Evidence map›Paper›PMID 40369095›Full record

ArticleNPJ digital medicine2025

A quantitative analysis of the use of anonymization in biomedical research.

Thierry Meurers, Karen Otte, Hammam Abu Attieh, Farah Briki, Jérémie Despraz, Mehmed Halilovic, Bayrem Kaabachi, Vladimir Milicevic, Armin Müller, Grigorios Papapostolou and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 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

13 authors.

Thierry MeurersHealth Data Science Center, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany. thierry.meurers@bih-charite.de.
Karen OtteHealth Data Science Center, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.
Hammam Abu AttiehHealth Data Science Center, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.
Farah BrikiBiomedical Data Science Center, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland.
Jérémie DesprazBiomedical Data Science Center, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland.
Mehmed HalilovicHealth Data Science Center, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.
Bayrem KaabachiBiomedical Data Science Center, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland.
Vladimir MilicevicHealth Data Science Center, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.
Armin MüllerHealth Data Science Center, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.
Grigorios PapapostolouHealth Data Science Center, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.
Felix Nikolaus WirthHealth Data Science Center, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.
Jean Louis RaisaroBiomedical Data Science Center, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland.
Fabian PrasserHealth Data Science Center, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany. fabian.prasser@bih-charite.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Anonymized biomedical data sharing faces several challenges. This systematic review analyzes 1084 PubMed-indexed studies (2018-2022) using anonymized biomedical data to quantify usage trends across geographic, regulatory, and cultural regions to identify effective approaches and inform implementation agendas. We identified a significant yearly increase in such studies with a slope of 2.16 articles per 100,000 when normalized against the total number of PubMed-indexed articles (p = 0.021). Most studies used data from the US, UK, and Australia (78.2%). This trend remained when normalized by country-specific research output. Cross-border sharing was rare (10.5% of studies). We identified twelve common data sources, primarily in the US (seven) and UK (three), including commercial (seven) and public entities (five). The prevalence of anonymization in the US, UK, and Australia suggests their practices could guide broader adoption. Rare cross-border anonymized data sharing and differences between countries with comparable regulations underscore the need for global standards.

Identifiers

PMID40369095
PMCPMC12078711

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.