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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
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PubMed
Summary
This summary is machine-generated.

Adverse event monitoring after COVID-19 vaccination revealed distinct time-varying patterns. Identifying these trajectories helps pinpoint individuals at higher risk for adverse reactions.

Keywords:
COVID-19 adverse effects monitoringadverse effects following immunization (AEFI)adverse eventsside-effectstrajectory analysesvaccines

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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.