Noortje Jacobs, Ethics by Committee: A History of Reasoning Together About Medicine, Science, Society, and the State
In: Social history of medicine, Band 37, Heft 1, S. 247-249
ISSN: 1477-4666
13 Ergebnisse
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In: Social history of medicine, Band 37, Heft 1, S. 247-249
ISSN: 1477-4666
In this article, we consider the possible application of the European General Data Protection Regulation (GDPR) to "citizen scientist"-led health research with mobile devices. We argue that the GDPR likely does cover this activity, depending on the specific context and the territorial scope. Remaining open questions that result from our analysis lead us to call for lex specialis that would provide greater clarity and certainty regarding the processing of health data by for research purposes, including these non-traditional researchers.
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Several recent data protection laws appear to afford a privileged position to scientific research, including health research. Provisions that might otherwise apply to data subjects and data controllers, including rights exercisable by data subjects against controllers, are lifted or lessened.However, when it comes to considering whether consent should serve as the lawful basis for processing data in the health research context, a fair degree of policy and regulatory divergence emerges. This divergence seems to stem from a normative link that some draw between consent as a research ethics principle and consent as a lawful basis in data protection law.We look at the EU General Data Protection Regulation (GDPR) and three national laws, either implementing the GDPR or inspired by it, to provide points of comparison: South Africa's Protection of Personal Information Act, 2013, the UK's Data Protection Act 2018, and Ireland's Health Research Regulations 2018. We supplement this analysis by considering other relevant laws and regulations governing health research in these jurisdictions.We argue that there is merit in distinguishing research ethics consent from data processing consent, to avoid what we call 'consent misconception', and come to advocate a middle-ground approach in data protection law, ie one that does not mandate consent as the lawful basis for processing personal data in health research projects—but does encourage it. This approach, we argue, achieves the best balance for protecting data subject/research participant rights and interests and promoting socially valuable health research.
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As the sustained and devastating extent of the coronavirus disease 2019 (COVID-19) pandemic becomes apparent, a key focus of public scrutiny in the UK has centred on the novel legal and regulatory measures introduced in response to the virus. When those measures were first implemented in March 2020 by the UK Government, it was thought that human rights obligations would limit excesses of governmental action and that the public had more to fear from unwarranted intrusion into civil liberties. However, within the first year of the pandemic's devastation in the UK, a different picture has emerged: rather than through action, it is governmental inaction that has given rise to greater human rights concerns. The UK Government has been roundly criticized for its inadequate response, including missteps in decision-making, delayed implementation and poor enforcement of lockdown measures, abandonment of testing, shortages of critical resources and inadequate test and trace methods. In this article, we analyse the UK Government's missteps and compare them with published international guidance; we also contrast the UK's decisions with those taken by several other countries (including the devolved administrations within the UK) to understand how its actions and inactions have contributed to unfavourable outcomes. Using an analytical perspective that demonstrates how human rights are both a protection from the power of the state and a requirement that governmental powers are used to protect the lives, health and wellbeing of citizens, we argue that the UK Government's failure to exercise their powers competently allowed the virus to spread without ensuring the country had the means to manage a high case load. This abject failure has led to one of the highest rates of deaths per capita worldwide. We offer several lessons that can be learnt from this unfortunate, but preventable, situation.
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While biobanks are established explicitly as scientific infrastructures, they are de facto political-economic ones too. Many biobanks, particularly population-based biobanks, are framed under the rubric of the bio-economy as national political-economic assets that benefit domestic business, while national populations are framed as a natural resource whose genomics, proteomics, and related biological material and national health data can be exploited. We outline how many biobanks epitomize this 'neoliberal' form of science and innovation in which research is driven by market priorities (e.g., profit, shareholder value) underpinned by state or government policies. As both scientific and political-economic infrastructures, biobanks end up entangled in an array of problems associated with market-driven science and innovation. These include: profit trumping other considerations; rentiership trumping entrepreneurship; and applied research trumping basic research. As a result, there has been a push behind new forms of 'post-neoliberal' science and innovation strategies based on principles of openness and collaboration, especially in relation to biobanks. The proliferation of biobanks and the putative transition in both scientific practice and political economy from neoliberalism to post-neoliberalism demands fresh social scientific analyses, particularly as biobanks become further established in fields such as oral health and personalized dentistry. To the best of our knowledge, this is the first analysis of biobanks with a view to what we can anticipate from biobanks and distributed post-genomics global science in the current era of oral health biomarkers.
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In: Ethics & human research: E&HR : a publication of the Hastings Center, Band 44, Heft 1, S. 2-17
ISSN: 2578-2363
ABSTRACTIn this article, we analyze legal and ethical issues raised in Big Data health research projects in the Covid‐19 era and consider how these issues might be addressed in ways that advance positive values (e.g., furtherance of respect for persons and accordance with relevant legal frameworks) while mitigating or eliminating any negative aspects (e.g., exacerbation of social inequality and injustice). We apply this analysis specifically to UK‐REACH (The United Kingdom Research Study into Ethnicity and Covid‐19 Outcomes in Healthcare Workers), a project with which we are involved. We argue that Big Data projects like UK‐REACH can be conducted in an ethically robust manner and that funders and sponsors ought to encourage similar projects to drive better evidence‐based public policy in public health. As part of this, we advocate that a Big Data ethics‐by‐design approach be undertaken when such projects are constructed. This principle extends the work of those who advocate ethics by design by addressing prominent issues in Big Data health research projects; it holds that ethical values and principles in Big Data health research projects are best adhered to when they are already integrated into the project aims and methods at the design stage. In advocating this principle, we present a unique perspective regarding pressing ethical problems around large‐scale, data‐driven Covid‐19 research, as well as legal issues associated with processing ostensibly anonymized health data.
Data sharing models designed to facilitate global business provide insights for improving transborder genomic data sharing. We argue that a flexible, externally endorsed, multilateral arrangement, combined with an objective third-party assurance mechanism, can effectively balance privacy with the need to share genomic data globally.
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In: IRB: ethics & human research, Band 40, Heft 6, S. 7-12
ISSN: 2326-2222
AbstractIn the United States, final amendments to the Federal Policy for the Protection of Human Subjects ("the Common Rule") were published on January 19, 2017, and they will take effect on January 21, 2019. One of the most widely discussed provisions is that for the first time, federal regulations governing research with humans authorize the use of broad consent for future, unspecified research on individually identifiable biospecimens and associated data. Many questions have been raised about broad consent, including what effect it will have on research and whether it adequately protects the interests of research participants.There are lessons to be learned for the U.S. and other countries by looking to countries that already have experience with broad consent for biobank collection and with the storage and subsequent use of the biospecimens and data. This article describes how broad consent works in five countries—Canada (in Quebec), Israel, Nigeria, Taiwan, and the United Kingdom—and with different types of biobanks: national biobanks, federated biobanks, and regional biobanks. Evaluating the provisions and challenges of the broad consent approaches in these countries can inform policies for this increasingly used approach to biobank regulation.
Genomic research and biobanking has undergone exponential growth in Africa and at the heart of this research is the sharing of biospecimens and associated clinical data amongst researchers in Africa and across the world. While this move towards open science is progressing, there has been a strengthening internationally of data protection regulations that seek to safeguard the rights of data subjects while promoting the movement of data for the benefit of research. In line with this global shift, many jurisdictions in Africa are introducing data protection regulations, but there has been limited consideration of the regulation of data sharing for genomic research and biobanking in Africa. South Africa (SA) is one country that has sought to regulate the international sharing of data and has enacted the Protection of Personal Information Act (POPIA) 2013 that will change the governance and regulation of data in SA, including health research data, once it is in force. To identify and discuss challenges and opportunities in the governance of data sharing for genomic and health research data in SA, a two-day meeting was convened in February 2019 in Cape Town, SA with over 30 participants with expertise in law, ethics, genomics and biobanking science, drawn from academia, industry, and government. This report sets out some of the key challenges identified during the workshop and the opportunities and limitations of the current regulatory framework in SA.
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The Protection of Personal Information Act 4 of 2013 (POPIA) was enacted by the South African (SA) parliament in 2013 after a long process of public consultation. To allow all sectors of SA society sufficient time to prepare to be compliant with POPIA, the SA government deferred the entering into force of the substantive provisions of POPIA for several years. Throughout this hiatus period, POPIA was widely publicised in the SA media, as is evident from any internet search. ; http://www.samj.org.za ; am2021 ; Immunology
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Biomedical science in the 21st century is embedded in, and draws from, a digital commons and "Big Data" created by high-throughput Omics technologies such as genomics. Classic Edisonian metaphors of science and scientists (i.e., "the lone genius" or other narrow definitions of expertise) are ill equipped to harness the vast promises of the 21st century digital commons. Moreover, in medicine and life sciences, experts often under-appreciate the important contributions made by citizen scholars and lead users of innovations to design innovative products and co-create new knowledge. We believe there are a large number of users waiting to be mobilized so as to engage with Big Data as citizen scientists—only if some funding were available. Yet many of these scholars may not meet the meta-criteria used to judge expertise, such as a track record in obtaining large research grants or a traditional academic curriculum vitae. This innovation research article describes a novel idea and action framework: micro-grants, each worth $1000, for genomics and Big Data. Though a relatively small amount at first glance, this far exceeds the annual income of the "bottom one billion"—the 1.4 billion people living below the extreme poverty level defined by the World Bank ($1.25/day). We describe two types of micro-grants. Type 1 micro-grants can be awarded through established funding agencies and philanthropies that create micro-granting programs to fund a broad and highly diverse array of small artisan labs and citizen scholars to connect genomics and Big Data with new models of discovery such as open user innovation. Type 2 micro-grants can be funded by existing or new science observatories and citizen think tanks through crowd-funding mechanisms described herein. Type 2 micro-grants would also facilitate global health diplomacy by co-creating crowd-funded micro-granting programs across nation-states in regions facing political and financial instability, while sharing similar disease burdens, therapeutics, and diagnostic needs. We ...
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Scholarship knows no geographical boundaries. This science diplomacy and biotechnology journalism article introduces an original concept and policy petition to innovate the global translational science, a Science Peace Corps. Service at the new Corps could entail volunteer work for a minimum of 6 weeks, and up to a maximum of 2 years, for translational research in any region of the world to build capacity manifestly for development and peace, instead of the narrow bench-to-bedside model of life science translation. Topics for translational research are envisioned to include all fields of life sciences and medicine, as long as they are linked to potential or concrete endpoints in development, foreign policy, conflict management, post-crisis capacity building, and/or peace scholarship domains. As a new instrument in the global science and technology governance toolbox, a Science Peace Corps could work effectively, for example, towards elucidating the emerging concept of "one health"-encompassing human, environmental, plant, microbial, ecosystem, and planet health-thus serving as an innovative crosscutting pillar of 21st century integrative biology. An interdisciplinary program of this caliber for development would link 21st century life sciences to foreign policy and peace, in ways that can benefit many nations despite their ideological differences. We note that a Science Peace Corps is timely. The Intergovernmental Panel on Climate Change (IPCC) of the United Nations released the Fifth Assessment Report on March 31, 2014. Worrisomely, the report underscores that no person or nation will remain untouched by the climate change, highlighting the shared pressing life sciences challenges for global society. To this end, we recall that President John F. Kennedy advocated for volunteer work that has enduring, transgenerational, and global impacts. This culminated in establishment of the Peace Corps in 1961. Earlier, President Abraham Lincoln aptly observed, "nearly all men can stand adversity, but if you want to test a man's character, give him power." We therefore petition President Barack Obama, other world leaders, and international development agencies in positions of power around the globe, to consider deploying a Science Peace Corps to cultivate the essential (and presently missing) ties among life sciences, foreign policy, development, and peace agendas. A Science Peace Corps requires support by a credible and independent intergovernmental organization or development agency for funding, and arbitration in the course of volunteer work when the global versus local (glocal) value-based priorities and human rights intersect in synergy or conflict. In all, Science Peace Corps is an invitation to a new pathway for competence in 21st century science that is locally productive and globally competitive. It can open up scientific institutions to broader considerations and broader inputs, and thus cultivate vital translational science in a world sorely in need of solidarity and sustainable responses to the challenges of 21st century science and society.
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The Global Alliance for Genomics and Health (GA4GH) aims to accelerate biomedical advances by enabling the responsible sharing of clinical and genomic data through both harmonized data aggregation and federated approaches. The decreasing cost of genomic sequencing (along with other genome-wide molecular assays) and increasing evidence of its clinical utility will soon drive the generation of sequence data from tens of millions of humans, with increasing levels of diversity. In this perspective, we present the GA4GH strategies for addressing the major challenges of this data revolution. We describe the GA4GH organization, which is fueled by the development efforts of eight Work Streams and informed by the needs of 24 Driver Projects and other key stakeholders. We present the GA4GH suite of secure, interoperable technical standards and policy frameworks and review the current status of standards, their relevance to key domains of research and clinical care, and future plans of GA4GH. Broad international participation in building, adopting, and deploying GA4GH standards and frameworks will catalyze an unprecedented effort in data sharing that will be critical to advancing genomic medicine and ensuring that all populations can access its benefits. ; B.P.C. acknowledges funding from Abigail Wexner Research Institute at Nationwide Children's Hospital; T.H. Nyrönen acknowledges funding from Academy of Finland grant #31996; A.M.-J., K.N., T.F.B., O.M.H., and Z.S. acknowledge funding from Australian Medical Research Future Fund; M.S. acknowledges funding from Biobank Japan; D. Bujold and S.J.M.J. acknowledge funding from Canada Foundation for Innovation; L.J.D. acknowledges funding from Canada Foundation for Innovation Cyber Infrastructure grant #34860; D. Bujold and G.B. acknowledge funding from CANARIE; L.J.D. acknowledges funding from CANARIE Research Data Management contract #RDM-090 (CHORD) and #RDM2-053 (ClinDIG); K.K.-L. acknowledges funding from CanSHARE; T.L.T. acknowledges funding from Chan Zuckerberg Initiative; T. Burdett acknowledges funding from Chan Zuckerberg Initiative grant #2017-171671; D. Bujold, G.B., and L.D.S. acknowledge funding from CIHR; L.J.D. acknowledges funding from CIHR grant #404896; M.J.S.B. acknowledges funding from CIHR grant #SBD-163124; M. Courtot and M. Linden acknowledge funding from CINECA project EU Horizon 2020 grant #825775; D. Bujold and G.B. acknowledge funding from Compute Canada; F.M.-G. acknowledges funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – NFDI 1/1 "GHGA – German Human Genome-Phenome Archive; R.M.H.-S. acknowledges funding from Duke-Margolis Center for Health Policy; S.B. and A.J.B. acknowledge funding from EJP-RD EU Horizon 2020 grant #825575; A. Niewielska, A.K., D.S., G.I.S., J.A.T., J.R., M.A.K., M. Baudis, M. Linden, S.B., S.S., T.H. Nyrönen, and T.M.K. acknowledge funding from ELIXIR; A. Niewielska acknowledges funding from EOSC-Life EU Horizon 2020 grant #824087; J.-P.H. acknowledges funding from ETH Domain Strategic Focal Area "Personalized Health and Related Technologies (PHRT)" grant #2017-201; F.M.-G. acknowledges funding from EUCANCan EU Horizon 2020 grant #825835; B.M.K., D. Bujold, G.B., L.D.S., M.J.S.B., N.S., S.E.W., and Y.J. acknowledge funding from Genome Canada; B.M.K., M.J.S.B., S.E.W., and Y.J. acknowledge funding from Genome Quebec; F.M.-G. acknowledges funding from German Human Genome-Phenome Archive; C. Voisin acknowledges funding from Google; A.J.B. acknowledges funding from Health Data Research UK Substantive Site Award; D.H. acknowledges funding from Howard Hughes Medical Institute; S.B. acknowledges funding from Instituto de Salud Carlos III; S.-S.K. and K.T. acknowledge funding from Japan Agency for Medical Research and Development (AMED); S. Ogishima acknowledges funding from Japan Agency for Medical Research and Development (AMED) grant #20kk0205014h0005; C.Y. and K. Kosaki acknowledge funding from Japan Agency for Medical Research and Development (AMED) grant #JP18kk0205012; GEM Japan acknowledges funding from Japan Agency for Medical Research and Development (AMED) grants #19kk0205014h0004, #20kk0205014h0005, #20kk0205013h0005, #20kk0205012h0005, #20km0405401h0003, and #19km0405001h0104; J.R. acknowledges funding from La Caixa Foundation under project #LCF/PR/GN13/50260009; R.R.F. acknowledges funding from Mayo Clinic Center for Individualized Medicine; Y.J. and S.E.W. acknowledge funding from Ministère de l'Économie et de l'Innovation du Québec for the Can-SHARE Connect Project; S.E.W. and S.O.M.D. acknowledge funding from Ministère de l'Économie et de l'Innovation du Québec for the Can-SHARE grant #141210; M.A.H., M.C.M.-T., J.O.J., H.E.P., and P.N.R. acknowledge funding from Monarch Initiative grant #R24OD011883 and Phenomics First NHGRI grant #1RM1HG010860; A.L.M. and E.B. acknowledge funding from MRC grant #MC_PC_19024; P.T. acknowledges funding from National University of Singapore and Agency for Science, Technology and Research; J.M.C. acknowledges funding from NHGRI; A.H.W. acknowledges funding from NHGRI awards K99HG010157, R00HG010157, and R35HG011949; A.M.-J., K.N., D.P.H., O.M.H., T.F.B., and Z.S. acknowledge funding from NHMRC grants #GNT1113531 and #GNT2000001; D.L.C. acknowledges funding from NHMRC Ideas grant #1188098; A.B.S. acknowledges funding from NHMRC Investigator Fellowship grant #APP177524; J.M.C. and L.D.S. acknowledge funding from NIH; A.A.P. acknowledges funding from NIH Anvil; A.V.S. acknowledges funding from NIH contract #HHSN268201800002I (TOPMed Informatics Research Center); S.U. acknowledges funding from NIH ENCODE grant #UM1HG009443; M.C.M.-T. and M.A.H. acknowledge funding from NIH grant #1U13CA221044; R.J.C. acknowledges funding from NIH grants #1U24HG010262 and #1U2COD023196; M.G. acknowledges funding from NIH grant #R00HG007940; J.B.A., S.L., P.G., E.B., H.L.R., and L.S. acknowledge funding from NIH grant #U24HG011025; K.P.E. acknowledges funding from NIH grant #U2C-RM-160010; J.A.E. acknowledges funding from NIH NCATS grant #U24TR002306; M.M. acknowledges funding from NIH NCI contract #HHSN261201400008c and ID/IQ Agreement #17X146 under contract #HHSN2612015000031 and #75N91019D00024; R.M.C.-D. acknowledges funding from NIH NCI grant #R01CA237118; M. Cline acknowledges funding from NIH NCI grant #U01CA242954; K.P.E. acknowledges funding from NIH NCI ITCR grant #1U24CA231877-01; O.L.G. acknowledges funding from NIH NCI ITCR grant #U24CA237719; R.L.G. acknowledges funding from NIH NCI task order #17X147F10 under contract #HHSN261200800001E; A.F.R. acknowledges funding from NIH NHGRI grant #RM1HG010461; N.M. and L.J.Z. acknowledge funding from NIH NHGRI grant #U24HG006941; R.R.F., T.H. Nelson, L.J.B., and H.L.R. acknowledge funding from NIH NHGRI grant #U41HG006834; B.J.W. acknowledges funding from NIH NHGRI grant #UM1HG009443A; M. Cline acknowledges funding from NIH NHLBI BioData Catalyst Fellowship grant #5118777; M.M. acknowledges funding from NIH NHLBI BioData Catalyst Program grant #1OT3HL142478-01; N.C.S. acknowledges funding from NIH NIGMS grant #R35-GM128636; M.C.M.-T., M.A.H., P.N.R., and R.R.F. acknowledge funding from NIH NLM contract #75N97019P00280; E.B. and A.L.M. acknowledge funding from NIHR; R.G. acknowledges funding from Project Ris3CAT VEIS; S.B. acknowledges funding from RD-Connect, Seventh Framework Program grant #305444; J.K. acknowledges funding from Robertson Foundation; S.B. and A.J.B. acknowledge funding from Solve-RD, EU Horizon 2020 grant #779257; T.S. and S. Oesterle acknowledge funding from Swiss Institute of Bioinformatics (SIB) and Swiss Personalized Health Network (SPHN), supported by the Swiss State Secretariat for Education, Research and Innovation SERI; S.J.M.J. acknowledges funding from Terry Fox Research Institute; A.E.H., M.P.B., M. Cupak, M.F., and J.F. acknowledge funding from the Digital Technology Supercluster; D.F.V. acknowledges funding from the Australian Medical Research Future Fund, as part of the Genomics Health Futures Mission grant #76749; M. Baudis acknowledges funding from the BioMedIT Network project of Swiss Institute of Bioinformatics (SIB) and Swiss Personalized Health Network (SPHN); B.M.K. acknowledges funding from the Canada Research Chair in Law and Medicine and CIHR grant #SBD-163124; D.S., G.I.S., M.A.K., S.B., S.S., and T.H. Nyrönen acknowledge funding from the EU Horizon 2020 Beyond 1 Million Genomes (B1MG) Project grant #951724; P.F., A.D.Y., F.C., H.S., I.U.L., D. Gupta, M. Courtot, S.E.H., T. Burdett, T.M.K., and S.F. acknowledge funding from the European Molecular Biology Laboratory; Y.J. and S.E.W. acknowledge funding from the Government of Canada; P.G. acknowledges funding from the Government of Canada through Genome Canada and the Ontario Genomics Institute (OGI-206); J.Z. acknowledges funding from the Government of Ontario; C.K.Y. acknowledges funding from the Government of Ontario, Canada Foundation for Innovation; C. Viner and M.M.H. acknowledge funding from the Natural Sciences and Engineering Research Council of Canada (grant #RGPIN-2015-03948 to M.M.H. and Alexander Graham Bell Canada Graduate Scholarship to C.V.); K.K.-L. acknowledges funding from the Program for Integrated Database of Clinical and Genomic Information; J.K. acknowledges funding from the Robertson Foundation; D.F.V. acknowledges funding from the Victorian State Government through the Operational Infrastructure Support (OIS) Program; A.M.L., R.N., and H.V.F. acknowledge funding from Wellcome (collaborative award); F.C., H.S., P.F., and S.E.H. acknowledge funding from Wellcome Trust grant #108749/Z/15/Z; A.D.Y., H.S., I.U.L., M. Courtot, H.E.P., P.F., and T.M.K. acknowledge funding from Wellcome Trust grant #201535/Z/16/Z; A.M., J.K.B., R.J.M., R.M.D., and T.M.K. acknowledge funding from Wellcome Trust grant #206194; E.B., P.F., P.G., and S.F. acknowledge funding from Wellcome Trust grant #220544/Z/20/Z; A. Hamosh acknowledges funding from NIH NHGRI grant U41HG006627 and U54HG006542; J.S.H. acknowledges funding from National Taiwan University #91F701-45C and #109T098-02; the work of K.W.R. was supported by the Intramural Research Program of the National Library of Medicine, NIH. For the purpose of open access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. H.V.F. acknowledges funding from Wellcome Grant 200990/A/16/Z 'Designing, developing and delivering integrated foundations for genomic medicine'. ; Peer reviewed
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