Evidence map›Paper›PMID 27784657›Full record

ArticleAmerican journal of epidemiology2016

Maternal Influenza Immunization and Adverse Birth Outcomes: Using Data and Practice to Inform Theory and Research Design.

Varun K Phadke, Mark C Steinhoff, Saad B Omer, Noni E MacDonald

Open access · bronzeAbstract readComment
In one paragraph

Article in American journal of epidemiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.2field-weighted citation impact, top 19% of its field
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

5 citing papers in PubMed, 7 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 2 countries.

Varun K Phadke
Mark C Steinhoff
Saad B Omer
Noni E MacDonald
Emory University · USDalhousie University · CA

Funding

Training a New Generation of VaccinologistsT32AI074492 · NIAID · EMORY UNIVERSITY · PI Rama Rao Amara, Benjamin A Lopman · 2009 to 2026
$6.4M
NIAID NIH HHS T32 AI074492
6 · The paper itself

Abstract

Maternal influenza immunization can reduce influenza-attributable morbidity and mortality among pregnant women and infants who are too young to be vaccinated. Data from empirical studies also support the hypothesis that immunization can protect the fetus against adverse outcomes if the mother is exposed to influenza. In their theoretical analysis in the Journal, Hutcheon et al. (Am J Epidemiol 2016;184(3):227-232) critiqued the existing evidence of the fetal benefits of maternal influenza immunization by calculating the sample sizes needed to demonstrate hypothetical reductions in risk and concluded that the benefits observed in empirical studies are likely implausible. However, in their analysis, they did not take into account multiple fundamental characteristics of influenza epidemiology, including the time-variable effects of influenza illness and vaccination during pregnancy, or well-known differences in disease epidemiology between seasons, populations, and geographic regions. Although these and other factors might affect the magnitude of fetal benefit conferred by maternal influenza immunization, studies in which investigators have accounted for influenza circulation have demonstrated a consistent protective effect against a variety of adverse birth outcomes; those studies include the only randomized controlled trial designed a priori and adequately powered to do so. Only a comprehensive and nuanced assessment of the evidence base will allow for effective translation of these data into a global immunization policy.

Indexed as

VaccinationFemaleHumansImmunizationInfluenza, HumanPregnancybirth outcomebirthweightinfluenzamaternal immunizationpregnancypreterm birth

Identifiers

PMID27784657
PMCPMC5152664
OpenAlexW2541829022

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.