University of Oxford

Academic Institute

Daniel Prieto-Alhambra - Chief Investigator - University of OxfordXintong Li - Corresponding Applicant - University of OxfordAntonella Delmestri - Collaborator - University of OxfordDanielle Robinson - Collaborator - University of OxfordEdward Burn - Collaborator - Oxford University HospitalsXintong Li - Collaborator - University of Oxford

CPRD893

Caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the global pandemic of the coronavirus disease of 2019 (COVID-19) had resulted in over 1 million cases and 50 thousand deaths in the United Kingdom, and over 50 million reported cases and over 1.2 million deaths worldwide. (Data as of 10-November-2020) While there are now hundreds of vaccine trials globally, checks on their safety will be paramount, and monitoring of side effects will be necessary. The ability to identify and understand the background rates of Adverse Events of Special Interests (AESI) for COVID-19 vaccines is critical for future vaccine safety surveillance.

The global pandemic of COVID-19 has resulted in over 50 million reported cases and over 1.2 million deaths globally. Meanwhile, hundreds of vaccines are in clinical evaluation and some show clinical efficacy. While planning for the large-scale immunization program, it is important to understand the potential adverse events after vaccination or viral infections. Electronic health records, including CPRD, have been increasingly used in safety studies. The ability to and the reliability of capturing the adverse events using suitable phenotyping algorithms in such databases are the foundation in conducting these studies. We will firstly identify the phenotyping algorithms of the AESI used in other studies, or develop the phenotypes if no existing one is found. Then we will evaluate the performance of these phenotypes using the diagnostic and evaluation tool that had been previously developed. The second objective is to estimate the background incidence rates (IR) of the AESI among the general population from the year 2006 to 2019. Individuals who were observed for at least 365 days during the study period will be included. The numerator of the incidence rate will be the total number of incident cases in each year, and the denominator will be person-time at risk each year. We will also estimate the IR among patients who were diagnosed or received a positive test for COVID-19 (after 31-Jan-2020) or seasonal influenza. We will apply different algorithms in identifying both the exposures and the outcomes. A tested negative cohort will be identified using primary care data as a negative control group as well. We will then carry on a self-controlled case series analysis to exam the association between developing the AESI and COVID-19 or influenza infections using the conditional Poisson regression model. A set of sensitivity analyses will be conducted to test the assumptions of this method.

19/01/2021

Release