DRIVE season 2018/2019

Acknowledgement
DRIVE project has received funding from the Innovative
Medicines Initiative 2 Joint Undertaking under grant
agreement No 777363, This Joint Undertaking receives
support from the European Union’s Horizon 2020
research and innovation programme and EFPIA.
DRIVE
Season 2018/2019
Acknowledgement
Anke Stuurman (P95), Stefano Mosca (CIRI-IT), Stefania Bellino
(ISS),Simon de Lusignan (RCGP RSC), Joël Mossong (LNS),Katrin
Katrin Wiedeschitz (MUV), Elisabetta Pandolfi (IT-BIVE-HOSP),
Javier Díez-Domingo (FISABIO), José Ángel Rodrigo Pendás
(HUVH), Rajija Auvinen (HUS), Ulrike Baum (THL), Jorne Biccler
(P95)
DRIVE Annual Forum
July 17th 2019, Helsinki

Introduction
• Objectives
• Sites
Primary objectives
• Data collection (TND sites, register-based cohort site)
• Context, methods and results 2018/19 season
Exploratory objectives
• Healthcare worker cohort
Discussion
Outline

DRIVE network building and expanding
2017/18: 5 sites; 2018/19 sites: 13; 2019/20: 15 sites
Site selection process improved
More sites joining next year
Sites visits done
Study harmonization
Generic protocols in use
Electronic Study Support Application (ESSA)
Minimum data requirements
DRIVE Season 2018/2019

DRIVE Study Platform– details later today

Statistical analysis
Improved SAP
Re-usable and quality controlled R-functinons
Quality Control Audit Committee
Data quality reports
Automated quality checks by ESSA
GDPR-compliant DRIVE Study Platform
Communication
Quarterly newsletter
Governance
Better defined roles ISC
DRIVE Season 2018/2019

Study sites
Vall d’Hebron University
Hospital (VHUH)
FISABIO, Valencia
National Institute of
Health (ISS)
National Institute for
Health and Welfare (THL)
Medizinische Universität
Vienna (MUV)
Helsinki University
Central Hospital (HUS)
University of Athens
Medical School (UoA)
Centro Interuniversitario
di Ricerca sull’Influenza
(CIRI-IT)
Italian Hospital
Network (IT-BIVE-
HOSP)
Infectious Diseases
(NIID)
Royal College of GPs
and Uni of Surrey
(RCGP&UNIS)
New sites 2018/19 season
Laboratoire National
de Santé (LNS)

Study sites: TND primary care
National Institute of
Health (ISS)
Medizinische Universität
Vienna (MUV)
(CIRI-IT)
Royal College of GPs
and Uni of Surrey
(RCGP&UNIS)
Laboratoire National
de Santé (LNS)

Study sites: TND hospital
Vall d’Hebron University
Hospital (VHUH)
FISABIO, Valencia
Italian Hospital
Network (IT-BIVE-
HOSP)
Infectious Diseases
(NIID)
Helsinki University
Central Hospital (HUS)

Study sites: register-based cohort
Health and Welfare (THL)

Study sites: clinical cohorts
University of Athens
Medical School (UoA)
(CIRI-IT)

Overall and vaccine brand-specific IVE against
laboratory-confirmed influenza, by age and setting
Primary objectives
Age
6m-17y 18-64y 65+y
Primary care
Hospital
Setting
A
B
H3N2
H1N1
Yamagata
Victoria

Vaccine type-specific IVE against laboratory-
confirmed influenza, by age and setting
Secondary objectives
Age
6m-17y 18-64y 65+y
Primary care
Hospital
Setting
A
B
H3N2
H1N1
Yamagata
Victoria

Primary objectives
and secondary objectives

Acknowledgement
Primary care TND
1) CIRI-IT, 2) ISS, 3) RCGP RSC, 4) LNS

Primary Care based
TEST NEGATIVE
DESIGN STUDY
CIRI-IT Centro Interuniversitario di Ricerca
sull’Influenza e sulle altre infezioni trasmissibili

Study characteristics
CIRI-IT is an interuniversity center for research on influenza and other transmissible
infections.
Since its inception (20 years ago), it has conducted clinical-epidemiological and virological influenza
surveillance with the aim of providing epidemiological information on seasonal trends, in order to
determine the onset, duration, intensity and burden of Influenza-Like Illness and acute respiratory
infections in the Italian population.
CIRI-IT team involved in DRIVE studies includes:
physicians, researchers, systems analysts, computer programmers and laboratory technicians.
Director: Prof. Giancarlo Icardi
Coordinator group of case control study (test-negative design studies) to measure type/brand-
specific seasonal influenza vaccine effectiveness against laboratory-confirmed influenza cases
in Italy, season 2018/19
Prof. Donatella Panatto (University of Genoa)
Prof. Andrea Orsi (University of Genoa and hospital Policlinico San Martino Genoa)
Dr. Piero Luigi Lai (University of Genoa and hospital Policlinico San Martino Genoa)
System manager and IT consultant: Stefano Mosca

Study size
Characteristic Subjects
Subjects 1094
Influenza cases 385
Vaccinated subjects 262
Age
Children (0-17y) 384
Adults (18-64y) 520
Elderly (65+y) 190
Influenza vaccine brand
Agripal 13
Fluad 24
Fluarix Tetra 214
Influvac Tetra 1
Vaxigrip Tetra 2
Unknown 8
•21 GPs and Paediatricians recruited in Genoa area
•>= 6-month age-group
•Dedicated digital platform for GPs and Lab operations
according to DRIVE protocols
•Pre-packaged enrolment kits with papers and swabs
•GPs and Paediatricians were trained one by one
•Direct operational support for every GP
•Distribution of kits (100 each) with unique code
•More than 1120 swabs collected and analysed
Every kit was composed of:
2 informed consent forms
1 questionnaire
1 swab
2 coded labels
1 instruction sheet
Each kit had an anonymous unique barcode

• All subjects were enrolled and swabbed by GPs during visit
• ECDC ILI case definition was used by GPs to check ILI eligibility
• Physicians loaded enrolment and swab data into web tools activating CIRI-IT
validation controls and collection cue
• Every valid swab was gathered by CIRI-IT personnel and delivered to Lab facilities
• Lab dedicated samples tracking software was implemented
• CIRI Lab executed samples analysis (RT PCR) and reported on CIRI-IT environment
• Swab test results were sent back to clinicians web interface and loaded into CIRI
database for further quality controls and internal reporting
• GPs activities were monitored day by day by CIRI-IT personnel
• Enrolment and swab data were analysed and conformed to DRIVE dataset and
converted for ESSA transmission and quality check
Data collection: description

• Reliability of the CIRI-IT medical network
• Data collected directly by GPs (enrolment and swab) to increase data quality
• Comorbidities and confounding factors confirmed by GPs
• Good protection and quality of the data (closed accounts – data anonymity)
• Homogeneous distribution in the territory
• Close connection between GPs and Lab via the digital platform
• Real-time control of every eCRF by CIRI-IT team
• Good perception of the study by GPs – in particular regarding swab results in real time
• Real-time connection between epidemiological and laboratory data
• Vaccination status, brand and date of vaccination confirmed by GP in every subject
• Monitoring of all age-groups
• Monitoring of all vaccine brands in the area
• The platform developed is robust and flexible enough to be extended to larger studies
Data Collection: what went well?

Data Collection: challenges
• Number of subjects enrolled depends on the intensity of the flu season (data
collection – delivery – analysis – reporting)
• GPs are very busy during FLU season – collecting a lot of data can be very difficult
at some times during the epidemic period
• Simplify data-collection input (ideally paperless?)
• Study methods and processes are easy to implement in other regions, not only for
FLU but for any respiratory infection
• Reduce reporting time and optimize swab logistics during epidemic period
• Increase swab numbers in elderly population

Istituto Superiore di Sanità
(National Institute of Health)
Stefania Bellino, Ornella Punzo
Antonino Bella, Maria Rita Castrucci, Simona Puzelli
DRIVE Annual Forum
July 17th 2019, Helsinki

• In Italy, clinical samples coming from general practioners
(GP) and hospitals were collected and analyzed by Regional
Reference Laboratories (RRL), present throughout the
country, and the National Influenza Center (NIC), located at
ISS, for molecular and phylogenetic characterization of the
circulating influenza virus strains.
• The influenza sentinel surveillance network (InfluNet),
coordinated by the ISS, involve nearly 1,000 GP covering
about 2.3% of the Italian population. Only aggregated data
are collected weekly, and ILI cases are not laboratory-
confirmed.
• A subset of sentinel GP participate on a voluntary basis
also to the virological surveillance aimed at evaluating the IVE
annually. The number of sentinel GP participating in InfluNet
(and who took throat swabs from ILI patients) was 245,
to which 316,237 patients refer; the Italian population
coverage was about 0.5%.

0
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14
16
0
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400
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42 43 44 45 46 47 48 49 50 51 52 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
IRILI/1,000
Numverofinfluenzapositivecases
Weeks of symptoms onset
A (not subtyped)
A(H1N1)
A(H3N2)
Virus B
IR ILI/1,000
Influenza virus detection n %
Virus A 6,392 99.9
A not subtyped 468
A subtyped 5,924
H1N1pdm09 2,969 50.1
H3N2 2,955 49.9
Virus B 9 0.1
Influenza positive 6,401
20,174 clinical samples were collected at national level, 32% were positive for influenza
Influenza epidemiology in Italy
The peak of IR (14/1,000) was
reached at week 5-2019 (28th
January-3rd February), and the
estimated number of ILI cases at
national level was around 8
millions.

• A test-negative case-control study was conducted within
the context of the InfluNet. The study population consisted
of patients consulting a participating GP for Influenza-like
illness (ILI).
• The aim of the study was to estimate seasonal overall and
age-specific influenza vaccine effectiveness (IVE) against
medically attended laboratory-confirmed influenza,
also stratifying by virus subtype (A/H1N1, A/H3N2)
• Sample size was 2,526 in line with the expected (2,380 to
detect at least 50% IVE).
• Crude and confounder-adjusted IVE were estimated
as (1-Odds Ratio)x100 by univariable and multivariable
logistic regression models.
TND Study - Methods

TND study flow-chart
Total ILI cases
n=2,655
Evaluated ILI cases
n=2,526
Influenza-negative
controls
n=1,349
Influenza-positive
cases
n=1,177
A(H1N1)pdm09 (n=584)
A(H3N2) (n=576)
A not subtyped (n=14)
B (n=3)
Excluded (n=129):
- outside of influenza season (n=18)
- <6 months of age at the symptoms onset (n=11)
- throat swabs >7 days after ILI onset (39)
- partially vaccinated (n=17)
- missing laboratory test for influenza (n=35)
- missing vaccination date (n=5)
- missing age (n=3)
- vaccinated with Intanza (n=1)

TND Study size
Age groups
Influenza
positive
Influenza
virus subtype
Vaccine
coverage
6-months-17 years 50.3%
A(H1N1) 45.5%
A(H3N2) 54.5%
7.4%
18-64 years 42.2%
A(H1N1) 58.5%
A(H3N2) 41.5%
10.4%
≥65 years 40.7%
A(H1N1) 41.0%
A(H3N2) 59.0%
66.5%
Total 46.6%
A(H1N1) 50.3%
A(H3N2) 49.7%
13.2%
Influenza vaccines used
QIV: 80.7%, Vaxigrip Tetra (64.1%) and Fluarix Tetra (35.9%)
aTIV: 18.4%, Fluad
TIV: 0.9%, Agrippal S1, Influpuozzi Sub.

• A systematic sampling of the first 2 ILI patients <65
years old that presented each week was used, whereas
all patients ≥65 years with ILI were sampled.
• GP interviewed ILI patients using an on-line
standardized questionnaire to collect data:
age and sex
date of symptoms onset
vaccination status, vaccination date and vaccine brand
flu vaccination in any of the previous two seasons
presence of chronic conditions
number of practitioner visits in the previous 12 months
number of hospitalizations due to chronic conditions in
the last year.
• Study period: started at week 42-2018 (15th October)
and ended at week 17-2019 (28th April).

Results (1)
Cases Controls
Type
A
A(H1N1) A(H3N2)
Influenza
negative
Cases
vs
Controls
Demographics and
clinical characteristics
n % n % n % n % p-value
All 1,174 584 576 1,349
Age groups
6 months-17 years 628 53.5 283 48.5 339 58.8 620 46.0
0.00118-64 years 467 39.8 269 46.0 191 33.2 614 45.5
≥65 years 79 6.7 32 5.5 46 8.0 115 8.5
Sex
Male 627 53.4 306 52.4 316 54.9 722 53.5
0.954
Female 547 46.6 278 47.6 260 45.1 627 46.5
Chronic conditions
Yes 372 31.7 153 26.2 215 37.3 411 30.5
0.509
No 802 68.3 431 73.8 361 62.7 938 69.5
N. of GP consultations
in the last year
0 194 17.5 107 19.3 86 16.0 219 16.8
0.0241-5 797 72.1 393 70.9 393 72.9 898 69.1
>5 115 10.4 54 9.8 60 11.1 183 14.1
N. of hospitalizations for
chronic conditions in the
last year
0 956 97.2 493 97.4 450 96.8 1,149 96.3
0.274
1-2 28 2.8 13 2.6 15 3.2 44 3.7
Positive and negative influenza subjects were similar for almost all the considered variables, however, controls
were older than cases and had more GP visits in the previous 12 months

Cases Controls
Type
A
A(H1N1) A(H3N2)
Influenza
negative
Cases
vs
Controls
Demographics and
clinical characteristics
n % n % n % n % p-value
Influenza vaccination in
any of the previous two
seasons
Yes 118 10.4 44 7.8 73 13.1 135 10.4
0.958
No 1,013 89.6 517 92.2 484 86.9 1,167 89.6
Influenza vaccination
in the current season
Yes 147 12.5 50 8.6 96 16.7 187 13.9
0.322
No 1,027 87.5 534 91.4 480 83.3 1,162 86.1
Type of flu vaccine
Quadrivalent 118 82.5 34 70.8 84 89.4 141 79.2
0.734Trivalent adjuvanted 24 16.8 13 27.1 10 10.6 35 19.7
Trivalent 1 0.7 1 2.1 0 0.0 2 1.1
Vaccine brand
Vaxigrip Tetra 69 48.2 18 37.5 51 54.3 97 54.5
0.370
Fluarix Tetra 49 34.3 16 33.3 33 35.1 44 24.7
Fluad 24 16.8 13 27.1 10 10.6 35 19.6
Agrippal S1 1 0.7 1 2.1 0 0.0 1 0.6
Influpozzi Sub 0 0.0 0 0.0 0 0.0 1 0.6
Results (2)

 Good quality of collected data, also due to automatic checks and
warnings included in the on-line questionnaire, and a good
feedback from GP to correct the data.
 Sentinel GP well trained and motivated.
 Good collaboration among Regions, GP and Reference
laboratories.
 Good GP participation that allowed the achievement of the planned
sample size
Data Collection:
What went well
Challenges
 Involve Italian Regions that do not participate to the virological
surveillance (currently 11/21 Regions participate in InfuNet).
 Increase the sample size in order to obtain more precise IVE
estimates.
 Improve the completeness of collected data.
 Reinforce the integration of epidemiological and virological data,
increasing the number of study specimens selected for genetic and
antigenic analysis.

Medical University
Vienna,
Austria
Katrin Wiedeschitz

• 90 sentinel physicians (general practitioners and pediatricians)
Coverage about 1-1,2% of the Austrian population
• 6 ICU sites for SARI surveillance
• Swabs for analyses sent to MUV
for analyses (typing, subtyping,
genetic and antigenic
characterisation)

• 1227 Datasets available, 340 datasets excluded
• Sutdy size: N=887, 432 Children, 422 Adults, 33 Elderly
• 374 influenza cases, 513 controls, 43 vaccinated subjects
Study size - TND
Vaccine coverage in Austria general very low: between 5% and 8%

• Description of data collection:
• Type: nasopharyngeal or nasal swabs; taken from the patient’s physician
• data collection procedure for case definition, covariates, vaccination
status and brand: standardized questionnaire
• Data were entered manually in the an Access Database
• data cleaning procedures and the data transformation was perfomed by
using an (for the DRIVE project designed) Access Database and
Dataexport was performed in xls-format to fit the DRIVE codebook.
• Sampling strategy that was implemented:
• ILI: all patients presenting to primary care physicians and fulfilling the
case definition (if more than 10 patients per sentinel physician per week
is fulfilling the criteria, every 4th patient is swabbed).
• SARI: all patients fulfilling the case definitions are included from the
sentinel ICU sites

• ready to use standardised swab kit including swab
material and standardized questionnaire was provided
by MUV and sent to each physician
• using pre-printed return envelopes for the sample
transfer to the MUV by mail
• postage was paid by addressee (MUV)
• use of access database for datamanagement

• standardized questionnaires were not completly filled in
(especially vaccination date)
• due to incomplete questionnaires a huge amount on
datasets needed to be excluded
• low influenza vaccine coverage in Austria is a big
problem for influenza VE studies in the austrian
population:
the group of the non-vaccineted will always far excceed
the group of the vaccinated

University of Surrey
Simon de Lusignan
Professor of Primary Care & Clinical Informatics,
Universities of Oxford and Surrey
Director
Royal College of GPs Research & Surveillance Centre
simon.delusignan@phc.ox.ac.uk

A pilot of near-patient testing for influenza in
primary care in the UK
• Site name - Royal College of General Practitioners (RCGP)
Research & Surveillance Centre (RSC), University of
Surrey
• Geographic location - England
• Setting – 6 primary care practices nested within the
English national sentinel surveillance network
• Who did the study –
• Prof Simon de Lusignan (Professor of Primary Care and Clinical Informatics)
• Dr Uy Hoang (Research Fellow)
• Dr Harshana Liyanage (Research Fellow)
• Manasa Tripathy (Practice Liaison Officer)
• Mariya Hriskova (Practice Liaison Officer)
• Ivelina Yonova (Project Manager)
• Dr Filipa Ferreira (Senior Project Manager)
• Dr Tristan Clark (Associate Professor and Honorary Consultant in Infectious Diseases)

• 6 practices with registered population ~ 78,000
• 312 POCT tests recorded (276 used for analysis)
• For TND: 60 influenza cases, 216 controls, 84
vaccinated subjects
Study size
0
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6
8
10
12
0 3 7 10 13 16 19 23 26 29 32 35 38 41 44 47 50 53 56 59 62 67 70 74 77 81 84 89
Numberofswabs
Age
Number of swabs by age for each POCT practice
F
E
D
C
B
A

• Description of data collection – Alere ID Now POCT
machines linked to patient’s electronic health record
• Sampling strategy that was implemented:
• All ILI and ARI subjects were swabbed
• Opportunistic sampling undertaken at clinician’s discretion

The aim of this slide is to reflect on the aspects of data collection
that went well. This is an opportunity to learn from each other.
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
7 8 9 10 11 12 13 14 15
swabbingrateper1000
registeredpopulation
ISO week
Other RCGP RSC virology sampling practices
All POCT practices
1. High swabbing rate for POCT
practices compared with other
RCGP RSC virology sampling
practices and comparable swab
positivity rate
2. Influence on clinical care of
patients with flu -> reduction in
antibiotic prescribing and
increase in antiviral prescribing
following positive influenza
POCT 0
0.1
0.2
0.3
0.4
Influenza +ve Invalid Negative
Proportionofswabbed
patientswhowere
prescribedantibiotics
POCT swab result
Average of antibiotic on swab day
Average of antibiotic 1 to 7 days after swab

The aim of this slide is to reflect on the aspects of data collection
that went less well or were challenging.
• Description of what went less well
• Late start to the study due to delay in ethical approval
• Did this affect data quality, if yes, how? Were any
biases introduced?
• smaller sample size than expected
• Challenges encountered, how they were addressed (if
applicable)
• Wide variation in swabbing rates btw practices addressed by
encouragement and visits from study team
• Ideas for improvement (if any)
• Earlier start to the study
• Encourage practices to collect vaccine brand information
• Anything you would like input on for the future?
• Save swabs for reference lab testing of influenza lineage

Contribution to DRIVE vaccine effectiveness
• Data provided from RCGP RSC:
• 326 RCGP RSC practices (N>300,000)
• 109,123 records of patients with ILI/ ARI
• 42% male
• 31% 5 years or younger
• 21% in ‘at-risk’ group for influenza
Influenza vaccination information
• 24% (n= 109123) received seasonal influenza vaccination
• 26,154 – administered influenza vaccination
• 20,458 – information on vaccine manufacturer
• 9,777 – vaccine brand information
Other data provided to DRIVE
(not from POCT study)

• Patients who allow their records to be used for
surveillance, quality improvement, research and education
• Practices who are members of the RCGP RSC network
• Co-authors listed on slide 2, other team contributors
• DRIVE consortium /IMI programme for funding
Acknowledgements:

Thanks for listening
Simon de Lusignan
simon.delusignan@phc.ox.ac.uk

Description of data collection
Sentinel surveillance of Influenza
Luxembourg
Joël Mossong PhD
Head of Microbiology (ad interim)
Laboratoire National de Santé
Luxembourg
DRIVE Annual Forum
17-18 July 2019

1) Airline
2) National beer(s)
3) National football team
Current FIFA ranking: 90
Above Cyprus, Estonia, Latvia, Malta and
Liechtenstein
Luxembourg passes
Frank Zappa country test

Influenza Sentinel surveillance
• In operation since 2003 as pandemic
plan measure
• Informal collaboration between
• Health Directorate
• Financial indemnities to participating sentinel
physicians
• Organisation of GPs
• Selects participating doctors
• Laboratoire National de Santé
• Laboratory testing and collection of EPI data
• WHO National Influenza Centre
• Nominated as Luxembourg representative @
ECDC/WHO

• 13 generalist practices
• Represent 3% of all generalists in Luxembourg
• 3 pediatricians
• Represent 3% all
• Geographic distribution proportional to
population density
• Annual fees paid to participating doctors
Sentinel doctor network

0
100
200
300
400
500
600
700
800
2010_7
2010_9
2010_11
2011_1
2011_3
2011_5
2011_7
2011_9
2011_11
2012_1
2012_3
2012_5
2012_7
2012_9
2012_11
2013_1
2013_3
2013_5
2013_7
2013_9
2013_11
2014_1
2014_3
2014_5
2014_7
2014_9
2014_11
2015_1
2015_3
2015_5
2015_7
2015_9
2015_11
2016_1
2016_3
2016_5
2016_7
2016_9
2016_11
2017_1
2017_3
2017_5
2017_7
2017_9
2017_11
2018_1
2018_3
2018_5
2018_7
2018_9
2018_11
2019_1
2019_3
2019_5
2019_7
Flu swabs tested per month
Flu swabs tested per month

0
200
400
600
800
1000
1200
1400
1600
1800
2000
Age distribution of patients tested for flu
Age distribution of patients tested for flu

Average season (less cases than in previous season)
Dominated by H3 (as opposed to H1 for many other
countries in EU)
Not much
excess
mortality
Flu Season 2018/19 in Luxembourg

•Until May 2019 case forms only had one
item:
• Vaccinated:  yes  no
•In 2018/19 due to shortage only one
vaccine Fluarix tetra (GSK) available in
pharmacies in Luxembourg
• Vaccine brand imputed for season 18/19
•As from Sept. 2019, new case forms with
more detailed info:
• dates + brand
• get informed consent
Vaccine data

FASTQ to GISAID pipeline
TESSy
Extract
(vaccine)
reference HA-
sequences of
current season
Extract HA-sequences
ETE Toolkit
A Python framework to work with trees
Distance
Matrix with
clade info
Download acknowledgement
table
(incl HA/NA/MP segment ID)
Merge clade,
segment IDs and
TESSy extract
TESSy
The
European
Surveillan
ce System
GISAID batch
upload

Characteristic Number of
subjects
Subjects 541
Influenza cases 257
Age
Adults (18-64y) 325
Elderly (65+y) 35
Fluarix Tetra 49
Contribution to DRIVE 18/19 data
collection

Trung Nguyen
Head of Virology
Guillaume Fournier
Deputy Head
Anke Wienecke,
Bioinformatician - NGS
Joël Mossong
Head of Department
Microbiology
Alain Lutgen
Lab technician
Catherine Ragimbeau
Next Generation Sequencing
The team…

Acknowledgement
Hospital TND
1) BIVE, 2) VHUH, 3) FISABIO

IT-BIVE-HOSP
Elisabetta Pandolfi, MD
Bambino Gesù Children’s Hospital
60

Multicenter hospital based TND
study
5 large Italian hospitals (>500
beds) participated in the network
with all Units involved, the
recruitment was done through
EMRs
Study population included all
community-dwelling individuals
aged ≥6 months, with SARI.
Study period: Week 47-2018
(mid November) and week 17-
2019 (mid April 2019),
At least one medical doctor,
together with medical residents
or nurses, were in charge of
checking for eligible SARI
patients daily

Study size
Characteristic Number of
subjects
Subjects 1598
Influenza cases 488
Age
Adults (18-64y) 278
Elderly (65+y) 500
Fluad 139
Fluarix Tetra 61
Vaxigrip Tetra 33
Unknown 79

• In all but 1 participating hospitals (Genova, Roma-
OPBG, Roma Sant’Andrea and Bari), every patient
presenting at the Emergency Department (ED), whose
symptoms suggests a SARI (through EMRs and ICD9
codes), were considered for recruitment.
• All SARI were swabbed
• Data were collected through a questionnaire at patients
bed
• Subsequently, data were entered on a dedicated web-
based system that allow to monitor data quality and
recruitments at hospital level
• As influenza program is delivered by GPs mainly in
Italy, enrolled patients’ GPs were contacted by
telephone, for confirming vaccination status and
collecting vaccine dates and brands.

• The participating hospitals are large academic
tertiary hospitals, of 600 to over 1000 beds.
• All SARI patients from the related catchment
area are admitted to these hospitals.
• The patients’ screening for enrolment was
systematic.
• The patients’ GPs, contacted by telephone,
provided highly reliable data on vaccination
dates and brands.
• We were able to collect all relevant
confounders, however….…

• …..we were not able to adjust for
indicators of health-seeking behavior,
however, the healthcare system in Italy is
a regionally based national health service
known as Servizio Sanitario Nazionale
(SSN)
• Sample size
• Increasing the number of participating
hospitals, especially those with larger
geriatric units, could help in increasing
the sample size in the elderly.

FISABIO – Public Health
(Valencia Region, Spain)
Javier Díez-Domingo

Hospital General Castellón
Population: 281,200
Number of beds: 509
Participating wards: General
Medicine, Paediatrics, ICU
Hospital La Fe
Population: 285,066
Number of beds: 975
Hospital Doctor Peset
Population: 278,344
Number of beds: 539
Hospital General Alicante
Population: 274,122
Number of beds: 794
Valencia Region (Spain)
5,000,000 inhabitants
Catchment population: 1,118,732
(22% of the Valencia Region)

Included
ILI
records
N=1,520
Study size
0-17
N=187
(12%)
18-64
N=234
(16%)
≥65
N=1099
(72%)
Controls
N=1297
(85%)
A(H1N1)pdm09
N=70
(31%)
A(H3N2)
N=106
(47%)
A not subtyped
N=47
(21%)
Cases
N=223
(15%)
Not
vaccinated
N=709
(46%)
Fluad
N=360
(44%)
Influvac
N=451
(56%)
Vaccinated
N=811
(54%)
Age group
RT-PCR result
Vaccination
status

Resident
Admitted in the last 48h
Diagnose related to flu
Not institutionalized
Not dicharged in the last 30 days
Informed consent
Resident
Not institutionalized
Not dicharged in the last 30 days
ILI-case definition (≥5 years old)
Symptoms in the last 7 days (<5)
Swabbing
Samples tested
by PCR in a
centralised lab in
FISABIO

Data weekly checks
Internal and external audits
ESSA application
Nurses’ training week
Doubts solved by the Coordination Office
Electronic CRF

Change of field nurses
Intensive training
Closely followed by the Coordination Office
Number and dates of vaccine dosis not collected for children
Studying the possibility of including these data
during the next season
Different SARI definition (DRIVE: symptoms within 7 days prior to
swabbing, FISABIO: symptoms within 7 days prior to admission)
FISABIO adapted the definition before sharing the data
Different age definition (DRIVE: age at symptoms onset,
FISABIO: age at admission)
FISABIO adapted the definition before sharing the data

Hospital Universitari
Vall d’Hebron (HUVH)
José Ángel Rodrigo Pendás

• Hospital Universitari Vall d’Hebron.
• Tertiary hospital, but also a community & secondary
hospital serving a population of ~400,000 people.
• Located in the upper part of Barcelona.
• Study setting:
• No primary care doctors involved.
• No restrictions for hospital wards.
• The study was carried out by:
• The Preventive Medicine &
Epidemiology Department.
• The Microbiology Department.

• 465 participants
Study size
Cases Controls
Subjects 233 232
Vaccinated (%)* 95 (41%) 104 (45%)
Age (%)*
6m – 17y 35 (15%) 35 (15%)
18 – 64y 63 (27%) 61 (26%)
>64y 135 (58%) 136 (59%)
Vaccine brand (%)**
Fluad® / Chiromas® 56 (59%) 65 (62%)
Agripal® / Chiroflu® 38 (40%) 33 (32%)
Fluarix Tetra® 1 (1%) 6 (6%)
* % of cases / controls ** % of vaccinated cases / controls

• Data collection:
• All the study participants were identified by the results of the
swab tests provided daily by the Microbiology department.
• The electronic medical record of each subject was reviewed
to check the inclusion criteria and to collect information on
the covariates.
• Patients are swabbed at the HUVH if they:
• Have severe respiratory symptoms or complications and
should be admitted to the hospital.
• Require antiviral treatment.

• The collaboration of the Virology Unit of the
Microbiology Department, which sent every day the
results of the swabs done in the hospital.
• The inclusion of patients whose healthcare provider
was the ICS (Institut Català de la Salut), in whose
hospital network the HUVH is included. By sharing the
same computer system, clinical information (both
primary care and that of other hospitals) was readily
available.

Challenges encountered
• Large amount of covariates ➜ Excessive workload ➜
Only mandatory variables were collected.
• Changes in the definition of the variables once the
study has begun.

Helsinki University
Hospital,
Jorvi Hospital,
Finland
Raija Auvinen,
study physician and clinical
coordinator,
HUS and THL

• HUS, Jorvi Hospital
• Located in Espoo, Finland
• Secondary and tertiary
care hospital with a
population base of over
330 000 people (~250
000 adults)
• TND study among adults
• Study wards:
• Internal Medicine Ward S4
• Internal Medicine Ward S6
• Cardiology Ward S7
• Pulmonary Disease Inpatient Ward Keu5
• HUS Emergency Ward
• Ward U2, Intensive Care and Burn Center
Source: https://www.ark-koivula.fi/hankkeet/hus-
jorvin-paivystyslisarakennus

• Study team at HUS:
• Kirsi Skogberg, M.D, PhD. Study
leader
• Raija Auvinen, M.D. Study
physician and coordinator
• Outi Debnam, Study nurse
• Marja-Leena Michelsson, part-
time study nurse
• Raisa Loginov, PhD, hospital
microbiologist, HUSLAB
• Close co-operation with THL
study team including
• Ritva Syrjänen, MD, PhD
• Niina Ikonen, MSc
• Anu Haveri, MSc
• Esa Ruokokoski, MSc
• Hanna Nohynek, MD, PhD

• 325 patients included in the study, 293 of whom
fulfilled DRIVE study criteria
• 74 laboratory-confirmed influenza (LCI) cases (25,3%
of DRIVE study patients) and 219 test negative
controls
• 179 (61,1%) vaccinated with Vaxigrip tetra, 112
(38,2%) not vaccinated in 2018-19, 2 information
missing (0,7%)
Study size

• All patients admitted to the study wards were screened
for SARI by the study nurse based on admission
diagnoses or reasons written in the daily patient report
lists
• Sampling strategy:
• All SARI subjects who consented to participate were swabbed
if a respiratory sample had not been taken for diagnostic
purposes. If a HUSLAB PCR sample was available, the residue
was obtained for the study.
• All respiratory samples were tested for influenza A, B and
RSV either at HUSLAB or THL virology laboratory
• All HUSLAB antigen test results and positive PCR findings
were confirmed and influenza positive samples subtyped at
THL virology laboratory
• After informed consent relevant background information
was collected from the patient, medical records and
national vaccination register (NVR)

• Positive reception of the study and co-operation with
the hospital and laboratory staff
• All patients screened by study nurse -> systematic
screening according to eligibility criteria
• Access to medical records and vaccination information
(Kanta archive, municipalities)
• Similar study questionnaires and the programme used
to enter patient records to the electronic study
database previously used in IMOVE TND study at
Tampere and adapted to the Jorvi TND study
• Support, advice and data management readily
available from THL study team, who had experience on
a similar study setup from the IMOVE study

• Screening method had to be changed from the one
described in our study protocol (ICD-10 admission
diagnoses -> admission reasons/diagnoses in patient
report lists):
• Missing of some potential study patients?
• Screening less systematic?
• Broad definition of SARI -> a lot of patients to be screened
• Complete information on the total amount of screened
patients not available
->More comprehensive and systematic screening for 2019-20
• Challenging schedules:
• Study permits and agreements
• Data gathering (vaccination information) and verification in
time for sending to DRIVE
• DRIVE quality management survey
• Room for improvement i.e. in sample logistics

Acknowledgement
Register-based
cohort
1) THL

Health and Welfare
(THL), Finland
Ulrike Baum,
Ritva Syrjänen,
Hanna Nohynek

• National Institute for Health and Welfare (THL), Finland
• Finland is one of the five Nordic Countries
• Register-based cohort study, whole country,
primary and secondary care
• Who are “we”?
• Hanna Nohynek, MD, PhD, chief physician
• Ritva Syrjänen, MD, PhD, senior researcher
• Ulrike Baum, MSc, statistical researcher

• Study population
• All registered Finnish residents aged
• 6 months to 6 years or
• 65 years to 100 years
• National registers
• Computerised
• Linked deterministically
using a unique person identifier
assigned to all permanent
(i.e., registered) residents
Study population and
National registers
Person
Identifier
Population
Information
System
National
Vaccination
Register
National
Infectious
Diseases
Register
Register of
Primary
Health Care
Visits
Care
Register for
Health Care

* only children aged 2 years to 6 years were eligible for vaccination with Fluenz Tetra
Study size
Children
(6 months – 6 years)
Elderly
(65 years – 100 years)
Study subjects 332166 1184780
Person-years 168020.7 600394.9
Vaccinated person-time
(Fluenz Tetra)
15.3%* ---*
Vaccinated person-time
(VaxigripTetra)
9.8% 39%
Influenza cases 1834 4545

• Data collection is ongoing
• Data are collected into computerised registers as part
of health care routines or statutory notification systems
Person
Identifier
Population
Information
System
National
Vaccination
Register
National
Infectious
Diseases
Register
Register of
Primary Health
Care Visits
Care
Register for
Health Care
• Person identifier, sex, place of residence,
date of birth, (date of death)
• Since 1969
• Person identifier, diagnostic code,
date of visit
• Public primary health care
• Since 2011
• Person identifier, diagnostic code,
date and duration of visit
• Secondary health care
• Since 1967
• Person identifier, vaccine
batch number and trade name,
date of vaccination
• Public primary health care
• Since 2009
• Person identifier, influenza type,
date of specimen
• Public/private primary and secondary
health care, no specific sampling strategy
• RT-PCR, antigen detection, culture
• Since 1995

• Data collection is an automated process
• Cheap
• Time-saving
(once the system is in place)
• Data are collected directly from the source
• Efficient
• No risk of recall bias e.g. regarding past vaccinations
Data collection: what went well?

• Data was collected study-independently
• No means to directly influence data quality,
sampling strategy or
utilised lab tests
• Only positive no negative records
• Assumption that an event did not occur if there is no record
• Exposure misclassification
• Completeness of exposure information is assumed to be high
but effectively unknown
• Exclusion of subjects with residence outside the vaccination
register’s catchment area
• Outcome misclassification
• Presumably many influenza cases remained unobserved in
the study as only laboratory-confirmed cases were considered
• Bias, if vaccinated subjects were less/more likely to undergo
laboratory testing
Data collection: challenges

Season 2018/2019 pooled
analysis
Context, methods, and
descriptive analyses

Data flow and analysis
Data collection (based on generic study protocols)

Individual-level data (TND studies)
Aggregated data (register-based cohort)
THL: data aggregation

Data transfer (ESSA)

Quality checks

Confounder-adjusted IVEs
TND: Logistic regression
Cohorts: Poisson regression
Central Calculation of VE
estimates for each site
 VE1Site1 , VE2Site1 , …
 VE1Site3 , VE2Site3 ,…
 VE1Site5 , VE2Site5 , .…

Central calculation of VE
estimates for each site
 VE1Site5 , VE2Site5 , .…
Meta-analysis of
site-specific VE to
generate pooled VE
VEpooled1, VEpooled2 , …
VEpooled1, VEpooled2 , …

Confounder-adjustment
TND primary care TND hospital
MUV CIRI ISS RCGP HUS BIVE NIID HUVH FISABIO
(Austria) (Italy) (Italy) (UK) (Finland) (Italy) (Romania) (Spain) (Spain)
Time since season
start
Sex
Age
Pregnancy
no pregnant
subjects
Chronic disease
Vaccinated 2017/18
Nr of GP visits /
hospitalizations
retained for final model
excluded, >10% missing values
not available

Site characteristics: age and design

Site characteristics: case definition

Site characteristics: laboratory-tests

Site characteristics: sampling strategy
Clinical
practice
Clinical
practice

Influenza activity intensity, 2018/19
Source: Adapted from Flu News Europe

Dominant influenza A virus, 2018/19
Source: Adapted from Flu News Europe (except UK), Public Health England (UK)

MUV – Austria CIRI-IT – Italy ISS - Italy
LNS – Luxembourg RCGP RCP - UK
ILI over time– TND primary care
A unspecified
A/H1N1
A/H3N2
B
Non-influenza

Subject characteristics – TND primary care
Age
Sex
Influenza vaccination status previous season
At least 1 chronic condition
Pregnancy
Nr of GP visits in previous 12 months
Not all covariates were available from all sites
% of all subjects

HUS – Finland BIVE – Italy NIID - Romania
Spain – FISABIO* Spain- HUVH
SARI over time– TND hospital
*ILI (>5y), acute hospitalization (<5y)
A unspecified
A/H1N1
A/H3N2
B
Non-influenza

Subject characteristics – TND hospital
Age
Sex
Influenza vaccination status previous season
At least 1 chronic condition
Pregnancy
Nr of hospitalizations in previous 12 months
Not all covariates were available from all sites
% of all subjects

Vaccine recommendations
Austria Italy Luxembourg UK Finland Italy Romania Catalonia Valencia
Children
2-10y 6m-6y
Adults
Older adults
Universal recommendation
Medical risk groups
Occupational risk groups
Pregnancy

Vaccine recommendations
Austria Italy Luxembourg UK Finland Italy Romania Catalonia Valencia
Children LAIV or QIV
LAIV
2-10y
LAIV or QIV
6m-6y
QIV (TIV) QIV LAIV LAIV or QIV QIV (TIV) TIV or QIV
TIV
(QIV very
high risk)
TIV
QIV
Adults aTIV or QIV QIV (TIV) QIV QIV QIV QIV (TIV) TIV or QIV
TIV
(QIV very
high risk)
TIV
QIV (TIV) QIV QIV QIV QIV (TIV) TIV or QIV TIV TIV
QIV (TIV) QIV (TIV) QIV QIV QIV QIV (TIV) TIV or QIV TIV TIV
Older adults aTIV (QIV)
65-75y: aTIV
(QIV, TIV)
75+y: aTIV
QIV aTIV QIV
65-75y: aTIV
or QIV or TIV)
75+y: aTIV
(QIV, TIV)
TIV or QIV aTIV
65-75y: TIV
75+y or
institutionalized
: aTIV
Universal recommendation
Medical risk groups
Occupational risk groups
Pregnancy

Vaccine coverage – TND
Primary care
Hospital
5% (43)
24% (262)
13% (305)
9% (51)
28% (39)
20% (312)
4% (43)
53% (811)
43% (199)
% vaccine coverage (n vaccinated)
61% (167)

Vaccine coverage – TND
Primary care
Hospital
5% (43)
24% (262)
13% (305)
9% (51)
28% (39)
61% (167)
20% (312)
4% (43)
53% (811)
43% (199)
% vaccine coverage (n vaccinated)

Results Influenza VE
pooled analysis
Season 2018/2019

•Many strata with limited data
• Low sample size
• Only 1 site-specific estimate
•Definition of robust VE estimate: CI width
<40%
• Often very wide CI, a small change in nr of
cases has large impact on point estimate
Considerations for interpretation

•Epidemiology
• Low vaccine coverage
• Mild season
• Mismatch A/H3N2
• Proportion of A/H1N1 and A/H3N2 varies
across sites  and impacts results for any
influenza/influenza A
Considerations for interpretation

Considerations for interpretation of
robust estimates
Source: deliverable D4.6 Guideline for
interpretation of IVE estimates

Overview data for primary objective -
TND studies
Age
Any
vaccine Agrippal Fluad
Fluarix
Tetra
Fluenz
Tetra
Influvac
Influvac
Tetra
Vaxigrip
Tetra
6m-17y 3 1 n/a 1 1 - - 2
18-64y 4 1 n/a 2 n/a - 1 2
65+y 2 - 2 1 n/a - - 1
6m-17y 3 1 n/a 2 - - - 2
18-64y 5 1 n/a 2 n/a 2 - 3
65+y 5 1 3 2 n/a 2 - 2
PrimarycareHospital
n/a: not applicable, not licensed for age group
N of sites contributing data for each stratum

Overview data for primary objective -
TND studies
Age
Any
vaccine Agrippal Fluad
Fluarix
Tetra
Fluenz
Tetra
Influvac
Influvac
Tetra
Vaxigrip
Tetra
6m-17y 3 1 n/a 1 1 - - 2
18-64y 4 1 n/a 2 n/a - 1 2
65+y 2 - 2 1 n/a - - 1
6m-17y 3 1 n/a 2 - - - 2
18-64y 5 1 n/a 2 n/a 2 - 3
65+y 5 1 3 2 n/a 2 - 2
PrimarycareHospital
Any influenza
Influenza A
Influenza A(H1N1)
Robust estimates, CI <40%
N of sites contributing data for each stratum

Pooled results TND studies– any vaccine
N = nr of estimates that were pooled
Adjusted IVE estimates

Robust VE estimates
Low VE
Sites (H1N1, H3N2)
Spain FISABIO (35%,65%)
Spain HUVH (48%,52%)
Italy BIVE (48%,52%)
Finland HUS (31%,69%)
(Romania NIID)
Setting Age Influenza Robust VE
(95%CI)
I2 N
TND hospital
65+y
Any influenza 27 (6-44) 0% 5
A 27 (6-44) 0% 4
Adjusted IVE estimates – hospital 65+y
Very low levels of influenza B strain
circulation
Match AH1N1 and mismatch AH3N2

Robust VE estimates
Very good VE
Setting Age Influenza Robust VE
(95%CI)
I2 N
TND primary care 6m-17y A(H1N1) 77 (53-89) 0% 3
Very low levels of influenza B strain
circulation
Match AH1N1 and mismatch AH3N2
Adjusted IVE estimates – primary care 6m-17y
Sites
Italy ISS
Italy CIRI-IT
Austria MUV

Ten influenza vaccine brands were licensed in the
European Union in the 2018/19 season:
• Abbott
• Influvac, Influvac Tetra
• AstraZeneca
• Fluenz Tetra
• GlaxoSmithKline
• Fluarix Tetra
• Sanofi
• TIV High Dose, Vaxigrip, Vaxigrip Tetra
• Seqirus
• Afluria, Agrippal, Fluad
Blue = in DRIVE dataset
Brands

Pooled results TND studies– Fluarix Tetra (QIV)

Pooled results TND studies– Vaxigrip Tetra (QIV)

Pooled results TND studies– Vaxigrip Tetra (QIV)
Adjusted IVE estimates: 6m-17y
Any Cases
unvax
Cases
vax
Controls
unvax
Controls
vax
Vax
coverage
Romania NIID 212 1 300 5 1.2%
Italy BIVE 231 4 562 2 0.8%
AH1N1 Cases
unvax
Cases
vax
Controls
unvax
Controls
vax
Vax
coverage
Italy ISS 256 2 522 18 2.5%
Austria MUV 109 1 255 4 1.4%

Pooled results TND studies– Influvac Tetra (QIV)

Pooled results TND studies– Influvac (TIV)

Pooled results TND studies– Agrippal (TIV)

Pooled results TND studies– Fluad (aTIV)

Pooled results TND studies– Fluad (aTIV)
Adjusted IVE estimates: hospital 65+y
Any Cases
unvax
Cases
vax
Controls
unvax
Controls
vax
Vax
coverage
Italy ISS 24 21 37 28 44.5%
Italy CIRI-IT 18 4 47 18 25.3%

Pooled results TND studies– Fluenz Tetra (LAIV)

•TIV
•QIV
•No robust results
Secondary objectives

Age Cases Controls
6m-17y 939 1071
18-64y 814 1222
65+y 144 277
Summary of pooled TND results
7 brands
Limited sample size
3 robust IVE estimates in TND (any vaccine)
No robust IVE estimates in TND by vaccine brand
Results interpretation:
• By setting, by age
• Pooled results for any influenza or influenza A are
impacted by proportion A/H1N1 and A/H3N2
Age Cases Controls
6m-17y 512 1083
18-64y 371 722
65+y 559 1635
Primarycare
Hospital

Register-based cohort THL
results

Subject characteristics - THL Register-
based cohort

Overview data for primary objective –
THL register-based cohort
Age
Any
vaccine
Fluenz
Tetra
Vaxigrip
Tetra
6m-6y yes yes yes
65+y yes n/a yes
Mixed
primarycare
andhospital

Results – THL register-based cohort
Age Any vaccine Fluenz Tetra Vaxigrip Tetra
6m-6y Any influenza 44.0 (36.0,51.0) 35.5 (24.1,45.1) 53.7 (43.3,62.2)
Influenza A
44.3 (36.3,51.3)
35.7 (24.4,45.3) 54 (43.6,62.4)
65+y Any influenza 30.3 (24.8,35.4) n/a 30.3 (24.8,35.4)
Influenza A
30.4 (24.8,35.5)
n/a 30.4 (24.8,35.5)
Mixedprimarycare
andhospital

Pregnant women
Healthcare workers
Patients with cardiovascular disease
Patients with lung disease
Patients with diabetes
IVE against laboratory-confirmed influenza in

Pregnant women
Healthcare workers
Patients with cardiovascular disease
Patients with lung disease
Patients with diabetes
IVE against laboratory-confirmed influenza in
Still undergoing QC
Based on TNDs,
too little data

HEALTH CARE WORKERS
COHORT STUDY
• CIRI-IT Centro Interuniversitario di Ricerca
sull’Influenza e sulle altre infezioni trasmissibili (CIRI)

CIRI-IT is an interuniversity center for research on influenza and other
transmissible infections.
Since its inception (20 years ago), it has conducted clinical-epidemiological and virological influenza
surveillance with the aim of providing epidemiological information on seasonal trends, in order to
determine the onset, duration, intensity and burden of Influenza-Like Illness and acute respiratory
infections in the Italian population.
CIRI-IT team involved in DRIVE studies includes:
physicians, researchers, systems analysts, computer programmers and laboratory technicians.
Director: Prof. Giancarlo Icardi
Coordinator group of clinical cohort study to measure type/brand-specific seasonal influenza
vaccine effectiveness against laboratory-confirmed influenza cases in Italy, season 2018/19
Prof. Donatella Panatto (University of Genoa)
Prof. Andrea Orsi (University of Genoa and hospital Policlinico San Martino Genoa)
Prof. Paolo Durando (University of Genoa and hospital Policlinico San Martino Genoa)
Dr. Piero Luigi Lai (University of Genoa and hospital Policlinico San Martino Genoa)
Prof. Elena Pariani (University of Milan)
Prof.ssa Silvana Castaldi (University of Milan and Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico,
Milan)
System manager and IT consultant: Stefano Mosca

Clinical cohort study: enrolment
• About 3000 enrolment kits were prepared (unique anonymous code)
• Dedicated software platform was developed and implemented
• All HCW were enrolled by CIRI-IT staff starting from week 40/2018
• HCW were enrolled in Genoa San Martino Polyclinic Hospital
• CIRI-IT staff uploaded all enrolment data to web tools, activating validation
controls and follow-up procedures
• About 2300 subjects were eligible for follow-up
• Population age: 18->65
• In the Liguria region, swabs were collected by CIRI-IT physicians
1 questionnaire
1 personal code and information guide
Every kit was associated to an anonymous unique
barcode

Clinical cohort study: enrolment
1 questionnaire
1 personal code and information guide
Every kit was associated to an anonymous unique barcode
• About 3000 enrolment kits were prepared (unique anonymous code)
• Dedicated software platform was developed and implemented
• All HCW were enrolled by CIRI-IT staff starting from week 40/2018
• HCW were enrolled in Milan Ca Granda Polyclinic Hospital
• CIRI-IT staff uploaded all enrolment data to web tools, activating validation
controls and follow-up procedures
• About 2400 subjects were eligible for follow-up
• Population age: 18->65
• In the Lombardy region, self swabbing were used

Clinical cohort study: follow-up
• Every single subject enrolled was contacted weekly via email/random messages/phone calls
• Vaccination status of every enrolled subject was collected during and after vaccination campaign
• Dedicated phone number/messages/email channels were activated
• CIRI-IT dedicated team of physicians was always available to receive calls
• Every ILI case identified was evaluated and a swab collection planned
• ECDC ILI case definition was used by physicians to check ILI eligibility
• During swab collection, vaccination status was checked and if necessary confirmed by means of the
data registry
• CIRI-IT team updated enrolment data and inserted matched swab data into web tools activating
CIRI-IT validation controls and lab cue
• Every valid swab was delivered by CIRI-IT personnel to the Genoa Laboratory
• CIRI-IT Lab dedicated "sample tracking" software was implemented
• CIRI-IT Lab performed sample analysis (RT PCR) and reported the results on CIRI-IT platform
• Swab test results were sent back to the subject and uploaded to the CIRI-IT database for further
quality controls and internal reporting
• Enrolment and swab data were analysed, adapted to the DRIVE dataset and converted for ESSA
transmission

Clinical cohort study: follow-up
• Every single subject enrolled was contacted weekly via email/random messages/phone calls
• Vaccination status of every enrolled subject was collected during and after vaccination campaign
• Dedicated phone number/messages/email channels were activated
• CIRI-IT dedicated team of physicians was always available to receive calls
• Every ILI identified case was evaluated by CIRI-IT physicians (via phone call) and then
authorized (self swabbing)
• ECDC ILI case definition was used by physicians to check ILI eligibility
• During swab authorization, vaccination status was checked and if necessary confirmed with data
registry
• CIRI-IT team updated enrolment data and inserted matched swab data into web tools activating
CIRI-IT validation controls and lab cue
• Every valid swab was sent by the HCW to CIRI-IT collection points then sent to the Milan
Lab
• CIRI-IT Lab dedicated "sample tracking" software was implemented
• CIRI-IT Lab executed samples analysis (RT PCR) and reported on CIRI-IT platform
• Swab test results were sent back to the subject and uploaded to the CIRI-IT database for further
quality controls and internal reporting
• Enrolment and swab data were analysed, adapted to the DRIVE dataset and converted for ESSA
transmission

•Sensitizing healthcare personnel to FLU vaccination
•Slight increase in the percentage of vaccinations in the two facilities
(about 5%)
Training for CIRI-IT Team in Genoa and Milan in a very short time was very
challenging but very positive.
•A very strong group of operators was built, who worked well together
•Shared digital platform simplified all procedures (web interface)
•Lab activities were fully connected via digital platform
•Vaccination status was easily confirmed, as almost all vaccinated subjects
had been vaccinated in CIRI-IT-monitored facilities
•Comparison of two or more vaccine brands
•Two different swab collection methods
•CIRI-IT Milan staff evaluated self-swabbing as a good collection method
(albeit more expensive)

•Large cohort not easy to manage, enroll and follow-up
•Communication campaign BEFORE the study is a key factor (needs enough
time)
•Strong sense of belonging to a study is not easy to create
•Good comprehension of study objectives is very important
•Misunderstanding of DRIVE adhesion and FLU vaccination obligation
•HCW are not so keen to be vaccinated (low percentage)
•If we enlarge the cohort, we increase swab numbers (but critical issues too...)
•Unvaccinated HCW are not so keen to call in the event of (probable) ILI
•HCW self-treat in the event of ILI
•Long-term follow-up needs frequent and effective reminders
•One-to-one follow-up of large cohorts requires huge human resources for a
long time (very expensive)
•Vaccination status of unvaccinated subjects is not easy to confirm
•Lack of national/regional vaccination registry is a critical point
•Increase active participation of HCW by providing more complete feedback of
results

Vaccine brands and influenza over time -
HCW cohort

Subject characteristics - HCW cohort

Non-influenza swabs
Non-influenza
swabs
Total number of
subjects
%
Vaccinated 111 1269 8.7%
Unvaccinated 116 2967 3.9%
More non-influenza ILI swabs among vaccinated
Alternative explanation:
Vaccinated might have been more frail/likely to get (non-influenza) ILI
symptoms?
Unlikely  % with chronic conditions and nr of hospitalizations similar
between vaccinated and unvaccinated
In case of health-care seeking behaviour, the % of subjects with a
non-influenza swab is expected to be higher among the
vaccinated:

• Due to possibility of a strong health-care seeking bias
in the cohort analysis a complementary nested TND
study was performed
• Nested TND study
• Based on swabbed subjects
• Analysis as for the TND studies
• Recomended by ISC to show only results from the
nested TND study
Analysis

Nested TND study
Age Liguria Lombardia
18-64y Any influenza -24.2[-270.4,58.3] 13.9[-125.8,67.2]
Influenza A -24.2[-270.4,58.3] 13.9[-125.8,67.2]
Influenza A H1N1 93.0[-156.8,99.8] 37.8[-122.9,82.6]
Influenza A H3N2
-789.7[-6252.2,-24.6] -38.8 [-411.8,62.4]
Liguria: subjects vaccinated with Fluarix Tetra
Lombardy: subjects vaccinated with Vaxigrip Tetra + self-swabbing

• Health-care seeking behaviour was likely
• Higher number of non-influenza swabs in the vaccinated
• Nested TND results different from cohort results (not
reported)
• Mechanism
• Follow-up: no answer = ‘no ILI’
• More enthousiastic reporting by vaccinated subjects
Interpretation and possible bias

Achievements
• ESSA worked, fully pilot and used for data uploaded
(presentation Kaat)
• Study harmonization
• Quality controls
• DRIVE network increased, new sites joining next year
Discussion

• Meeting primary objectives
• Met for THL: all estimates were robust
• Poorly met for TND
• Few robust results for any vaccine IVE
• No robust results for brand-specific IVE
• Studies in special populations
• Any single study is unlikely to have sufficient sample size for
robust estimates
• Very low numbers for CVD, lung disease, diabetes (in TND
studies)
Discussion

• Minimum data requirements
• Could be different for studies with primary vs secondary data
collection
• Less strict for secondary data
• For studies with primary data collection
• Setting primary care vs. hospital
• Influenza subtype/lineage
• Selected covariates: rediscuss which ones must be included
• Inclusion at cost of discarding records in the event of missing data
• “at least 1 chronic condition” vs type of chronic condition
• Influenza vaccination in previous season not sufficiently granular to be
meaningful  remove?
Discussion

How to meet sample size requirements for primary
objectives in the future?
• Further explore use of secondary data
• All IVE estimates from THL were robust
• Potential sustainable solution to problem of sample size?
• Data from virological surveillance
• Limited clinical data, but more sample size
• Focus on IVE monitoring
• Participatory epidemiology?
Discussion

Acknowledgments
Thank you to all the sites that contributed data and all
the patients that participated in the studies.

• Medical University Vienna (MUV), Austria
• Istituto Superiore di Sanita (ISS), Italy
• Royal College of General Practitioners & University of Surrey (RCGP
RSC), United Kingdom
• Laboratoire National de Santé (LNS), Luxembourg
• Centro Interuniversitario di Ricerca sull’Influenza e sulle altre infezioni
trasmissibili (CIRI-IT), Italy
• Italian Hospital Network (IT-BIVE-HOSP), Italy
• National Institute for Infectious Diseases “Prof. Dr. Matei Balș”, Romania
• Helsinki University Central Hospital (HUS), Finland
• Fundación para el Fomento de la Investigación Sanitaria y Biomédica de
la Comunitat Valenciana (FISABIO), Spain
• Vall d’Hebron University Hospital (HUVH), Barcelona, Spain
• 1st Department of Obstetrics and Gynecology, “Alexandra” General
Hospital of Athens, National and Kapodistrian University of Athens
(UoA), Medical School, Athens, Greece
• The National Institute for Health and Welfare (THL), Finland
Acknowledgments

www.drive-eu.org
Acknowledgement
Thank you
for your attention and
contribution!

www.drive-eu.org
Acknowledgement
Back up slides

Vaccine brands – TND primary care
MUV – Austria CIRI-IT – Italy ISS - Italy
LNS – Luxembourg RCGP RCP - UK Brands with most vaccinees:
• Fluarix Tetra (n=314)
• Vaxigrip Tetra (n=178)

Vaccine brands – TND hospital
HUS – Finland BIVE – Italy NIID - Romania
Spain – FISABIO Spain- HUVH Brands with most vaccinees:
• Fluad (n=620)
• Influvac Tetra (n=478)
• Vaxigrip Tetra (n=216)

DRIVE season 2018/2019

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