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.
Proportionality in health research regulation can, at its broadest level, be understood as an attempt to balance two considerations that sometimes compete: the protection of individuals affected by researchespecially, but not limited to, human subjectsand the promotion of socially valuable research. This chapter will explore the concept of proportionality through three sections: First, a clarification on what I mean by proportionality in this context and why it is important; second, an exploration of how particularly challenging it is to assess proportionality; and third, a proposal for a procedural approach to proportionality that may assist with those challenges. In particular, I will propose that adopting a facilitative attitude, undertaking rigorous justification, ensuring transparency and engaging with relevant stakeholders may be effective procedural means of overcoming the challenges of proportionality. 1 what is proportionality?The term 'proportionality' has several meanings even within the context of health research regulations. We can roughly distinguish between the first-order or study-level sense of the term, and second-order or policy-level sense.First-order proportionality refers to the benefits of a studyinclusive of benefits to the subjects as well as society as a wholebeing proportionate to its risks and burdens. It is interchangeable with 'favourable risk-benefit ratio' as found in the classic article 'What makes clinical research ethical?', 2 and a variety of authors have followed suit. 3 On this understanding, the benefits of a given study need to be of sufficient strength or magnitude to justify the risks individuals are exposed to. Research Ethics Committees (RECs), Institutional Review Boards (IRBs) or 12
We live in the era of the internet as well as the era of big data. These naturally intersect to the point that those involved in the conduct and oversight of contemporary internet research must grapple with ethical issues raised by the promulgation of big data. In this chapter the author summarizes the key challenges of internet research in the big data context, particularly in light of limitations on consent and anonymization as means to protect human subjects. The author critically appraises the “contextual pluralism” of recent guidance from the Association of Internet Researchers and more deeply explores the ethical nuances of one particular and pressing domain of internet research that relies on big data: data scraping. This ethical analysis is indicative of the need for careful contextual attention to the issues raised by contemporary internet research, where novel platforms and study designs may require commensurately novel approaches to ensuring responsible research.
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