Exploring the Use of Genomic and Routinely Collected Data: Narrative Literature Review and Interview Study.

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.

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

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.

Toward the Development of Data Governance Standards for Using Clinical Free-Text Data in Health Research: Position Paper.

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.

Common governance model: a way to avoid data segregation between existing trusted research environment.

Background: Trusted Research Environments provide a legitimate basis for data access along with a set of technologies to support implementation of the "five-safes" framework for privacy protection. Lack of standard approaches in achieving compliance with the "five-safes" framework results in a diversity of approaches across different TREs. Data access and analysis across multiple TREs has a range of benefits including improved precision of analysis due to larger sample sizes and broader availability of out-of-sample records, particularly in the study of rare conditions. Knowledge of governance approaches used across UK-TREs is limited. Objective: To document key governance features in major UK-TRE contributing to UK wide analysis and to identify elements that would directly facilitate multi TRE collaborations and federated analysis in future. Method: We summarised three main characteristics across 15 major UK-based TREs: 1) data access environment; 2) data access requests and disclosure control procedures; and 3) governance models. We undertook case studies of collaborative analyses conducted in more than one TRE. We identified an array of TREs operating on an equivalent level of governance. We further identify commonly governed TREs with architectural considerations for achieving an equivalent level of information security management system standards to facilitate multi TRE functionality and federated analytics. Results: All 15 UK-TREs allow pooling and analysis of aggregated research outputs only when they have passed human-operated disclosure control checks. Data access requests procedures are unique to each TRE. We also observed a variability in disclosure control procedures across various TREs with no or minimal researcher guidance on best practices for file out request procedures. In 2023, six TREs (40.0%) held ISO 20071 accreditation, while 9 TREs (56.2%) participated in four-nation analyses. Conclusion: Secure analysis of individual-level data from multiple TREs is possible through existing technical solutions but requires development of a well-established governance framework meeting all stakeholder requirements and addressing public and patient concerns. Formation of a standard model could act as the catalyst for evolution of current TREs governance models to a multi TRE ecosystem within the UK and beyond.

Public involvement & engagement in the work of a data safe haven: a case study of the SAIL Databank.

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 & engagement in connection with the SAIL Databank and related Population Data Science initiatives. APPROACH: We have a public involvement & 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 & 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.

Linking pre- and post-adoption records for research in anonymised form in a data safe haven: legal implications and support for a social licence.

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 National Health Service (NHS) number (or equivalent) at adoption, as part of creating the new identity. The SAIL Databank holds data on child and family court cases from 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, or members of 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.

Toward an Ethically Founded Framework for the Use of Mobile Phone Call Detail Records in Health Research.

Data derived from the plethora of networked digital devices hold great potential for public benefit. Among these, mobile phone call detail records (CDRs) present novel opportunities for research and are being used in a variety of health geography studies. Research suggests that the public is amenable to the use of anonymized CDRs for research; however, further work is needed to show that such data can be used appropriately. This study works toward an ethically founded data governance framework with social acceptability. Using a multifaceted approach, this study draws upon data governance arrangements in published health research using CDRs, with a consideration of public views and the public's information expectations from mobile network operators, and data use scenarios of CDRs in health research. The findings were considered against a backdrop of legislative and regulatory requirements. CDRs can be used at various levels of data and geographic granularity and may be integrated with additional, publicly available or restricted datasets. As such, there may be a significant risk of identity disclosure, which must be mitigated with proportionate control measures. An indicative relative risk of the disclosure model is proposed to aid this process. Subsequently, a set of recommendations is presented, including the need for greater transparency, accountability, and incorporation of public views for social acceptability. This study addresses the need for greater clarity and consistency in data governance for CDRs in health research. While recognizing the need to protect commercial interests, we propose that these recommendations be used to contribute toward an ethically founded practical framework to promote the safe, socially acceptable use of CDR data for public benefit. This pattern needs to be repeated for the appropriate use of new and emerging data types from other networking devices and the wider internet of things.

Public Views on Using Mobile Phone Call Detail Records in Health Research: Qualitative Study.

BACKGROUND: Mobile phone call detail records (CDRs) are increasingly being used in health research. The location element in CDRs is used in various health geographic studies, for example, to track population movement and infectious disease transmission. Vast volumes of CDRs are held by multinational organizations, which may make them available for research under various data governance regimes. However, there is an identified lack of public engagement on using CDRs for health research to contribute to an ethically founded framework. OBJECTIVE: This study aimed to explore public views on the use of call detail records in health research. METHODS: Views on using CDRs in health research were gained via a series of three public workshops (N=61) informed by a pilot workshop of 25 people. The workshops included an initial questionnaire to gauge participants' prior views, discussion on health research using CDRs, and a final questionnaire to record workshop outcome views. The resulting data were analyzed for frequencies and emerging themes. RESULTS: At the outset, most participants (66%, 40/61) knew that location data were collected by operators, but only 3% (2/61) knew they were being used for health research. Initially, the majority of the participants (62%, 38/61) was content for their anonymous CDRs to be used, and this increased (80%, 49/61) after the discussion explained that safeguards were in place. Participants highlighted that terms and conditions should be clearer, as should information to phone users on data collection, privacy safeguards, sharing, and uses in research. CONCLUSIONS: This is the first known study exploring public views of using mobile phone CDRs in health research. It revealed a lack of knowledge among the public on uses of CDRs and indicated that people are generally amenable to the use of anonymized data for research, but they want to be properly informed and safeguarded. We recommend that public views be incorporated into an ethically founded framework for the use of CDRs in health research to promote awareness and social acceptability in data use.

The Good, the Bad, the Clunky: Improving the Use of Administrative Data for Research.

INTRODUCTION: Administrative data arising via the operation of public service delivery systems hold great benefits for citizens and society by enabling new research questions to be addressed, providing they can be made available in a safe, socially acceptable way. In recognition of this potential, the UK Administrative Data Research Network was established in 2013 to enable new research for public benefit. However, there are considerable challenges to be overcome for effective data use, and many of these are common to administrative data enterprises in general. Using this network as a practical case study, we set out to explore the issues and propose how to share the 'good', suggest solutions to the 'bad', and improve the 'clunky' issues, to lead to improvements in administrative data use. METHODS: A qualitative survey representing the data use pathway was carried out across the network, followed by a workshop to discuss the summarised findings and make further suggestions. This led to a set of recommendations to inform the development of an action plan for implementation. RESULTS: The survey respondents (N=27) and workshop participants (N=95) comprised multi disciplinary staff from across the network. The responses were summarised by consensus of three researchers and grouped into six areas: A) Data acquisition pathway; B) Approval processes; C) Controls on access & disclosure; D) Data and metadata; E) Researcher support; and F) Data reuse & retention, leading to an embedded set of 18 recommendations. Key developments promoted by this study were the development of themed research partnerships to progress data acquisition, and a policy of data retention and reuse for research. CONCLUSIONS: The network has broken new ground in using administrative data for research. This study informed the development of an evidence-based action plan to address many challenges in the effective use of administrative data. It represents a practical worked example, and the learning is widely relevant to enterprises working with administrative data across the world.

Challenges and Potential Opportunities of Mobile Phone Call Detail Records in Health Research: Review.

BACKGROUND: Call detail records (CDRs) are collected by mobile network operators in the course of providing their service. CDRs are increasingly being used in research along with other forms of big data and represent an emerging data type with potential for public good. Many jurisdictions have infrastructures for health data research that could benefit from the integration of CDRs with health data. OBJECTIVE: The objective of this study was to review how CDRs have been used in health research and to identify challenges and potential opportunities for their wider use in conjunction with health data. METHODS: A literature review was conducted using structured search terms making use of major search engines. Initially, 4066 items were identified. Following screening, 46 full text articles were included in the qualitative synthesis. Information extracted included research topic area, population of study, datasets used, information governance and ethical considerations, study findings, and data limitations. RESULTS: The majority of published studies were focused on low-income and middle-income countries. Making use of the location element in CDRs, studies often modeled the transmission of infectious diseases or estimated population movement following natural disasters with a view to implementing interventions. CDRs were used in anonymized or aggregated form, and the process of gaining regulatory approvals varied with data provider and by jurisdiction. None included public views on the use of CDRs in health research. CONCLUSIONS: Despite various challenges and limitations, anonymized mobile phone CDRs have been used successfully in health research. The use of aggregated data is a safeguard but also a further limitation. Greater opportunities could be gained if validated anonymized CDRs were integrated with routine health records at an individual level, provided that permissions and safeguards could be put in place. Further work is needed, including gaining public views, to develop an ethically founded framework for the use of CDRs in health research.