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      Toward a Risk-Utility Data Governance Framework for Research Using Genomic and Phenotypic Data in Safe Havens: Multifaceted Review

      research-article
      , BSc, PhD 1 , , , BSc, PhD 1 , , BSc 1 , , BSc, PhD 1 , , MBA 1
      (Reviewer), (Reviewer)
      Journal of Medical Internet Research
      JMIR Publications
      genomic data, data safe havens, data governance

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          Abstract

          Background

          Research using genomic data opens up new insights into health and disease. Being able to use the data in association with health and administrative record data held in safe havens can multiply the benefits. However, there is much discussion about the use of genomic data with perceptions of particular challenges in doing so safely and effectively.

          Objective

          This study aimed to work toward a risk-utility data governance framework for research using genomic and phenotypic data in an anonymized form for research in safe havens.

          Methods

          We carried out a multifaceted review drawing upon data governance arrangements in published research, case studies of organizations working with genomic and phenotypic data, public views and expectations, and example studies using genomic and phenotypic data in combination. The findings were contextualized against a backdrop of legislative and regulatory requirements and used to create recommendations.

          Results

          We proposed recommendations toward a risk-utility model with a flexible suite of controls to safeguard privacy and retain data utility for research. These were presented as overarching principles aligned to the core elements in the data sharing framework produced by the Global Alliance for Genomics and Health and as practical control measures distilled from published literature and case studies of operational safe havens to be applied as required at a project-specific level.

          Conclusions

          The recommendations presented can be used to contribute toward a proportionate data governance framework to promote the safe, socially acceptable use of genomic and phenotypic data in safe havens. They do not purport to eradicate risk but propose case-by-case assessment with transparency and accountability. If the risks are adequately understood and mitigated, there should be no reason that linked genomic and phenotypic data should not be used in an anonymized form for research in safe havens.

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          Most cited references26

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          GINA, genetic discrimination, and genomic medicine.

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            Privacy in the Genomic Era.

            Genome sequencing technology has advanced at a rapid pace and it is now possible to generate highly-detailed genotypes inexpensively. The collection and analysis of such data has the potential to support various applications, including personalized medical services. While the benefits of the genomics revolution are trumpeted by the biomedical community, the increased availability of such data has major implications for personal privacy; notably because the genome has certain essential features, which include (but are not limited to) (i) an association with traits and certain diseases, (ii) identification capability (e.g., forensics), and (iii) revelation of family relationships. Moreover, direct-to-consumer DNA testing increases the likelihood that genome data will be made available in less regulated environments, such as the Internet and for-profit companies. The problem of genome data privacy thus resides at the crossroads of computer science, medicine, and public policy. While the computer scientists have addressed data privacy for various data types, there has been less attention dedicated to genomic data. Thus, the goal of this paper is to provide a systematization of knowledge for the computer science community. In doing so, we address some of the (sometimes erroneous) beliefs of this field and we report on a survey we conducted about genome data privacy with biomedical specialists. Then, after characterizing the genome privacy problem, we review the state-of-the-art regarding privacy attacks on genomic data and strategies for mitigating such attacks, as well as contextualizing these attacks from the perspective of medicine and public policy. This paper concludes with an enumeration of the challenges for genome data privacy and presents a framework to systematize the analysis of threats and the design of countermeasures as the field moves forward.
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              A Profile of the SAIL Databank on the UK Secure Research Platform

              Background The Secure Anonymised Information Linkage (SAIL) Databank is a national data safe haven of de identified datasets principally about the population of Wales, made available in anonymised form to researchers across the world. It was established to enable the vast arrays of data collected about individuals in the course of health and other public service delivery to be made available to answer important questions that could not otherwise be addressed without prohibitive effort. The SAIL Databank is the bedrock of other funded centres relying on the data for research. Approach SAIL is a data repository surrounded by a suite of physical, technical and procedural control measures embodying a proportionate privacy-by-design governance model, informed by public engagement, to safeguard the data and facilitate data utility. SAIL operates on the UK Secure Research Platform (SeRP), which is a customisable technology and analysis platform. Researchers access anonymised data via this secure research environment, from which results can be released following scrutiny for disclosure risk. SAIL data are being used in multiple research areas to evaluate the impact of health and social exposures and policy interventions. Discussion Lessons learned and their applications include: managing evolving legislative and regulatory requirements; employing multiple, tiered security mechanisms; working hard to increase analytical capacity efficiency; and developing a multi-faceted programme of public engagement. Further work includes: incorporating new data types; enabling alternative means of data access; and developing further efficiencies across our operations. Conclusion SAIL represents an ongoing programme of work to develop and maintain an extensive, whole population data resource for research. Its privacy-by-design model and UK SeRP technology have received international acclaim, and we continually endeavour to demonstrate trustworthiness to support data provider assurance and public acceptability in data use. We strive for further improvement and continue a mutual learning process with our contemporaries in this rapidly developing field
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                Author and article information

                Contributors
                Journal
                J Med Internet Res
                J. Med. Internet Res
                JMIR
                Journal of Medical Internet Research
                JMIR Publications (Toronto, Canada )
                1439-4456
                1438-8871
                May 2020
                15 May 2020
                : 22
                : 5
                : e16346
                Affiliations
                [1 ] Population Data Science Swansea University Medical School Swansea University Swansea United Kingdom
                Author notes
                Corresponding Author: Kerina Jones k.h.jones@ 123456swansea.ac.uk
                Author information
                https://orcid.org/0000-0001-8164-3718
                https://orcid.org/0000-0001-8899-0333
                https://orcid.org/0000-0002-3007-644X
                https://orcid.org/0000-0001-7983-8073
                https://orcid.org/0000-0001-6551-721X
                Article
                v22i5e16346
                10.2196/16346
                7260661
                32412420
                4e88bbcb-dd8d-49e8-8c23-da97075657dd
                ©Kerina Jones, Helen Daniels, Sharon Heys, Arron Lacey, David V Ford. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 15.05.2020.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

                History
                : 20 September 2019
                : 12 December 2019
                : 13 January 2020
                : 30 January 2020
                Categories
                Original Paper
                Original Paper

                Medicine
                genomic data,data safe havens,data governance
                Medicine
                genomic data, data safe havens, data governance

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