Active Surveillance for COVID-19 Vaccine Safety Using Sequential Analysis in Korea: Population-Based Retrospective Observational Study
Bibliographic record
Abstract
Background: With the advent of new vaccines, including the COVID-19 vaccines introduced during the recent pandemic, the need for near real-time active surveillance has increased to support timely regulatory decision-making. Objective: This study aimed to assess the feasibility of sequential monitoring for potential adverse events following immunization in Korea, focusing on COVID-19 vaccines. Methods: This population-based study used a linked database that combined the COVID-19 registry with national health insurance claims data. Participants included individuals older than 12 years who received either monovalent or bivalent COVID-19 vaccines in Korea between February 2021 and March 2023. Monthly retrospective sequential testing was performed for 3 prespecified outcomes (acute myocardial infarction, myocarditis, and anaphylaxis) as well as a negative control event (colonic diverticulitis). A Poisson-based maximized sequential probability ratio test was applied to compare postvaccination incidence rates with historical background rates, accounting for multiple testing and claims processing delays. Analyses were stratified by age group, vaccine platform, and dose. Results: This study included over 43 million monovalent and 6.3 million bivalent vaccine recipients. Sequential analyses identified statistical signals for myocarditis following mRNA vaccines in individuals aged 12 to 64 years and protein subunit vaccines in those aged 40 to 64 years. Signals for anaphylaxis were observed following mRNA and nonreplicating viral vector vaccines in individuals older than 18 years. No safety signals were identified for acute myocardial infarction or colonic diverticulitis. Sequential monitoring detected signals for myocarditis and anaphylaxis before regulatory authorities took safety actions, with the earliest signals observed on September 30, 2021, and April 30, 2021, respectively. Conclusions: Near real-time sequential testing detected statistical safety signals for myocarditis and anaphylaxis following COVID-19 vaccination. These signals were recognized by the regulatory authority as being associated with the vaccines, demonstrating the potential of this approach to detect signals requiring further causality assessments, particularly for newly introduced vaccines at an early stage.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".