The descriptive epidemiology of Adverse Events of Special Interest for COVID-19 and other vaccines in the general population and after seasonal influenza and COVID-19 disease

Date of Approval
Application Number
20_000211
Technical Summary

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.

Health Outcomes to be Measured

1.Neurologic/ neuropsychiatric (Guillain Barré Syndrome (GBS), Miller Fisher syndrome, Encephalomyelitis (include Acute disseminated encephalomyelitis (ADEM)), Meningitis, Encephalitis, Aseptic meningitis, Encephalitis / Encephalomyelitis, Meningoencephalitis, Anosmia, ageusia, Generalized convulsion, Transverse myelitis, Narcolepsy, Bell’s palsy, Other Cranial nerve disorders (other than VII)); 2.Immunologic (Acute aseptic arthritis, Anaphylaxis, Vasculitis, Arthritis, Type I Diabetes); 3. Hematologic (Thrombocytopenia, Thrombotic thrombocytopenia purpura, Idiopathic thrombocytopenic purpura (ITP), Coagulation disorder: Deep vein thrombosis , Pulmonary embolus , Cerebrovascular stroke, Limb ischemia, Hemorrhagic disease); 4.Cardiovascular system (Acute cardiac injury :Microangiopathy, Heart failure and cardiogenic shock, Stress cardiomyopathy, Coronary artery disease, Arrhythmia, Myocarditis, pericarditis, Ischemic stroke, intracerebral haemorrhage, Cerebral venous sinus thrombosis and related disorders, Intracranial venous thrombosis and related disorders, CNS vasculitis (ANCA-associated vasculitis, Takayasu Arteritis and Giant Cell Arteritis)); 5.Respiratory system (Acute respiratory distress syndrome, spontaneous pneumothorax); 6.Dermatologic (Single organ cutaneous vasculitis, Erythema multiforme, Chilblain-like lesions); and 7.Others (Rhabdomyolysis).

Collaborators

Daniel Prieto-Alhambra - Chief Investigator - University of Oxford
Xintong Li - Corresponding Applicant - University of Oxford
Antonella Delmestri - Collaborator - University of Oxford
Daniel Dedman - Collaborator - CPRD
Danielle Robinson - Collaborator - University of Oxford
Edward Burn - Collaborator - Oxford University Hospitals
Xintong Li - Collaborator - University of Oxford
Zara Cuccu - Collaborator - CPRD

Linkages

HES Admitted Patient Care;ONS Death Registration Data;Patient Level Index of Multiple Deprivation