Real-time benefit-risk assessment of influenza vaccine exposure across a nationally representative primary care sentinel network
Lead Research Organisation:
University of Oxford
Department Name: Primary Care Health Sciences
Abstract
Key terms:
Vaccine administration and dosage, vaccine adverse effects, human influenza, influenza vaccine, telemedicine, vaccine-preventable diseases, immunologic surveillance, sentinel surveillance, public health informatics, clinical informatics
Innovation within influenza vaccination has enabled the release of a number of flu vaccine types to counter seasonal flu each year, each with their own unique characteristics. This can span live attenuated vs inactivated methodologies, egg vs cell cultures, trivalent (protective against three strains) vs quadrivalent (protective against four strains) composition and adjuvanted (added ingredients to maximise immune response) vs high-dosage delivery methods. It is necessary to monitor and compare how these vaccines and their characteristics perform each season; doing so in real-time can even lead to in-season rather than post-season composition changes, maximising the effectiveness of flu response both nationally and internationally.
Equally, the unprecedented speed of vaccine development achieved this year to counteract critical illness associated with the novel coronavirus (COVID-19) has also been accompanied with intense levels of scrutiny related to their effectiveness over time and associated adverse events of interest (AEI). Influenza vaccines have never amassed this level of public attention, hesitation and politicisation by comparison. In this context, it will be more important than ever to monitor how and when protectiveness of vaccines wane and to identify at-risk demographics for both adverse events of interest and vaccine hesitancy; differentiating these metrics by vaccine brands, batches and characteristics will be key for informing both disease preparedness and response going forwards.
As such, informed by robust original research and rigorous literature review, the primary output of this work will be to develop a real-time influenza vaccine benefit-risk (V-BR) platform capable of monitoring the efficacy, uptake and adverse effects associated with different types and brands of influenza vaccines. This platform will leverage data from the Oxford-Royal College of General Practitioners Research and Surveillance Centre (twice weekly extracts of ~ 15,000,000 pseudoanonymised patient records linked to UKHSA virology databases and hospital and death records), the expertise of its underpinning Clinical Informatics and Outcomes Research Group and a combination of daily extracts and direct questionnaire data supplied by the EMIS 'Patient Access' app (~ 10 million registered users).
Benefitting from both the 55-year strong legacy of RCGP RSC surveillance, dashboards and observatories and the richness of data offered through patient experience surveys hosted on the Patient Access app, this studentship will be uniquely positioned to generate a world-class influenza V-BR platform. This work also aligns with MRC research priorities (most notably health informatics, disease prevention, stratified medicine and epidemiology and public health) due to its ability to provide insight into how influenza vaccine uptake, adverse effects and efficacy may differ between population groups. Further, by illuminating where vaccine uptake is lowest, where efficacy wavers and/or where post-vaccination adverse effects are most pronounced, this platform will contribute to efforts to reduce vaccine hesitancy and refusal - a WHO and ECDC priority. Finally, the individual differences that may be revealed by this platform will also make a strong case for exploring a more personalised approach to vaccination in the future.
As well as becoming responsible for the ongoing maintenance and actioning of this platform, by the studentship's end the student will have established themselves as an expert within the field of vaccine research, will have upskilled in R, SQL, Tableau and PowerBI and will have authored or co-authored a range of high-level research outputs and publications
Vaccine administration and dosage, vaccine adverse effects, human influenza, influenza vaccine, telemedicine, vaccine-preventable diseases, immunologic surveillance, sentinel surveillance, public health informatics, clinical informatics
Innovation within influenza vaccination has enabled the release of a number of flu vaccine types to counter seasonal flu each year, each with their own unique characteristics. This can span live attenuated vs inactivated methodologies, egg vs cell cultures, trivalent (protective against three strains) vs quadrivalent (protective against four strains) composition and adjuvanted (added ingredients to maximise immune response) vs high-dosage delivery methods. It is necessary to monitor and compare how these vaccines and their characteristics perform each season; doing so in real-time can even lead to in-season rather than post-season composition changes, maximising the effectiveness of flu response both nationally and internationally.
Equally, the unprecedented speed of vaccine development achieved this year to counteract critical illness associated with the novel coronavirus (COVID-19) has also been accompanied with intense levels of scrutiny related to their effectiveness over time and associated adverse events of interest (AEI). Influenza vaccines have never amassed this level of public attention, hesitation and politicisation by comparison. In this context, it will be more important than ever to monitor how and when protectiveness of vaccines wane and to identify at-risk demographics for both adverse events of interest and vaccine hesitancy; differentiating these metrics by vaccine brands, batches and characteristics will be key for informing both disease preparedness and response going forwards.
As such, informed by robust original research and rigorous literature review, the primary output of this work will be to develop a real-time influenza vaccine benefit-risk (V-BR) platform capable of monitoring the efficacy, uptake and adverse effects associated with different types and brands of influenza vaccines. This platform will leverage data from the Oxford-Royal College of General Practitioners Research and Surveillance Centre (twice weekly extracts of ~ 15,000,000 pseudoanonymised patient records linked to UKHSA virology databases and hospital and death records), the expertise of its underpinning Clinical Informatics and Outcomes Research Group and a combination of daily extracts and direct questionnaire data supplied by the EMIS 'Patient Access' app (~ 10 million registered users).
Benefitting from both the 55-year strong legacy of RCGP RSC surveillance, dashboards and observatories and the richness of data offered through patient experience surveys hosted on the Patient Access app, this studentship will be uniquely positioned to generate a world-class influenza V-BR platform. This work also aligns with MRC research priorities (most notably health informatics, disease prevention, stratified medicine and epidemiology and public health) due to its ability to provide insight into how influenza vaccine uptake, adverse effects and efficacy may differ between population groups. Further, by illuminating where vaccine uptake is lowest, where efficacy wavers and/or where post-vaccination adverse effects are most pronounced, this platform will contribute to efforts to reduce vaccine hesitancy and refusal - a WHO and ECDC priority. Finally, the individual differences that may be revealed by this platform will also make a strong case for exploring a more personalised approach to vaccination in the future.
As well as becoming responsible for the ongoing maintenance and actioning of this platform, by the studentship's end the student will have established themselves as an expert within the field of vaccine research, will have upskilled in R, SQL, Tableau and PowerBI and will have authored or co-authored a range of high-level research outputs and publications
Organisations
People |
ORCID iD |
| Simon De Lusignan (Primary Supervisor) |
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| MR/R015708/1 | 30/09/2018 | 29/09/2025 | |||
| 2597950 | Studentship | MR/R015708/1 | 30/09/2021 | 29/09/2025 |