Current Approaches in Postapproval Vaccine Safety Studies Using Real-World Data: A Systematic Review of Published Literature.

PURPOSE

Well-designed observational postmarketing studies using real-world data (RWD) are critical in supporting an evidence base and bolstering public confidence in vaccine safety. This systematic review presents current research methodologies in vaccine safety research in postapproval settings, technological advancements contributing to research resources and capabilities, and their major strengths and limitations.

METHODS

A comprehensive search was conducted using PubMed to identify relevant articles published from January 1, 2019, to December 31, 2022. Eligible studies were summarized overall by study design and other study characteristics (eg, country, vaccine studied, types of data source, and study population). An in-depth review of select studies representative of conventional or new designs, analytical approaches, or data collection methods was conducted to summarize current methods in vaccine safety research.

FINDINGS

Out of 977 articles screened for inclusion, 135 were reviewed. The review shows that recent advancements in scientific methods, digital technology, and analytic approaches have significantly contributed to postapproval vaccine safety studies using RWD. "Near real-time surveillance" using large datasets (via collaborative or distributed databases) has been used to facilitate rapid signal detection that complements passive surveillance. There was increasing appreciation for self-controlled case-only designs (self-controlled case series and self-controlled risk interval) to assess acute-onset safety outcomes, artificial intelligence, and natural language processing to improve outcome accuracy and study timeliness and emerging artificial intelligence-based analysis to capture adverse events from social media platforms.

IMPLICATIONS

Continued development in the area of vaccine safety research methodologies using RWD is warranted. The future of successful vaccine safety research, especially evaluation of rare safety events, is likely to comprise digital technologies including linking RWD networks, machine learning, and advanced analytic methods to generate rapid and robust real-world safety information.