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Menstrual Cycle Length Changes Following Vaccination Against Influenza Alone or With COVID-19

2025· article· en· W4409920319 on OpenAlexaboutno aff
Emily R. Boniface, Blair G. Darney, Agathe van Lamsweerde, Eleonora Benhar, Alexandra Alvergne, Alison Edelman

Bibliographic record

VenueJAMA Network Open · 2025
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Impact on Reproduction
Canadian institutionsnot available
FundersNational Institute of Child Health and Human DevelopmentEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentCenters for Disease Control and PreventionNational Institutes of HealthWorld Health OrganizationBill and Melinda Gates Foundation
KeywordsVaccinationMedicineMenstrual cycleReceiptDemographyGynecologyObstetricsImmunologyInternal medicineHormone

Abstract

fetched live from OpenAlex

Importance: Multiple studies have identified an association between COVID-19 vaccination and menstrual disturbances. Data on whether menstrual health is impacted by other vaccines are needed to counsel individuals about what to expect and to address vaccine hesitancy. Objective: To assess the association of changes in length of the menstrual cycle with influenza vaccination, with or without concurrent receipt of a COVID-19 vaccine. Design, Setting, and Participants: This global retrospective cohort study prospectively collected menstrual cycle data from April 25, 2023, to February 27, 2024 (4-5 cycles per individual), among international English-speaking users of a digital birth control application. Participants included individuals aged 18 to 45 years, not using hormonal contraception, and with average cycle lengths of 24 to 38 days in 3 consecutive cycles before receipt of vaccines. Exposure: Seasonal influenza vaccination with or without concurrent receipt of COVID-19 vaccine. Main Outcome and Measure: The primary outcome consisted of adjusted mean within-individual changes of menstrual cycle length assessed by vaccination group. Secondary analysis evaluated the phase of menstrual cycle at time of vaccination. Results: A total of 1501 individuals met the inclusion criteria, of whom 791 were vaccinated for influenza only and 710 were concurrently vaccinated for influenza and COVID-19. By race and ethnicity, 1 participant (0.1%) was American Indian or Alaska Native; 10 (0.7%), Asian; 3 (0.2%), Black; 15 (1.0%), Hispanic or Latina; 1 (0.1%), Middle Eastern or North African; 368 (24.5%), White; and 19 (1.3%), other; and 1084 (72.2%), missing. Most of the cohort was younger than 35 years (1230 [82.0%]), had at least a college degree (1122 [74.8%]), and was located in the US or Canada (938 [62.5%]). Individuals vaccinated for influenza alone experienced an adjusted mean increase of 0.40 (95% CI, 0.08-0.72) days, while those vaccinated concurrently for influenza and COVID-19 experienced a mean increase of 0.49 (95% CI, 0.16-0.83) days (P = .69 for difference between vaccine groups). A total of 37 individuals (4.7%) experienced a change in cycle length of at least 8 days with influenza vaccine only and 42 (5.9%) with concurrent receipt of both vaccines (P = .28). In the postvaccination cycle, both vaccination groups returned to their prevaccination cycle lengths. Menstrual cycle changes occurred with vaccination in the follicular phase but not the luteal phase. Conclusions and Relevance: In this cohort study of individuals with regular menstrual cycles, influenza vaccine given alone or in combination with a COVID-19 vaccine was associated with a small but temporary change in menstrual cycle length. These findings may help clinicians confirm the utility of vaccination for patients with concerns about menstrual adverse effects of vaccination.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.469
Threshold uncertainty score0.828

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.045
GPT teacher head0.386
Teacher spread0.341 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

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Citations1
Published2025
Admission routes1
Has abstractyes

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