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Introduction Birth cohorts are valuable resources for studying early life, the determinants of health, disease, and development. They are essential for studying life course. Dynamic longitudinal electronic cohorts use routinely collected data, are live, and can reduce selection bias specifically associated with direct recruitment in traditional birth cohorts. However, they are limited to health and administrative data and may lack contextual information. The MIREDA (Mother and Infant Research Electronic Data Analysis) partnership creates a UK-wide birth cohort by aligning existing electronic birth cohorts to have the same structure, content, and vocabularies, enabling UK-wide federated analyses. Objectives 1) Create a core dynamic, live UK-wide electronic birth cohort with approximately 500,000 new births per year using a common data model (CDM). 2) Provide data linkage and automation for long-term follow up of births from the Clinical Practice Research Datalink (CPRD), MuM-PreDiCT and the `Born in' initiatives of Bradford, Wales, Scotland, and South London for comparable analyses. Methods We will establish core data content and collate linkable data. A suite of extraction, transformation, and load (ETL) tools will be used to transform data for each birth cohort into the CDM. Transformed datasets will remain within each cohort's trusted research environment (TRE). Metadata will be uploaded for the public to the Health Data Research (HDRUK) Innovation Gateway. We will develop a single online data access request for researchers. A cohort profile will be developed for researchers to reference the resource. Ethics Each cohort has approval from their TRE through compliance with their project application processes and information governance. Dissemination We will engage with researchers in the field to promote our resource through partnership networking, publication, research collaborations, conferences, social media, and marketing communications strategies.

Objective Construct an innovative open-learning solution that provides comprehensive training specific to Trusted Research Environments (TRE) and the broader research community of administrative data users, irrespective of their proficiency levels. DOL offers training opportunities tailored to meet each user's unique learning needs, enabling them to utilise complex, linked administrative datasets confidently and effectively for meaningful research outcomes, thereby building capacity for sustainable national data infrastructure. Approach DOL’s innovative open-learning solution offers two learning formats: adaptive and experiential. Adaptive learning provides registered users with bitesize self-paced training based on Administrative Data Research UK's priorities. Experiential learning involves online workgroups with real-world context and practical application. They meet twice a year and are designed around specific topics with frequent guest speakers who are experts in their fields. Conclusion DOL’s innovative open-learning solution empowers TREs, such as SAIL Databank, to provide well-rounded learning that fosters community support, knowledge-sharing, and networking opportunities for its users, while gathering valuable user feedback. Users can personalise learning and test their knowledge in a flexible training environment, allowing them to take charge of their learning journey. Implication In response to the increasing demand for training services from novice to advanced users, SAIL Databank adopted DOL's dual learning approach by (1) developing training courses that cover access, process, methodology, integration, and analytical tools for SAIL TRE users, (2) engaging users in a series of workgroups focused on themed datasets including Justice, Environmental, Maternal, Education, and Core Health.

Introduction When a child has suffered, or is at risk of suffering, significant harm from parents or caregivers, the local authority may issue Section 31 (s.31) Care and Supervision proceedings under the Children Act (1989). Objectives We compared the healthcare use of infants less than one year old subject to s.31 proceedings in Wales (n = 1,332),to that of a comparison group of infants not subject to s.31 proceedings (n = 204,417), between January 2011 and February 2020. Methods Population-based e-cohort study utilising data held in the Secure Anonymised Information Linkage (SAIL) Databank. Infants in s.31 proceedings were identified using the Children and Family Court Advisory and Support Service dataset. This was linked to demographic and healthcare datasets, to identify General Practice (GP) visits, emergency department (ED) attendances, and hospital admissions (emergency and elective); before the study end date or the child's first birthday for the comparison group, orbefore the s.31 application date.Regression analysis calculated event rate ratios [RR] and incidence rate ratios [IRR] for healthcare events, adjusting for widerdeterminants of health (e.g. perinatal factors, maternal mental health, deprivation), and investigated reasons for healthcare use. Results Infants in s.31 proceedings had ahigher number and incidence of healthcare events compared with the comparison group, across all healthcare settings. Differences were greatest for emergency hospital admissions (IRR = 4.03, 95% confidence interval [CI] = 3.53 - 4.59; RR = 4.60, CI = 3.90 - 5.41). "Injury and poisoning" was the main reason for emergency admissions amongst infants in s.31 proceedings. For ED presentations, emergency hospital admissions, and GP visits, there were proportionally more events for these infants across all top ten reasons for healthcare. Conclusions Findings highlight greater healthcare utilisation for infants involved in s.31 proceedings in Wales, helping to build a better understanding of their needs and vulnerabilities.

Objectives Under section 31 (s.31) of the UK Children Act 1989, public law care proceedings can be issued if there is concern a child is subject to, or at risk of significant harm. We examined health vulnerabilities of parents involved in public law care proceedings in the two-year period prior to involvement. Approach Our study created an anonymised individual-level population-based cohort, with a matched comparison group of parents in Wales who were not subject to care proceedings, matched on age, sex and deprivation. Family court data provided by Cafcass Cymru were linked to population-level healthcare records held within the Secure Anonymised Information Linkage (SAIL) Databank. Demographic characteristics, overall health service use and health profiles of parents of children subject to s.31 care proceedings between 2011 and 2019 were examined. Results Data were available for 8,821 parents involved in care proceedings between 2011 and 2019, with a comparison group of 32,006 parents. Nearly half (47.6%) of cohort parents resided in the most deprived quintile. Higher levels of healthcare use were found for cohort mothers and fathers compared to the comparison group across multiple healthcare settings, with the most pronounced differences for emergency department attendances (59.3% vs 37.0%). Health conditions with the largest variation between groups were related to mental health (43.6% vs 16.0%), substance use (19.4% vs 1.6%) and injuries (41.5% vs 23.6%). Conclusion This study highlights the heightened socioeconomic and health vulnerabilities of parents who experience care proceedings concerning a child. Better understanding of the needs and vulnerabilities of this population may provide opportunities to improve a range of support and preventative interventions that respond to crises in the community.

Objectives The BREATHE Health Data Research Hub for Respiratory Health was formed in October 2019 as a multi-site academic consortium with multiple industrial partners via an Industry Forum and across its wider network. BREATHE’s main mission is enhancing data services within respiratory science, funded by the UKRI Industrial Strategy Challenge Fund. Approach BREATHE leveraged expertise across its founding sites and industrial partners to create data services which could be used by multiple sectors of collaborator. Across the founding sites, BREATHE was able to mobilise datasets housed within national TREs to form real-world evidence eCohorts for rapid and efficient respiratory study (Asthma, COPD, ILD), and has worked with specialists in cohort study and genomic data to house and supply these from within our partner TRE, SAIL Databank. As well as data assets, BREATHE is able to provide clinical and data expertise to collaborators for grant submissions and on bespoke respiratory science projects. Results Including a significant period of work during the pandemic supporting COVID-19 research and also focusing on other respiratory disease science support, BREATHE is now well-placed to move towards a sustainable operating plan post-grant from March 2023. Due to the approach taken in maximising data services for multiple sectors, BREATHE is positioned to provide data linkage and sharing services (making use of its TRE, SAIL Databank), providing analytic and clinical support to respiratory research projects for customers in multiple sectors (Pharma, SMEs, Academia, NHS, Charities), and advancing synthetic data and software development, again in partnership with SAIL and our wider industry partners. Conclusion As of March 2022, BREATHE has established a portfolio of data services and projects interfacing with multiple sectors of collaborator in enhancing respiratory science projects across the UK. With population-level data assets representing Wales, Scotland, and England and the ability to work with the Northern Ireland infrastructure housed at Swansea University, BREATHE supports 4-nation respiratory science in RWE data, and provides clinical and data linkage expertise to studies such as longitudinal cohorts, pharma companies, and contract research organisations.

Introduction: On June 24 in the United Kingdom, there were 277,989 cases of COVID-19 and 39,369 deaths recorded. The government enforced a complete lockdown on March 23 that resulted in cessation of all elective admissions on 24th onward, with only acute trauma cases being admitted to hospital. This study aims to characterize the changes in trauma admissions during the first 5-week lockdown period. The hypothesis states that there would be a significant reduction in overall orthopedic trauma admissions, polytrauma, and high-energy outdoor trauma during this COVID-19 period. Methods: All trauma admissions over nearly a 5-week period from March 23, 2020, to April 26, 2020, were collated as the “COVID cohort” and compared to the “control” group of patients from the same hospitals 1 year before between March 23, 2019, and April 26, 2019. Spinal admissions and pediatrics were excluded from the study as they were managed in other regional units. Results: There was a 56% reduction in trauma admissions during the COVID-19 lockdown (133 vs. 304). A majority of the COVID cohort were admitted with fractures (89 vs. 164, P = 0.017, Chi-square test) from home with low-energy falls. Overall, fewer operations were performed than the year before. However, a greater proportion of admitted patients had a surgical orthopedic intervention rather than admission and nonoperative management. Conclusions: There was a reduction in admissions as well as reductions in high energy and occupational injuries. Elderly patients continued to fall at home or in care, sustaining hip fractures. This vulnerable group requires beds, orthogeriatric management followed by surgical intervention and social care. Orthogeriatric services must be maintained to ensure the best clinical outcomes for this group.

Introduction In Wales, the Children in Need (CIN) dataset includes information relating to needs of children and social care support. Before the Social Services and Well-being (Wales) Act 2014 came into force in April 2016, this data collection was named the Children in Need census, changing to Children Receiving Care and Support (CRCS) after this date to reflect better the children eligible for inclusion. This paper describes these datasets, their potential for research and their limitations. We describe data that researchers can access via the Secure Anonymised Information Linkage (SAIL) Databank and exploratory linkages made to health records. Methods CIN and CRCS data were transferred to the SAIL Databank using a standardised approach to provide de-identified data with Anonymised Linking Fields (ALF) for successfully matched records. The linkage method relies on the use of Unique Pupil Numbers (UPN). As such, no records are currently available for children without a UPN, which includes most under age three. ALFs enabled linkage to individual-level health data within SAIL. Health service use was compared to non-CIN/CRCS populations. Results CRCS data held within the SAIL Databank comprises 25,972 records, 81% of the total number of records reported by the Welsh Government. The CIN data contains 108,449 records, 79% of the Welsh Government's records for this data collection. Health service use of children in need, and children receiving care and support, was roughly equal to that of the non-CIN/CRCS population, except GP visits, where children in need had fewer consultations, and children receiving care and support had more consultations than the comparison population. Conclusion Researchers can access Welsh CIN and CRCS datasets through the SAIL Databank, enabling research opportunities. Work is ongoing to improve records and to understand better the health and health service use among children captured by CIN and CRCS censuses.

Introduction Under section 31 of the Children Act 1989, public law care proceedings can be issued if there is concern a child is subject to, or at risk of significant harm, which can lead to removal of a child from parents. Appropriate and effective health and social support are required to potentially prevent some of the need for these proceedings. More comprehensive evidence of the health needs and vulnerabilities of parents will enable enhanced response from family courts and integrated other services. Objective To examine health vulnerabilities of parents involved in care proceedings in the two-year period prior to involvement. Methods Family court data provided by Cafcass Cymru were linked to population-based health records held within the Secure Anonymised Information Linkage Databank. Linked data were available for 8,821 parents of children involved in care proceedings between 2011 and 2019. Findings were benchmarked with reference to a comparison group of parents matched on sex, age, and deprivation (n = 32,006), not subject to care proceedings. Demographic characteristics, overall health service use, and health profiles of parents were examined. Descriptive and statistical tests of independence were used. Results Nearly half of cohort parents (47.6%) resided in the most deprived quintile. They had higher levels of healthcare use compared to the comparison group across multiple healthcare settings, with the most pronounced differences for emergency department attendances (59.3% vs 37.0%). Health conditions with the largest variation between groups were related to mental health (43.6% vs 16.0%), substance use (19.4% vs 1.6%) and injuries (41.5% vs 23.6%). Conclusion This study highlights the heightened socioeconomic and health vulnerabilities of parents who experience care proceedings concerning a child. Better understanding of the needs and vulnerabilities of this population may provide opportunities to improve a range of support and preventative interventions that respond to crises in the community.

BackgroundSeveral countries restricted the administration of ChAdOx1 to older age groups in 2021 over safety concerns following case reports and observed versus expected analyses suggesting a possible association with cerebral venous sinus thrombosis (CVST). Large datasets are required to precisely estimate the association between Coronavirus Disease 2019 (COVID-19) vaccination and CVST due to the extreme rarity of this event. We aimed to accomplish this by combining national data from England, Scotland, and Wales.Methods and findingsWe created data platforms consisting of linked primary care, secondary care, mortality, and virological testing data in each of England, Scotland, and Wales, with a combined cohort of 11,637,157 people and 6,808,293 person years of follow-up. The cohort start date was December 8, 2020, and the end date was June 30, 2021. The outcome measure we examined was incident CVST events recorded in either primary or secondary care records. We carried out a self-controlled case series (SCCS) analysis of this outcome following first dose vaccination with ChAdOx1 and BNT162b2. The observation period consisted of an initial 90-day reference period, followed by a 2-week prerisk period directly prior to vaccination, and a 4-week risk period following vaccination. Counts of CVST cases from each country were tallied, then expanded into a full dataset with 1 row for each individual and observation time period. There was a combined total of 201 incident CVST events in the cohorts (29.5 per million person years). There were 81 CVST events in the observation period among those who a received first dose of ChAdOx1 (approximately 16.34 per million doses) and 40 for those who received a first dose of BNT162b2 (approximately 12.60 per million doses). We fitted conditional Poisson models to estimate incidence rate ratios (IRRs). Vaccination with ChAdOx1 was associated with an elevated risk of incident CVST events in the 28 days following vaccination, IRR = 1.93 (95% confidence interval (CI) 1.20 to 3.11). We did not find an association between BNT162b2 and CVST in the 28 days following vaccination, IRR = 0.78 (95% CI 0.34 to 1.77). Our study had some limitations. The SCCS study design implicitly controls for variables that are constant over the observation period, but also assumes that outcome events are independent of exposure. This assumption may not be satisfied in the case of CVST, firstly because it is a serious adverse event, and secondly because the vaccination programme in the United Kingdom prioritised the clinically extremely vulnerable and those with underlying health conditions, which may have caused a selection effect for individuals more prone to CVST. Although we pooled data from several large datasets, there was still a low number of events, which may have caused imprecision in our estimates.ConclusionsIn this study, we observed a small elevated risk of CVST events following vaccination with ChAdOx1, but not BNT162b2. Our analysis pooled information from large datasets from England, Scotland, and Wales. This evidence may be useful in risk-benefit analyses of vaccine policies and in providing quantification of risks associated with vaccination to the general public.

Introduction The COVID-19 pandemic has resulted in significant morbidity and mortality and devastated economies globally. Among groups at increased risk are healthcare workers (HCWs) and ethnic minority groups. Emerging evidence suggests that HCWs from ethnic minority groups are at increased risk of adverse COVID-19-related outcomes. To date, there has been no large-scale analysis of these risks in UK HCWs or ancillary workers in healthcare settings, stratified by ethnicity or occupation, and adjusted for confounders. This paper reports the protocol for a prospective longitudinal questionnaire study of UK HCWs, as part of the UK-REACH programme (The United Kingdom Research study into Ethnicity And COVID-19 outcomes in Healthcare workers).Methods and analysis A baseline questionnaire will be administered to a national cohort of UK HCWs and ancillary workers in healthcare settings, and those registered with UK healthcare regulators, with follow-up questionnaires administered at 4 and 8 months. With consent, questionnaire data will be linked to health records with 25-year follow-up. Univariate associations between ethnicity and clinical COVID-19 outcomes, physical and mental health, and key confounders/explanatory variables will be tested. Multivariable analyses will test for associations between ethnicity and key outcomes adjusted for the confounder/explanatory variables. We will model changes over time by ethnic group, facilitating understanding of absolute and relative risks in different ethnic groups, and generalisability of findings.Ethics and dissemination The study is approved by Health Research Authority (reference 20/HRA/4718), and carries minimal risk. We aim to manage the small risk of participant distress about questions on sensitive topics by clearly participant information that the questionnaire covers sensitive topics and there is no obligation to answer these or any other questions, and by providing support organisation links. Results will be disseminated with reports to Government and papers submitted to pre-print servers and peer reviewed journals.Trial registration number ISRCTN11811602; Pre-results.

Introduction As the world continues to grapple with the COVID-19 pandemic, emerging evidence suggests that individuals from ethnic minority backgrounds may be disproportionately affected. The United Kingdom Research study into Ethnicity And COVID-19 outcomes in Healthcare workers (UK-REACH) project has been initiated to generate rapid evidence on whether and why ethnicity affects COVID-19 diagnosis and clinical outcomes in healthcare workers (HCWs) in the UK, through five interlinked work packages/work streams, three of which form the basis of this protocol. The ethico-legal work (Work Package 3) aims to understand and address legal, ethical and acceptability issues around big data research; the HCWs experiences study (Work Package 4) explores their work and personal experiences, perceptions of risk, support and coping mechanisms; the stakeholder engagement work (Work Package 5) aims to provide feedback and support with the formulation and dissemination of the project recommendations.Methods and analysis Work Package 3 has two different research strands: (A) desk-based doctrinal research; and (B) empirical qualitative research with key opinion leaders. For the empirical research, in-depth interviews will be conducted digitally and recorded with participants permission. Recordings will be transcribed, coded and analysed using thematic analysis. In Work Package 4, online in-depth interviews and focus groups will be conducted with approximately 150 HCWs, from across the UK, and these will be recorded with participants consent. The recordings will be transcribed and coded and data will be analysed using thematic analysis. Work Package 5 will achieve its objectives through regular group meetings and in-group discussions.Ethics and dissemination Ethical approval has been received from the London-Brighton & Sussex Research Ethics Committee of the Health Research Authority (Ref No 20/HRA/4718). Results of the study will be published in open-access journals, and disseminated through conference presentations, project website, stakeholder organisations, media and scientific advisory groups.Trial registration number ISRCTN11811602.

Abstract Background Medanta - The Medicity, a multi-super specialty corporate hospital in Gurugram, Haryana launched a “TB-Free Haryana” Campaign; mobile van equipped with a digital CXR machine to screen patients with presumptive Tuberculosis (TB). Objectives: In this study, we aimed to assess the (1) yield and cost analysis of two strategies using mobile digital x-ray to detect Pulmonary TB in rural Haryana. Methods An observational study was conducted on all individuals screened by either of the two case finding strategies using a mobile x-ray unit (MXU) mounted on a mobile van in District Mewat, Haryana during Jan-March 2016. Results Strategy 1: Out of 121 smear negative cases, x-rays were suggestive of TB in 39(32%), of which 24 were started on TB treatment. Cost of identifying a smear negative TB was US$ 32. Strategy 2: Out of 596 presumptive TB, chest x-rays were suggestive of TB in 108 (18%), of which 67 were started on TB treatment (56 were smear negative TB). Cost of detecting any case of TB was US$ 08 (1 USD = 64 INR). Conclusion The study reports a new initiative within a PPM model to improve the diagnosis of PTB by filling the gap in the current diagnostic infrastructure. We believe there is potential for replication of strategy 2 model in other states, although further evidence is required.

Introduction The importance of the determinants of health to health outcomes has long been established. Historically, data from each of these sectors has been captured in disparate, often siloed, sources. Attempts to integrate these data have faced a number of challenges including technical, legislative and interpretative barriers, creating inefficiencies and inhibiting knowledge sharing. Despite this, there have been notable successes where intersectoral data and health data have been brought together in a meaningful way. The establishment of strong partnerships, with academia, governments, privacy and legal sectors, and other bodies, across sectors has been key to this success. These partnerships ensure data are integrated, analyzed, and interpreted accurately and appropriately, while also leveraging existing investments and expertise. Objectives and Approach The objective of this session is to explore the role of partnerships throughout the data integration life cycle, from initial discussions, to data integration, through to connecting research output to policy impact. Each of the presenters will discuss the successes, barriers and mitigation strategies they have experienced across different jurisdictions using real world examples. Results Health research institutes globally are increasingly able to access routinely collected intersectoral data from non-health sectors. In each institute, data are unique, complex and have been collected in a manner consistent with the needs of the sector. As health research institutes work to understand the data structures and determine the best way to link, use and interpret the information according to national and international best practice guidelines, it has become clear that it is critical to undertake this in partnership with experts from each sector, who understand how the data was collected and can guide appropriate interpretation. In addition, these partnerships have enabled the connection of policy priorities in other sectors with research done in the health sector using intersectoral data. For example, in addition to supporting government health departments, health research institutes have collaborated with other government ministries including immigration, social services, and education. This session will present real world examples from local (provincial), national and international contexts, and highlight a novel data platform, being developed to minimize barriers to data access and use across sectors and jurisdictions. Conclusion / Implications The participants on this panel will demonstrate the importance of partnership throughout the data integration life cycle when working with intersectoral data using real world examples. Collaboration increases the value of integrated data to both health and non-health sectors, through the connection of policy priorities and support of research across the determinants of health.

Introduction There is no power for change greater than a community discovering what it cares about.1 The Health Data Platform (HDP) will democratize British Columbia’s (population of approximately 4.6 million) health sector data by creating common enabling infrastructure that supports cross-organization analytics and research used by both decision makers and cademics. HDP will provide streamlined, proportionate processes that provide timelier access to data with increased transparency for the data consumer and provide shared data related services that elevate best practices by enabling consistency across data contributors, while maintaining continued stewardship of their data. HDP will be built in collaboration with Swansea University following an agile pragmatic approach starting with a minimum viable product. Objectives and Approach Build a data sharing environment that harnesses the data and the understanding and expertise about health data across academe, decision makers, and clinicians in the province by: • Enabling a common harmonized approach across the sector on: • Data stewardship • Data access • Data security and privacy • Data management • Data standards • To: • Enhance data consumer data access experience • Increase process consistency and transparency • Reduce burden of liberating data from a data source • Build trust in the data and what it is telling us and therefore the decisions made • Increase data accessibility safely and responsibly Working within the jurisdiction’s existing legislation, the Five Safes Privacy and Security Framework will be implemented, tailored to address the requirements of data contributors. Results The minimum viable product will provide the necessary enabling infrastructure including governance to enable timelier access, safely to administrative data to a limited set of data consumers. The MVP will be expanded with another release planned for early 2021. Conclusion / Implications Collaboration with Swansea University has enabled BC to accelerate its journey to increasing timelier access to data, safely and increasing the maturity of analytics by creating the enabling infrastructure that promotes collaboration and sharing of data and data approaches. 1 Margaret Wheatley

Introduction The BREATHE Health Data Research Hub is a consortium of five academic institutions and several industry partners seeking to facilitate and accelerate respiratory science initiatives and outcomes. Unlocking organisational, jurisdictional, and scientific challenges, such as differing and inherent complexities with data standards, incongruous governance, and disparate data access mechanisms for over 100 diverse UK datasets are key aims. Objectives and Approach Central to the data effort is the UK Secure eResearch Platform (SeRP UK), and its flagship tenancy, the SAIL Databank. Onboarding datasets, making them remotely available to the respiratory research community, is a key approach. Datasets targeted range from population cohort studies, to respiratory trials data, routine healthcare datasets, and specialist ‘omics data. Partnerships with national safe havens and providers such as eDRIS and NHS Digital will enable BREATHE to expedite and improve wider sharing of datasets for the respiratory science. Data improvements focus on datasets from primary, secondary, and tertiary care from national healthcare systems, ‘respiratorising’ these datasets and increasing utility for academic and industry respiratory scientists. Incorporating dataset metadata and access permutations into national cataloguing systems at HDR UK, standardising metadata, and interoperability for in-scope datasets form a concerted data quality improvement effort. Results Facilitating data sharing through initiatives such as BREATHE will increase visibility and accessibility for datasets within respiratory science, whilst addressing national cultural and governance issues to data sharing. BREATHE data sharing processes will allow for team science to be undertaken in a highly collaborative manner and allow for best practise in data collection and sharing to flow to nationwide datasets in respiratory science. Conclusion / Implications Collaborative hubs with scientific domain expertise can be created and leveraged to accelerate data sharing and data science within the scientific area. These collaborative efforts can however be translated to other disease-specific efforts, and indeed disease agnostic platform solutions.

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 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.

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/Rationale Linked administrative datasets offer great potential for research, but also present major challenges—including the preparation of operational data into a form suitable for efficient research, complex and computationally demanding analysis, and the need to capture and share information about dataset contents and research methods. Main Aim The analytical services team in the Secure Anonymised Information Linkage (SAIL) Databank is creating interconnected tools and systems to automate the preparation and analysis of research data and to curate information about datasets and research methods. Our underlying goal is to make linked data research orders of magnitude faster and cheaper, as well as improve its consistency and quality. Methods Several key developments are ongoing: • Automation of data quality checking. • Management of dataset metadata. • Processing of raw source datasets into cleaned, research-ready data assets. • The Concept Library, an application for creating, using, and sharing knowledge about research definitions and methods. • A suite of R packages for analysis. Web Application Programming Interfaces will allow these pieces to work together as an integrated system enabling efficient research. Results Initial versions of dataset quality checking, cleaned datasets, and R code to implement common tasks are already in day-to-day use by researchers within SAIL. An advisory group has been convened to help guide the work. For example, shared library code that flags conditions within health data has been used across multiple projects; a cleaned dataset measuring follow-up within primary care has been used by more than 100 projects. Conclusion Our proof-of-concept work demonstrates the ability of shared code and cleaned data to meet needs across multiple projects, saving effort and standardizing results. Ongoing work to develop and integrate these tools should further streamline the research process, increasing the output and public benefit of SAIL and other data sources.

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. Methods Family 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. Results Children 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. Conclusion We 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/rationale Nations 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. Aim To 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/approach Data 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. Results The 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. Conclusion High 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.

Ocean ecosystems are subject to a multitude of stressors, including changes in ocean physics and biogeochemistry, and direct anthropogenic influences. Implementation of protective and adaptive measures for ocean ecosystems requires a combination of ocean observations with analysis and prediction tools. These can guide assessments of the current state of ocean ecosystems, elucidate ongoing trends and shifts, and anticipate impacts of climate change and management policies. Analysis and prediction tools are defined here as ocean circulation models that are coupled to biogeochemical or ecological models. The range of potential applications for these systems is broad, ranging from reanalyses for the assessment of past and current states, and short-term and seasonal forecasts, to scenario simulations including climate change projections. The objectives of this article are to illustrate current capabilities with regard to the three types of applications, and to discuss the challenges and opportunities. Representative examples of global and regional systems are described with particular emphasis on those in operational or pre-operational use. With regard to the benefits and challenges, similar considerations apply to biogeochemical and ecological prediction systems as do to physical systems. However, at present there are at least two major differences: (1) biogeochemical observation streams are much sparser than physical streams presenting a significant hinderance, and (2) biogeochemical and ecological models are largely unconstrained because of insufficient observations. Expansion of biogeochemical and ecological observation systems will allow for significant advances in the development and application of analysis and prediction tools for ocean biogeochemistry and ecosystems, with multiple societal benefits.

Overall objectives or goal Many 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 structure Prior 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 outcome We 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

Introduction Sets of clinical codes that define conditions and events of interest are a key knowledge product in health data research. Documenting such lists is essential for transparency and repeatability, and there is great potential benefit in their sharing and reuse. We designed and implemented software to address these goals. Objectives and Approach Our goals were threefold: • Provide a graphical user interface (GUI) to allow easier creation of code lists, for less technical users. • Allow clear documentation of code lists, preserving the history of their creation and capturing metadata about their meaning, provenance, and use. • Facilitate programmatic access, so that the software is not just documentation but can be integrated into data preparation and analysis. To these ends, we developed a web application using Python and PostgreSQL that allows creating, editing, and accessing via a GUI, as well as a REST API for integration into SQL, R, and other environments. Results The software allows users to view and create lists through a familiar web paradigm. Lists can be built by identifying codes in a variety of ways, including keyword searches, regular expressions, and more complex rules. A change history is stored. Information such as a description, whether there was clinical reviewed, and relevant publications is captured. The REST API allows access and use in a variety of settings. We have implemented a DB2 SQL interface to enable code lists to be used within database queries, and other interfaces such as an R package are planned for the future. It will be used within the SAIL Databank initially, with a public version for sharing across institutions planned. The code will be open source to enable further development. Conclusion/Implications We expect this tool to facilitate faster, higher-quality, more reproducible research in Wales and beyond. Hopefully it will be not just a standalone effort, but one small piece in a set of better tools and methods that will enable our field to truly realized the benefit of large linked datasets.

Introduction The Dementias Platform UK (DPUK) Data Portal is a secure, accessible environment facilitating provision of rich data towards the largest Dementia, cognition and ageing community of cohort studies in the world. DPUK is also providing services for cohort studies and researchers to maximise the research potential of the programme’s community. Objectives and Approach As part of the engagement of DPUK cohorts with the Data Portal, cohorts will upload data onto the DPUK instance of UK Secure eResearch Platform infrastructure. The Data Portal allows access to a collaborative working space that allows cohorts to enrich their own data, perform their own analysis, and enhance the research potential of their data whilst making use of expertise at various DPUK sites, such as data linking, curation and multi-modal specialism. Cohort data divided into ontologies allows researchers to access data specific to their study needs and can be requested from multiple cohorts simultaneously. Results By utilising the Data Portal researchers have access to cohort data that has been prepared for dementia epidemiology using the agreed ontologies, providing more rapid access to cohort data that otherwise may be large and complex. The knowledge and experience of DPUK staff and collaborators can also help to guide nascent cohorts and feasibility studies into producing research-ready datasets, enabling them to achieve greater impact with their data. A range of analytical tools are provided on the Data Portal making analysis of a cohort’s own data or multiple independent datasets more accessible. Alongside data curation, DPUK also facilitates data linkage to routine sources, beginning with a Wales-wide use case that will expand to the UK over the course of the project. Conclusion/Implications Data from international sources accessible using a central platform permits international collaboration, with ontologies allowing previously disparate data to be combined and analysed to build knowledge and research impact. DPUK projects create policy leading results and operational research standards, enhancing cohort impact and discovery of benefits for Dementia patients.

Introduction Electronic health records (EHR) are a powerful resource in enabling large-scale healthcare research. EHRs often lack detailed disease-specific information that is collected in free text within clinical settings. This challenge can be addressed by using Natural Language Processing (NLP) to derive and extract detailed clinical information from free text. Objectives and Approach Using a training sample of 40 letters, we used the General Architecture for Text Engineering (GATE) framework to build custom rule sets for nine categories of epilepsy information as well as clinic date and date of birth. We used a validation set of 200 clinic letters to compare the results of our algorithm to a separate manual review by a clinician, where we evaluated a “per item” and a “per letter” approach for each category. Results The “per letter” approach identified 1,939 items of information with overall precision, recall and F1-score of 92.7%, 77.7% and 85.6%. Precision and recall for epilepsy specific categories were: diagnosis (85.3%,92.4%), type (93.7%,83.2%), focal seizure (99.0%,68.3%), generalised seizure (92.5%,57.0%), seizure frequency (92.0%,52.3%), medication (96.1%,94.0%), CT (66.7%,47.1%), MRI (96.6%,51.4%) and EEG (95.8%,40.6%). By combining all items per category, per letter we were able to achieve higher precision, recall and F1-scores of 94.6%, 84.2% and 89.0% across all categories. Conclusion/Implications Our results demonstrate that NLP techniques can be used to accurately extract rich phenotypic details from clinic letters that is often missing from routinely-collected data. Capturing these new data types provides a platform for conducting novel precision neurology research, in addition to potential applicability to other disease areas.

Introduction Call 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 Approach The 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. Results The 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/Implications Despite 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.

Introduction Modern team science requires effective sharing of data and skills. The DPUK Data Portal is a collection of tools, datasets and networks that allows for epidemiologists and specialist researchers alike to access, analyse and investigate cohort and different modalities of routine data across UK and international sources. Objectives and Approach The Portal is housed on an instance of UKSeRP (UK Secure eResearch Platform), that allows customisable infrastructure to be used for multi-modal research (thus far live in genetics, imaging and clinical data) for researchers across the world using remote access technology whilst allowing governance to remain with the data provider. A central team at Swansea University is responsible for data curation and processing, and runs an access procedure for researchers to apply to use data from multiple sources to be analysed in a central analysis environment. Other modalities are similarly hosted, with input from partner sites in Cardiff and Oxford. Results DPUK facilitates data access and research on 49 cohorts, 40 UK-based and 9 international. The centralised repository model including remote access and ability to store and make available different modalities of data, from phenotypic data, to genetic and imaging data, has allowed DPUK to begin to support research of varying topics, from those studying cognitive decline and Dementia as a disease, to those maturing analytical models. By providing access to data platforms specialising in genetics, imaging and routine clinical data, as well as to specialists in disease and biology to aid with its understanding, DPUK has realised a large-scale research exercise combining major data modalities on a central platform, and allow access to such rich data across the world under an umbrella of robust governance. Conclusion/Implications Globally, cohorts are pooling data, expertise and desire to enrich their own aims in partnership with a federated research community to enable in-depth scrutiny of the biological origins of dementia and the development and evaluation of novel approach to disease prevention and cure.

DPUK relaunched the Data Portal in November 2017 to present openly available information on the data availability and technical capability of the Data Portal, which supports multi-modal research studies with various objectives from disease model validation to observation investigation. DPUK not only brings clinical data together from cohorts, but is now supporting multi-modal studies in genetics and imaging, as well as linkage opportunities to routine data using world-leading technical solutions to data sharing. The capacity, adaptability and sophistication of the UK Secure eResearch Platform which the Portal is housed on, allows for unprecedented levels of centralised access to rich cohort and routine data, which is consequentially leading to international collaboration and development ambition within epidemiology, bioinformatics, research methodology and technical research solutions. As of March 2018, DPUK is supporting 50 cohorts, 41 from the UK and 9 from across the rest of the world, alongside furthering links and access to routine data held in the UK and across the world. 20 research studies are underway, and the DPUK mission to enhance data science within dementia research is leading the conversation for developing a community of excellence in this field and across other research genres.

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.

ABSTRACT Objectives Whilst 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. Approach Using 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. Results The 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. Conclusion The 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.

ABSTRACT Objectives The UK MS Register is a research project that aims to capture real world data about living with Multiple Sclerosis(MS) in the UK. Launched in 2011, identified data sources were: Directly from People with MS (PwMS) via the internet, from NHS treatment centers via ‘traditional’ database capture and by linkage to routine datasets from the SAIL databank. Data received from the NHS, though ‘gold standard’ in terms of diagnosis, is dependent on clinical staff finding both time and information to enter into a clinical system. System implementations across the NHS are variable, as is clinical time. Therefore, we looked to other complementary methodologies. Approach The Clix enrich natural language processing (NLP) software was chosen to see if it could capture a portion of the MS Register minimum clinical dataset, the software matches clinical phrases against SNOMED-CT. 40 letters, from 2 NHS Trusts, from 28 patients were loaded. The letters were a mix of MS patients with differing disease subtypes and were dictated by Neurologists, Specialist General Practitioners and MS Specialist Nurses. 20 of the letters were in docx format and 20 as PDF. The letters were parsed by a domain expert for clinical content, scored by data item for sensitivity and specificity. Next the output from the software was scored by another researcher to see if the 12 relevant clinical concepts from the Register dataset had been elicited. Lastly a ruleset was created to look for particular clinical concepts and scored in the same way. Results Of the 40 letters one failed to load, the rest were analysed for the specific data items. Date related items were clearly challenging, with only 7% of appointment dates being matched and 22% for date of diagnosis. MS Type (93.3%) and EDSS score (93.75%) were well recognised, additionally symptoms of MS that would be poorly reported in traditional databases were recognised, with fatigue being well highlighted (78.5%) and gait and walking issues (68.7%) Of concern, were a number of false positive results in DMT’s with 15% patients being identified as being on a DMT when this was just being ‘considered’. Conclusion The NLP pathway could be extremely useful for obtaining hard to capture clinical data for the Register. Further work is needed to reduce errors, even with the current minimal configuration, it's possible to ascertain MS Type, functional score of MS, current medication and potentially disabling symptomology within the condition.

ABSTRACT Objectives The UK MS Register is a large scale observational research platform, capturing data from patients, NHS and carries out linkage with routine data from the SAIL databank. We have 14,000 People with MS (PwMS) submitting Patient Reported Outcome Measures (PRoMS) quarterly and over 3000 patients consenting at NHS Sites around the UK A differentiating factor between Register and previous attempts to capture PRoMS and clinical data was the goal that it be paperless. One area, where paper had to be used, was obtaining informed consent. Clinical participants are consented using a triplicate consent form, one copy for the patient, one for medical notes and one for the Register It’s desirable for patients to be able to electronically consent, providing the following benefits: • Tablet computers already in use to collect PRoMS • Printing costs • Participant expectations • Improved content and user experience: improved feedback, of multimedia elements about informed consent • Increased familiarity with tablets Approach Changing consent methodology is complex, all documentation, processes and changes are reviewed by the ethics committee. A privacy protecting, secure software package (iConsent) was developed by modifying an existing package from Welsh Cancer Bank. The software is server based, running on a Secured MS SQLServer 2014 and developed in .net to iOS/Android tablets The practitioner taking consent explains the process, participants then see the approved documentation and materials. Finally they fill in their email address and name, and are presented with the consent form, the participant uses a stylus to sign. The practitioner then countersigns. Once completed a digitally signed, secure pdf is generated on the server. Links are sent by email to the participant, the Register and unit administrator. The pdf is functionally identical to the paper. Results The South West Central Bristol ethics committee approved the software following guidance on security and documentation design. Staff were trained in system usage. A number of patients were successfully e-consented, Of note was a potential issue with some patients and how MS impacts their ability to sign without resting a hand on the screen. Conclusion Patients who have been e-consented have expressed satisfaction in the ease of use and security of the software. Patients being unable to rest their hands on the screen is being examined. Newer tablets can ignore inputs other than the stylus. The MS Register intends to use the software in additional centres to capture patient consent.

ABSTRACT Objectives It is widely acknowledged that breaches and misuses of health-related data can have serious implications and consequently they often carry penalties. However, harm due to the omission of health data usage, or data non use, is a subject that lacks attention. A better understanding of this other side of the coin is required before it can be addressed effectively. Approach This article uses an international case study approach to explore why data non use is difficult to ascertain, the sources and types of health-related data non-use, its implications for citizens and society and some of the reasons it occurs. It does this by focussing on issues with clinical care records, research data and governance frameworks and associated examples of non-use. Results The non-use of health-related data is a complex issue with multiple sources and reasons contributing to it. Instances of data non-use can be associated with harm, but taken together they describe a trail of data non-use, and this may complicate and compound its impacts. Actual evidence of data non-use is sparse and harm due to data non use is difficult to prove. But although it can be nebulous, it is a real problem with largely unquantifiable consequences. There is ample indirect evidence that health data non-use is implicated in the deaths of many thousands of people and potentially £billions in financial burdens to societies. Conclusion The most effective initiatives to address specific contexts of data non-use will be those that are cognisant of the multiple aspects to this complex issue, in order to move towards socially responsible reuse of data becoming the norm to save lives and resources.

We present a model that estimates the spectral phytoplankton absorption coefficient ( a p h ( λ ) ) of four phytoplankton groups (picophytoplankton, nanophytoplankton, dinoflagellates, and diatoms) as a function of the total chlorophyll-a concentration (C) and sea surface temperature (SST). Concurrent data on a p h ( λ ) (at 12 visible wavelengths), C and SST, from the surface layer ( a p h ( λ ) with a similar performance to two earlier models, using either in situ or satellite data as input. Although more complex, the new model has the advantage of being able to determine a p h ( λ ) for four phytoplankton groups and of incorporating the influence of SST on the composition of the four groups. We integrate the new four-population absorption model into a simple model of ocean colour, to illustrate the influence of changes in SST on phytoplankton community structure, and consequently, the blue-to-green ratio of remote-sensing reflectance. We also present a method of propagating error through the model and illustrate the technique by mapping errors in group-specific a p h ( λ ) using a satellite image. We envisage the model will be useful for ecosystem model validation and assimilation exercises and for investigating the influence of temperature change on ocean colour.

A 62-year-old man presented with obstructive sleep apnea, congestive heart failure and Cheyne-Stokes respiration. Further evaluation revealed a chronic dissecting aneurysm of the aorta causing vena caval obstruction. Surgical correction or the aneurysm dramatically improved ventricular function with resolution of Cheyne-Stokes respiration. Nasal continuous positive airway pressure corrected the obstructive sleep apnea and associated clinical features. The cardiopulmonary interactions between sleep apnea and aortic dissection are discussed.