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The COVID-19 pandemic has had a significant impact globally. Most affected, however, are those individuals and groups routinely disadvantaged by the social injustice created by the misdistribution of power, money, and resources. Simple measures that prevent the spread of COVID-19, such as frequent hand washing and social distancing, are unavailable to millions of people in the wealthiest of nations and in the poorest of nations. Disadvantaged groups are impacted more directly and in disproportionately higher numbers due to existing poor health, and the disruption of services central to securing an income and an education will have lasting consequences for their futures. The unintended effect of exclusionary government policies is that privileged citizens and healthcare systems are also at greater risk. This paper seeks to highlight the impact of COVID-19 on those already suffering health inequities through consideration of some of the social determinants of health on groups in affluent and poorer nations. It also highlights some of the factors that may assist in tackling health inequities as we emerge from this pandemic.

Governments are investing in precision medicine (PM) with the aim of improving healthcare through the use of genomic analyses and data analytics to develop tailored treatment approaches for individual patients. The success of PM is contingent upon clear public communications that engender trust and secure the social licence to collect and share large population-wide data sets because specific consent for each data re-use is impractical. Variation in the terminology used by different programmes used to describe PM may hinder clear communication and threaten trust. Language is used to create common understanding and expectations regarding precision medicine between researchers, clinicians and the volunteers. There is a need to better understand public interpretations of PM-related terminology. This paper reports on a qualitative study involving 24 focus group participants in the multi-lingual context of Singapore. The study explored how Singaporeans interpret and understand the terms 'precision medicine' and 'personalised medicine', and which term they felt more aptly communicates the concept and goals of PM. Results suggest that participants were unable to readily link the terms with this area of medicine and initially displayed preferences for the more familiar term of 'personalised'. The use of visual aids to convey key concepts resonated with participants, some of whom then indicated preferences for the term 'precision' as being a more accurate description of PM research. These aids helped to facilitate dialogue around the ethical and social value, as well as the risks, of PM. Implications for programme developers and policy makers are discussed.

Much has been written about gene modifying technologies (GMTs), with a particularly strong focus on human germline genome editing (HGGE) sparked by its unprecedented clinical research application in 2018, shocking the scientific community. This paper applies political, ethical, and social lenses to aspects of HGGE to uncover previously underexplored considerations that are important to reflect on in global discussions. By exploring 4 areas—(1) just distribution of HGGE benefits through a realist lens; (2) HGGE through a national interest lens; (3) "broad societal consensus" through a structural injustice lens; and (4) HGGE through a scientific trustworthiness lens—a broader perspective is offered, which ultimately aims to enrich further debates and inform well-considered solutions for developments in this field. The application of these lenses also brings to light the fact that all discussions about scientific developments involve a conscious or unconscious application of a lens that shapes the direction of our thinking.

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 ...

Objective: To provide insights into complexities of seeking access to state and federal cross-jurisdictional data for linkage with the Australian Childhood Immunisation Register (ACIR). We provide recommendations for improving access and receipt of linked datasets involving Australian Government-administered data. Methods: We describe requirements for linking eleven federal and state data sources to establish a national linked dataset for safety evaluation of vaccines. The required data linkage methodology for integrating cross-jurisdictional data sources is also described. Results: Extensive negotiation was required with 18 different agencies for 21 separate authorisations and 12 ethics approvals. Three variations of the 'best practice' linkage model were implemented. Australian Government approval requests spanned nearly four years from initial request for data, with a further year before ACIR data transfer to the linkage agency. Conclusions: Integration of immunisation registers with other data collections is achievable in Australia but infeasible for routine and rapid identification of vaccine safety concerns. Lengthy authorisation requirements, convoluted disparate application processes and inconsistencies in data supplied all contribute to delayed data availability. Implications for public health: Delayed data access for safety surveillance prevents timely epidemiological reviews. Poor responsiveness to safety concerns may erode public confidence, compromising effectiveness of vaccination programs through reduced participation.