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Utilizing group-based models to identify adverse event patterns after an intervention.
Wei Wang1, Sara Abbaspour2, Kimberly G Blumenthal3
1Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
Frontiers in Medicine
|October 2, 2025
Summary
Adverse event monitoring after COVID-19 vaccination revealed distinct time-varying patterns. Identifying these trajectories helps pinpoint individuals at higher risk for adverse reactions.
Area of Science:
- Vaccinology
- Epidemiology
- Biostatistics
Background:
- Standard adverse event (AE) monitoring lacks temporal analysis, failing to capture event variations over time.
- Current methods do not assess if AE patterns differ between individuals post-intervention.
Purpose of the Study:
- To analyze time-varying trajectories of AEs following COVID-19 vaccination.
- To identify individual characteristics associated with distinct AE patterns.
Main Methods:
- Group-based trajectory models were applied to an observational study.
- Data from 50,484 healthcare personnel receiving mRNA COVID-19 vaccines were analyzed.
- AEs were monitored for 1-3 days post-vaccination.
Main Results:
- Distinct AE trajectory groups were identified post-vaccination: two after the first dose and five after the second.
- These groups varied significantly based on demographics, age, prior medical history, vaccination timing, and vaccine manufacturer.
Conclusions:
- Time-based AE trajectories after vaccination reveal distinct individual patterns.
- These findings can inform risk stratification, future physiological studies, and patient counseling.
- Trajectory-based methods should be integrated into post-intervention surveillance strategies.
Keywords:
COVID-19 adverse effects monitoringadverse effects following immunization (AEFI)adverse eventsside-effectstrajectory analysesvaccinesMore Related Videos
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