Evidence map›Paper›PMID 39741942›Full record

ArticleFrontiers in public health2024

Policy brief: Improving national vaccination decision-making through data.

Sandra Evans, Joe Schmitt, Dipak Kalra, Tomislav Sokol, Daphne Holt

Abstract read
In one paragraph

Article in Frontiers in public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
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

5 authors.

Sandra EvansSandra Evans Health Policy, Liverpool, United Kingdom.
Joe SchmittGlobal Health Press, Singapore, Singapore.
Dipak KalraThe European Institute for Innovation through Health Data, Ghent, Belgium.
Tomislav SokolEuropean Parliament, Brussels, Belgium.
Daphne HoltCoalition for Life Course Immunisation, Brussels, Belgium.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Life course immunisation looks at the broad value of vaccination across multiple generations, calling for more data power, collaboration, and multi-disciplinary work. Rapid strides in artificial intelligence, such as machine learning and natural language processing, can enhance data analysis, conceptual modelling, and real-time surveillance. The GRADE process is a valuable tool in informing public health decisions. It must be enhanced by real-world data which can span and capture immediate needs in diverse populations and vaccination administration scenarios. Analysis of data from multiple study designs is required to understand the nuances of health behaviors and interventions, address gaps, and mitigate the risk of bias or confounding presented by any single data collection methodology. Secure and responsible health data sharing across European countries can contribute to a deeper understanding of vaccines.

Indexed as

Decision MakingVaccinationEuropeHealth PolicyHumansPublic HealthAI technologiesbig data analysislife course immunisationNational Immunisation ProgramsNational Immunisation Technical Advisory Groupsvaccine policyvaccine-preventable diseases

Identifiers

PMID39741942
PMCPMC11685149

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.