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Trust and Trade-Offs in Sharing Data for Precision Medicine: A National Survey of Singapore
Background: Precision 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 appropria...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8465970/ https://www.ncbi.nlm.nih.gov/pubmed/34575698 http://dx.doi.org/10.3390/jpm11090921 |
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author | Lysaght, Tamra Ballantyne, Angela Toh, Hui Jin Lau, Andrew Ong, Serene Schaefer, Owen Shiraishi, Makoto van den Boom, Willem Xafis, Vicki Tai, E Shyong |
author_facet | Lysaght, Tamra Ballantyne, Angela Toh, Hui Jin Lau, Andrew Ong, Serene Schaefer, Owen Shiraishi, Makoto van den Boom, Willem Xafis, Vicki Tai, E Shyong |
author_sort | Lysaght, Tamra |
collection | PubMed |
description | Background: Precision 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. Methods: The 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 n = 1000 respondents for an in-person household survey. Results: Of 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%). Conclusions: While 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. |
format | Online Article Text |
id | pubmed-8465970 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-84659702021-09-27 Trust and Trade-Offs in Sharing Data for Precision Medicine: A National Survey of Singapore Lysaght, Tamra Ballantyne, Angela Toh, Hui Jin Lau, Andrew Ong, Serene Schaefer, Owen Shiraishi, Makoto van den Boom, Willem Xafis, Vicki Tai, E Shyong J Pers Med Article Background: Precision 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. Methods: The 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 n = 1000 respondents for an in-person household survey. Results: Of 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%). Conclusions: While 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. MDPI 2021-09-16 /pmc/articles/PMC8465970/ /pubmed/34575698 http://dx.doi.org/10.3390/jpm11090921 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Lysaght, Tamra Ballantyne, Angela Toh, Hui Jin Lau, Andrew Ong, Serene Schaefer, Owen Shiraishi, Makoto van den Boom, Willem Xafis, Vicki Tai, E Shyong Trust and Trade-Offs in Sharing Data for Precision Medicine: A National Survey of Singapore |
title | Trust and Trade-Offs in Sharing Data for Precision Medicine: A National Survey of Singapore |
title_full | Trust and Trade-Offs in Sharing Data for Precision Medicine: A National Survey of Singapore |
title_fullStr | Trust and Trade-Offs in Sharing Data for Precision Medicine: A National Survey of Singapore |
title_full_unstemmed | Trust and Trade-Offs in Sharing Data for Precision Medicine: A National Survey of Singapore |
title_short | Trust and Trade-Offs in Sharing Data for Precision Medicine: A National Survey of Singapore |
title_sort | trust and trade-offs in sharing data for precision medicine: a national survey of singapore |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8465970/ https://www.ncbi.nlm.nih.gov/pubmed/34575698 http://dx.doi.org/10.3390/jpm11090921 |
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