Evidence map›Paper›PMID 41369166›Full record

ReviewHuman vaccines & immunotherapeutics2025

Protecting pregnancy during pandemics: What recent outbreaks teach us.

Annette K Regan, Flor M Muñoz

Abstract readReview
In one paragraph

Review in Human vaccines & immunotherapeutics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Article
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

2 authors.

Annette K ReganDepartment of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA.ORCID 0000-0002-3879-6193
Flor M MuñozDivision of Infectious Diseases, Molecular Virology and Microbiology, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA.ORCID 0000-0002-0457-7689

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Public health emergences often place pregnant people at a greater risk of severe disease. In this commentary, we highlight some of the insights from recent public health emergencies, spanning the 2009 influenza A/H1N1 pandemic to the recent mpox outbreaks. These epidemics have resulted in a large amount of perinatal pharmacovigilance expertise and capacity, improving the ability to conduct post-authorization evaluation of the safety of vaccines and other therapeutics during pregnancy. Although pregnant people have not historically been included in the clinical development of vaccines and other therapeutics intended for the general population, experiences from more recent public health emergencies show this trend is changing, with ongoing clinical trials either including or specifically targeting pregnant people. Pandemics and other public health emergencies that strain the healthcare system can disrupt routine prenatal care, birthing, and post-delivery care practices and this should be considered as part of pandemic planning. Finally, experiences from several epidemics show that vaccination during pregnancy is achievable, but vaccination rates during pregnancy are often suboptimal. A better understanding of vaccine hesitancy and acceptance during pregnancy and evidence-based strategies to address such hesitancy remain needed to better protect pregnant people against emerging infectious disease threats.

Indexed as

Disease OutbreaksInfluenza, HumanInfluenza VaccinesPandemicsPregnancy Complications, InfectiousVaccinationFemaleHumansInfluenza A Virus, H1N1 SubtypePharmacovigilancePregnancyVaccination HesitancyInfluenza Vaccinesemerging infectious diseasesPandemicpregnancysurveillance

Identifiers

PMID41369166
PMCPMC12698041

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.