Active post-vaccination surveillance is vital for ensuring vaccine safety, particularly in monitoring Adverse Events of Special Interest (AESIs). This scoping review synthesises evidence on methodologies employed in active surveillance studies, with a focus on influenza and COVID-19 vaccines. Literature was identified via PubMed, Embase, Web of Science, Scopus, Google Scholar, and manual searches, using key terms including influenza, COVID-19, vaccine, and AESI. Two authors independently screened studies and extracted data on study characteristics, methods, and outcomes. Findings were synthesised narratively and presented in tables and figures, following the PRISMA-ScR guideline. Of 427 included studies, most were published after 2020 (74.0%) and focused on COVID-19 vaccines (69.3%), particularly mRNA platforms (51.3%). The majority were conducted in North America and Europe, with 85.5% from high-income countries; multinational studies accounted for 6.6%, and single-centre studies with national or subnational coverage for 63.0%. Cohort designs predominated (40.5%), mostly retrospective (74.2%), utilising registries (24.0%) and electronic health records (22.4%), including artificial intelligence for signal detection and prediction (2.7%). Nearly half (49.6%) linked multiple data sources, though outcome verification was reported in fewer than half (45.9%). Incidence rates (16.5%) and risk/hazard ratios (14.8%) were the most reported measures. Neurological (21.8%) and cardiac (21.3%) AESIs, particularly Guillain–Barré Syndrome and myocarditis, were most frequently investigated. Active surveillance for vaccine safety has increased but remains concentrated in high-income countries. Methodological approaches to detection, verification, and validation vary widely. Introducing active surveillance methodologies in low- and middle-income countries is crucial to achieving more equitable global monitoring of vaccine safety. • Active vaccine-safety surveillance is still limited for detecting rare adverse events especially in LMICs. • Linked multi-source databases can improve signal detection and validation. • AI use in active surveillance is emerging, especially NLP for case identification. • Standard case definitions (e.g., Brighton) can aid outcome verification, but challenges remain. • Modified designs (e.g., self-controlled) can support case-only vaccine safety research.
Chandra et al. (Fri,) studied this question.
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