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Cohort profile: Early pandemic evaluation and enhanced surveillance of COVID-19 (EAVE II) database

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Cohort Profile
Cohort profile: Early pandemic evaluation and
enhanced surveillance of COVID-19 (EAVE II)
database
Rachel H. Mulholland ,
1
Eleftheria Vasileiou,
1
* Colin R. Simpson,
1,2
Chris Robertson,
3,4
Lewis D. Ritchie,
5
Utkarsh Agrawal,
6
Mark Woolhouse,
1
Josephine L.K. Murray,
4
Helen R. Stagg,
1
Annemarie B. Docherty,
1
Colin McCowan ,
6
Rachael Wood,
1,4
Sarah J. Stock
1
and Aziz Sheikh
1
1
Usher Institute, University of Edinburgh, Edinburgh, UK,
2
School of Health, Wellington Faculty of
Health, Victoria University of Wellington, Wellington, New Zealand,
3
Department of Mathematics and
Statistics, University of Strathclyde, Glasgow, UK,
4
Public Health Scotland, Glasgow and Edinburgh,
UK,
5
Centre of Academic Primary Care, University of Aberdeen, Aberdeen, UK,
6
School of Medicine,
University of St Andrews, St Andrews, UK
*Corresponding author. Usher Institute, University of Edinburgh, NINE Edinburgh BioQuarter, Edinburgh EH16 4UX, UK.
E-mail: eleftheria.vasileiou@ed.ac.uk
Editorial decision 29 January 2021; Accepted 19 February 2021
Why was the cohort set up?
In December 2019, a novel coronavirus COVID-19
emerged from Wuhan, China, and was soon declared as
pandemic by the World Health Organization (WHO) on
the 11 March 2020.
1
The UK soon followed suit and
implemented a national lockdown on the 23 March 2020.
As of 9 December 2020, according to WHO, this highly in-
fectious virus has infected more than 67 million people and
led to over 1.5 million deaths across the world.
2
There is a
growing body of evidence on the epidemiology of the con-
dition, risk factors for poor outcomes and effects of inter-
ventions.
39
The rapid generation of robust data is crucial to moni-
tor, understand and mitigate the effects of COVID-19. The
Early Pandemic Evaluation and Enhanced Surveillance of
COVID-19 (EAVE II) database creates a national, real-
time prospective cohort using Scotland’s health data infra-
structure, to describe the epidemiology of COVID-19 in-
fection, patterns of healthcare use and outcomes, and
insights into the effectiveness of and safety of vaccines and
treatments for COVID-19.
10
This work builds on an established cohort for seasonal
and pandemic influenza vaccine and anti-viral assessment
in Scotland EAVE (Early Estimation of Vaccine and Anti-
Viral Effectiveness).
11,12
EAVE is a dormant pandemic
protocol that is part of the National Institute for Health
Research (NIHR) Pandemic Preparedness Research
Portfolio and a platform for previous studies on influenza
vaccine and antiviral assessment.
1216
Who is in the cohort?
We obtained ethical approval from the National Research
Ethics Service Committee, Southeast Scotland 02.
This prospective baseline cohort study contains all 5.4
million individuals registered with a general practitioner
(GP) in Scotland from 23 February 2020 which, according
to the National Records of Scotland (NRS) 2019 mid-year
V
CThe Author(s) 2021; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association 1
IEA
International Epidemiological Association
International Journal of Epidemiology, 2021, 1–11
doi: 10.1093/ije/dyab028
Cohort Profile
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estimates, covers around 98–99% of the Scottish popula-
tion.
10,17
A map of the baseline EAVE II cohort by the
National Health Service (NHS) Health Board shows that
most of the cohort are based in the central belt of Scotland
(Figure 1).
A summary of the baseline population by sex, age group
(as of 23 February) and deprivation used the Scottish Index
of Multiple Deprivation (SIMD)
18
and Scottish
Government Urban Rural Classification.
19
SIMD is a mea-
sure of deprivation built on seven domains and is unique to
Scotland, with lower quintiles representing the most de-
prived areas.
18
These primary care records are linked to other data
sources from out-of-hours, emergency and secondary care.
There are additional linkages to other datasets such as lab-
oratory testing data, registration and mortality data, self-
reported data and enhanced surveillance data such as the
COVID-19 Clinical Information Network (CO-CIN). This
is done using the Community Health Index (CHI), the
unique identifier provided by NHS Scotland. It is allocated
to all residents in Scotland registered with a GP and to all
patients who receive care in Scotland, even if they are non-
Scottish residents.
10
Summaries of these data sources are
given in Table 2, with a data flow diagram on how they
are linked together in Figure 2.
This cohort therefore consists of specific groups of in-
terest that are used in EAVE II sub-studies such as the
COVID-19 in Pregnancy in Scotland (COPS)
37
and for in-
vestigating ethnic and social inequalities in COVID-19.
How often have they been followed up?
The baseline GP records will be updated on a biannual to
3-monthly basis, if possible. The first update in early 2021
will contain COVID-19-specific GP codes that were cre-
ated during the pandemic and were therefore missed in the
initial extract. This will capture information on COVID-
19 related appointments, vaccinations, therapies and
vaccination-induced adverse effects. Information on influ-
enza will also be included to facilitate analyses on the
effectiveness of and safety of COVID-19-specific and pre-
existing vaccines, therapies and treatments. To facilitate
Figure 1 Baseline Early Pandemic Evaluation and Enhanced
Surveillance of COVID-19 (EAVE II) cohort population by National
Health Service (NHS) Health Board. (1 ¼NHS Ayrshire and Arran;
2¼NHS Borders; 3 ¼NHS Dumfries and Galloway; 4 ¼NHS Forth
Valley; 5 ¼NH S Grampian; 6 ¼NHS Highland; 7 ¼NHS Lothian; 8 ¼NHS
Orkney; 9 ¼NHS Shetland; 10 ¼NHS Western Isles; 11 ¼NHS Fife;
12 ¼NHS Tayside; 13 ¼NHS Greater Glasgow and Clyde; 14 ¼NHS
Lanarkshire ordered by Health board code).
Table 1 Baseline characteristics of the population in the Early
Pandemic Evaluation and Enhanced Surveillance of COVID-
19 (EAVE II) cohort study (n¼5 431 034). Update: 23 February
2020
Characteristics Total number of
individuals (% of total)
Sex
Female 2733477 (50.3)
Male 2697557 (49.7)
Age group (years)
0–4 245423 (4.5)
5–14 574389 (10.6)
15–24 624070 (11.5)
25–44 1479594 (27.2)
45–64 1503617 (27.7)
65–74 563605 (10.4)
75–84 323812 (6.0)
>85 116524 (2.1)
Deprivation quintile
a
1: most deprived 1100521 (20.3)
2 1074842 (19.8)
3 1050369 (19.3)
4 1079282 (19.9)
5: least deprived 1080775 (19.9)
Urban/rural score
b
1: large urban areas 1920932 (35.4)
2: other urban areas 1959281 (36.1)
3: accessible small towns 501557 (9.2)
4: remote small towns 257264 (4.7)
5: accessible rural 486665 (9.0)
6: remote rural 260090 (4.8)
Missing values (%) as below.
a
Deprivation score not available for 45 245 (0.8%) individuals. Score cal-
culated via the Scottish Index of Multiple Deprivation (SIMD).
b
Urban/rural score not available for 45 245 (0.8%) individuals.
c
NHS Health Board not available for 45 245 (0.8%) individuals.
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Table 2 Details of data sources within the different settings for the Early Pandemic Evaluation and Enhanced Surveillance of
COVID-19 (EAVE II) cohort
Setting Data sources Description
Primary care General practice (GP) data
a
Data from all patients registered with GP. GP data (demographic,
consultation data—categorized into risk groups, prescribing and
categorized measurements) will be extracted using the Enhanced
Services Contract Reporting Options (ESCRO) system by the
trusted third party Albasoft Ltd
10
Prescribing Information System
(PIS)
a
Information on all prescribing relating to all prescriptions dispensed
in the community. Prescriptions written in hospitals which are dis-
pensed in the community are also included
20
Out-of-hours (OOH)
b
Data on the services a patient receives for primary care when their reg-
istered GP practice is closed
21
Scottish Morbidity Record 00
(SMR00)
a
Relates to all outpatients (new and follow-up) in specialties other than
Accident & Emergency (A&E), and Genito-urinary Medicine
22
Telephone consultation National Health Service (NHS)
24
a
Delivers telephone and online services across Scotland for initial
assessments, which are then passed on to the appropriate services if
required
10
COVID-19 Community Hubs and
Assessment Centres
b
A network established by NHS Health Boards in Scotland to provide
a direct and rapid route of people with COVID-19
10
. Data from
these centres will derive from National Health Service (NHS) 24
and the COVID-19 Enhanced Surveillance dataset
10
.
Secondary care Scottish Morbidity Record (SMR)
including:
SMR01
a
SMR02
a
SMR01: Episode-based patient record for all inpatients and day cases
discharged from non-obstetric and non-psychiatric specialties in
Scotland. This includes Accident & Emergency (A&E)
attendances
10
SMR02: Episode-based patient record for all inpatients and day cases
discharged from obstetric specialties in Scotland
10
Scottish Hospital Electronic
Prescribing and Medicines
Administration (HEMPA)
system
a
Data on prescription and administration of medicines for inpatients
from a subgroup of hospitals with HEPMA systems
10
Scottish Ambulance Service (SAS)
a
Scottish database for all patients requiring emergency ambulance serv-
ices or needing support to reach their health care appointments due
to their medical and mobility needs
23
Scottish Intensive Care Society
Audit Group (SICSAG)
a
Scottish database for adult patients admitted to all general intensive
care units (ICU) and combined ICU/high dependency units (HDU)
10
COVID19 Clinical Information
Network/International Severe
Acute Respiratory and emerging
Infection Consortium
(CO-CIN/ISARIC)
p
Data of the clinical characteristics of patients admitted to hospital
with COVID-19 infection in Scotland recruited to CO-CIN/
ISARIC.
24
As of 22 June 2020 this comprised 65% of the hospital-
ized patients in Scotland
Rapid Preliminary Inpatient Data
(RAPID)
b
Contains hospital inpatient admission data which have been used to
predict emergency admissions and bed occupancy
25
Mortality data National Records of Scotland
(NRS) deaths
a
Data on Scottish death certificates and the cause of death
26
Laboratory and
serology data
Electronic Communication of
Surveillance in Scotland
(ECOSS)
a
Surveillance data on laboratory results from microorganisms, infec-
tions and microbial intoxications. Contains all reverse transcriptase
polymerase chain reaction (RT-PCR) tests carried out in Scotland
10
Serology data
a
All serology data will be provided by the ‘Seroprevalence’ work car-
ried out and commissioned by the COVID-19 Enhanced
Surveillance cell of Public Health Scotland (PHS)
10
Genome sequencing data
b
Positive laboratory RT-PCR swab samples for COVID-19 will also be
sent to national sequencing centres where 500 COVID-19 genome
sequences will be performed
10
(Continued)
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UK-wide research, QCOVID groups will also be added to
allow validation of the QCOVID ‘living’ risk prediction
model on the Scottish population.
38
Information on
shielded risk groups will also be included to assess the im-
pact of COVID-19 on those most at risk for severe illness
where a 12-month self-isolation was recommended by the
UK government on 23 March 2020.
39
Regular updates on a number of linked datasets and the
underlying GP data will be undertaken on a daily, weekly
or monthly basis, as available and necessary (see Table 3).
Those who have transferred GPs within Scotland will stay
in the cohort. Participants who die or permanently leave
Scotland (and deregister from general practices) will drop
out of the cohort. Characteristics of individuals lost to
follow-up compared with those remaining in the cohort
will also be provided in the study. Missing data will also be
reported for each variable.
What has been measured?
Combining these rich data sources together provides a
wealth of information on the natural history of the condi-
tion and patients’ journeys across Scotland’s NHS. We
provide a high-level summary of key available data in
Table 4.
What has it found?
Permissions to link these datasets were received in May
2020 and the flow of linked data began in June 2020. The
initial GP data extract contained the baseline cohort and
the EAVE II risk groups, which were based on the risk
groups for seasonal influenza, as research at the time of ex-
tract did not know exact risk groups for COVID-19. This
includes comorbidities and household characteristics, for
Table 2 Continued
Setting Data sources Description
Self-reported data Test and Protect data
b
A service which identifies positive cases of COVID-19 and who they
have had close, recent contact with
27
Surveys
b
Surveys on how people have been affected by COVID-19 in Scotland
Census 2011 data
b
Residents in Scotland are asked to fill in a census questionnaire every
10 years and provide information on their demographic (e.g. ethnic-
ity), socioeconomic, health and other circumstances. NRS will pro-
vide data from the latest Scottish Census in 2011
28
Derived data COVID-19 shielding patient list
b
Uses a combination of primary and secondary care held in Public
Health Scotland to derive groups considered to be at high risk if
they contract COVID-19
29
Births and pregnancy-
related data
Scottish Birth Record (SBR)
b
The SBR is a web-based system developed on the NHSNet to ensure
that every baby born in Scotland will have one record which will
act as the foundation for future information collection. The system
has been implemented to varying degrees in all Scottish hospitals
providing midwifery and/or neonatal care
30
NHS live birth notifications
b
Notification of live births from NHS Board maternity units to child
health administration departments
31
NRS births
b
Record of statutory registration of a live birth (live-born baby at any
gestation)
32
NRS statutory stillbirth
registrationsb
Record of statutory registration of a stillbirth (baby born at 24
weeks, showing no signs of life)
33
NHS antenatal care notifications
b
Public Health Scotland (PHS) has developed a new national data re-
turn as part of the response to the COVID-19 pandemic, providing
information on women booking for antenatal care with NHS ma-
ternity services: for identification of women with ongoing pregnan-
cies in near real-time
Abortion Act Scotland (AAS)
Notifications
b
Record of statutory notification of all terminations of pregnancy in
Scotland
34
Vaccine treatment Child Health Systems
Programme—School (CHSP-S)
a
Facilitates the call/recall of both primary and secondary school pupils
for screening, review and immunization
35
Scottish Immunisation Recall
System (SIRS)
b
Data on recorded immunization in children when scheduled for a vac-
cination, including children of pre-school age
36
HEPMA, Hospital Electronic Prescribing and Medicines Administration.
a
Data sources approved as of May 2020.
b
Data sources pursuing.
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example an indicator of living in a care home. This EAVE
II risk group dataset contained more individuals than the
baseline cohort, with over-representation in certain popu-
lations. This is likely to have resulted in residents being
registered at multiple GP practices, people who have left
Scotland or visitors. To overcome this, weights were calcu-
lated by comparing the age and sex profile in the EAVE II
cohort with the age and sex profile for the 2019 NRS mid-
year population estimates in Scotland.
17
A summary of the
number of EAVE II risk groups using these weights is
shown in the Supplementary material, along with the indi-
vidual risk groups (Supplementary Table S1, available as
Supplementary data at IJE online). The following analyses
were performed using these weights.
Initial explorations showed that as age increased, lower
levels of deprivation using SIMD quintiles slightly
Figure 2 Flow diagram for the Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE II) cohort. Primary care consultations (SMR;
Scottish Morbidity Record; OOH: Out-of-hours); Hospital Admission (SICSAG: Scottish Intensive Care Society Audit Group; CO-CIN: COVID19 Clinical
Information Network; RAPID: Rapid Preliminary Inpatient Data; SAS: Scottish Ambulan ce Service); Prescribing (PIS: Prescribing Information System;
HEMPA: Hospital Electronic Prescribing and Medicines Administration); Laboratory (ECOSS: Electronic Communication of Surveillance in Scotland;
RT-PCR: Reverse transcription polymerase chain reaction); Vaccine Treatment (CHSP-S: Child Health Systems Programme School; SIRS: Scottish
Immunisation Recall System); Birth and Pregnancy (SBR: Scottish Birth Record; NRS: National Records of Scotland; AAS: Abortion Act Scotland).
Table 3 Details on frequency of data linkages
Daily or weekly
linkages
Weekly or
monthly linkages
Monthly
linkages
ECOSS SMR01 SBR
NHS 24 SMR02 NRS births
SAS PIS NRS stillbirths
Serology data NRS deaths NHS antenatal care
SICSAG CO-CIN AAS
RAPID OOH
AAS, Abortion Act Scotland; CO-CIN, COVID-19 Clinical Information
Network; ECOSS, Electronic Communication of Surveillance in Scotland;
NHS, National Health Service; NRS, National Records of Scotland; OOH,
Out-of-hours; PIS, Prescribing Information System; RAPID, Rapid
Preliminary Inpatient Data; SAS, Scottish Ambulance Service; SBR, Scottish
Birth Record; SICSAG, Scottish Intensive Care Society Audit Group; SMR;
Scottish Morbidity Record.
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Table 4 Variables captured and their relevant data sources
Category Variable group Specific variables Source(s)
COVID-19 outcomes Testing Tested; date of positive/negative test; test results; type
of test; antibody tests (if available)
COVID-19 Community Hubs
and Assessment Centres;
ECOSS; serology data; genome
sequencing data; Test and
Protect data
Severity Severity; symptoms; hospital admission; admitted to
ICU; treatment in ICU
NHS 24; COVID-19 Community
Hubs and Assessment Centres;
SMR; SAS; SICSAG; CO-CIN;
RAPID; Test and Protect data;
Surveys; SMR01; SMR00
Mortality Death; cause of death NRS deaths; SMR
Treatment Type of vaccination; date of vaccination GP data; ECOSS;
CHSP-S; SIRS
Potential risk factors Sociodemographic Age; sex; ethnicity; country of birth; BMI; smoking;
employment status; occupation; country of birth;
religion; tenure
GP data; 2011 Census
Geographical Data zone; socioeconomic status (SES) through
Scottish Multiple Deprivation Index (SIMD)
18
;
Urban Rural Index
19
; pollution exposure
40
; popu-
lation density
GP data (use postcode to link to
relevant datasets)
Clinical Comorbidities including chronic respiratory disease
(with chronic obstructive pulmonary disease and
asthma as subsets); chronic heart disease; chronic
liver disease; chronic kidney disease; chronic neuro-
logical disease; diabetes types 1 and 2; conditions
or medications causing impaired immune function;
pregnancy; asplenia or dysfunction of spleen; obe-
sity; hypertension (subsets controlled/uncontrolled
hypertension); tuberculosis; multimorbidity;
Charlson Comorbidity Index
GP data; SMR
Medications Prescription drugs including asthma (including GINA
management steps and oral steroids) and COPD-re-
lated prescriptions; regular inhalers; COVID/pan-
demic acute therapies and chronic therapy for long-
term sequelae; statins; rhinitis therapy; immuno-
therapy; diabetes therapy; cardiovascular disease
therapy; antihypertensives; antibiotics; NSAIDs;
Cox2; paracetamol; antiviral prescriptions; drugs
for previous primary care consultations; polyphar-
macy; high-risk prescribing
GP data; PIS; HEPMA
Pregnancy and babies Pregnancy indicator; miscarriage, ectopic pregnancy,
pregnancy termination (incl. date, gestation,
grounds); stillbirth (incl. date, gestation, cause of
death); live birth (incl. date, gestation, sex of baby);
congenital anomaly flag; neonatal outcomes fol-
lowing maternal infection
GP data; SMR; SICSAG; CO-
CIN; SBR; NHS live birth;
NRS births; NRS stillbirths;
NHS antenatal care; AAS
AAS, Abortion Act Scotland; CO-CIN, COVID-19 Clinical Information Network; ECOSS, Electronic Communication of Surveillance in Scotland; ICU, iten-
sive care unit; NHS, National Health Service; NRS, National Records of Scotland; OOH, Out-of-hours; PIS, Prescribing Information System; RAPID, Rapid
Preliminary Inpatient Data; SAS, Scottish Ambulance Service; SBR, Scottish Birth Record; SICSAG, Scottish Intensive Care Society Audit Group; SMR, Scottish
Morbidity Record; NSAID, non-steroidal anti-inflammatory drug; incl., including; HEPMA, Hospital Electronic Prescribing and Medicines Administration.
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increased and the number of risk groups increased (Figure
3). These did not differ substantially between sexes (Figure
3).
Since the first follow-up of COVID-19 outcomes from 1
March to 10 November, there have been a total of 835
803 (15.4%) tested, 57 416 (1.1%) with a positive test
(out of the total cohort), 9847 (0.2%) hospitalized with
COVID-19, 5350 (0.1%) admitted to an intensive care
unit (ICU) or died with COVID-19 on the death certificate
and 4726 (0.1%) who have died with COVID-19 on the
death certificate within the EAVE II cohort. The propor-
tions of these outcomes split by age and sex for the same
time period show that more elderly residents have been
tested with a resulting positive test (Figure 4). Elderly resi-
dents, particularly males, are also more represented in the
more severe outcomes (Figure 4).
These age profiles were repeated for deprivation levels
(using SIMD quintiles), the number of risk groups and the
20 most frequent individual risk groups within the EAVE
II study (Supplementary material). This showed that there
were higher proportions of positive tests and more severe
outcomes in more deprived areas, residents belonging to
multiple risk groups and those who had comorbidities
Figure 3 Baseline Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE II) cohort population by National Health Service (NHS)
Health Board. (1¼NHS Ayrshire and Arran; 2 ¼NHS Borders; 3 ¼NHS Dumfries and Galloway; 4 ¼NH S Forth Valley; 5 ¼NHS Grampian; 6 ¼NHS
Highland; 7 ¼NHS Lothian; 8 ¼NHS Orkney; 9 ¼NHS Shetland; 10 ¼NHS Western Isles; 11 ¼NHS Fife; 12 ¼NHS Tayside; 13 ¼NHS Greater Glasgow
and Clyde; 14 ¼NHS Lanarkshire ordered by Health board code).
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(Supplementary material, available as Supplementary data
at IJE online).
The map of the proportion of these outcomes by NHS
Health Board demonstrated that despite high rates of test-
ing in more rural areas in the northern and southern parts
of Scotland, positive tests were low (Figure 5). The central
belt had a higher proportion of positive tests out of the to-
tal baseline population and higher rates of more severe
COVID-19 outcomes (Figure 5).
All relevant R code scripts for the summary tables and
figures will be made available on the EAVE II GitHub page
[https://github.com/EAVE-II]. This will also contain a data
dictionary for the entire EAVE cohort which will be
updated when new updates and data linkages are made.
We are currently working on the development of a
national risk prediction algorithm to identify risk factors
for poor outcomes i.e. hospitalisation and death from
COVID-19,
10
and the validation of the QCOVID-19
algorithm.
38
Figure 4 Baseline Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE II) cohort population by National Health Service (NHS)
Health Board. (1 ¼NHS Ayrshire and Arran; 2 ¼NHS Borders; 3 ¼NHS Dumfries and Galloway; 4 ¼NHS Forth Valley; 5 ¼NHS Grampian; 6 ¼NHS
Highland; 7 ¼NHS Lothian; 8 ¼NHS Orkney; 9 ¼NHS Shetland; 10 ¼NHS Western Isles; 11 ¼NHS Fife; 12 ¼NHS Tayside; 13 ¼NHS Greater Glasgow
and Clyde; 14 ¼NHS Lanarkshire ordered by Health board code).
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What are the main strengths and
weaknesses?
The EAVE II cohort will be widely generalizable to the
Scottish population as it contains all individuals registered
within GP practices in Scotland, with exception of home-
less, itinerant or travelling groups, those in prison, those
who are institutionalized due to mental health reasons and
other reasons. Regularly updating and monitoring this co-
hort over a long period of time will also be quick and cost
effective as the underlying data sources are mainly rou-
tinely collected, quality assured and easily linkable using
unique CHI numbers. This in turn means insights can be
kept up to date with the rapidly evolving pandemic situa-
tion. The completeness and coverage, in terms of both pop-
ulation and breadth of data, are also a major strength.
The key limitations are the possibility of some selection
biases because of excluded patients, although this is
estimated to be under 2% of the Scottish population, and
the risk of residual confounding in the context of analytical
epidemiological studies. Considerable care will need to be
taken when making inferences about the effectiveness of
interventions, because of non-randomized comparisons.
Can I get hold of the data? Where can I find
out more?
Data can be accessed by contacting the corresponding au-
thor. For more information on the cohort, refer to the pub-
lished EAVE II protocol.
10
The study findings will be
presented at international conferences and published in
peer-reviewed journals.
Supplementary Data
Supplementary data are available at IJE online.
Figure 5 Baseline Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE II) cohort population by National Health Service (NHS)
Health Board. (1 ¼NHS Ayrshire and Arran; 2 ¼NHS Borders; 3 ¼NHS Dumfries and Galloway; 4 ¼NHS Forth Valley; 5 ¼NHS Grampian; 6 ¼NHS
Highland; 7 ¼NHS Lothian; 8 ¼NHS Orkney; 9 ¼NHS Shetland; 10 ¼NHS Western Isles; 11 ¼NHS Fife; 12 ¼NHS Tayside; 13 ¼NHS Greater Glasgow
and Clyde; 14 ¼NHS Lanarkshire ordered by Health board code).
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Author Contributions
A.S. initiated the manuscript. R.H.M. and E.V. led the
writing of the manuscript, and R.H.M. and C.R. led the
analysis. All co-authors reviewed and contributed to the
writing of the manuscript.
Funding
The original EAVE project was funded by the National Institute for
Health Research Health Technology Assessment Programme (proj-
ect number 13/34/14). EAVE II is funded by the Medical Research
Council [MR/R008345/1] and supported by the Scottish
Government. This work is supported by BREATHE—The Health
Data Research Hub for Respiratory Health [MC_PC_19004].
BREATHE is funded through 10 the UK Research and Innovation
Industrial Strategy Challenge Fund and delivered through Health
Data Research UK.
Conflict of interest
Details on competing interests are included in the study’s
protocol [http://dx.doi.org/10.1136/bmjopen-2020-039097].
Remaining co-authors (R.H.M., C.M., U.A., R.W., A.B.D.,
S.J.S.) do not report conflict of interest. A.D. and S.J.S. are
also funded by Wellcome Trust Clinical Career
Development. H.R.S. is supported by the Medical Research
Council (MR/R008345/1).
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Profile in a nutshell
The Early Pandemic Evaluation and Enhanced
Surveillance of COVID-19 (EAVE II) database creates
a national, real-time prospective cohort using
Scotland’s health data infrastructure, to describe the
epidemiology of COVID-19, patterns of health care
use and outcomes, and insights into the
effectiveness and safety of vaccines and treatments
for COVID-19. As far as we are aware, EAVE II is the
first national end-to-end clinical surveillance platform
for COVID-19 predominantly using routinely available
data.
This study contains all 5.4 million individuals
registered with a GP in Scotland from 23 February
2020, covering 98–99% of the Scottish population.
These primary care records are linked to other data
sources from out-of-hours, community, emergency
and secondary care, in addition to data on
registrations and mortality, laboratory testing, self-
report and enhanced surveillance.
These data will be updated throughout the course of
the pandemic. Participants who die or permanently
leave Scotland (and deregister from general
practices) will drop out of the cohort.
Combining these rich data sources together provides
a wealth of information on the natural history of the
condition and patients’ journeys across Scotland’s
National Health Service (NHS).
Data will be hosted in Scotland’s National Safe
Haven within the electronic Data Research and
Innovation Service (eDRIS) of Public Health Scotland
(PHS). Applicants must submit an enquiry to the
corresponding author.
10 International Journal of Epidemiology, 2021, Vol. 00, No. 00
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... 25 26 EAVE II comprises routinely collected primary care, secondary care, laboratory and serology data from 5.4 million Scottish residents registered with a GP (~99% of the Scottish population) from February 2020. 25 26 We will primarily focus on adults (aged ≥18 years) but will consider extending the cohort to include children (aged <18 years) if there are sufficient numbers of individuals in this age group. We intend to use data from February 2020 up to March 2023. ...
... In order to include data from primary care encounters when GP practices are closed, we will use outof-hours (OOH) records derived from the Public Health Scotland (PHS) Primary Care OOH Data Mart. 26 Secondary care data Activity in hospital-based care will be extracted from the Scottish Morbidity Record (SMR) 01 which holds detailed information on hospital admissions, such as the specific area of clinical activity (specialty), the facility of care, patient management and new diagnoses. 28 Diagnoses in SMR01 will be extracted using ICD-10 codes. ...
... This surveillance data contains all reverse transcriptase PCR (RT-PCR) tests, carried out in Scotland. 26 Sequencing data will be obtained from the Centre of Genomics and will make it possible to account for the variant of SARS-CoV-2 during model building. ...
Article
Introduction: COVID-19 is commonly experienced as an acute illness, yet some people continue to have symptoms that persist for weeks, or months (commonly referred to as 'long-COVID'). It remains unclear which patients are at highest risk of developing long-COVID. In this protocol, we describe plans to develop a prediction model to identify individuals at risk of developing long-COVID. Methods and analysis: We will use the national Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE II) platform, a population-level linked dataset of routine electronic healthcare data from 5.4 million individuals in Scotland. We will identify potential indicators for long-COVID by identifying patterns in primary care data linked to information from out-of-hours general practitioner encounters, accident and emergency visits, hospital admissions, outpatient visits, medication prescribing/dispensing and mortality. We will investigate the potential indicators of long-COVID by performing a matched analysis between those with a positive reverse transcriptase PCR (RT-PCR) test for SARS-CoV-2 infection and two control groups: (1) individuals with at least one negative RT-PCR test and never tested positive; (2) the general population (everyone who did not test positive) of Scotland. Cluster analysis will then be used to determine the final definition of the outcome measure for long-COVID. We will then derive, internally and externally validate a prediction model to identify the epidemiological risk factors associated with long-COVID. Ethics and dissemination: The EAVE II study has obtained approvals from the Research Ethics Committee (reference: 12/SS/0201), and the Public Benefit and Privacy Panel for Health and Social Care (reference: 1920-0279). Study findings will be published in peer-reviewed journals and presented at conferences. Understanding the predictors for long-COVID and identifying the patient groups at greatest risk of persisting symptoms will inform future treatments and preventative strategies for long-COVID.
... EAVE II is a Scotland-wide COVID-19 surveillance platform that has been used to track and forecast the epidemiology of COVID-19, inform risk stratification assessment, and investigate vaccine effectiveness and safety. [10][11][12][13][14] It comprises national health-care datasets for 5·4 million people (approximately 99% of the Scottish population) deterministically linked through the Community Health Index (CHI) number, which is a unique identifier used in all health-care contacts across National Health Service (NHS) Scotland. We used data from EAVE II to describe the demographic profile of children with asthma who had SARS-CoV-2 infections and COVID-19 hospital admissions. ...
... The sensitivity analysis including both prednisolone and dexamethasone prescriptions as the marker of uncontrolled asthma (HR 1·34 [95% CI 0·98-1·83] for no courses, 1·49 [0·89-2·52] for one course, 3·82 [2·08-7·00] for two courses, and 3·32 [1·81-6·11] for three or more courses, compared with those without asthma), the sensitivity analysis using 1-year look back before March 1, 2020, for both markers of asthma control (table 3), the subset analysis of those who tested positive for SARS-CoV-2 measuring the markers of uncontrolled asthma at the date of test (appendix p 9), and the post-hoc sensitivity analysis including regional health board as an adjusted variable in the model (appendix p 12), all showed similar results to the primary analysis. In our study, when focusing on COVID-19 hospital admission with more than 1 day length of hospital stay or focusing on COVID-19 hospital admission with previous positive test, the results did not differ much (appendix pp [10][11]. The full models of HR for COVID-19 hospitalisation using both markers of uncontrolled asthma are available in appendix pp 13-14. ...
... This might be because they might be more likely to be admitted to hospital and therefore more likely to have routine SARS-CoV-2 testing and screening in hospital than those with well controlled asthma or without asthma. In our study, when focusing on COVID-19 hospital admission with more than 1 day length of hospital stay or focusing on COVID-19 hospital admission with previous positive test, the results did not differ much from the primary analysis (appendix pp [10][11]. There might also have been different health-care seeking behaviours and a lower threshold for COVID-19 admission (influenced by physician and hospital factors) in children with poorly controlled asthma, which might have resulted in greater chances of being tested for SARS-CoV-2. ...
Article
Full-text available
Background There is an urgent need to inform policy deliberations about whether children with asthma should be vaccinated against SARS-CoV-2 and, if so, which subset of children with asthma should be prioritised. We were asked by the UK's Joint Commission on Vaccination and Immunisation to undertake an urgent analysis to identify which children with asthma were at increased risk of serious COVID-19 outcomes. Methods This national incident cohort study was done in all children in Scotland aged 5–17 years who were included in the linked dataset of Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE II). We used data from EAVE II to investigate the risk of COVID-19 hospitalisation among children with markers of uncontrolled asthma defined by either previous asthma hospital admission or oral corticosteroid prescription in the previous 2 years. A Cox proportional hazard model was used to derive hazard ratios (HRs) and 95% CIs for the association between asthma and COVID-19 hospital admission, stratified by markers of asthma control (previous asthma hospital admission and number of previous prescriptions for oral corticosteroids within 2 years of the study start date). Analyses were adjusted for age, sex, socioeconomic status, comorbidity, and previous hospital admission. Findings Between March 1, 2020, and July 27, 2021, 752 867 children were included in the EAVE II dataset, 63 463 (8·4%) of whom had clinician-diagnosed-and-recorded asthma. Of these, 4339 (6·8%) had RT-PCR confirmed SARS-CoV-2 infection. In those with confirmed infection, 67 (1·5%) were admitted to hospital with COVID-19. Among the 689 404 children without asthma, 40 231 (5·8%) had confirmed SARS-CoV-2 infections, of whom 382 (0·9%) were admitted to hospital with COVID-19. The rate of COVID-19 hospital admission was higher in children with poorly controlled asthma than in those with well controlled asthma or without asthma. When using previous hospital admission for asthma as the marker of uncontrolled asthma, the adjusted HR was 6·40 (95% CI 3·27–12·53) for those with poorly controlled asthma and 1·36 (1·02–1·80) for those with well controlled asthma, compared with those with no asthma. When using oral corticosteroid prescriptions as the marker of uncontrolled asthma, the adjusted HR was 3·38 (1·84–6·21) for those with three or more prescribed courses of corticosteroids, 3·53 (1·87–6·67) for those with two prescribed courses of corticosteroids, 1·52 (0·90–2·57) for those with one prescribed course of corticosteroids, and 1·34 (0·98–1·82) for those with no prescribed course, compared with those with no asthma. Interpretation School-aged children with asthma with previous recent hospital admission or two or more courses of oral corticosteroids are at markedly increased risk of COVID-19 hospital admission and should be considered a priority for vaccinations. This would translate into 9124 children across Scotland and an estimated 109 448 children across the UK. Funding UK Research and Innovation (Medical Research Council), Research and Innovation Industrial Strategy Challenge Fund, Health Data Research UK, and Scottish Government.
... Our methods have been described in detail in a number of previous publications. 12 -15 We used a Scotland-wide prospective cohort, which comprises linked datasets on 5·4 million people in Scotland (around 99% coverage), to construct a nested test-negative design study among individuals with incident symptomatic infections. ...
... We studied second doses of BNT162b2 (Pfizer-BioNTech), 19 ChAdOx1 (Oxford-AstraZeneca), 20 and mRNA-1273 (Moderna) 12 vaccines, and third or booster doses of BNT162b2 and mRNA-1273. Vaccination status was defined on the date of the positive RT-PCR (symptom date for test-negative design) test and coded using the following categories: unvaccinated; 1-27 days after first dose; 28 days or more after first dose; 0-13 days after second dose; 14-41 days after second dose; 42-69 days after second dose; and 10 weeks or more (≥70 days) after ...
Article
Full-text available
Background Since its emergence in November, 2021, in southern Africa, the SARS-CoV-2 omicron variant of concern (VOC) has rapidly spread across the world. We aimed to investigate the severity of omicron and the extent to which booster vaccines are effective in preventing symptomatic infection. Methods In this study, using the Scotland-wide Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE II) platform, we did a cohort analysis with a nested test-negative design incident case-control study covering the period Nov 1–Dec 19, 2021, to provide initial estimates of omicron severity and the effectiveness of vaccine boosters against symptomatic disease relative to 25 weeks or more after the second vaccine dose. Primary care data derived from 940 general practices across Scotland were linked to laboratory data and hospital admission data. We compared outcomes between infection with the delta VOC (defined as S-gene positive) and the omicron VOC (defined as S-gene negative). We assessed effectiveness against symptomatic SARS-CoV-2 infection, with infection confirmed through a positive RT-PCR. Findings By Dec 19, 2021, there were 23 840 S-gene-negative cases in Scotland, which were predominantly among those aged 20–39 years (11 732 [49·2%]). The proportion of S-gene-negative cases that were possible reinfections was more than ten times that of S-gene-positive cases (7·6% vs 0·7%; p<0·0001). There were 15 hospital admissions in S-gene-negative individuals, giving an adjusted observed-to-expected admissions ratio of 0·32 (95% CI 0·19–0·52). The booster vaccine dose was associated with a 57% (54–60) reduction in the risk of symptomatic S-gene-negative infection relative to individuals who tested positive 25 weeks or more after the second vaccine dose. Interpretation These early national data suggest that omicron is associated with a two-thirds reduction in the risk of COVID-19 hospitalisation compared with delta. Although offering the greatest protection against delta, the booster dose of vaccination offers substantial additional protection against the risk of symptomatic COVID-19 for omicron compared with 25 weeks or more after the second vaccine dose. Funding Health Data Research UK, National Core Studies, Public Health Scotland, Scottish Government, UK Research and Innovation, and University of Edinburgh.
... Early Pandemic Evaluation and Enhanced Surveillance (EAVE II) is a COVID-19 surveillance platform that comprises linked primary care, secondary care, mortality, virological sequencing, and COVID-19 testing data covering 5.4 million (~99% population coverage) people in Scotland. EAVE II has been used to track and forecast the epidemiology of COVID-19, inform deliberations on risk stratification, and investigate vaccine effectiveness and safety [4][5][6][7][8][9][10][11][12][13]. ...
Article
Background: In July 2021, a new variant of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) in the Delta lineage was detected in the United Kingdom (UK), named AY.4.2 or "Delta plus". By October 2021, the AY.4.2 variant accounted for approximately 10-11% of cases in the UK. AY.4.2 was designated as a variant under investigation by the UK Health and Security Agency on 20 October 2021. This study aimed to investigate vaccine effectiveness (VE) against symptomatic COVID-19 (Coronavirus disease 2019) infection and COVID-19 hospitalisation/death for the AY.4.2 variant. Methods: We used the Scotland-wide Early Pandemic Evaluation and Enhanced Surveillance (EAVE-II) platform to estimate the VE of the ChAdOx1, BNT162b2, and mRNA-1273 vaccines against symptomatic infection and severe COVID-19 outcomes in adults. The study was conducted from June 8 to October 25, 2021. We used a test-negative design (TND) to estimate VE against reverse transcriptase polymerase chain reaction (RT-PCR) confirmed symptomatic SARS-CoV-2 infection while adjusting for sex, socioeconomic status, number of coexisting conditions, and splines in time and age. We also performed a cohort study using a Cox proportional hazards model to estimate VE against a composite outcome of COVID-19 hospital admission or death, with the same adjustments. Results: We found an overall VE against symptomatic SARS-CoV-2 infection due to AY.4.2 of 73% (95% confidence interval (CI) = 62-81) for >14 days post-second vaccine dose. Good protection against AY.4.2 symptomatic infection was observed for BNT162b2, ChAdOx1, and mRNA-1273. In unvaccinated individuals, the hazard ratio (HR) for COVID-19 hospital admission or death from AY.4.2 among community detected cases was 1.77 (95% CI = 1.02-3.07) relative to unvaccinated individuals who were infected with Delta, after adjusting for multiple potential confounders. VE against AY.4.2 COVID-19 admissions or deaths was 87% (95% CI = 74-93) >28 days post-second vaccination relative to unvaccinated. Conclusions: We found that AY.4.2 was associated with an increased risk of COVID-19 hospitalisations or deaths in unvaccinated individuals compared with Delta and that vaccination provided substantial protection against symptomatic SARS-CoV-2 and severe COVID-19 outcomes following Delta AY.4.2 infection. High levels of vaccine uptake and protection offered by existing vaccines, as well as the rapid emergence of the Omicron variant may have contributed to the AY.4.2 variant never progressing to a variant of concern.
... Participants aged 50-75 who were current or ex-smokers with at least 20 years pack history were recruited to ECLS between December 2013 and April 2015, and all baseline assessments of plasma antibody levels occurred during this time (14). SARS-CoV-2 status and outcome data for ECLS participants during 2020 were obtained from the EAVE II database, which is a national, real-time prospective cohort using Scotland's health data infrastructure, to describe the epidemiology of SARS-CoV-2 infection, patterns of healthcare use and outcomes (15,16). Data from both sources was linked using Scotland's Community Health Index (CHI) number at the University of Dundee's Health Informatics Centre (HIC) (17,18). ...
Article
Full-text available
Background: Patients with more severe forms of SARS-CoV-2 exhibit activation of immunological cascades. Participants (current or ex-smokers with at least 20 years pack history) in a trial (Early Diagnosis of Lung Cancer, Scotland [ECLS]) of autoantibody detection to predict lung cancer risk had seven autoantibodies measured 5 years before the pandemic. This study compared the response to Covid infection in study participants who tested positive and negative to antibodies to tumour-associated antigens: p53, NY-ESO-1, CAGE, GBU4-5, HuD, MAGE A4 and SOX2. Methods: Autoantibody data from the ECLS study was deterministically linked to the EAVE II database, a national, real-time prospective cohort using Scotland’s health data infrastructure, to describe the epidemiology of SARS-CoV-2 infection, patterns of healthcare use and outcomes. The strength of associations was explored using a network algorithm for exact contingency table significance testing by permutation. Results: There were no significant differences discerned between SARS-CoV-2 test results and EarlyCDT-Lung test results (p = 0.734). An additional analysis of intensive care unit (ICU) admissions detected no significant differences between those who tested positive and negative. Subgroup analyses showed no difference in COVID-19 positivity or death rates amongst those diagnosed with chronic obstructive pulmonary disease (COPD) with positive and negative EarlyCDT results. Conclusions: This hypothesis-generating study demonstrated no clinically valuable or statistically significant associations between EarlyCDT positivity in 2013-15 and the likelihood of SARS-CoV-2 positivity in 2020, ICU admission or death in all participants (current or ex-smokers with at least 20 years pack history) or in those with COPD or lung cancer.
... CDC will update information as new evidence becomes available. [30] ...
Article
Full-text available
The pandemic of COVID-19 had caused millions of deaths and left hundred millions of patients. Actually the disease will be controlled only through vaccination. With the prevalence of wrong information, convincing people to get vaccinated is very difficult. So, accurate vaccine information is critical and essential. The most common myths and rumors were related to COVID-19 vaccine safety and utilization. They include: The vaccines were developed rapidly without research, It is not safe and not necessary for children, They cause many variants of the virus, many side effects, complications and deaths, The mRNA vaccine is not considered a vaccine, They contain microchips, fetal cells and around 99% graphene oxide, They cause the body to be magnetic, They shed or release their components, They will alter DNA, It is unsafe for women planning to have a baby one day, They cause COVID-19 infection, abortion and miscarriage, They cause test positive for COVID-19 on a viral test, They should be postponed for certain period of time after getting a flu vaccine or another vaccine, Vaccines not needed for patients recovered from COVID-19, Pfizer-BioNTech vaccine is the best one, Fully vaccinated do not need to avoid close contact with others or wear a mask, The second dose of vaccine is not necessarily the same as first dose and medical conditions are contraindication for vaccination. While social media posts and some news outlets may make it harder to know what is fact or fiction, the science is clear, approved COVID-19 vaccines safe and effective.
... COVID-19, inform risk stratification assessment, and investigate vaccine effectiveness and safety. 1,[14][15][16][17][18][19] It comprises national health-care datasets on 5·4 million people (approximately 99% of the Scottish population) deterministically linked through the Community Health Index (CHI) number, which is a unique identifier used in all health-care contacts across NHS Scotland. We used data from EAVE II to describe the demographic profile of adults with asthma who had SARS-CoV-2 infections, COVID-19 hospital admissions, and ICU admissions or deaths. ...
Article
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Background There is considerable uncertainty over whether adults with asthma should be offered booster vaccines against SARS-CoV-2 and, if so, who should be prioritised for booster vaccination. We were asked by the UK's Joint Commission on Vaccination and Immunisation to undertake an urgent analysis to identify which adults with asthma were at an increased risk of serious COVID-19 outcomes to inform deliberations on booster COVID-19 vaccines. Methods This national incident cohort study was done in all adults in Scotland aged 18 years and older who were included in the linked dataset of Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE II). We used data from EAVE II to investigate the risk of COVID-19 hospitalisation and the composite outcome of intensive care unit (ICU) admission or death from COVID-19 among adults with asthma. A Cox proportional hazard model was used to derive adjusted hazard ratios (HRs) and 95% CIs for the association between asthma and COVID-19 hospital admission and ICU admission or death, stratified by markers of history of an asthma attack defined by either oral corticosteroid prescription (prednisolone, prednisone, and dexamethasone) in the 2 years before March 1, 2020, or hospitalisation for asthma before March 1, 2020. Analyses were adjusted for age, sex, socioeconomic status, comorbidity, previous hospitalisation, and vaccine status. Findings Between March 1, 2020, and July 27, 2021, 561 279 (12·7%) of 4 421 663 adults in Scotland had clinician-diagnosed-and-recorded-asthma. Among adults with asthma, 39 253 (7·0%) had confirmed SARS-CoV-2 infections, of whom 4828 (12·3%) were admitted to hospital for COVID-19 (among them, an estimated 600 [12·4%] might have been due to nosocomial infections). Adults with asthma were found to be at an increased risk of COVID-19 hospital admission (adjusted HR 1·27, 95% CI 1·23–1·32) compared with those without asthma. When using oral corticosteroid prescribing in the preceding 2 years as a marker for history of an asthma attack, the adjusted HR was 1·54 (95% CI 1·46–1·61) for those with three or more prescribed courses of oral corticosteroids, 1·37 (1·26–1·48) for those with two prescribed courses, 1·30 (1·23–1·37) for those with one prescribed course, and 1·15 (1·11–1·21) for those without any courses, compared with those aged 18 years or older without asthma. Adults with asthma were found to be at an increased risk of COVID-19 ICU admission or death compared with those without asthma (adjusted HR 1·13, 95 % CI 1·05–1·22). The adjusted HR was 1·44 (95% CI 1·31–1·58) for those with three or more prescribed courses of oral corticosteroids, 1·27 (1·09–1·48) for those with two prescribed courses, 1·04 (0·93–1·16) for those with one prescribed course, and 1·06 (0·97–1·17) for those without any course, compared with adults without asthma. Interpretation Adults with asthma who have required two or more courses of oral corticosteroids in the previous 2 years or a hospital admission for asthma before March 1, 2020, are at increased risk of both COVID-19 hospitalisation and ICU admission or death. Patients with a recent asthma attack should be considered a priority group for booster COVID-19 vaccines. Funding UK Research and Innovation (Medical Research Council), Research and Innovation Industrial Strategy Challenge Fund, Health Data Research UK, and Scottish Government.
... The COVID-19 in Pregnancy in Scotland (COPS) study (a sub-study of EAVE II (Early Pandemic Evaluation and Enhanced Surveillance of COVID- 19) 22,23 ) is a national, prospective dynamic cohort of all women who were pregnant on, or became pregnant Infection and vaccination in pregnancy was defined as infection diagnosed or vaccination given at any point from the date of conception (2 + 0 weeks gestation) to the date the pregnancy ends inclusive. The date of first positive viral RT−PCR sample collection was taken as the date of onset of the first episode of COVID-19. ...
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Population-level data on COVID-19 vaccine uptake in pregnancy and SARS-CoV-2 infection outcomes are lacking. We describe COVID-19 vaccine uptake and SARS-CoV-2 infection in pregnant women in Scotland, using whole-population data from a national, prospective cohort. Between the start of a COVID-19 vaccine program in Scotland, on 8 December 2020 and 31 October 2021, 25,917 COVID-19 vaccinations were given to 18,457 pregnant women. Vaccine coverage was substantially lower in pregnant women than in the general female population of 18−44 years; 32.3% of women giving birth in October 2021 had two doses of vaccine compared to 77.4% in all women. The extended perinatal mortality rate for women who gave birth within 28 d of a COVID-19 diagnosis was 22.6 per 1,000 births (95% CI 12.9−38.5; pandemic background rate 5.6 per 1,000 births; 452 out of 80,456; 95% CI 5.1−6.2). Overall, 77.4% (3,833 out of 4,950; 95% CI 76.2−78.6) of SARS-CoV-2 infections, 90.9% (748 out of 823; 95% CI 88.7−92.7) of SARS-CoV-2 associated with hospital admission and 98% (102 out of 104; 95% CI 92.5−99.7) of SARS-CoV-2 associated with critical care admission, as well as all baby deaths, occurred in pregnant women who were unvaccinated at the time of COVID-19 diagnosis. Addressing low vaccine uptake rates in pregnant women is imperative to protect the health of women and babies in the ongoing pandemic.
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Background Little is known about vaccine effectiveness over time among adolescents, especially against the SARS-CoV-2 omicron (B.1.1.529) variant. This study assessed the associations between time since two-dose vaccination with BNT162b2 and the occurrence of symptomatic SARS-CoV-2 infection and severe COVID-19 among adolescents in Brazil and Scotland. Methods We did test-negative, case-control studies in adolescents aged 12–17 years with COVID-19-related symptoms in Brazil and Scotland. We linked records of SARS-CoV-2 RT-PCR and antigen tests to national vaccination and clinical records. We excluded tests from individuals who did not have symptoms, were vaccinated before the start of the national vaccination programme, received vaccines other than BNT162b2 or a SARS-CoV-2 booster dose of any kind, or had an interval between their first and second dose of fewer than 21 days. Additionally, we excluded negative SARS-CoV-2 tests recorded within 14 days of a previous negative test, negative tests recorded within 7 days after a positive test, any test done within 90 days after a positive test, and tests with missing sex and location information. Cases (SARS-CoV-2 test-positive adolescents) and controls (test-negative adolescents) were drawn from a sample of individuals in whom tests were collected within 10 days of symptom onset. We estimated the adjusted odds ratio and vaccine effectiveness against symptomatic COVID-19 for both countries and against severe COVID-19 (hospitalisation or death) for Brazil across fortnightly periods. Findings We analysed 503 776 tests from 2 948 538 adolescents in Brazil between Sept 2, 2021, and April 19, 2022, and 127 168 tests from 404 673 adolescents in Scotland between Aug 6, 2021, and April 19, 2022. Vaccine effectiveness peaked at 14–27 days after the second dose in both countries during both waves, and was significantly lower against symptomatic infection during the omicron-dominant period in Brazil (64·7% [95% CI 63·0–66·3]) and in Scotland (82·6% [80·6–84·5]), than it was in the delta-dominant period (80·7% [95% CI 77·8–83·3] in Brazil and 92·8% [85·7–96·4] in Scotland). Vaccine efficacy started to decline from 27 days after the second dose for both countries, reducing to 5·9% (95% CI 2·2–9·4) in Brazil and 50·6% (42·7–57·4) in Scotland at 98 days or more during the omicron-dominant period. In Brazil, protection against severe disease remained above 80% from 28 days after the second dose and was 82·7% (95% CI 68·8–90·4) at 98 days or more after receiving the second dose. Interpretation We found waning vaccine protection of BNT162b2 against symptomatic COVID-19 infection among adolescents in Brazil and Scotland from 27 days after the second dose. However, protection against severe COVID-19 outcomes remained high at 98 days or more after the second dose in the omicron-dominant period. Booster doses for adolescents need to be considered. Funding UK Research and Innovation (Medical Research Council), Scottish Government, Health Data Research UK BREATHE Hub, Fiocruz, Fazer o Bem Faz Bem programme, Brazilian National Research Council, and Wellcome Trust. Translation For the Portuguese translation of the abstract see Supplementary Materials section.
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The detection of SARS-CoV-2 variants correlates to the rapid increase in the number of COVID-19 cases. The variants may emerge with fatal consequences to the usual resistance to the immunity arbitrated by the available vaccines to prevent the pandemic of COVID-19. When some variants of interest have increased transmissibility or virulence, the priority of appropriate public health measures and vaccination programs will increase. Still and all, the genomic surveillance must continue to monitor this trend as these observations have grave implications for mitigation and vaccination policies and must be considered by policy makers when designing public health interventions. Moreover, the global response must be directed toward urgency with scientific reliability. Bangladesh J Med Microbiol 2021; 15 (2): 30-35
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Introduction: The effects of SARS-CoV-2 in pregnancy are not fully delineated. We will describe the incidence of COVID-19 in pregnancy at population level in Scotland, in a prospective cohort study using linked data. We will determine associations between COVID-19 and adverse pregnancy, neonatal and maternal outcomes and the proportion of confirmed cases of SARS-CoV-2 infection in neonates associated with maternal COVID-19. Methods and analysis: Prospective cohort study using national linked data sets. We will include all women in Scotland, UK, who were pregnant on or became pregnant after, 1 March 2020 (the date of the first confirmed case of SARS-CoV-2 infection in Scotland) and all births in Scotland from 1 March 2020 onwards. Individual-level data will be extracted from data sets containing details of all livebirths, stillbirth, terminations of pregnancy and miscarriages and ectopic pregnancies treated in hospital or attending general practice. Records will be linked within the Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE II) platform, which includes primary care records, virology and serology results and details of COVID-19 Community Hubs and Assessment Centre contacts and deaths. We will perform analyses using definitions for confirmed, probable and possible COVID-19 and report serology results (where available). Outcomes will include congenital anomaly, miscarriage, stillbirth, termination of pregnancy, preterm birth, neonatal infection, severe maternal disease and maternal deaths. We will perform descriptive analyses and appropriate modelling, adjusting for demographic and pregnancy characteristics and the presence of comorbidities. The cohort will provide a platform for future studies of the effectiveness and safety of therapeutic interventions and immunisations for COVID-19 and their effects on childhood and developmental outcomes. Ethics and dissemination: COVID-19 in Pregnancy in Scotland is a substudy of EAVE II(, which has approval from the National Research Ethics Service Committee. Findings will be reported to Scottish Government, Public Health Scotland and published in peer-reviewed journals.
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Objective To derive and validate a risk prediction algorithm to estimate hospital admission and mortality outcomes from coronavirus disease 2019 (covid-19) in adults. Design Population based cohort study. Setting and participants QResearch database, comprising 1205 general practices in England with linkage to covid-19 test results, Hospital Episode Statistics, and death registry data. 6.08 million adults aged 19-100 years were included in the derivation dataset and 2.17 million in the validation dataset. The derivation and first validation cohort period was 24 January 2020 to 30 April 2020. The second temporal validation cohort covered the period 1 May 2020 to 30 June 2020. Main outcome measures The primary outcome was time to death from covid-19, defined as death due to confirmed or suspected covid-19 as per the death certification or death occurring in a person with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the period 24 January to 30 April 2020. The secondary outcome was time to hospital admission with confirmed SARS-CoV-2 infection. Models were fitted in the derivation cohort to derive risk equations using a range of predictor variables. Performance, including measures of discrimination and calibration, was evaluated in each validation time period. Results 4384 deaths from covid-19 occurred in the derivation cohort during follow-up and 1722 in the first validation cohort period and 621 in the second validation cohort period. The final risk algorithms included age, ethnicity, deprivation, body mass index, and a range of comorbidities. The algorithm had good calibration in the first validation cohort. For deaths from covid-19 in men, it explained 73.1% (95% confidence interval 71.9% to 74.3%) of the variation in time to death (R ² ); the D statistic was 3.37 (95% confidence interval 3.27 to 3.47), and Harrell’s C was 0.928 (0.919 to 0.938). Similar results were obtained for women, for both outcomes, and in both time periods. In the top 5% of patients with the highest predicted risks of death, the sensitivity for identifying deaths within 97 days was 75.7%. People in the top 20% of predicted risk of death accounted for 94% of all deaths from covid-19. Conclusion The QCOVID population based risk algorithm performed well, showing very high levels of discrimination for deaths and hospital admissions due to covid-19. The absolute risks presented, however, will change over time in line with the prevailing SARS-C0V-2 infection rate and the extent of social distancing measures in place, so they should be interpreted with caution. The model can be recalibrated for different time periods, however, and has the potential to be dynamically updated as the pandemic evolves.
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Introduction Following the emergence of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in December 2019 and the ensuing COVID-19 pandemic, population-level surveillance and rapid assessment of the effectiveness of existing or new therapeutic or preventive interventions are required to ensure that interventions are targeted to those at highest risk of serious illness or death from COVID-19. We aim to repurpose and expand an existing pandemic reporting platform to determine the attack rate of SARS-CoV-2, the uptake and effectiveness of any new pandemic vaccine (once available) and any protective effect conferred by existing or new antimicrobial drugs and other therapies. Methods and analysis A prospective observational cohort will be used to monitor daily/weekly the progress of the COVID-19 epidemic and to evaluate the effectiveness of therapeutic interventions in approximately 5.4 million individuals registered in general practices across Scotland. A national linked dataset of patient-level primary care data, out-of-hours, hospitalisation, mortality and laboratory data will be assembled. The primary outcomes will measure association between: (A) laboratory confirmed SARS-CoV-2 infection, morbidity and mortality, and demographic, socioeconomic and clinical population characteristics; and (B) healthcare burden of COVID-19 and demographic, socioeconomic and clinical population characteristics. The secondary outcomes will estimate: (A) the uptake (for vaccines only); (B) effectiveness; and (C) safety of new or existing therapies, vaccines and antimicrobials against SARS-CoV-2 infection. The association between population characteristics and primary outcomes will be assessed via multivariate logistic regression models. The effectiveness of therapies, vaccines and antimicrobials will be assessed from time-dependent Cox models or Poisson regression models. Self-controlled study designs will be explored to estimate the risk of therapeutic and prophylactic-related adverse events. Ethics and dissemination We obtained approval from the National Research Ethics Service Committee, Southeast Scotland 02. The study findings will be presented at international conferences and published in peer-reviewed journals.
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Objective To characterise the clinical features of patients admitted to hospital with coronavirus disease 2019 (covid-19) in the United Kingdom during the growth phase of the first wave of this outbreak who were enrolled in the International Severe Acute Respiratory and emerging Infections Consortium (ISARIC) World Health Organization (WHO) Clinical Characterisation Protocol UK (CCP-UK) study, and to explore risk factors associated with mortality in hospital. Design Prospective observational cohort study with rapid data gathering and near real time analysis. Setting 208 acute care hospitals in England, Wales, and Scotland between 6 February and 19 April 2020. A case report form developed by ISARIC and WHO was used to collect clinical data. A minimal follow-up time of two weeks (to 3 May 2020) allowed most patients to complete their hospital admission. Participants 20 133 hospital inpatients with covid-19. Main outcome measures Admission to critical care (high dependency unit or intensive care unit) and mortality in hospital. Results The median age of patients admitted to hospital with covid-19, or with a diagnosis of covid-19 made in hospital, was 73 years (interquartile range 58-82, range 0-104). More men were admitted than women (men 60%, n=12 068; women 40%, n=8065). The median duration of symptoms before admission was 4 days (interquartile range 1-8). The commonest comorbidities were chronic cardiac disease (31%, 5469/17 702), uncomplicated diabetes (21%, 3650/17 599), non-asthmatic chronic pulmonary disease (18%, 3128/17 634), and chronic kidney disease (16%, 2830/17 506); 23% (4161/18 525) had no reported major comorbidity. Overall, 41% (8199/20 133) of patients were discharged alive, 26% (5165/20 133) died, and 34% (6769/20 133) continued to receive care at the reporting date. 17% (3001/18 183) required admission to high dependency or intensive care units; of these, 28% (826/3001) were discharged alive, 32% (958/3001) died, and 41% (1217/3001) continued to receive care at the reporting date. Of those receiving mechanical ventilation, 17% (276/1658) were discharged alive, 37% (618/1658) died, and 46% (764/1658) remained in hospital. Increasing age, male sex, and comorbidities including chronic cardiac disease, non-asthmatic chronic pulmonary disease, chronic kidney disease, liver disease and obesity were associated with higher mortality in hospital. Conclusions ISARIC WHO CCP-UK is a large prospective cohort study of patients in hospital with covid-19. The study continues to enrol at the time of this report. In study participants, mortality was high, independent risk factors were increasing age, male sex, and chronic comorbidity, including obesity. This study has shown the importance of pandemic preparedness and the need to maintain readiness to launch research studies in response to outbreaks. Study registration ISRCTN66726260.
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Introduction Seasonal (inactivated) influenza vaccination is recommended for all individuals aged 65+ and in individuals under 65 who are at an increased risk of complications of influenza infection, for example, people with asthma. Live attenuated influenza vaccine (LAIV) was recommended for children as they are thought to be responsible for much of the transmission of influenza to the populations at risk of serious complications from influenza. A phased roll-out of the LAIV pilot programme began in 2013/2014. There is limited evidence for vaccine effectiveness (VE) in the populations targeted for influenza vaccination. The aim of this study is to examine the safety and effectiveness of the live attenuated seasonal influenza vaccine programme in children and the inactivated seasonal influenza vaccination programme among different age and at-risk groups of people. Methods and analysis Test negative and cohort study designs will be used to estimate VE. A primary care database covering 1.25 million people in Scotland for the period 2000/2001 to 2015/2016 will be linked to the Scottish Immunisation Recall Service (SIRS), Health Protection Scotland virology database, admissions to Scottish hospitals and the Scottish death register. Vaccination status (including LAIV uptake) will be determined from the primary care and SIRS database. The primary outcome will be influenza-positive real-time PCR tests carried out in sentinel general practices and other healthcare settings. Secondary outcomes include influenza-like illness and asthma-related general practice consultations, hospitalisations and death. An instrumental variable analysis will be carried out to account for confounding. Self-controlled study designs will be used to estimate the risk of adverse events associated with influenza vaccination. Ethics and dissemination We obtained approval from the National Research Ethics Service Committee, West Midlands—Edgbaston. The study findings will be presented at international conferences and published in peer-reviewed journals. Trial registration number ISRCTN88072400; Pre-results.
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After the introduction of any new pandemic influenza, population-level surveillance and rapid assessment of the effectiveness of a new vaccination will be required to ensure that it is targeted to those at increased risk of serious illness or death from influenza. Objective We aimed to build a pandemic influenza reporting platform that will determine, once a new pandemic is under way: the uptake and effectiveness of any new pandemic vaccine or any protective effect conferred by antiviral drugs once available; the clinical attack rate of pandemic influenza; and the existence of protection provided by previous exposure to, and vaccination from, A/H1N1 pandemic or seasonal influenza/identification of susceptible groups. Design An observational cohort and test-negative study design will be used (post pandemic). Setting A national linkage of patient-level general practice data from 41 Practice Team Information general practices, hospitalisation and death certification, virological swab and serology-linked data. Participants We will study a nationally representative sample of the Scottish population comprising 300,000 patients. Confirmation of influenza using reverse transcription polymerase chain reaction and, in a subset of the population, serology. Interventions Future available pandemic influenza vaccination and antivirals will be evaluated. Main outcome measures To build a reporting platform tailored towards the evaluation of pandemic influenza vaccination. This system will rapidly measure vaccine effectiveness (VE), adjusting for confounders, estimated by determining laboratory-confirmed influenza; influenza-related morbidity and mortality, including general practice influenza-like illnesses (ILIs); and hospitalisation and death from influenza and pneumonia. Once a validated haemagglutination inhibition assay has been developed (and prior to the introduction of any vaccination), cross-reactivity with previous exposure to A/H1N1 or A/H1N1 vaccination, other pandemic influenza or other seasonal influenza vaccination or exposure will be measured. Conclusions A new sentinel system, capable of rapidly determining the estimated incidence of pandemic influenza, and pandemic influenza vaccine and antiviral uptake and effectiveness in preventing influenza and influenza-related clinical outcomes, has been created. We have all of the required regulatory approvals to allow rapid activation of the sentinel systems in the event of a pandemic. Of the 41 practices expressing an interest in participating, 40 have completed all of the necessary paperwork to take part in the reporting platform. The data extraction tool has been installed in these practices. Data extraction and deterministic linkage systems have been tested. Four biochemistry laboratories have been recruited, and systems for serology collection and linkage of samples to general practice data have been put in place. Future work The reporting platform has been set up and is ready to be activated in the event of any pandemic of influenza. Building on this infrastructure, there is now the opportunity to extend the network of general practices to allow important subgroup analyses of VE (e.g. for patients with comorbidities, at risk of serious ILI) and to link to other data sources, in particular to test for maternal outcomes in pregnant patients. Study registration This study is registered as ISRCTN55398410. Funding The National Institute for Health Research Health Technology Assessment programme.
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Globally, seasonal influenza is responsible for an estimated 3 to 5 million cases of severe illness and 250,000 to 500,000 deaths per year. It is uncertain to what extent national vaccination programmes can prevent this morbidity and mortality. Objective To determine the effectiveness of the seasonal trivalent inactivated influenza vaccine. Design We undertook a retrospective observational cohort study. A propensity score model was constructed and adjusted odds ratios (ORs) were calculated to assess differences in vaccine uptake according to a number of patient characteristics. Adjusted illness and mortality hazard ratios (HRs) were estimated from a Cox proportional hazards model adjusted for sex, age, socioeconomic status, smoking status, urban/rural location, clinical at-risk groups (i.e. patients with chronic respiratory, heart, kidney, liver or neurological disease, immunosuppression and diabetes), Charlson comorbidity index, previous pneumococcal and influenza vaccination, and number of previous primary care consultations, prescribed drugs and hospital admissions. We also included nursing home residence and social care support. Vaccine effectiveness (VE) was expressed as a percentage, and represents a reduction in risk provided by the vaccine for a given outcome (e.g. laboratory-confirmed influenza). This was calculated as 1 − HR, where HR is that of the measured clinical outcome in vaccinated compared with unvaccinated individuals. For estimates of VE derived from linked virological swab data, we carried out a nested case–control study design. Setting A national linkage of patient-level primary care, hospital, death certification and virological swab-linked data across nine influenza seasons (2000–9). Participants A nationally representative sample of the Scottish population during 1,767,919 person-seasons of observation. Cases of influenza were confirmed using reverse transcription-polymerase chain reaction (RT-PCR) in a subset of the population ( n = 3323). Interventions Trivalent inactivated seasonal influenza vaccination ( n = 274,071). Main outcome measures VE, pooled across seasons and adjusting for confounders, was estimated by determining laboratory-confirmed influenza, influenza-related morbidity and mortality including primary care influenza-like illnesses, hospitalisation and death from influenza and pneumonia. Results Most vaccines (93.6%; n = 256,474 vaccines) were administered to at-risk patients targeted for vaccination, with a 69.3% uptake among those aged ≥ 65 years (178,754 vaccinations during 258,100 person-seasons). For at-risk patients aged
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The 2009 A/H1N1 influenza pandemic was responsible for considerable global morbidity and mortality. In 2009, UK funders, including the National Institute for Health Research (NIHR) rapidly funded and activated a number of research studies to inform clinical and public health actions. However, even with accelerated processes, some studies were completed too late for their results to have an early and significant impact on clinical care. This was in contrast to a study funded separately in 2008, which was published within a matter of weeks after the first two cases of 2009 A/H1N1 influenza virus infection were detected in the UK. In recognition of the impact of the NIHR-funded 2009 A/H1N1 influenza work and following reflection on the inherent delays in calling for research proposals, assessing, funding, and setting up the subsequent projects, including obtaining relevant ethical and regulatory approvals, the NIHR funded a second wave of studies in 2012. Our portfolio of projects have now been set-up (relevant permissions put in place, arrangements made for data collection) and pilot tested where relevant. All studies are now in ‘hibernation’ - that is, they have been put on standby mode in a maintenance-only state to await activation in the event of a pandemic being declared. In this thought piece, we describe the projects that were set up, the challenges of putting these projects into hibernation, on-going activities to maintain readiness for activation, and discuss how we think about planning research for a range of major incidents (e.g. other emerging infectious diseases, chemical, biological, radiological or nuclear (CBRN) risks, extreme weather events or industrial accidents).
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A targeted vaccination programme for pandemic H1N1 2009 influenza was introduced in Scotland, UK, in October, 2009. We sought to assess the effectiveness of this vaccine in a sample of the Scottish population during the 2009-10 pandemic. We assessed the effectiveness of the Scottish pandemic H1N1 2009 influenza vaccination with a retrospective cohort design. We linked data of patient-level primary care, hospital records, death certification, and virological swabs to construct our cohort. We estimated vaccine effectiveness in a nationally representative sample of the Scottish population by establishing the risk of hospital admission and death (adjusted for potential confounders) resulting from influenza-related morbidity in vaccinated and unvaccinated patients and laboratory-confirmed cases of influenza H1N1 2009 in a subset of patients. Pandemic H1N1 2009 influenza vaccination began in week 43 of 2009 (Oct 21, 2009) and was given to 38,296 (15·5%, 95% CI 15·4-15·6) of 247,178 people by the end of the study period (Jan 31, 2010). 208,882 (85%) people were unvaccinated. There were 5207 emergency hospital admissions and 579 deaths in the unvaccinated population and 924 hospital admissions and 71 deaths in the vaccinated population during 23,893,359 person-days of observation. The effectiveness of H1N1 vaccination for prevention of emergency hospital admissions from influenza-related disorders was 19·5% (95% CI 0·8-34·7). The vaccine's effectiveness in preventing laboratory-confirmed influenza was 77·0% (95% CI 2·0-95·0). Pandemic H1N1 2009 influenza vaccination was associated with protection against pandemic influenza and a reduction in hospital admissions from influenza-related disorders in Scotland during the 2009-10 pandemic. National Institute for Health Research Health Technology Assessment Programme (UK).