Evidence map›Paper›PMID 34575698›Full record

ArticleJournal of personalized medicine2021

Trust and Trade-Offs in Sharing Data for Precision Medicine: A National Survey of Singapore.

Tamra Lysaght, Angela Ballantyne, Hui Jin Toh, Andrew Lau, Serene Ong, Owen Schaefer, Makoto Shiraishi, Willem van den Boom, Vicki Xafis, E Shyong Tai

Open access · goldAbstract read
In one paragraph

Article in Journal of personalized medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 2 pooled it
3.8field-weighted citation impact, top 6% 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

19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 25 citations in OpenAlex.

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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

10 authors at 1 institution in 2 countries.

Tamra LysaghtCentre for Biomedical Ethics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore.
Angela BallantyneCentre for Biomedical Ethics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore.
Hui Jin TohCentre for Biomedical Ethics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore.
Andrew LauProjective Insights Consultants, Singapore 590003, Singapore.
Serene OngCentre for Biomedical Ethics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore.
Owen SchaeferCentre for Biomedical Ethics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore.
Makoto ShiraishiCentre for Biomedical Ethics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore.
Willem van den BoomYale-NUS College, National University of Singapore, Singapore 138527, Singapore.ORCID 0000-0002-1777-3857
Vicki XafisCentre for Biomedical Ethics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore.
E Shyong TaiYong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore.
National University of Singapore · SG

Funding

Ministry of Education - Singapore MOE2017-SSRTG-028Ministry of Health -Singapore NANational Research Foundation Singapore NA
6 · The paper itself

Abstract

backgroundPrecision medicine (PM) programs typically use broad consent. This approach requires maintenance of the social license and public trust. The ultimate success of PM programs will thus likely be contingent upon understanding public expectations about data sharing and establishing appropriate governance structures. There is a lack of data on public attitudes towards PM in Asia.

methodsThe aim of the research was to measure the priorities and preferences of Singaporeans for sharing health-related data for PM. We used adaptive choice-based conjoint analysis (ACBC) with four attributes: uses, users, data sensitivity and consent. We recruited a representative sample of

resultsOf the 1000 respondents, 52% were female and majority were in the age range of 40-59 years (40%), followed by 21-39 years (33%) and 60 years and above (27%). A total of 64% were generally willing to share de-identified health data for IRB-approved research without re-consent for each study. Government agencies and public institutions were the most trusted users of data. The importance of the four attributes on respondents' willingness to share data were: users (39.5%), uses (28.5%), data sensitivity (19.5%), consent (12.6%). Most respondents found it acceptable for government agencies and hospitals to use de-identified data for health research with broad consent. Our sample was consistent with official government data on the target population with 52% being female and majority in the age range of 40-59 years (40%), followed by 21-39 years (33%) and 60 years and above (27%).

conclusionsWhile a significant body of prior research focuses on preferences for consent, our conjoint analysis found consent was the least important attribute for sharing data. Our findings suggest the social license for PM data sharing in Singapore currently supports linking health and genomic data, sharing with public institutions for health research and quality improvement; but does not support sharing with private health insurers or for private commercial use.

Indexed as

bioethicsdata sharingPrecision medicineSingaporesurveytrust

Identifiers

PMID34575698
PMCPMC8465970
OpenAlexW3200329293

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.