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  1. Advancing Regulatory Science

Real-World Population Characteristics, Safety and Effectiveness of COVID-19 Vaccines

CERSI Collaborator: University of California San Francisco: Atul Butte, MD, PhD; Michelle Wang, PharmD; Rohit Vashisht, PhD; Thomas Peterson, PhD

FDA Collaborators: Center for Biologics Evaluation and Research: Barbee Whitaker, PhD; Artur Belov, PhD; Hussein Ezzeldin, PhD; Steven Anderson, PhD, MPP; Hui-Lee Wong, PhD; Jane Mutanga, PhD

CERSI Subcontractors: IBM collaborators for FDA Biologics Effectiveness and Safety (BEST) Initiative

Project Start Date: 09/2021

Regulatory Science Challenge

Analysis of real-world data (RWD) sources, such as patient electronic health records (EHRs), can support FDA’s mission to assess safety and effectiveness of COVID-19 vaccines after they are approved. Developing and applying methods for real-time analysis of COVID-19 vaccination RWD may reduce the time required to generate safety and effectiveness information on COVID-19 vaccines. There is an immediate need for developing real-time data generation and analysis methods for COVID-19 vaccine data, which is vital to inform COVID-19 vaccine-related policies.

Project Description and Goals

The goal of this study is to analyze COVID-19 related RWD for potential adverse events (AE) after COVID-19 vaccination. UCSF-Stanford CERSI will collaborate with partners within the FDA Biologics Effectiveness and Safety (BEST) initiative and IBM to characterize the association between adverse events (AE) and COVID-19 vaccine exposure. This will be done by analyzing EHR data collected from over 8 million patients accessing care at the University of California Health (UC Health) system. Researchers will determine the extent to which the UC Health population can be used to understand COVID-19 vaccine-associated AE in the general population. Frequency of AE prior to the COVID-19 pandemic, and after vaccination will be determined. EHR data will also be used to develop novel methods for identifying and characterizing AE. Demographics of the vaccinated UC Health population will be described using characteristics such as sex, age, and race. Methods will be used to describe the socioeconomic profile of the UC Health population and to assess the impact of socioeconomic conditions on the frequency of AE post-COVID-19 vaccination.


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