Document text
Research paper
Factors associated with deaths due to COVID-19 versus other causes:
population-based cohort analysis of UK primary care data and linkednational death registrations within the OpenSAFELY platform
Krishnan Bhaskarana,1,*, Sebastian Baconb,1, Stephen JW Evansa,1, Chris J Batesc,
Christopher T Rentscha, Brian MacKennab, Laurie Tomlinsona, Alex J Walkerb, Anna Schultzea,
Caroline E Mortonb, Daniel Grinta, Amir Mehrkarb, Rosalind M Eggoa, Peter Inglesbyb,
Ian J Douglasa, Helen I McDonalda, Jonathan Cockburnc, Elizabeth J Williamsona, David Evansb,
Helen J Curtisb, William J Hulmeb, John Parryc, Frank Hesterc, Sam Harperc,
David Spiegelhalterd, Liam Smeetha,2, Ben Goldacreb,2
aFaculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine
bThe DataLab, Nuf field Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
cThe Phoenix Partnership, TPP House, Horsforth, Leeds, UK
dWinton Centre for Risk and Evidence Communication, Statistical Laboratory Centre for Mathematical Sciences, Cambridge, UK
ARTICLE INFO
Article History:
Received 4 March 2021Revised 26 March 2021Accepted 1 April 2021Available online 8 May 2021ABSTRACT
Background: Mortality from COVID-19 shows a strong relationship with age and pre-existing medical condi-
tions, as does mortality from other causes. We aimed to investigate how speci fic factors are differentially
associated with COVID-19 mortality as compared to mortality from causes other than COVID-19.
Methods: Working on behalf of NHS England, we carried out a cohort study within the OpenSAFELY platform.
Primary care data from England were linked to national death registrations. We included all adults (aged/C2118 years) in the database on 1
stFebruary 2020 and with >1 year of continuous prior registration; the cut-
off date for deaths was 9thNovember 2020. Associations between individual-level characteristics and
COVID-19 and non-COVID deaths, classifi ed according to the presence of a COVID-19 code as the underlying
cause of death on the death certi ficate, were estimated by fitting age- and sex-adjusted logistic models for
these two outcomes.Findings: 17,456,515 individuals were included. 17,063 died from COVID-19 and 134,316 from other
causes. Most factors associated with COVID-19 death were similarly associated with non-COVID death,
but the magnitudes of association differed. Older age was more strongly associated with COVID-19
death than non-COVID death (e.g. ORs 40.7 [95% CI 37.7-43.8] and 29.6 [28.9-30.3] respectively for/C2180 vs 50-59 years), as was male sex, deprivation, obesity, and some comorbidities. Smoking, history
of cancer and chronic liver disease had stronger associations with non-COVID than COVID-19 death. All
non-white ethnic groups had higher odds than white of COVID-19 death (OR for Black: 2.20 [1.96-
2.47], South Asian: 2.33 [2.16-2.52]), but lower odds than white of non-COVID death (Black: 0.88[0.83-0.94], South Asian: 0.78 [0.75-0.81]).
Interpretation: Similar associations of most individual-level factors with COVID-19 and non-COVID death sug-
gest that COVID-19 largely multiplies existing risks faced by patients, with some notable exceptions. Identify-
ing the unique factors contributing to the excess COVID-19 mortality risk among non-white groups is a
priority to inform efforts to reduce deaths from COVID-19.
Funding: Wellcome, Royal Society, National Institute for Health Research, National Institute for Health
Research Oxford Biomedical Research Centre, UK Medical Research Council, Health Data Research UK.
© 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/by/4.0/)Keywords:
COVID-19
MortalityEpidemiology
* Corresponding author.
E-mail address: [email protected] (K. Bhaskaran).
1Contributed equally
2Joint senior authors
https://doi.org/10.1016/j.lanepe.2021.100109
2666-7762/© 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)The Lancet Regional Health - Europe 6 (2021) 100109
Contents lists available at ScienceDirect
The Lancet Regional Health - Europe
journal homepage: www.elsevier.com/lanepe
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Research in context
Evidence before this study
A range of demographic and clinical risk factors for COVID 19
death were established in the first few months of the pandemic
but little evidence is available on whether such factors havesimilar associations with COVID 19 death and non COVID 19
death.
Added value of this study
We used data from a large database linking richly detailed pri
mary care records and death registrations for 40% of the popu
lation of England. We conducted analyses comparing factors
associated with COVID 19 and non COVID deaths to generate
unique insights into the extent to which risk factors for COVID
19 death mirror broader risk factors for death. We carried out arange of sensitivity analyses to ensure that our findings were
robust.
Implications of all the available evidence
COVID 19 appears to largely act as if multiplying existing risks
faced by patients. Public health decisions requiring prioritisa
tion of vulnerable subgroups can therefore be informed by our
knowledge of pre pandemic mortality risks based on estab
lished risk factors. However there were some key exceptions;
notably, higher risks of COVID 19 death for non white ethnic
groups were in contrast to lower risks of non COVID deaths in
these groups. Improved understanding of the unique drivers of
COVID 19 mortality in non white groups should be a research
priority.
1. Introduction
Severe acute respiratory syndrome coronavirus 2 (SARS CoV 2)
has infected tens of millions of people worldwide, causing substantialmortality [1]. Numerous factors have emerged as being associated
with a higher risk of severe outcomes and death from COVID 19 [2].Mortality appears to rise exponentially with increasing age. Male sex,
obesity, socioeconomic deprivation, and a number of comorbidities
are also associated with higher risk. [3,4] Substantial variation in
mortality by ethnicity has also been observed in several studies, with
evidence from both the UK and US suggesting worse COVID related
outcomes among minority ethnic groups, compared with the major
ity White populations. [5 7] However, little evidence is available on
how the factors associated with COVID 19 mortality compare with
the factors associated with mortality from other causes, and hence
the extent to which a person 's risk of dying from COVID 19 is likely
to be governed by their broader mortality risk. We know that increasing age is the major risk factor for all cause mortality. It is possiblethat COVID 19 simply multiplies everyone 's risk of death by a con
stant factor, or it could be that some factors have a different effect onCOVID 19 deaths speci fically. A better understanding of this would
help inform strategies to identify and protect those most at risk ofpoor outcomes during the pandemic.
A previous analysis of death registration data in England and
Wales showed exponential relationships between adult age and both
rates of COVID 19 death (between March and June 2020), and pre
pandemic rates of all cause mortality derived from life tables, with a
slightly steeper age mortality association for COVID 19 death [8].A
study using UK Biobank data found that both modi fiable and nonmodifi able risk factors for COVID 19 infection were somewhat stron
ger than for other infections, but did not assess severity of disease or
mortality [9]. A further study using UK Biobank data from before the
current pandemic examined how demographic characteristics and
non communicable disease comorbidities were associated with
deaths from infections versus other causes; the authors observed
broadly similar patterns of risk for the two outcomes, though the
magnitude of associations differed [10]. However, it is unclear to
what extent findings from pre pandemic infection related deaths
can be used to draw conclusions about COVID 19.
To our knowledge, no study to date has directly compared factors
associated with COVID 19 versus non COVID deaths in the same
cohort. We aimed to address this by conducting parallel analyses of
COVID 19 and non COVID death outcomes using population based
data from England within the OpenSAFELY platform.
2. Methods
2.1. Study design and
study population
Aretrospective cohort study was carried out within OpenSAFELY,
a new data analytics platform in England created to address urgent
COVID 19 related questions, which has been described previously
[4]. We used routinely collected electronic data from primary care
practices using TPP SystmOne software, covering 2816 practices and
approximately 40% of the population in England, linked to Of fice of
National Statistics (ONS) death registrations. We included all adults
(aged 18 years or over) alive and under follow up on 1stFebruary
2020, and with at least one year of continuous GP registration priorto this date, to ensure that baseline data could be adequately cap
tured. We excluded people with missing age, sex, or index of multiple
deprivation, since these are likely to indicate poor data quality. For a
secondary analysis of deaths prior to the pandemic, a second cohort
was extracted comprising all adults alive and under follow up on 1
st
February 2019 and with at least one year of GP registration prior tothat date (hereafter referred to as the “2019 cohort”). Finally, we
compared directly those that died due to COVID 19 and those thatdied from other causes to assess associations between individual
level factors and cause of death (analogous to a case control analy
sis).
2.2. Outcome and covariates
The outcomes were COVID 19 death, and deaths from causes
other than COVID 19 (hereafter “non COVID death ”). Cause of death
was assigned using the underlying cause of death field (main/primary
cause of death, coded in ICD 10) in the death registration. COVID 19
death was de fined as any death with the underlying cause coded as
U07.1 ( “COVID 19, virus identi fied”) or U07.2 ( “COVID 19, virus not
identi fied”) [11]. Non COVID deaths comprised all other deaths;
these were also further sub divided into categories covering themost common causes of death, namely cancer (ICD 10 chapter C),
cardiovascular disease (chapter I), respiratory (chapter J), dementia/
Alzheimer 's disease (F00 03 or G30), and other (all other ICD 10
codes). Two sensitivity analyses were done to check that our findings
were robust to the way COVID 19 deaths were de fined: (i) only using
the U07.1 ( “virus identi fied”) code which would likely have higher
speci ficity; (ii) counting a U07.1/U07.2 code anywhere on the death
certifi cate as a COVID 19 death, in case of variation in how underly
ing causes were assigned. For the secondary analysis of deaths priorto the pandemic, the outcome was all cause mortality; this was based
on a record for death in primary care, because ONS death registration
linkage for 2019 was not available.
Covariates considered in the analysis included health conditions
listed in UK guidance on higher risk groups; [12] other common con
ditions that may cause immunode ficiency inherently or through2 K. Bhaskaran et al. / The Lancet Regional Health - Europe 6 (2021) 100109
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medication, and other postulated risk factors for severe outcomes
among COVID 19 cases. We included age (grouped as 18 39, 40 49,
50 59, 60 69, 70 79 and /C2180 years for descriptive analysis), sex, eth
nicity (White, Mixed, South Asian, Black, Other, categories from theUK census), obesity (categorised as class I [body mass index 30
34.9kg/m
2], II [35 39.9kg/m2], III [/C2140kg/m2]), smoking status (never,
former, current), index of multiple deprivation quintile (derived fromthe patient 's postcode at lower super output area level). We also con
sidered the following comorbidities: diagnosed hypertension, chronicrespiratory diseases other than asthma, asthma (categorised as with
or without recent use of oral steroids), chronic heart disease, diabetes
(categorised according to the most recent glycated haemoglobin
(HbA1c) recorded in the 15 months prior to 1
stFebruary 2020), non
haematological and haematological cancer (both categorised byrecency of diagnosis, <1, 1 4.9, /C215 years), reduced kidney function
(categorised by estimated glomerular filtration rate derived from the
most recent serum creatinine measure (30 <60, 15 <30,<15 mL/
min/1.73m
2or a record of dialysis), chronic liver disease, stroke,
dementia, other neurological disease (motor neurone disease, myas
thenia gravis, multiple sclerosis, Parkinson 's disease, cerebral palsy,
quadriplegia or hemiplegia, and progressive cerebellar disease),organ transplant, asplenia (splenectomy or a spleen dysfunction,
including sickle cell disease), rheumatoid arthritis/lupus/psoriasis,
and other immunosuppressive conditions (permanent immunode fi
ciency ever diagnosed, or aplastic anaemia or temporary immunodeficiency recorded within the last year). The Sustainability and
Transformation Partnership (STP, an NHS administrative region) ofthe patient 's general practice was included as an additional adjust
ment for geographical variation in infection rates across the country.
Information on all clinical covariates was obtained by searching
TPP SystmOne records prior to 1
stFebruary 2020 (or prior to 1stFeb
ruary 2019, for the 2019 secondary analysis cohort) for speci fic coded
data, based on a subset of SNOMED CT mapped to Read version 3
codes. All codelists, along with detailed information on their compila
tion are available at https://codelists.opensafely.org for inspection
and re use by the wider research community.
2.3. Statistical analysis
Follow up time for mortality in the main cohort was from 1stFeb
ruary 2020 until 9thNovember 2020, which was the last date for
which mortality data were complete. Overall absolute probabilities of
COVID 19 death and non COVID death (by cause of death category)
over this period were calculated for each age group, and standardised
by sex; this was done by fitting a multinomial logistic regression
model with outcome levels of died (by cause of death category) versus alive at end of follow up, including covariates of age group and
sex, and using fitted covariate and constant terms to predict risks for
each outcome under a 50:50 male:female split [13]. Con fidence inter
vals were computed by taking the 5
thand 95thcentiles of estimates
from 1000 bootstrapped replications of this process.
To estimate differential associations between potential risk factors
and mortality from COVID or non COVID causes, binary logisticregression models were then fitted, with outcomes of (i) COVID 19
death and (ii) non COVID death (all non COVID causes combined).
Logistic regression was chosen because the use of linked national
mortality data meant that there was effectively a fixed follow up
period for outcome ascertainment; however a sensitivity analysiswas also done using time to event Cox regression. For each outcome,
separate models adjusted for age, sex, and STP were fitted for each
covariate, with age parametrised as a 4 knot restricted cubic spline inregression models and STP included as a fixed effect. An additional
model was fitted with age group sex, and STP only, to provide odds
ratios for age group (since model estimates for spline terms are dif fi
cult to interpret). For the secondary analysis using the 2019 cohort, asimilar set of age, sex and STP adjusted logistic models was fittedusing a follow up time period from 1
stFebruary to 9thNovember
2019, with the outcome of all cause mortality, which by de finition
represents non COVID mortality during this pre pandemic timeperiod.
In further secondary analyses, models for each outcome were also
fitted, adjusting for all covariates simultaneously to identify indepen
dent associations between covariates and outcomes. In a post hocanalysis driven by the striking pattern of results for ethnicity, further
age, sex, and STP adjusted logistic models were fitted to explore eth
nic differences in the odds of death from speci fic cause of death cate
gories. Separate secondary analyses were done for wave 1 and 2 of
the pandemic in England, based on the periods 1
stFebruary 31st
August and 1stSeptember 9thNovember respectively. Finally, to
directly estimate the comparative association between individual
level factors and COVID 19 versus non COVID deaths, we fitted age,
sex, and STP adjusted logistic models only including people whodied, with cause of death (COVID 19 versus non COVID) as the binary
outcome variable. In these analyses, odds ratios >1 indicate that a
variable has a more positive association with COVID 19 death thanwith non COVID death, and vice versa.
Multiple imputation (10 imputations) was used to account for
missing ethnicity, with the imputation model including all covariates
from the main model and an indicator for the outcome. Those with
missing body mass index were assumed to be non obese, and those
with missing smoking data were assumed to be never smokers; we
did not use multiple imputation for these variables as they are
expected to be missing not at random in UK primary care [14]. In sen
sitivity analyses, we excluded individuals with missing data (com
plete case analysis).
2.4. Information governance and ethics
NHS England is the data controller; TPP is the data processor; and
the key researchers on OpenSAFELY are acting on behalf of NHS Eng
land. OpenSAFELY is hosted within the TPP environment which is
accredited to the ISO 27001 information security standard and is NHS
IG Toolkit compliant; [15,16] patient data are pseudonymised for
analysis and linkage using industry standard cryptographic hashing
techniques; all pseudonymised datasets transmitted for linkage onto
OpenSAFELY are encrypted; access to the platform is via a virtual pri
vate network (VPN) connection, restricted to a small group of
researchers who hold contracts with NHS England and only access
the platform to initiate database queries and statistical models. All
database activity is logged. No patient level data leave the platform;
only aggregate statistical outputs leave the platform environment fol
lowing best practice for anonymisation of results such as statistical
disclosure control for low cell counts [17]. The OpenSAFELY platform
adheres to the data protection principles of the UK Data ProtectionAct 2018 and the EU General Data Protection Regulation (GDPR)
2016. In March 2020, the Secretary of State for Health and Social Care
used powers under the UK Health Service (Control of Patient Infor
mation) Regulations 2002 (COPI) to require organisations to process
confidential patient information for the purposes of protecting public
health, providing healthcare services to the public and monitoringand managing the COVID 19 outbreak and incidents of exposure
[18]. Taken together, these provide the legal bases to link patient
datasets on the OpenSAFELY platform. This study was approved by
the Health Research Authority (REC reference 20/LO/0651) and bythe LSHTM Ethics Board (ref 21863).
2.5. Role of the funding source
The funders had no role in study design, data collection, data anal
ysis, interpretation, or writing of the report.K. Bhaskaran et al. / The Lancet Regional Health - Europe 6 (2021) 100109 3
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3. Results
17,456,515 individuals were included in the analysis, of whom
17,063 died with COVID 19 listed as the underlying cause, while
134,316 died of other underlying causes (Figure 1). Demographic
characteristics are shown in Table 1.
As expected, the probabilities of both COVID 19 death and
death from other causes, among people in the general populationduring the 283 day study period were highly dependent on age.
The probability of COVID 19 death over this period ranged from
0.0012% in those aged 18 39 years to 0.93% in those aged
/C2180 years, similar to other respiratory causes combined ( Figure 2 ,
appendix Table A1). Probabilities of death from cardiovasculardisease and cancer during this period were higher (ranging from0.0033% to 1.8% in the youngest to oldest age groups for cardiovascular disease death, and from 0.0062% to 1.4% for cancer
death); in those aged /C2180, the risk of dementia/ Alzheimer 's
death was 1.2%.
Most individual level factors associated with odds of COVID 19
death had qualitatively similar associations with non COVID death,but the magnitudes of association differed (Figures 3 4). Age, male
gender, obesity, deprivation, and some comorbidities (notably
uncontrolled diabetes, severe asthma, dementia and organ trans
plant) had more pronounced positive associations with COVID 19
death than non COVID death. Smoking, history of cancer and chronic
liver disease, while positively associated with both outcomes, had
more pronounced positive associations with non COVID death than
COVID death.
Fig. 1. Flow chart of participants in the primary study cohort.4 K. Bhaskaran et al. / The Lancet Regional Health - Europe 6 (2021) 100109
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Non white ethnicity had opposite associations with COVID 19
and non COVID death: non white ethnic groups had higher odds
than white of COVID 19 death, but lower odds than white of death
from non COVID causes (Figure 3). This was also seen when non
COVID deaths were divided into more speci fic cause of death catego
ries: non white groups had similar or lower odds than white of deathfrom cancer, cardiovascular disease, dementia/Alzheimer 's, and respi
ratory causes of death (Figure 5).
An analysis restricted to those who died, directly modelling the
odds of COVID 19 versus non COVID cause of death, con firmed the
pattern of results from our primary analyses (Figure 6). Older age,
male sex, obesity, ethnicity, higher deprivation, diabetes, reducedkidney function, asthma, stroke, dementia, other neurological condi
tions, history of organ transplant, and autoimmune diseases were to
varying degrees stronger risk factors for COVID 19 deaths, whilesmoking, haematological and non haematological cancers, andchronic liver disease were stronger risk factors for non COVID death.
Associations between individual level factors and non COVID
deaths were similar when deaths from 2019 were used to represent
non COVID deaths, and patterns of results were also similar in analy
ses mutually adjusted for all variables (appendix Table A2). In analy
ses of separate pandemic waves, patterns of associations were mostly
similar between the two waves, but the pattern of results for ethnic
ity changed in wave 2, with the South Asian group having continuing
higher odds of COVID 19 death while other minority ethnic groups
had similar odds to White; it was also notable that in contrast with
wave 1, the second wave saw dementia having similar associationswith COVID 19 and non COVID death (appendix Figure A1(a v). In
sensitivity analyses, varying how COVID 19 deaths were de fined,
using complete case analysis to deal with missing data, and using aTable 1
Characteristics of the primary study cohort and distribution of COVID-19/non-COVID deaths
N (%) COVID-19 deaths Non-COVID deaths
N 17456515 (100.0) 17063 (100.00) 134316 (100.00)
Age (yrs) 18-39 5965744 (34.2) 73 (0.43) 1346 (1.00)
40-49 2877525 (16.5) 226 (1.32) 2711 (2.02)
50-59 3085141 (17.7) 729 (4.27) 7487 (5.57)60-69 2421249 (13.9) 1631 (9.56) 15242 (11.35)70-79 1963205 (11.2) 3984 (23.35) 32093 (23.89)80+ 1143651 (6.6) 10420 (61.07) 75437 (56.16)
Sex Female 8739169 (50.1) 7617 (44.64) 67774 (50.46)
Male 8717346 (49.9) 9446 (55.36) 66542 (49.54)
Body mass index (kg/m
2)* Not obese 13621506 (78.0) 12541 (73.50) 108236 (80.58)30-34.9 (Obese class I) 2419268 (13.9) 2761 (16.18) 16630 (12.38)35-39.9 (Obese class II) 940080 (5.4) 1152 (6.75) 6222 (4.63)/C2140 (Obese class III) 475661 (2.7) 609 (3.57) 3228 (2.40)
Smoking status* Never 8739756 (50.1) 5678 (33.28) 43947 (32.72)
Former 5747053 (32.9) 10276 (60.22) 72206 (53.76)
Current 2969706 (17.0) 1109 (6.50) 18163 (13.52)
Ethnicity White 11163018 (63.9) 11280 (66.11) 90619 (67.47)
Mixed 172141 (1.0) 77 (0.45) 353 (0.26)
South Asian 1027068 (5.9) 951 (5.57) 2734 (2.04)
Black 343094 (2.0) 308 (1.81) 1054 (0.78)
Other 323893 (1.9) 145 (0.85) 549 (0.41)
Missing 4427301 (25.4) 4302 (25.21) 39007 (29.04)
Index of Multiple Deprivation 1 (least deprived) 3519427 (20.2) 2882 (16.89) 25941 (19.31)
2 3555666 (20.4) 3144 (18.43) 27357 (20.37)
3 3515186 (20.1) 3258 (19.09) 27696 (20.62)
4 3491534 (20.0) 3727 (21.84) 26996 (20.10)
5 (most deprived) 3374702 (19.3) 4052 (23.75) 26326 (19.60)
ComorbiditiesHypertension 5990510 (34.3) 12635 (74.05) 97064 (72.27)
Chronic respiratory disease 711370 (4.1) 3598 (21.09) 28461 (21.19)
Asthma With no oral steroid use 2484264 (14.2) 1902 (11.15) 14054 (10.46)
With oral steroid use 296251 (1.7) 545 (3.19) 3361 (2.50)
Chronic heart disease 1179367 (6.8) 6202 (36.35) 46465 (34.59)
Diabetes With HbA1c <58 mmol/mol 1053215 (6.0) 3604 (21.12) 23929 (17.82)
With HbA1c >=58 mmol/mol 491874 (2.8) 1937 (11.35) 10860 (8.09)
With no recent HbA1c measure 196831 (1.1) 664 (3.89) 4636 (3.45)
Cancer (non-haematological) Diagnosed <1 year ago 81070 (0.5) 299 (1.75) 9754 (7.26)
Diagnosed 1-4.9 years ago 237331 (1.4) 669 (3.92) 11671 (8.69)
Diagnosed /C215 years ago 547778 (3.1) 1788 (10.48) 17467 (13.00)
Haematological malignancy Diagnosed <1 year ago 8878 (0.1) 59 (0.35) 835 (0.62)
Diagnosed 1-4.9 years ago 28130 (0.2) 168 (0.98) 1453 (1.08)
Diagnosed /C215 years ago 64022 (0.4) 272 (1.59) 2164 (1.61)
Reduced kidney function Estimated GFR 30-60 1012185 (5.8) 6296 (36.90) 43973 (32.74)
Estimated GFR 15- <30 60836 (0.3) 1014 (5.94) 7339 (5.46)
Estimated GFR <15 or dialysis 31027 (0.2) 372 (2.18) 2516 (1.87)
Chronic liver disease 100844 (0.6) 266 (1.56) 3718 (2.77)
Dementia 41460 (0.2) 1334 (7.82) 6747 (5.02)
Stroke 367717 (2.1) 2937 (17.21) 19769 (14.72)
Other neurological disease 172055 (1.0) 1068 (6.26) 6761 (5.03)
Organ transplant 20194 (0.1) 94 (0.55) 494 (0.37)
Asplenia 28083 (0.2) 58 (0.34) 631 (0.47)
Rheumatoid arthritis/lupus/psoriasis 885573 (5.1) 1543 (9.04) 11263 (8.39)
Other immunosuppressive conditions 45307 (0.3) 70 (0.41) 650 (0.48)
* missing BMI included in 'not obese '(n = 3,711,186 (21.3%); missing smoking included in 'never smoker '(n = 732,342 (4.2%)). “Data from 1
st
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Cox regression modelling framework made little difference to the
results (appendix Figure A1a v).
4. Discussion4.1. Key findings
Patterns of association between individual level factors and risk of
COVID 19 death largely mirrored those for non COVID deaths, sug
gesting that COVID 19 has largely acted to multiplying existing mor
tality risks faced by patients. However, there were notable
exceptions. People from non white ethnic groups were at substan
tially raised risk of COVID 19 death compared with white people,
despite having similar or lower risks of deaths from other causes.
Several other demographic characteristics, lifestyle related factors
and comorbidities had qualitatively similar associations with risk of
both COVID 19 and non COVID death but with different magnitudes
of association: age, male sex, obesity, deprivation and some comor
bidities including severe asthma, uncontrolled diabetes, dementia
and organ transplant, had stronger associations with COVID 19
deaths than non COVID deaths. The opposite was true of smoking
and other comorbidities including cancers and chronic liver disease,
which were more strongly associated with non COVID deaths. During
the period from February to November 2020, COVID 19 was a com
mon cause of death in England, though the incidence of cancer and
cardiovascular disease deaths was higher in all age groups, and the
incidence of deaths recorded as being due to dementia/Alzheimer 's
disease was also higher in the oldest individuals.
4.2. Findings in the context of other evidence
Although individual level factors were generally similarly associ
ated with COVID 19 and non COVID death, some of the observed dif
ferences were striking, including the discrepant effects of ethnicity
on the two outcomes. A lower overall mortality risk in Black, South
Asian and other minority ethnic groups has been observed before in
an analysis of linked death registration data from 2001 2013 in Scot
land, with suggested reasons including self selection of healthyindividuals among migrants, and healthier lifestyles and behaviours
among these groups [19]. A study using pre pandemic data from UK
Biobank also observed a reduced risk of mortality in non white ethnic
groups that was consistent for both infectious and non infectious
deaths; [10] UK Biobank participants are not representative of the
broader UK population, with evidence of a healthy volunteer selection bias, [20] and it is possible that this bias may have operated
more strongly in non white ethnic groups. Nevertheless the evidencefrom both the internal comparison in the present study, and related
data from other studies, suggests that the higher risk of poor COVID
19 outcomes re flects unique features of the pandemic rather than a
generalised higher risk of death in non white groups. Reasons mightinclude a high likelihood of working in at risk occupations with high
exposure risk, such as health and social care, hospitality and public
transportation; and a high likelihood of living in large, high density
or multigenerational households, which might act individually or in
combination to increase the risk of infection, and thus the overall risk
of COVID 19 death, particularly if a high exposure risk in younger
people leads to increased infection in older people via households
and community settings [21]. Changes between wave 1 and 2 in the
patterns of results for ethnicity are consistent with data recently pub
lished by ONS, [22] as well as a detailed analysis of OpenSAFELY
focussing on associations between ethnicity and COVID 19 outcomes,
[5] and suggest that ethnic differences in risk of COVID 19 death may
be largely driven by exposure risk, which is more likely to have
changed rapidly over time than susceptibility to severe disease.
Another notable finding was that, although current smoking was
associated with a higher risk of both COVID 19 and non COVID death,
the association with COVID 19 death was substantially smaller than
with non COVID death. Current smokers also had a slightly smaller
risk of COVID 19 death than ex smokers, but the association was as
expected for non COVID death (current smokers at substantially
higher risk than ex smokers) suggesting that the finding was not
driven by exposure misclassi fication or model mis speci fication.
Other studies have found smoking to be a signi ficant risk factor for
mortality among those hospitalised for COVID 19 [23]. If smokers
have a raised risk of severe disease and death once infected, then it is
possible that this has been diluted by a lower risk of infection in ourFig. 2. Estimated probability of death from different causes over the period between 1stFebruary and 9thNovember 2020, by age group
FOOTNOTES: Data from 1stFebruary 2020 to 9thNovember 2020. Probabilities estimated from a multinomial logistic regression model with alive versus died from specifi c
causes as outcomes, and with age group and sex fitted as covariates; estimates are standardised to a 50% male/female gender balance within each age group. Dementia includes Alz-
heimer 's. CVD = cardiovascular diseases. For numerical estimates and 95% CIs please see appendix Table A1.6 K. Bhaskaran et al. / The Lancet Regional Health - Europe 6 (2021) 100109
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Fig. 3. Odds ratios for the association between demographic and lifestyle-related factors and COVID-19 and non-COVID mortality, adjusted for age, sex and STP
FOOTNOTES: Estimates for each covariate were produced by fitting two age, sex and STP-adjusted logistic models with outcomes of COVID-19 death and death from other
causes respectively. Data from 1stFebruary 2020 to 9thNovember 2020.K. Bhaskaran et al. / The Lancet Regional Health - Europe 6 (2021) 100109 7
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Fig. 4. Odds ratios for the association between comorbidities and COVID-19 and non-COVID mortality, adjusted for age, sex and STP
FOOTNOTES: Estimates for each covariate were produced by fitting two age, sex and STP-adjusted logistic models with outcomes of COVID-19 death and death from other
causes respectively. Data from 1stFebruary 2020 to 9thNovember 2020.8 K. Bhaskaran et al. / The Lancet Regional Health - Europe 6 (2021) 100109
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general population based study, but at present there is limited evi
dence to assess possible mechanisms that might explain such a
reduced infection risk among smokers. Of note, the UK Biobank study
found smoking to be more strongly associated with infection deaths
than non infection deaths in a pre pandemic time period [10].
Among comorbidities, history of non haematological cancer stood
out as having a substantially smaller association with COVID 19
death compared with non COVID death; cancer patients and survi
vors are likely to have a high long term risk of cancer recurrence
driving a raised risk of non COVID (cancer) death; our results suggest
that this is proportionately larger than the more modest raised risk of
COVID 19 death that might arise from compromised immunity or
risk of infection complications. Any underlying raised COVID 19 risk
in this group may have been mitigated if cancer patients and survi
vors were more likely to shield and/or be compliant with social dis
tancing measures. The difference between COVID 19 and non COVID
mortality risk was less stark in haematological cancer survivors, in
keeping with evidence that these cancers are likely associated with a
larger raised risk of COVID 19 death. [4,24] Chronic liver disease also
showed a smaller association with COVID 19 death compared with
non COVID death, perhaps again re flecting shielding behaviour or
social interactions during the pandemic; unfortunately we lacked thedata to investigate the role of alcohol in this association. Dementia
was associated with disproportionately raised risks of COVID 19
death, but this appeared to be driven by data from wave 1 of the pan
demic, likely due to signi ficant outbreaks in residential care homes
during that period; by wave 2, dementia was associated with similarrisks of COVID 19 and non COVID death.
4.3. Strengths and limitations
Study strengths include the large size of the study, providing high
statistical power to investigate associations between a wide range offactors and mortality. Our use of routinely collected primary care
data meant that information on a wide range of longitudinal, detailed
patient characteristics and comorbidities were available. Our data
extraction and data management processes ensured that only records
meeting data basic quality criteria were included; in addition, the
underlying primary care data that we accessed are routinely
extracted to support GP payment incentive schemes and national
audits, ensuring that problems with data quality and flow are quickly
addressed. Furthermore, individual level linkage to death registrations provided near complete ascertainment of mortality, with the
caveat that a small proportion of deaths may have been missed
among people who died outside the UK. UK primary care data have
been shown to have good validity for ascertainment of a range of
comorbidities, ethnicity and body mass index. [25 27] Ourfindings
were robust in a number of sensitivity analyses.
There were also some limitations. Comorbidity ascertainment
relied on conditions being coded in the primary care record; conditions will only be coded when patients consult, which may not hap
pen for early stage or mild illness. Conversely, acute conditions
requiring hospitalisation may have been missed if feedback from hos
pital to primary care providers was imperfect. Missing data was an
issue for some variables, notably ethnicity and body mass index. We
used multiple imputation to deal with missing ethnicity, and our
findings were robust to an alternative “complete case analysis ”
approach. Recording of body mass index in primary care is highlylikely to be missing not at random, violating a key assumption
required for multiple imputation, [14] so we instead assumed those
with missing data to be non obese, since obese individuals are morelikely to have their weight recorded; however this could have caused
some misclassi fication. Our results were again robust to an alterna
tive “complete case approach”. We did not have any data on country
of birth, to explore the effect of this variable and its interplay withethnicity. We also lacked reliable information on some potentiallyFig. 5. Odds ratios for the association between ethnicity and COVID-19 death and death from specifi c other causes, adjusted for age, sex, and STP
FOOTNOTES:From separate logistic regression models for each cause-speci fic mortality outcome, with age (spline), sex, STP and ethnicity as covariates. Note: the dementia out-
come included Alzheimer 's and the model was restricted to those aged /C2140y due to non-convergence when younger people were included. Data from 1stFebruary 2020 to 9th
November 2020.K. Bhaskaran et al. / The Lancet Regional Health - Europe 6 (2021) 100109 9
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Fig. 6. Odds ratio for COVID-19 cause of death (versus non-COVID causes) among those who died
FOOTNOTES: Note that the odds ratio presented here are modelling the association between individual factors and the odds of a COVID-19 cause of death, among those who
died. They cannot be interpreted as showing how factors are associated with the odds of death occurring (for this, see Figures 3-4). Estimates are from individual age, sex and STP-
adjusted logistic regression models for each factor of interest, including only individuals that died, and with an outcome of COVID-19 cause of death. Age was parameterised as a 4-
knot restricted cubic spline in all models, except to estimate the effect of age itself, where a linear age term was used for ease of presentation and interpretation. Data from 1stFebru-
ary 2020 to 9thNovember 2020.10 K. Bhaskaran et al. / The Lancet Regional Health - Europe 6 (2021) 100109
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important lifestyle related variables such as alcohol use, diet and
physical activity. We used the underlying cause of death field from
the death registration to assign deaths as being due to COVID 19 orother causes, though results were similar in a sensitivity analysis
where a COVID 19 death was de fined based on a COVID 19 code any
where on the death certifi cate. There may have been some misclassi
fication of cause of death. Early in the epidemic, some COVID 19
deaths may have been misclassi fied as being due to other causes; for
example a proportion of deaths due to COVID 19 occurring in care
homes may have been misclassi fied as being due to dementia/Alz
heimer 's, biasing downwards our estimate of the absolute contribu
tion of COVID 19 to overall mortality [28]. Conversely, during peak
epidemic periods, a bias in the opposite direction may have occurred,
if COVID 19 were entered as the presumed cause of death in uncer
tain cases. However, 16,049/17,063 COVID 19 deaths (94%) had the
ICD 10 code U07.1 ( “COVID 19 /C0virus identi fied”) implying that
infection was con firmed by laboratory testing, so any misclassi fica
tion in this direction is likely to have been minimal, and results were
unchanged in a sensitivity analysis using an outcome de finition
restricted to this code. A caveat to this is that we could not be sure ofthe method of virus identi fication, as this was not part of the available
data, or of the validity of the “COVID 19 /C0virus identi fied”code. We
were further reassured given that we found patterns of results for
non COVID deaths in 2020 to be similar to those for pre pandemic
deaths in the equivalent period of 2019. COVID 19 mortality may
have been affected by the competing risk of non COVID death, and
vice versa; our use of logistic regression over a fixed time period con
ceptually accounts for competing risks in a similar way to Fine & Gray
modelling in a time to event framework, [29] since the estimated
odds ratios target the association between covariate and odds of outcome, including any part of the association that is driven by the com
peting outcome. Our primary results describe associations between
individual level factors and outcomes adjusted for age, sex, and geog
raphy and in the supplementary appendix we also include results
with mutual covariate adjustment to provide a more complete
description of independent associations. However, we caution
against interpreting our estimates as causal effects, which are chal
lenging to estimate in observational data, and in this case would
require the development of detailed variable speci fic confounder
models. In addition, since we estimated factors associated withCOVID 19 death in the general population, rather than those with
confirmed infection, it is not possible to disentangle from our data
whether associations were driven by risk of infection, susceptibilityto severe disease or a combination of the two. In the absence of wide
spread representative testing data, any attempt to look at factors
associated with survival from infection would be highly vulnerable to
collider bias because, for example, people with underlying ill health
would be more likely to be tested and have infection ascertained, as
well as being at increased risk of COVID 19 death [30]. Finally, TPP
primary care software is not used uniformly around the country so
our data are not fully geographically representative of England: in
particular, the East of England, East Midlands, Yorkshire and the
Humber regions are over represented in the data, while London is
underrepresented. Our adjustment for STP, which divided our datainto 32 NHS administrative areas,
should have avoided confounding
by factors relating to geography in the estimation of associations, butobserved absolute risks are not necessarily generalisable to England
as whole.
4.4. Implications for public health and research
In all age groups, the probability of COVID 19 death was approxi
mately similar to that for death from other respiratory causes com
bined during the time period of this study, but lower than the
probability of death from cancer, cardiovascular disease and (among
older individuals) dementia/Alzheimer 's. This highlights theimportance of maintaining care and prevention services targetingthese high burden non COVID conditions. It should be remembered
that the absolute probabilities of COVID 19 deaths we observed were
speci fic to our study period, and were in the context of major national
efforts to suppress infection rates and targeted shielding for particular risk groups. The relative contribution of COVID 19 to mortality
would undoubtedly have been higher in the absence of these policies.
The broadly similar relationships between most individual level fac
tors and COVID 19 and non COVID mortality suggest that COVID 19
largely ampli fies a person' s underlying mortality risk (by a factor that
will depend on current levels of circulating virus) based on theircharacteristics and medical history; public health decisions requiring
prioritisation of vulnerable subgroups can therefore be informed by
our knowledge of pre pandemic mortality risks based on established
risk factors. However there were important exceptions to this overall
pattern. Understanding the drivers of uniquely raised risks of COVID
19 death associated with non white ethnicity is a clear research pri
ority; research into the role of occupations involving high levels of
public contact, population density, and household composition will
be key in further exploring the reasons, and thus informing mitiga
tion strategies. Cancers were notably more strongly associated with
non COVID than COVID 19 deaths; a similar pattern was seen for
chronic liver disease. However, this finding should not affect policies
aimed at reducing risk in these groups, as even given a high underlying risk of non COVID death, COVID 19 represents a potentially
avoidable addition to the mortality risk. Emerging risk prediction
tools can help to quantify this excess risk for individuals with differ
ent combinations of risk factors, and thus inform the targeting of mitigation measures, including prioritisation for vaccines. [3,31]
5. Conclusion
Demographic characteristics, lifestyle related factors and comor
bidities generally had qualitatively similar relationships with the
risks of both COVID 19 death and non COVID deaths, with some dif
ferences in the magnitudes of association. People from non white
ethnic groups had higher risks of COVID 19 death than white people,
contrasting with similar or lower risks of deaths from other causes.
This strongly suggests there are risk factors for mortality, speci fict o
COVID 19, that are disproportionately affecting non white groups;various factors along the causal chain culminating in COVID 19 death
might contribute to the raised risks in non white groups, including
risk of exposure to the virus, risk of infection once exposed, underly
ing health and susceptibility to severe disease, health seeking behav
iour and health care received. Improved understanding of these
factors is needed in order to tackle the increased mortality from
COVID 19 among non white groups. In conclusion, COVID 19
appears to largely act as if multiplying existing risks faced by patients,
with the notable exceptions described in this paper.
Contributors
BG conceived the OpenSAFELY platform and the approach. BG and
LS lead the project overall and are guarantors. KB and SJWE led on
study design. KB did the analysis and wrote the first draft. SB led on
software development. AM led on information governance. Othercontributions were /C0data curation CB, JP, JC, SH, SB, DE, PI and
CEM; disease category conceptualisation and codelists KB CTR BMCB JC CEM AJW HIM IJD HJC JP; statistical analysis code: KB, EW; ethi
cal approvals HJC EW LS BG; software SB, DE, PI, AJW, WJH, CEM,
CB, FH, JC; writing (reviewing and editing) KB, CTR, LT, BM, AS, AM,
RME, CEM, IJD, SJWE, DS, LS, BG. All authors were involved in design
and conceptual development and reviewed and approved the final
manuscript.K. Bhaskaran et al. / The Lancet Regional Health - Europe 6 (2021) 100109 11
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Declaration of interests
TPP provided technical expertise and infrastructure within their
data centre pro bono in the context of a national emergency. BG 's
work on better use of data in healthcare more broadly is currently
funded in part by: NIHR Oxford Biomedical Research Centre, NIHR
Applied Research Collaboration Oxford and Thames Valley, the
Mohn Westlake Foundation, NHS England, and the Health Founda
tion; all DataLab staff are supported by BG 's grants on this work. LS
reports grants from Wellcome, MRC, NIHR, UKRI, British Council,
GSK, British Heart Foundation, and Diabetes UK outside this work. KB
held a Sir Henry Dale fellowship (grant: 107731/Z/15/Z) jointly
funded by Wellcome and the Royal Society and a Wellcome Senior
Research Fellowship (grant: 220283/Z/20/Z) during this work. HIM is
funded by the National Institute for Health Research (NIHR) Health
Protection Research Unit in Immunisation, a partnership between
Public Health England and LSHTM. EW holds grants from MRC. ID
golds grants from NIHR and GSK. HF holds a UKRI fellowship. RME is
funded by HDR UK (grant: MR/S003975/1) and MRC (grant: MC_PC
19065). The views expressed are those of the authors and not neces
sarily those of the NIHR, NHS England, Public Health England or the
Department of Health and Social Care. Funders had no role in the
study design, collection, analysis, and interpretation of data; in the
writing of the report; and in the decision to submit the article for
publication.
Data availability statement
All data were linked, stored and analysed securely within the
OpenSAFELY platform https://opensafely.org/. Data include pseudo
nymized data such as coded diagnoses, medications and physiologi
cal parameters. No free text data are included. All code is shared
openly for review and re use under MIT open license (https://github.
com/opensafely/covid vs noncovid deaths research). Detailed pseu
donymised patient data are potentially re identi fiable and therefore
not shared. We rapidly delivered the OpenSAFELY data analysis plat
form without prior funding to deliver timely analyses on urgent
research questions in the context of the global Covid 19 health emer
gency: now that the platform is established we are developing a for
mal process for external users to request access in collaboration with
NHS England; details of this process will be published on Open
SAFELY.org.Patient and Public Involvement
Patients were not formally involved in developing this specific
study design that was developed rapidly in the context of a global
health emergency. We have developed a publicly available website
https://opensafely.org/ through which we invite any patient or mem
ber of the public to contact us regarding this study or the broader
OpenSAFELY project.
Supplementary materials
Supplementary material associated with this article can be found,
in the online version, at doi:10.1016/j.lanepe.2021.100109.
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