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Quality deliberation is essential for societies to address the challenges presented by the coronavirus pandemic effectively and legitimately. Critics of deliberative and participatory democracy are highly skeptical that most citizens can engage with such complex issues in good circumstances and these are far from ideal circumstances. The need for rapid action and decision-making is a challenge for inclusivity and quality of deliberation. Additionally, policy responses to the virus need to be even more co-ordinated than usual, which intensifies their complexity. The digitalisation of the public sphere may be seen as a further challenge to deliberating. Furthermore, these are stressful and emotional times, making a considered judgement on these issues potentially challenging. We employ a modified version of the Discourse Quality Index to assess the deliberative quality in two facilitated synchronous digital platforms to consider aspects of data use in light of COVID 19. Our study is the first to perform a comprehensive, systematic and in-depth analysis of the deliberative capacity of citizens in a pandemic. Our evidence indicates that deliberation can be resilient in a crisis. The findings will have relevance to those interested in pandemic democracy, deliberative democracy in a crisis, data use and digital public spheres. [ABSTRACT FROM AUTHOR] Copyright of Javnost-The Public is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article outlines the value of administrative data for family justice research. Although socio-legal scholars have extended their research beyond purely theoretical or doctrinal analyses, studi...

Background Many countries remain in the grip of the COVID-19 global pandemic, with a considerable journey still ahead toward normalcy and free mobility. Contact tracing smartphone apps are among a raft of measures introduced to reduce spread of the virus, but their uptake depends on public choice. Objective The objective of this study was to ascertain the views of citizens in Wales on their intended use of a COVID-19 contact tracing smartphone app, including self-proposed reasons for or against use and what could lead to a change of decision. Methods We distributed an anonymous survey among 4000 HealthWise Wales participants in May 2020. We adopted a mixed methods approach: responses to closed questions were analyzed using descriptive and inferential statistics; open question responses were analyzed and grouped into categories. Results A total of 976 (24.4%) people completed the survey. Smartphone usage was 91.5% overall, but this varied among age groups. In total, 97.1% were aware of contact tracing apps, but only 67.2% felt sufficiently informed. Furthermore, 55.7% intended to use an app, 23.3% refused, and 21.0% were unsure. The top reasons for app use were as follows: controlling the spread of the virus, mitigating risks for others and for oneself, and increasing freedoms. The top reasons against app use were as follows: mistrusting the government, concerns about data security and privacy, and doubts about efficacy. The top response for changing one’s mind about app use from being willing to being unwilling was that nothing would; that is, they felt that nothing would cause them to become unwilling to use a contact tracing app. This was also the top response for changing one’s mind from being unwilling to being willing to use contact tracing apps. Among those who were unsure of using contact tracing apps, the top response was the need for more information. Conclusions Respondents demonstrated a keenness to help themselves, others, society, and the government to avoid contracting the virus and to control its spread. However, digital inclusion varied among age groups, precluding participation for some people. Nonetheless, unwillingness was significant, and considering the nature of the concerns raised and the perceived lack of information, policy and decision-makers need to do more to act openly, increase communication, and demonstrate trustworthiness if members of the public are to be confident in using an app.

Background: Text-mining techniques are advancing all the time and vast corpora of social media text can be analyzed for users' views and experiences related to their health. There is great promise for new insights into health issues such as drug side effects and spread of disease, as well as patient experiences of health conditions and health care. However, this emerging field lacks ethical consensus and guidance. We aimed to bring together a comprehensive body of opinion, views, and recommendations in this area so that academic researchers new to the field can understand relevant ethical issues.Methods: After registration of a protocol in PROSPERO, three parallel systematic searches were conducted, to identify academic articles comprising commentaries, opinion, and recommendations on ethical practice in social media text mining for health research and gray literature guidelines and recommendations. These were integrated with social media users' views from qualitative studies. Papers and reports that met the inclusion criteria were analyzed thematically to identify key themes, and an overarching set of themes was deduced.Results: A total of 47 reports and articles were reviewed, and eight themes were identified. Commentators suggested that publicly posted social media data could be used without consent and formal research ethics approval, provided that the anonymity of users is ensured, although we note that privacy settings are difficult for users to navigate on some sites. Even without the need for formal approvals, we note ethical issues: to actively identify and minimize possible harms, to conduct research for public benefit rather than private gain, to ensure transparency and quality of data access and analysis methods, and to abide by the law and terms and conditions of social media sites.Conclusion: Although social media text mining can often legally and reasonably proceed without formal ethics approvals, we recommend improving ethical standards in health-related research by increasing transparency of the purpose of research, data access, and analysis methods; consultation with social media users and target groups to identify and mitigate against potential harms that could arise; and ensuring the anonymity of social media users.

Introduction: The long-term health and wellbeing of adoptees is under-researched. One reason for this has been limited data accessibility regarding the adoption process, and another is a practice common in some UK jurisdictions of changing the NHS number (or equivalent) at adoption, as part of creating the new identity. The SAIL Databank holds data from the Wales Children and Family Court Advisory and Support Service (Cafcass Cymru), together with children’s social care data, and can link these with routine health and administrative data in anonymised form. However, because the linkage key at SAIL is based on an encryption of the NHS number, working with pre- and post-adoption records for longitudinal research remains a major challenge. We set out to explore the legal implications of, and social support for, linking these records for use in anonymised form for longitudinal research. Methods: We reviewed the main legislation and regulations governing the use of data about adoptees in England and Wales. We gauged support for a social licence in Wales by carrying out interviews with individuals who had been involved in the adoptions process, and by engaging with general public groups for their views. We drew out the main emerging themes and, in combination with the review, propose a way forward. Results: The legal review indicated that there are provisions in the Family Procedure Rules (England and Wales) and the General Data Protection Regulation that can be relied upon for the lawful processing of adoption data into anonymised form for research. The main points of concern about linking pre- and post-adoption records were privacy, data security, the need to limit the number of organisations involved in data sharing, and re-identification risk. The over-riding message was favourable with longitudinal research seen as strongly beneficial. Conclusion: This study has indicated that in Wales, there is no legal impediment, nor major objection from individuals involved in the adoptions process, as well as the general public for the use of adoption data in anonymised form, in a data safe haven. This includes the linkage of pre- and post‑adoption records to enable novel longitudinal research to take place. The provisos were that robust safeguards must be in place, and that the research should aim to benefit adoptees and to improve policy and practice. We conclude that it is reasonable to proceed with caution to develop practical ways to link pre- and post‑adoption records in a data safe haven.

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.

Background: The SAIL Databank is a data safe haven established in 2007 at Swansea University (Wales). It was set up to create new opportunities for research using routinely-collected health and other public service datasets in linkable anonymised form. SAIL forms the bedrock of other Population Data Science initiatives made possible by the data and safe haven environment. Aim: The aim of this paper is to provide an overview of public involvement and engagement in connection with the SAIL Databank and related Population Data Science initiatives. Approach: We have a public involvement and engagement policy for SAIL in the context of Population Data Science. We established a Consumer Panel to provide advice on the work of SAIL and associated initiatives, including on proposed uses of SAIL data. We reviewed the topics discussed and provide examples of advice to researchers. We carried out a survey with members on their experiences of being on the Panel and their perceptions of the work of SAIL. We have a programme of wider public engagement and provide illustrations of this work. Discussion: We summarise what this paper adds and some lessons learned. In the rapidly developing area of Population Data Science it is important that people feel welcome, that they are encouraged to ask questions and are provided with digestible information and adequate consideration time. Citizens have provided us with valuable anticipated and unanticipated opinions and novel viewpoints. We seek to take a pragmatic approach, prioritising the communication modes that allow maximum public input commensurate with the purpose of the activity. Conclusion: This paper has set out our policy, rationale, scope and practical approaches to public involvement and engagement for SAIL and our related Population Data Science initiatives. Although there will be jurisdictional, cultural and organizational differences, we believe that the material covered in this paper will be of interest to other data focused enterprises across the world.

The global coronavirus pandemic has clearly demonstrated the great urgency to collect and use patient data effectively to understand, track and manage the spread of Covid-19. The value of patient data in this pandemic is undeniable, however considerations around how - and by whom - such data should be collected, accessed and used, and for what purposes, remain to be fully debated and resolved. Who decides, and how such decisions are made remain unclear. We argue that public engagement and deliberation are essential for good governance and are key to establish and maintain a legitimate social licence for data practices around Covid-19.

Background Clinical free-text data (eg, outpatient letters or nursing notes) represent a vast, untapped source of rich information that, if more accessible for research, would clarify and supplement information coded in structured data fields. Data usually need to be deidentified or anonymized before they can be reused for research, but there is a lack of established guidelines to govern effective deidentification and use of free-text information and avoid damaging data utility as a by-product. Objective This study aimed to develop recommendations for the creation of data governance standards to integrate with existing frameworks for personal data use, to enable free-text data to be used safely for research for patient and public benefit. Methods We outlined data protection legislation and regulations relating to the United Kingdom for context and conducted a rapid literature review and UK-based case studies to explore data governance models used in working with free-text data. We also engaged with stakeholders, including text-mining researchers and the general public, to explore perceived barriers and solutions in working with clinical free-text. Results We proposed a set of recommendations, including the need for authoritative guidance on data governance for the reuse of free-text data, to ensure public transparency in data flows and uses, to treat deidentified free-text data as potentially identifiable with use limited to accredited data safe havens, and to commit to a culture of continuous improvement to understand the relationships between the efficacy of deidentification and reidentification risks, so this can be communicated to all stakeholders. Conclusions By drawing together the findings of a combination of activities, we present a position paper to contribute to the development of data governance standards for the reuse of clinical free-text data for secondary purposes. While working in accordance with existing data governance frameworks, there is a need for further work to take forward the recommendations we have proposed, with commitment and investment, to assure and expand the safe reuse of clinical free-text data for public benefit.

Introduction Better use of administrative data is essential to enhance understanding about the family justice system, and characteristics and outcomes for children and families. The Nuffield Family Justice Observatory Data Partnership supports this aim through analyses of core family justice datasets. When a child is involved in family court proceedings in Wales, Cafcass Cymru are employed to represent a child’s best interests. This paper provides an overview of the Cafcass Cymru data, and linkage to population level health and other administrative datasets held within the Secure Anonymised Information Linkage (SAIL) Databank. Two data linkage example analyses are described. Further research opportunities are outlined. Methods Cafcass Cymru data was transferred to SAIL using a standardised approach to provide de-identified data with Anonymised Linking Fields (ALF) for successfully matched records. Three cohorts were created: all individuals involved in family court applications; all individuals with an ALF allowing subsequent health data linkage; and all individuals with a Residential Anonymised Linking Field (RALF) and Lower Super Output Area (LSOA) enabling area level deprivation analysis. Results Cafcass Cymru data are available containing 12,745 public law applications between 2011 and 2019, with 52,023 applications from 2005 to 2019 for private law. The overall match rate was 80%, with variations observed by time, law type, roles, gender and age. Forty per cent had hospital inpatient admissions 2 years prior or after application receipt at Cafcass Cymru, of which 27% were for emergency admissions; 54% had an emergency department attendance and 61% an outpatient appointment during the same period. Individuals involved in public or private law applications were more likely to reside in deprived areas. Conclusion The Nuffield Family Justice Observatory Data Partnership will enhance research opportunities to better understand the family justice system and outcomes for children and families. Population level Cafcass Cymru data can be accessed through the SAIL Databank. Forthcoming data acquisition will also facilitate further analyses and insight.

The concept of precision medicine aims to tailor treatment based on data unique to the patient. An example is the use of genetic data from malignant tumours to select the most appropriate oncological treatment. The competing interests of utilitarianism and egoism create dilemmas for decisions regarding investment in precision medicine. The need to balance the perceived rights and needs of individuals against those of society as a whole is an on-going challenge in the distribution of limited health service resources. There is need for proper planning, organisation and investment into precision medicine to cope with the consequences of both direct-to-consumer and healthcare-directed genetic testing for genetic counselling, therapeutics and diagnostic networks. Consideration needs to be given to providing adequate time and training to allow for meaningful shared decision-making with patients and there is a strong case in support of a hub-and-spoke model to provide rapid, solid tumour genetic mutational analysis to prevent patients missing out on beneficial treatments.

Objectives To investigate through survey and data linkage, healthcare resource use and costs (except drugs), including who bears the cost, of multiple sclerosis in the United Kingdom by disease severity and type. Methods The United Kingdom Multiple Sclerosis Register deployed a cost of illness survey, completed by people with multiple sclerosis and linked this with data within the United Kingdom Multiple Sclerosis Register and from their hospital records. Resource consumption was categorised as being medical or non-medical and costed by National Health Service and social services estimates for 2018. Results We calculated £509,003 in non-medical costs over a year and £435,488 in medical costs generated over 3 months. People with multiple sclerosis reported self-funding 75% of non-medical costs with non-medical interventions having long-term potential benefits. Costs increased with disability as measured by patient-reported Expanded Disability Status Score and Multiple Sclerosis Impact Scale, with Multiple Sclerosis Impact Scale physical being a more powerful predictor of costs than the patient-reported Expanded Disability Status Score. Two distinct groups were identified: medical and non-medical interventions ( n = 138); and medical interventions only ( n = 399). The medical and non-medical group reported increased disease severity and reduced employment but incurred 80% more medical costs per person than the medical-only group. Conclusions The importance of disability in driving costs is illustrated with balance between medical and non-medical costs consistent with the United Kingdom health environment. People with multiple sclerosis and their families fund a considerable proportion of non-medical costs but non-medical interventions with longer term impact could affect future medical costs.

Supplemental material, MSO901727 Supplemental material for Personal and societal costs of multiple sclerosis in the UK: A population-based MS Registry study by Richard S Nicholas, Martin L Heaven, Rodden M Middleton, Manoj Chevli, Ruth Pulikottil-Jacob, Kerina H Jones and David V Ford in Multiple Sclerosis Journal – Experimental, Translational and Clinical

Background Advancing the use of genomic data with routinely collected health data holds great promise for health care and research. Increasing the use of these data is a high priority to understand and address the causes of disease. Objective This study aims to provide an outline of the use of genomic data alongside routinely collected data in health research to date. As this field prepares to move forward, it is important to take stock of the current state of play in order to highlight new avenues for development, identify challenges, and ensure that adequate data governance models are in place for safe and socially acceptable progress. Methods We conducted a literature review to draw information from past studies that have used genomic and routinely collected data and conducted interviews with individuals who use these data for health research. We collected data on the following: the rationale of using genomic data in conjunction with routinely collected data, types of genomic and routinely collected data used, data sources, project approvals, governance and access models, and challenges encountered. Results The main purpose of using genomic and routinely collected data was to conduct genome-wide and phenome-wide association studies. Routine data sources included electronic health records, disease and death registries, health insurance systems, and deprivation indices. The types of genomic data included polygenic risk scores, single nucleotide polymorphisms, and measures of genetic activity, and biobanks generally provided these data. Although the literature search showed that biobanks released data to researchers, the case studies revealed a growing tendency for use within a data safe haven. Challenges of working with these data revolved around data collection, data storage, technical, and data privacy issues. Conclusions Using genomic and routinely collected data holds great promise for progressing health research. Several challenges are involved, particularly in terms of privacy. Overcoming these barriers will ensure that the use of these data to progress health research can be exploited to its full potential.

Background Not enough is known about how the family justice system is working, the children and families using these services, and their wider outcomes beyond their involvement with the family court. The Nuffield Family Justice Observatory (FJO) Data Partnership, comprising a bespoke analysis platform hosted within the Secure Anonymised Information Linkage (SAIL) Databank and analytical teams at Lancaster and Swansea Universities, has been established to address this knowledge deficit. MethodsFamily justice data is being deposited in the SAIL Databank. Data are acquired using a standardised split-file approach, stored in an anonymised format and made available to projects as linked data using a unique encrypted project anonymised linkage field. These data can be augmented with a wealth of available health, education and other governmental/social routinely collected datasets, and future data acquired from other sources, for a range of research projects. ResultsChildren and Family Court Advisory and Support Service (Cafcass) Cymru data has been transferred to the SAIL Databank, and agreements to transfer the Cafcass England data are being finalised. Applications are now welcomed to use these data to enhance understanding of the family justice system and children and families involved with the family courts in public and private law. Access will be facilitated through the SAIL Databank, subject to relevant governance procedures. ConclusionWe will discuss the rationale of the Nuffield FJO Data Partnership, and how it aims to a) increase capacity and capability of researchers and data scientists utilising family justice and other relevant administrative datasets, b) improve understanding about the family justice system using data from the Cafcass in England and Wales, c) demonstrate the complexities and value of data linkage, and d) assist future policy and practice development. We will discuss matching rates for the Cafcass Cymru data and linkages that have been made to other datasets within the SAIL Databank. We will also set out the support available from the partnership to those wishing to access and utilise family justice data.

Background/rationaleNations with advanced child protection systems place considerable emphasis on the developmental salience of infancy. However, this emphasis is not matched by any differentiated analysis of the timing of family court intervention in the lives of infants or the final legal order outcomes of these cases. This presentation shares findings from the first ever population profiling study of infants subject to care proceedings within the family justice system in England and Wales. AimTo estimate the proportion of all infant care proceedings cases issued within 7 days and 4 weeks of birth and describe case and infant characteristics; to calculate incidence rates over time and by local authority and family court region; to describe and compare legal order outcomes according to age. Methods/approachData was extracted from case management records produced by the Children and Family Court Advisory and Support Service (Cafcass) England and Wales. Records were first restructured to link infants to legal order outcome data and birth mother records. Incidence rates were calculated using ONS mid-year population estimates and annual live births. Within the SAIL Databank, Welsh infant records were linked to birth registration and community child health data to produce a fuller picture of infant characteristics in Wales. ResultsThe cohort we created comprised all infants recorded as subjects within care proceedings in England (2007/08-2016/17) and Wales (2011/12-2018/19). The study captured the high proportion of infant cases that are issued at/close to birth, but also marked regional and local authority variation in incidence rates. ConclusionHigh rates of adoption, particularly for babies born to mothers without a previous family court history, have prompted calls for new preventative solutions. The President of the Family (Court) Division in England has initiated a review of legal proceedings at birth.

BACKGROUND Clinical free-text data (eg, outpatient letters or nursing notes) represent a vast, untapped source of rich information that, if more accessible for research, would clarify and supplement information coded in structured data fields. Data usually need to be deidentified or anonymized before they can be reused for research, but there is a lack of established guidelines to govern effective deidentification and use of free-text information and avoid damaging data utility as a by-product. OBJECTIVE This study aimed to develop recommendations for the creation of data governance standards to integrate with existing frameworks for personal data use, to enable free-text data to be used safely for research for patient and public benefit. METHODS We outlined data protection legislation and regulations relating to the United Kingdom for context and conducted a rapid literature review and UK-based case studies to explore data governance models used in working with free-text data. We also engaged with stakeholders, including text-mining researchers and the general public, to explore perceived barriers and solutions in working with clinical free-text. RESULTS We proposed a set of recommendations, including the need for authoritative guidance on data governance for the reuse of free-text data, to ensure public transparency in data flows and uses, to treat deidentified free-text data as potentially identifiable with use limited to accredited data safe havens, and to commit to a culture of continuous improvement to understand the relationships between the efficacy of deidentification and reidentification risks, so this can be communicated to all stakeholders. CONCLUSIONS By drawing together the findings of a combination of activities, we present a position paper to contribute to the development of data governance standards for the reuse of clinical free-text data for secondary purposes. While working in accordance with existing data governance frameworks, there is a need for further work to take forward the recommendations we have proposed, with commitment and investment, to assure and expand the safe reuse of clinical free-text data for public benefit.

Background The literature abounds with increasing numbers of research studies using genomic data in combination with health data (eg, health records and phenotypic and lifestyle data), with great potential for large-scale research and precision medicine. However, concerns have been raised about social acceptability and risks posed for individuals and their kin. Although there has been public engagement on various aspects of this topic, there is a lack of information about public views on data access models. Objective This study aimed to address the lack of information on the social acceptability of access models for reusing genomic data collected for research in conjunction with health data. Models considered were open web-based access, released externally to researchers, and access within a data safe haven. Methods Views were ascertained using a series of 8 public workshops (N=116). The workshops included an explanation of benefits and risks in using genomic data with health data, a facilitated discussion, and an exit questionnaire. The resulting quantitative data were analyzed using descriptive and inferential statistics, and the qualitative data were analyzed for emerging themes. Results Respondents placed a high value on the reuse of genomic data but raised concerns including data misuse, information governance, and discrimination. They showed a preference for giving consent and use of data within a safe haven over external release or open access. Perceived risks with open access included data being used by unscrupulous parties, with external release included data security, and with safe havens included the need for robust safeguards. Conclusions This is the first known study exploring public views of access models for reusing anonymized genomic and health data in research. It indicated that people are generally amenable but prefer data safe havens because of perceived sensitivities. We recommend that public views be incorporated into guidance on models for the reuse of genomic and health data.

BackgroundFree-text data represent a vast, untapped source of rich information to guide research and public service delivery. Free-text data contain a wealth of additional detail that, if more accessible, would clarify and supplement information coded in structured data fields. Personal data usually need to be de-identified or anonymised before they can be used for purposes such as audit and research, but there are major challenges in finding effective methods to de-identify free-text that do not damage data utility as a by-product. The main aim of the TexGov project is to work towards data governance standards to enable free-text data to be used safely for public benefit. MethodsWe conducted: a rapid literature review to explore the data governance models used in working with free-text data, plus case studies of systems making de-identified free-text data available for research; we engaged with text mining researchers and the general public to explore barriers and solutions in working with free-text; and we outlined (UK) data protection legislation and regulations for context. ResultsWe reviewed 50 articles and the models of 4 systems providing access to de-identified free-text. The main emerging themes were: i) patient involvement at identifiable and de-identified data stages; ii) questions of consent and notification for the reuse of free-text data; iii) working with identifiable data for Natural Language Processing algorithm development; and iv) de-identification methods and thresholds of reliability. ConclusionWe have proposed a set of recommendations, including: ensuring public transparency in data flows and uses; adhering to the principles of minimal data extraction; treating de-identified blacklisted free-text as potentially identifiable with use limited to accredited data safe-havens; and, the need to commit to a culture of continuous improvement to understand the relationships between accuracy of de-identification and re-identification risk, so this can be communicated to all stakeholders.

The creation of vast, complex datasets made possible by technological advances over recent decades, has resulted in data becoming big business across many sectors and disciplines world-wide. Everyday life is increasingly networked via a growing array of digital devices to which individuals provide data, passively and actively. The pace of development has led to questions about the role of such ‘data donors’ and how individuals can be safeguarded when they might not be fully cognisant of the extent or destinations of data provided. We show that the many ways in which individuals provide data about themselves can result in incongruities and dilemmas in apparent decision making. We argue that it is not ethical for the vast swathes of data provided by individuals not to be used for public good. We explore whether we can make truly informed choices with the panoply of issues that may influence our decisions. We conclude without a straightforward yes or no, but propose that if we provide the best available information and engage with information presented, we stand a more reasonable chance. Do that, there is a need for demonstrable trustworthiness and clarity, greater awareness so that trust can be placed wisely, and for us to hone our juggling skills.

Background The great benefits of linking health datasets for research in the public interest have long been demonstrated. More recently, we are seeing an increase in the availability of wider administrative data, such as employment, education and housing, to add new opportunities for population data science. However, there are challenges to be overcome in selecting a data linkage approach. Main Aim We set out to examine various data linkage approaches, and to formulate some high level questions to inform decision-making. Methods We used published literature to review various data linkage methods in theory and in practical settings. The study was commissioned by the UK Government Statistical Service and a key focus was privacy and confidentiality in data linkage. Results The questions we formulated are based on: Legislative position; Information systems; Nature of datasets; Knowledge-base; Aims and purposes; Ground truth; and Environment. Conclusion There are many factors influencing the selection of a data linkage approach. While not exhaustive, our set of questions covers some of the major ones. The findings of the study are being taken forward by UK Government Statistical Service and government departments to inform decision-making on options for data linkage research and the greater availability of their datasets.

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.

IntroductionSocietal and individual benefits of data-intensive science are substantial but raise challenges of balancing individual privacy and public good, while building appropriate governance and socio-technical systems to support data-intensive science. We set out to define a new field of inquiry to move collective interests forward. Objectives and ApproachOur objectives were: 1. To create a concise definition of the emerging field of Population Data Science; 2. To highlight the characteristics and challenges of Population Data Science; 3. To differentiate Population Data Science from existing fields of data science and informatics; and 4. To discuss the implications and future opportunities for Population Data Science. Objectives 1 and 2 were met largely through International Population Data Linkage Network (IPDLN) member engagement, Objective 3 was evaluated via literature review, and Objective 4 was achieved through iterative and collective work on a peer-reviewed position paper. ResultsWe define Population Data Science succinctly as the science of data about people. It is related to, but distinct from, the fields of data science and informatics. A broader definition includes four characteristics of: i) data use for positive impact on individuals and populations; ii) bringing together and analyzing data from multiple sources; iii) identifying population-level insights; and iv) developing safe, privacy-sensitive and ethical infrastructure to support research. One implication of these characteristics is that few individuals or organisations possess all of the requisite knowledge and skills comprising Population Data Science, so this is by nature a multi-disciplinary “team science” field. There is a need to advance various aspects of science, such as data linkage technology, various forms of analytics, and methods of public engagement. Conclusion/ImplicationsThese implications are the beginnings of a research agenda for Population Data Science, which if approached as a collective field, will catalyze significant advances in our understanding of society, health, and human behavior and increase the impact of our research.

Background and rationaleThe International Journal of Population Data Science (IJPDS) was launched in April 2017. It is an electronic, open-access, peer reviewed journal, publishing articles on all aspects of research, development and evaluation connected with about people and populations. It represents an internationally unique vehicle for publishing a broader range of articles than most journals in related fields by including in scope: working papers, methodological developments, informative reports, and other pieces of interest, in addition to more traditional manuscripts. As such, it provides a focal point (and a home) for all areas of Population Data Science. The creation of the IJPDS was inspired by the IPDLN, and places great importance on the viewpoints and activities of Network members to guide the development of the journal. ObjectiveInformation dissemination – stakeholder consultation – informing future directionsThe main objective of this collaborative session is to present the audience with an up-to-date summary of journal strategy and progress to date, and to use this forum to gain further viewpoints to better target future directions of the journal to meet the needs of those working in Population Data Science. PlanThe session will comprise 5 sections: A short presentation overviewing the journal, its historical origins, its remit and relationship to the IPDLN and Network members. Primary objectives of the journal and performance metrics from the first 18 months of operation will be presented. Gauging audience opinion on: What they like/dislike about the journal What is working well/not so well Feedback and discussion on the survey results Discussions with audience in groups, each focusing on some/all of the following questions, and a question of their own choice if something arises: How can we make IJPDS articles more accessible to the general reader/non-specialist researcher?Example options: short video/audio; general reader summaries; infographics; other How can we increase the reach of, and interest in, the journal? Open answers What would you suggest as a special issue?These topics for group discussion will be posed to the session audience with the aim of using the feedback in defining our priorities for the coming year. Feedback and summing up The groups will be asked to give their feedback. Feedback from the discussions and voting will be used to inform the next steps for IJPDS. Facilitators Kerina Jones, Founding Editor-in-Chief, Swansea University, Wales Kim McGrail, Deputy Editor, University of British Columbia, Canada Claudia Medina Coeli, Editorial Board member, Rio de Janeiro Federal University, Brazil Rainer Schnell, Editorial Board member, University of Duisburg-Essen, Germany Stephanie Lee, Journal Director of Operations, Swansea University, Wales

IntroductionMany jurisdictions have programmes for the large-scale reuse of health and administrative data that would benefit from greater cross-centre working. The Advancing Cross centre Research Networks (ACoRN) project considered barriers and drivers for joint working and information sharing using the UK Farr Institute as a case study, and applicable widely. Objectives and ApproachACoRN collected information from researchers, analysts, academics and the public to gauge the acceptability of sharing data across institutions and jurisdictions. It considered international researcher experiences and evidence from a variety of cross centre projects to reveal barriers and potential solutions to joint working. It reviewed the legal and regulatory provisions that surround data sharing and cross-centre working, including issues of information governance to provide the context and backdrop. The emerging issues were grouped into five themes and used to propose a set of recommendations. ResultsThe five themes identified were: organisational structures and legal entities; people and culture; information governance; technology and infrastructure; and finance and strategic planning. Recommendations within these included: standardised terms and conditions including agreements and contractual templates; performance indicators for frequency of dataset sharing; communities of practice and virtual teams to develop cooperation; standardised policies and procedures to underpin data sharing; an accredited quality seal for organisations sharing data; a dashboard for data availability and sharing; and adequate resource to move towards greater uniformity and to drive data sharing initiatives. Conclusion/ImplicationsThe challenges posed by cross-centre information sharing are considerable but the public benefits associated with the greater use of health and administrative data are inestimable, particularly as novel and emerging data become increasingly available. The proposed recommendations will assist in achieving the benefits of cross-centre working.

IntroductionCall Detail Records (CDRs) are collected by mobile network operators in the course of service provision, and they are increasingly being used in health research. It has been identified that further work is needed to show that CDRs can be used within an ethically-founded framework that meets with social acceptability. Objectives and ApproachThe published research literature was reviewed to identify data governance arrangements, challenges and potential opportunities for the greater use of the location element of CDRs in health research. A series of 3 workshops with members of the public (N=61) were conducted to gain views on the use of CDRs for health research. Data use scenarios of CDRs for health research were constructed to consider risk and mitigating controls. The findings were drawn together against a backdrop of legislative and regulatory requirements. ResultsThe majority of published studies focused on low and middle income countries, often modelling the transmission of infectious diseases, and population movement following natural disasters. CDRs were used in anonymised or aggregated form, and gaining regulatory approvals varied with data provider and by jurisdiction. Only 2 people knew CDR data was being used for health research, but ultimately, most (N=49) were happy for their anonymised CDRs to be used, provided that safeguards were in place. Recommendations towards an ethically-founded framework for using CDR locations in health research are proposed, including the need for greater transparency, accountability, and the incorporation of public views for social acceptability. Conclusion/ImplicationsDespite limitations inherent in the data, mobile phone CDRs have been used successfully in health research. People are generally amenable to the use of anonymised CDR data, but they want to be properly informed. The proposed recommendations should be taken into consideration to contribute towards a consistent, socially-acceptable, ethically founded framework.

BackgroundThere are there are considerable challenges to be addressed so the benefits of administrative data for research can be realised. Significant headway is being made, but there is great scope and appetite for further improvement. ObjectivesThis study set out to explore good practice, barriers and bottlenecks in effective administrative data use, and to gain suggestions on how to share the good, solve the bad and improve the clunky issues. MethodsUsing the ESRC-funded UK Administrative Data Research Network (ADRN) as the case study, a qualitative survey, focusing on the data use pathway, was carried out across the network. This encompassed a set of 18 questions spanning from acquisition to archiving. Survey responses were grouped into six themes: data acquisition; approval processes; controls on access and disclosure; data and metadata; researcher support; and data reuse and retention. The resulting information matrix was presented to participants at the All Hands meeting (April-May 2017) to facilitate discussion. FindingsSurvey responses were received from across the network (N=27) and 95 people took part in the workshop. The combined information from the survey and workshop was used to inform set of 18 recommendations across the 6 themes, and this has been used by the ADRN directors to develop an action plan for implementation. ConclusionsThe ADRN has broken new ground in overcoming many challenges in using administrative data for research in the UK. The recommendations and action plan show how further improvements will be made in the ADRCs, and the findings of this study are relevant more widely to other organisations working with administrative data.

Information is increasingly digital, creating opportunities to respond to pressing issues about human populations in near real time using linked datasets that are large, complex, and diverse. The potential social and individual benefits that can come from data-intensive science are large, but raise challenges of balancing individual privacy and the public good, building appropriate socio-technical systems to support data-intensive science, and determining whether defining a new field of inquiry might help move those collective interests and activities forward. A combination of expert engagement, literature review, and iterative conversations led to our conclusion that defining the field of Population Data Science (challenge 3) will help address the other two challenges as well. We define Population Data Science succinctly as the science of data about people and note that it is related to but distinct from the fields of data science and informatics. A broader definition names four characteristics of: data use for positive impact on citizens and society; bringing together and analyzing data from multiple sources; finding population-level insights; and developing safe, privacy-sensitive and ethical infrastructure to support research. One implication of these characteristics is that few people possess all of the requisite knowledge and skills of Population Data Science, so this is by nature a multi-disciplinary field. Other implications include the need to advance various aspects of science, such as data linkage technology, various forms of analytics, and methods of public engagement. These implications are the beginnings of a research agenda for Population Data Science, which if approached as a collective field, can catalyze significant advances in our understanding of trends in society, health, and human behavior.

Overall objectives or goalMany health data linkage ecosystems across the world have designed and implemented secure data analysis environments as one of their controls to protect patient privacy and confidentiality. These have been shaped by local legislation and data governance policies, available IT infrastructure and resources, and the skills and imagination of their architects. However, at present their various features and functionalities have not been reviewed, synthesised or contrasted. Burton et al [1] have proposed 12 criteria for Data Safe Havens in health and healthcare, which they conceptualise broadly as encompassing data governance and ethics, quality and curation of data repositories, and data security. Under this definition, secure analysis environments, which may or may not be integrated with data repositories, are a component of a Data Safe Haven, addressing the criterion “Appropriate secure access to individually identifying data”. To guide those building and operating these environments, and data custodians and stewards who need to assess their fitness-for-purpose, it would be of great value to discuss and agree an aggregate term (e.g. “Secure Data Lab”) that describes them, and to develop a more detailed set of criteria for what entails “Appropriate secure access” to linked health data. The goal of this session is to describe and document the approaches that have been taken by flagship secure data analysis environments internationally, including their approaches to authentication, assigning permissions, managing the ingress and egress of files and auditing transactions, and their responses to emerging opportunities, including cloud computing and national and international data sharing. We will explore how the interplay of physical, technical and procedural controls have been combined to create existing models, and the extent to which these can balance each other and be applied with flexibility depending on perceived risk and regimes. Session structurePrior to the session, we will develop a draft set of criteria for “Appropriate secure access” to linked health data. The session will comprise presentations describing existing secure analysis environments against the draft criteria, followed by a facilitated discussion. The secure data analysis environments that will be presented include: UNSW Sydney E-Research Institutional Cloud Architecture (ERICA) PopData BC Secure Research Environment (SRE) Institute for Clinical Evaluative Sciences (ICES) Data and Analytic Virtual Environment (IDAVE) Secure Anonymised Information Linkage (SAIL) Gateway Intended output or outcomeWe will write up the outcomes of the session as a scientific paper that proposes an aggregate term for secure data analysis environments for linked health data and a set of criteria for what entails “Appropriate secure access” to linked health data. Presenters and Facilitators Professor Louisa Jorm, Centre for Big Data Research in Health, UNSW Sydney, Australia Dr Tim Churches, South Western Sydney Clinical School, UNSW Sydney, Australia Professor Kim McGrail, Population Data BC, The University of British Columbia, Vancouver, Canada J. Charles Victor, Institute for Clinical Evaluative Sciences, Toronto, Canada Dr Kerina Jones, Swansea University Medical School, Wales, United Kingdom Professor David Ford, Swansea University Medical School, Wales, United Kingdom 1. Burton PR, Murtagh MJ, Boyd A, et al. Data Safe Havens in health research and healthcare. Bioinformatics 2015; 31(20): 3241–3248

ObjectivesData safe havens can bring together and combine a rich array of anonymised person-based data for research and policy evaluation within a secure setting. To date, the majority of available datasets have been structured micro-data derived from routine health-related records. Possibilities are opening up for the greater reuse of genomic data such as Genome Wide Association studies (GWAS) and Whole Exome/Genome Sequencing (WES or WGS). However, there are considerable challenges to be addressed if the benefits of using these data in combination with health-related data are to be realized safely. ApproachWe explore the benefits and challenges of using genomic datasets with health-related data, and using the Secure Anonymised Information Linkage (SAIL) system as a case study, the implications and way forward for Data Safe Havens in seeking to incorporate genomic data for use with health-related data. ResultsThe benefits of using GWAS, WES and WGS data in conjunction with health-related data include the potential to explore genetics at a population level and open up novel research areas. These include the ability to increasingly stratify and personalize how medical indications are detected and treated through precision medicine by understanding rare conditions and adding socioeconomic and environmental context to genomic data. Among the challenges are: data availability, computing capacity, technical solutions, legal and regulatory frameworks, public perceptions, individual privacy and organizational risk. Many of the challenges within these areas are common to person-based data in general, and often Data Safe Havens have been designed to address these. But there are also aspects of these challenges, and other challenges, specific to genomic data. These include issues due to the unknown clinical significance of genomic information now or in the future, with corresponding risks for privacy and impact on individuals. ConclusionGenomic data sets contain vast amounts of valuable information, some of which is currently undefined, but which may have direct bearing on individual health at some point. The use of these data in combination with health-related data has the potential to bring great benefits, better clinical trial stratification, epidemiology project design and clinical improvements. It is, therefore, essential that such data are surrounded by a properly-designed, robust governance framework including technical and procedural access controls that enable the data to be used safely.

ObjectivesWhilst the current expansion of health-related big data and data linkage research are exciting developments with great potential, they bring a major challenge. This is how to strike an appropriate balance between making the data accessible for beneficial uses, whilst respecting the rights of individuals, the duty of confidentiality and protecting the privacy of person-level data, without undue burden to research. ApproachUsing a case study approach, we describe how the UK Secure Research Platform (UKSeRP) for the Secure Anonymised Information Linkage (SAIL) databank addresses this challenge. We outline the principles, features and operating model of the SAIL UKSeRP, and how we are addressing the challenges of making health-related data safely accessible to increasing numbers of research users within a secure environment. ResultsThe SAIL UKSeRP has four basic principles to ensure that it is able to meet the needs of the growing data user community, and these are to: A) operate a remote access system that provides secure data access to approved data users; B) host an environment that provides a powerful platform for data analysis activities; (C) have a robust mechanism for the safe transfer of approved files in and out of the system; and (D) ensure that the system is efficient and scalable to accommodate a growing data user base. Subject to independent Information Governance approval and within a robust, proportionate Governance framework, the SAIL UKSeRP provides data users with a familiar Windows interface and their usual toolsets to access anonymously-linked datasets for research and evaluation. ConclusionThe SAIL UKSeRP represents a powerful analytical environment within a privacy-protecting safe haven and secure remote access system which has been designed to be scalable and adaptable to meet the needs of the rapidly growing data linkage community. Further challenges lie ahead as the landscape develops and emerging data types become more available. UKSeRP technology is available and customisable for other use cases within the UK and international jurisdictions, to operate within their respective governance frameworks.