Evidence map›Paper›PMID 41035868›Full record

ArticleFrontiers in medicine2025

Utilizing group-based models to identify adverse event patterns after an intervention.

Wei Wang, Sara Abbaspour, Kimberly G Blumenthal, Dean M Hashimoto, Gregory K Robbins, Elizabeth B Klerman

Abstract read
In one paragraph

Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Wei WangDivision of Sleep and Circadian Disorders, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
Sara AbbaspourDivision of Sleep, Department of Neurology, Massachusetts General Hospital, Boston, MA, United States.
Kimberly G BlumenthalDivision of Rheumatology, Allergy and Immunology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.
Dean M HashimotoWorkplace Health, Mass General Brigham, Boston, MA, United States.
Gregory K RobbinsDivision of Infectious Diseases, Massachusetts General Hospital, Boston, MA, United States.
Elizabeth B KlermanDivision of Sleep and Circadian Disorders, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Standard adverse event (AE) monitoring only records whether events occur after the intervention, and not whether these events vary over time. Objective: To test whether there were statistically distinct time-varying trajectories of AE (e.g., "side effects") after an intervention and identify characteristics of individuals associated with these patterns. Design: Group-based trajectory models applied to an observational study of individuals who received one or two doses of a mRNA COVID-19 vaccine (i.e., the intervention). Participants: 50,484 healthcare personnel who received their vaccinations within the Mass General Brigham (MGB) healthcare system. Interventions: Vaccination. Main measures: Allergic and non-allergic AE for 1-3 days after each of two COVID-19 vaccinations. Key results: Trajectories models identified distinct groups with different trajectories after intervention: two groups after the first vaccination and five groups after the second vaccination. These groups differed by demographics, age, prior prescription for epinephrine auto-injectors, prior COVID-19 history, time-of-day of vaccination, and vaccine manufacturer. Conclusion: 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.

Indexed as

adverse effects following immunization (AEFI)adverse eventsCOVID-19 adverse effects monitoringside-effectstrajectory analysesvaccines

Identifiers

PMID41035868
PMCPMC12479506

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