker, Alex J
- Bhaskaran, Krishnan
- Bacon, Seb
- Morton, Caroline E
- Curtis, Helen J
- Inglesby, Peter
- Cockburn, Jonathan
- McDonald, Helen I
- MacKenna, Brian
- Tomlinson, Laurie
- Douglas, Ian J
- Rentsch, Christopher T
- Mathur, Rohini
- Wong, Angel
- Grieve, Richard
- Harrison, David
- Schultze, Anna
- Croker, Richard
- Hester, Frank
- Perera, Raf
- Evans, Stephen
- Smeeth, Liam
- Goldacre, Ben
- Venue
- Nature
- Publisher
- openRxiv
- Published
- 2020-05-07
- Volume
- 584
- Issue
- 7821
- Pages
- 430-436
- ISSN
- 0028-0836 (ISSN-L)
- PMC
- PMC7611074
- OpenAlex
- W3047144258
- Cited by
- 6,674
- References
- 32
- Citations by year
- 2026: 176 · 2025: 303 · 2024: 698 · 2023: 904 · 2022: 1505 · 2021: 2173 · 2020: 908
- FWCI
- 179.94
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Nature (publishedVersion)
- medRxiv (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- LSHTM Research Online (London School of Hygiene and Tropical Medicine) (acceptedVersion)
- PubMed Central (acceptedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Queen Mary Research Online (Queen Mary University of London) (submittedVersion)
- Funders
- London School of Hygiene and Tropical Medicine
- Wellcome Trust (107731/Z/15/Z, 201375/Z/16/Z, 201375, 107731)
- UK Research and Innovation
- National Institute for Health Research Health Protection Research Unit
- National Institute for Health and Care Research
- British Heart Foundation (FS/19/19/34175)
- Department of Health and Social Care
- Royal Society
- Diabetes UK
- Public Health England
- Medical Research Council (MR/S003940/1)
- NIHR Oxford Biomedical Research Centre
- National Institute for Health and Care Research Applied Research Collaboration Oxford and Thames Valley
- Topics
- Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
- Health Sciences › Medicine › Oncology › COVID-19 and healthcare impacts
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Keywords
- COVID-19 pandemic, COVID-19 mortality, risk factors, OpenSAFELY, primary care electronic health records, sex differences, age, socioeconomic deprivation, diabetes mellitus, severe asthma, ethnicity, NHS England, cohort studies
- MeSH terms
- Betacoronavirus, COVID-19, SARS-CoV-2, Adolescent, Adult, Age Factors, Aged, Aged, 80 and over, Aging, Asthma, Diabetes Mellitus, Female, Humans, Hypertension, Male, Middle Aged, Pneumonia, Viral, Sex Characteristics, Smoking, State Medicine, Cohort Studies, Proportional Hazards Models, Age Distribution, Coronavirus Infections, Risk Assessment, Black People, Asian People, Young Adult, Pandemics
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