Article Text

Original research
Smoking and COVID-19 outcomes: an observational and Mendelian randomisation study using the UK Biobank cohort
  1. Ashley K Clift1,2,
  2. Adam von Ende3,
  3. Pui San Tan1,
  4. Hannah M Sallis4,5,6,
  5. Nicola Lindson1,
  6. Carol A C Coupland1,7,
  7. Marcus R Munafò4,5,6,
  8. Paul Aveyard1,
  9. Julia Hippisley-Cox1,
  10. Jemma C Hopewell3
  1. 1Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
  2. 2Cancer Research UK Oxford Centre, Department of Oncology, University of Oxford, Oxford, UK
  3. 3Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
  4. 4MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
  5. 5School of Psychological Science, University of Bristol, Bristol, UK
  6. 6NIHR Bristol Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust and University of Bristol, Bristol, UK
  7. 7Division of Primary Care, University of Nottingham, Nottingham, UK
  1. Correspondence to Dr Ashley K Clift, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK; ashley.clift{at}phc.ox.ac.uk

Abstract

Background Conflicting evidence has emerged regarding the relevance of smoking on risk of COVID-19 and its severity.

Methods We undertook large-scale observational and Mendelian randomisation (MR) analyses using UK Biobank. Most recent smoking status was determined from primary care records (70.8%) and UK Biobank questionnaire data (29.2%). COVID-19 outcomes were derived from Public Health England SARS-CoV-2 testing data, hospital admissions data, and death certificates (until 18 August 2020). Logistic regression was used to estimate associations between smoking status and confirmed SARS-CoV-2 infection, COVID-19-related hospitalisation, and COVID-19-related death. Inverse variance-weighted MR analyses using established genetic instruments for smoking initiation and smoking heaviness were undertaken (reported per SD increase).

Results There were 421 469 eligible participants, 1649 confirmed infections, 968 COVID-19-related hospitalisations and 444 COVID-19-related deaths. Compared with never-smokers, current smokers had higher risks of hospitalisation (OR 1.80, 95% CI 1.26 to 2.29) and mortality (smoking 1–9/day: OR 2.14, 95% CI 0.87 to 5.24; 10–19/day: OR 5.91, 95% CI 3.66 to 9.54; 20+/day: OR 6.11, 95% CI 3.59 to 10.42). In MR analyses of 281 105 White British participants, genetically predicted propensity to initiate smoking was associated with higher risks of infection (OR 1.45, 95% CI 1.10 to 1.91) and hospitalisation (OR 1.60, 95% CI 1.13 to 2.27). Genetically predicted higher number of cigarettes smoked per day was associated with higher risks of all outcomes (infection OR 2.51, 95% CI 1.20 to 5.24; hospitalisation OR 5.08, 95% CI 2.04 to 12.66; and death OR 10.02, 95% CI 2.53 to 39.72).

Interpretation Congruent results from two analytical approaches support a causal effect of smoking on risk of severe COVID-19.

  • COVID-19
  • clinical epidemiology
  • tobacco control

Data availability statement

Data are available upon reasonable request. Data may be obtained from a third party and are not publicly available. The individual participant data from UK Biobank is available on application by bona fide researchers to the UK Biobank (via the Access Management Team). Individual level data is not permitted to be shared by the authorship team. A data dictionary defining ICD-10 codes used to define comorbidities, or Read/SNOMED codes to define smoking exposures from primary care data, can be made available on request to the corresponding author.

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Data availability statement

Data are available upon reasonable request. Data may be obtained from a third party and are not publicly available. The individual participant data from UK Biobank is available on application by bona fide researchers to the UK Biobank (via the Access Management Team). Individual level data is not permitted to be shared by the authorship team. A data dictionary defining ICD-10 codes used to define comorbidities, or Read/SNOMED codes to define smoking exposures from primary care data, can be made available on request to the corresponding author.

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Footnotes

  • AKC and AvE are joint first authors.

  • JH-C and JCH are joint senior authors.

  • Twitter @AshClift

  • Contributors AKC: conceptualisation, study design, data analysis, writing first draft of manuscript. AvE: conceptualisation, study design, data analysis, writing first draft of manuscript. PST: conceptualisation, study design, data analysis, revision of manuscript. HMS: conceptualisation, study design, revision of manuscript. NL: conceptualisation, study design, revision of manuscript. CACC: study design, advice on data analysis, interpretation, revision of manuscript. MRM: conceptualisation, study design, revision of manuscript. PA: conceptualisation, study design, revision of manuscript. JH-C: conceptualisation, study design, revision of manuscript. JCH: conceptualisation, study design, data analysis, revision of manuscript. AKC, PST and JH-C have verified the underlying observational data, and AvE and JCH have verified the underlying genetic data.

  • Funding AKC is supported by a Clinical Research Training Fellowship from Cancer Research UK (DCS-CRUK-CRTF20-AC). HMS and MRM work in a research unit funded by the UK Medical Research Council (MC_UU_00011/7). HMS is also supported by the European Research Council (grant ref: 758813 MHINT). This work was supported by the National Institute for Health Research (NIHR) Biomedical Research Centre at the University Hospitals Bristol National Health Service Foundation Trust. PA is an NIHR senior investigator and is funded by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC) Obesity, Diet and Lifestyle Theme and NIHR Oxford and Thames Valley Applied Research Collaboration. JHC has received grants from the National Institute for Health Research Biomedical Research Centre, Oxford, John Fell Oxford University Press Research Fund, Cancer Research UK (grant no: C5255/A18085) through the Cancer Research UK Oxford Centre, and the Oxford Wellcome Institutional Strategic Support Fund (204826/Z/16/Z). JCH is supported by a British Heart Foundation Fellowship (FS/14/55/30806), and acknowledges support from the Oxford Biomedical Research Centre, BHF Oxford Centre for Research Excellence, and Nuffield Department of Population Health. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health and Social Care. We are grateful to the participants of the UK Biobank as well as all the research staff who worked on the data collection and synthesis. This research has been conducted under UK Biobank application numbers 14568 and 40628.

  • Competing interests PST reports personal fees from AstraZeneca, and personal fees from Duke-NUS, outside the submitted work. CACC reports receiving personal fees from ClinRisk, outside this work. MRM reports grants from Pfizer and Rusan, outside the submitted work. JHC is an unpaid director of QResearch, a not-for-profit organisation which is a partnership between the University of Oxford and EMIS Health. JHC is a founder and shareholder of ClinRisk Ltd and was its medical director until 31 May 2019; ClinRisk produces open and closed source software to implement clinical risk algorithms (outside this work) into clinical computer systems. AvE and JCH work at the Clinical Trial Service Unit and Epidemiological Studies Unit, which receives research grants from industry that are governed by University of Oxford contracts that protect its independence, and has a staff policy of not taking personal payments from industry; further details can be found at https://www.ndph.ox.ac.uk/files/about/ndph-independence-of-research-policy-jun-20.pdf. AKC, HMS, NL and PA have no conflicts to disclose.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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