Evidence map›Paper›PMID 36260402›Full record

ArticleJMIR medical informatics2021

Preferences of the Public for Sharing Health Data: Discrete Choice Experiment.

Jennifer Viberg Johansson, Heidi Beate Bentzen, Nisha Shah, Eik Haraldsdóttir, Guðbjörg Andrea Jónsdóttir, Jane Kaye, Deborah Mascalzoni, Jorien Veldwijk

Abstract read
In one paragraph

Article in JMIR medical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 2 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

16 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Unraveling acceptance of healthcare innovations in neurorehabilitation: results from a discrete choice experiment.The European journal of health economics : HEPAC : health economics in prevention and care · 2026
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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

8 authors.

Jennifer Viberg JohanssonCentre for Research Ethics & Bioethics, Department of Public Health and Caring Sciences, Uppsala Universitet, Uppsala, Sweden.ORCID https://orcid.org/0000-0001-9533-9274
Heidi Beate BentzenNorwegian Research Center for Computers and Law, Faculty of Law, University of Oslo, Oslo, Norway.ORCID https://orcid.org/0000-0001-8285-818X
Nisha ShahCentre for Health, Law, and Emerging Technologies, Faculty of Law, University of Oxford, Oxford, United Kingdom.ORCID https://orcid.org/0000-0002-1872-4598
Eik HaraldsdóttirSocial Science Research Institute, University of Iceland, Reykjavik, Iceland.ORCID https://orcid.org/0000-0001-7215-3127
Guðbjörg Andrea JónsdóttirSocial Science Research Institute, University of Iceland, Reykjavik, Iceland.ORCID https://orcid.org/0000-0002-8852-9560
Jane KayeCentre for Health, Law, and Emerging Technologies, Faculty of Law, University of Oxford, Oxford, United Kingdom.ORCID https://orcid.org/0000-0002-7311-4725
Deborah MascalzoniCentre for Research Ethics & Bioethics, Department of Public Health and Caring Sciences, Uppsala Universitet, Uppsala, Sweden.ORCID https://orcid.org/0000-0003-4156-1464
Jorien VeldwijkCentre for Research Ethics & Bioethics, Department of Public Health and Caring Sciences, Uppsala Universitet, Uppsala, Sweden.ORCID https://orcid.org/0000-0003-4822-5068

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDigital technological development in the last 20 years has led to significant growth in digital collection, use, and sharing of health data. To maintain public trust in the digital society and to enable acceptable policy-making in the future, it is important to investigate people's preferences for sharing digital health data.

objectiveThe aim of this study is to elicit the preferences of the public in different Northern European countries (the United Kingdom, Norway, Iceland, and Sweden) for sharing health information in different contexts.

methodsRespondents in this discrete choice experiment completed several choice tasks, in which they were asked if data sharing in the described hypothetical situation was acceptable to them. Latent class logistic regression models were used to determine attribute-level estimates and heterogeneity in preferences. We calculated the relative importance of the attributes and the predicted acceptability for different contexts in which the data were shared from the estimates.

resultsIn the final analysis, we used 37.83% (1967/5199) questionnaires. All attributes influenced the respondents' willingness to share health information (P<.001). The most important attribute was whether the respondents were informed about their data being shared. The possibility of opting out from sharing data was preferred over the opportunity to consent (opt-in). Four classes were identified in the latent class model, and the average probabilities of belonging were 27% for class 1, 32% for class 2, 23% for class 3, and 18% for class 4. The uptake probability varied between 14% and 85%, depending on the least to most preferred combination of levels.

conclusionsRespondents from different countries have different preferences for sharing their health data regarding the value of a review process and the reason for their new use. Offering respondents information about the use of their data and the possibility to opt out is the most preferred governance mechanism.

Indexed as

discrete choice experimenthealth datapreferencessecondary usewillingness to share

Identifiers

PMID36260402
PMCPMC8406119

What OpenQuestion holds

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LicenceCC BY
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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.