Post-authorisation Passive Enhanced Safety Surveillance of Seasonal Influenza Vaccines: Pilot Study in England 2017/18
Conditions: Influenza, Human
Sponsor: GlaxoSmithKline

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Post-authorisation passive enhanced safety 
surveillance of seasonal influenza vaccines: 
Pilot study in England 2017/18 
Date of Final Protocol: 30 June 2017 
GSK study number (e-track): 207781  
GSK study abbreviated title: EPI FLU-055 VS UK 
 
Collaborating Study Sponsor: University of Surrey, Guildford UK 
Collaborating Study Sponsor: GlaxoSmithKline Biologicals, Rue de l’Institut, 89, 
1330 Rixensart, Belgium 
 
University of Surrey investigators and primary protocol authors: 
Prof 
  
(Principal Investigator)*  
Professor of Primary Care and Clinical 
Informatics 
Chair in Health Care Management, 
University of Surrey 
 
* Department of Clinical and Experimental 
Medicine 
University of Surrey 
Guildford 
 
W: 
 
  
Programme Manager 
Department of Clinical and Experimental 
Medicine, University of Surrey 
 
 
 
Practice Liaison Officer 
Department of Clinical and Experimental 
Medicine, University of Surrey 
  
 
 
Senior Database Developer 
Department of Clinical and Experimental 
Medicine, University of Surrey 
  
 
 
Primary Care Scientist 
Department of Clinical and Experimental 
Medicine, University of Surrey 
 
 
 
Senior Research Fellow 
Department of Clinical and Experimental 
Medicine, University of Surrey 
 
 
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Contributing authors from GlaxoSmithKline: 
  
Epidemiology, Clinical R&D US/BE*  
 
*GSK Vaccines 
Rue de l’Institut 89 
1330 Rixensart 
Belgium  
  
Clinical Epidemiology Project Lead, Clinical 
R&D US  
 
 
 
Statistician 
 
 
 
Epi Lead Statistician 
 
 
  
SERM Head  
 
 
 
Safety Scientist  
 
 
 
Funding: 
GlaxoSmithKline Biologicals SA 
PPD
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Table of contents 
 
1. 
SYNOPSIS ......................................................................................................................................... 4 
2. 
LIST OF ABBREVIATIONS AND GLOSSARY ...................................................................................... 6 
3. 
RESPONSIBLE SPONSORS ................................................................................................................ 8 
4. 
INTRODUCTION ............................................................................................................................... 9 
a. 
Rationale for the pilot study and background ............................................................................. 9 
b. 
Objectives and endpoints .......................................................................................................... 12 
5. 
RESEARCH METHODS .................................................................................................................... 14 
a. 
Study Design .............................................................................................................................. 14 
6. 
PROJECT MANAGEMENT .............................................................................................................. 33 
7. 
ETHICAL CONSIDERATIONS ........................................................................................................... 35 
8. 
INFORMATION GOVERNANCE CONSIDERATIONS ....................................................................... 36 
9. 
DISSEMINATION AND PUBLIC REGISTER DISCLOSURE ................................................................ 37 
10. 
APPENDIX .................................................................................................................................. 39 
11. 
REFERENCES .............................................................................................................................. 54 
 

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1. SYNOPSIS 
 
Background: 
The European Medicines Agency (EMA) set out new requirements for influenza vaccine safety 
surveillance that all Marketing Authorisation Holders (MAHs) providing vaccines in the EU must 
address. The EMA guideline came into effect in February 2017 and included its last 
Pharmacovigilance Risk Assessment Committee (PRAC) recommendations on passive enhanced 
safety surveillance for seasonal influenza vaccines in the EU. In 2014/2015 and 2015/2016, 
GlaxoSmithKline Biologicals (GSK) collaboratively with the University of Surrey carried out pilot 
studies (EPI-FLU-045 - EPI-FLU-046). These surveillance studies were conducted in England since 
nearly all primary care consultations and vaccinations are recorded in computerised medical record 
(electronic health record, EHR) systems. 
 
Aim: 
The EPI-FLU-055 pilot study aims to fulfil the EMA guideline. The requirement is to rapidly detect an 
increase in the frequency or severity of expected reactions (local, systemic or allergic reactions) that 
may indicate a potential or more serious risk, with increased exposure to the vaccine. 
 
Objectives: 
The eligible study period for both primary and secondary objectives is expected to be from 
01 September until 30 November 2017. 
 
Primary objective: To estimate the weekly and cumulative incidence rates of adverse events of 
interest (AEIs) within 7 days following vaccination with any seasonal influenza vaccine using card-
based adverse drug reaction (ADR) reporting system. Data will be presented overall, by brand 
(Fluarix Tetra vs. others), by age strata, and UK Chief Medical Officer (CMO)-specified risk groups 
status (at risk/not at risk).  
 
Secondary objective: To estimate the weekly and cumulative incidence rates of AEIs within 7 days 
following vaccination with any seasonal influenza vaccines using the card-based ADR reporting 
system as well as routinely collected AEIs. Data will be presented overall, by brand (Fluarix Tetra 
vs. others), by age strata, and UK CMO-specified risk groups status (at risk/not at risk).  
 
Method: 
The EPI-FLU-055 pilot study will build on the key learnings from the EPI-FLU-045 and EPI-FLU-046 
pilot studies carried out in the 2 previous influenza seasons (2015/2016 and 2016/2017) in order to 
adapt the approach to collect and report adverse events. The EPI-FLU-055 pilot study is intended to 
collect data about vaccination status and adverse events following immunisation (AEFI) on a weekly 
basis, from 01 September 2017 onwards, using a standardised approach. 
 
Study design: The third pilot study will be a prospective passive enhanced safety surveillance study 
with weekly and cumulative analysis of incidence rate of reported AEIs. A combination of card-based 
ADR reported data and routinely collected medical data will be used to provide relevant information 
about influenza vaccine safety, and analyse these data in a near to real time manner, ideally within a 
week or so of data collection. 
  

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Setting: 10 volunteer general practices in England, primarily using the GSK influenza vaccine will be 
enrolled. After the end of each influenza seasons, these general practices select which brand of 
influenza vaccine they will use in the subsequent season. The observation period will coincide with 
the start of influenza vaccination period in the respective GP practices and is intended to end on 30th 
November 2017.  
 
Participants: Influenza vaccine recipients who are registered with participating GP practices, and/or 
their guardian or carer.  
Registered patients who have explicitly opted out of data sharing will be excluded from the analysis. 
 
Data sources: All data pertaining to the study will be extracted from practice electronic health record 
(EHR) systems. Anonymised data, (strictly defined as “pseudonymised”), will be transferred to the 
secure network at University of Surrey where analysis will occur on its secure network. No individual 
patient level data will leave this network. Data from the ADR forms completed by the patients will be 
entered into the GP practice EHR system.  
 
Variables: The extract will include: demographic data, information about vaccine exposure, data 
about co-morbidities supporting eligibility for influenza vaccination as defined by the UK CMO’s high 
risk groups will be collected, as well as EMA specified AEIs occurring within 7 days of vaccination, 
combining data routinely collected during GP-consultation and data reported using a customised 
card-based ADR reporting system listing EMA defined AEIs.  
 
Bias: Any disparities in the data generated compared with the national population and the 
immunisation recommendations in the UK will be discussed.  
 
Study size: As per EMA requirement and Vaccine Working Party (VWP) recommendations, a target of 
at least 1000 vaccinees across all age groups (6 months to 5 years; 6 to 12 years; 13 to 17 years; ≥18-
65 years; >65 years). The sample size proposed in this study accounts for the probability of observing 
at least one event in the 10 enrolled GP practices together with the level of precision associated with 
the finding. 
 
Statistical methods: Weekly and cumulative incidence rates of AEIs within 7 days following 
vaccination with a seasonal influenza vaccine (GSK’s Fluarix Tetra or another influenza vaccine) will 
be estimated, with 95% confidence intervals (CIs).  
 
Outputs: 
Weekly analysis: A weekly analysis of influenza vaccination and uptake by vaccine brand, different 
age and at-risk (as per UK CMO recommendations) groups and reports of AEIs in vaccinees will be 
produced. Those reported at consultations in the practice (medically attended) as well as those 
reported through ADR cards will be listed. 
 
Final analysis: Interim weekly safety reports and a final comprehensive study report at the end of 
observation period will be produced. The findings will be discussed in light of the rates of adverse 
reactions observed in the RCGP network, in clinical trials performed with GSK’s seasonal influenza 
vaccines and, as appropriate, with rates observed in EPI-FLU-045 and EPI-FLU-046 studies. 

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2. LIST OF ABBREVIATIONS AND GLOSSARY 
 
ADR 
Adverse drug reaction 
AEFI 
Adverse events following immunization 
AEI 
Adverse events of interest – as defined by EMA for this report 
BMI 
Body Mass Index 
CAG 
Confidential Advisory Group 
CI 
Confidence interval 
CMO 
Chief Medical Officer at Department of Health, London 
CRN 
Clinical Research Network 
DCEM 
Department of Clinical and Experimental Medicine, at University of Surrey 
DES 
Directed enhanced services 
EDPPS 
European Data Protection Supervisor 
EHR 
Electronic Health Record (used in EMA publications) 
EMA 
European Medicines Agency 
EPI-FLU-045 The first pilot study in 2015/2016: Enhanced safety surveillance of seasonal influenza 
vaccines: feasibility study in England  
EPI-FLU-046 
The second pilot study in 2016/17: Post-authorisation passive enhanced safety 
surveillance of seasonal influenza vaccines: Pilot study in England 
EU 
European Union 
GIS 
Geographical Information System 
GMS 
General Medical services – the standard NHS primary care provision 
GP 
General Practitioner – A family physician providing NHS care to a registered list of 
patients 
GPSoC 
GP System of Choice, range of NHS approved computerised medical record systems 
that provide the required level of functionality to support primary care delivery 
GSK 
GlaxoSmithKline Biologicals 
IGT 
Information Governance Toolkit 
JCVI 
Joint Committee on Vaccination and Immunisation 
LAIV 
Live attenuated influenza vaccine 
MAH 
Marketing Authorisation Holders 
NIHR 
National Institute of Health Research 
NHS 
National Health Service 
NHS Digital 
NHS DIGITAL (source of National data against which denominators and other 
population data can be checked & security policy, including its IGT) 
NRES 
National Research Ethics Service 
ODS – NHS 
Digital 
Organisation Data Service NHS Digital – system that provides codes for all NHS bodies, 
including general practices and population data about these bodies 
PASS 
Post Authorisation Safety study 
PHE 
Public Health England 
PRAC 
Pharmacovigilance Risk Assessment Committee of EMA 
QIV 
Quadrivalent Influenza Vaccine 
QOF 
Quality and Outcomes Framework 

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RCGP RSC 
Royal College of General Practitioners Research and Surveillance Centre 
RSC 
Research and Surveillance Centre (part of RCGP) 
REC 
Research Ethics Committee 
RES 
Research and Enterprise Support 
RSE 
Relative Standard Error 
SAE 
Serious Adverse Event – 
An SAE (experience) or reaction is any untoward medical occurrence that at any dose: 
results in death, is life-threatening, requires inpatient hospitalisation or prolongation 
of existing hospitalisation, results in persistent or significant disability/incapacity, or is 
a congenital anomaly/birth defect. 
SLA 
Service level agreement 
SOAP 
Simple Object Access Protocol 
UK 
United Kingdom 
VWP 
Vaccine Working Party 
 

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3. RESPONSIBLE SPONSORS 
 
University of Surrey 
Guildford, Surrey 
GU2 7XH  
United Kingdom 
 
GSK Vaccines 
Rue de l’Institut 89 
1330 Rixensart 
Belgium  

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4. INTRODUCTION 
 
a. Rationale for the pilot study and background 
The European Medicines Agency (EMA) is a decentralised body of the European Union (EU 
responsible for the scientific evaluation of medicines developed by pharmaceutical companies for 
use in the EU. Part of this responsibility is to coordinate the EU's safety-monitoring or 
pharmacovigilance system for medicines, monitor the safety of medicines through the EU network, 
and take action, if information indicates that the benefit-risk balance of a medicine has changed 
since it was authorised. 
 
In response to a recent expansion of national vaccination programmes in EU member states, the 
European Medicines Agency has released interim guidance on enhanced safety surveillance for 
seasonal influenza vaccines in the EUi. This set out new standards for surveillance that all Marketing 
Authorisation Holders (MAHs) providing vaccines in the EU must address. The key objective of the 
EMA enhanced safety surveillance is to rapidly detect a significant increase in the frequency and/or 
severity of expected reactions (local, systemic or allergic reactions) that may indicate a potential or 
more serious risk, as exposure to the vaccine increases. Of note, since 2015, European regulatory 
requirements to evaluate the safety and immunogenicity of seasonal influenza vaccines in small scale 
clinical trials were withdrawnii. Such trials had insufficient power to adequately evaluate safety 
concerns arising from annual formulation changes (e.g. adverse events occurring at a rate of 1–2%). 
These clinical trials are replaced by enhanced, preferably active, safety monitoring and vaccine 
effectiveness assessments. 
 
In the initial EMA Interim Guidance on enhanced safety surveillance for seasonal influenza vaccines in 
the EU suggested that there would be three options envisioned for enhanced surveillance: 
 
Enhanced Active surveillance (post authorisation safety studies [PASS]): Active follow-up of a 
cohort of children and adults for 7 days after immunisation for reactogenicity endpoints/adverse 
events. 
 
Enhanced Passive Surveillance: Rapidly estimate vaccine usage and facilitate adverse drug 
reaction (ADR) reporting, in order to determine reporting rate as a surrogate of incidence of the 
adverse events of interest (AEIs).  
 
Data mining or other use of electronic health record/ computerized medical record. 
 
The present collaborative pilot study between MAH GlaxoSmithKline Biologicals (GSK) and the 
Clinical Informatics and Health Outcomes Research Group at the University of Surrey builds on the 
lessons learned from the pilot studies (EPI-FLU-045 VS UK and EPI-FLU-046 VS UK). The study 
addresses the EMA commitment for enhanced safety surveillance of seasonal vaccines in Europe. The 
EPI-FLU-045 VS UK pilot study showed that the proposed surveillance setting in the UK was suitable 
for rapid detection and evaluation of AEIs during an influenza season. Nevertheless, the outcomes 
were not available in a near real time manner. This was successfully addressed in the EPI-FLU-046 VS 
UK pilot study. 
 
Both pilot studies confirmed that a card-based ADR reporting system in addition to report of AEI 
from routinely collected was a valid methodology. In the EPI-FLU-046 VS UK pilot study, the ADR 
cards were customized (including pre-specified EMA AEIs) and a specific field was created to allow 

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reporting that no AEI was experienced during the period of interest. Thanks to those modifications, 
the return rate drastically increased up to approximately 50%. In addition, the encoding of AEIs was 
facilitated for GPs which led to a more standardised and more accurate way to ultimately collect the 
AEIs. 
 
The EHR data provided a reliable estimate of the denominator of vaccines administered throughout 
the participating GP practices. The use of routinely collected data provided additionally demographic 
characteristics and account for underlying conditions, or comorbidities, discharged letter from 
hospital or prescriptions from pharmacist for patients registered in the participating GP practices. 
 
The primary purpose of the 2016/17 pilot study was to improve the combination of a card-based ADR 
reporting system and the use of routine data to collect adverse events following vaccination with 
seasonal influenza vaccines, as per EMA guidance and PRAC requirements. The ADR cards had been 
further customized since the first pilot study to account for the EU requirements. The 2017/18 pilot 
study will build on the lessons learned from the first 2 pilot studies and aim to continue standardizing 
the approach to collect and report AEIs.  
 
The results of the current study will further inform decisions regarding future influenza vaccine safety 
surveillance and contribute to the cumulative awareness and knowledge associated with reporting of 
adverse event following immunisation (AEFI) in Europe. 
 
The Clinical Informatics Research Group, in the Department of Clinical and Experimental Medicine 
(DCEM) at the University of Surrey is home of the data and analysis hub for the Royal College of 
General Practitioners Research and Surveillance Centre (RCGP RSC). The RCGP RSC provides a 
national primary care surveillance system and is supported by Public Health England (PHE). The RCGP 
RSC network of GP practices has a membership designed to give national coverage of 1.5%-2% of the 
English population.iii The data processing, analysis capability, and leadership of the RCGP RSC 
developed by and are performed at the University of Surrey will be used for this investigation.  
 
The most important work of the RCGP RSC network is its influenza surveillance; many GP practices 
have been involved in this work for decadesiv. Data are uploaded from the network on a weekly basis 
to a secure sever with the possibility to switch the frequency of the release to a twice weekly upload 
during epidemics. The methods developed by the University of Surrey will be used in this passive 
enhanced safety surveillance study, with a focus on adverse events reporting after vaccination. 
 
Seasonal influenza vaccines present several specific challenges for pharmacovigilance. These include 
immunisation in large population cohorts in a relatively short and fixed time period each year, and 
multiplicity of vaccine products on the market with the need to conduct product-specific safety 
surveillance.  
 
Routine pharmacovigilance systems for influenza vaccines would need capability to rapidly detect 
and evaluate potential new safety concerns each influenza season. The main objective of enhanced 
safety surveillance is to detect and evaluate a potential increase in product and batch-specific 
reactogenicity and allergic events in a near real-time manner in the earliest vaccinated cohorts in 
order to react accordingly as promptly as possible.  

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Similarly to the first two pilot studies, the Enhanced Passive Surveillance approach is the one chosen 
for this third pilot study. The use of customised ADR cards is the enhancement provided over simple 
AEI surveillance through routine data collection. 
 
The UK national flu immunisation programme 2017/18 – recommendations  
Groups eligible for flu vaccination are based on the recommendation of the Joint Committee on 
Vaccination and Immunisation (JCVI). The national flu immunisation programme aims to provide 
direct protection to those who are at higher risk of flu associated morbidity and mortality. This 
includes older people, pregnant women, and those with certain underlying medical conditions. In 
2012 JCVI recommended extending vaccination to children to provide both individual protection to 
the children themselves and reduce transmission across all age groups by recommending an 
intranasal live attenuated influenza vaccine (LAIV).  
 
In the UK, 2017/2018 influenza plan recommended the following groups to be vaccinatedv: 
 
all children aged two to eight (but not nine years or older) on 31 August 2017 (with LAIV)  
 
All primary school-aged children in former primary school pilot areas (with LAIV)  
 
Those aged six months to under 65 years in clinical risk groups  
 
Pregnant women  
 
Those aged 65 years and over  
 
Those in long-stay residential care homes  
 
Carers  
 
In 2017/18 changes to the programme are as follows:  
 
Morbidly obese: Vaccination of the morbidly obese (defined as body mass index [BMI] of 40 and 
above) will attract a payment under the directed enhanced services (DES) in 2017/18.  
 
Age segments in children to receive the seasonal vaccination (children aged 4-5 years): These 
children will now be offered flu vaccination (LAIV) in reception class, rather than through general 
practice. No payment will be made under the DES if they are vaccinated in general practice 
(unless the child is in an at risk group);  
 
School Year 4 (children aged 8-9 years): As part of the phased roll-out of the children’s 
programme, this year children in school year 4 will also be offered the vaccination.  
 
Eligible adults aged 18 years and over will have the choice of getting their flu vaccine at a pharmacy 
the Community Pharmacy Seasonal Influenza Vaccination Advanced Service. Of note, the intent will 
be to issue cards to all vaccinated patients belonging the recruited practices, however, considering 
the UK influenza vaccination plan, some vaccinations are expected to occur outside of the GP settings 
describe as opportunistic vaccination, perform by third parties including pharmacist and thus it 
cannot be excluded that for some patients, ADR cards are not distributed. The AEIs will however be 
captured through the routine data collection process.  
 
The list above is not exhaustive, and the healthcare practitioner should apply clinical judgement to 
take into account the risk of influenza exacerbating any underlying disease 
 

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Expansion of national vaccination has increased the need for timely information and reassurance on 
the balance of risks and benefits for those receiving the vaccines. The collaborative pilot study is 
conceived in response to the EU requirements triggered by the EMA’s call for enhanced safety 
surveillance in Europe. This third pilot study in the 2017/18 season will help to build a framework for 
passive enhanced safety surveillance in the UK, but will also contribute to an EU-wider programme of 
enhanced safety surveillance for seasonal influenza vaccines. 
 
b. Objectives and endpoints 
Per EMA guideline on enhanced safety surveillance for seasonal influenza vaccines in the EU, the EPI-
FLU-055 pilot study intends to rapidly detect a clinically meaningful change to the known safety 
profile of influenza vaccines, in terms of the frequency and/or severity of expected reactogenicity 
(local, systemic or allergic reactions) that may indicate a potential safety signal. 
 
Vaccine coverage will be estimated through the EHR system from the participating GP practices and 
rates of AEIs following the receipt of seasonal influenza vaccine will be calculated by combining a 
card-based ADR reporting system and routinely collected. 
 
As per EU requirement, data quality will be evaluated, with special focus on data completeness and 
timeliness.  
 
The eligible study period is expected to be from 01 September until 30 November 2017. 
 
Primary objective: 
 
To estimate the weekly and cumulative incidence rates of adverse events of interest (AEIs) within 
7 days following vaccination with any seasonal influenza vaccine using card-based adverse drug 
reaction (ADR) reporting system. Data will be presented overall, by brand (Fluarix Tetra vs. 
others), by age strata, and UK Chief Medical Officer (CMO)-specified risk groups status (at 
risk/not at risk).  
 
Secondary objective: 
 
To estimate the weekly and cumulative incidence rates of AEIs within 7 days following 
vaccination with any seasonal influenza vaccines using the card-based ADR reporting system as 
well as medically attended AEIs. Data will be presented overall, by brand (Fluarix Tetra vs. 
others), by age strata, and UK CMO-specified risk groups status (at risk/not at risk).  
 
Tertiary objective:  
 
To estimate on a weekly basis the vaccine uptake among the subjects registered in the enrolled 
GP practices, by age strata (6 months to 5 years; 6 to 12 years; 13 to 17 years; ≥18-65 years; >65 
years) and CMO-specified risk groups. 
 
To assess the completeness of vaccination data in the EHR 
 
To assess the timeliness of availability of vaccination data in the EHR 
 
To evaluate the return rate of ADR cards  
 
To assess the timeliness of AEI reports in the EHR from medically attended AEIs and from the 
card-based ADR reporting system 

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 
To assess the timeliness of generating weekly reports  
 
Primary endpoint: 
 
Occurrence of AEIs (Appendix 2) within 7 days post vaccination reported using a card-based ADR 
reporting system overall, by age strata (6 months to 5 years; 6 to 12 years; 13 to 17 years; ≥18-65 
years; >65 years) and UK CMO-specified risk groups status (at risk/not at risk), each week and 
cumulatively, overall and by vaccine brand (Fluarix Tetra vs. others). AEIs will be presented by 
system organ categories. 
 
Secondary endpoint: 
 
Occurrence of AEIs within 7 days post vaccination reported using data entered in EHR (i.e., AEIs 
derived from a card-based ADR reporting system and medically attended AEIs) overall, by age 
strata (6 months to 5 years; 6 to 12 years; 13 to 17 years; ≥18-65 years; >65 years) and UK CMO-
specified risk groups status (at risk/not at risk), each week and cumulatively, overall and by 
vaccine brand (Fluarix Tetra vs. others). AEIs will be presented by system organ categories. 
 
Tertiary endpoints: 
 
Seasonal influenza vaccination status among the subjects registered in the enrolled GP practices, 
overall, by vaccine brand, by age strata (6 months to 5 years; 6 to 12 years; 13 to 17 years; ≥18-
65 years; >65 years) and UK CMO-specified risk groups and date of vaccine administration 
collected in the EHR system 
 
Level of missing data related to vaccination information (date of event, vaccine brand, vaccine 
batch).  
 
Lag time between date of vaccine administration and date at which vaccination record is 
encoded in the EHR system 
 
Return of ADR cards  
 
Time interval between AEI onset date and recording in the EHR by source (medically attended vs 
via ADR cards). 
 
Time interval between the date data extraction and date at which the weekly report is 
generated.  
 

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5. RESEARCH METHODS 
 
a. Study Design 
Study setting and population 
Routinely collected primary care data from up to ten GP practices will be extracted, to support 
passive surveillance. Additionally, this passive surveillance will be enhanced by the use of a card-
based ADR reporting system. ADR card will distributed to patients and upon return entered into the 
EHR system. 
 
EPI-FLU-055 VS UK study targets to follow vaccinated patients between 01/09/2017 and 30/11/2017.  
 
A customized ADR card will be used, which lists pre-defined categories of AEIs to be reported, 
similarly to study EPI-FLU-046 (2016/2017 season). A field will also be included to report AEIs not 
listed as well as a field to indicate that no AEI occurred within the 7 days-time window. Patients will 
be provided with the ADR cards and asked to complete the ADR cards with any AEIs occurring within 
7 days post vaccination and to return the cards to the GP practices not later than 14 days post-
vaccinationvi. 
 
GP practices ordering mainly GSK’s Fluarix Tetra vaccine for the 2017/18 season will be contacted to 
inquire about their interest in participating in this study. This may also include existing research 
contacts and networks of the University of Surrey. GP practices selection will ensure distributed 
location across England (in London, a Northern city, and rural settings in the North and South) and 
that the participating population spans different age strata, different levels of deprivation, different 
ethnic mix, different brand of computerised medical record systems, and different practice sizes. 
However, this will be tempered by our need to recruit before the start of the influenza immunisation 
season.  
 
GP practices will also be included based on their ability to comply with the protocol requirements 
(e.g., number of subjects registered, IT system used). GP practices will be reimbursed for their 
involvement in this study, according to the National Institute of Health Research (NIHR) guidelines for 
industry sponsored studiesvii.  
 
Of note, regulatory compliance studies can be registered with the National Institute for Health 
Research (NIHR) Clinical Research Network (CRN) Portfolioviii. Advice will be sought as to whether this 
study qualifies. 
 
Inclusion criteria 
1. Surveillance is planned to apply to vaccination between 1st September and 30th November 2017  
As this is a GP sentinel safety surveillance study, all individuals who receive influenza vaccination in 
the 10 participating GP practices between 1 September and 30 November 2017 are planned to be 
eligible for inclusion in the analysis. A date of 30 November allows for safety evaluation and potential 
signal detection early in the annual vaccination period and was selected for the 2016/2017 season.  
 

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2. GP Practices primary vaccine supplier will be preferentially GSK 
To fulfil its commitment, the Company will recruit from GP practices ordering preferentially GSK’s 
Fluarix Tetra. Therefore, an inclusion criterion is that GP practices use GSK as their principal vaccine 
supplier.  
 
Exclusion criteria 
Data will not be extracted from patients having “opted out” in respect of use of their medical data 
Registered patients who have explicitly opted out of data sharing will be excluded from the analysis. 
These patients will be identified by the opt-out codes within GP information systems. Patients will be 
informed of their option to opt-out via posters in the GP practices and information sheets 
accompanying the ADR cards.  
 
Data extraction and data management 
There are a number of GP EHR systems in use; the systems eligible for use in English primary care 
must be part of GP System of Choice (GPSoC)ix. GP practices have a single EHR system, which contains 
comprehensive data about patients, their medical history including treatment and all the aspects of 
providing General Medical Services (GMS – the standard NHS primary care provision) or other 
primary care schema. There are predominantly 3 brands; the market leader is Egton Medical 
Information Systems (EMIS), followed by The Phoenix Partnership (TPP) SystmOne, and In Practice 
Systems (INPS) Vision.  
 
Two data sources are considered for this study, i.e., the general practice EHR data (routine data 
collection) which provides the standard passive surveillance component and the ADR cards system 
completed by patients corresponding to the enhanced component. The ADR cards are being returned 
to the patient’s own practice to ensure confidentiality. Using a specific code, the data from these 
cards will be also coded into the EHR and uploaded weekly.  
1. General Practice EHR data recorded by the practice team. Weekly data about vaccine 
exposure, and any subsequent AEIs will be uploaded (anonymised) to University of Surrey. 
The EHR data contains both AEIs recorded by the practice team, as well as data reported to 
the GP practice on an ADR card by a vaccinated patient.  
2. ADR cards completed by patients. Among the 10 participating GP practices, patients who are 
vaccinated against influenza will be provided ADR cards. These ADR cards customised 
following practice feedback to match EMA requirements will be used to collect AEIs reported 
after the receipt of influenza vaccination.  
 
The method and governance procedure has been developed by the University of Surrey, using an 
approved provider, Apollo. If not applicable, alternatively, another approved data extraction supplier 
will be chosen, or the relevant study data will be directly extracted by the University of Surrey team 
using standard data extraction tools such as Morbidity Information Query Export Syntax (MIQUEST), 
tool sponsored by the Department of Health.  
 
Data extractions will be conducted in accordance with the Research Group’s standard operating 
procedures in data extraction, pseudonymisation, and transfer.  
 

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Pseudonymisation is a process that involves the removal of all personal identifiers – such as name, 
date of birth, etc. Furthermore, encrypted data will be kept during transfer and on a secure network 
that meets NHS Information Governance standards to minimise the risk of re-identification. 
Pseudonymisation is the standard approach for this type of surveillance. A legally binding definition 
of pseudonymisation has been introduced into European lawx on the recommendation of the 
European Data Protection Supervisor (EDPS)xi.  
 
Data are anonymised (strictly defined as “pseudonymised”) as near to source as possible. All data are 
strongly encrypted by a combination of symmetric and asymmetric encryption algorithms: Triple 
DES1 and RSA 10242 before transmission, and utilises public and private key pairs unique to each 
project. Pseudonymisation is applied at this stage to allow for backwards identification should there 
be a need to do so as part of an ethically approved study. 
 
For this study, it is required to link an adverse event to the vaccine (specific brand and batch number) 
administered. Pseudonymisation allows this without knowing any of the strong personal identifiers of 
that individual.  
 
All data processing and analysis in the study will be conducted within the secure IT environment of 
the Clinical Informatics Research Group, at the University of Surrey. The information security policies 
and procedures of the Research Group have been approved by the NHS Digital as meeting 
Information Governance Toolkit (IGT) standardsxii.  
 
The system is continuously being updated and modernized in respect of information processing, 
security and governance processes. The data are automatically extracted from the network of GP 
practices using a Simple Object Access Protocol (SOAP) web service, on a weekly basis. Data are 
uploaded to a secure Microsoft SQL server, and processed into aggregated tables; these are then 
linked to a pre-defined report structure using business intelligence software (Tableau Software, Inc. 
Seattle, WA, www.tablleau.com), to produce a weekly surveillance report in a timely manner. 
 
Using the EHR system, routine data as well as the content of encoded ADR cards will be extracted 
using methods that Surrey developed and deploys to extract RCGP RSC surveillance data. Sensitive 
coded data and free-text data will not be extracted. Only relevant EHR data for this study (e.g. 
disease or symptom, vaccination status and brand) coded by the GPs or other health professional will 
                                                          
 
 
1 This is also referred to as “3DES”, which is the commonly used name for the triple data encryption algorithm 
(TDEA, also written Triple DEA) symmetric-key block cipher. 
2 RSA stands for Rivest, Shamir and Aldeman who founded RSA Laboratories. They created large numbers with 
only two prime factors, a core component of the encryption process 

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be extractedxiii. Large volume of research that has come out of UK primary care is based on coded 
dataxiv. The quality of primary care data are such that the expectation will be able to detect frequent 
AEIs.xv  
 
GP practices will be required to use the relevant Read code for ADR notifications, when recording 
data from a returned card (Read Code: 
 - Adverse drug reaction notification). 
 
Relevant coded data will be extracted, however these are limited to the administration regime, AEIs 
and batch number fields of prescribing data.  
 
The following routinely collected patient data will be extracted for the study: 
 
Demographic information: age, gender, ethnicity, date of registration. 
 
Seasonal influenza vaccine information: date of administration, brand and batch number when 
available  
 
To understand any inequities in access according to level of social deprivation using Geographical 
Information System (GIS) methods, full postcodes will be immediately transformed into 
deprivation scores, using the Index of Multiple Deprivation, within GP computer systems upon 
date extraction. 
 
Primary care consultations following vaccination, any other markers of health care utilisation, 
and referral to further care. 
 
AEIs (Appendix 2) or any other reported AE recorded in the EHR.  
 
Data from at least one year prior to the start of the study to determine the category of UK CMO-
specified risk group for influenza vaccination the subjects belong to: 
 Life-style/risk factors, CMO risk status – e.g. BMI, smoking status. 
 Records of other diseases and long term conditions – e.g. chronic respiratory disease, 
chronic heart disease, chronic kidney disease, chronic liver disease, chronic neurological 
disease, diabetes, immunosuppression, pneumonia, etc. 
 Pregnancy status during the course of the study period. 
 
Sample size consideration  
The average practice size in England is 7,034 patientsxvi, an estimate of approximately 70,340 patients 
are expected to be registered (across the ten participating GP practices). Of note, in the period from 
September to December 2016, the seasonal influenza vaccine uptake for over 65 year olds was 
71.0%; for those in a clinical risk group aged 6 months to 65 years old, the uptake was 45.1%; and for 
pregnant women, it was 42.3%. The estimate of influenza vaccine uptake had been made estimated 
using the coverage estimates published by Public Health England (PHE)xvii. 
 
The eligible target population to be medically followed by the GPs would be estimated at 50,000 
subjects (approximately 5,000 per practice using a conservative approach). As per EMA 
Pharmacovigilance Risk Assessment Committee (PRAC)/Vaccine Working Party (VWP) request, at 
least 1,000 vaccinated subjects with 7 days of follow-up after vaccination are targeted to be enrolled. 
In this study, up to 5,000 vaccinated subjects with 7 days of follow-up after vaccination are expected 
to be enrolled. This sample size estimation sets out to estimate the probability to observe at least 
PPD

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one AEI in the study population and evaluate the level of “certainty” around this finding; over a 14 
week-surveillance period (01 September – 30 November 2017).  
The recruitment will be performed by GP practice and this might create a clustering effect. Based on 
the previous year study (EPI-FLU-046 study) the clustering effect is expected to be negligible. 
Similarly to last year, in the present study, the clustering effect was not accounted for in the power 
calculation but will be considered during the analysis. 
 
Cluster effect 
Cluster effect requires special statistical considerations when designing the study, and later when 
analysing the data. Groups tend to form because of certain selection factors, so individuals within the 
group tend to be more similar to each other with respect to important potential confounders than 
those selected truly at random.  
 
For instance, patients medically followed by the same GP are usually more prone to receive similar 
treatment for a given condition than those being treated for the same condition by different 
physicians. Furthermore, patients attending a single GP practice are likely to share similarities 
including geography, socioeconomic status, ethnic background, or age by virtue of the area they have 
all chosen to live In the same way, GPs who have chosen to work together are likely to share 
similarities 
 
Similarities, or homogeneity, between subjects in clusters reduces the variability of their responses, 
compared with that expected from a random sample. As a consequence, a compensatory increase in 
sample size is required to maintain power in studies characterised by a cluster effect and the degree 
of similarity of within clusters should also be assessed. The intra-cluster correlation coefficient (ICC) 
is a measure of the relatedness or similarity of clustered data. There are different methods of 
calculating the ICC, usually requiring a pilot study, but all compare the variance within clusters with 
the variance between clusters. 
 
Sample size calculation  
 
Table 1 shows the exact 95% CI, the probability of observing at least one AEI during the study period 
in the study cohort and the relative standard error (RSE) for a range of scenarios in term of cohort 
size, vaccine coverage and expected probability of AEIxviii. With an overall cohort size of about 
50,000 subjects medically followed by the participating GP practices, an estimated study period of 14 
weeks, a vaccine coverage of 2%, 5%, 10% or 20% and an expected probability of AEI varying from 
0,01% to 20%, the corresponding probability to observe at least one event in our study population 
varies from 9 to >99%, and the associated relative standard error varies from 2.0% to 316% 
depending on the scenario.  
 
Table 2 shows the evolution by week of the exact 95% CI, the cumulative probability of observing at 
least one AEI in the study cohort and the relative standard error (RSE) in the course of the study for a 
range of scenarios in term of cohort size, vaccine coverage and probability of AEI of 1%. With an 
overall sample size of a minimum of about 50,000 subjects medically followed by the enrolled GP 
practices, a follow-up period of 14 weeks, a vaccine coverage of 2%, 5%, 10% or 20%, the 

CONFIDENTIAL 
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corresponding cumulative probability to observe at least one event in our study population varies 
from 51% to 100% after week 1, and the associated relative standard error varies from 9% to 118% 
depending on the scenario.  

CONFIDENTIAL 
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Table 1 
Exact 95% Confidence Intervals, Relative Standard Error and probability 
to observe at least one AEI according to expected probabilities of 
occurrence of AEIxix 
Expected 
Population 
medically 
followed by 
the enrolled 
practices 
Vaccine 
coverage 
Vaccinated 
subjects 
Subjects 
with 
events 
Expected 
Proportion 
of subjects 
with ≥1 AEI 
reported 
Lower 
95%CL 
Upper 
95%CL 
Probability 
to observe 
≥1 AEI in the 
study 
population 
Associated 
Relative 
standard 
error (RSE) 
50000 
20% 
10000 
2000 
20.00% 
19.2% 
20.8% 
>99.99% 
2.0% 
50000 
20% 
10000 
1500 
15.00% 
14.3% 
15.7% 
>99.99% 
2.4% 
50000 
20% 
10000 
1000 
10.00% 
9.4% 
10.6% 
>99.99% 
3.0% 
50000 
20% 
10000 
500 
5.00% 
4.6% 
5.4% 
>99.99% 
4.4% 
50000 
20% 
10000 
400 
4.00% 
3.6% 
4.4% 
>99.99% 
4.9% 
50000 
20% 
10000 
200 
2.00% 
1.7% 
2.3% 
>99.99% 
7.0% 
50000 
20% 
10000 
100 
1.00% 
0.8% 
1.2% 
>99.99% 
9.9% 
50000 
20% 
10000 
10 
0.10% 
0.0% 
0.2% 
>99.99% 
31.6% 
50000 
20% 
10000 
9 
0.09% 
0.0% 
0.2% 
99.99% 
33.3% 
50000 
20% 
10000 
8 
0.08% 
0.0% 
0.2% 
99.97% 
35.3% 
50000 
20% 
10000 
7 
0.07% 
0.0% 
0.1% 
99.91% 
37.8% 
50000 
20% 
10000 
6 
0.06% 
0.0% 
0.1% 
99.75% 
40.8% 
50000 
20% 
10000 
5 
0.05% 
0.0% 
0.1% 
99.33% 
44.7% 
50000 
20% 
10000 
4 
0.04% 
0.0% 
0.1% 
98.17% 
50.0% 
50000 
20% 
10000 
3 
0.03% 
0.0% 
0.1% 
95.02% 
57.7% 
50000 
20% 
10000 
2 
0.02% 
0.0% 
0.1% 
86.47% 
70.7% 
50000 
20% 
10000 
1 
0.01% 
0.0% 
0.1% 
63.21% 
100.0% 
50000 
10% 
5000 
250 
5.00% 
4.4% 
5.6% 
>99.99% 
6.2% 
50000 
10% 
5000 
200 
4.00% 
3.5% 
4.6% 
>99.99% 
6.9% 
50000 
10% 
5000 
100 
2.00% 
1.6% 
2.4% 
>99.99% 
9.9% 
50000 
10% 
5000 
50 
1.00% 
0.7% 
1.3% 
>99.99% 
14.1% 
50000 
10% 
5000 
5 
0.10% 
0.0% 
0.2% 
99.33% 
44.7% 
50000 
10% 
5000 
4.5 
0.09% 
0.0% 
0.2% 
98.89% 
47.1% 
50000 
10% 
5000 
4 
0.08% 
0.0% 
0.2% 
98.17% 
50.0% 
50000 
10% 
5000 
3.5 
0.07% 
0.0% 
0.2% 
96.98% 
53.4% 
50000 
10% 
5000 
3 
0.06% 
0.0% 
0.2% 
95.03% 
57.7% 
50000 
10% 
5000 
2.5 
0.05% 
0.0% 
0.2% 
91.80% 
63.2% 
50000 
10% 
5000 
2 
0.04% 
0.0% 
0.1% 
86.47% 
70.7% 
50000 
10% 
5000 
1.5 
0.03% 
0.0% 
0.1% 
77.69% 
81.6% 
50000 
10% 
5000 
1 
0.02% 
0.0% 
0.1% 
63.22% 
100.0% 
50000 
10% 
5000 
0.5 
0.01% 
0.0% 
0.1% 
39.35% 
141.4% 
50000 
5% 
2500 
125 
5.00% 
4.2% 
5.9% 
>99.99% 
8.7% 
50000 
5% 
2500 
100 
4.00% 
3.3% 
4.8% 
>99.99% 
9.8% 
50000 
5% 
2500 
50 
2.00% 
1.5% 
2.6% 
>99.99% 
14.0% 
50000 
5% 
2500 
25 
1.00% 
0.6% 
1.5% 
>99.99% 
19.9% 
50000 
5% 
2500 
12.5 
0.50% 
0.3% 
0.9% 
>99.99% 
28.2% 
50000 
5% 
2500 
2.5 
0.10% 
0.0% 
0.3% 
91.80% 
63.2% 
50000 
5% 
2500 
2.25 
0.09% 
0.0% 
0.3% 
89.47% 
66.6% 
50000 
5% 
2500 
2 
0.08% 
0.0% 
0.3% 
86.48% 
70.7% 
50000 
5% 
2500 
1.75 
0.07% 
0.0% 
0.3% 
82.63% 
75.6% 
50000 
5% 
2500 
1.5 
0.06% 
0.0% 
0.3% 
77.70% 
81.6% 
50000 
5% 
2500 
1.25 
0.05% 
0.0% 
0.3% 
71.36% 
89.4% 
50000 
5% 
2500 
1 
0.04% 
0.0% 
0.2% 
63.22% 
100.0% 
50000 
5% 
2500 
0.75 
0.03% 
0.0% 
0.2% 
52.77% 
115.5% 
50000 
5% 
2500 
0.5 
0.02% 
0.0% 
0.2% 
39.35% 
141.4% 
50000 
5% 
2500 
0.25 
0.01% 
0.0% 
0.2% 
22.12% 
200.0% 
50000 
2% 
1000 
50 
5.00% 
3.7% 
6.5% 
>99.99% 
13.8% 
50000 
2% 
1000 
40 
4.00% 
2.9% 
5.4% 
>99.99% 
15.5% 
50000 
2% 
1000 
20 
2.00% 
1.2% 
3.1% 
>99.99% 
22.1% 
50000 
2% 
1000 
10 
1.00% 
0.5% 
1.8% 
>99.99% 
31.5% 
50000 
2% 
1000 
1 
0.10% 
0.0% 
0.6% 
63.23% 
99.9% 
50000 
2% 
1000 
0.9 
0.09% 
0.0% 
0.6% 
59.36% 
105.4% 

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Expected 
Population 
medically 
followed by 
the enrolled 
practices 
Vaccine 
coverage 
Vaccinated 
subjects 
Subjects 
with 
events 
Expected 
Proportion 
of subjects 
with ≥1 AEI 
reported 
Lower 
95%CL 
Upper 
95%CL 
Probability 
to observe 
≥1 AEI in the 
study 
population 
Associated 
Relative 
standard 
error (RSE) 
50000 
2% 
1000 
0.8 
0.08% 
0.0% 
0.6% 
55.08% 
111.8% 
50000 
2% 
1000 
0.7 
0.07% 
0.0% 
0.5% 
50.35% 
119.5% 
50000 
2% 
1000 
0.6 
0.06% 
0.0% 
0.5% 
43.13% 
129.1% 
50000 
2% 
1000 
0.5 
0.05% 
0.0% 
0.5% 
39.35% 
141.4% 
50000 
2% 
1000 
0.4 
0.04% 
0.0% 
0.5% 
32.97% 
158.1% 
50000 
2% 
1000 
0.3 
0.03% 
0.0% 
0.5% 
25.92% 
182.5% 
50000 
2% 
1000 
0.2 
0.02% 
0.0% 
0.4% 
18.13% 
223.6% 
50000 
2% 
1000 
0.1 
0.01% 
0.0% 
0.4% 
9.52% 
316.2% 
 

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Table 2 
Exact 95% Confidence Intervals, Relative Standard Error and cumulative 
probability to observe at least one AEI by week associated with a 
probability of occurrence of event of 1%  
Week 
Expected 
Population 
medically 
followed 
by the 
enrolled 
practices 
Cumulative 
Vaccine 
coverage 
after 14 
weeks 
Cumulative 
number of 
Vaccinated 
subjects 
Cumulative 
number of 
Subjects 
reported 
≥1 AEI 
Average 
Proportion 
of AEI 
reported 
Lower 
95%CL 
Upper 
95%CL 
Cumulative 
Probability 
to observe 
at least 
one event 
Associated 
Relative 
standard 
error (RSE) 
1 
50000 
20% 
714 
7 
1.00% 
0.4% 
2.0% 
99.92% 
37.2% 
2 
50000 
20% 
1428 
14 
1.00% 
0.5% 
1.6% 
>99.99% 
26.3% 
3 
50000 
20% 
2142 
21 
1.00% 
0.6% 
1.5% 
>99.99% 
21.5% 
4 
50000 
20% 
2857 
28 
1.00% 
0.7% 
1.4% 
>99.99% 
18.6% 
5 
50000 
20% 
3571 
35 
1.00% 
0.7% 
1.4% 
>99.99% 
16.7% 
6 
50000 
20% 
4285 
42 
1.00% 
0.7% 
1.3% 
>99.99% 
15.2% 
7 
50000 
20% 
5000 
50 
1.00% 
0.7% 
1.3% 
>99.99% 
14.1% 
8 
50000 
20% 
5714 
57 
1.00% 
0.8% 
1.3% 
>99.99% 
13.2% 
9 
50000 
20% 
6428 
64 
1.00% 
0.8% 
1.3% 
>99.99% 
12.4% 
10 
50000 
20% 
7142 
71 
1.00% 
0.8% 
1.3% 
>99.99% 
11.8% 
11 
50000 
20% 
7857 
78 
1.00% 
0.8% 
1.2% 
>99.99% 
11.2% 
12 
50000 
20% 
8571 
85 
1.00% 
0.8% 
1.2% 
>99.99% 
10.7% 
13 
50000 
20% 
9285 
92 
1.00% 
0.8% 
1.2% 
>99.99% 
10.3% 
14 
50000 
20% 
10000 
100 
1.00% 
0.8% 
1.2% 
>99.99% 
9.9% 
1 
50000 
10% 
357 
3 
1.00% 
0.2% 
2.4% 
97.23% 
52.7% 
2 
50000 
10% 
714 
7 
1.00% 
0.4% 
2.0% 
99.92% 
37.2% 
3 
50000 
10% 
1071 
10 
1.00% 
0.4% 
1.7% 
>99.99% 
30.4% 
4 
50000 
10% 
1428 
14 
1.00% 
0.5% 
1.6% 
>99.99% 
26.3% 
5 
50000 
10% 
1785 
17 
1.00% 
0.6% 
1.5% 
>99.99% 
23.6% 
6 
50000 
10% 
2142 
21 
1.00% 
0.6% 
1.5% 
>99.99% 
21.5% 
7 
50000 
10% 
2500 
25 
1.00% 
0.6% 
1.5% 
>99.99% 
19.9% 
8 
50000 
10% 
2857 
28 
1.00% 
0.7% 
1.4% 
>99.99% 
18.6% 
9 
50000 
10% 
3214 
32 
1.00% 
0.7% 
1.4% 
>99.99% 
17.6% 
10 
50000 
10% 
3571 
35 
1.00% 
0.7% 
1.4% 
>99.99% 
16.7% 
11 
50000 
10% 
3928 
39 
1.00% 
0.7% 
1.4% 
>99.99% 
15.9% 
12 
50000 
10% 
4285 
42 
1.00% 
0.7% 
1.3% 
>99.99% 
15.2% 
13 
50000 
10% 
4642 
46 
1.00% 
0.7% 
1.3% 
>99.99% 
14.6% 
14 
50000 
10% 
5000 
50 
1.00% 
0.7% 
1.3% 
>99.99% 
14.1% 
1 
50000 
5% 
178 
1 
1.00% 
0.0% 
3.1% 
83.29% 
74.6% 
2 
50000 
5% 
357 
3 
1.00% 
0.2% 
2.4% 
97.23% 
52.7% 
3 
50000 
5% 
535 
5 
1.00% 
0.3% 
2.2% 
99.54% 
43.0% 
4 
50000 
5% 
714 
7 
1.00% 
0.4% 
2.0% 
99.92% 
37.2% 
5 
50000 
5% 
892 
8 
1.00% 
0.4% 
1.8% 
99.99% 
33.3% 
6 
50000 
5% 
1071 
10 
1.00% 
0.4% 
1.7% 
>99.99% 
30.4% 
7 
50000 
5% 
1250 
12 
1.00% 
0.5% 
1.7% 
>99.99% 
28.1% 
8 
50000 
5% 
1428 
14 
1.00% 
0.5% 
1.6% 
>99.99% 
26.3% 
9 
50000 
5% 
1607 
16 
1.00% 
0.6% 
1.6% 
>99.99% 
24.8% 
10 
50000 
5% 
1785 
17 
1.00% 
0.6% 
1.5% 
>99.99% 
23.6% 
11 
50000 
5% 
1964 
19 
1.00% 
0.6% 
1.5% 
>99.99% 
22.5% 
12 
50000 
5% 
2142 
21 
1.00% 
0.6% 
1.5% 
>99.99% 
21.5% 
13 
50000 
5% 
2321 
23 
1.00% 
0.6% 
1.5% 
>99.99% 
20.7% 
14 
50000 
5% 
2500 
25 
1.00% 
0.6% 
1.5% 
>99.99% 
19.9% 
1 
50000 
2% 
71 
0 
1.00% 
0.0% 
5.1% 
51.01% 
118.1% 
2 
50000 
2% 
142 
1 
1.00% 
0.0% 
3.9% 
76.00% 
83.5% 
3 
50000 
2% 
214 
2 
1.00% 
0.1% 
3.3% 
88.36% 
68.0% 
4 
50000 
2% 
285 
2 
1.00% 
0.1% 
2.5% 
94.30% 
58.9% 
5 
50000 
2% 
357 
3 
1.00% 
0.2% 
2.4% 
97.23% 
52.7% 
6 
50000 
2% 
428 
4 
1.00% 
0.3% 
2.4% 
98.65% 
48.1% 
7 
50000 
2% 
500 
5 
1.00% 
0.3% 
2.3% 
99.34% 
44.5% 
8 
50000 
2% 
571 
5 
1.00% 
0.3% 
2.0% 
99.68% 
41.6% 
9 
50000 
2% 
642 
6 
1.00% 
0.3% 
2.0% 
99.84% 
39.3% 

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Week 
Expected 
Population 
medically 
followed 
by the 
enrolled 
practices 
Cumulative 
Vaccine 
coverage 
after 14 
weeks 
Cumulative 
number of 
Vaccinated 
subjects 
Cumulative 
number of 
Subjects 
reported 
≥1 AEI 
Average 
Proportion 
of AEI 
reported 
Lower 
95%CL 
Upper 
95%CL 
Cumulative 
Probability 
to observe 
at least 
one event 
Associated 
Relative 
standard 
error (RSE) 
10 
50000 
2% 
714 
7 
1.00% 
0.4% 
2.0% 
99.92% 
37.2% 
11 
50000 
2% 
785 
7 
1.00% 
0.4% 
1.8% 
99.96% 
35.5% 
12 
50000 
2% 
857 
8 
1.00% 
0.4% 
1.8% 
99.98% 
34.0% 
13 
50000 
2% 
928 
9 
1.00% 
0.4% 
1.8% 
99.99% 
32.7% 
14 
50000 
2% 
1000 
10 
1.00% 
0.5% 
1.8% 
>99.99% 
31.5% 
 
Statistical analyses 
All data processing and statistical analysis will be performed within the secure IT environment of the 
Clinical Informatics Research Group, at the University of Surrey. R and SAS Software will be used for 
the statistical analyses. Statistical analyses will be described in details in a statistical analysis plan, 
including the methodology to account for the clustering effect. 
Coded data will be interpreted by the creation of ontologies allowing to map to case-definitions, 
where available. However, no in depth descriptions required for case definition will be used such as 
in clinical trials. Meaning will be inferred from brief clinical coded information.  
 
Sequence of analysis 
 
Interim analysis 
Weekly safety report 
Weekly safety reports will be generated in order to be able to detect and potentially report any 
safety concerns in near real time manner. Analyses will be performed overall and by vaccine brand 
(Fluarix Tetra vs. others). 
 
The weekly incidence rates of AEIs within 7 days will be estimated as follows: 
 
The denominator will consist of the number of subjects vaccinated two weeks before the week of 
interest 
 
The numerator will encompass all subjects from the denominator reporting the AEI within 7 days 
following vaccination with a seasonal influenza vaccine 
 
Final analysis after end of the surveillance period 
Analyses of demographics/baseline characteristics 
Characteristics of the participating GP practices (Clinical Commissioning Group (i.e. the 
administrative health division used in England), North/South, Urban/Rural, IMD, number of 
registered subjects (rounded to the nearest 1000 to avoid the identification of the GP practices) will 
be tabulated.  
 
Demographic characteristics will be summarized using descriptive statistics: 
 
Frequency tables will be generated for categorical variables such as gender 
 
Mean, median, standard deviation, maximum and minimum will be provided for continuous data 
such as age. 

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Analyses of the primary objective 
All analyses will be carried out overall and by vaccine brand (Fluarix Tetra vs. others), by age strata 
(6 months to 5 years; 6 to 12 years; 13 to 17 years; ≥18-65 years; >65 years) and UK CMO-specified 
risk groups status (at risk/not at risk).  
 
The weekly incidence rates (per 100 subjects) of AEIs within 7 days will be estimated as follows: 
 
The denominator will consist of the number subjects vaccinated the week before the week of 
interest (so reaching up to 7 days of follow-up post vaccination during the week of interest) and 
having received an ADR card.  
 
The numerator will encompass all subjects from the denominator reporting the AEI within 7 days 
following vaccination with a seasonal influenza vaccine derived from the ADR card 
 
The cumulative incidence rates (per 100 subjects) of AEIs within 7 days will be estimated as follows: 
 
The denominator will be the number of subjects vaccinated at any point from study start up to 
the week before the week of interest (i.e. cumulatively since the beginning of the study) and 
having received an ADR card.  
 
The numerator will encompass all subjects from the denominator reporting the AEI within 7 days 
following vaccination with a seasonal influenza vaccine derived from the ADR card 
 
95% confidence interval (CI) will be computed on the estimated incidence rates. 
 
Analyses of the secondary objective 
All analyses will be carried out overall and by vaccine brand (Fluarix Tetra vs. others), by age strata 
(6 months to 5 years; 6 to 12 years; 13 to 17 years; ≥18-65 years; >65 years) and UK CMO-specified 
risk groups status (at risk/not at risk). 
 
The weekly incidence rates (per 100 subjects) of AEIs within 7 days will be estimated as follows: 
 
The denominator will be the number of all subjects vaccinated the week before the week of 
interest (so reaching up to 7 days of follow-up post vaccination during the week of interest) 
 
The numerator will encompass all vaccinated subjects reporting the AEI within 7 days following 
vaccination with a seasonal influenza vaccine using data entered in the EHR (i.e., AEIs derived 
from ADR card and medically attended AEIs) 
 
The cumulative incidence rates (per 100 subjects) of AEIs within 7 days will be estimated as follows: 
 
The denominator will be the number of all subjects vaccinated at any point from study start up to 
the week before the week of interest (i.e. cumulatively since the beginning of the study)  
 
The numerator will encompass all subjects from the denominator reporting the AEI within 7 days 
following vaccination with a seasonal influenza vaccine using data entered in the EHR (i.e., AEIs 
derived from ADR card and medically attended AEIs) 
 
95% CI will be computed on the estimated incidence rates. 
 

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Analyses of the tertiary objectives 
The percentage of subjects vaccinated with a seasonal influenza vaccine during the study vaccination 
period in the 10 participating GP practices will be tabulated by vaccine brand, age categories and 
CMO-specified risk groups. 
 
Completeness of seasonal influenza vaccination data in the EHR will be assessed by computing the 
percentage of subjects with data on influenza vaccines recorded in EHR (date of vaccination, vaccine 
brand, vaccine batch number). 
 
Timeliness of vaccination data in the EHR will be assessed as follows: 
 
Time interval in days between the seasonal influenza vaccination dates and the dates at which 
the records were entered in EHR will be summarized using descriptive statistics (mean, standard 
deviation, median, range, first and third quartile). 
 
Timeliness of AEI reporting in EHR (medically attended AEs and from the card-based ADR reporting 
system) will be assessed as follows: 
 
Time interval in days between the first onset date of AEIs within the 7 days post-vaccination 
period and the dates at which the records were entered in EHR will be summarized using 
descriptive statistics (mean, standard deviation, median, range, first and third quartile). 
 
The percentage of subjects who received and who returned the ADR card will be tabulated by 
vaccine brand. 
 
Sensitive analyses  
The cumulative incidence rates (per 100 subjects) of AEIs within the 7 days post-vaccination period 
derived from the ADR card, with 95%CI, will also be tabulated for subjects who returned the ADR 
card. 
 
The cumulative incidence rates (per 100 subjects) of AEIs within the 7 days post-vaccination period, 
with 95%CI, will also be tabulated for subjects from the RCGP RSC network of GP practices. 
 

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Schematic description of process from site selection to data extraction  
 
A series of flow charts have been developed to facilitate understanding of recruitment flow, the 
training and other process that have to be developed as participating GP practices, and to explain the 
data flow in the practice. 
 
The flow charts are presented below 
 

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The added value of combining those two systems is to be able to enhance the collection of AEIs 
(using the customized cards) and ensure further that the data is comprehensively collected (using the 
routine EHR system).  
 
Flow chart summarizing the automated data extraction process performed using Apollo system 
 
 
Safety reporting, including routine pharmacovigilance 
Safety reporting 
An AE is any untoward medical occurrence in a patient or clinical investigation subject, temporally 
associated with the use of a medicinal product, whether or not considered related to the medicinal 
product.  
 
An AEI is any untoward medical occurrence which follows immunisation and which does not 
necessarily have a causal relationship with the usage of the vaccine. The AEIs following vaccination 
may be any unfavourable or unintended sign, abnormal laboratory finding, symptom or disease. EMA 
recommendations regarding AEIs collected after vaccination in this study are specified in Appendix 2.  
 
A Serious Adverse Event (SAE) is defined as any adverse event which is fatal, life threatening, 
disabling or incapacitating, requires in-patient treatment or prolongs existing hospitalization, is a 
congenital anomaly in the off-spring of the patient or which may require intervention to prevent the 
previously stated outcomes.  
 
It will be clearly communicated to participating GP practices that the study does not replace 
reporting of AEs/SAEs that should occur as part of routine practice as specified by their local 
regulations. GPs should continue to report any AEs/SAEs they would typically report using the 
mechanisms routinely used in their GP practices. Therefore, although the data collected for this study 

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is primarily safety-related, reporting mechanisms of AEs to regulatory authorities should not be 
altered and is to continue according to each practice’s standards. 
 
In addition, if the team at the University of Surrey becomes aware of an SAE experienced by a study 
participant that is deemed to be related to a GSK flu vaccine, the SAE should be reported to GSK 
within 24 hours of awareness using the GSK reporting forms (forms are provided in the contract 
agreement). If GSK deems additional information necessary, request of additional information will be 
sent through the team at the University of Surrey.  
Study Contact for Reporting SAEs 
 GSK Biologicals Clinical Safety & Pharmacovigilance 
 Fax: +
 or 
 
Email:
 
 
PPD
PPD
PPD

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6. PROJECT MANAGEMENT 
 
This study is conducted within the University of Surrey’s formal frameworks for information and 
research governance. In addition, all externally funded projects and collaborative projects with 
external partners are supported and guided by the University’s Research and Enterprise Support 
(RES) service. RES ensures that university-supported projects are financially viable, and that legal 
issues of knowledge transfer and intellectual properties are addressed. The project team is 
supported by IT services dedicated to the Faculty and to the Department of Clinical and Experimental 
Medicine. At the University of Surrey, secure analysis servers are optimised for routine healthcare 
data processing, to provide faster deliveries for our projects. 
 
The project is accountable to the Project Steering Board, with the day-to-day operational issues 
managed by the Project Operational team. 
 
Project Steering Board 
The Steering Board will meet bi-annually to receive regular and exceptional reports, including 
reporting of adverse events, from the Operational Team, monitor progress against set milestones, 
and ensure that resources and support are available to enable the successful delivery of the project 
within the funding agreement. In the event of a report of adverse incidents, the Project Steering 
Board will co-ordinate an effective management of the adverse incidents in line with local and 
national guidance, and if appropriate, onward reporting to the University, GSK, external partners or 
external research and information governance authorities. 
 
The Project Steering Board consists of senior academics from the University of Surrey and 
collaborating universities, a patient representative, senior practitioners involved in the domain of 
influenza vaccine, and a representative of the GSK of the study. 
Steering Board Member  
Role and Organisation 
Prof 
 
Principal Investigator, University of Surrey 
Dr 
 
Research Representative, GSK 
  
Statistician, GSK 
After practice recruitment 
GP/Practice representative 
After practice recruitment 
Patient Representative 
Dr 
 
Project Manager, University of Surrey 
 
Project operational team 
The operational team is responsible for the completion of the project objectives against set 
milestones, and submit regular and ad-hoc reports to the Project Steering Board. The Team will meet 
fortnightly in person and/or via teleconference, particularly in the early stages of the project, to 
ensure the project meets with the milestones agreed for the project. 
 
PPD
PPD
PPD
PPD

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The Operational Team consists of research staff, the project manager and the Principal Investigator 
of this project: 
Team Member  
Lead responsibility in the project and 
organisation 
Prof 
 
Senior Clinical Lead, University of Surrey 
Dr 
 
Project Manager, University of Surrey 
Dr 
 
Research Representative, GSK 
 
Statistician, GSK 
  
Senior Database developer, University of Surrey 
Dr 
 
Senior Research Fellow, University of Surrey 
 
Research Fellow, University of Surrey 
 
Practice Liaison Officer 
 
These arrangements are standard University of Surrey research and surveillance governance 
requirements for projects. 
 
Peer review of the study protocol 
In May 2017, the draft study protocol had been reviewed by GSK’s peer review committee and in 
parallel by the Surrey University Peer review committee consisting of pharmacologists, general 
practitioners and lay advisors as well as patients’ association representative. 
 
Patient involvement 
Patients were involved in the protocol review as part of the Surrey university Peer review process. 
Their comments were taken into consideration in the development of the protocol to help ensure its 
acceptability to patients. A patient representative is intended to be part of the steering committee. 
 
PPD
PPD
PPD
PPD
PPD
PPD
PPD
PPD

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7. ETHICAL CONSIDERATIONS 
 
The primary purpose of this study is to work with practitioners, governance experts, and a 
commercial MAH to develop robust process for the annual enhanced safety surveillance of seasonal 
influenza vaccines recommended by the EMA. 
 
The principal ethical issue is concerned with the protection and use of anonymised patient level 
information for the purpose of surveillance of safety of seasonal influenza vaccination as 
recommended by the EMA. NHS guidelines specify that a Section 251 approval is required when 
conducting research using anonymised patient level data, without individual level patient consent; 
approval is also dependent on the requesting institution meeting specific requirements of 
information governance, which the University of Surrey secure network exceeds. The protection and 
use of anonymised patient level information is addressed more fully in the next section: information 
governance considerations. 
 
The University of Surrey team will seek approval from the University Ethics Review Committee. In 
addition, the formal opinion of the Proportional Review System of the National Ethics Review Service 
will be sought regarding the need for NHS Research Ethics Committee (REC) approval. 
 
‘Defining 
Research’ 
(http://www.hra.nhs.uk/documents/2013/09/defining-research.pdf), 
the 
National Research Ethics Service (NRES) guidance suggests that surveillance does not require formal 
review by a Research Ethics Committee. The research team will however seek an opinion from the 
NRES’s Proportional Review system to check if formal approval from a NHS Research Ethics 
Committee (REC) is needed prior to the commencement of the study, as well as Section 251 
approvalxx. If the proportional review suggests that a full NHS REC review is necessary, then 
applications will be submitted to the REC as well as the Clinical Research Network (CRN) and, if 
advised, the Confidential Advisory Group (CAG) for formal approval for Section 251 of NHS Act 2006 
and Health Service (Control of Patient Information) Regulations 2002 exemptions. 
 
Section 251 of the Health and Social Care Act 2001, allowed the Secretary of State to set aside the 
common law duty of confidentiality for defined medical purposes. Surveillance is generally taken to 
be one of the defined medical purposes for which data can be used. As it has not been tested 
whether the Health and Social Care Act is retrospective data are generally not extracted for periods 
prior to that Act, without a clear need generally approved by an ethics committee.  
 
This study is piloting enhanced passive surveillance as recommended by EMA. Such surveillance is 
not expected to require taking active consent. Generally, collecting surveillance data in an 
anonymised form is lawful, acceptable as use of data for public health purposes is recognised to be in 
the public interest. Based on our experience with the EPI-FLU-045 & EPI-FLU-046 pilot studies, the 
expectation is that this investigation meets the Health Research Authority’s definition of Service 
Evaluationxxi. The expectation is that the current enhanced passive surveillance (EPI-FLU-055 pilot 
studies) falls under the same criteriaxxi.  

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8. INFORMATION GOVERNANCE CONSIDERATIONS 
 
The Clinical Informatics Research Group at the University of Surrey has worked with routinely 
collected healthcare data in a number of research and evaluation projects for over 15 years. The 
Research Group works within the research and Information Governance frameworks for health and 
social care in the United Kingdom, and is compliant with the University’s best practice standards. The 
University of Surrey is registered with the Information Commissioner’s Office Data Protection 
Register, and is compliant with the Data Protection Act, and other legislations. 
 
In addition, the Research Group reviewed its departmental information governance policies and 
procedures, against the requirements of the NHS IGT for Hosted Secondary Use Team/ Project, 
Version 12xxii. The review was approved by the Health and Social Care Information Centre, and was 
deemed satisfactory to support application to Confidentiality Advisory Group or the Data Access 
Advisory Group. 
 
In line with the principle of the Data Protection Act 1998, data subjects will be informed of the uses 
of their data in this study. Participating GP practices will be asked to display project information in 
their website, and project information posters in reception areas, from when the practice has 
consented to take part in the study and until the study is completed.  
 
The project information will specifically refer to the right of the patients to opt out if they do not 
wish their data to be included in this study. The codes in the data indicating that a patient does not 
wish to have their record available for research will be carefully considered. However, the number of 
patients within a practice who have chosen to opt out will be reported.  
 
No Personally Identifiable Information (PII) such as NHS numbers, postcodes, dates of birth, etc. will 
be available to GSK, third parties, or disclosed in publications. Additionally, no patient level data will 
be sent to GSK to remove any possibility that any individual patient might be re-identified. GSK will 
also be blind to practice identities, and the locality at which any AEI occurs; other than where the 
patient gives consent, or their own chooses to report any condition in line with best practice. 

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9. DISSEMINATION AND PUBLIC REGISTER DISCLOSURE 
 
The final agreed protocol of this study is intended be published in a peer review open access journal. 
 
The outputs from the research will be disseminated primarily through peer review papers within the 
domains of primary care, surveillance, vaccines, and infectious diseasesxxiii xxiv. Findings will be 
presented at relevant seminars and conferences. 
 
The University of Surrey, in accordance with GSK policy, will post a summary of the study protocol 
and subsequently results within 12 months of study completion and following review and comment 
by GSK on GSK’s Clinical Study Register, accessible at http://www.gsk-clinicalstudyregister.com and 
at www.clinicaltrials.gov.  
 

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10. APPENDIX 
 
Appendix 1 
Data extraction is by automated routine as detailed below: 
Currently, data are extracted by weekly bulk upload. Apollo extracts data using the Apollo automated 
extraction system. Communication is via a SOAP (Simple Object Access Protocol) web service, no 
special firewall configuration is needed.  
At the point of the data drop the data are filtered and processed through a pseudonymisation 
package encrypting the NHS number. All data are strongly encrypted by a combination of symmetric 
and asymmetric encryption algorithms: Triple DES and RSA 1024 before transmission, and utilises 
public and private key pairs unique to each project. 
Pseudonymisation is applied at this stage to allow for backwards identification should there be a 
need to do so as part of an ethically approved study. However, the application of pseudonymisation 
at this stage also allows the same algorithm to be applied to additional data sources which may be 
linked data in future years; for example, enabling the linkage of patients’ primary care and hospital 
data without the need to identify a person in the process of conducting this linkage. 
Once the data are extracted, they are transferred using the above methodology to the custom built 
Data Warehouse located within University of Surrey for analysis in secure networks that meet the 
NHS Information Governance toolkit level 2 standard. These arrangements may change in the future 
in accordance with developments in technology. 
 

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Appendix 5 
Information Sheet – GP  
 

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Appendix 6 
Information Sheet - Patients  
 

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Appendix 7 
Practice feedback sample  
 

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xv de Lusignan S, van Weel C. The use of routinely collected computer data for research in primary care: 
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xxiii MRC Policy and Guidance on Sharing of Research Data from Population and Patient Studies Revised January 
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