Guidance

Developing smoking-attributable fractions (SAFs) for COVID-19

Published 7 July 2026

Applies to England

Introduction

The Smoking profile, produced by the Office for Health Improvement and Disparities (OHID), provides data on the extent of smoking, smoking-related harm, and measures being taken to reduce smoking at a national and local level.

It includes indicators of smoking-attributable mortality and hospital admissions which, until the July 2026 release of the profile, had not been updated since 2019. This is because producing these indicators requires accurate estimates of the proportion of disease which can be attributed to smoking tobacco.

As COVID-19 has been a cause of mortality and hospital admission since 2020, it has been necessary to establish what proportion of these could be attributed to smoking. This report outlines the approach and methodology used to update indicators of smoking-attributable mortality and hospital admissions.

Background

There are well-documented links between smoking tobacco and various diseases, including:

• some cancers
• respiratory diseases
• diseases of the digestive system
• heart diseases

For these diseases, it is possible to estimate the increased risk of developing a disease (or increased risk of death from a disease) for people who smoke, or former smokers, compared with the risk for people who have never smoked. These are termed ‘relative risks’.

The relative risks used for indicators in the Smoking profile are reported in table 2.5 of the Royal College of Physicians (RCP) report Hiding in plain sight: treating tobacco dependency in the NHS (RCP, 2018). These are based on a review of meta-analyses of the most up-to-date literature available at the time of publication.

For example, RCP reports a relative risk for lung cancer based on a 2016 meta-analysis of 34 studies which showed the risk of developing this cancer to be increased approximately 11-fold in current smokers relative to non-smokers. There was a 4-fold increase in risk for former smokers relative to non-smokers.

The same relative risks are used for indicators of both smoking-attributable mortality and hospital admissions. The estimated relative risks are combined with smoking prevalence information to create a smoking-attributable fraction (SAF). This is the proportion of a disease that can be attributed to smoking.

SAF = [current_p × (current_rr – 1) + ex_p × (ex_rr – 1)] ÷ [1 + current_p × (current_rr – 1) + ex_p × (ex_rr – 1)]

This is calculated using:

• current_p - the proportion of current smokers
• ex_p - the proportion of former smokers
• current_rr - the relative risk for current smokers
• ex_rr - the relative risk for former smokers

The estimated number of smoking-attributable deaths or admissions is then calculated by multiplying the observed number of deaths or admissions for each cause by the relevant SAF.

SAFs are estimated by age and sex at a local authority level. Regional rates are based on the sum of deaths and populations for all local authorities within a given region. England rates are based on the sum of regional data.

The RCP report was published before the pandemic and so does not include relative risks for COVID-19. This means that SAFs for COVID-19 could not be generated without new evidence. As COVID-19 primarily affects the respiratory system, smoking may increase the risk of severe disease. Smoking could therefore be a contributing factor in some deaths and hospital admissions from COVID-19. Estimates of smoking-attributable harm produced without a COVID-19 SAF could, since 2020, underestimate smoking-attributable mortality and hospital admissions.

Additionally, some people who were admitted to hospital, or died with COVID-19, may still have been admitted or died due to another illness if they had not contracted COVID-19. For example, the death of someone who died of COVID-19 and who also had terminal lung cancer could have been registered with COVID-19 as the underlying cause rather than lung cancer. Therefore, without an accurate SAF for COVID-19, these deaths would be excluded from any estimate of smoking-related mortality as analysis is based solely on the underlying cause of death. Similarly, indicators of hospital admissions are based solely on the primary diagnosis for the admission.

This publication outlines the methodology used to create a relative risk for COVID-19, which then allows the calculation of SAFs. With these, rates of smoking-attributable mortality and hospital admission, which include COVID-19 as an underlying cause of death or primary diagnosis for admission, can be calculated.

Estimates of smoking-attributable harm for 2020 and 2021, the peak years of the COVID-19 pandemic, have not been produced as they are likely to be unreliable. There were just over 130,000 deaths in England with COVID-19 as an underlying cause in those 2 years and risk of death was influenced by multiple factors, including age, sex, geography, deprivation, ethnicity, occupation and care home residence (Public Health England, 2020). Risk also changed over time as an increasing proportion of the population was vaccinated.

Methods

A rapid literature review was carried out to find a source for relative risks of death from COVID-19 among smokers. Relative risks were sought for current and former smokers as this was consistent with the current OHID methodology. The review concentrated on mortality from COVID-19 as an outcome, and the same relative risks have then been used for indicators of hospital admission.

The following describes the search strategy used to identify relative risks for COVID-19.

Databases

The following databases were included in the literature review:

• Ovid MEDLINE(R)
• Embase
• Web of Science
• Scopus

Search terms

The detail of the search terms used can be found in the appendix (below).

Inclusion criteria

Full texts were screened against the following criteria. Only results from studies meeting all criteria were considered for use:

• large sample size covering general population
• primary research study (such as a cohort study)
• published, peer-reviewed journal article
• follow-up period must be after 2020 and for a minimum of 9 months
• smoking included as exposure or covariate
• mortality included as an outcome
• based on a UK sample

As relative risks were required for reporting in the post-pandemic period, evidence was sought which was not based solely on deaths during the peak of the COVID-19 pandemic. The sample was limited to the UK because countries differed in their pandemic responses and mortality rates. This could influence the relative risks.

Results of the rapid literature review

An initial literature search found 346 papers which were potentially relevant. An additional paper was found by a hand search (a manual rather than a digital search). Titles and abstracts of all 347 papers were screened by 2 reviewers independently to assess evidence of usable results. Any inconsistencies in the outcomes were resolved through discussion. Following this process, 20 were found to have potentially relevant findings. The completion of full text screening of these 20 papers excluded 17 of them (table 1).

The review was limited to papers where mortality was included as an outcome. It was considered a more definitive, consistently recorded outcome than hospital admission, and hospitalisation may have reflected admission practices during the pandemic. Papers which only concentrated on hospitalisation were included in the initial search strategy but were not included for title and abstract screening. Relative risks from papers which concentrated only on hospitalisation would have been considered if no appropriate relative risks for mortality could be identified.

Table 1: list of papers for full text screen and reasons for exclusion

Authors	Year	Reason excluded
Agrawal U and others	2021	Short follow-up (December 2020 to April 2021)
Ahmadi MN and others	2021	Short follow-up (start of cohort to June 2020)
Bhaskaran K and others	2021a	Short follow-up (February to November 2020)
Bhaskaran K and others	2021b	No reported smoking estimates
Bhaskaran K and others	2022	Short follow-up (February to June 2020)
Chudasama YV and others	2021	Short follow-up (March to July 2020)
Clift A K and others	2022	Short follow-up (January to August 2020)
Cooksey R and others	2022	Conference abstract - insufficient detail to assess methodology or did not give detailed estimates
Gao M and others	2022	Short follow-up (January to April 2020)
Newman CB	2021	No reported smoking estimates
Topless RK and others	2021	Conference abstract - insufficient detail to assess methodology or did not give detailed estimates
Topless R and others	2022a	Short follow-up (August 2020)
Wang L and others	2021	Unclear methods and R package
Wang Y and others	2024	Starts with COVID-19 positive patients
Wong A and others	2022	Conference abstract - insufficient detail to assess methodology or did not give detailed estimates
Zhao L and others	2023	No relevant smoking data
Zheng J and others	2024	Conference abstract - insufficient detail to assess methodology or did not give detailed estimates

In addition to the papers identified in the literature review (table 2), a further relevant paper was identified through the peer review process for this report. This was Simons (2021). This paper used a rapid review of observational and experimental studies. Its aim was to estimate the association of smoking status with rates of infection, hospitalisation, disease severity in hospitalised patients and mortality from COVID-19. It concluded that, when compared with people who had never smoked, people who currently smoked were at:

increased risk of greater in-hospital disease severity, while former smokers appear to be at increased risk of hospitalisation, greater in-hospital disease severity and mortality from COVID-19.

While Simons (2021) provided valuable evidence during the pandemic, it has not been updated since August 2021. At that point it found that evidence of increased mortality from COVID-19 for current smokers was “inconclusive and favoured there being no important association.” Its findings were therefore not considered suitable for use for ongoing monitoring of smoking-attributable harm. 

Following the full text screening, 3 papers were identified which could potentially provide suitable relative risks. These papers differed in the periods for which mortality data was included:

• Nab (2023) - deaths up to 3 August 2022
• Topless (2022b) - deaths up to 12 November 2021
• Topless (2022c) - deaths up to 23 March 2021

The relative risk of death from COVID-19 reported by Topless (2022c) for people who smoke was 1.27, which was similar to Nab (2023) for the first 2 waves of the pandemic (table 2). Topless (2022b) reported a relative risk of 0.77 for people who had never smoked compared with current smokers.

When looking at the 3 papers, only Nab (2023) had results for the later period of the pandemic (broken down for waves 1 to 5) until August 2022. Evidence for these later periods is particularly important to establish ongoing risks and allow consistent reporting of smoking-attributable harm to continue beyond the period of the pandemic. In these later periods of the pandemic, the majority of the population had been vaccinated against COVID-19. They are therefore more representative of post-pandemic conditions for ongoing reporting. The Nab (2023) paper was also based on OpenSAFELY, which is a very large sample, even when compared with the UK Biobank, which was used for analysis in the Topless papers.

The relative risks from Nab (2023) were therefore selected for use.

Table 2: relative risk of death from COVID-19 for current and former smokers based on Nab (2023)

Pandemic wave	Value for current smokers	Value for former smokers
1. Wild type (23 March to 30 May 2020)	1.20 (lower confidence interval 1.12, upper confidence interval 1.28)	1.43 (lower confidence interval 1.38, upper confidence interval 1.48)
2. Alpha (B.1.1.7) (7 September 2020 to 24 April 2021)	1.41 (lower confidence interval 1.35, upper confidence interval 1.48)	1.46 (lower confidence interval 1.43, upper confidence interval 1.50)
3. Delta (28 May to 14 December 2021)	1.32 (lower confidence interval 1.21, upper confidence interval 1.45)	1.64 (lower confidence interval 1.55, upper confidence interval 1.73)
4. Omicron (15 December 2021 to 29 April 2022)	2.28 (lower confidence interval 2.10, upper confidence interval 2.48)	1.44 (lower confidence interval 1.37, upper confidence interval 1.52)
5. Omicron (24 June to 3 August 2022)	2.80 (lower confidence interval 2.34, upper confidence interval 3.35)	1.37 (lower confidence interval 1.22, upper confidence interval 1.55)

Smoking-related COVID-19 relative risk

Following the literature review, relative risks for smoking-related COVID-19 taken from Nab (2023) have been added to the current list of relative risks in the methodology to calculate SAFs. The study reported hazard ratios, which can reasonably be applied as approximate relative risks, as the paper’s authors did. To provide the most robust estimates for ongoing monitoring, figures from the later waves were considered most relevant. However, the hazard ratios for wave 5 were based on a short follow-up period, the numbers of deaths were small and confidence intervals consequently wide. Since the confidence intervals for waves 4 and 5 overlapped, it was felt appropriate to combine the hazard ratios for both waves, using inverse variance weighting. The variances of the log hazard ratios were estimated from the published confidence intervals and the weights calculated as:

1 ÷ ((u – l) ÷ (2 × z))²

‘l’ and ‘u’ are the lower and upper 95% confidence limits for the log hazard ratios respectively, and ‘z’ is the critical value of the normal distribution (1.96 for 95% confidence intervals).

Using this methodology results in relative risks of:

• current smoker = 2.37
• former smoker = 1.43

That is to say, based on data from 21 December 2021 to 3 August 2022, the risk of death from COVID-19 was estimated to be 2.37 times higher for someone who currently smokes, compared with someone who has never smoked. For a former smoker, the risk was 1.43 times higher. 

Smoking-attributable mortality results

Effect on statistics

Table 3 compares the smoking-attributable directly age-standardised mortality rates (DSRs) using the previously published methodology without the COVID-19 SAFs and the updated methodology with the COVID-19 SAFs. Rates have not been calculated for 2020 or 2021 using the updated methodology, for the reasons already noted.

Including the COVID-19 SAFs results in a smoking-attributable mortality rate 4.7% higher in 2022 to 2024 than the rate without the COVID-19 SAF.

Table 3: smoking-attributable mortality for people age 35 and over, DSR per 100,000 population, England, 2022 to 2024

	Without COVID-19 SAFs	With COVID-19 SAFs
Number of smoking-attributable deaths	176,021	184,316
DSR (per 100,000 population)	172.6	180.7
Lower confidence interval (95%)	171.8	179.9
Upper confidence interval (95%)	173.4	181.6

Source: calculated by OHID, based on Office for National Statistics (ONS) death registration data and ONS mid-year population estimates.

Including the COVID-19 SAFs results in a higher smoking-attributable admission rate of 4.5% in the financial year ending March 2023 (table 4).

Table 4: smoking-attributable hospital admissions for people aged 35 and over, DSR per 100,000 population, England, financial year ending March 2023

	Without COVID-19 SAFs	With COVID-19 SAFs
Number of smoking-attributable admissions	394,540	412,171
DSR (per 100,000 population)	1,189.0	1,241.9
Lower confidence interval (95%)	1,185.2	1,238.1
Upper confidence interval (95%)	1,192.7	1,245.7

Source: calculated by OHID, based on NHS England hospital episode statistics (HES) data and ONS mid-year population estimates.

Limitations

A limitation for using the COVID-19 relative risks from the Nab (2023) paper is that they are available only for all ages. The same relative risks have therefore been applied to all age groups in the calculation of rates of alcohol-attributable harm. However, we know that severe harm from COVID-19 is strongly associated with age, with the highest mortality and hospital admission rates in the oldest age groups. This is, however, a limitation which applies to many of the other cause groups in the existing methodology. For many of them, deaths and hospital admissions increase with age but the only cause group in the RCP (2018) paper which is stratified by age is ischaemic heart disease. For that, relative risks are provided by both sex and age (35 to 64 and 65 and over).

More broadly, other limitations are likely to exist around the complex interactions between:

• smoking status
• differences in COVID-19 exposure for different population subgroups
• vaccination status
• different characteristics of COVID-19 variants

These are not accounted for in the results.

Conclusion

Establishing appropriate relative risks of mortality from COVID-19 for people who smoke or who used to smoke is complex because of the multiple interactions noted in the limitations section. However, if COVID-19 was not taken into account, statistics of smoking-attributable harm would be underestimates. For example, although deaths from COVID-19 have fallen substantially since 2020 and 2021, there were still over 38,000 deaths in England in 2022 to 2024 which had COVID-19 as an underlying cause. 

Despite limitations in currently available evidence, the relative risks identified in the Nab (2023) paper provide reasonable approximations for the risk of an adult smoker or former smoker dying from COVID-19 compared with someone who has never smoked.

These have been used to calculate smoking-attributable fractions for COVID-19 and to update estimates of smoking-attributable deaths for the period 2022 to 2024, and smoking-attributable hospital admissions for the financial year ending March 2023.

Acknowledgements

We thank the topic experts who kindly gave their time to review and offer comments on a draft version of this document.

Appendix: database search strategy

This appendix shows the search terms used to identify relevant studies in the 4 academic databases:

• Ovid MEDLINE(R)
• Embase
• Web of Science
• Scopus

For Ovid MEDLINE(R), Embase and Web of Science, each table below shows:

• the search terms used
• the order in which the searches were run
• the number of results each search returned

Individual searches were then combined using Boolean operators (AND, OR, NOT) to progressively narrow or broaden results.

For Scopus, the full search strategy is entered as a single combined string.

The following symbols and conventions are used in the search strings:

• * (asterisk) - a truncation or wildcard symbol that retrieves all words beginning with the preceding characters. For example, smok* retrieves ‘smoker’, ‘smoking’, ‘smoked’, and so on
• ? (question mark) - a wildcard that represents zero or one character. For example, hospitali?ation retrieves both ‘hospitalisation’ and ‘hospitalization’
• adj2 - finds 2 terms with up to one word in between them, in any order. For example, hospital* adj2 admi* retrieves phrases such as ‘hospital admission’ or ‘admitted to hospital’.
• NEAR/2 - finds 2 terms with up to 2 words separating them, in any order - used in Web of Science
• W/2 - finds 2 terms within 2 words of each other in any order - used in Scopus
• “quoted phrases” - in Ovid, Embase and Web of Science, quotation marks are used for exact phrases. In Scopus, quotation marks indicate a loose or approximate phrase
• .tw,kf. - limits the search to title, abstract and keyword fields (used in Ovid databases)
• .ti,ab,in. - limits the search to title, abstract and institution name fields (used in Ovid databases)
• .ti,ab,jw,in. - limits the search to title, abstract, journal name and institution name fields (used in Ovid databases)
• TS= - searches the ‘topic’ field in Web of Science, which covers title, abstract and keywords
• TITLE-ABS-KEY - searches title, abstract and keyword fields in Scopus
• exp - ‘explode’, meaning the search includes the selected subject heading and all narrower related terms within the database’s controlled vocabulary
• or/8-11 - combines search results from lines 8 to 11 using OR, retrieving records that appear in any of those searches

Search terms and results, Ovid MEDLINE(R) ALL database, 1946 to 21 January 2025

Note: ‘ALL’ indicates the complete database, including in-process records.

Search number	Search term	Results
1	Ex-Smokers/ or Smokers/ or Non-Smokers/	5,308
2	exp Smoking/	165,704
3	(smok* or tobacco or cigar*).tw,kf.	434,230
4	1 or 2 or 3	466,802
5	exp Coronavirus/ or exp Coronavirus Infections/	309,708
6	(coronavir* or 2019nCoV* or 19nCoV* or “2019 novel*” or Ncov* or “n-cov” or “SARS-CoV-2*” or “SARSCoV-2*” or SARSCoV2* or “SARS-CoV2*” or “severe acute respiratory syndrome*” or COVID*).tw,kf.	468,251
7	5 or 6	482,787
8	hospitalization/ or patient admission/ or patient readmission/	189,890
9	(hospitali?* or (hospital* adj2 admi*)).tw,kf.	457,129
10	exp Mortality/	436,715
11	(mortality or death*).tw,kf.	1,983,446
12	or/8-11	2,575,998
13	7 and 12	97,250
14	exp United Kingdom/	401,807
15	(national health service* or nhs*).ti,ab,in.	307,173
16	(english not ((published or publication* or translat* or written or language* or speak* or literature or citation*) adj5 english)).ti,ab.	138,716
17	(gb or “g.b.” or britain* or (british* not “british columbia”) or uk or “u.k.” or united kingdom* or (england* not “new england”) or northern ireland* or northern irish* or scotland* or scottish* or ((wales or “south wales”) not “new south wales”) or welsh*).ti,ab,jw,in.	2,632,920
18	(bath or “bath’s” or ((birmingham not alabama) or (“birmingham’s” not alabama) or bradford or “bradford’s” or brighton or “brighton’s” or bristol or “bristol’s” or carlisle* or “carlisle’s” or (cambridge not (massachusetts* or boston* or harvard)) or (“cambridge’s” not (massachusetts or boston* or harvard)) or (canterbury not zealand) or (“canterbury’s” not zealand) or chelmsford or “chelmsford’s” or chester or “chester’s” or chichester or “chichester’s” or coventry or “coventry’s” or derby or “derby’s” or (durham not (carolina or nc)) or (“durham’s” not (carolina* or nc)) or ely or “ely’s” or exeter or “exeter’s” or gloucester or “gloucester’s” or hereford or “hereford’s” or hull or “hull’s” or lancaster or “lancaster’s” or leeds* or leicester or “leicester’s” or (lincoln not nebraska) or (“lincoln’s” not nebraska) or (liverpool not (new south wales* or nsw)) or (“liverpool’s” not (new south wales* or nsw)) or ((london not (ontario* or ont or toronto)) or (“london’s” not (ontario or ont or toronto)) or manchester or “manchester’s” or (newcastle not (new south wales or nsw)) or (“newcastle’s” not (new south wales* or nsw)) or norwich or “norwich’s” or nottingham or “nottingham’s” or oxford or “oxford’s” or peterborough or “peterborough’s” or plymouth or “plymouth’s” or portsmouth or “portsmouth’s” or preston or “preston’s” or ripon or “ripon’s” or salford or “salford’s” or salisbury or “salisbury’s” or sheffield or “sheffield’s” or southampton or “southampton’s” or st albans or stoke or “stoke’s” or sunderland or “sunderland’s” or truro or “truro’s” or wakefield or “wakefield’s” or wells or westminster or “westminster’s” or winchester or “winchester’s” or wolverhampton or “wolverhampton’s” or (worcester not (massachusetts* or boston* or harvard)) or (“worcester’s” not (massachusetts or boston* or harvard)) or (york not (“new york*” or ny or ontario or ont or toronto)) or (“york’s” not (“new york*” or ny or ontario or ont or toronto*))))).ti,ab,in.	1,910,504
19	(bangor or “bangor’s” or cardiff or “cardiff’s” or newport or “newport’s” or st asaph or “st asaph’s” or st davids or swansea or “swansea’s”).ti,ab,in.	77,577
20	(aberdeen or “aberdeen’s” or dundee or “dundee’s” or edinburgh or “edinburgh’s” or glasgow or “glasgow’s” or inverness or (perth not australia) or (“perth’s” not australia) or stirling or “stirling’s”).ti,ab,in.	281,017
21	(armagh or “armagh’s” or belfast or “belfast’s” or lisburn or “lisburn’s” or londonderry or “londonderry’s” or derry or “derry’s” or newry or “newry’s”).ti,ab,in.	37,633
22	or/14-21	3,378,739
23	(exp africa/ or exp americas/ or exp antarctic regions/ or exp arctic regions/ or exp asia/ or exp australia/ or exp oceania/) not (exp United Kingdom/ or europe/)	3,520,973
24	22 not 23	3,165,750
25	cohort.tw,kf.	923,075
26	exp Cohort Studies/	2,698,863
27	25 or 26	3,088,325
28	UK Biobank/	619
29	“uk biobank”.tw,kf.	10,414
30	opensafely.tw,kf.	101
31	(cprd or “clinical practice research datalink”).tw,kf.	2,493
32	or/28-31	13,000
33	4 and 13 and 24 and 27	117
34	4 and 13 and 32	44
35	33 or 34	131

Search terms and results, Embase database, 1974 to 21 January 2025

Search number	Search term	Results
1	exp non-smoker/	25,171
2	exp Smoking/	506,374
3	(smok* or tobacco or cigar*).tw,kf.	624,512
4	1 or 2 or 3	754,222
5	exp coronavirus disease 2019/	436,048
6	(coronavir* or 2019nCoV* or 19nCoV* or “2019 novel*” or Ncov* or “n-cov” or “SARS-CoV-2*” or “SARSCoV-2*” or SARSCoV2* or “SARS-CoV2*” or “severe acute respiratory syndrome*” or COVID*).tw,kf.	533,179
7	5 or 6	569,818
8	hospitalization/ or hospital admission/	854,377
9	(hospitali?* or (hospital* adj2 admi*)).tw,kf.	729,128
10	exp death/	2,165,018
11	(mortality or death*).tw,kf.	2,847,163
12	or/8-11	4,306,079
13	7 and 12	167,307
14	exp United Kingdom/	482,864
15	(national health service* or nhs*).ti,ab,in.	449,788
16	(english not ((published or publication* or translat* or written or language* or speak* or literature or citation*) adj5 english)).ti,ab.	67,825
17	(gb or “g.b.” or britain* or (british* not “british columbia”) or uk or “u.k.” or united kingdom* or (england* not “new england”) or northern ireland* or northern irish* or scotland* or scottish* or ((wales or “south wales”) not “new south wales”) or welsh*).ti,ab,jw,in.	3,830,358
18	(bath or “bath’s” or ((birmingham not alabama) or (“birmingham’s” not alabama) or bradford or “bradford’s” or brighton or “brighton’s” or bristol or “bristol’s” or carlisle* or “carlisle’s” or (cambridge not (massachusetts* or boston* or harvard)) or (“cambridge’s” not (massachusetts or boston* or harvard)) or (canterbury not zealand) or (“canterbury’s” not zealand) or chelmsford or “chelmsford’s” or chester or “chester’s” or chichester or “chichester’s” or coventry or “coventry’s” or derby or “derby’s” or (durham not (carolina or nc)) or (“durham’s” not (carolina* or nc)) or ely or “ely’s” or exeter or “exeter’s” or gloucester or “gloucester’s” or hereford or “hereford’s” or hull or “hull’s” or lancaster or “lancaster’s” or leeds* or leicester or “leicester’s” or (lincoln not nebraska) or (“lincoln’s” not nebraska) or (liverpool not (new south wales* or nsw)) or (“liverpool’s” not (new south wales* or nsw)) or ((london not (ontario* or ont or toronto)) or (“london’s” not (ontario or ont or toronto)) or manchester or “manchester’s” or (newcastle not (new south wales or nsw)) or (“newcastle’s” not (new south wales* or nsw)) or norwich or “norwich’s” or nottingham or “nottingham’s” or oxford or “oxford’s” or peterborough or “peterborough’s” or plymouth or “plymouth’s” or portsmouth or “portsmouth’s” or preston or “preston’s” or ripon or “ripon’s” or salford or “salford’s” or salisbury or “salisbury’s” or sheffield or “sheffield’s” or southampton or “southampton’s” or st albans or stoke or “stoke’s” or sunderland or “sunderland’s” or truro or “truro’s” or wakefield or “wakefield’s” or wells or westminster or “westminster’s” or winchester or “winchester’s” or wolverhampton or “wolverhampton’s” or (worcester not (massachusetts* or boston* or harvard)) or (“worcester’s” not (massachusetts or boston* or harvard)) or (york not (“new york*” or ny or ontario or ont or toronto)) or (“york’s” not (“new york*” or ny or ontario or ont or toronto*))))).ti,ab,in.	3,058,454
19	(bangor or “bangor’s” or cardiff or “cardiff’s” or newport or “newport’s” or st asaph or “st asaph’s” or st davids or swansea or “swansea’s”).ti,ab,in.	126,333
20	(aberdeen or “aberdeen’s” or dundee or “dundee’s” or edinburgh or “edinburgh’s” or glasgow or “glasgow’s” or inverness or (perth not australia) or (“perth’s” not australia) or stirling or “stirling’s”).ti,ab,in.	421,319
21	(armagh or “armagh’s” or belfast or “belfast’s” or lisburn or “lisburn’s” or londonderry or “londonderry’s” or derry or “derry’s” or newry or “newry’s”).ti,ab,in.	59,340
22	or/14-21	4,701,053
23	(exp africa/ or exp Western Hemisphere/ or exp “arctic and antarctic”/ or exp asia/ or exp “Australia and New Zealand”/ or exp Pacific islands/) not (exp United Kingdom/ or europe/)	3,911,727
24	22 not 23	4,411,902
25	cohort.tw,kf.	1,534,679
26	cohort analysis/	1,264,934
27	25 or 26	1,866,357
28	uk biobank/	2,789
29	“uk biobank”.tw,kf.	14,944
30	opensafely.tw,kf.	156
31	(cprd or “clinical practice research datalink”).tw,kf.	4,722
32	or/28-31	19,880
33	4 and 13 and 24 and 27	249
34	4 and 13 and 32	70
35	33 or 34	270
36	limit 35 to “remove medline records”	120

Search terms and results, Web of Science database

Search number	Search term	Results
1	TS=((smok* or tobacco or cigar*))	1,769,243
2	TS=((coronavir* or 2019nCoV* or 19nCoV* or “2019 novel*” or Ncov* or “n-cov” or “SARS-CoV-2*” or “SARSCoV-2*” or SARSCoV2* or “SARS-CoV2*” or “severe acute respiratory syndrome*” or COVID*))	925,957
3	(((((TS=(mortality)) OR TS=(death)) OR TS=(hospitalis)) OR TS=(hospitaliz)) OR TS=(hospitalNEAR/2 admi*)	5,226,594
4	TS=(cohort)	1,672,837
5	#1 AND #2 AND #3 AND #4	854
6	#1 AND #2 AND #3 AND #4 and ENGLAND or UK or SCOTLAND or WALES or NORTH IRELAND (Countries/Regions)	100
7	(((TS=(“uk biobank”)) OR TS=(opensafely)) OR TS=(cprd)) OR TS=(“clinical practice research datalink”)	20,245
8	#1 AND #2 AND #3 AND #7	67

Search terms and results, Scopus database

Unlike the other databases searched, Scopus does not use a numbered, step-by-step search interface. Instead, the full search strategy is entered as a single combined string.

Search term:

( TITLE-ABS-KEY ( smok* OR tobacco OR cigar* ) ) AND ( TITLE-ABS-KEY ( coronavir* OR 2019ncov* OR 19ncov* OR “2019 novel*” OR ncov* OR “n-cov” OR “SARS-CoV-2*” OR “SARSCoV-2*” OR sarscov2* OR “SARS-CoV2*” OR “severe acute respiratory syndrome*” OR covid* ) ) AND ( TITLE-ABS-KEY ( hospital* W/2 admi* ) OR TITLE-ABS-KEY ( hospitalis* ) OR TITLE-ABS-KEY ( hospitaliz* ) OR TITLE-ABS-KEY ( mortality ) OR TITLE-ABS-KEY ( death* ) ) AND ( TITLE-ABS-KEY ( “uk biobank” ) OR TITLE-ABS-KEY ( opensafely ) OR TITLE-ABS-KEY ( cprd ) OR TITLE-ABS-KEY ( “clinical practice research datalink” ) )

OR

( TITLE-ABS-KEY ( smok* OR tobacco OR cigar* ) ) AND ( TITLE-ABS-KEY ( coronavir* OR 2019ncov* OR 19ncov* OR “2019 novel*” OR ncov* OR “n-cov” OR “SARS-CoV-2*” OR “SARSCoV-2*” OR sarscov2* OR “SARS-CoV2*” OR “severe acute respiratory syndrome*” OR covid* ) ) AND ( TITLE-ABS-KEY ( hospital* W/2 admi* ) OR TITLE-ABS-KEY ( hospitalis* ) OR TITLE-ABS-KEY ( hospitaliz* ) OR TITLE-ABS-KEY ( mortality ) OR TITLE-ABS-KEY ( death* ) ) AND ( TITLE-ABS-KEY ( cohort ) ) AND ( LIMIT-TO ( AFFILCOUNTRY , “United Kingdom” ) )

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