Utilizing group-based models to identify adverse event patterns after an intervention
Several different time-based trajectories after the intervention (e.g., first two COVID-19 vaccinations) were noted; individuals in these groups varied by demographic and clinical criteria. These time-based methods may be able to identify groups at higher risk of future adverse reactions, provide a basis for future studies of the physiology underlying these risk differentials, and improve counseling surrounding interventions associated with AEs. We suggest that trajectory-based methods be added to post-intervention surveillance.
Authors
- Elizabeth B. Klerman (ORCID: https://orcid.org/0000-0002-7402-3171)
- Sara Abbaspour (ORCID: https://orcid.org/0000-0001-8294-861X)
- Gregory K. Robbins (ORCID: https://orcid.org/0000-0001-7545-5817)
- Dean M. Hashimoto (ORCID: https://orcid.org/0009-0005-8289-4945)
- Kimberly G. Blumenthal (ORCID: https://orcid.org/0000-0003-4773-9817)
- Wei Wang (ORCID: https://orcid.org/0000-0001-7333-3701)
Institutions
- Brigham and Women's Hospital (US)
- Harvard University (US)
- Massachusetts General Hospital (US)
- Workplace Health, Safety and Compensation Commission (CA)
- Circadian (United States) (US)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-09-16
- DOI
- https://doi.org/10.3389/fmed.2025.1637091
- Primary Topic
- Advanced Causal Inference Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Centers for Disease Control and Prevention