Evidence map›Paper›PMID 35752778›Full record

ArticleBMC medical ethics2022

Data privacy protection in scientific publications: process implementation at a pharmaceutical company.

Friedrich Maritsch, Ingeborg Cil, Colin McKinnon, Jesse Potash, Nicole Baumgartner, Valérie Philippon, Borislava G Pavlova

Open access · goldAbstract read
In one paragraph

Article in BMC medical ethics, 2022. 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
2.3field-weighted citation impact, top 10% 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

1 citing paper in PubMed, 17 citations in OpenAlex.

  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

7 authors at 3 institutions in 3 countries.

Friedrich MaritschBaxalta Innovations GmbH, A Takeda Company, Vienna, Austria.
Ingeborg CilBaxalta Innovations GmbH, A Takeda Company, Vienna, Austria.
Colin McKinnonTakeda Pharmaceuticals International AG, Zurich, Switzerland.
Jesse PotashTakeda Development Center Americas, Inc., Cambridge, MA, USA.
Nicole BaumgartnerBaxalta Innovations GmbH, A Takeda Company, Vienna, Austria.
Valérie PhilipponTakeda Development Center Americas, Inc., Cambridge, MA, USA.
Borislava G PavlovaBaxalta Innovations GmbH, A Takeda Company, Vienna, Austria. borislava.pavlova@takeda.com.
Takeda (Austria) · ATTakeda (United States) · USTakeda (Switzerland) · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSharing anonymized/de-identified clinical trial data and publishing research outcomes in scientific journals, or presenting them at conferences, is key to data-driven scientific exchange. However, when data from scientific publications are linked to other publicly available personal information, the risk of reidentification of trial participants increases, raising privacy concerns. Therefore, we defined a set of criteria allowing us to determine and minimize the risk of data reidentification. We also implemented a review process at Takeda for clinical publications prior to submission for publication in journals or presentation at medical conferences.

methodsAbstracts, manuscripts, posters, and oral presentations containing study participant information were reviewed and the potential impact on study participant privacy was assessed. Our focus was on direct (participant ID, initials) and indirect identifiers, such as sex, age or geographical indicators in rare disease studies or studies with small sample size treatment groups. Risk minimization was sought using a generalized presentation of identifier-relevant information and decision-making on data sharing for further research. Additional risk identification was performed based on study participant/personnel parameters present in materials destined for the public domain. The potential for participant/personnel identification was then calculated to facilitate presentation of meaningful but de-identified information.

resultsThe potential for reidentification was calculated using a risk ratio of the exposed versus available individuals, with a value above the threshold of 0.09 deemed an unacceptable level of reidentification risk. We found that in 13% of Takeda clinical trial publications reviewed, either individuals could potentially be reidentified (despite the use of anonymized data sets) or inappropriate data sharing plans could pose a data privacy risk to study participants. In 1/110 abstracts, 58/275 manuscripts, 5/87 posters and 3/58 presentations, changes were necessary due to data privacy concerns/rules. Despite the implementation of risk-minimization measures prior to release, direct and indirect identifiers were found in 11% and 34% of the analysed documents, respectively.

conclusionsRisk minimization using de-identification of clinical trial data presented in scientific publications and controlled data sharing conditions improved privacy protection for study participants. Our results also suggest that additional safeguards should be implemented to ensure that higher data privacy standards are met.

Indexed as

Computer SecurityPrivacyHumansInformation DisseminationPharmaceutical PreparationsPharmaceutical PreparationsClinical trialsData de‑identificationData sharingMedical publicationsPatient privacyReidentification riskTrial participant privacy

Identifiers

PMID35752778
PMCPMC9233846
OpenAlexW4283463213

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.