Core protocol for ECDC studies of COVID-19 vaccine effectiveness against hospitalisation with severe acute respiratory infection laboratory-confirmed with SARS-CoV-2 or with seasonal influenza : version 2.0.
Date: 2023-05-11
Institutions: European Centre for Disease Prevention and Control
Subjects: European Economic Area, European Union, coronavirus disease, data collection, disease prevention, disease surveillance, hospitalisation, research report, vaccine

TECHNICAL REPORT

Core protocol for ECDC studies of 
COVID-19 vaccine effectiveness against 
hospitalisation with Severe Acute 
Respiratory Infection, laboratory-
confirmed with SARS-CoV-2 or with 
seasonal influenza - Version 2.0

www.ecdc.europa.eu

ECDC TECHNICAL REPORT 

Core protocol for ECDC VEBIS studies of 
vaccine effectiveness against 
hospitalisation with Severe Acute 
Respiratory Infection, laboratory-
confirmed with SARS-CoV-2 or with 
seasonal influenza - Version 2.0 

 
 
This core protocol was commissioned by the European Centre for Disease Prevention and Control (ECDC), as part of the activities 
referring to Framework contract N. ECDC/2021/016 ‘Vaccine Effectiveness, Burden and Impact Studies (VEBIS) of COVID-19 and 
Influenza, Lot 1’, and coordinated Nathalie Nicolay.  

Authors (alphabetical order) 
Jenny Howard, Esther Kissling, Ruoran Li, Alain Moren, Anthony Nardone, Angie Rose, Marta Valenciano (Epiconcept) 

Acknowledgements 
The following names are of those currently participating in the hospital study (for experts who participated in the first ECDC 
expert meeting on COVID-19 VE studies on 29 January 2021, please see core protocol v01). 

Cyril Barbezang,e Nathalie Bossuyt, Sarah Denayer, François Dufrasne, Sébastien Fierens, Melissa Vermeulen (Sciensano, 
Belgium); Thomas Demuyser, Xavier Holemans, Benedicte Lissoir, Lucie Seyler, Els Van Nedervelde (Universitair Ziekenhuis 
Brussel, Belgium), (Grand Hôpital de Charleroi, Belgium); Marieke Bleyen, Door Jouck, Koen Magerman, (Jessa Ziekenhuis, 
Belgium); Marc Bourgeois, Benedicte Delaere (Université catholique de Louvain, Belgium); Evelyn Petit, Marijke Reynders 
(Algemeen Ziekenhuis Sint-Jan Bugge-Oostende, Belgium); Nicolas Dauby, Marc Hainaut (CHU Saint-Pierre, Belgium); Maja Ilić, 
Pero Ivanko, Zvjezdana Lovrić Makarić, Iva Pem Novosel, Goranka Petrović, Petra Smoljo, Irena Tabain (Croatian Institute of 
Public Health, Croatia); Diana Nonković (Teaching Public Health Institute of Split-Dalmatia County, Croatia); Petr Husa, Lenka 
Součková (University Hospital Brno, Czechia); Hana Orliková (National Institute of Public Health, NIPH, Czechia); Anna Maisa, 
Isabelle Parent, Sibylle Bernard-Stoecklin (Santé Publique, France); Odile Launay, Zineb Lesieur, Liem Luong, Claire Rekacewicz, 
Yacine Saidi (REIVAC, France); Silke Buda, Ralf Dürrwald, Ute Preuß, Janine Reiche, Kristin Tolksdorf, Marianne Wedde (Robert 
Koch Institute, Germany); Annamaria Ferenczi, Krisztin J Horváth, Beatrix Oroszi (Semmelweis Univeristy, Hungary); Lisa 
Domegan, Róisín Duffy, Joan O’Donnell (Health Protection Surveillance Centre, Health Service Executive, Ireland); Giedre 
Gefenaite, Indrė Jonikaitė, Monika Kuliešė, Aukse Mickiene, Roberta Vaikutytė (Lithuanian University of Health Sciences); 
Françoise Berthet, Ala'a Al Kerwi (National Health Directorate, Luxembourg); Myriam Alexandre, Nassera Aouali, Guy Fagherazzi 
(Luxembourg Institute of Health); Marc Simon (Centre Hospitalier Luxembourg); Maria-Louise Borg, John Paul Cauchi, Ausra 
Dziugyte, Tanya Melillo (Ministry of Health, Malta); Verónica Gómez, Raquel Guiomar, Irina Kislaya, Ausenda Machado, Ana Paula 
Ambrósio Rodrigues (Instituto Nacional de Saúde Doutor, Portugal); Mihaela Lazar, Odette Popovici (‘Cantacuzino’ National 
Military-Medical Institute for Research and Development, Romania); Isabela Ioana Loghin (Clinical Hospital of Infectious Diseases 
and University of Medicine and Pharmacy ‘Gr. T. Popa’, Iasi, Romania); Corneliu Petru Popescu (Dr Victor Babes Clinical Hospital 
of Infectious and Tropical Diseases, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania); Grupo SiVIRA de 
vigilancia y efectividad vacunal (Acute Respiratory Infections Surveillance System, Spain); Iván Martínez-Baz, Cristina Burgui, 
Itziar Casado Buesa, Jesús Castilla (Instituto de Salud Pública y Laboral de Navarra – IdiSNA – CIBERESP, Spain). 

The following ECDC staff provided substantial input during the production/revision of the document (alphabetical order): Karam 
Adel Ali, Sabrina Bacci, Piotr Kramarz and Nathalie Nicolay.  

This core protocol is based on the ‘Core protocol for ECDC studies of vaccine effectiveness against hospitalisation with Severe 
Acute Respiratory Infection, laboratory-confirmed with SARS-CoV-2 or with seasonal influenza, version 1.0’ as well as scientific 
literature; I-MOVE generic influenza protocol for hospitalised older adults 2019–2020; I-MOVE-COVID-19 WP4 protocol for 
investigation into risk factors against severe COVID-19 among hospitalised patients; Guidance document from WHO Regional 
Office for Europe: COVID-19 VE against SARI hospitalisations associated with lab-confirmed SARS-CoV-1 version 5 (20 January 
2021); first ECDC expert meeting on COVID-19 vaccine effectiveness studies (29 January 2021) SARI COVID-19 Vaccine 
Effectiveness questionnaire v7 from WHO Regional Office for Europe (3 February 2021); I-MOVE-COVID-19 protocol: European 
study of COVID-19 vaccine effectiveness against hospitalised SARI patients laboratory confirmed with SARS-CoV-2 (version 8 
February 2021). 

Suggested citation: European Centre for Disease Prevention and Control. Core protocol for ECDC VEBIS studies of COVID-19 
vaccine effectiveness against hospitalisation with Severe Acute Respiratory Infection laboratory-confirmed with SARS-CoV-2 or 
seasonal influenza - Version 2.0. Stockholm: ECDC; 2023. 

Stockholm, January 2023 

ISBN: 978-92-9498-615-3 
doi: 10.2900/69245 
Catalogue number TQ-07-23-040-EN-N 

© European Centre for Disease Prevention and Control, 2023 

Reproduction is authorised, provided the source is acknowledged 

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TECHNICAL REPORT  Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0 

Contents 
Abbreviations ................................................................................................................................................ v 
Executive summary ........................................................................................................................................ 1 
1 Background ................................................................................................................................................ 3 
1.1 Context: COVID-19 .............................................................................................................................. 3 
1.2 Context: influenza ............................................................................................................................... 4 
1.3 ECDC CVE and IVE studies ................................................................................................................... 5 
1.4 Aim of the core protocol ...................................................................................................................... 5 
2 Objectives .................................................................................................................................................. 7 
2.1 Primary objective ................................................................................................................................ 7 
2.2 Secondary objectives ........................................................................................................................... 7 
3 Methods ..................................................................................................................................................... 8 
3.1 Study design ....................................................................................................................................... 8 
3.2 Study population ................................................................................................................................. 8 
3.3 Study period ....................................................................................................................................... 8 
3.4 Outcomes ........................................................................................................................................... 8 
CVE studies .......................................................................................................................................... 8 
IVE studies........................................................................................................................................... 9 
3.5 Definitions .......................................................................................................................................... 9 
3.5.1 Hospitalised patient ...................................................................................................................... 9 
3.5.2 SARI patient ................................................................................................................................ 9 
3.5.3 SARI patients confirmed as COVID-19 (confirmed cases: CVE) ......................................................... 9 
3.5.4 SARI patients confirmed as influenza (confirmed cases: IVE) ............................................................ 9 
3.5.5 SARI patients testing negative for SARS-CoV-2/influenza (test-negative controls) ............................... 9 
3.5.6 Exclusion criteria ........................................................................................................................ 10 
3.6 SARI patient identification – algorithm for patient inclusion ................................................................... 11 
3.6.1 Recruitment strategies ................................................................................................................ 11 
3.7 Laboratory methods .......................................................................................................................... 13 
3.8 Exposure (vaccination) ...................................................................................................................... 14 
3.8.1 Definition of vaccination status .................................................................................................... 14 
3.9 Potential confounding factors and effect modifiers................................................................................ 15 
3.9.1 Pre-existing chronic conditions..................................................................................................... 15 
3.9.2 Severity of underlying condition/healthcare utilisation .................................................................... 15 
3.9.3 Ethnicity (optional for CVE; not required for IVE) .......................................................................... 15 
3.9.4 Medication status for chronic condition(s) (optional for CVE; for IVE: statin use only) ....................... 16 
3.9.6 Pregnancy status ........................................................................................................................ 16 
3.9.7 Smoking history ......................................................................................................................... 16 
3.9.8 Healthcare worker ...................................................................................................................... 16 
3.9.9 Other occupations (optional for CVE; not required for IVE) ............................................................. 16 
3.9.10 Other vaccinations .................................................................................................................... 17 
3.9.11 Antiviral administration ............................................................................................................. 17 
3.9.12 Presence of other respiratory virus infections .............................................................................. 17 
3.9.13 Setting (LTCF vs community) ..................................................................................................... 17 
3.10 Sample size .................................................................................................................................... 18 
3.11 Data ............................................................................................................................................... 21 
3.11.1 Sources of information .............................................................................................................. 21 
3.12 Collected information ....................................................................................................................... 21 
3.12.1 Study identifiers ....................................................................................................................... 22 
3.12.2 Hospital/ward information ......................................................................................................... 22 
3.12.3 Patient characteristics ............................................................................................................... 22 
3.12.4 Clinical characteristics (symptoms and markers of severity) .......................................................... 22 
3.12.5 Case definition ......................................................................................................................... 23 
3.12.6 Data entry validation................................................................................................................. 23 
3.13 Data analysis .................................................................................................................................. 23 
3.13.1 Individual (country/site level) analysis ........................................................................................ 23 
3.13.2 Pooled analysis ......................................................................................................................... 25 
3.14 Personal data protection .................................................................................................................. 25 
3.15 Training .......................................................................................................................................... 25 
4 Logistical aspects ...................................................................................................................................... 26 
4.1 Respiratory specimen collection .......................................................................................................... 26 
4.2 Laboratory tests ................................................................................................................................ 26 

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Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0  TECHNICAL REPORT 

5 Limitations................................................................................................................................................ 27 
5.1 Potential biases ................................................................................................................................. 27 
5.1.1 Bias from pooled estimates ......................................................................................................... 27 
5.1.2 Negative confounding ................................................................................................................. 27 
5.1.3 Positive confounding ................................................................................................................... 27 
5.1.4 Unmeasured confounding ........................................................................................................... 27 
5.1.5 Previous infection in cases or controls; inclusion of asymptomatic controls ...................................... 28 
5.1.6 Inclusion of influenza-positive controls for CVE and SARS-CoV-2-positive controls for IVE ................. 28 
5.1.7 Validation of exposure ................................................................................................................ 28 
5.1.8 Misclassification.......................................................................................................................... 28 
5.1.9 Other potential biases ................................................................................................................. 28 
5.2 Representativeness of subjects included in the study ............................................................................ 29 
References .................................................................................................................................................. 30 
Annex 1. List of variables, definitions and coding ............................................................................................ 33 
Annex 2. Detailed analysis plan ..................................................................................................................... 43 
Annex 3. Genetic characterisation in vaccine effectiveness studies .................................................................... 47 
Annex 4. List of ICD-9 and ICD-10 codes for pre-existing chronic conditions ..................................................... 50 

Figures 

Figure 1. Proposed inclusion algorithm for hospitals/services relying on common use of ICD codes, hospital-based 
COVID-19/influenza vaccine effectiveness study ............................................................................................. 11 
Figure 2. Proposed inclusion algorithm for hospitals/services systematic screening of all admitted patients, hospital-
based COVID-19/influenza vaccine effectiveness study .................................................................................... 12 

Tables 
Table 1. COVID-19 vaccines authorised and in used in the EU/EEA as of December 2022 ..................................... 3 
Table 2. Type of studies and settings within the VEBIS infrastructure as of November 2022 ....................................... 5 
Table 3. List of diagnosis codes for which patients could be screened for onset of SARI symptoms, COVID-
19/influenza hospital-based VE studies .......................................................................................................... 12 
Table 4. List of recommended and optional pre-existing conditions as potential effect modifiers or confounding factors ...... 15 
Table 5. Sample size calculations ................................................................................................................... 18 
Table 6. Example of descriptive table for cases and controls; COVID-19/influenza hospital-based vaccine 
effectiveness study, ECDC multi-centre study, 2021 ........................................................................................ 24 
Table 7. Example of table showing vaccine effectiveness against COVID-19/influenza adjusted for various co-variables by 
age group, hospital-based COVID-19/influenza vaccine effectiveness ECDC multi-centre study, 2022 ............................ 24 

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TECHNICAL REPORT  Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0 

Abbreviations 

COVID-19 

Coronavirus disease 2019 

Ct 

CVE 

IVE 

EEA 

EU 

GP 

HDU  

ICD  

ICU  

Cycle threshold 

COVID-19 vaccine effectiveness 

Influenza vaccine effectiveness 

European Economic Area 

European Union 

General practitioner 

High Dependency Unit 

International classification of diseases 

Intensive Care Unit 

I-MOVE 

Influenza – Monitoring Vaccine Effectiveness in Europe 

OR 

PCV 

PPV  

RF 

RSV  

RT- PCR 

SARI 

Odds ratio 

Pneumococcal Conjugate Vaccine  

Pneumococcal Polysaccharide Vaccine  

Risk factor 

Respiratory Syncytial Virus  

Reverse-transcription polymerase chain reaction 

Severe acute respiratory infection 

SARS-CoV-2 

Severe acute respiratory syndrome – coronavirus 2 

SES 

VE 

Socioeconomic status 

Vaccine effectiveness 

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Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0  TECHNICAL REPORT 

Executive summary 

The end of 2019 saw the emergence of a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) 
causing coronavirus disease 2019 (COVID-19). As of week 39, 2022, over 168 million cases and more than 1.1 million 
deaths had been reported in the European Union/European Economic Area (EU/EEA) [1]. As of December 2022, 
seven vaccines had been authorised by the European Commission based on the scientific opinion of the European 
Medicines Agency (EMA) for use in the European Union (Comirnaty, COVID-19 Vaccine Valneva, Nuvaxovid [previously 
Novavax], Spikevax [previously COVID-19 vaccine Moderna], Vaxzevria [previously AstraZeneca], and Jcovden 
[previously
been authorised as boosters (Comirnaty Original/Omicron BA.1, Comirnaty Original/Omicron BA.4-5, Spikevax bivalent 
Original/Omicron BA.1). Many other vaccines are under rolling review [2]. 

Covid-19 Vaccine Janssen] and VidPrevtyn Beta (indicated as a booster)) and three adapted vaccines had 

Influenza viruses undergo frequent genetic and antigenic changes. The influenza vaccine is reformulated each year 
and annual re-vaccination is recommended. Observed influenza vaccine effectiveness (IVE) varies from year to year 
between population sub-groups (age groups, risk groups) and differs for the various influenza types, subtypes and 
genetic clades, and outcomes measured. Immunological correlates of protection are not well defined. In 2017, the 
European Medicines Agency (EMA) formally adopted new guidelines on influenza vaccines covering, inter alia, post-
authorisation studies of vaccine effectiveness, including brand-specific IVE data [3].  

The available vaccine products currently in use for EU/EEA immunisation programmes, the target groups for 
vaccination and the vaccination coverage all vary across countries. New vaccines are being developed for which 
limited or no effectiveness data are yet available in the EU. A comparison by vaccine type (adjuvanted vs non-
adjuvanted, live attenuated vs inactivated, egg- vs cell-based), group (split virion, subunit, etc.) and product could 
provide essential information for vaccine recommendations and health economic assessments.  

In 2020, the European Commission stressed the importance of continuously monitoring the safety and 
effectiveness of vaccines in the EU/EEA in the post-authorisation phase, with particular emphasis on COVID-19 
vaccines in the context of the ongoing pandemic [4]. The 2018 ‘Council Recommendation on Strengthened 
Cooperation against Vaccine-preventable Diseases’ already called on ECDC and EMA to cooperate to ensure the 
continued monitoring of vaccines and vaccination in use in EU/EEA vaccination programmes [5]. This request was 
subsequently formalised as part of the extended EMA regulatory mandate [6] and ECDC’s newly amended mandate 
[7], requiring the two Agencies to develop a structured and independent post-authorisation vaccine monitoring 
platform, initially prioritising COVID-19 vaccines. ECDC and EMA officially established and launched the platform in 
May 2022, with the intention of bringing together public health and regulatory experts to discuss the studies 
needed to generate real-life evidence on the safety and effectiveness of vaccines in use in EU/EEA immunisation 
programmes. 

ECDC already began building infrastructure to perform COVID-19 vaccine effectiveness (CVE) studies in 2020, 
using the lessons learned from other vaccine effectiveness studies. The infrastructure will be used to build a system 
that regularly monitors vaccine effectiveness and performs studies, including impact and burden of disease studies, 
in different settings. Depending on the setting, information will be provided on different outcomes (severe disease, 
moderate disease, transmission, etc). The overall project is called VEBIS (Vaccine Effectiveness, Burden and Impact 
Studies) and it includes different networks of study sites/countries/infrastructures, where the multi-country studies 
are conducted. 

This core protocol for ECDC studies of vaccine effectiveness against hospitalisation with Severe Acute Respiratory 
Infection laboratory-confirmed with SARS-CoV-2 or with influenza, version 2.0, represents an update to the main 
elements for a multi-country hospital-based study of COVID-19 vaccine effectiveness in patients hospitalised with 
Severe Acute Respiratory Infection (SARI), already published as version 1.0 [8]. This version includes a section on 
influenza vaccine effectiveness, outlining the agreed methods for collecting data related to SARS-CoV-2 and 
influenza infections at country level, and including a plan for pooled analyses. The larger sample size achieved by 
combining data from multiple sites will provide more statistical power to meet more specific objectives. The 
protocol can be implemented for COVID-19 and/or influenza. 

The proposed method is a case-control study using a test negative design. The study population consists of 
individuals of all ages, belonging to the target group for COVID-19 or influenza vaccination, hospitalised with SARI 
symptoms and no contra-indication for being vaccinated with the vaccine of interest. The study period runs during 
the influenza circulation season for influenza vaccine effectiveness study. It would be beneficial if those countries 
only measuring vaccine effectiveness (VE) for COVID-19 also test for influenza during the influenza season, as well 
as for all other respiratory viruses (as appropriate depending on time of year).  

This core protocol is primarily intended to guide the implementation of ECDC-funded studies. However, ECDC 
encourages the conduct of vaccine effectiveness studies, using this protocol as a basis, in countries not currently 
planning to participate in ECDC-funded studies. The use of consistent protocols will facilitate the comparability of 
study results across studies, countries and study sites. 

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TECHNICAL REPORT  Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0 

This document represents version 2.0 of the core protocol, and succeeds version 1.0, which has already been 
adapted and implemented at country level. Results from the implementation of the VEBIS multi-country study from 
version 1 are available publicly on ECDC’s website1. This document and its country-level adaptations will continue 
to be updated and revised on a regular basis. 

1 https://www.ecdc.europa.eu/en/covid-19/prevention-and-control/vaccines 

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Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0   TECHNICAL REPORT 

1 Background 

1.1 Context: COVID-19 
The end of 2019 saw the emergence of a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), 
which causes coronavirus disease 2019 (COVID-19) with substantial disease burden [1]. 

As of December 2022, seven of the originally authorised COVID-19 vaccines and three adapted vaccines for use as 
boosters had received conditional marketing authorisation from the European Commission (EC) based on the 
scientific opinion of the European Medicine Agency (EMA) (Table 1) [2,9].  

Table 1. COVID-19 vaccines authorised and used in the EU/EEA, as of December 2022 
Vaccine  

Primary dose regimen 

Booster dose 

Age of 
recommendation 

Vaxzevria (AZD1222) 

18+ years 

Comirnaty (BNT162b2) 30 
micrograms per dose (12+ 
years), 10 micrograms per 
dose (5-11 years), 3 
micrograms per dose (six 
months – four years). 

Six months and 
above 

Comirnaty Original/Omicron 
BA.1 

12+ years 

Two doses given between 
four and 12 weeks after 
the first dose.  
Two doses three weeks 
apart in individuals aged 
five years and above. 
Three doses. In children 
from six months to four 
years, the first two doses are 
given three weeks apart, 
followed by a third dose 
given at least eight weeks 
after the second dose. 
No 

Comirnaty  Original/Omicron 
BA.4-5 

Five years and 
above 

May be given for primary 
vaccination in non-
immune individuals. 

Jcovden (Ad26.COV 2.5) 

18+ years 

Single dose  

Spikevax (mRNA-1273) 50 
micrograms per dose (12+ 
years) 25 micrograms per 
dose (six months - five years) 
Spikevax bivalent 
Original/Omicron BA.1 50 
micrograms per dose (12+ 
years) 25 micrograms per 
dose (six months - five 
years). 
Spikevax bivalent 
Original/Omicron BA.4-5, 

Six months and 
above 

Two injections 28 days 
apart. 

Six years and 
above 

No 

12+ years 

No 

Nuvaxovid (NVX-CoV2373) 

12+ years 

Two doses given three 
weeks apart. 

VidPrevtyn Beta  

18+ years 

No. 

COVID-19 vaccine Valneva 
(inactivated, adjuvanted) 
(VLA2001). 

18 to 50 years 

Two doses given four 
weeks apart. 

A booster dose may be given at least three 
months after the second dose. 

May be given as a booster given at least 
three months after the second dose in 
individuals aged 12+ years. 
May be given as a booster at least six 
months after the second dose of primary 
vaccination in individuals aged 5-11 years.  

May be given as a booster at least three 
months after the second dose in 
individuals aged 12+ years.  
May be given as a booster at least six 
months after the second dose of primary 
vaccination in individuals aged 5-11 years  
May be given as a second booster to those 
aged 12+ years three months after first 
booster.  
May be given at least three months after 
primary vaccination or a booster dose with a 
COVID-19 vaccine to those aged 12+ years. 
May be given as a booster dose three 
months after primary vaccination or first 
booster in those aged five years and over. 
May be used as booster dose given at least 
two months after the first dose of Jcovden. 

May be given as a booster at least three 
months after the second dose in 
individuals aged 12+ years. 

May be given to adults and children from six 
years, at least three months after primary 
vaccination or a booster dose with a COVID-
19 vaccine. 

May be given to adults and children from six 
years, at least three months after primary 
vaccination or a booster dose with a COVID-
19 vaccine. 
May be used as a booster in adults who have 
had Nuvaxovid, an mRNA vaccine or an 
adenoviral vector vaccine as their primary 
vaccination. 
May be used as a booster dose at least four 
months after a previous mRNA or adenoviral 
vector COVID-19 vaccine. 
No. 

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TECHNICAL REPORT  Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0 

By January 2021, all 30 EU/EEA countries had started COVID-19 vaccination campaigns, and different COVID-19 
vaccine products have been gradually introduced as they have become available through the EU Vaccines Strategy 
[10]. In addition to healthcare workers and residents of long-term care facilities, the vaccination strategy in the 
general population had an age-based staggered approach prioritising older individuals over younger ones. Most of 
the vaccines used so far, including booster vaccines, have been mRNA vaccines [11]. 

1.2 Context: influenza 
Despite a recommendation in 2009 by the Council of Ministers that all EU Member States achieve an influenza 
vaccination coverage of 75% in all risk groups by the winter season 2014–15, a survey by ECDC at the end of 2018 
showed that very few had reached this goal in the specific risk groups [12]. Risk groups are defined as children 
aged 6–59 months, pregnant women, healthcare workers, individuals 60 or 65 years and older, and people with a 
range of underlying medical conditions [13,14]. 

Influenza viruses undergo frequent genetic and antigenic changes and vaccine-induced immunity usually does not 
last more than 6–12 months (sometimes less). As a consequence, the influenza vaccine is evaluated each year and 
annual re-vaccination is recommended. Available seasonal influenza vaccines are only moderately effective and 
influenza vaccine effectiveness (IVE) may vary depending on the vaccine product, the time since last vaccination 
and previous influenza infection and influenza vaccine history. Observed IVE varies from year to year among 
population sub-groups (age groups, risk groups) and differs for the various influenza types, subtypes, antigenic 
groups and outcomes measured.  

Influenza VE is only partially correlated with the degree of virological match between the virus strains included in 
the vaccine and the circulating strains in an influenza season or a pandemic. Immunological correlates of protection 
are not well defined. As a consequence, EMA has stopped requiring annual immunogenicity studies from vaccine 
producers prior to marketing their products, and now requires brand-specific IVE data [3]. Although it recognises 
the challenges in collecting brand-specific data for every product and season, EMA expects that results of 
effectiveness studies by brand will contribute important information to the overall clinical evidence available for 
each influenza vaccine, especially new vaccines. Results of vaccine effectiveness studies may yield potential signals 
that require further investigation to determine the drivers of estimated effectiveness, facilitating the consideration 
of regulatory actions [3]. 

With the exception of some of the 2009 pandemic vaccines and of some new vaccine formulations (live attenuated 
influenza vaccine (LAIV)), seasonal trivalent and quadrivalent influenza vaccines may be authorised nationally. The 
available vaccine products, the target groups for vaccination and vaccination coverage all vary across countries. 
New vaccines are being developed for which limited or no effectiveness data are yet available in the EU. Several 
studies suggest that adjuvanted vaccines are more immunogenic against seasonal influenza than non-adjuvanted 
in the elderly population, but their protective effect against clinical disease is unclear. A comparison by vaccine type 
(adjuvanted vs non-adjuvanted, live attenuated vs inactivated, egg- vs cell-based), group (split virion, subunit, 
etc.) and product (vaccine brands) would provide essential information for vaccine recommendations and health 
economic assessments.  

Early-season IVE by product and influenza type/subtype is also critical for the WHO vaccine composition meeting 
discussions, in order to formulate vaccine selections for the subsequent season. A lack of early season IVE results 
could also result in a failure to use alternative measures (antivirals as a preventive measure if there are low IVE 
estimates), leading to increased disease burden and costs.  

Two questions have recently been raised and the answers to these may modify our understanding of influenza 
immunology, the vaccines needed to prevent influenza and the recommended strategies required. The first 
question is to understand why the measured IVE decreases during some influenza seasons [15-18]. Potential 
explanations are the respective role of amino acid substitutions in widely circulating viruses during the season and 
the potential decreasing protection conferred by seasonal vaccines given from October each season. In addition, 
for influenza A(H3N2) there is an issue with egg- versus cell-based vaccines. It has been observed that mutations 
that emerge during cultivation of the virus for vaccine manufacturing may affect VE. The second question is to 
understand if, and how, former seasonal influenza vaccinations modify the effectiveness of current seasonal 
vaccines [19,20]. Long-term multi-centre studies are needed, allowing for the early and late season measurement 
of IVE, with a sufficient sample size to be able to answer these questions.  

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Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0   TECHNICAL REPORT 

1.3 ECDC CVE and IVE studies 
In 2018, the Council ‘Recommendation on Strengthened Cooperation against Vaccine-preventable Diseases’ 
(2018/C 466/01) called on the European Commission to work with the Member States, with the support of EMA 
and in cooperation with ECDC, to ‘continuously monitor the benefits and risks of vaccines and vaccinations at EU 
level, including through post-marketing authorisation studies’. In 2020, the European Commission stressed the 
importance of continuously monitoring the safety and effectiveness of vaccines in the EU/EEA in the post-
authorisation phase, with particular emphasis on COVID-19 vaccines in the context of the ongoing pandemic [4]. 
This was subsequently formalised as part of the extended EMA regulatory mandate [6] and ECDC’s newly amended 
mandate [7], requiring the two Agencies to develop a structured and independent post-authorisation vaccine 
monitoring platform, initially prioritising COVID-19 vaccines. ECDC and EMA officially established and launched such 
platform in May 2022, with the intention of bringing together public health and regulatory experts to discuss the 
studies needed to generate real-life evidence on the safety and effectiveness of vaccines in use for EU/EEA 
immunisation programmes. 

ECDC already began building infrastructure to perform COVID-19 vaccine effectiveness (CVE) studies in 2020, using 
the lessons learned from other vaccine effectiveness studies (Table 2). The infrastructure is being used to build a 
system that regularly monitors CVE and performs studies in different settings. Depending on the setting, information 
will be provided on different outcomes (severe disease, moderate disease, transmission, etc). At the start of 2022, 
ECDC expanded this infrastructure to include IVE monitoring. The multi-country approach of the effectiveness studies 
is also one of the key features, with a progressive inclusion of further countries foreseen over time. 

Table 2. Type of studies and settings within the VEBIS infrastructure, as of November 2022 

Setting  

Hospitals 

Type of study 

Main outcome 

References 

Test negative design 

Severe disease; influenza and 
COVID-19 

[8,21-24]  

Healthcare workers cohort  

Cohort study 

Infection, COVID-19 

[25,26] 

Electronic healthcare 
databases 

Cohort study 

Hospitalisation, death, COVID-19 

[27] 

Primary care  

Test negative design 

Moderate disease (~ARI/ILI), 
influenza and COVID-19 

[28] 

One of the first studies implemented, and for which the core ECDC protocol version 1.0 was published in October 
2021 [8], was a multi-country study to estimate CVE against severe disease, by assessing it in individuals 
hospitalised for SARI. The update, including estimation of influenza vaccine effectiveness (IVE), is presented in this 
document as version 2.0. 

1.4 Aim of the core protocol 
This core protocol for ECDC studies of CVE and IVE against hospitalisation with SARI laboratory-confirmed with 
SARS-CoV-2, and SARI laboratory-confirmed with influenza, version 2.0, presents the main elements for a multi-
centre (multi-country) hospital-based study of CVE/IVE in patients hospitalised with SARI. It outlines the agreed 
methods for collecting data related to SARS-CoV-2, COVID-19 and influenza virus and disease at country level, and 
includes a plan for the pooled analysis. The combination of data from multiple sites will facilitate studies with more 
statistical power in order to meet more specific objectives. 

ECDC has worked closely with EU Member States with the aim of implementing a system for the regular monitoring 
of vaccine effectiveness among hospitalised SARI patients, taking advantage of parallel efforts to enhance SARI 
surveillance in the EU/EEA and countries of the Western Balkan region2. Through the Member States hospitals have 
been recruited that are capable of applying the core protocol and therefore contributing to the EU-level monitoring 
of CVE and ultimately IVE. Each study site has been identified through a process involving ECDC’s relevant National 
Coordinator3. 

This core protocol is therefore primarily intended to guide the implementation of ECDC-funded studies. However, 
ECDC encourages the conduct of VE studies, using this protocol as a basis, in countries not currently planning to 
participate in ECDC-funded studies. The use of consistent protocols will facilitate the comparability of study results 
across studies, countries, and study sites. 

2 Western Balkans, i.e. Albania, Bosnia and Herzegovina, Montenegro, Serbia, North Macedonia, and Kosovo (this designation is without prejudice to 
positions on status, and is in line with UNSCR 1244/1999 and the ICJ Opinion on the Kosovo declaration of independence). 
3 https://www.ecdc.europa.eu/sites/portal/files/media/en/aboutus/governance/competent-bodies/Documents/coordinating-competent-bodies-
structures-terms-of-reference-and-interactions-w-Annexes.pdf 

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TECHNICAL REPORT  Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0 

This document presents version 2.0 of the core protocol. Version 1.0 has already been implemented at country 
level. This document will be updated and revised on a regular basis. 

This core protocol is complemented by a questionnaire, a list of variables to be collected and their coding, all of which are 
available upon request at vpd.vpd@ecdc.europa.eu (the list of variables and coding also appear in Annex 1). 

Under each paragraph, arrow marks indicate the points that countries/hospitals/study sites could further 
expand/detail when creating a country-specific protocol using the core ECDC protocol.  

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Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0   TECHNICAL REPORT 

2 Objectives  

2.1 Primary objective 
The primary objective is to measure, within each European country/site participating in both CVE and IVE4 and in a 
pooled, multi-country analysis, the direct effect (effectiveness) of overall and product-specific COVID-19 and 
influenza vaccines against SARI due to laboratory-confirmed SARS-CoV-2 and/or influenza in hospitalised patients. 
This will provide up-to-date information on the ability of COVID-19 and influenza vaccines to prevent severe 
disease under real conditions of use. 

2.2 Secondary objectives 
The secondary objectives are, among SARI patients requiring hospitalisation:  

• 

• 

• 

to measure overall and product-specific CVE and IVE against SARI due to laboratory-confirmed SARS-CoV-2 
or influenza in hospitalised patients by participating study country, risk group (e.g. specific chronic 
conditions), age group, COVID-19 or influenza vaccination target group, time since vaccination, calendar 
time and vaccine doses when applicable; 
to measure overall and product-specific CVE and IVE against specific genetic variant(s) of laboratory-
confirmed SARS-CoV-2, type, subtype/lineage and specific clades of influenza (where possible), and more 
severe outcomes (ICU admission, invasive ventilation, in-hospital mortality);  
to identify potential factors that may modify CVE or IVE: prior SARS-CoV-2/influenza infection, chronic 
conditions, the role of influenza/COVID-19 vaccination, etc.  

The aim of these three secondary objectives is to understand the duration of vaccine protection and identify any 
differences in CVE/IVE among each of these strata, potential target groups for vaccination, and key SARS-CoV-2 or 
influenza virus phenotypic or genotypic evolutions that could affect vaccine performance.  

 
 

Each study site/country to specify primary objectives of their study as measuring CVE only or CVE and IVE. 
Each study site/country to specify the (additional) secondary objectives of their study. 

4 Countries only participating in COVID-19 vaccine effectiveness studies can ignore references to measurement of influenza vaccine 
effectiveness throughout the document. 

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TECHNICAL REPORT  Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0 

3 Methods 

3.1 Study design 
The following study design is applied:  

At country level: a hospital-based, test-negative, case–control study at each participating hospital. 

• 

At EU/EEA level: a multi-centre hospital-based, test-negative, case–control study, using pooled data from 
several countries/regions. 

In addition to the test-negative controls, some sites/countries may be able to recruit a second additional control 
group (e.g. non-SARI hospitalised patients matched to cases by date of admission; see Section 3.5.5). 

 

Each study site/country to specify if, in addition to the test-negative controls, other control groups are selected. 

3.2 Study population 
This study is intended to be conducted primarily in countries with pre-existing SARI surveillance systems, to recruit 
patients for hospital-based CVE/IVE studies. The study population for the CVE/IVE study will therefore consist of 
individuals of all ages, belonging to the target group for vaccination, hospitalised with SARI symptoms in one of the 
participating hospitals/services, with no contra-indication for COVID-19 vaccination (for CVE studies) or for 
influenza vaccination (for IVE studies).  

 

 
 
 

 

Each study site/country to describe the setting (number of hospitals included, number of beds, number and 
type of wards/specialties/services included). 
Each study site/country to describe the existing SARI surveillance system in place. 
Each study site/country to describe the study population for IVE and for CVE. 
Each study site/country to describe target group(s) for vaccination and order/timeline of vaccination by group 
(if known). 
Each study site/country to describe the epidemiological situation (incidence, number of COVID-19 or influenza 
hospitalisations, mortality). 

3.3 Study period 
The study period for CVE starts once COVID-19 vaccine is available for each target group and SARS-CoV-2 is 
circulating in the participating country. In order to determine SARS-Cov-2 variant dominance, ECDC surveillance 
data on variants of concern can be used. 

The study period for IVE begins when the seasonal influenza vaccine for the corresponding season becomes 
available and the influenza season begins in the country/region and it finishes at the end of the influenza period. 
Cases and controls are included from the week of onset of the season’s first influenza-positive case in each 
country-specific study. Each country/study site should clearly describe the criteria used to define the beginning and 
the end of the influenza circulating period.  

Participating hospitals carry out the study throughout the year.  

 
 

 

Each study site/country to define the beginning of the CVE study period (day/month/year). 
Each study site/country to define the beginning and end of the IVE study period (day/month/year) and 
criteria for defining the IVE study period in their country. 
Each study site/country to specify the start date of their COVID-19 and influenza vaccination campaign, by 
target group and for the general population, for primary course vaccination. For COVID-19, each site/country 
should additionally specify dates for each booster dose, by target group and for the general population. 

3.4 Outcomes 

CVE studies 

The outcome of interest for the primary analysis is SARS-CoV-2 infection in patients of all ages hospitalised with 
SARI symptoms, laboratory-confirmed by PCR, documented either on admission to hospital or in the 14 days 
before admission.  

Secondary outcomes of interest, in the same patient group, are laboratory-confirmed infections with genetic 
variants of SARS-CoV-2 and confirmed SARS-CoV-2 patients with severe outcomes (ICU admission, invasive 
ventilation or death). 

8 

 
 
Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0   TECHNICAL REPORT 

IVE studies 

The outcome of interest is laboratory-confirmed influenza in patients of all ages, hospitalised with SARI symptoms.  

More specifically, this will be:  
• 
• 
• 
• 

subtype-specific laboratory-confirmed influenza A; 
laboratory-confirmed influenza B overall and, if available, by lineage (B Victoria/B Yamagata); 
laboratory-confirmed influenza by clade (where possible); 
laboratory confirmed as co-infected with SARS-CoV-2 (where possible). 

3.5 Definitions 

3.5.1 Hospitalised patient 

A hospitalised patient is a SARI patient who has been admitted to one of the participating hospitals during the 
study period and has not been discharged to their home or home equivalent, or died within 24 hours. 

3.5.2 SARI patient 

A SARI patient is defined as a hospitalised person with at least one of the following symptoms: 
• 
• 
• 
• 

cough;  
fever;  
shortness of breath; or  
sudden onset of anosmia, ageusia or dysgeusia. 

SARI patients with onset of symptoms in the 14 days prior to hospital admission will be included in the study. Note 
that hospitals already participating in SARI surveillance systems should not modify the SARI inclusion criteria for 
surveillance. However, for the CVE analysis, only those patients with onset of symptoms in the 14 days prior to 
hospital admission will be included. 

In a later protocol version, a cut-off for days between onset of symptoms and swabbing may be decided (if 
appropriate).  

A SARI patient is defined using ECDC’s clinical case definition for a hospitalised possible COVID-19 case5. 
Compared with the World Health Organization (WHO) SARI case definition6 (an acute respiratory infection with a 
history of fever or measured fever of ≥38 C° and cough, onset within the last 10 days and requiring 
hospitalisation), ECDC’s possible COVID-19 case definition is more sensitive. Each study site/country will report 
individual SARI symptoms separately to allow sensitivity analyses comparing CVE and IVE estimates, based on 
alternative SARI case definitions and depending on the pathogen. 

3.5.3 SARI patients confirmed as COVID-19 (confirmed cases: CVE) 

A confirmed COVID-19 case will be defined as a hospitalised patient fulfilling the SARI case definition, with a 
respiratory sample positive for SARS-CoV-2 by PCR, [29] either on admission to hospital or documented in the 14 
days prior to hospital admission. 

3.5.4 SARI patients confirmed as influenza (confirmed cases: IVE) 

A confirmed influenza case will be defined as a hospitalised patient fulfilling the SARI case definition, with a 
respiratory sample PCR-positive for influenza7.  

3.5.5 SARI patients testing negative for SARS-CoV-2/influenza (test-
negative controls) 

A control will be defined as a hospitalised patient fulfilling the SARI case definition, with a respiratory sample 
negative for SARS-CoV-2 (for CVE) by PCR on admission to hospital or a respiratory sample negative for influenza 
(for IVE) [27,28]. 

It is advised that all sites test for both SARS-CoV-2 and influenza during influenza season, as well as for all other 
respiratory viruses (as appropriate, depending on time of year), even if they are not participating in IVE. If this is 
not feasible, then during influenza season all samples that are negative for SARS-CoV-2 should also be tested for 
influenza (if not already tested at primary care level).  

5 ECDC possible COVID-19 case definition: https://www.ecdc.europa.eu/en/covid-19/surveillance/case-definition  
6 WHO SARI case definition: https://www.who.int/teams/global-influenza-programme/surveillance-and-monitoring/case-
definitions-for-ili-and-sari 
7 No requirement for the dates of the test. There are additional requirements in the exclusion criteria relating to test date and 
symptom onset. 

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TECHNICAL REPORT  Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0 

Controls that are negative by PCR but have Ct results suggestive of COVID-198, and those with prior SARS-CoV-2 
infection in the three months before admission, may be excluded as controls in sensitivity analyses (see Section 
3.5.7 ‘Exclusion criteria’). 

 

 

A study site/country measuring only CVE should indicate which testing strategy they will use (testing all 
samples for both SARS-CoV-2 and influenza, or only testing for influenza in those negative for SARS-CoV-2). 
Each study site/country to indicate whether they can test for other respiratory viruses, only SARS-CoV-2 and 
influenza, or only SARS-CoV-2. 

Other control groups (optional) 
During periods when respiratory viruses are in low circulation the number of SARI patients testing negative for 
SARS-CoV-2/influenza may be limited. Some sites may therefore wish to include other control groups, either in 
addition to SARI controls or as an alternative, and these must be recruited throughout the whole study period.  

Examples of other control groups include:  

• 

● 

• 

Example source of hospitalised non-SARI cases: hospital wards admitting patients without COVID-19. 

Patients hospitalised with non-SARI related symptoms matched by time, age group and, if possible, 
underlying conditions.  
− 
Primary care: selection of GP patients belonging to the hospital catchment area and vaccination target 
group matched by time and age group.  
− 

Example source of GP patients: contact the GP for the case and select patients from his/her list 
(matching by GP). 

Community controls  
− 

− 

− 

− 

Random selection of community controls belonging to the vaccination target group, matched by time 
and age group (e.g. vaccine registry, telephone random survey, other planned survey). 
Vaccination coverage in cases will be compared to vaccination coverage in the vaccination target 
population (screening method). Vaccination coverage should be available by time, age group and 
comorbidities.  
Vaccination coverage among GP patients in the hospital catchment area: a random sample of GPs in 
the hospital catchment area can be used to compute the proportion of GP patients who are 
vaccinated.  
Vaccination coverage using vaccination centres in the hospital catchment area: the vaccination 
coverage can be computed by dividing the number of individuals vaccinated (by age group, target 
group) by the number of individuals eligible for vaccination in the hospital catchment area (by age 
group, target group). Several methods can be used to estimate the population in the hospital 
catchment area.  

All control groups should represent the vaccination coverage of the population giving rise to the cases. As the 
circulation of SARS-CoV-2/influenza and vaccination coverage changes over time, it is recommended that cases and 
controls be matched by time (e.g. onset of SARI symptoms) or adjusted by time in the analysis.  

 

Each study site/country including other control groups should define the control group, how controls will be 
selected, representativeness (vaccination coverage in the population giving rise to the cases) and potential 
limitations.  

3.5.6 Exclusion criteria 

The patient will not be enrolled in the study if she/he: 

• 

• 
• 

• 

• 

• 

• 

• 

is unwilling to participate or unable to communicate and give consent (the consent may also be given by 
her/his legal representative, or by specific consent procedures, acceptable according to the local ethical 
review process); 
has a contraindication for the COVID-19 (for CVE) or influenza (for IVE) vaccination; 
cannot be swabbed due to severe septum deviation, obstruction or other conditions that contra-indicate 
swabbing; 
has a history of hospitalisation within the 14 days immediately prior to this admission (including transfers 
from another hospital); 
had his/her SARI onset ≥ 48 hours after admission to the hospital (for IVE) or ≥ 7 days after admission to 
the hospital (for CVE); 
was institutionalised at the time of symptom onset (lives at a care-home/residence for people who require 
continual nursing care and have difficulty with the required activities of daily living); 
had a respiratory specimen taken ≥ 8 days after SARI symptom onset (for IVE) or ≥ 10 days after SARI 
symptom onset (for CVE);  
had a respiratory sample taken ≤ 3 days before SARI symptom onset (for CVE). 

8 https://www.who.int/publications/i/item/diagnostic-testing-for-sars-cov-2  

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Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0   TECHNICAL REPORT 

Information will be collected on these and other potential exclusion factors and patients will be excluded from 
primary analyses according to available evidence on these factors (not all available at time of writing).  

In sensitivity analyses, the CVE/IVE will be estimated: 

• 

• 

• 

• 

with different cut-offs for numbers of days between onset and swabbing, onset and hospitalisation, and 
between vaccination and onset of symptoms; 
excluding those positive for seasonal coronaviruses (e.g. HCoV-NL63, HCoV-229E, HCoV-OC43 and HCoV-
HKU1); 
excluding those who are current controls (SARS-CoV-2 negative for CVE; influenza negative for IVE) but 
were positive by PCR or serology in the previous year before the current hospitalisation, or reported 
clinically confirmed COVID-19, so as to determine the best cut-off period for having had a previous positive 
test during the preceding year versus ‘any previous positive test’ irrespective of date; 
excluding those who have received antivirals ≤14 days prior to swabbing (to avoid false negatives; the 
exact cut-off and types of antivirals will be determined as more research becomes available). 

Please see Annex 2 for further analysis. 

 

The study site/country should define how they obtain informed consent from those who are too unwell at 
time of recruitment (e.g. oral consent with witness for those in isolation, until written consent possible, 
and/or consent of next of kin by telephone, etc). 

3.6 SARI patient identification – algorithm for patient 
inclusion 
The SARI patients are identified from among patients hospitalised for at least 24 hours in one of the participating 
hospitals. SARI patients should be enrolled and swabbed within 48 hours of hospital admission. 

3.6.1 Recruitment strategies 

For hospitals with electronic patient records and/or diagnosis codes commonly displayed, SARI-related ICD codes 
(or other codes used for SARI surveillance) may be used. Patients admitted with any of the ICD codes listed in 
Table 3 will be approached; those meeting the SARI case definition and the inclusion criteria will be invited to be 
part of the study and give consent, if required by the country’s legislation (Figure 1). 

Figure 1. Proposed inclusion algorithm for hospitals/services relying on common use of ICD codes, 
hospital-based COVID-19/influenza vaccine effectiveness study 

For hospitals where ICD codes on admission are not systematically collected or accessible, all patients will be 
systematically screened. This should be done by sensitising the medical staff at the beginning of the study (Figure 
2) and following up with regular study coordinator reviews.  

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TECHNICAL REPORT  Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0 

Figure 2. Proposed inclusion algorithm for hospitals/services systematic screening of all admitted 
patients, hospital-based COVID-19/influenza vaccine effectiveness study 

Retrospective recruitment (or ‘catching-up’ on already diagnosed patients) is not recommended for the CVE study, 
as not all COVID-19 patients exhibit SARI symptoms and it will be difficult to determine retrospectively the reasons 
for testing.  

 

Each study site/country to describe procedures used to identify study participants.  

In the event of test scarcity, extreme workloads, or budget limiting inclusion to a threshold of patients, the study 
sites/countries may need to switch from exhaustive to systematic sampling (e.g. inclusion of patients every second 
day, or only on certain days in the week). Forward planning should be undertaken by the study sites for systematic 
sampling procedures. During the period of systematic selection, the study sites should make sure that they 
document the sampling fraction.  

  Where a study site/country is not testing all SARI cases to describe the systematic sampling procedure, if 

systematic sampling is not being carried out, the criteria for testing should be explained. 

 

Each study site/country using ICD-10 codes for case identification should list the codes used. 

Table 3. List of diagnosis codes for which patients could be screened for onset of SARI symptoms, 
COVID-19/influenza hospital-based VE studies 

Morbidity 

Cough 
Difficulty breathing 
Sore throat 
Dysphagia 
Fever 
Headache 
Myalgia 
Fatigue/malaise 

ICD-9 

786.2 
786.05 
784.1 
787.20 
780.6 
784.0 
729.1 
780.79 

ICD-10 

R05 
R06 
R07.0 
R13 
R50.9 
R51 
M79.1 
R53.1, R53.81, R53.83 

Acute myocardial infarction/acute coronary syndrome 

410-411, 413-414 

I20-23, I24-25 

Category 

Influenza-like 
illness 

Cardiovascular 
diagnosis 

Respiratory 
diagnosis 

Heart failure 

Emphysema 

Chronic obstructive pulmonary disease 

Asthma 

Myalgia 

Dyspnoea/respiratory abnormality 

Respiratory abnormality 

Shortness of breath 

Tachypnoea 

Other respiratory abnormalities 

Infections 

Pneumonia and influenza 

Other acute lower respiratory infections 

12 

428 to 429.0 

I50, I51 

492 

496 

493 

729.1 

786.0 

786.00 

786.05 

786.06 

786.09 

J43.9 

J44.9 

J45 

M79.1 

R06.0 

R06.9 

R06.02 

R06.82 

R06.00, R06.09, R06.3, R06.89 

480-488.1 

466, 519.8 

J09-J18 

J20-J22 

 
Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0   TECHNICAL REPORT 

Category 

Morbidity 

Viral infection, unspecified 

Bacterial infection, unspecified 

Myocarditis 

Bronchitis 

Inflammation 

SIRS* non-infectious without acute organ 
dysfunction 

SIRS* non-infectious with acute organ dysfunction 

Abdominal 
symptoms 

Vomiting 

Diarrhoea 

Abdominal pain 

Diagnoses related 
to deterioration of 
general condition 
or functional 
status 

General physical deterioration, lethargy, tiredness 

Anorexia 

Feeding difficulties 

Abnormal weight loss 

Other symptoms/signs concerning food and fluid intake 

Disorientation/altered mental status 

Dizziness and giddiness 

Infective delirium 

Coma 

Transient alteration of awareness 

Other alteration of consciousness (somnolence, stupor) 

Febrile convulsions (simple), unspecified 

Complex febrile convulsions 

ICD-9 

790.8 

041.9 

429.0 

490, 491 

995.93 

995.94 

787.0 

ICD-10 

B34.9 

A49.9 

I40.9 

J40, 41 

R65.10 

R65.11 

R11 

009.3, 787.91 

A07.9, K52.9 

789.0 

780.79 

783.0 

783.3 

783.21 

783.9 

780.97 

780.4 

293.0, 293.1 

780.01 

780.02 

780.09 

780.31 

780.32 

R10 

R53.1, R53.81, R53.83 

R63.0 

R63.3 

R63.4 

R63.8 

R41.0 

R42 

F05 

R40.2 

R40.4 

R40.0, R40.1 

R56.00 

R56.01 

Other 

Anosmia, ageusia, myalgia 

781.1, 729.1 

R43.0, R43.2, M79.1 

* SIRS: Systemic inflammatory response syndrome 

3.7 Laboratory methods  
Study nurses or physicians will collect respiratory specimens (see Section 4.4) from all eligible patients, respecting 
safety standards for COVID-19 and influenza, and following WHO biosafety guidelines.9 

 

Each study site/country to describe the type (nasopharyngeal/oropharyngeal or both) and number of swabs 
taken for each patient. 

Influenza laboratory confirmation will be done using RT-PCR or multiplex RT-PCR. Specimens will undergo 
molecular analysis for currently circulating influenza viruses.  

PCR should be run with the inclusion of an internal/external quality control. It is recommended that the quality of 
the samples be monitored using an internal control to check presence of human cells in the respiratory specimens.  

ECDC’s recommendation ford SARS-CoV-2 laboratory confirmation is by viral RNA detection with nucleic acid 
amplification tests, such as RT-PCR. Specimens will undergo molecular analysis for currently circulating SARS-CoV-2 
virus. During the influenza season, influenza virus tests will also be performed (and this is also recommended for 
countries not performing IVE), provided that there is circulation of influenza viruses in the community [30,31]. This 
is especially important to identify any potential association between influenza and SARS-CoV-2.  

Following the procedures outlined by each study, all or a systematic sample of the detected viruses will undergo 
gene sequencing. The sampling procedure can include sequencing all viruses, or a systematic sample. The 
systematic sample should be representative of cases and be large enough to provide reasonable precision when 
calculating proportions of virus change over time. Guidance on random sampling for selection of samples for 
sequencing is provided in Annex 3. 

9 Any non-propagative diagnostics (e.g. sequencing, RT-PCR) should be conducted at a facility using procedures equivalent to 
biosafety level 2 (BSL-2), while propagative work (e.g. virus culture, isolation or neutralisation assays) should be conducted at a 
containment laboratory with inward directional airflow (BSL-3). Patient specimens from suspected or confirmed cases should be 
transported as UN3373, ‘biological substance category B’. Viral cultures or isolates should be transported as category A, UN2814, 
‘infectious substance, affecting humans’.[32] 

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TECHNICAL REPORT  Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0 

If sequencing is performed, gene sequences should also be uploaded to GISAID’s open access EpiCoV or EpiFlu platform 
(https://gisaid.org/ and raw WGS data should be uploaded to the EU portal (ENA) database). Gene sequence information 
can be provided directly to the coordinating central hub for the study, or the GISAID EpiCoV/EpiFlu accession number can 
be provided alongside the study’s unique identifier to link these data (see Annex 3, after the section on random 
sampling). Processed genetic information (e.g. name of genetic clade) can also be included within the epidemiological 
database. The proportion of specific COVID-19 subvariants may be used to define a subvariant dominating period (e.g. 
>= 90% of isolates of a specific subtype during a defined period) [21-24].  
 
 

Each study site/country to describe the laboratory procedures (samples taken, storage, transport). 
Each study country to describe the tests and kits used (and their sensitivity, specificity and positive predictive 
value for COVID-19 and influenza, and if necessary, for the detection of other respiratory viruses). 
Each study site/country to describe if the laboratory participates in QA/QC (Quality Assurance/Quality Control) 
schemes. 
Each study site/country to describe the selection of specimens (including Ct value threshold) and the 
procedures for genetic and antigenic characterisation, where appropriate. 
Each study site/country to describe sequencing methods and the process for selection of samples for further 
characterisation. 
Each study site/country to describe whether specimens are tested for other respiratory viruses (e.g. 
whether influenza continues to be tested systematically during the season and stops once the influenza 
season is over, or is only tested if the COVID-19 result is negative, etc.) 

 

 

 

 

3.8 Exposure (vaccination) 

3.8.1 Definition of vaccination status 

Current pandemic COVID-19 vaccine 
An individual will be considered as vaccinated against COVID-19 with a product-specific vaccine during the current 
pandemic under the following categories: 

• 

• 

• 

• 

• 

Fully vaccinated with a primary course plus booster(s) (two-dose vaccine): SARI patients will be 
considered fully vaccinated with booster(s) if they have received both doses followed by booster 
dose(s) at least 14 days 10 before SARI symptom onset. 
Fully vaccinated with a primary course (two-dose vaccine): SARI patients will be considered fully 
vaccinated if they have received both doses at least 14 days* before SARI symptom onset. 
Fully vaccinated with a primary course (single-dose vaccine): SARI patients will be considered fully 
vaccinated if they have received one dose at least 14 days* before SARI symptom onset. 
Partially vaccinated (two-dose vaccine only): SARI patients will be considered partially vaccinated if they 
have received one of two doses at least 14 days* before SARI symptom onset. 
SARI patients will be considered as unvaccinated if they did not receive COVID-19 vaccine or if they 
were vaccinated after onset of symptoms.  

It is crucial that the vaccination status and date of vaccination variables are collected with the utmost care to 
ensure data completeness and quality. The definition of vaccination status will be updated in light of evolving 
decisions related to vaccination programmes, such as the use of different products for subsequent doses as well as 
additional doses. 

Current seasonal influenza vaccine 
• 

A SARI patient will be considered as vaccinated against influenza with any vaccine during the current season if 
he/she has received at least one dose of the influenza vaccine ≥14 days before SARI symptom onset. 
A SARI patient will be considered as unvaccinated if he/she did not receive influenza vaccine in the 
current season of if he/she was vaccinated after onset of symptoms. (Anyone vaccinated <14 days before 
SARI symptom onset will be excluded from the primary analysis). 

Product-specific seasonal influenza vaccine 
• 

A SARI patient is considered as vaccinated against influenza with a product-specific vaccine during the 
current season if he/she has received a vaccination with an influenza vaccine of a named product ≥14 days 
before symptom onset. 
A SARI patient is considered as unvaccinated if he/she did not receive influenza vaccine with a named 
product in the current season of if he/she was vaccinated after onset of symptoms. (Anyone vaccinated <14 
days before SARI symptom onset will be excluded from analysis.) 

Each study site/country to document COVID-19 and seasonal influenza vaccine products used. 

• 

• 

 

10 The exact number of days will depend on the vaccine; this number may change and the protocol will be updated when more 
information is available. If any patients were vaccinated earlier or later than recommended by manufacturers, sensitivity analyses 
will be performed for differing delays between first and second dose. 
14 

 
Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0   TECHNICAL REPORT 

3.9 Potential confounding factors and effect modifiers 

3.9.1 Pre-existing chronic conditions 

SARI patients (in particular, those who are ultimately included as controls) with underlying conditions may be 
included due to an exacerbation of these conditions, unrelated to SARI. These patients may be more likely to be 
infected with SARS-CoV-2/influenza, or to develop more severe disease than the source population. Furthermore, 
these patients may be more likely to be vaccinated against COVID-19/influenza than the source population and the 
CVE/IVE may therefore be underestimated. The presence of underlying conditions will be documented among all 
recruited SARI patients. 

Underlying conditions which could be potential confounding factors/effect modifiers are shown in Table 4 (for their 
ICD codes, see Annex 4). 

Table 4. List of recommended and optional pre-existing conditions as potential effect modifiers or 
confounding factors 

Recommended 

Asthma 

Immunodeficiency (including HIV infection) and organ 
transplant 
Cancer (solid organ and haematological) 

Optional 

Anaemia/chronic haematologic disease 

Asplenia 

Chronic liver disease/cirrhosis 

Diabetes mellitus 

Dementia 

Heart disease (excluding hypertension) 

Neuromuscular disorders 

Hypertension  

Lung disease  

Obesity or 

●  height and weight, or 

●  BMI11 (sites to include whichever is feasible/available) 

Renal disease (exclude acute renal failure) 

Rheumatologic diseases 

Stroke 

Tuberculosis 

 

 

Each study site/country to keep a list to include pre-existing conditions defining target groups for vaccination 
in their country. 
Each study site/country to define the list of chronic conditions to be included and state whether they are used 
to define target groups for vaccination, as well as any pre-existing medications being taken, and describe 
what the sources of information for these will be. 

3.9.2 Severity of underlying condition/healthcare utilisation 

The severity of an underlying condition could be an effect modifier or a confounding factor (i.e. not just presence 
of underlying condition). To document and control for healthcare-seeking behaviour in control groups and the 
severity of the underlying conditions, information will be collected on the number of hospital admissions due to 
underlying conditions in the 12 months prior to inclusion in the study.  

3.9.3 Ethnicity (optional for CVE; not required for IVE) 

Some studies have shown that certain ethnic groups may be at higher risk, either of becoming infected with SARS-
CoV-2, or of developing severe COVID-19. Uptake of, or access to vaccination may also be linked to ethnicity. Not 
all sites/countries collect, or are able to collect, this information. Even if all sites /countries were able to collect 
information on ethnic group, each group may be defined differently in different countries. The definitions for each 
ethnic group will need to be standardised across study sites before this information can be collected robustly and 
used to investigate CVE by ethnic group for pooled data. However, any site(s)/countries which can or do already 
collect this information may find it useful for examining their national CVE estimates by ethnic group. 

 

Each study site/country to indicate whether they can or do collect information on ethnic groups, and define 
their ethnic groups and (proposed) method of collecting the data (e.g. by self-reported ethnicity, from patient 
notes, or any other method). 

11 Obesity defined as BMI>29. 

15 

 
 
 
 
TECHNICAL REPORT  Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0 

3.9.4 Medication status for chronic condition(s) (optional for CVE; for 
IVE: statin use only) 

The use of specific types of chronic medications prior to vaccination or illness may modify or confound the effect of 
the vaccine. For influenza, data on statin use should be collected. 

Definition of medication status for pre-existing chronic condition(s): 
• 

− 

An individual is considered as ‘on medication’ if he/she has received the medication at least once during the 
six months before:  
− 

the first dose of pandemic COVID-19 vaccination (if date of medication use available: for the analysis 
measuring the effect of chronic medications on CVE); or 
onset of SARI symptoms (for unvaccinated individuals: for the analysis measuring the effect of 
chronic medications on COVID-19); or 
hospitalisation. 

− 
An individual is considered as ‘not on chronic medication’ if he/she did not receive medication before the 
periods specified in the protocol above to document such medication use. 

• 

3.9.5 Chronic medication use status ascertainment (optional for CVE; 
not required for IVE) 

The medication history includes the date the patient started the medication(s) where known; otherwise just the 
year, if the patient was known to have been on medication before vaccination or symptom onset, or if the precise 
date is unknown. If both of these are unknown, then a simple yes/no response will be used as to whether the 
patient was on the chronic medication before hospitalisation. In addition, medication history will include 
medication brand name, dose, and number/ frequency of doses.  

The sources of information for chronic medication status may include:  

• 
• 
• 
• 

• 

 
 

consultation of the patient’s hospital record; 
interview with the patient’s GP; 
interview with the patient’s pharmacist; 
data from the patient’s insurance company showing evidence of pharmacy delivery or re-imbursement for 
chronic medication during the relevant period; 
interview with the patient and/or his/her relatives. 

Each study site/country will define chronic medication use based on the data collected.  
Each study site/country to describe how chronic medication status ascertainment will be performed. 

3.9.6 Pregnancy status 

Pregnancy status will be collected and coded for women aged 15–55 years as follows: pregnant (yes/no/do not 
know), and if yes: trimester (1/2/3/do not know). 

3.9.7 Smoking history 

Smoking history will be collected and coded as follows: never-smoker, former smoker (stopped smoking at least 
one year before inclusion in the study), current smoker (smoking currently or stopped within a year of study 
recruitment). 

3.9.8 Healthcare worker 

The definition of a healthcare worker for the purposes of this study is anyone working (paid or on a regular 
voluntary basis) in healthcare who has contact with any type of patient) during his/her work. This includes doctors, 
nurses, emergency medical personnel, medical and nursing students having contact with patients, and porters and 
cleaners. It also includes anybody working in a nursing/residential home for the elderly who has contact with 
residents. Study sites should collect information on healthcare worker status where possible. 

 

Each study site/country to indicate whether they can collect information on healthcare worker status. 

3.9.9 Other occupations (optional for CVE; not required for IVE) 

As some occupations predispose individuals to greater exposure, and may be a proxy for attitudes towards 
vaccination, where possible countries may collect additional information on occupation for stratification in analysis 
by type of occupation (optional). 

Each study site/country to indicate whether they intend to/can also collect information on occupation in 
general (optional). 

 

16 

Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0   TECHNICAL REPORT 

3.9.10 Other vaccinations 

It is important to collect information on vaccinations received for pneumococcal disease (pneumococcal 
polysaccharide vaccine (PPV) and pneumococcal conjugate vaccine (PCV)), where available. For study countries 
that are not participating in IVE, information on previous influenza vaccination (including date of vaccination) will 
also be collected. 

Previous pneumococcal vaccinations (and influenza vaccination, for study 
countries not participating in IVE) 
Date of prior influenza vaccination and year of vaccination against pneumococcal disease are part of the data 
collection.  

The sources of information for these vaccinations may include:  

• 
• 
• 
• 
• 

• 
• 

 

 

vaccination registry; 
consultation of the patient’s vaccination card/certificate; 
interview with the patient’s GP;  
interview with the patient’s pharmacist;  
data from the patient’s insurance company showing evidence of pharmacy delivery or re-imbursement of 
the vaccine during the relevant period; 
interview of the patient and/or his/her relatives; 
self-reported vaccination status via photo on mobile phone. 

Each study site/country to indicate whether vaccination information (including date of vaccination) will be 
available for influenza and/or pneumococcal disease. 
Each study site/country to describe how previous influenza/pneumococcal vaccination status is documented. 

3.9.11 Antiviral administration 
The use of antivirals 12 prior to swabbing may lead to misclassification biases. Sensitivity analyses are planned to 
exclude patients who were administered antivirals prior to swabbing, and to document whether the patients 
received any antiviral treatment in the two weeks preceding symptom onset and the type (curative or preventive) 
of antivirals received. 

 

Each study site/country to list any antivirals administered.  

3.9.12 Presence of other respiratory virus infections 

It is important to document the presence of influenza (for study countries not participating in IVE) and other 
respiratory viruses in cases and controls. Analysis will document the presence of other respiratory infections (e.g. 
Respiratory Syncytial Virus (RSV)) among patients testing negative for SARS-CoV-2, and those who are positive. 

 

Each study site/country to list the other respiratory infection viruses that they test for (including influenza). 

3.9.13 Setting (long-term care facilities versus community) 

Older and vulnerable populations, already at greater risk of severe disease, are often situated in localised settings 
such as long-term care facilities (LTCF), where they are more at risk of localised outbreaks than residents in the 
general community (as was observed in the early phase of the pandemic, when there were many hospitalisations of 
people in long-term care). It is also possible, however, that an entire LTCF (residents and staff) is vaccinated, 
providing them with less chance of exposure than the general population. Stratifying by setting (LTCF versus 
community) will help to adjust for differences between SARI patients who are LTCF residents and those who are not. 

 

Each study site/country to ensure setting is captured, particularly for SARI patients aged over 64 years. 

3.9.15 Previous SARS-CoV-2/influenza infection 

Individuals who have been previously infected may have a greater response to the vaccine or be less likely to be 
reinfected, even if unvaccinated. Study participants should be asked about prior test results and prior symptoms to 
determine possibility of prior infection. A sensitivity analysis will be conducted by excluding individuals with prior 
infection.  

 

Each study site/country to collect and describe measure(s) used to capture prior SARS-CoV-2/influenza 
infection. 

12 Types of antivirals will be determined as more research becomes available. 

17 

 
 
 
TECHNICAL REPORT  Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0 

3.10 Sample size  
Providing VE estimates for each separate study is one of the objectives of this project. Therefore, the minimum 
sample size should be estimated for each study to obtain precise CVE/IVE estimates. The pooled analyses should 
not prevent study teams at country level from including a large enough sample size to obtain exact estimates for 
each country-specific study.  

 

Each study site/country to specify the minimum sample size calculation.  

Sample size estimation in VE estimation is different from sample size estimation in hypothesis testing. Rather than 
focussing on whether a VE estimation includes 0% or not, it is the precision of the estimate which is more 
important. For example, with a VE of 70%, a lower boundary confidence interval of 1% does not provide a very 
informative VE estimate, even if the confidence interval does not include 0%. It is more important to have a VE 
estimate that is precise around the point estimate of 70% (e.g. with a lower boundary of 50%). In fact, a low VE 
estimate, which can be the case in particular stratified analyses, needs a very large sample size to provide a VE 
estimate where the confidence interval does not include 0%. For example, if the true VE is 5–10%, then a study 
providing a lower boundary not including 0% would be unreasonably large.  

The following sample size estimates focus on precision of the VE estimate (Table 5).  

The odds ratio (OR) has confidence intervals that are symmetrical around the point estimate in the log scale. 
However, due to the properties of the OR, these are asymmetrical around the point estimate on the arithmetic 
scale. The length of the confidence interval of an odds ratio on the arithmetic scale will therefore always be shorter 
at the lower limit (which will never reach zero). When converted to VE (VE=1-OR), the length of the confidence 
interval will always be shorter at the upper limit. Hence, the focus is on the precision of the lower limit of the 
confidence interval, and for Table 5 below a potential range of between 10 and 30% (or 0.1–0.3) has been 
selected for this limit. A case-to-control ratio of 1:1 is also assumed, including varying vaccine coverage among the 
source population between 30% and 90%, and varying VE (with the OR between 0.1 and 0.7). 

Table 5. Sample size calculations  

Lower CI 
boundary 

Controls/ 
case 

Detectable 
OR 

Vaccine 
coverage in 
source 
population/
controls 

Number of 
cases 

Number of 
controls 

IVE/CVE 

CI 

0.3 

0.3 

0.3 

0.3 

0.3 

0.3 

0.3 

0.2 

0.2 

0.2 

0.2 

0.2 

0.2 

0.2 

0.1 

0.1 

0.1 

0.1 

0.1 

0.1 

0.1 

0.3 

0.3 

0.3 

0.3 

0.3 

0.3 

0.3 

0.2 

0.2 

0.2 

0.2 

0.2 

18 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

0.1 

0.2 

0.3 

0.4 

0.5 

0.6 

0.7 

0.1 

0.2 

0.3 

0.4 

0.5 

0.6 

0.7 

0.1 

0.2 

0.3 

0.4 

0.5 

0.6 

0.7 

0.1 

0.2 

0.3 

0.4 

0.5 

0.6 

0.7 

0.1 

0.2 

0.3 

0.4 

0.5 

0.3 

0.3 

0.3 

0.3 

0.3 

0.3 

0.3 

0.3 

0.3 

0.3 

0.3 

0.3 

0.3 

0.3 

0.3 

0.3 

0.3 

0.3 

0.3 

0.3 

0.3 

0.4 

0.4 

0.4 

0.4 

0.4 

0.4 

0.4 

0.4 

0.4 

0.4 

0.4 

0.4 

60 

85 

118 

157 

203 

255 

314 

96 

148 

216 

299 

395 

507 

633 

241 

433 

681 

985 

1 346 

1 764 

2 240 

42 

91 

125 

165 

212 

265 

68 

111 

168 

238 

323 

60 

85 

118 

157 

203 

255 

314 

96 

148 

216 

299 

395 

507 

633 

241 

433 

681 

985 

1 346 

1 764 

2 240 

42 

63 

91 

125 

165 

212 

265 

68 

111 

168 

238 

323 

90 

80 

70 

60 

50 

40 

30 

90 

80 

70 

60 

50 

40 

30 

90 

80 

70 

60 

50 

40 

30 

90 

80 

70 

60 

50 

40 

30 

90 

80 

70 

60 

50 

60–98 

51–92 

40–85 

30–77 

20–69 

10–60 

0–51 

70–97 

60–90 

50–82 

40–73 

30–64 

20–55 

10–46 

80–95 

70–87 

60–77 

50–68 

40–58 

30–49 

20–39 

60–98 

49–92 

40–85 

30–77 

20–69 

10–60 

0–51 

70–97 

60–90 

50–82 

40–73 

30–64 

 
Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0   TECHNICAL REPORT 

Lower CI 
boundary 

Controls/ 
case 

Detectable 
OR 

Vaccine 
coverage in 
source 
population/
controls 

Number of 
cases 

Number of 
controls 

IVE/CVE 

CI 

0.2 

0.2 

0.1 

0.1 

0.1 

0.1 

0.1 

0.1 

0.1 

0.3 

0.3 

0.3 

0.3 

0.3 

0.3 

0.3 

0.2 

0.2 

0.2 

0.2 

0.2 

0.2 

0.2 

0.1 

0.1 

0.1 

0.1 

0.1 

0.1 

0.1 

0.3 

0.3 

0.3 

0.3 

0.3 

0.3 

0.3 

0.2 

0.2 

0.2 

0.2 

0.2 

0.2 

0.2 

0.1 

0.1 

0.1 

0.1 

0.1 

0.1 

0.1 

0.3 

0.3 

0.3 

0.3 

0.3 

0.3 

0.3 

0.2 

0.2 

0.2 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

0.6 

0.7 

0.1 

0.2 

0.3 

0.4 

0.5 

0.6 

0.7 

0.1 

0.2 

0.3 

0.4 

0.5 

0.6 

0.7 

0.1 

0.2 

0.3 

0.4 

0.5 

0.6 

0.7 

0.1 

0.2 

0.3 

0.4 

0.5 

0.6 

0.7 

0.1 

0.20 

0.30 

0.40 

0.50 

0.60 

0.70 

0.10 

0.20 

0.30 

0.40 

0.50 

0.60 

0.70 

0.10 

0.20 

0.30 

0.40 

0.50 

0.60 

0.70 

0.10 

0.20 

0.30 

0.40 

0.50 

0.60 

0.70 

0.10 

0.20 

0.30 

0.4 

0.4 

0.4 

0.4 

0.4 

0.4 

0.4 

0.4 

0.4 

0.5 

0.5 

0.5 

0.5 

0.5 

0.5 

0.5 

0.5 

0.5 

0.5 

0.5 

0.5 

0.5 

0.5 

0.5 

0.5 

0.5 

0.5 

0.5 

0.5 

0.5 

0.6 

0.60 

0.60 

0.60 

0.60 

0.60 

0.60 

0.60 

0.60 

0.60 

0.60 

0.60 

0.60 

0.60 

0.60 

0.60 

0.60 

0.60 

0.60 

0.60 

0.60 

0.70 

0.70 

0.70 

0.70 

0.70 

0.70 

0.70 

0.70 

0.70 

0.70 

421 

534 

170 

323 

528 

786 

1 098 

1 466 

1 891 

32 

51 

77 

109 

148 

193 

246 

51 

90 

142 

208 

289 

384 

495 

129 

262 

447 

687 

983 

421 

534 

170 

323 

528 

786 

1 098 

1 466 

1 891 

32 

51 

77 

109 

148 

193 

246 

51 

90 

142 

208 

289 

384 

495 

129 

262 

447 

687 

983 

1 337 

1 751 

1 337 

1 751 

26 

45 

71 

103 

143 

191 

247 

41 

78 

130 

197 

280 

380 

497 

104 

229 

410 

651 

953 

26 

45 

71 

103 

143 

191 

247 

41 

78 

130 

197 

280 

380 

497 

104 

229 

410 

651 

953 

1 322 

1 760 

1 322 

1 760 

23 

43 

71 

108 

153 

207 

272 

36 

75 

131 

23 

43 

71 

108 

153 

207 

272 

36 

75 

131 

40 

30 

90 

80 

70 

60 

50 

40 

30 

90 

80 

70 

60 

50 

40 

30 

90 

80 

70 

60 

50 

40 

30 

90 

80 

70 

60 

50 

40 

30 

90 

80 

70 

60 

50 

40 

30 

90 

80 

70 

60 

50 

40 

30 

90 

80 

70 

60 

50 

40 

30 

90 

80 

70 

60 

50 

40 

30 

90 

80 

70 

20–55 

10–46 

80–95 

70–87 

60–77 

50–68 

40–58 

30–49 

20–39 

60–98 

51–92 

40–85 

30–77 

20–69 

10–60 

0–51 

70–97 

60–90 

50–82 

40–73 

30–64 

20–55 

10–46 

80–95 

70–87 

60–78 

50–68 

40–58 

30–49 

20–39 
60-98 

50-92 

40-85 

30-77 

20-69 

10-60 

0-51 

70-97 

60-90 

50-82 

40-73 

30-64 

20-55 

10-46 

80-95 

70-87 

60-78 

50-68 

40-58 

30-49 

20-39 

60-98 

50-92 

40-85 

30-77 

20-69 

10-60 

0-51 

70-97 

60-90 

50-82 

19 

TECHNICAL REPORT  Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0 

Lower CI 
boundary 

Controls/ 
case 

Detectable 
OR 

Vaccine 
coverage in 
source 
population/
controls 

Number of 
cases 

Number of 
controls 

IVE/CVE 

CI 

0.2 

0.2 

0.2 

0.2 

0.1 

0.1 

0.1 

0.1 

0.1 

0.1 

0.1 

0.3 

0.3 

0.3 

0.3 

0.3 

0.3 

0.3 

0.2 

0.2 

0.2 

0.2 

0.2 

0.2 

0.2 

0.1 

0.1 

0.1 

0.1 

0.1 

0.1 

0.1 

0.3 

0.3 

0.3 

0.3 

0.3 

0.3 

0.3 

0.2 

0.2 

0.2 

0.2 

0.2 

0.2 

0.2 

0.1 

0.1 

0.1 

0.1 

0.1 

0.1 

0.1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

0.40 

0.50 

0.60 

0.70 

0.10 

0.20 

0.30 

0.40 

0.50 

0.60 

0.70 

0.10 

0.20 

0.30 

0.40 

0.50 

0.60 

0.70 

0.10 

0.20 

0.30 

0.40 

0.50 

0.60 

0.70 

0.10 

0.20 

0.30 

0.40 

0.50 

0.60 

0.70 

0.10 

0.20 

0.30 

0.40 

0.50 

0.60 

0.70 

0.10 

0.20 

0.30 

0.40 

0.50 

0.60 

0.70 

0.10 

0.20 

0.30 

0.40 

0.50 

0.60 

0.70 

0.70 

0.70 

0.70 

0.70 

0.70 

0.70 

0.70 

0.70 

0.70 

0.70 

0.70 

0.80 

0.80 

0.80 

0.80 

0.80 

0.80 

0.80 

0.80 

0.80 

0.80 

0.80 

0.80 

0.80 

0.80 

0.80 

0.80 

0.80 

0.80 

0.80 

0.80 

0.80 
0.90 

0.90 

0.90 

0.90 

0.90 

0.90 

0.90 

0.90 

0.90 

0.90 

0.90 

0.90 

0.90 

0.90 

0.90 

0.90 

0.90 

0.90 

0.90 

0.90 

0.90 

205 

298 

412 

548 

90 

219 

413 

676 

1 015 

1 435 

1 941 

22 

47 

82 

128 

187 

259 

344 

35 

82 

151 

245 

365 

514 

694 

89 

241 

477 

808 

1 242 

1 789 

2 458 

30 

71 

129 

208 

310 

437 

591 

48 

124 

238 

397 

605 

868 

1 190 

121 

361 

751 

1 311 

2 061 

3 022 

4 216 

205 

298 

412 

548 

90 

219 

413 

676 

1 015 

1 435 

1 941 

22 

47 

82 

128 

187 

259 

344 

35 

82 

151 

245 

365 

514 

694 

89 

241 

477 

808 

1 242 

1 789 

2 458 

30 

71 

129 

208 

310 

437 

591 

48 

124 

238 

397 

605 

868 

1 190 

121 

361 

751 

1 311 

2 061 

3 022 

4 216 

60 

50 

40 

30 

90 

80 

70 

60 

50 

40 

30 

90 

80 

70 

60 

50 

40 

30 

90 

80 

70 

60 

50 

40 

30 

90 

80 

70 

60 

50 

40 

30 

90 

80 

70 

60 

50 

40 

30 

90 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

40-73 

30-64 

20-55 

10-46 

80-95 

70-87 

60-78 

50-68 

40-58 

30-49 

20-39 

60-98 

50-92 

40-85 

30-77 

20-69 

10-60 

0-51 

70-97 

60-90 

50-82 

40-73 

30-64 

20-55 

10-46 

80-95 

70-87 

60-78 

50-68 

40-58 

30-49 

20-39 

60-98 

50-92 

40-85 

30-77 

20-69 

10-60 

0-51 

70-97 

60-90 

50-82 

40-73 

30-64 

20-55 

10-46 

80-95 

70-87 

60-78 

50-68 

40-58 

30-49 

20-39 

The sample size estimates above are for the crude analysis and an adjusted analysis would require a higher sample size. 

The sample size should also be respected for each population subgroup for which a sub (stratified) analysis (e.g. effect 
modification) is planned.  

See also the Analysis section on sample size requirements for analyses. 

20 

 
 
Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0   TECHNICAL REPORT 

3.11 Data 

3.11.1 Sources of information 

Data are to be collected using a standardised questionnaire/data collection form. The source(s) of data may 
include: 

• 
• 
• 
• 
• 
• 
• 
• 

 

vaccination card/certificate; 
hospital medical records; 
interview with patient or his/her family; 
communication with the patient or his/her family via mobile phone; 
interview with patient’s GP; 
interview with patent’s pharmacist; 
vaccination register; 
laboratory records. 

Each study site/country to define the sources of information used for each variable collected and the 
potential limitations. 

3.12 Collected information 
Collected information falls under the following main categories:  

• 

• 
• 

• 
• 

• 

• 

• 
• 

• 
• 
• 

study identification 
− 
country, hospital; 
− 
vaccination target groups; 
− 
first ward of referral; 
− 
ICU/other ward of admission. 
Patient characteristics (ethnic group optional) 
SARI signs, symptoms  
− 
current; 
− 
previous clinical symptoms (if no prior tests done). 
Other symptoms (optional) 
Dates 
− 
− 
− 
− 
Laboratory 
− 
− 
− 
− 

vaccination (COVID-19, influenza, pneumococcal disease); 
onset of SARI symptoms; 
admission, discharge; 
swabbing. 

type of swab/sample (nasopharyngeal/sputum, etc.) (optional); 
type of test;  
results (including information from antigenic and genetic analysis, where available); 
previous positive PCR for SARS-CoV-2/influenza or antigen test for SARS-CoV-2, if feasible (for 
sensitivity analyses). 

use of medications for chronic conditions (optional); 
number of hospitalisations for chronic conditions in the previous 12 months (optional); 
number of GP consultations in the previous 12 months (optional). 

Underlying chronic conditions, including obesity (see Sections 3.9.1–3.9.5) 
− 
− 
− 
Presence of other respiratory viruses infection (see section 3.9.12). 
Vaccination and antivirals (see Sections 3.8 and 3.9.10–3.9.11) 
− 
− 
− 
− 
Setting (e.g. LTCF) 
Socioeconomic status (SES)/deprivation (optional) 
Outcome. 

COVID-19 vaccination including number of doses, date, product/brand; 
influenza vaccination from the current and two previous seasons; 
pneumococcal vaccination status, type of vaccine and either date or year of vaccination (optional); 
antiviral administration.  

COVID-19 vaccine data collected will be revised as more information on the vaccine(s) and target groups becomes 
available. 

These are described in more detail below (see also Annex 1 for a complete variable list including coding).  

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TECHNICAL REPORT  Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0 

3.12.1 Study identifiers 
For each country/study sites, the following characteristics need to be documented:  
• 
• 
• 

Country, site, priority vaccination target group(s) and their modifications; 
Hospital (unique number not including hospital name, to allow adjustment by hospital in analyses); 
Patient unique ID (note: this is not a patient identifiable ID such as date-of-birth or national ID number, but 
a unique identifier for a pooled database). 

3.12.2 Hospital/ward information 
The following dates and other hospital information need to be documented: 
• 
• 
• 
• 

Date of onset, admission, discharge, death; 
First ward of referral; 
Any hospital stay (for pre-existing chronic condition) in previous 12 months (optional); 
Date of swab/sample. 

3.12.3 Patient characteristics 
The following patient characteristics need to be documented to describe the study population. 
• 
• 
• 
• 
• 
• 
• 
• 

Age; 
Sex; 
Smoking history (see Section 3.9.5); 
Pregnancy status; 
Healthcare worker status; 
Occupation (optional); 
Ethnic group (optional); 
SES/deprivation (optional). 

 

Each study site/country to describe community measures in place to limit exposures. 

3.12.4 Clinical characteristics (symptoms and markers of severity) 
The following clinical characteristics and markers of disease severity should be documented: 

The four following key symptoms which are part of the WHO and ECDC COVID-19/influenza case definitions: 
• 
• 
• 
• 

fever or feverishness; 
cough; 
shortness of breath; 
sore throat. 

The following three symptoms which are associated with COVID-19 illness and are part of ECDC’s COVID-19 case 
definition: 
• 
• 
• 

anosmia; 
ageusia; 
dysgeusia. 

In addition, for study countries participating in IVE estimation, information on the following four symptoms should 
also be collected: 
• 
• 
• 
• 

headache; 
myalgia; 
malaise; 
deterioration of general condition (asthenia, weight loss, anorexia). 

The following 14 symptoms are optional for the hospital-based CVE study: 
• 
• 
• 
• 
• 
• 
• 
• 
• 
• 
• 
• 
• 
• 

coryza, rhinitis; 
chest pain; 
chills; 
fatigue; 
nausea; 
vomiting; 
abdominal pain; 
diarrhoea; 
conjunctivitis; 
confusion; 
dizziness; 
tachypnoea or other signs of low oxygen saturation (restlessness); 
rash or other dermatological manifestation; 
palpitations/rapid heartbeat. 

22 

Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0   TECHNICAL REPORT 

The following information can be used to indicate severity when measuring CVE to prevent severe disease: 
• 
• 
• 
• 

oxygen use; 
ICU admission; 
invasive ventilation; 
death. 

Date of first key symptom onset should be collected, as well as information on COVID-19/influenza test(s) and 
laboratory results, including information on antigenic and genetic analysis, if available. It is vital that this 
information is collected, as well as date of vaccination, date of swab or date sample obtained (to allow 
estimation of and stratification by delay from swab to onset), date of admission (to allow estimation of and 
stratification by time from onset to hospitalisation, and to measure length of hospital stay), and date of 
discharge/death (to allow the length of hospital stay to be measured.) 

fever or feverishness 
− 

3.12.5 Case definition 
Collection of good-quality symptom information is crucial for the CVE/IVE studies to be able to validate the case 
definition used. As a minimum, there is a need to collect data on the symptoms required for the SARI case 
definition. The following variables are imperative for application of the SARI case definition: 
• 

if fever: measured fever (with temperature), or feverishness (to allow sensitivity analyses with other 
SARI case definitions); 

• 
• 
• 
• 
• 
• 
• 

cough; 
shortness of breath; 
sudden onset of anosmia; 
sudden onset of ageusia; 
sudden onset of dysgeusia; 
onset date; 
admission date. 

3.12.6 Data entry validation 
For hospitals using electronic medical records, if paper questionnaires are used, a sample of them will be checked 
against the medical records and against the study database. Concurrence between patient records/reports by study 
participants will be measured when/if records are available. 

 

Each study site/country to specify how data are validated. 

3.13 Data analysis 

3.13.1 Individual (country /site level) analysis  
Cases and controls will first be described by baseline characteristics. The timing to conduct each interim analysis 
will depend on the time needed to achieve the appropriate sample size; regular updates are foreseen. This will 
depend mainly on the incidence of hospitalisation, COVID-19/influenza incidence, vaccination coverage, the 
recruitment strategy within hospital/s and the number of participating hospitals/services per hospital.  

Patients will be described according to: 
• 
• 
• 
• 
• 
• 
• 
• 
• 
• 
• 
• 
• 
• 

sex; 
age group; 
healthcare worker status; 
time: month of symptom onset; 
COVID-19/influenza vaccination status; 
symptoms; 
absence/presence of at least one, presence of more than one high-risk condition; 
specific chronic conditions (e.g. respiratory, cardiovascular diseases); 
pregnancy, smoking status; 
pneumococcal vaccination status; 
respiratory co-infections (where available); 
severity (ICU, oxygen use, invasive ventilation, death); 
SARS-CoV-2 variant/influenza (sub)type and clade (where possible) for cases; 
vaccination status, with vaccinated patients described by vaccine product.  

An sample layout of this descriptive analysis is provided in Table 6 below.  

This study is a case–control study (test-negative design). The measure of association is an odds ratio (OR). This 
can be estimated by logistic regression. An OR = 1 indicates no association between an exposure and the 
outcome. An OR>1 indicates a potential risk factor, an OR < 1 indicates a potential protective factor, noting that 
the confidence interval around the OR helps with its interpretation. 

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TECHNICAL REPORT  Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0 

For vaccination as preventive factor, the CVE/IVE can be computed as CVE/IVE = (1 – OR)*100. A 95% confidence 
interval is computed around the point estimate.  

Univariable analysis will be carried out to measure the CVE/IVE against being a laboratory-confirmed COVID-
19/influenza SARI case. Stratified analyses (e.g. by sex and age group) can follow to better understand potential 
effect modifiers and confounders. 

Prior to multivariable analysis, a model development strategy will be determined and included in the plan of analysis. In 
the final step, multivariable analysis will be carried out to take confounding factors and potential effect modifiers into 
account. This will provide adjusted ORs from which the CVE/IVE can be estimated using the formula above. 

Output tables presenting CVE/IVE estimates 
To present the results in the most transparent manner and to enable the reader to best understand the data, 
tables similar to the one illustrated in Table 7 can be used (variables presented just as example of the output 
format). Useful information includes numbers of cases and controls (overall and vaccinated) and presentation of 
results for different models. 

Table 6. Example of descriptive table for cases and controls; COVID-19/influenza hospital-based 
vaccine effectiveness study, ECDC multi-centre study, 2021 

Variables 

Median age (IQR) 
Missing 
Age groups (years) 
20–59 
60–79 
≥ 80 
Missing 
Sex 
Female 
Missing 
Healthcare worker 
Missing 
Days between onset of symptoms 
and swabbing 
0 
1 
2 
3 
4–7 
COVID-19/influenza vaccination 
Missing 
etc. 

Number of laboratory-confirmed 
COVID-19 (or influenza) 
cases/total n (%) 
X 
X 

x/x (x) 
x/x (x) 
x/x (x) 
X 

x/x (x) 
X 
x/x (x) 
X 

x/x (x) 
x/x (x) 
x/x (x) 
x/x (x) 
x/x (x) 
x/x (x) 
x 

Number of test-negative 
controls/total n (%) 

X 
X 

x/x (x) 
x/x (x) 
x/x (x) 
X 

x/x (x) 
X 
x/x (x) 
X 

x/x (x) 
x/x (x) 
x/x (x) 
x/x (x) 
x/x (x) 
x/x (x) 
X 

Table 7. Sample table - vaccine effectiveness against COVID-19/influenza adjusted for various co-variables by 
age group, hospital-based COVID-19/influenza vaccine effectiveness ECDC multi-centre study, 2022 

CVE (or 
IVE) (%) 

(95% CI) 

Clade/variant 

COVID-19  
(or influenza) 

Population 
included 
All ages 

20–59 years 

60 years and over 

Analysis scenarios/adjustments made 

N (cases/ vaccinated; controls/ vaccinated) 
Crude 
Adjusted for onset date (cubic spline) 
Adjusted for sex 
Adjusted for chronic condition 
Adjusted for age (cubic spline) 
Adjusted for onset date, age (cubic spline) 
Adjusted for onset date, chronic condition 
Adjusted for onset date, age (cubic spline), 
chronic conditions, sex 
N (cases/ vaccinated; controls/ vaccinated) 
Crude 
Adjusted for onset month, age (cubic spline) 
N (cases/ vaccinated; controls/ vaccinated) 
Crude 
Adjusted for onset date, age (cubic spline), 
chronic condition, sex 

24 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0   TECHNICAL REPORT 

3.13.2 Pooled analysis  

See Annex 2 for details of the pooled analysis plan. For the pooled data, interim analyses will be conducted during 
different periods, if appropriate, and according to the available sample size. 

The timing to conduct each interim analysis will depend on the time needed to achieve the appropriate sample 
size. This will depend mainly on the incidence of hospitalisation, COVID-19/influenza incidence, vaccination 
coverage, the recruitment strategy within hospitals and the number of participating hospitals/services per hospital.  

The pooled analysis will be carried out in a similar way to the site-specific analysis. Country or study site will 
potentially be included as a fixed effect, or as a random effect in a multi-level model. Statistical heterogeneity 
between study sites will be determined using the Q-test and the I2 index [33]. 

3.14 Personal data protection 
Each country conducting the study shall comply with requirements stemming from data protection legislation and 
national ethics committee provisions. Informed consent will be required from all participants or their legal 
representatives. Where data protection legislation permits, the national ethics committees will specify whether oral 
or written consent is required. Specific consent procedures may be needed for unconscious patients and patients 
with deterioration of general condition or functional status, unable to sign the consent (e.g. oral witnessed consent, 
consent by next of kin, etc).  

3.15 Training 
Investigators and data collectors will be trained on the study protocol before the study begins. They will receive the 
protocol, questionnaires and laboratory respiratory specimen collection procedures. 

 

Each study site/country to describe the training to be organised.  

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TECHNICAL REPORT  Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0 

4 Logistical aspects 

4.1 Respiratory specimen collection 
The default collection method for the respiratory specimen will be by means of nasal/nasopharyngeal swabbing or 
concurrent nasal and oral/oropharyngeal swabbing (or endotracheal aspirates in ICU). Personal protection 
equipment must be used in accordance with guidelines.  

 

Each study site/country to describe the specimen collection procedures. 

4.2 Laboratory tests 
High specificity is needed for COVID-19 confirmation. COVID-19 laboratory confirmation will be carried out using 
RT-PCR or multiplex RT-PCR. Influenza laboratory confirmation will be carried out using RT-PCR or by culture. 

 

 

Each study site/country to describe the tests and kits used for COVID-19 and influenza; and, if necessary, 
other respiratory virus detection. 
Each study site/country to specify sequencing methods. 

PCR should include an internal/external quality control. It is advisable to monitor the quality of the respiratory 
samples by checking for presence of cells in the specimens. In addition, quality assurance of assay performance at 
sites should be evaluated by participating in External Quality Assessment Programmes (EQAS). 

 

 

Each study site/country to describe quality controls for specimens. 

Each study site/country to describe genetic and antigenic analyses. 

26 

 
 
Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0   TECHNICAL REPORT 

5 Limitations 

With any multi-centre study, there is always the potential for heterogeneity across study sites. In addition, during a 
pandemic with such high caseloads for hospitals, there may be difficulties in collecting all data, and not all cases 
included will have laboratory confirmation. There is also the possibility that very severely ill patients (e.g. those 
who are ≥80 years old, or who are extremely frail and/or in nursing homes) may not be admitted to hospital at all, 
and would be missed by the study. For both influenza and COVID-19, there is symptom variability across age 
groups (e.g. less fever seen in the elderly). Potential limitations to the CVE/IVE estimates are discussed below. 

5.1 Potential biases 

5.1.1 Bias from pooled estimates  
With data from several hospitals in different countries being pooled, any bias in the individual studies will influence 
the pooled estimate. The power of the test for the presence of heterogeneity between individual studies is low if 
there are few sites/countries. In this case, the test may not be able to detect heterogeneity between them, despite 
this being present. It is important that heterogeneity is also assessed using qualitative knowledge about differences 
between studies. Depending on the nature of the bias, the inclusion of biased studies in the pooled estimate could 
lead to over- or underestimation of the true association between COVID-19/influenza vaccination and the outcome.  

There are many conditions which could lead to bias at a single site or hospital. For CVE, with this new virus, there 
are new and evolving surveillance systems and strategies in each participating country. There are not only different 
tests being used, but a variation in the number of tests used to declare an individual negative. Another example is 
that, when under high pressure (e.g. high volume of patients to be admitted during a peak in the epidemic for any 
site), it is possible that some hospitals may switch to admitting only suspected COVID-19 patients, while others 
focus on non-COVID-19 patients. In the event of the former type of hospital participating, this could affect the 
recruitment of controls and result in cases being predominantly recruited from one hospital over another. If a 
participating site only has one hospital providing data, this could mean they are only able to provide information on 
cases. Conversely, if the single participating hospital was designated a non-COVID-19 admitting hospital, this site 
would only be able to provide information on controls. 

To allow for complete assessment of heterogeneity, sites need to document all changes in their COVID-19 
surveillance system and any in the influenza surveillance system during the study period. 

 

Each study site/country to document any changes in COVID-19/influenza surveillance during the study period, 
including allocation of participating hospitals to COVID-19 or non-COVID-19 admission status. 

5.1.2 Negative confounding 
Negative confounding refers to biases that reflect the fact that high-risk groups (people more likely to develop 
severe complications) will be more likely to be vaccinated and therefore reduce CVE/IVE. If negative confounding is 
present, the CVE/IVE will be underestimated. Adjustment for potential negative confounding factors documented in 
the study (e.g. age, presence of chronic diseases) will minimise negative confounding.  

5.1.3 Positive confounding 
Positive confounding refers to biases that reflect a ‘healthy vaccinee effect’. People with a healthy lifestyle will be 
more likely to accept vaccination, thus leading to an increase in measured CVE/IVE. Similarly, people in a state of 
‘extreme frailty’ will not be offered vaccination and, because they are frail, may be more likely to have severe 
disease. Individuals with risk-taking behaviour may also be averse to vaccination, which may also increase their 
exposure to disease. If positive confounding is present, CVE/IVE will be overestimated.  

5.1.4 Unmeasured confounding 
Positive and negative confounding will be minimised through stratification and multivariable analysis. It will not be 
possible to rule out the presence of characteristics in the study population for which no information is collected in 
the study questionnaire and this could lead to positive or negative confounding. Therefore, some residual 
unmeasured confounding may remain.  
 

Each study site/country to describe the potential limitations and representativeness of the subjects included. 

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TECHNICAL REPORT  Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0 

5.1.5 Previous infection in cases or controls; inclusion of 
asymptomatic controls  
Individuals who have previously been infected may have a stronger response to the vaccine or be less likely to be 
reinfected, even if unvaccinated. It is possible that some of the controls (those testing negative for influenza/SARS-
CoV-2) may have themselves been positive for influenza/SARS-CoV-2 some time before. The proportion of these 
(potentially immune individuals) in each country’s dataset would depend on the circulation of the virus in the 
community during the months before the hospitalisation of the control. Knowledge of their prior infection could 
affect their likelihood to be vaccinated. For example, if someone knew that they had had COVID-19, despite having 
no symptoms (e.g. if they had had a screening test), they may be subsequently less likely to be vaccinated. This 
would lower vaccination coverage among controls and underestimate CVE. Similarly, if someone believed that they 
had had influenza but had not had a test, they may be subsequently be less likely to be vaccinated. This would 
lower vaccination coverage among controls and underestimate IVE. 

As time goes on, there will be more and more individuals in the population who have either had symptomatic or 
asymptomatic prior COVID-19. Many may not have been tested, especially if asymptomatic. Ascertainment of 
previous SARS-CoV-2/influenza infection in both cases and controls would therefore be very difficult and largely 
subjective. Results should be interpreted in light of this, and an estimate of a range of potential bias should be 
calculated around the CVE and IVE estimates. Where possible, sensitivity analyses should be conducted, excluding 
any SARI patient with previous SARS-CoV-2 or influenza infection confirmed either by PCR or by serological tests. 

As antibody tests become more widespread, then this may be included in the protocol. 

5.1.6 Inclusion of influenza-positive controls for CVE and SARS-CoV-
2-positive controls for IVE 

It is possible that SARI patients who are also influenza-positive will be unsuitable controls for CVE, and those who 
are also SARS-CoV-2-positive will be unsuitable controls for IVE. There is limited information on co-infection with 
influenza and COVID-19 from the first wave of the pandemic in Europe, partly due to the timing of the pandemic 
being towards or after the end of the 2019–20 influenza season in many countries. The low number of co-
infections described in the literature [34,35] could be due to lack of opportunity (there being little influenza 
circulating at that time) or a negative correlation between the two infections, with those positive for COVID-19 
being unlikely to also be positive for influenza. In addition, those receiving COVID-19 vaccination are highly likely 
to have also received influenza vaccine. There is therefore the potential for a relationship between being positive 
for influenza and receiving COVID-19 vaccination, which introduces bias. 

A similar situation could be observed for the reverse (i.e. inclusion of SARS-CoV-2-positive controls for IVE).  

Sensitivity analyses will be conducted, excluding controls who are positive for influenza from CVE estimation and 
controls who are positive for SARS-CoV-2 from IVE estimation. 

5.1.7 Validation of exposure 

The vaccination status is the exposure of interest and the validity of vaccination data should therefore be checked 
carefully. If the vaccination status is reported by the patient only, without further proof, information bias may occur. 
Vaccination status of cases and controls should be validated using an independent source (i.e. vaccination register, GPs). 

 

Each study site/country to describe the source of exposure validation and its potential limitations. 

5.1.8 Misclassification 

The use of antivirals prior to swabbing in CVE/IVE studies may lead to misclassification biases. Sensitivity analyses 
will be run to exclude patients who were administered antivirals prior to swabbing. In addition, misclassification can 
occur due to test performance. In analysis, adjustment can be made for sensitivity and specificity of the tests. Sites 
may use different tests, so investigators should seek to use common international, national or research standards 
to address possible variation in test performance at sites. The UK National Institute of Biological Standards and 
Control currently offers international standards for molecular and serological testing [36].  

5.1.9 Other potential biases 

Controls could come from different source populations with varying risk of infection with SARS-CoV-2/influenza, 
varying probability for acquiring COVID-19/influenza vaccination, etc. (e.g. depending on time of year). Time 
(onset date) will be used to adjust for seasonal differences. Analyses will also be stratified by time (e.g. onset 
quarter of the year). 

Each study site/country to describe timeline of vaccination for different target groups. 

 

28 

 
 
Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0   TECHNICAL REPORT 

5.2 Representativeness of subjects included in the study 
The study only includes cases that are hospitalised. Health-seeking behaviour may differ by country depending on 
the case management strategy (e.g. recommendation to stay at home with mild symptoms, and only see a GP if 
symptoms persist, followed by hospitalisation if severe). In some cases, the management strategy will have an 
impact on the delay between onset of symptoms and hospitalisation. This, in turn, may have an impact on the time 
lag between onset and respiratory specimen collection, and may affect positivity rates between study sites. In 
addition to the collection of dates of onset/admission/respiratory specimen collection, health-seeking behaviour and 
case-management strategies should be described for each study and it should be noted how these may affect the 
CVE/IVE estimates. 

Some very severely-ill patients will not be able to give informed consent and this group may therefore be under-
represented among the subjects included.  

It is important to consider the representativeness of the controls. For example, if controls were to be all influenza 
and COVID-19 negative, consideration should be given to whether they are representative of the source 
population in terms of vaccine coverage. After two years of vaccination, the COVID-19 vaccine coverage rates in 
most EU/EEA countries are quite high. In 2022, most SARI patients may be vaccinated against COVID-19 and 
vaccine coverage among hospitalised controls could be 70% or higher. This may not be representative of the 
vaccine coverage in the general population. 

 

 

Each study site/country to describe the potential limitations in terms of representativeness of the subjects 
included. 
Each study site/country to describe case-management strategy in their country. 

This core protocol will be updated in the light of evolving scientific evidence and methodological considerations. 

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References 

1. 

2. 

3. 

European Centre for Disease Prevention and Control (ECDC). Communicable Disease Threats Report Week 3, 16–22 January 
2023. ECDC: Stockholm 2023. Available 
from: https://www.ecdc.europa.eu/sites/default/files/documents/Weekly%20Communicable%20Disease%20Threats%20Re
port%20Public%202023w03.pdf 

European Centre for Disease Prevention and Control (ECDC). COVID-19 vaccines. Stockholm: ECDC; 2022. Available from: 
https://www.ema.europa.eu/en/human-regulatory/overview/public-health-threats/coronavirus-disease-covid-19/treatments-
vaccines/covid-19-vaccines 

European Medicines Agency (EMA). Committee for Medicinal Products for Human Use. Guideline on influenza vaccines. Non-
clinical and clinical module. London, UK: European Medicines Agency; 2014 Jul. Available from: 
https://www.ema.europa.eu/en/documents/scientific-guideline/influenza-vaccines-non-clinical-clinical-module_en.pdf 

4.   European Centre for Disease Prevention and Control (ECDC). ‘EMA and ECDC join forces for enhanced post-marketing 

monitoring of COVID-19 vaccines in Europe’. Press Release. 26 April 2022. Available from: 
https://www.ecdc.europa.eu/en/news-events/ema-and-ecdc-join-forces-enhanced-post-marketing-monitoring-covid-19-
vaccines-europe 

5.   Council Recommendation of 7 December 2018 on strengthened cooperation against vaccine-preventable diseases 

(2018/C466/01). Available from: https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32018H1228(01) 

6.   Regulation (EU) 2022/123 of the European Parliament and of the Council of 25 January 2022 on a reinforced role for the 

European Medicines Agency in crisis preparedness and management for medicinal products and medical devices. Available 
from: https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=OJ:L:2022:020:FULL&from=EN 

7.   Regulation (EU) 2022/2370 of the European Parliament and of the Council of 23 November 2022 amending Regulation (EC) 

No 851/2004 establishing a European centre for disease prevention and control. Available from: https://eur-
lex.europa.eu/eli/reg/2022/2370/oj 

8.   European Centre for Disease Prevention and Control (ECDC). Core protocol for ECDC studies of COVID-19 vaccine 

effectiveness against hospitalisation with Severe Acute Respiratory Infection laboratory-confirmed with SARS-CoV-2, version 
1.0. Stockholm: ECDC; 2021. Available from: https://www.ecdc.europa.eu/en/publications-data/core-protocol-ecdc-studies-
covid-19-vaccine-effectiveness-against-hospitalisation 

9 

European Medicine Agency (EMA). COVID-19 vaccines: authorised. Available at:https://www.ema.europa.eu/en/human-
regulatory/overview/public-health-threats/coronavirus-disease-covid-19/treatments-vaccines/vaccines-covid-19/covid-19-
vaccines-authorised#authorised-covid-19-vaccines-section 

10.   European Centre for Disease Prevention and Control (ECDC). Overview of the implementation of COVID-19 vaccination 
strategies and vaccine deployment plans in the EU/EEA – 29 March 2021. ECDC: Stockholm; 2021. Available from: 
https://www.ecdc.europa.eu/sites/default/files/documents/Overview-implementation-COVID-19-vaccination-strategies-
vaccine-deployment-plans.pdf 

11.   European Centre for Disease Prevention and Control (ECDC). Overview of the implementation of COVID-19 vaccination 

strategies and deployment plans in the EU/EEA. 8 September 2022. Stockholm: ECDC; 2022. Available at: 
https://www.ecdc.europa.eu/en/publications-data/overview-implementation-covid-19-vaccination-strategies-and-
deployment-plans 

12.   European Centre for Disease Prevention and Control (ECDC). Seasonal influenza vaccination and antiviral use in EU/EEA 

Member States: Overview of vaccine recommendations for 2017–2018 and vaccination coverage rates for 2015–2016 and 
2016–2017 influenza seasons. Stockholm: ECDC; 2018 Nov. Available from: 
https://www.ecdc.europa.eu/sites/default/files/documents/seasonal-influenza-antiviral-use-2018.pdf 

13.   European Commission (EC). Proposal for a Council recommendation on seasonal influenza vaccination. Brussels, Belgium: 

European Commission; 2009. Report No: COM(2009) 353/final/2. Available from: 
https://ec.europa.eu/health/ph_threats/com/Influenza/docs/seasonflu_rec2009_en.pdf 

14.   European Centre for Disease Prevention and Control (ECDC). Seasonal influenza vaccines. Stockholm: ECDC; Available from: 

https://www.ecdc.europa.eu/en/seasonal-influenza/prevention-and-control/seasonal-influenza-vaccines 

15.   Kissling E, Valenciano M, Larrauri A, Oroszi B, Cohen JM, Nunes B, et al. Low and decreasing vaccine effectiveness against 
influenza A(H3) in 2011/12 among vaccination target groups in Europe: results from the I-MOVE multi-centre case-control 
study. Euro Surveill. 2013 Jan 31;18(5).  

16.   Pebody RG, Andrews N, McMenamin J, Durnall H, Ellis J, Thompson CI, et al. Vaccine effectiveness of 2011/12 trivalent 

seasonal influenza vaccine in preventing laboratory-confirmed influenza in primary care in the United Kingdom: evidence of 
waning intra-seasonal protection. Euro Surveill. 2013 Jan 31;18(5).  

17.   Castilla J, Martínez-Baz I, Martínez-Artola V, Reina G, Pozo F, García Cenoz M, et al. Decline in influenza vaccine 

effectiveness with time after vaccination, Navarre, Spain, season 2011/12. Euro Surveill. 2013 Jan 31;18(5).  

18.   Skowronski DM, Janjua NZ, Sabaiduc S, De Serres G, Winter AL, Gubbay JB, et al. Influenza A/subtype and B/lineage 

effectiveness estimates for the 2011-2012 trivalent vaccine: cross-season and cross-lineage protection with unchanged 
vaccine. J Infect Dis. 2014 Jul 1;210(1):126–37.  

19.   Ohmit SE, Thompson MG, Petrie JG, Thaker SN, Jackson ML, Belongia EA, et al. Influenza vaccine effectiveness in the 2011-
2012 season: protection against each circulating virus and the effect of prior vaccination on estimates. Clin Infect Dis. 2014 
Feb;58(3):319–27.  

20.   Sullivan SG, Kelly H. Stratified estimates of influenza vaccine effectiveness by prior vaccination: caution required. Clin Infect 

Dis. 2013 Aug;57(3):474–6.  

21.   European Centre for Disease Prevention and Control (ECDC). Interim analysis of COVID-19 vaccine effectiveness against 
Severe Acute Respiratory Infection due to laboratory-confirmed SARS-CoV-2 among individuals aged 65 years and older, 

30 

Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0   TECHNICAL REPORT 

ECDC multi-country study. ECDC: Stockholm; 2021. Available from: 
https://www.ecdc.europa.eu/sites/default/files/documents/TRP-20211004-1860_0.pdf 

22.   European Centre for Disease Prevention and Control (ECDC). Interim analysis of COVID-19 vaccine effectiveness against 
Severe Acute Respiratory Infection due to laboratory-confirmed SARS-CoV-2 among individuals aged 50 years and older, 
ECDC multi-country study – first update. ECDC: Stockholm; 2022. Available from: 
https://www.ecdc.europa.eu/sites/default/files/documents/Interim-analysis-of-COVID-19-vaccine-effectiveness-against-
SARI-2022.pdf 

23.   European Centre for Disease Prevention and Control (ECDC). Interim analysis of COVID-19 vaccine effectiveness against 
Severe Acute Respiratory Infection due to laboratory-confirmed SARS-CoV-2 among individuals aged 30 years and older, 
ECDC multi-country study – second update. 14 March 2022. ECDC: Stockholm; 2022. Available from: 
https://www.ecdc.europa.eu/sites/default/files/documents/covid-19-vaccine-effectiveness-interim-analysis-second-
update_0.pdf 

24.   European Centre for Disease Prevention and Control (ECDC). Interim analysis of COVID-19 vaccine effectiveness against 

Severe Acute Respiratory Infection due to SARS-CoV-2 in individuals aged 20 years and older – third update. 8 November 
2022. ECDC: Stockholm; 2022. Available from: https://www.ecdc.europa.eu/sites/default/files/documents/Interim-analysis-
COVID-19-vaccine-over-20.pdf 

25.   European Centre for Disease Prevention and Control (ECDC). Generic protocol for ECDC studies of COVID-19 vaccine 

effectiveness against confirmed SARS-CoV-2 using healthcare worker cohorts, v.2.0. Stockholm: ECDC; 2022. Available 
from: https://www.ecdc.europa.eu/sites/default/files/documents/covid-19-generic-protocol-ECDC-studies-vaccine-
effectiveness-healthcare-worker-cohorts-version-2-0.pdf  

26.   European Centre for Disease Prevention and Control (ECDC). Interim analysis of COVID-19 vaccine effectiveness in 

healthcare workers, an ECDC multi-country study, May 2021–July 2022. ECDC: Stockholm; 2022. Available from: 
https://www.ecdc.europa.eu/sites/default/files/documents/covid-19-analysis-vaccine-effectiveness-healthcare-workers.pdf 

27.   Sentís A, Kislaya I, Nicolay N, Meijerink H, Starrfelt J, Martínez-Baz I, et al. Estimation of COVID-19 vaccine effectiveness 
against hospitalisation in individuals aged ≥ 65 years using electronic health registries; a pilot study in four EU/EEA 
countries, October 2021 to March 2022. Euro Surveill. 2022;27(30):pii=2200551. https://doi.org/10.2807/1560-
7917.ES.2022.27.30.2200551 

28.   Kissling E, Hooiveld M, Martínez-Baz I, Mazagatos C, William N, Vilcu A-M, et al. Effectiveness of complete primary 

vaccination against COVID-19 at primary care and community level during predominant Delta circulation in Europe: multi-
centre analysis, I-MOVE-COVID-19 and ECDC networks, July to August 2021. Euro Surveill. 2022;27(21):pii=2101104. 
https://doi.org/10.2807/1560-7917.ES.2022.27.21.2101104 

29.   European Centre for Disease Prevention and Control (ECDC). Rapid risk assessment: Coronavirus disease 2019 (COVID-19) 

pandemic: increased transmission in the EU/EEA and the UK – seventh update. ECDC. 2020 [cited 27 March 2020]. 
Available from: https://www.ecdc.europa.eu/en/publications-data/rapid-risk-assessment-coronavirus-disease-2019-covid-19-
pandemic 

30.   European Centre for Disease Prevention and Control (ECDC). Operational considerations for influenza surveillance in the 

WHO European Region during COVID-19: interim guidance. 16 October 2020. Stockholm; ECDC: 2020 [cited 27 March 
2021]. Available from: https://www.ecdc.europa.eu/en/publications-data/operational-considerations-influenza-surveillance-
european-region-during-covid-19 

31.   European Centre for Disease Prevention and Control (ECDC). Testing strategies for SARS-CoV-2. Stockholm; ECDC: 2020 

[cited 27 March 2021]. Available from: https://www.ecdc.europa.eu/en/covid-19/surveillance/testing-strategies 

32.   World Health Organization (WHO). Laboratory testing for 2019 novel coronavirus (2019-nCoV) in suspected human cases: 
Interim guidance. 19 March 2020 [cited 6 April 2020]. Available from: https://www.who.int/publications/i/item/10665-
331501 

33.  Huedo-Medina TB, Sánchez-Meca J, Marín-Martínez F, Botella J. Assessing heterogeneity in meta-analysis: Q statistic or I2 

index? Psychol Methods. 2006 Jun;11(2):193–206.  

34.   Ozaras R, Cirpin R, Duran A, Duman H, Arslan O, Bakcan Y, et al. Influenza and COVID-19 coinfection: Report of six cases 

and review of the literature. Journal of Medical Virology. 2020 Jun 4;  

35.  Antony SJ, Almaghlouth NK, Heydemann EL. Are coinfections with COVID-19 and influenza low or underreported? An 
observational study examining current published literature including three new unpublished cases. Journal of Medical 
Virology. 2020 Jun 12;  

36.  UK National Institute for Biological Standards and Control (NIBSC). Coronavirus (COVID-19)-related research reagents 
available from the NIBSC. UK: NIBSC; Available from: https://www.nibsc.org/science_and_research/idd/cfar/covid-
19_reagents.aspx 

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Bibliography 

Blettner, M, Sauerbrei, W, Schlehofer, B, Scheuchenpflug T, Friedenreich C. Traditional reviews, metaanalyses and pooled 
analyses in epidemiology. Int J Epidemiol 28, 1–9(1999). 

Lemeshow S, Hosmer D, Klar J. Sample size requirements for studies estimating odds ratios and relative risks. Stat Med 
1988 ;(7) :759–64. 

National Research Council. Combining information – statistical issues and opportunities for research. Washington, DC: National 
Research Council; 1992. doi: https://doi.org/10.17226/20865  

Rodrigues L, Kirkwood BR. Case–control designs in the study of common diseases: updates on the demise of the rare disease 
assumption and the choice of sampling scheme for controls. Int J Epidemiol 1990 Mar;19(1):205–13. 

Rothman, K, Greenland, S, Lash, T. Modern Epidemiology (ed.). Philadephia: Lippincott–Williams–Wilkins, 2008. 

Smith-Warner SA, Spiegelman D, Ritz J, Albanes D, Beeson WL, Bernstein L et al. Methods for pooling results of epidemiologic 
studies: the Pooling Project of Prospective Studies of Diet and Cancer. Am J Epidemiol 2006 ;163 :1053–64. 

Stukel TA, Demidenko E, Dykes J, Karagas MR. Two-stage methods for the analysis of pooled data. Stat Med 2001 Jul 
30;20(14):2115–30. 

Legislative references 

ECDC Founding Regulation (EC) No 851/2004 of the European Parliament and of the Council of 21 April 2004 establishing a 
European Centre for disease prevention and control. 

Decision No. 1082/2013/EU of the European Parliament and of the Council of 22 October 2013 on serious cross-border threats to 
health and repealing Decision No 2119/98/EC  

Council Recommendation of 7 December 2018 on strengthened cooperation against vaccine preventable diseases (2018/C 
466/01). 

Commission Implementing Decision (EU) 2018/945 of 22 June 2018 on the communicable diseases and related special health 
issues to be covered by epidemiological surveillance as well as relevant case definitions. 

Communication from the Commission to the European Parliament and the Council of 15 October 2020 on Preparedness for 
COVID-19 vaccination strategies and vaccine deployment, COM (2020)680 final. 

Regulation of the European Parliament and of the Council amending Regulation (EC) No 851/2004 establishing a European centre 
for disease prevention and control. 12 October 2022.  

Council Recommendation of 7 December 2018 on strengthened cooperation against vaccine-preventable diseases 
(2018/C466/01). Available from: https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32018H1228(01)  

Regulation (EU) 2022/123 of the European Parliament and of the Council of 25 January 2022 on a reinforced role for the 
European Medicines Agency in crisis preparedness and management for medicinal products and medical devices. Available from: 
https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=OJ:L:2022:020:FULL&from=EN 

Regulation (EU) 2022/2370 of the European Parliament and of the Council of 23 November 2022 amending Regulation (EC) No 
851/2004 establishing a European centre for disease prevention and control. Available from: https://eur-lex.europa.eu/legal-
content/EN/TXT/PDF/?uri=CELEX:32022R2370&qid=1670582843806&from=EN  

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Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0   TECHNICAL REPORT 

Annex 1. List of variables, definitions and 
coding (hospital-based COVID-19 and 
influenza vaccine effectiveness studies 
minimum dataset) 

Individual data 

 
 

Each study site/country to list all the variables collected and their coding 
Each study site/country to indicate all modifications in the variables collected compared to variables below. 

Optional variables are moved to the end of the table and shaded in grey (for both new and old variables) 

Variable 

Type 

Values and coding 

Definition 

Section A 
Administrative 
information 
(study 
identifiers) 

Section B 
Patient 
information/ 
characteristics 

idcountry 

Numeric 
(categorical) 

Coded according to 
international country codes 

id 

Numeric  

Unique integer 

hospitalcode 

Numeric  

Unique integer 

consent 

Numeric 

consent_sp 

Text 

Numeric (integer) 

Numeric 
(categorical) 

0 = No 

1 = Yes 

2 = Not required 

8 = Do not know 

0 = female 

1 = male 

3 = other 

8 = do not know 

Numeric 
(categorical) 

0 = No 

1 = Yes 

8 = Do not know 

Numeric 
(categorical) 

0 = No 

1 = Yes 

age 

sex 

ltcf 

hcw 

smoking 

Numeric 
(categorical) 

Section B 
continued 

pregnant 

Numeric 
(categorical) 

8 = Do not know 

0 = Never 

1 = Former 

2 = Current 

8 = Do not know 

0 = No 

1 = Yes 

8 = Do not know 

Identifier uniquely identifying 
the country (for pooled 
datasets only) 

Unique number for 
each patient 

Unique number for each 
hospital  

Agreement of patient to 
participate (where appropriate, 
i.e. for countries requiring 
consent) 

Reason provided for non-
participation 

Age of patient 

Sex of patient (Note coding 
change to match with ECDC 
coding) 

Patient residence at time of 
SARI onset. Whether patient 
was living in long-term care 
facility (Note coding change 
from ‘residence’ to match with 
TESSy coding of ltcf) 

Whether the patient is a 
healthcare worker 

Never, former (stopped 
smoking at least one year 
before inclusion in the study), 
current smoker (or stopped in 
the past year) 

Whether patient is pregnant (for 
women aged 15–55 years) 

Section C 

admitdate 

Date 

dd/mm/yyyy 

Date of hospital admission 

33 

  
 
 
TECHNICAL REPORT  Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0 

Variable 

Type 

Values and coding 

Definition 

Hospital/ward, 
clinical course 
and outcomes 

outcome 

Numeric 
(categorical) 

1 = died 

2 = discharged from hospital 

3 = still on treatment 

8 = unknown outcome 

Indicate the outcome of the 
patient known at the time of 
data collection (note: this may 
be updated later) (updated to 
match TESSy) 

outcomedate 

Date (dd/mm/yyyy) 

Date of outcome 

icu 

Numeric 
(categorical) 

0 = No 

1 = Yes 

Admission to intensive care unit 
(ICU) or high-dependency unit 
(HDU) 

8 = Do not know 

icuadmitdate 

Date 

dd/mm/yyyy 

Date of ICU/HDU admission 

numberdaysicu 

Numeric 

resp_support 

Numeric 
(categorical) 

hospitalward 

Numeric 
(categorical) 

hospitalward_oth 

Text 

0 = None 

1 = High-flow oxygen therapy 
(non-invasive ventilation) 

2 = Invasive ventilation 

3 = Extra corporeal membrane 
oxygenation (ECMO) 

4 = Other 

8 = Do not know 

0 = Special COVID-19 ward 

1 = Lung, pulm/respir. 

2 = Internal medicine 

3 = Infectious diseases 

4 = Emergency or A&E 

5 = Cardiology 

6 = Geriatric 

7 = ICU or HDU 

9 = Other 

10 = Obstetrics/gynaecology 

11 = Surgery 

12 = Paediatric 

8 = Do not know 

asthma 

Section D 
Pre-existing 
chronic 
conditions 

Numeric 
(categorical) 

0 = No 

1 = Yes 

Section D 
continued 

cancer 

Numeric 
(categorical) 

8 = Do not know 

0 = No 

1 = Yes 

8 = Do not know 

Number of days in ICU/HDU 
(updated to match TESSy) 

Level of respiratory support 
(updated to match TESSy) 

First ward of referral (NOTE: 
this may alternatively be sent 
as text, as for influenza) 

Specify other ward 

Asthma  

Cancer (any) 

heartdis 

Numeric 
(categorical) 

0 = No 

1 = Yes 

Heart / cardiac disease 
(excluding hypertension) 

8 = Do not know 

lungdis 

Numeric 

0 = No 

Chronic lung disease (excluding 

34 

  
 
 
 
Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0   TECHNICAL REPORT 

Variable 

Type 

Values and coding 

Definition 

(categorical) 

1 = Yes 

asthma) 

diabetes 

hypert 

immuno 

obese 

8 = Do not know 

Numeric 
(categorical) 

0 = No 

1 = Yes 

8 = Do not know 

Numeric 
(categorical) 

0 = No 

1 = Yes 

8 = Do not know 

Diabetes 

Hypertension 

Numeric 
(categorical) 

0 = No 

1 = Yes 

HIV or other immunodeficiency 
disorders including organ 
transplantation 

8 = Do not know 

Numeric 
(categorical) 

0 = No 

1 = Yes 

Obesity (only if height, weight 
and BMI not collected; can be 
calculated) 

8 = Do not know 

hosp_visit 

Numeric (integer) 

Section E 
Medications for 
chronic medical 
conditions 

statin_pre 

Numeric 
(categorical) 

antivir_pre 

Numeric 
(categorical) 

Section F 
Vaccination 
status: 
pandemic 
COVID-19 
vaccination 

panvacc1dose 

Numeric 
(categorical) 

panvacc1date 

panvacc1type 

Date 

Text 

panvacc2dose 

Numeric 

Number of times patient was 
admitted to hospital for an 
underlying chronic condition in 
the 12 months prior to current 
admission  

Patient was on statins since or 
from 01 Sept 2022 

Antivirals given prior to 
swabbing 

Received pandemic COVID-19 
vaccination, first dose 

0 = No 

1 = Yes 

8 = Do not know 

0 = No 

1 = Yes 

8 = Do not know 

0 = No 

1 = Yes 

8 = Do not know 

dd/mm/yyyy 

Vaccination date, first dose 

Type of vaccine (product name) 

Received pandemic COVID-19 
vaccination, second dose 

0 = No 

1 = Yes 

2 = second dose NA 

8 = Do not know 

Section F 
continued 

panvacc2date 

panvacc2type 

panvacc3dose 

panvacc3date 

panvacc3type 

panvacc4dose 

Date 

Text 

Numeric 
(categorical) 

Date 

Text 

Numeric 
(categorical) 

dd/mm/yyyy 

Vaccination date second dose 

Type of vaccine (product name) 

Received pandemic COVID-19 
vaccination, third dose 

0 = No 

1 = Yes 

8 = Do not know 

dd/mm/yyyy 

Vaccination date third dose 

0 = No 

1 = Yes 

Type of vaccine (product name) 

Received pandemic COVID-19 
vaccination, fourth dose 

35 

  
 
 
 
 
 
TECHNICAL REPORT  Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0 

Variable 

Type 

Values and coding 

Definition 

8 = Do not know 

dd/mm/yyyy 

Vaccination date fourth dose 

panvacc4date 

panvacc4type 

flu_vacc 

Date 

Text 

Numeric 
(categorical) 

Section G 
Vaccination 
status: other 
vaccinations 

0 = No 

1 = Yes 

8 = Do not know 

flu_vaccdate 

Date 

dd/mm/yyyy 

flu_vaccbrand 

Text 

seasvacc_n1 

Numeric 
(categorical) 

seasvacc_n2 

Numeric 
(categorical) 

pneumo_vacc 

Numeric 
(categorical) 

ppv_vaccdate 

Integer 

pcv_vaccdate 

Integer 

feverish 

Numeric 
(categorical) 

0 = No 

1 = Yes 

8 = Do not know 

0 = No 

1 = Yes 

8 = Do not know 

0 = No 

1 = Yes 

8 = Do not know 

yyyy 

yyyy 

0 = No 

1 = Yes 

fever 

cough  

8 = Do not know 

Numeric 
(categorical) 

0 = No 

1 = Yes 

8 = Do not know 

Numeric 
(categorical) 

0 = No 

1 = Yes 

Section H 
Symptoms at or 
prior to 
admission (for 
SARI case 
definition) 

sorethroat  

Numeric 
(categorical) 

Section H 
Continued 

sob  

Numeric 
(categorical) 

anosmia  

Numeric 
(categorical) 

8 = Do not know 

0 = No 

1 = Yes 

8 = Do not know 

0 = No 

1 = Yes 

8 = Do not know 

0 = No 

1 = Yes 

8 = Do not know 

Type of vaccine (product name) 

Received current seasonal 
influenza vaccination 

Date of receiving the current 
season’s influenza vaccination 

Brand of the current season’s 
influenza vaccination 

Received a seasonal influenza 
vaccination during the last 
season 

Received a seasonal influenza 
vaccination two seasons ago 

Pneumococcal vaccination 
received (any type, ever) 

Year of last PPV23 vaccination 

Year of last PCV7/10 or 13 
vaccination 

Sub-febrility (37–38°C)  

History of fever ≥ 38°C 

Cough  

Sore throat 

Shortness of breath  

Loss of sense of smell  

ageusia 

Numeric 
(categorical) 

0 = No 

1 = Yes 

Loss of sense of taste 

8 = Do not know 

36 

  
 
 
Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0   TECHNICAL REPORT 

Variable 

Type 

Values and coding 

Definition 

dysgeusia 

Numeric 
(categorical) 

headache 

Numeric 
(categorical) 

0 = No 

1 = Yes 

8 = Do not know 

0 = No 

1 = Yes 

8 = Do not know 

Numeric 
(categorical) 

0 = No 

1 = Yes 

8 = Do not know 

myalgia 

malaise 

Alteration of sense of taste 

Headache 

Myalgia (muscle pains) 

Numeric 
(categorical) 

0 = No 

1 = Yes 

Malaise  

general_deter 

Numeric 
(categorical) 

8 = Do not know 

0 = No 

1 = Yes 

8 = Do not know 

Deterioration of general 
condition (including asthenia, 
weight loss, anorexia, fatigue) 

onsetdate 

Date 

dd/mm/yyyy 

Date of onset of first symptom 

Section I 
Laboratory tests 
and results 
(SARS-CoV-2): 
before 
hospitalisation  

prev_labcovid 

Numeric 
(categorical) 

0 = No 

1 = Yes 

8 = Do not know 

prev_labcovid_type  Numeric 

1 = RT-PCR 

(categorical) 

prev_labcovid_sp 

Text 

prev_labcovid_date  Date 

lab_covtest 

Numeric 
(categorical) 

lab_covtesttype 

Numeric 
(categorical) 

Section J 
Laboratory tests 
and results 
(SARS-CoV-2): 
during 
hospitalisation  

2 = Serology 

3 = Rapid test 

4 = Other 

8 = Do not know 

0 = No 

1 = Yes 

8 = Do not know 

1 = RT-PCR 

2 = Serology 

3 = Rapid test 

4 = Other 

8 = Do not know 

lab_covtesttype_sp  Text 

Swabdate cov 

Date 

dd/mm/yyyy 

lab_covid  

Numeric 
(categorical) 

0 = Negative 

1 = Positive 

2 = inconclusive / 
undetermined 

8 = Do not know 

Whether patient had a prior 
positive COVID-19 test 

Type of prior COVID-19 test 
used (for positive result above) 

Specify other type of test used 

Date of prior positive COVID-19 
test 

Whether patient was tested for 
SARS-CoV-2 (during 
hospitalisation) 

Type of lab test used 

Specify other type of lab test 

Respiratory  specimen  collection 
date for SARS-CoV-2 testing 

Laboratory result: SARS-CoV-2  

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TECHNICAL REPORT  Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0 

Variable 

Type 

Values and coding 

Definition 

seq 

Numeric 
(categorical) 

0 = No 

1 = Yes 

Whether patient sample was 
sequenced/sent for sequencing 

Section K 
Laboratory tests 
(other 
respiratory 
viruses) 

genetic_group 

swabdate_flu 

Text 

Date 

lab_fluany 

Numeric 
(categorical) 

lab_flu_type 

Numeric 
(categorical) 

Laboratory result: genetic group 

Respiratory  specimen  collection 
date for influenza testing 

Laboratory result: any influenza 
virus type 

If positive for influenza, indicate 
which  type  and  subtype, 
if 
known 

8 = Do not know 

dd/mm/yyyy 

0 = Negative 

1 = Positive 

2 = Not done 

8 = Do not know 

1 = influenza A(H1N1) 

2 = influenza A(H3N2) 

3 = influenza A (untyped) 

4 = influenza B/Yamagata 

5 = influenza B/Victoria 

6 = influenza B (untyped) 

lab_mers 

Numeric 
(categorical) 

lab_othcov 

Numeric 
(categorical) 

lab_rsv 

Numeric 
(categorical) 

lab_metap 

Numeric 
(categorical) 

lab_adeno 

Numeric 
(categorical) 

resp_path 

Numeric 
(categorical) 

8 = Do not know 

0 = Negative 

1 = Positive 

2 = Not done 

8 = Do not know 

0 = Negative 

1 = Positive 

2 = Not done 

8 = Do not know 

0 = Negative 

1 = Positive 

2 = Not done 

8 = Do not know 

0 = Negative 

1 = Positive 

2 = Not done 

8 = Do not know 

0 = Negative 

1 = Positive 

2 = Not done 

8 = Do not know 

0 = Negative 

1 = Positive 

2 = Not done 

8 = Do not know 

Laboratory result: MERS-CoV 

Laboratory result: other 
coronavirus 

Laboratory result: RSV 

Laboratory result: 
metapneumovirus 

Laboratory result: adenovirus 

Other respiratory pathogen 
patient tests positive for 

resp_path_oth 

Text 

Specify other respiratory 
pathogen 

38 

  
 
 
Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0   TECHNICAL REPORT 

Variable 

Type 

Values and coding 

Definition 

Section B 
Patient 
information/ 
characteristics 

Optional 
variables 

dob 

Date 

dd/mm/yyyy 

ethnic 

Numeric 
(categorical) 

ethnic_sp 

Text 

essential_worker 

Numeric 
(categorical) 

esswork_sp 

Text 

occupation 

Text 

0 = No 

1 = Yes 

8 = Do not know 

height 

Numeric (integer) 

weight 

Numeric (integer) 

ses 

Section B 

trimester 

Numeric 
(categorical) 

Numeric 
(categorical) 

1 = first trimester 

2 = second trimester 

3 – third trimester 

8 = Do not know 

Date of birth (only if no age) 
(optional) 

Patient’s ethic group (note: 
codes will be country-specific) 
(optional) 

Other ethnic group not 
specified in coding above 
(optional) 

Whether the patient is any 
other type of essential worker 
with much human contact (e.g. 
teacher, police person, 
supermarket worker) (optional) 

Specify which other type of 
essential worker (optional) 

Patient’s occupation, if not 
already captured (note: this 
may be collected another way, 
e.g. by national occupational 
code, depending on country) 
(optional) 

Height of patient in metres 
(optional) 

Weight of patient in kg 
(optional) 

Indicate results from 
socioeconomic or deprivation 
index used (optional) 

If patient is pregnant, indicate 
which trimester (if known) 
(optional) 

deathcause 

Numeric 
(categorical) 

1 = died from COVID-19 

Cause of death (optional) 

bmi 

Numeric (1 d.p.) 

anaemia 

asplenia 

liverdis 

rendis 

Numeric 
(categorical) 

0 = No 

1 = Yes 

8 = Do not know 

Numeric 
(categorical) 

0 = No 

1 = Yes 

8 = Do not know 

Numeric 
(categorical) 

0 = No 

1 = Yes 

8 = Do not know 

Numeric 
(categorical) 

0 = No 

1 = Yes 

BMI of patient (only if available 
in place of missing 
weight/height) (optional) 

Anaemia/chronic haematologic 
disease (optional) 

Asplenia (absence of/damage 
to spleen) (optional) 

Chronic liver disease (excluding 
cancer) (optional) 

39 

Optional 
variables 
continued 

Section C 
Hospital/ward, 
clinical course 
and outcomes 

Optional 
variables 

Section D 
Pre-existing 
chronic 
conditions 

Optional 
variables 

  
 
 
 
 
 
 
 
 
 
 
 
 
TECHNICAL REPORT  Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0 

Variable 

Type 

Values and coding 

Definition 

8 = Do not know 

dement 

Numeric 
(categorical) 

0 = No 

1 = Yes 

neuromusc 

Numeric 
(categorical) 

8 = Do not know 

0 = No 

1 = Yes 

8 = Do not know 

Renal disease (excluding cancer 
and acute renal failure) 
(optional) 

Dementia (optional) 

Neuromuscular disorder 
(optional) 

rheumat 

Numeric 
(categorical) 

0 = No 

1 = Yes 

Rheumatologic disease 
(optional) 

Section D 

stroke 

Optional 
variables 
continued 

8 = Do not know 

Numeric 
(categorical) 

0 = No 

1 = Yes 

8 = Do not know 

Stroke (optional) 

tuberc 

Numeric 
(categorical) 

0 = No 

1 = Yes 

Tuberculosis (optional) 

chronic_other_targ
etcovid 

Numeric 
(categorical) 

chronic_other_targ
et_flu 

Numeric 
(categorical) 

chronic_other_targ
et_sp 

Text 

Section E 
Medications for 
chronic medical 
conditions 

metform_pre  

Numeric 
(categorical) 

Optional 
variables 

steroids_pre  

Numeric 
(categorical) 

corticost_pre 

Numeric 
(categorical) 

nsaid_pre 

Numeric 
(categorical) 

8 = Do not know 

0 = No 

1 = Yes 

8 = Do not know 

0 = No 

1 = Yes 

8 = Do not know 

0 = No 

1 = Yes 

8 = Do not know 

0 = No 

1 = Yes 

8 = Do not know 

0 = No 

1 = Yes 

8 = Do not know 

0 = No 

1 = Yes 

8 = Do not know 

Whether the patient has 
another chronic condition which 
the country target for COVID-
19 vaccination  

Whether the patient has 
another chronic condition which 
the country target for seasonal 
influenza vaccination  

Specify other chronic condition 
and whether they are targeted 
for COVID-19 or seasonal 
influenza vaccination 

Metformin (optional)  

Steroids (optional)  

Corticosteroids (optional)  

NSAID (non-steroidal anti-
inflammatory drugs) (optional) 

ace_pre 

Numeric 
(categorical) 

0 = No 

1 = Yes 

ACE inhibitor (angiotensin 
converting enzyme inhibitors) 
(optional) 

Section E 

arb_pre 

40 

8 = Do not know 

0 = No 

  
 
 
 
Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0   TECHNICAL REPORT 

Variable 

Type 

Values and coding 

Definition 

Optional 
variables 
continued 

dmards_pre 

Numeric 
(categorical) 

Numeric 
(categorical) 

chemo_pre 

Numeric 
(categorical) 

gliclaz_pre 

Numeric 
(categorical) 

psychotrop_pre 

Numeric 
(categorical) 

chloroq_pre 

Numeric 
(categorical) 

hydroxychloroq_pre  Numeric 

(categorical) 

other1_pre_sp 

Text 

other2_pre_sp 

Text 

other3_pre_sp 

Text 

1 = Yes 

8 = Do not know 

0 = No 

1 = Yes 

8 = Do not know 

0 = No 

1 = Yes 

8 = Do not know 

0 = No 

1 = Yes 

8 = Do not know 

0 = No 

1 = Yes 

8 = Do not know 

0 = No 

1 = Yes 

8 = Do not know 

0 = No 

1 = Yes 

8 = Do not know 

chills 

Numeric 
(categorical) 

0 = No 

1 = Yes 

8 = Do not know 

Section H 
Symptoms at or 
prior to 
admission (for 
SARI case 
definition) 

Optional 
variables 

Section H 

coryza  

Optional 
variables 
continued 

vomit 

nausea  

tach 

Numeric 
(categorical) 

0 = No 

1 = Yes 

8 = Do not know 

Numeric 
(categorical) 

0 = No 

1 = Yes 

8 = Do not know 

Numeric 
(categorical) 

0 = No 

1 = Yes 

8 = Do not know 

Numeric 
(categorical) 

0 = No 

1 = Yes 

ARB (angiotensin II receptor 
blockers) (optional) 

Biological disease-modifying 
anti-rheumatic drugs (DMARDs) 
e.g. rituximab, tocilizumab, etc. 
(optional) 

Chemotherapy (within 6 
months or currently) for cancer 
(optional) 

Gliclazides (for diabetes or 
heart failure) (optional) 

Psychotropic drugs (including 
benzodiazepine, etc.) (optional) 

Chloroquine (optional) 

Hydroxychloroquine (optional) 

Other pre-symptomatic 
medication #1 (optional) 

Other pre-symptomatic 
medication #2 (optional) 

Other pre-symptomatic 
medication #3 (optional) 

“Chills”, or shivering (optional) 

Coryza (optional) 

Vomiting (optional) 

Nausea (optional)  

Tachypnoea or signs of low 
oxygen saturation (optional) 

41 

  
 
 
 
 
 
 
TECHNICAL REPORT  Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0 

Variable 

Type 

Values and coding 

Definition 

8 = Do not know 

abdopain 

Numeric 
(categorical) 

0 = No 

1 = Yes 

Abdominal pain (optional) 

diarr 

palp 

chest 

dizzy 

8 = Do not know 

Numeric 
(categorical) 

0 = No 

1 = Yes 

8 = Do not know 

Numeric 
(categorical) 

0 = No 

1 = Yes 

8 = Do not know 

Numeric 
(categorical) 

0 = No 

1 = Yes 

8 = Do not know 

Numeric 
(categorical) 

0 = No 

1 = Yes 

8 = Do not know 

dermato 

Numeric 
(categorical) 

0 = No 

1 = Yes 

8 = Do not know 

Numeric 
(categorical) 

0 = No 

1 = Yes 

8 = Do not know 

Diarrhoea (optional) 

Heart palpitations (optional) 

Chest pain (optional) 

Dizziness (optional) 

Rash or other dermatological 
manifestation of COVID-19 
(optional) 

Confusion (optional) 

pcr2 

Numeric 
(categorical) 

0 = No 

1 = Yes 

Whether a second PCR was 
done (if first PCR was negative) 
(optional) 

lab_covidpcr2 

Numeric 
(categorical) 

8 = Do not know 

0 = Negative 

1 = Positive 

8 = Do not know 

Second PCR result for virus 
type SARS-COV-2 (optional) 

Section H 

confusion 

Optional 
variables 
continued 

Section J 
Laboratory tests 
and results 
(SARS-CoV-2): 
during 
hospitalisation  

Optional 
variables 

42 

  
 
 
 
 
 
Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0   TECHNICAL REPORT 

Annex 2. Detailed analysis plan 

Pooled analysis outline 
A pooled analysis is part of the primary objectives of the ECDC study. Country will be included potentially as a fixed 
effect or as a random effect in a multi-level model. Statistical heterogeneity between study sites will be 
determined, using the Q-test and the I2 index. [8] 

Cases and controls will be described by baseline characteristics, and uni- and multivariable analyses performed as 
described in Section 3.14.1 for individual analysis.  

Pooled analysis plan 

Descriptive pooled analysis 

The proportion of eligible hospitalised cases and controls who agreed to participate in the study will be calculated. 
The proportion of patients not consenting will be documented, along with reasons for not participating. Patients 
excluded will be described in a study flowchart. 

Patient inclusion depends on the SARI case definition(s) used for CVE and IVE estimates. Sensitivity analyses 
comparing CVE and IVE estimates based on alternative SARI case definitions are planned. In the primary analysis, 
CVE is estimated among SARI patients as defined in Section 3.5.2, while IVE is estimated among SARI patient 
defined as a hospitalised person with: 

• 

AND 

• 

at least one systemic symptom or sign: fever or feverishness, malaise, headache or myalgia or deterioration 
of general condition (asthenia or loss of weight or anorexia or confusion or dizziness); 

at least one respiratory symptom or sign (cough, sore throat or shortness of breath) 

on admission or within 48 hours after admission. The symptoms should not have started (or clearly worsened, if 
chronic) more than seven days before swabbing. 

Cases and controls will be described by baseline characteristics.  

The main characteristics of each study will be summarised individually, and will include: 

• 
• 

• 
• 

Number of hospitals participating and catchment population;  
Beginning of vaccination campaigns for COVID-19 and for influenza vaccines;  
− 
− 
− 
− 
Number of patients screened; 
Number of patients excluded per reasons for exclusion.  

Beginning of the study; 
End of the study; 
Vaccine product(s) used; 
Estimated vaccine coverage in the country/region by vaccine brand, by target vaccine group; 

Measure of effect 

This study is a case control study (test-negative design). The measure of association is an odds ratio (OR). This 
can be measured by logistic regression. An OR = 1 indicates no association between an exposure and the 
outcome. An OR>1 indicates a potential risk factor, an OR < 1 indicates a potential protective factor, noting that 
the confidence interval around the OR helps with its interpretation. 

For vaccination as preventive factor, the VE can be computed as VE = (1 – OR)*100. A 95% confidence interval is 
computed around the point estimate.  

Pooled univariable analyses 
Baseline characteristics of cases and controls will be compared using the chi-square test, Fisher’s exact test, t-test 
or the Mann-Whitney test (depending on the nature of the variable and the sample size). The association (OR) 
between vaccination status and baseline characteristics will be measured for both case and control groups. 

Stratified analysis 
Depending on sample size, the analysis by vaccine product will be further stratified according to: 

• 
• 
• 
• 

sex; 
age group, e.g. 0–14 years, 15–49 years, 50–64 years, 65–79 years, 80+ years; 
specific chronic conditions (e.g. respiratory, diabetes, obesity); 
absence, presence of at least one, presence of more than one high-risk condition; 

43 

TECHNICAL REPORT  Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0 

• 

• 
• 
• 
• 
• 
• 
• 

• 
• 

• 
• 
• 

time: this will depend on timing of the pandemic/influenza season at sites/in countries and may just include 
one period at the start of the study once vaccines are available, and a specified period later on; 
dose (for CVE): partial vaccination, full vaccination, full vaccination plus booster; 
swab delay (0–3 days, 4–7 days; 8+ days); 
vaccination delay (<8 days, 8–14 days, >14 days, etc.); 
hospital admission delay (0–4 days, 5–9 days, 10 days +, onset after hospitalisation); 
previous vaccination against influenza and pneumococcal disease; 
prior infection with influenza or SARS-CoV-2 (prior to hospital admission for SARI); 
current co-infection with influenza (for CVE) or SARS-CoV-2 (for IVE) or other respiratory viruses (for both 
CVE and IVE); 
severity (ICU admission, ventilation/oxygen, death; for CVE); 
CVE/IVE at different time points in calendar time, e.g. by week or group of weeks (e.g. CVE for weeks 2–
20, 21–42, etc.; IVE for interim season (to end January), and for the whole season); 
for the various groups of vaccines (if available/applicable), mode of injection (intradermal vs intramuscular); 
use of medications for chronic conditions (e.g. statins, for IVE);  
Other stratifications may be included. 

Virus variant-specific outcomes will be used, if available and feasible at the time of analysis. 

A sufficient sample size should be planned in order to ensure enough individuals in each stratum for a precise 
estimate. Effect modification will be assessed comparing the OR across the strata of the potential effect modifiers. 
Confounding will be assessed by comparing crude and adjusted OR for each potential confounder. 

Multivariable analysis 
A multivariable logistic regression analysis will be conducted to control for negative and positive confounding. Odds 
ratios and standard errors will be obtained. Variables will be tested for multicollinearity. Interactions will be tested 
using the likelihood ratio test or Wald’s test and will be included in the model if significant at the 5% level. Factors 
other than statistical significance (prevalence of exposure, magnitude of OR) will also be used as criteria for 
inclusion of a variable or an interaction term. If possible, a variable for sex, age and onset time should always be 
included in the model. 

Continuous variables 
Continuous variables in the COVID-19/influenza datasets include age, time of onset of symptoms, and 
hospitalisations in the past 12 months. These variables can be coded as categories (e.g. age group, week of 
symptom onset, etc.) However, when coding continuous variables as categories, information may be lost, or 
introduce residual confounding and increase the standard error of the model. Tests will be carried out to see if 
these variables could be coded as a linear term, polynomial or a spline. In addition, a balance will be sought 
between simplicity of a model (so a non-expert can understand it), precision and a model that estimates the 
vaccine effect with the least bias. 

Identifying heterogeneity, testing for heterogeneity 
Country-specific crude and adjusted ORs and their confidence intervals will be plotted in separate forest plots. 
Following the core protocol minimises heterogeneity between studies. However, adherence to the protocol and 
study design and study quality characteristics will also be checked. Other study site characteristics will be assessed 
where feasible, such as types of circulating virus, information on healthcare use and organisation of the vaccination 
campaign. A qualitative decision will then be taken if one or more studies are substantially different from the other 
and should be excluded from the pooled analysis. 

Statistical heterogeneity between studies will be tested using the Q-test and the I2 index (see boxes for formulae 
below). The Q statistic follows a Chi2 distribution (with k-1 degrees of freedom). The Q-test reports presence or 
absence of heterogeneity, while the I2 index (based on the Q-statistic) quantifies the extent of the heterogeneity. 
According to the Higgins and Thompson classification, an I2 index of around 25% indicates low, 50% indicates 
medium and 75% indicated high heterogeneity between studies. 

Where: 

w
i

/1=

v

i

vi is the inverse variance of the estimated log odds ratio of study i 

log(

OR
i

)

∑ ×
w
i
∑

w
i

log(

OR

F

)

=

44 

 
 
 
Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0   TECHNICAL REPORT 

2

I

=

−

)1

×

−
kQ
(
Q

2

I

=

0

    %100

(Qfor 

>

k

−

)1

≤
 Qfor 

(

k

−

)1

Formulae are given here for completeness, in practice these measures are automatically calculated by many 
statistical software packages as part of the meta-analysis commands. 

One-stage pooled analysis approach 
If sample sizes are too small to measure vaccine effectiveness controlling for all potential confounders for each 
individual study site, a one-stage pooled approach will be used for analysis. 

Individual study data will be pooled into one dataset and analysed as a one-stage model with study site as a fixed 
effect. This could provide a large enough sample size to obtain (for example) an estimate of CVE/IVE early in the 
study/season with reasonable precision. However, the results of this analysis should be interpreted with caution as 
it assumes not only that the underlying true exposure effect is the same in all studies, but also that the association 
of all covariates with the outcome is the same in all studies.  

Formal tests of interaction between study site and covariates will be carried out to determine if the effect of each 
covariate differs across studies, to test the assumptions of the one-stage pooled fixed effect analysis.  

The significance of interaction terms are themselves influenced by sample size and should also be interpreted with 
caution. Particular care needs to be taken if heterogeneity is found between study sites when using a one-stage 
fixed effects approach (see above section). Reasons for heterogeneity need to be thoroughly investigated and the 
assumptions underlying the one-stage pooling approach need to be revisited.  

Controlling for hospital effect 
Primary analysis will be carried out using simple logistic regression to obtain the individual study estimates. 
However, there could be an effect of the hospital that is related both to the exposure (propensity to vaccinate) and 
the outcome (in terms of swabbing behaviour). To adjust for this cluster effect, a multi-level logistic regression with 
each hospital as a random effect will be carried out when using one-stage pooled analysis.  

Multi-level logistic regression can also be carried out for each individual study with hospital as a random effect. The 
two-stage model as outlined above will then be used to obtain a summary CVE/IVE measure, using these 
estimates. 

The same applies to stratified analyses. The point estimates and confidence intervals from the multi-level and 
simple logistic regression will be compared in a sensitivity analysis. 

Two-stage pooled analysis approach 
If adequate sample size by study is achieved to obtain an adjusted OR, then a two-stage approach to pooled 
analysis will be taken.  

Country-specific adjusted ORs and standard errors for the effect of COVID-19/influenza vaccination obtained from 
the individual studies will be combined in a model that incorporates random effects of the studies, to account for 
unmeasured country- and hospital-specific factors that differ between countries.  

The country-specific exposure-disease effects (ORs) are then weighted by the inverse of their marginal variances. 
The marginal variance is the sum of the individual study-specific variances and the variance of the random study 
effects (τ2). This will give the pooled odds ratio and standard error. 

log(

ORi

)

×
*
∑

w
i

*

log(

OR

)

=

R

∑

w
i

wi

*

=

1
2
τ+

vi

The country-specific ORs and their confidence intervals, along with the pooled OR, will be presented graphically in 
a forest plot. This model will also be compared against a two-stage analysis with fixed study effects, to assess the 
effects of model assumptions. 

If, despite the common protocol, covariates were not uniformly collected in the different studies, then an analysis 
will be carried out to exclude certain studies and a comparison made with the analysis, including all studies. In a 
different scenario, analyses can also be carried out excluding certain study participants for whom variables were 
collected differently.  

45 

  
          
          
          
 
 
 
 
TECHNICAL REPORT  Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0 

Further analyses  
Where sample size allows, further analyses will be carried out. These include: 

• 

• 

• 

• 

• 

• 

CVE/IVE by time since vaccination. Time since vaccination can be calculated by subtracting the date of 
vaccination from the date of onset. Time since vaccination can then be modelled as a continuous variable, 
including correction for either stable or increased rate of COVID-19/influenza over time; cumulative risk of 
COVID-19/influenza. 
CVE and IVE for patients with previous influenza vaccination (current influenza season) vs no previous 
influenza vaccination. 
IVE and CVE for patients with previous COVID-19 vaccination (within six months) versus no previous 
COVID-19 vaccination. 
If negative CVE is found in some target groups 
− 

assess possibility of vaccine-mediated enhanced disease (VMED), which could manifest as negative 
CVE, by comparing severity in vaccinated and unvaccinated patients. Results should show reduced 
severity among vaccinated patients; findings of increased severity in vaccinated patients could 
suggest VMED. 

As a sensitivity analysis, CVE/IVE will be calculated: 
− 
− 

using WHO versus ECDC case definitions for SARI; 
considering those vaccinated <X days before onset of symptoms as unvaccinated (in the main 
analysis these records will be excluded); 
including SARI patients testing positive for influenza/SARS-CoV-2 in the control group; 
including SARI patients testing positive for influenza/SARS-CoV-2 in the case group; 
including SARI patients whose influenza/COVID-19 vaccine status is unknown in the control group; 
using, as a control group, only SARI patients testing positive for at least one non-influenza 
respiratory virus/non-coronavirus; 
considering different restrictions according to swabbing delay (e.g. <14 days, <10 days, etc.); 
considering the sensitivity and specificity of PCR; 
based on assumptions of previous infections;  
excluding participants who received antivirals ≤14 days prior to swabbing; 
excluding all participants with lab-confirmed influenza/SARS-CoV-2 at any time after symptom onset, 
to reduce bias. 

− 
− 
− 
− 

− 
− 
− 
− 
− 

This can then be repeated using RSV as a sham outcome (if multiplex results are available for any sites). 
There should be no association between COVID-19/influenza vaccination and RSV-positivity in the absence 
of confounding. 

Time can be input as a variable in the model to assess whether it can be an effect modifier.  

Minimum sample size 

Sample sizes may be very small for some sub-analyses. Different criteria can be used to determine whether the 
sample size is large enough to obtain a valid measure of VE; 

• 

• 
• 

there are at least 10–15 cases (or controls, whichever is smaller) in the sub-analysis for crude analyses and 
more for adjusted analyses (e.g. at least 10 for each parameter in the model); 
there are ≥5 records in each cell of the two-by-two table of case and vaccination status; 
the precision of the estimate does not span both -200% and 90% (uninformative). 

With low sample size, approaches include collapsing categories, or modelling continuous variables in a different 
way (if applicable). Sensitivity analyses can be carried out using penalised logistic regression. 

Use of propensity scores  

To limit the number of co-variables to include in the multivariable model, if sample size allows, estimates will be 
built and adjusted based on propensity scores. Propensity scores can be defined as the conditional probability of 
receiving the vaccine given a number of observed co-variables.  

In propensity score matching, a propensity score for vaccination is calculated for cases and controls. Cases and 
controls are then matched by propensity score and all non-matched patients are discarded. Variables used to 
calculate the propensity score will include variables related to the vaccination and outcome. Care will be taken to 
avoid correlation and overmatching. 

46 

 
 
Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0   TECHNICAL REPORT 

Annex 3. Genetic characterisation in vaccine 
effectiveness studies 

Virus selection 

Objectives 
• 

To describe the viruses included in ECDC’s COVID-19 and influenza vaccine effectiveness (CVE/IVE) studies 
(overall, by site and by time period), in order to identify key SARS-CoV-2/influenza virus genotypic evolution 
that could affect vaccine effectiveness. 
To measure genetic variant-specific CVE and (sub)type-specific IVE among study sites. 

• 

To meet these objectives, it is important that the viruses sequenced are representative of the influenza/SARS-CoV-
2 viruses from cases belonging to the VE study population. To achieve this, either all or a random selection of 
viruses are sequenced (among those that are technically feasible to sequence). In this way the selection will be 
independent of vaccination or clinical outcomes. 

Study sites/country will select viruses from SARI patients included in the CVE/IVE studies testing positive for SARS-
CoV-2/influenza. If feasible, before virus selection, study sites/countries should verify if the influenza/SARS-CoV-2 
positive cases meet the criteria used to include cases in the CVE/IVE pooled analysis (e.g. target group for 
vaccination, vaccination status and date documented, delay symptom onset swabbing no more than 10 days, etc.).  

Proportion of SARS-CoV-2/influenza viruses to characterise sampling 
fraction 
Each study country has different resources, different incidence and a different proportion of genetic variants 
circulating.  

Ideally, a study site will sequence all viruses that for which sequencing is technically feasible. If this is not feasible, 
then the proportion sequenced (the sampling fraction) will be based on the study site resources and the 
epidemiological/virological situation. We suggest that study sites/countries sequence as a minimum 50% of viruses 
for which sequencing is technically feasible, to be reviewed during the course of the pandemic. 

If study sites are sequencing a proportion of viruses (as opposed to all viruses), the sampling fraction can change 
over time, depending on resources. For example, during peak incidence only 50% of viruses could be sequenced 
and during periods of low incidence all viruses could be sequenced. When sequencing a proportion of viruses it is 
then important to adhere to the random selection process. 

The proportions sampled over time should be documented in the ‘Example of sampling fraction definition’ Excel 
spreadsheet. An example appears in Figure A1. 

Figure A1. Example of how to define sampling fractions over time using the Excel spreadsheet 
‘Example of sampling fraction definition.xlsx’ 

Each study country to  

 
 

 

define the sampling fraction used for each time interval (if all viruses characterised, then indicate 100%);  

document the sampling fraction for each time interval in the ‘Example of sampling fraction definition.xlsx’ 
document; 

take the sampling fraction into account when measuring site-specific genetic variant-specific VE (if 
applicable).  

Procedures for random selection of specimens to be characterised 

If a study country is not genetically characterising all viruses, then the random selection proposed should be used to select 
the viruses included in the multi-centre study. This should be done independent of any routine virological surveillance. 

As it is difficult to prospectively randomise viruses to sequence, study sites can use a list of viruses by a predefined 
period (e.g. each week or month) to use for randomisation.  

At the end of each period defined for the selection of strains, study sites will select viruses using random selection (e.g. 
the Bernoulli sampling method). This method ensures that each strain has the same probability of being selected.  

⮚ 

Each site to define who selects the strains to sequence (e.g. team of epidemiologists, team of virologists). 

47 

 
TECHNICAL REPORT  Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0 

Steps to randomly select the strains 

Step 1: Sampling frame 
• 

Create a list of all positive cases recruited for the period that study sites/country would like to sequence 
(week/month). 
Viruses already characterised by the National Reference Centre (or other laboratories) during that period 
will be part of the sampling frame to ensure representativity. 
Viruses with low viral load will be part of the sampling frame. 
If possible, sites/countries will exclude from the sampling frame the viruses from cases that could later be 
excluded from any pooled analysis (e.g. target group for vaccination, vaccination status and date 
documented, delay symptom onset swabbing no more than 10 days, etc.) 

Step 2: Randomisation 
• 
• 

List order all positive cases (viruses) by swab date. 
Assign a random number to each virus: The Excel function =RAND() can be used (this may be different if 
using a different language version of Excel). 
Copy the random number column and paste as values (this is important, otherwise they will keep changing) and 
then sort the list of cases (viruses) by random number in the pasted column (e.g. in order of high to low). 

• 

• 
• 

• 

The STATA syntax below can be used. The example is a selection of 50% of 88 cases (44 strains to characterise).  

sort swabdate 
set seed 500 
gen naleat=runiform(), 
sort naleat 
gen select=0 
replace select=1 if _n<=44 
list IDnumber strain select if select==1, noobs separator (44) 

where 500 is the number used to set the seed. You can select another number but it is recommended that the ‘set 
seed’ be used to be able to replicate the selection. 

Step 3: Selection 
Based on the proportion of viruses sites/countries would like to sequence for this period (e.g. 50% or 75%), select 
the number of cases/viruses needed: start from the first case in the list and continue selecting the following cases 
until reaching the desired number (e.g. if 88 cases have been recruited in the study and the sampling fraction is 
0.5, the first 44 cases in the list will be selected).  

Step 4: Replacement of viruses randomly selected, but not characterised 
Viruses that cannot be sequenced should be replaced using the same variant (or subtype/lineage) sampling frame. 
The strains will be replaced by the next ones in the list. So, for example, if 44 viruses would have been chosen 
from the randomised list and two are not feasible to sequence, then the viruses in the 45th and 46th line can be 
selected to sequence. The reasons for not sequencing selected specimens should be documented (e.g. low viral 
loads) and all study sites should document their Ct threshold for sequencing (if applicable). 

Step 5: Increase in proportion sequenced if needed 
If during the pandemic, the study site decides to increase the proportion sequenced for a given time period, it 
should go back to the sampling frame for that time period and continue selecting the subsequent strains from the 
ordered original list.  

Data collected 
For the viruses characterised, study sites/countries should fill in an Excel spreadsheet as shown below, with at least 
the following information available (see Figure A2 for an example):  

Country; 
Patient’s study ID number; 
GISAID sequence database accession number; 
Selected for characterisation? (Y/N); 
Reasons for not characterising; 
If possible: Ct value; 
If possible: Type of sample (primary specimen or isolate). 

• 
• 
• 
• 
• 
• 
• 

48 

 
Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0   TECHNICAL REPORT 

Figure A2. Example of information collected on viruses using an Excel spreadsheet  

Screenshot of Excel spreadsheet to collect information on proportions sequenced over time (sampling fraction): 

Figure A3. Example of sampling fraction definition 

Screenshot of Excel spreadsheet to collect information on proportions sequenced over time (sampling fraction):  

Genetic and antigenic analysis data (example) 

The minimum amount of data needed to obtain genetic data from GISAID (sequences of all viruses should be sent 
to GISAID’s open access EpiCoV platform) is country, ECDC SARI VE study patient ID number and GISAID 
accession number. Additional information on Ct value and selection for characterisation and reasons for not 
characterising can also be collected (see Table A1 below). 

Table A1. Example of a data collection form for genetic data 

Country 

Study ID 
number 

GISAID 
accession ID 
number 

Selected for 
characterisation? 

Reasons for not 
characterising? 

Ct 
value 

Type of sample 
(primary 
specimen or 
isolate) 

Strain
1 

Strain
2 

Where not all viruses were sequenced, but only a random selection of them, additional information on sampling 
fraction should be provided, to better understand how viruses were selected for sequencing over time. An example 
can be seen in Table A2 below. 

Table A2. Example of document outlining how viruses were selected for sequencing over time  

Period 

First date  

Last date 

of period 

Sampling 
fraction 

Date used for definition of 
time unit (onset date, 
swab date, other) 

Comments 

1 

2 

Example1 

01/10/2020 

31/12/2020 

1 

Date of onset 

Example2 

01/01/2021 

15/02/2021 

0.2 

Date of onset 

For example: all specimens 
were characterised 
For example: 20% of all 
specimens were 
characterised 

49 

 
 
 
 
 
  
 
  
  
 
 
 
  
 
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
TECHNICAL REPORT  Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0 

Annex 4. List of ICD-9 and ICD-10 codes for 
pre-existing chronic conditions 

Category 

ICD-9 

ICD-10 

Underlying conditions included  

Anaemia  

280–285 

D50-64 

Asplenia 

746.87, 759.0 

Q89.01, Q20.6, Z90.81 

Asthma 

493.0, 493.1, 493.9 

J45 

Chronic liver disease 

571 

Cardiovascular diseases 

093, 112.81, 130.3, 
391, 393–398, 402, 
404, 410–429, 745, 
746, 747.1, 747.49, 
759.82, 785.2-3 

K70, K72-74, K754, 
K769 

A52.01, B37.6, B58.81, 
I05-9, I11, I13, I20-25, 
I26.09, I26.9, I27, I30-
51, I97.0-1, R00.1, 
T81.718A, T81.72XA, 
T82.817A, T82.818A, 
Q20-24, Q25.1-2, 
Q26.0-1, Q26.8, Q87.4, 
R01.1-2 

Nutritional anaemias, Haemolytic anaemias, 
Aplastic and other anaemias and other bone 
marrow failure syndromes 
Malposition of heart, Anomalies of spleen, 
Isomerism of atrial appendages, Acquired and 
Congenital absence of spleen 

Extrinsic asthma, Intrinsic asthma, Predominantly 
allergic asthma, Non-allergic asthma, Mixed 
asthma, Asthma unspecified 

Alcoholic liver disease, Hepatic failure, Chronic 
hepatitis, Fibrosis and cirrhosis of liver, Other 
inflammatory liver diseases 
Syphilitic aneurysm of aorta, Candidal 
endocarditis, Toxoplasma myocarditis, Chronic 
rheumatic heart diseases,  
Ischemic heart diseases, Hypertensive heart and 
chronic kidney disease, pulmonary embolism with 
acute cor pulmonale, pulmonary heart diseases, 
diseases of pulmonary vessels, Other forms of 
heart disease (including Nonrheumatic valve 
disorders, pericarditis, endocarditis, myocarditis, 
cardiomyophathy, heart failure, block, cardiac 
arrhythmias, heart failure), Complication of other 
artery / vein following a procedure, Embolism of 
cardiac/vascular prosthetic devices, implants and 
grafts, congenital malformations of cardiac 
chambers and connections or heart, Coarctation or 
atresia of aorta, Congenital malformations of great 
veins, Marfan's syndrome, Cardiac murmur 

250 

E10-11 

Type 1 and Type 2 diabetes mellitus 

Diabetes 

Hypertension 

401, 401.0, 401.9, 405, 
405.91, 405.99,  

I10, I15.8, I15, I15.1, 
I15.2, I97.3, I27.0 

Obesity 

27800, 278.01, 278.03 

E66.01, E66.2, E66.9 

Immunodeficiency* or 
organ transplant 
Neuromuscular 
disorders 

042, 279, V08, V42  

358.00-358.1, 358.8, 
358.9, 378.73, 775.2 

B20, D80-84, D89.8-9, 
Z21, Z94 
G70-G70.01, G70.2, 
G70.80, G70.81, G70.9, 
G70.89, G73.7,  

Renal disease  

274.1, 408, 580–591, 
593.71–593.73, 593.9  

M10.30, N00-19, N20.0, 
N28.9 

Dementia 

290, 294, 331 

F01, F03, F05, G30, 
G31, G91, G94 

Stroke 

348, 438 

G93, I67.83, I69 

Rheumatologic 
diseases  

446, 710, 714 

M30-34, M35.0, M35.5, 
M35.8-9, M05-06, M08, 
M12.00 

Cancer 

140–208 

C00-96 

50 

Hypertension (essential and secondary), 
Secondary to other [renal or endocrine] disorders, 
Malignant hypertension 
Obesity 

HIV, immune deficiency, organ or tissue replaced 
by transplant 
Myasthenia gravis, Myoneural disorders NEC/NOS, 
Neuromuscular disease strabism, Congenital and 
developmental myasthenia, Lambert-Eaton 
syndrome, Myoneural disorder NOS 
Gout due to renal impairment, Glomerular 
diseases, Renal tubulo-interstitial diseases, Acute 
kidney failure and chronic kidney disease, Calculus 
of kidney, Disorder of kidney and ureter, 
unspecified 
Vascular dementia, other dementia, Delirium due 
to known physiological condition, Alzheimer's 
disease, Other degenerative diseases of nervous 
system 
Brain disorders, Posterior reversible 
encephalopathy syndrome, Sequelae of 
cerebrovascular disease 
Polyarteritis nodosa and related conditions, Other 
necrotising vasculopathies, Systemic lupus 
erythematosus (SLE), Dermatopolymyositis, 
Systemic sclerosis, Sicca syndrome, Multifocal 
fibrosclerosis, other systemic involvement of 
connective tissue, Rheumatoid arthritis with 
rheumatoid factor, Other rheumatoid arthritis, 
Juvenile arthritis, Chronic post-rheumatic 
arthropathy  
Malignant neoplasms and neuroendocrine tumours 

Core protocol for ECDC VEBIS studies of COVID-19 vaccine effectiveness against hospitalisation with SARI, Ver 2.0   TECHNICAL REPORT 

Category 

ICD-9 

ICD-10 

Underlying conditions included  

Lung disease 

011, 490–511, 512.8, 
513–517, 518.3, 518.8, 
519.9, 714.81 

A15, J40–47, J60–94, 
J96, J99, J182,  

M34.81, M05.10 

Tuberculosis 

A15–A19 

Respiratory tuberculosis, Bronchitis, not specified 
as acute or chronic, Chronic bronchitis, 
Emphysema, Other chronic obstructive pulmonary 
disease, Asthma, Bronchiectasis, Hypersensitivity 
pneumonitis due to organic dust, Pneumoconiosis, 
Airway disease due to specific organic dust, 
Hypersensitivity pneumonitis due to organic dust, 
Respiratory conditions due to inhalation of 
chemicals, gases, fumes and vapor, Pneumonitis 
due to solids and liquids, Respiratory conditions 
due to other external agents, Acute respiratory 
distress syndrome, Pulmonary oedema, Pulmonary 
eosinophilia, not elsewhere classified, Other 
interstitial pulmonary diseases, Abscess of lung 
and mediastinum, Pyothorax, Pleural effusion, 
Pneumothorax and air leak, Other pleural 
conditions, Intraoperative and postprocedural 
complications and disorders of respiratory system, 
not elsewhere classified, Other diseases of the 
respiratory system, Hypostatic pneumonia, 
unspecified organism,  
Systemic sclerosis with lung involvement, 
Rheumatoid lung disease with rheumatoid 
arthritis 
Primary respiratory tuberculosis, Respiratory 
tuberculosis unspecified, Tuberculosis of nervous 
system, Tuberculosis of other organs, Miliary 
tuberculosis 

*Note: patients who are only treated with glucocorticoids and have no other immune deficiency, are considered 
immune suppressed when treated with high-dose corticosteroids (≥ 20 mg/day of prednisone or equivalent for ≥2 
weeks) in the last three months.  

51 

 
 
 
 
 
 
 
 
 
 
 
 
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