A Case-Based Monitoring Approach to Evaluate Safety of COVID-19 Vaccines in a Partially Integrated Health Information System: A Study Protocol

Front Pharmacol. 2022 Jul 13:13:834940. doi: 10.3389/fphar.2022.834940. eCollection 2022.

Abstract

In response to Coronavirus disease 2019 (COVID-19) global pandemic, various COVID-19 vaccines were rapidly administered under emergency use authorization. Rare outcomes associated with COVID-19 vaccines might be less likely to be captured in clinical trials, leading to a knowledge gap in real-world vaccine safety. In contrast with high-income countries, many low-to-middle income countries have limited capacity to conduct active surveillance, owing to the absence of large and fully-integrated health information databases. This paper describes the study protocol, which aims to investigate risk of prespecified adverse events of special interests following COVID-19 vaccination in a partially integrated health information system with non-shareable electronic health records. The SAFECOVAC study is a longitudinal, observational retrospective study of active safety surveillance using case-based monitoring approach. This involves linkage of several administrative databases and hospitalization data monitoring to identify adverse events of special interests following administration of COVID-19 vaccines in Malaysia. The source population comprises of all individuals who received at least one dose of COVID-19 vaccine. Self-controlled design and vaccinated case-coverage design will be employed to assess risk of adverse events of special interests and determine the association with vaccine exposure. Data on vaccination records will be obtained from the national COVID-19 vaccination register to identify the vaccination platforms, doses and the timing of vaccinations. The outcome of this study is hospitalization for the adverse events of special interests between March 2021 and June 2022. The outcomes will be obtained through linkage with hospital admission database and national pharmacovigilance database. Findings will provide analysis of real-world data which can inform deliberations by government and public health decision makers relative to the refinement of COVID-19 vaccination recommendations.

Keywords: COVID-19; active surveillance; adverse events of special interest (AESI); real world evidence; safety surveillance; self-controlled; vaccine safety.