Search

Abstract The first dose of COVID-19 vaccines led to an overall reduction in cardiovascular events, and in rare cases, cardiovascular complications. There is less information about the effect of second and booster doses on cardiovascular diseases. Using longitudinal health records from 45.7 million adults in England between December 2020 and January 2022, our study compared the incidence of thrombotic and cardiovascular complications up to 26 weeks after first, second and booster doses of brands and combinations of COVID-19 vaccines used during the UK vaccination program with the incidence before or without the corresponding vaccination. The incidence of common arterial thrombotic events (mainly acute myocardial infarction and ischaemic stroke) was generally lower after each vaccine dose, brand and combination. Similarly, the incidence of common venous thrombotic events, (mainly pulmonary embolism and lower limb deep venous thrombosis) was lower after vaccination. There was a higher incidence of previously reported rare harms after vaccination: vaccine-induced thrombotic thrombocytopenia after first ChAdOx1 vaccination, and myocarditis and pericarditis after first, second and transiently after booster mRNA vaccination (BNT-162b2 and mRNA-1273). These findings support the wide uptake of future COVID-19 vaccination programs.

Objectives Generation Scotland (GS) is a family-based genetic epidemiology study. Initial recruitment between 2006-11 recruited ~24,000 adults from ~7000 families across Scotland with consent for medical record linkage and re-contact. In 2022 we began recruiting another 20,000, with consent extended to administrative records, with age range now 12+. Methods Original volunteers completed a baseline questionnaire, provided biological samples and underwent clinical assessment. The samples, phenotype and genotype (including methylation) data are linked to routine NHS hospital, maternity, lab test, prescriptions, dentistry, mortality, imaging, cancer screening, GP data records, Covid-19 testing and vaccinations, and follow-up questionnaires. The new wave of recruitment is all online and can be done on a smartphone, with DNA from saliva collected by post. Teenagers aged 12-15 can join with parental consent. Results Researchers can find prevalent and incident disease cases and controls, to test research hypotheses on a stratified population. GS is a vanguard cohort testing emerging linkage opportunities in Scotland – including neonatal bloodspots and medical imaging reports. GS has established and validated E-HR linkage with the NHS Scotland CHI Register, overcoming technical and governance issues in the process. We contribute to major international consortia, with collaborators from institutions worldwide, both academic and commercial. Conclusion We plan to extend the linkage process to include other administrative data from national datasets as and when approvals are obtained. New types of data can also be collected by online questionnaires. The Research Tissue Bank resources are available to academic and commercial researchers through a managed access process.

Objectives Generation Scotland (GS) is a family-based genetic epidemiology study. Initial recruitment was ~24,000 adult volunteers from across Scotland in 2006-11 with consent for medical record linkage and re-contact. In 2022 we began recruiting another 20,000, with consent extended to administrative records, with age range now 12+. Methods People join the study and complete a questionnaire online, with postal saliva sample collection for DNA. Initially, the consent process required a parent/guardian to join GS, confirm their 12-15 year-old’s capacity for enduring consent and invite their child to join. After launch, parents and young people fed back that the communication and consent methods had a negative effect on the ability of young people to join GS - impacting their autonomy, as they were unable join GS independently of their parents. Results The GS Team designed a new sign-up process for 12–15 year-olds that allowed the teenager to sign up first and request that their parent agree they have capacity to consent. Creating this process was done in collaboration with the new GS YPAG. This new sign-up process was launched in September 2023. We also enabled sign-up using a mobile phone number instead of an email address. Conclusion Co-design of GS recruitment with young people and parents has greatly improved the sign-up process, making GS the first UK longitudinal study where people aged 12-15 can provide enduring consent online with parental confirmation of their capacity. This provides young people with greater ownership over their ability to take part in health research.

Objective: To identify highest-risk subgroups for COVID-19 and Long COVID(LC), particularly in contexts of influenza and cardiovascular disease(CVD). Methods: Using national, linked electronic health records for England (NHS England Secure Data Environment via CVD-COVID-UK/COVID-IMPACT Consortium), we studied individuals (of all ages) with COVID-19 and LC (2020-2023). We compared all-cause hospitalization and mortality by prior CVD, high CV risk, vaccination status (COVID-19/influenza), and CVD drugs, investigating impact of vaccination and CVD prevention using population preventable fractions. Results: Hospitalization and mortality were 15.3% and 2.0% among 17,373,850 individuals with COVID-19 (LC rate 1.3%), and 16.8% and 1.4% among 301,115 with LC. Adjusted risk of mortality and hospitalization were reduced with COVID-19 vaccination ≥ 2 doses(COVID-19:HR 0.36 and 0.69; LC:0.44 and 0.90). With influenza vaccination, mortality was reduced, but not hospitalization (COVID-19:0.86 and 1.01, and LC:0.72 and 1.05). Mortality and hospitalization were reduced by CVD prevention in those with CVD, e.g., anticoagulants- COVID:19:0.69 and 0.92; LC:0.59 and 0.88; lipid lowering- COVID-19:0.69 and 0.86; LC:0.68 and 0.90. COVID-19 vaccination averted 245044 of 321383 and 7586 of 8738 preventable deaths after COVID-19 and LC, respectively. Interpretation: Prior CVD and high CV risk are associated with increased hospitalization and mortality in COVID-19 and LC. Targeted COVID-19 vaccination and CVD prevention are priority interventions. Funding: NIHR. HDR UK.

Purpose Generation Scotland (GS) is a large family-based cohort study established as a longitudinal resource for research into the genetic, lifestyle and environmental determinants of physical and mental health. It comprises extensive genetic, sociodemographic and clinical data from volunteers in Scotland.Participants A total of 24 084 adult participants, including 5501 families, were recruited between 2006 and 2011. Within the cohort, 59% (approximately 14 209) are women, with an average age at recruitment of 49 years. Participants completed a health questionnaire and attended an in-person clinic visit, where detailed baseline data were collected on lifestyle information, cognitive function, personality traits and mental and physical health. Genotype array data are available for 20 026 (83%) participants, and blood-based DNA methylation (DNAm) data for 18 869 (78%) participants. Linkage to routine National Health Service datasets has been possible for 93% (n=22 402) of the cohort, creating a longitudinal resource that includes primary care, hospital attendance, prescription and mortality records. Multimodal brain imaging is available in 1069 individuals.Findings to date GS has been widely used by researchers across the world to study the genetic and environmental basis of common complex diseases. Over 350 peer-reviewed papers have been published using GS data, contributing to research areas such as ageing, cancer, cardiovascular disease and mental health. Recontact studies have built on the GS cohort to collect additional prospective data to study chronic pain, major depressive disorder and COVID-19.Future plans To create a larger, richer, longitudinal resource, ‘Next Generation Scotland’ launched in May 2022 to expand the existing cohort by a target of 20 000 additional volunteers, now including anyone aged 12+ years. New participants complete online consent and questionnaires and provide postal saliva samples, from which genotype and salivary DNAm array data will be generated. The latest cohort information and how to access data can be found on the GS website (www.generationscotland.org).

Abstract Intersectional social determinants including ethnicity are vital in health research. We curated a population-wide data resource of self-identified ethnicity data from over 60 million individuals in England primary care, linking it to hospital records. We assessed ethnicity data in terms of completeness, consistency, and granularity and found one in ten individuals do not have ethnicity information recorded in primary care. By linking to hospital records, ethnicity data were completed for 94% of individuals. By reconciling SNOMED-CT concepts and census-level categories into a consistent hierarchy, we organised more than 250 ethnicity sub-groups including and beyond “White”, “Black”, “Asian”, “Mixed” and “Other, and found them to be distributed in proportions similar to the general population. This large observational dataset presents an algorithmic hierarchy to represent self-identified ethnicity data collected across heterogeneous healthcare settings. Accurate and easily accessible ethnicity data can lead to a better understanding of population diversity, which is important to address disparities and influence policy recommendations that can translate into better, fairer health for all.

Background Generation Scotland (GS) is large family-based cohort study established as a resource for research into the genetic and environmental determinants of physical and mental health. Participants completed a baseline questionnaire and clinic visit in 2006-2011. 24,084 Participants 5,501 Families • NHS data linkage 22,402 (93%) • Genotype array data 20,026 (83%) • DNA methylation data 18,876 (78%) In 2019 funding was obtained to expand the existing GS cohort. By 2025 NextGenScot aims to: • Recruit 20,000 new participants online • Expand eligibility to 12-17 year olds • Collect saliva samples by post for genotyping • Linkage to NHS and other administrative data Details on how to join at www.gen.scot Participants consented to linkage to NHS Scotland medical records which offer longitudinal phenotype information. Linkage is conducted using the Community Health Index (CHI). Introduction GS receives 30-40 requests for access to data and/or samples every year, and has been included in over 380 publications. Research proposals are subject to review by the GS Access Committee. Details of the application process terms and conditions for access are available on the GS website, and now on the new research access website. The previous GS access process was based on an application form in Word, downloaded from the main GS website. We are replacing this with a new online research access system, adhering to transparency standards1, helped by HDR Alliance Transparency funding. After researchers publish their results, a link to the publication and a lay summary is currently posted on the GS website, with a spreadsheet listing papers. We will now make this information available and searchable for researchers, participants and the public on the new research access website. Methods We are developing a website, using in-house staff, with HDR Alliance Transparency contributing to their salaries. The system coding uses Django (a Python tool) linked to a MySQL database, a platform widely used for such projects, enabling faster development in a secure environment. Transparent access to GS data for registered users • View summary of data available • Query the summary data to see if it fits research requirements • Select data requested from data dictionaries • Get a detailed quote for access charges • Complete application form showing the project complies with the Five Safes2 • Upload supporting documentation (e.g. protocol, DPIA, ethics, etc) • Track progress of their application review, approval and delivery • Researchers will be required to submit a lay summary of their application GS participants, researchers and the public can view: • List of approved applications with lay summaries • Summaries of ongoing projects which will link to eventual publications • Searchable list of publications Result The new system is undergoing internal testing, using data imported from previous and ongoing access requests (May 2024). We will then pilot it with selected external researchers and GS participants (June 2024), with a plan to launch it for public access in July/August 2024. The GS Access Committee and admin team will be able to streamline the review and approval process, giving an improved turnaround time. Conclusions We were already planning to develop a new online application system, but the Transparency grant funding has enabled us to extend the functionality beyond a simple form-filling system. The new access website will give researchers more information about the application process, the data and samples available, see what other research has been approved in their area of interest, and track the progress of their project from enquiry through to publication. By adding a participant/public access area, with lay summaries of projects and publications, GS participants will be able to see how their data is being used for public benefit, as can potential new volunteers or other interested parties. Notes The URL for the new website is www.gsaccess.org The main GS website is at www.generationscotland.org References • Transparency Standards https://zenodo.org/record/8262453 • Five Safes https://ukdataservice.ac.uk/help/secure-lab/what-is-the-five-safes-framework/

Objectives We started a family-based genetic epidemiology study in 2006-11 which recruited ~24,000 adult volunteers from ~7000 families across Scotland with consent for follow-up through medical record linkage and re-contact. In 2022-23 we are recruiting another 20,000, with consent extended to administrative records, with age range now 12+. Methods Original volunteers completed a demographic, health and lifestyle questionnaire, provided biological samples, and underwent detailed clinical assessment. The samples, phenotype and genotype data form a resource for research on the genetics of conditions of public health importance. This has become a longitudinal dataset by linkage to routine NHS hospital, maternity, lab test, prescriptions, dentistry, mortality, imaging, cancer screening, GP data records, Covid-19 testing and vaccinations, as well as follow-up questionnaires. The new wave of recruitment is all online and can be done on a smartphone, with DNA from saliva collected by post. Teenagers aged 12-15 can join with parental consent. Results GWAS has been done on quantitative traits and biomarkers, with DNA methylation data and proteomics available for most of the cohort. Our “CovidLife” surveys collected data on effects of the pandemic. Researchers can find prevalent and incident disease cases and controls, to test research hypotheses on a stratified population. They can also do targeted recruitment of participants to new studies, including recall by genotype. We have established and validated E-HR linkage with the NHS Scotland CHI Register,,overcoming technical and governance issues in the process. We contribute to major international consortia, with collaborators from institutions worldwide, both academic and commercial. Recruits are asked to give consent to linkage to other administrative data, and reuse of samples from routine NHS tests for medical research. Conclusion We plan to extend the linkage process to include other administrative data from national datasets as and when approvals are obtained. New types of data can also be collected by online questionnaires. The Research Tissue Bank resources are available to academic and commercial researchers through a managed access process.

Abstract Background Epigenetic clocks can track both chronological age (cAge) and biological age (bAge). The latter is typically defined by physiological biomarkers and risk of adverse health outcomes, including all-cause mortality. As cohort sample sizes increase, estimates of cAge and bAge become more precise. Here, we aim to develop accurate epigenetic predictors of cAge and bAge, whilst improving our understanding of their epigenomic architecture. Methods First, we perform large-scale (N = 18,413) epigenome-wide association studies (EWAS) of chronological age and all-cause mortality. Next, to create a cAge predictor, we use methylation data from 24,674 participants from the Generation Scotland study, the Lothian Birth Cohorts (LBC) of 1921 and 1936, and 8 other cohorts with publicly available data. In addition, we train a predictor of time to all-cause mortality as a proxy for bAge using the Generation Scotland cohort (1214 observed deaths). For this purpose, we use epigenetic surrogates (EpiScores) for 109 plasma proteins and the 8 component parts of GrimAge, one of the current best epigenetic predictors of survival. We test this bAge predictor in four external cohorts (LBC1921, LBC1936, the Framingham Heart Study and the Women’s Health Initiative study). Results Through the inclusion of linear and non-linear age-CpG associations from the EWAS, feature pre-selection in advance of elastic net regression, and a leave-one-cohort-out (LOCO) cross-validation framework, we obtain cAge prediction with a median absolute error equal to 2.3 years. Our bAge predictor was found to slightly outperform GrimAge in terms of the strength of its association to survival (HRGrimAge = 1.47 [1.40, 1.54] with p = 1.08 × 10−52, and HRbAge = 1.52 [1.44, 1.59] with p = 2.20 × 10−60). Finally, we introduce MethylBrowsR, an online tool to visualise epigenome-wide CpG-age associations. Conclusions The integration of multiple large datasets, EpiScores, non-linear DNAm effects, and new approaches to feature selection has facilitated improvements to the blood-based epigenetic prediction of biological and chronological age.

Abstract Background The CVD-COVID-UK consortium was formed to understand the relationship between COVID-19 and cardiovascular diseases through analyses of harmonised electronic health records (EHRs) across the four UK nations. Beyond COVID-19, data harmonisation and common approaches enable analysis within and across independent Trusted Research Environments. Here we describe the reproducible harmonisation method developed using large-scale EHRs in Wales to accommodate the fast and efficient implementation of cross-nation analysis in England and Wales as part of the CVD-COVID-UK programme. We characterise current challenges and share lessons learnt. Methods Serving the scope and scalability of multiple study protocols, we used linked, anonymised individual-level EHR, demographic and administrative data held within the SAIL Databank for the population of Wales. The harmonisation method was implemented as a four-layer reproducible process, starting from raw data in the first layer. Then each of the layers two to four is framed by, but not limited to, the characterised challenges and lessons learnt. We achieved curated data as part of our second layer, followed by extracting phenotyped data in the third layer. We captured any project-specific requirements in the fourth layer. Results Using the implemented four-layer harmonisation method, we retrieved approximately 100 health-related variables for the 3.2 million individuals in Wales, which are harmonised with corresponding variables for > 56 million individuals in England. We processed 13 data sources into the first layer of our harmonisation method: five of these are updated daily or weekly, and the rest at various frequencies providing sufficient data flow updates for frequent capturing of up-to-date demographic, administrative and clinical information. Conclusions We implemented an efficient, transparent, scalable, and reproducible harmonisation method that enables multi-nation collaborative research. With a current focus on COVID-19 and its relationship with cardiovascular outcomes, the harmonised data has supported a wide range of research activities across the UK.

Abstract Much of the knowledge and information needed for enabling high-quality clinical research is stored in free-text format. Natural language processing (NLP) has been used to extract information from these sources at scale for several decades. This paper aims to present a comprehensive review of clinical NLP for the past 15 years in the UK to identify the community, depict its evolution, analyse methodologies and applications, and identify the main barriers. We collect a dataset of clinical NLP projects (n = 94; £ = 41.97 m) funded by UK funders or the European Union’s funding programmes. Additionally, we extract details on 9 funders, 137 organisations, 139 persons and 431 research papers. Networks are created from timestamped data interlinking all entities, and network analysis is subsequently applied to generate insights. 431 publications are identified as part of a literature review, of which 107 are eligible for final analysis. Results show, not surprisingly, clinical NLP in the UK has increased substantially in the last 15 years: the total budget in the period of 2019–2022 was 80 times that of 2007–2010. However, the effort is required to deepen areas such as disease (sub-)phenotyping and broaden application domains. There is also a need to improve links between academia and industry and enable deployments in real-world settings for the realisation of clinical NLP’s great potential in care delivery. The major barriers include research and development access to hospital data, lack of capable computational resources in the right places, the scarcity of labelled data and barriers to sharing of pretrained models.

Cunningham-Burley, McCartney, Campbell, Flaig, Orange et al. assess the ethics and potential benefit of secondary data use from newborn blood spot archives for research in a Citizen’s jury that agreed this was in the public interest. They also investigate and demonstrate the feasibility of using the archives for research purposes.

The use of data from smartphones and wearable devices has huge potential for population health research, given the high level of device ownership; the range of novel health-relevant data types available from consumer devices; and the frequency and duration with which data are, or could be, collected. Yet, the uptake and success of large-scale mobile health research in the last decade have not met this intensely promoted opportunity. We make the argument that digital person-generated health data are required and necessary to answer many top priority research questions, using illustrative examples taken from the James Lind Alliance Priority Setting Partnerships. We then summarize the findings from 2 UK initiatives that considered the challenges and possible solutions for what needs to be done and how such solutions can be implemented to realize the future opportunities of digital person-generated health data for clinically important population health research. Examples of important areas that must be addressed to advance the field include digital inequality and possible selection bias; easy access for researchers to the appropriate data collection tools, including how best to harmonize data items; analysis methodologies for time series data; patient and public involvement and engagement methods for optimizing recruitment, retention, and public trust; and methods for providing research participants with greater control over their data. There is also a major opportunity, provided through the linkage of digital person-generated health data to routinely collected data, to support novel population health research, bringing together clinician-reported and patient-reported measures. We recognize that well-conducted studies need a wide range of diverse challenges to be skillfully addressed in unison (eg, challenges regarding epidemiology, data science and biostatistics, psychometrics, behavioral and social science, software engineering, user interface design, information governance, data management, and patient and public involvement and engagement). Consequently, progress would be accelerated by the establishment of a new interdisciplinary community where all relevant and necessary skills are brought together to allow for excellence throughout the life cycle of a research study. This will require a partnership of diverse people, methods, and technologies. If done right, the synergy of such a partnership has the potential to transform many millions of people’s lives for the better.

Abstract Background Better phenotyping of routinely collected coded data would be useful for research and health improvement. For example, the precision of coded data for hemorrhagic stroke (intracerebral hemorrhage [ICH] and subarachnoid hemorrhage [SAH]) may be as poor as

Abstract Background Dementia outcomes include memory loss, language impairment, reduced quality of life and personality changes. Research suggests that outcomes selected for dementia clinical trials might not be the most important to people affected. Objective One of the goals of the ‘Real world Outcomes across the Alzheimer's Disease spectrum for better care: Multi‐modal data Access Platform’ (ROADMAP) project was to identify important outcomes from the perspective of people with dementia and their caregivers. We review how ROADMAP's Public Involvement shaped the programme, impacted the research process and gave voice to people affected by dementia. Design The European Working Group of People with Dementia (EWGPWD) were invited to participate. In‐person consultations were held with people with dementia and caregivers, with advance information provided on ROADMAP activities. Constructive criticism of survey content, layout and accessibility was sought, as were views and perspectives on terminology and key concepts around disease progression. Results The working group provided significant improvements to survey accessibility and acceptability. They promoted better understanding of concepts around disease progression and how researchers might approach measuring and interpreting findings. They effectively expressed difficult concepts through real‐world examples. Conclusions The role of the EWGPWD in ROADMAP was crucial, and its impact was highly influential. Involvement from the design stage helped shape the ethos of the programme and ultimately its meaningfulness. Public contribution People with dementia and their carers were involved through structured consultations and invited to provide feedback on project materials, methods and insight into terminology and relevant concepts.

Objectives Generation Scotland (GS) is a family-based genetic epidemiology study of around 24,000 volunteers from 7000 families recruited across Scotland between 2006 and 2011 with follow-up through record linkage and re-contact. Broad consent was obtained for linkage to medical records for 98% of the cohort. Recruitment has recommenced in 2022. Approach Participants completed a demographic, health and lifestyle questionnaire, provided biological samples, and underwent detailed clinical assessment. The samples, phenotype and genotype data form a resource with broad consent for health-related research of current and projected public health importance. This has become a longitudinal dataset by linkage to routine NHS hospital, maternity, lab test, prescriptions, dentistry, mortality, cancer screening, GP data records, Covid-19 testing and vaccinations. Genome-wide association studies (GWAS) have been done on quantitative traits and biomarkers, with DNA methylation data and proteomics available for most of the cohort. Our “CovidLife” surveys collected data on the effects of the pandemic. Results Researchers can use the linked datasets to ascertain prevalent and incident disease cases and controls to test a wide range of research hypotheses. They can also recruit participants to new studies, including recall by genotype, utilising the NHS Scotland Community Health Index (CHI) Register for current contact details. We have established and validated electronic health record linkage, overcoming technical and governance issues in the process. Using consented data avoids some limitations of safe havens for analysis. GS is a contributor to major international consortia, with collaborators from many institutions worldwide, both academic and commercial. New recruits are now asked to give consent to linkage to other administrative data, and reuse of samples from routine NHS tests for medical research. Conclusion GS is now extending linkages to include radiology and health-relevant administrative data (e.g. education); and reopening recruitment to double the cohort size, including teenagers age 12+, collecting new data online and using remote sample collection. GS resources are available to academic and commercial researchers through a managed access process (www.generationscotland.org).

Background Cerebral small‐vessel disease (cSVD) is an important cause of stroke and vascular dementia. Most cases are multifactorial, but an emerging minority have a monogenic cause. While NOTCH3 is the best‐known gene, several others have been reported. We aimed to summarize the cerebral phenotypes associated with these more recent cSVD genes. Methods and Results We performed a systematic review (PROSPERO [International Prospective Register of Systematic Reviews]: CRD42020196720), searching Medline/Embase (conception to July 2020) for any language publications describing COL4A1/2, TREX1, HTRA1, ADA2, or CTSA pathogenic variant carriers. We extracted data about individuals’ characteristics and clinical and vascular radiological cerebral phenotypes. We summarized phenotype frequencies per gene, comparing patterns across genes. We screened 6485 publications including 402, and extracted data on 390 individuals with COL4A1, 123 with TREX1, 44 with HTRA1 homozygous, 41 with COL4A2, 346 with ADA2, 82 with HTRA1 heterozygous, and 14 with CTSA. Mean age ranged from 15 (ADA2) to 59 years (HTRA1 heterozygotes). Clinical phenotype frequencies varied widely: stroke, 9% (TREX1) to 52% (HTRA1 heterozygotes); cognitive features, 0% (ADA2) to 64% (HTRA1 homozygotes); and psychiatric features, 0% (COL4A2; ADA2) to 57% (CTSA). Among individuals with neuroimaging, vascular radiological phenotypes appeared common, ranging from 62% (ADA2) to 100% (HTRA1 homozygotes; CTSA). White matter lesions were the most common pathology, except in ADA2 and COL4A2 cases, where ischemic and hemorrhagic lesions dominated, respectively. Conclusions There appear to be differences in cerebral manifestations across cSVD genes. Vascular radiological changes were more common than clinical neurological phenotypes, and present in the majority of individuals with reported neuroimaging. However, these results may be affected by age and biases inherent to case reports. In the future, better characterization of associated phenotypes, as well as insights from population‐based studies, should improve our understanding of monogenic cSVD to inform genetic testing, guide clinical management, and help unravel underlying disease mechanisms.

RuralCovidLife is part of Generation Scotland’s CovidLife project, investigating the impact of the COVID-19 pandemic and mitigation measures on people in Scotland. The RuralCovidLife project focuses on Scotland’s rural communities, and how they have been impacted by the pandemic. During survey development, Generation Scotland consulted with people living or working in rural communities, and collaborated with a patient and public involvement and engagement (PPIE) group composed of rural community leaders. Through this consultation work, the RuralCovidLife survey was developed to assess the issues most pertinent to people in rural communities, such as mental health, employment, transport, connectivity, and local communities. Between 14th October and 30th November 2020, 3,365 participants from rural areas in Scotland took part in the survey. Participant ages ranged from 16 to 96 (mean = 58.4, standard deviation [SD] = 13.3), and the majority of the participants were female (70.5%). Over half (51.3%) had taken part in the original CovidLife survey. RuralCovidLife includes a subsample (n = 523) of participants from the Generation Scotland cohort. Pre-pandemic data on health and lifestyle, as well as biological samples, are available for these participants. These participants’ data can also be linked to past and future healthcare records, allowing analysis of retrospective and prospective health outcomes. Like Generation Scotland, RuralCovidLife is designed as a resource for researchers. RuralCovidLife data, as well as the linked Generation Scotland data, is available for use by external researchers following approval from the Generation Scotland Access Committee. RuralCovidLife can be used to investigate mental health, well-being, and behaviour in participants living in rural areas during the COVID-19 pandemic, as well as comparisons with non-rural samples. Moreover, the sub-sample with full Generation Scotland data and linkage can be used to investigate the long-term health consequences of the COVID-19 pandemic in rural communities.

TeenCovidLife is part of Generation Scotland’s CovidLife projects, a set of longitudinal observational studies designed to assess the psychosocial and health impacts of the COVID-19 pandemic. TeenCovidLife focused on how adolescents in Scotland were coping during the pandemic. As of September 2021, Generation Scotland had conducted three TeenCovidLife surveys. Participants from previous surveys were invited to participate in the next, meaning the age ranges shifted over time. TeenCovidLife Survey 1 consists of data from 5,543 young people age 12 to 17, collected from 22 May to 5 July 2020, during the first school closures period in Scotland. TeenCovidLife Survey 2 consists of data from 2,245 young people aged 12 to 18, collected from 18 August to 14 October 2020, when the initial lockdown measures were beginning to ease, and schools reopened in Scotland. TeenCovidLife Survey 3 consists of data from 597 young people age 12 to 19, collected from 12 May to 27 June 2021, a year after the first survey, after the schools returned following the second lockdown in 2021. A total of 316 participants took part in all three surveys. TeenCovidLife collected data on general health and well-being, as well as topics specific to COVID-19, such as adherence to COVID-19 health guidance, feelings about school closures, and the impact of exam cancellations. Limited work has examined the impact of the COVID-19 pandemic on young people. TeenCovidLife provides relevant and timely data to assess the impact of the pandemic on young people in Scotland. The dataset is available under authorised access from Generation Scotland; see the Generation Scotland website for more information.

BackgroundStroke in UK Biobank (UKB) is ascertained via linkages to coded administrative datasets and self-report. We studied the accuracy of these codes using genetic validation.MethodsWe compiled stroke-specific and broad cerebrovascular disease (CVD) code lists (Read V2/V3, ICD-9/-10) for medical settings (hospital, death record, primary care) and self-report. Among 408,210 UKB participants, we identified all with a relevant code, creating 12 stroke definitions based on the code type and source. We performed genome-wide association studies (GWASs) for each definition, comparing summary results against the largest published stroke GWAS (MEGASTROKE), assessing genetic correlations, and replicating 32 stroke-associated loci.ResultsThe stroke case numbers identified varied widely from 3,976 (primary care stroke-specific codes) to 19,449 (all codes, all sources). All 12 UKB stroke definitions were significantly correlated with the MEGASTROKE summary GWAS results (rg.81-1) and each other (rg.4-1). However, Bonferroni-corrected confidence intervals were wide, suggesting limited precision of some results. Six previously reported stroke-associated loci were replicated using ≥1 UKB stroke definition.ConclusionsStroke case numbers in UKB depend on the code source and type used, with a 5-fold difference in the maximum case-sample size. All stroke definitions are significantly genetically correlated with the largest stroke GWAS to date.

BackgroundThromboses in unusual locations after the Coronavirus Disease 2019 (COVID-19) vaccine ChAdOx1-S have been reported, although their frequency with vaccines of different types is uncertain at a population level. The aim of this study was to estimate the population-level risks of hospitalised thrombocytopenia and major arterial and venous thromboses after COVID-19 vaccination.Methods and findingsIn this whole-population cohort study, we analysed linked electronic health records from adults living in England, from 8 December 2020 to 18 March 2021. We estimated incidence rates and hazard ratios (HRs) for major arterial, venous, and thrombocytopenic outcomes 1 to 28 and >28 days after first vaccination dose for ChAdOx1-S and BNT162b2 vaccines. Analyses were performed separately for ages 28 days post-ChAdOx1-S, 359 (338 to 382), ≤28 days post-BNT162b2-S, 241 (229 to 253); >28 days post-BNT162b2-S 277 (263 to 291). The crude incidence rates (per 100,000 person-years) of all arterial events were as follows: prevaccination, 546 (95% CI: 541 to 555); ≤28 days post-ChAdOx1-S, 1,211 (1,185 to 1,237); >28 days post-ChAdOx1-S, 1678 (1,630 to 1,726), ≤28 days post-BNT162b2-S, 1,242 (1,214 to 1,269); >28 days post-BNT162b2-S, 1,539 (1,507 to 1,572). Adjusted HRs (aHRs) 1 to 28 days after ChAdOx1-S, compared with unvaccinated rates, at ages ConclusionsIn this study, we observed increases in rates of ICVT and thrombocytopenia after ChAdOx1-S vaccination in adults aged

ImportanceA lack of internationally agreed standards for combining available data sources at scale risks inconsistent disease phenotyping limiting research reproducibility.ObjectiveTo develop and then evaluate if a rules-based algorithm can identify coronary artery disease (CAD) sub-phenotypes using electronic health records (EHR) and questionnaire data from UK Biobank (UKB).DesignCase-control and cohort study.SettingProspective cohort study of 502K individuals aged 40-69 years recruited between 2006-2010 into the UK Biobank with linked hospitalization and mortality data and genotyping.ParticipantsWe included all individuals for phenotyping into 6 predefined CAD phenotypes using hospital admission and procedure codes, mortality records and baseline survey data. Of these, 408,470 unrelated individuals of European descent had a polygenic risk score (PRS) for CAD estimated.ExposureCAD Phenotypes.Main outcomes and measuresAssociation with baseline risk factors, mortality (n = 14,419 over 7.8 years median f/u), and a PRS for CAD.ResultsThe algorithm classified individuals with CAD into prevalent MI (n = 4,900); incident MI (n = 4,621), prevalent CAD without MI (n = 10,910), incident CAD without MI (n = 8,668), prevalent self-reported MI (n = 2,754); prevalent self-reported CAD without MI (n = 5,623), yielding 37,476 individuals with any type of CAD. Risk factors were similar across the six CAD phenotypes, except for fewer men in the self-reported CAD without MI group (46.7% v 70.1% for the overall group). In age- and sex- adjusted survival analyses, mortality was highest following incident MI (HR 6.66, 95% CI 6.07-7.31) and lowest for prevalent self-reported CAD without MI at baseline (HR 1.31, 95% CI 1.15-1.50) compared to disease-free controls. There were similar graded associations across the six phenotypes per SD increase in PRS, with the strongest association for prevalent MI (OR 1.50, 95% CI 1.46-1.55) and the weakest for prevalent self-reported CAD without MI (OR 1.08, 95% CI 1.05-1.12). The algorithm is available in the open phenotype HDR UK phenotype library (https://portal.caliberresearch.org/).ConclusionsAn algorithmic, EHR-based approach distinguished six phenotypes of CAD with distinct survival and PRS associations, supporting adoption of open approaches to help standardize CAD phenotyping and its wider potential value for reproducible research in other conditions.

Abstract Introduction Clinical trials involving patients with Alzheimer's disease (AD) continue to try to identify disease‐modifying treatments. Although trials are designed to meet regulatory and registration requirements, many do not measure outcomes of the disease most relevant to key stakeholders. Methods A systematic review sought research that elicited information from people with AD, their caregivers, and health‐care professionals on which outcomes of the disease were important. Studies published in any language between 2008 and 2017 were included. Results Participants in 34 studies described 32 outcomes of AD. These included clinical (memory, mental health), practical (ability to undertake activities of daily living, access to health information), and personal (desire for patient autonomy, maintenance of identity) outcomes of the disease. Discussion Evidence elicited directly from the people most affected by AD reveals a range of disease outcomes that are relevant to them but are not commonly captured in clinical trials of new treatments.

The prevalence of the coronavirus SARS-CoV-2 disease has resulted in the unprecedented collection of health data to support research. Historically, coordinating the collation of such datasets on a national scale has been challenging to execute for several reasons, including issues with data privacy, the lack of data reporting standards, interoperable technologies, and distribution methods. The coronavirus SARS-CoV-2 disease pandemic has highlighted the importance of collaboration between government bodies, healthcare institutions, academic researchers and commercial companies in overcoming these issues during times of urgency. The National COVID-19 Chest Imaging Database, led by NHSX, British Society of Thoracic Imaging, Royal Surrey NHS Foundation Trust and Faculty, is an example of such a national initiative. Here, we summarise the experiences and challenges of setting up the National COVID-19 Chest Imaging Database, and the implications for future ambitions of national data curation in medical imaging to advance the safe adoption of artificial intelligence in healthcare.

Objective There is contrasting evidence on the relationship between socioeconomic status (SES) and age-related macular degeneration (AMD), the most common cause of visual impairment (VI) in developed countries. This study examines the relationship between SES, cardiovascular risk factors and self-reported AMD.Methods and analysis Over 500000 people participated in the UK Biobank study from 2006 to 2019, with sociodemographic data and clinical measurements collected using standardised procedures. Visual acuity was measured in 117907 participants with VI defined as LogMAR ≤0.3. We used logistic regression to examine the cross-sectional associations between SES and self-reported AMD.Results Self-reported AMD was available for 133339 participants aged 50 and older. People reporting AMD had higher academic qualifications, lower income, were unable to work due to disability, have higher BMI, diabetes and vascular heart disease after adjusting for age and sex. In a multivariable analysis, higher income was protective of AMD and economic inactivity due to disability increased the odds of AMD (2.02, 95% CI 1.13 to 3.61). Both associations were independent of cardiovascular factors, but was no longer significant after adjusting for VI.Conclusions The association between education, employment and household income with AMD was independent of cardiovascular risk factors.

CovidLife is a longitudinal observational study designed to investigate the impact of the COVID-19 pandemic on mental health, well-being and behaviour in adults living in the UK. In total, 18,518 participants (mean age = 56.43, SD = 14.35) completed the first CovidLife questionnaire (CovidLife1) between April and June 2020. To date, participants have completed two follow-up assessments. CovidLife2 took place between July and August 2020 (n = 11,319), and CovidLife3 took place in February 2021 (n = 10,386). A range of social and psychological measures were administered at each wave including assessments of anxiety, depression, well-being, loneliness and isolation. Information on sociodemographic, health, and economic circumstances was also collected. Questions also assessed information on COVID-19 infections and symptoms, compliance to COVID-19 restrictions, and opinions on the UK and Scottish Governments’ handling of the pandemic. CovidLife includes a subsample of 4,847 participants from the Generation Scotland cohort (N~24,000, collected 2006-2011); a well-characterised cohort of families in Scotland with pre-pandemic data on mental health, physical health, lifestyle, and socioeconomic factors, along with biochemical and genomic data derived from biological samples. These participants also consented to their study data being linked to Scottish health records. CovidLife and Generation Scotland data can be accessed and used by external researchers following approval from the Generation Scotland Access Committee. CovidLife can be used to investigate mental health, well-being and behaviour during COVID-19; how these vary according to sociodemographic, health and economic circumstances; and how these change over time. The Generation Scotland subsample with pre-pandemic data and linkage to health records can be used to investigate the predictors of health and well-being during COVID-19 and the future health consequences of the COVID-19 pandemic.

Abstract Background With the improvements to text mining technology and the availability of large unstructured Electronic Healthcare Records (EHR) datasets, it is now possible to extract structured information from raw text contained within EHR at reasonably high accuracy. We describe a text mining system for classifying radiologists’ reports of CT and MRI brain scans, assigning labels indicating occurrence and type of stroke, as well as other observations. Our system, the Edinburgh Information Extraction for Radiology reports (EdIE-R) system, which we describe here, was developed and tested on a collection of radiology reports.The work reported in this paper is based on 1168 radiology reports from the Edinburgh Stroke Study (ESS), a hospital-based register of stroke and transient ischaemic attack patients. We manually created annotations for this data in parallel with developing the rule-based EdIE-R system to identify phenotype information related to stroke in radiology reports. This process was iterative and domain expert feedback was considered at each iteration to adapt and tune the EdIE-R text mining system which identifies entities, negation and relations between entities in each report and determines report-level labels (phenotypes). Results The inter-annotator agreement (IAA) for all types of annotations is high at 96.96 for entities, 96.46 for negation, 95.84 for relations and 94.02 for labels. The equivalent system scores on the blind test set are equally high at 95.49 for entities, 94.41 for negation, 98.27 for relations and 96.39 for labels for the first annotator and 96.86, 96.01, 96.53 and 92.61, respectively for the second annotator. Conclusion Automated reading of such EHR data at such high levels of accuracies opens up avenues for population health monitoring and audit, and can provide a resource for epidemiological studies. We are in the process of validating EdIE-R in separate larger cohorts in NHS England and Scotland. The manually annotated ESS corpus will be available for research purposes on application.

Abstract Background Manual coding of phenotypes in brain radiology reports is time consuming. We developed a natural language processing (NLP) algorithm to enable automatic identification of brain imaging in radiology reports performed in routine clinical practice in the UK National Health Service (NHS). Methods We used anonymized text brain imaging reports from a cohort study of stroke/TIA patients and from a regional hospital to develop and test an NLP algorithm. Two experts marked up text in 1692 reports for 24 cerebrovascular and other neurological phenotypes. We developed and tested a rule-based NLP algorithm first within the cohort study, and further evaluated it in the reports from the regional hospital. Results The agreement between expert readers was excellent (Cohen’s κ =0.93) in both datasets. In the final test dataset (n = 700) in unseen regional hospital reports, the algorithm had very good performance for a report of any ischaemic stroke [sensitivity 89% (95% CI:81–94); positive predictive value (PPV) 85% (76–90); specificity 100% (95% CI:0.99–1.00)]; any haemorrhagic stroke [sensitivity 96% (95% CI: 80–99), PPV 72% (95% CI:55–84); specificity 100% (95% CI:0.99–1.00)]; brain tumours [sensitivity 96% (CI:87–99); PPV 84% (73–91); specificity: 100% (95% CI:0.99–1.00)] and cerebral small vessel disease and cerebral atrophy (sensitivity, PPV and specificity all > 97%). We obtained few reports of subarachnoid haemorrhage, microbleeds or subdural haematomas. In 110,695 reports from NHS Tayside, atrophy (n = 28,757, 26%), small vessel disease (15,015, 14%) and old, deep ischaemic strokes (10,636, 10%) were the commonest findings. Conclusions An NLP algorithm can be developed in UK NHS radiology records to allow identification of cohorts of patients with important brain imaging phenotypes at a scale that would otherwise not be possible.

Regardless of prognosis, surgery is often considered in metastatic bone disease (MBD) as a palliative procedure to improve function and quality of life. Traditional focus on objective outcomes such as mortality is inappropriate in this group, and there is a drive to assess outcomes via patient-reported outcome measures (PROMs). This is an overview of current understanding of MBD outcomes and how this should influence future decision-making and research. The objectives of this review were to identify difficulties in measuring PROMs in the MBD patient population and explore alternatives to patientreported outcomes. We also provide an overview of current understanding of outcomes in MBD and how this should influence decision-making and direct research.

BackgroundUK Biobank is a well-characterised cohort of over 500 000 participants including genetics, environmental data and imaging. An online mental health questionnaire was designed for UK Biobank participants to expand its potential.AimsDescribe the development, implementation and results of this questionnaire.MethodAn expert working group designed the questionnaire, using established measures where possible, and consulting a patient group. Operational criteria were agreed for defining likely disorder and risk states, including lifetime depression, mania/hypomania, generalised anxiety disorder, unusual experiences and self-harm, and current post-traumatic stress and hazardous/harmful alcohol use.ResultsA total of 157 366 completed online questionnaires were available by August 2017. Participants were aged 45–82 (53% were ≥65 years) and 57% women. Comparison of self-reported diagnosed mental disorder with a contemporary study shows a similar prevalence, despite respondents being of higher average socioeconomic status. Lifetime depression was a common finding, with 24% (37 434) of participants meeting criteria and current hazardous/harmful alcohol use criteria were met by 21% (32 602), whereas other criteria were met by less than 8% of the participants. There was extensive comorbidity among the syndromes. Mental disorders were associated with a high neuroticism score, adverse life events and long-term illness; addiction and bipolar affective disorder in particular were associated with measures of deprivation.ConclusionsThe UK Biobank questionnaire represents a very large mental health survey in itself, and the results presented here show high face validity, although caution is needed because of selection bias. Built into UK Biobank, these data intersect with other health data to offer unparalleled potential for crosscutting biomedical research involving mental health.

BackgroundUK Biobank is a well-characterised cohort of over 500 000 participants that offers unique opportunities to investigate multiple diseases and risk factors.AimsAn online mental health questionnaire completed by UK Biobank participants was expected to expand the potential for research into mental disorders.MethodAn expert working group designed the questionnaire, using established measures where possible, and consulting with a patient group regarding acceptability. Case definitions were defined using operational criteria for lifetime depression, mania, anxiety disorder, psychotic-like experiences and self-harm, as well as current post-traumatic stress and alcohol use disorders.Results157 366 completed online questionnaires were available by August 2017. Comparison of self-reported diagnosed mental disorder with a contemporary study shows a similar prevalence, despite respondents being of higher average socioeconomic status than the general population across a range of indicators. Thirty-five per cent (55 750) of participants had at least one defined syndrome, of which lifetime depression was the most common at 24% (37 434). There was extensive comorbidity among the syndromes. Mental disorders were associated with high neuroticism score, adverse life events and long-term illness; addiction and bipolar affective disorder in particular were associated with measures of deprivation.ConclusionsThe questionnaire represents a very large mental health survey in itself, and the results presented here show high face validity, although caution is needed owing to selection bias. Built into UK Biobank, these data intersect with other health data to offer unparalleled potential for crosscutting biomedical research involving mental health.Declaration of interestG.B. received grants from the National Institute for Health Research during the study; and support from Illumina Ltd. and the European Commission outside the submitted work. B.C. received grants from the Scottish Executive Chief Scientist Office and from The Dr Mortimer and Theresa Sackler Foundation during the study. C.S. received grants from the Medical Research Council and Wellcome Trust during the study, and is the Chief Scientist for UK Biobank. M.H. received grants from the Innovative Medicines Initiative via the RADAR-CNS programme and personal fees as an expert witness outside the submitted work.

Introduction Dementia Platform UK (DPUK) brings together over 50 different dementia-related cohorts. Most studies have restricted follow-up times and all are based on information from people who volunteer time and data for research. Participants are therefore often not representative of the 'wider population' and generalization of results is complicated. The Secure Anonymised Information Linkage databank (SAIL) holds long-time information on every person in Wales registered with the national health service, so generalization of study results is easier; however, data management and analysis of SAIL data is not trivial. We used data from SAIL to construct an easily accessible, well described dementia e-cohort. Methods With some age restrictions, all Welsh residents for whom primary care data were available were included. Within SAIL, a table was created holding demographic information for every participant including follow-up times and several dementia indicators. Using validated diagnostic code lists, this table was linked to information on every dementia-related diagnostic event and several covariates and co-morbidites. SAIL-DeC can be modified according to varying study designs using annotated SQL-based scripts. Information on SAIL-DeC can easily be updated and linked to additional data on the SAIL database. Interactive visualisations effectively summarise cohort characteristics, aiding researchers to quickly determine cohort eligibility for dementia studies. Results From 4.4 million participants in SAIL, 1.2 million met the cohort inclusion criteria, resulting in 18.8 million person-years of follow-up. Of these, 129,650 (10%) developed all-cause dementia during follow-up, with 77,978 (60%) having dementia subtype codes. Seventy-nine percent of participants who developed dementia died during follow-up. Median survival was 12.3 years for participants diagnosed with dementia when aged 50-60, 6.8 years when aged 60-70, 4.2 years when aged 70-80 and 2.4 years when aged 80-90. Conclusions We have created a generalisable, national dementia e-cohort, aimed at facilitating epidemiological dementia research.

BackgroundThe long-term risk of stroke or myocardial infarction (MI) in patients with minor neurological symptoms who are not clinically diagnosed with transient ischaemic attack (TIA) or minor stroke is uncertain.MethodsWe used data from a rapid access clinic for patients with suspected TIA or minor stroke and follow-up from four overlapping data sources for a diagnosis of ischaemic or haemorrhagic stroke, MI, major haemorrhage and death. We identified patients with and without a clinical diagnosis of TIA or minor stroke. We estimated hazard ratios of stroke, MI, major haemorrhage and death in early and late time periods.Results5,997 patients were seen from 2005-2013, who were diagnosed with TIA or minor stroke (n = 3604, 60%) or with other diagnoses (n = 2392, 40%). By 5 years the proportion of patients who had a subsequent ischaemic stroke or MI, in patients with a clinical diagnosis of minor stroke or TIA was 19% [95% confidence interval (CI): 17-20%], and in patients with other diagnoses was 10% (95%CI: 8-15%). Patients with clinical diagnosis of TIA or minor stroke had three times the hazard of stroke or MI compared to patients with other diagnoses [hazard ratio (HR)2.83 95%CI:2.13-3.76, adjusted age and sex] by 90 days post-event; however from 90 days to end of follow up, this difference was attenuated (HR 1.52, 95%CI:1.25-1.86). Older patients and those who had a history of vascular disease had a high risk of stroke or MI, whether or not they were diagnosed with minor stroke or TIA.ConclusionsCareful attention to vascular risk factors in patients presenting with transient or minor neurological symptoms not thought to be due to stroke or TIA is justified, particularly those who are older or have a history of vascular disease.

Abstract Background Electronic linkage of UK cohorts to routinely collected National Health Service (NHS) records provides virtually complete follow-up for cause-specific hospital admissions and deaths. The reliability of dementia diagnoses recorded in NHS hospital data is not well documented. Methods For a sample of Million Women Study participants in England we compared dementia recorded in routinely collected NHS hospital data (Hospital Episode Statistics: HES) with dementia recorded in two separate sources of primary care information: a primary care database [Clinical Practice Research Datalink (CPRD), n = 340] and a survey of study participants’ General Practitioners (GPs, n = 244). Results Dementia recorded in HES fully agreed both with CPRD and with GP survey data for 85% of women; it did not agree for 1 and 4%, respectively. Agreement was uncertain for the remaining 14 and 11%, respectively; and among those classified as having uncertain agreement in CPRD, non-specific terms compatible with dementia, such as ‘memory loss’, were recorded in the CPRD database for 79% of the women. Agreement was significantly better (p

[This corrects the article DOI: 10.1371/journal.pone.0199026.].

Background and purposePolymorphisms in coagulation genes have been associated with early-onset ischemic stroke. Here we pursue an a priori hypothesis that genetic variation in the endothelial-based receptors of the thrombomodulin-protein C system (THBD and PROCR) may similarly be associated with early-onset ischemic stroke. We explored this hypothesis utilizing a multi-stage design of discovery and replication.MethodsDiscovery was performed in the Genetics-of-Early-Onset Stroke (GEOS) Study, a biracial population-based case-control study of ischemic stroke among men and women aged 15-49 including 829 cases of first ischemic stroke (42.2% African-American) and 850 age-comparable stroke-free controls (38.1% African-American). Twenty-four single-nucleotide-polymorphisms (SNPs) in THBD and 22 SNPs in PROCR were evaluated. Following LD pruning (r2≥0.8), we advanced uncorrelated SNPs forward for association analyses. Associated SNPs were evaluated for replication in an early-onset ischemic stroke population (onset-ageResultsAmong GEOS Caucasians, PROCR rs9574, which was in strong LD with 8 other SNPs, and one additional independent SNP rs2069951, were significantly associated with ischemic stroke (rs9574, OR = 1.33, p = 0.003; rs2069951, OR = 1.80, p = 0.006) using an additive-model adjusting for age, gender and population-structure. Adjusting for risk factors did not change the associations; however, associations were strengthened among those without risk factors. PROCR rs9574 also associated with early-onset ischemic stroke in the replication sample (OR = 1.08, p = 0.015), but not older-onset stroke. There were no PROCR associations in African-Americans, nor were there any THBD associations in either ethnicity.ConclusionPROCR polymorphisms are associated with early-onset ischemic stroke in Caucasians.

OBJECTIVES:Electronic health records (EHR) might be a useful resource to study the risk factors and clinical care of people with dementia. We sought to determine the diagnostic validity of dementia captured in linked EHR. METHODS AND FINDINGS:A cohort of adults in linked primary care, hospital, disease registry and mortality records in England, [CALIBER (CArdiovascular disease research using LInked Bespoke studies and Electronic health Records)]. The proportion of individuals with dementia, Alzheimer's disease, vascular and rare dementia in each data source was determined. A comparison was made of symptoms and care between people with dementia and age-, sex- and general practice-matched controls, using conditional logistic regression. The lifetime risk and prevalence of dementia and mortality rates in people with and without dementia were estimated with random-effects Poisson models. There were 47,386 people with dementia: 12,633 with Alzheimer's disease, 9540 with vascular and 1539 with rare dementia. Seventy-four percent of cases had corroborating evidence of dementia. People with dementia were more likely to live in a deprived area (conditional OR 1.26;95%CI:1.20-1.31 most vs least deprived), have documented memory impairment (cOR = 11.97;95%CI:11.24-12.75), falls (cOR = 2.36;95%CI:2.31-2.41), depression (cOR = 2.03; 95%CI:1.98-2.09) or anxiety (cOR = 1.27; 95%CI:1.23-1.32). The lifetime risk of dementia at age 65 was 9.2% (95%CI:9.0%-9.4%), in men and 14.9% (95%CI:14.7%-15.1%) in women. The population prevalence of recorded dementia increased from 0.3% in 2000 to 0.7% in 2010. A higher mortality rate was observed in people with than without dementia (IRR = 1.56;95%CI:1.54-1.58). CONCLUSIONS:Most people with a record of dementia in linked UK EHR had some corroborating evidence for diagnosis. The estimated 10-year risk of dementia was higher than published population-based estimations. EHR are therefore a promising source of data for dementia research.

Background: Monoallelic and biallelic mutations in the exonuclease TREX1 cause monogenic small vessel diseases (SVD). Given recent evidence for genetic and pathophysiological overlap between monogenic and polygenic forms of SVD, evaluation of TREX1 in small vessel stroke is warranted. Methods: We sequenced the TREX1 gene in an exploratory cohort of patients with lacunar stroke (Edinburgh Stroke Study, n=290 lacunar stroke cases). We subsequently performed a fully blinded case-control study of early onset MRI-confirmed small vessel stroke within the UK Young Lacunar Stroke Resource (990 cases, 939 controls). Results: No patients with canonical disease-causing mutations of TREX1 were identified in cases or controls. Analysis of an exploratory cohort identified a potential association between rare variants of TREX1 and patients with lacunar stroke. However, subsequent controlled and blinded evaluation of TREX1 in a larger and MRI-confirmed patient cohort, the UK Young Lacunar Stroke Resource, identified heterozygous rare variants in 2.1% of cases and 2.3% of controls. No association was observed with stroke risk (odds ratio = 0.90; 95% confidence interval, 0.49-1.65 p=0.74). Similarly no association was seen with rare TREX1 variants with predicted deleterious effects on enzyme function (odds ratio = 1.05; 95% confidence interval, 0.43-2.61 p=0.91). Conclusions: No patients with early-onset lacunar stroke had genetic evidence of a TREX1-associated monogenic microangiopathy. These results show no evidence of association between rare variants of TREX1 and early onset lacunar stroke. This includes rare variants that significantly affect protein and enzyme function. Routine sequencing of the TREX1 gene in patients with early onset lacunar stroke is therefore unlikely to be of diagnostic utility, in the absence of syndromic features or family history.

Background: Excess weight is associated with poor health and increased health-care costs. However, a detailed understanding of the effects of excess weight on total hospital costs and costs for different health conditions is needed. Methods: Women in England aged 50–64 years were recruited into the prospective Million Women Study cohort in 1996–2001 through 60 NHS breast cancer screening centres. Participants were followed up and annual hospital costs and admission rates were estimated for April 1, 2006, to March 31, 2011, in relation to body-mass index (BMI) at recruitment, overall and for categories of health conditions defined by the International Classification of Diseases 10th revision chapter of the primary diagnosis at admission. Associations of BMI with hospital costs were projected to the 2013 population of women aged 55–79 years in England. Findings: 1 093 866 women who provided information on height and weight, had a BMI of at least 18·5 kg/m2, and had no previous cancer at recruitment, were followed up for an average of 4·9 years from April 1, 2006 (12·3 years from recruitment), during which time 1·84 million hospital admissions were recorded. Annual hospital costs were lowest for women with a BMI of 20·0 kg/m2 to less than 22·5 kg/m2 (£567 per woman per year, 99% CI 556–577). Every 2 kg/m2 increase in BMI above 20 kg/m2 was associated with a 7·4% (7·1–7·6) increase in annual hospital costs. Excess weight was associated with increased costs for all diagnostic categories, except respiratory conditions and fractures. £662 million (14·6%) of the estimated £4·5 billion of total annual hospital costs among all women aged 55–79 years in England was attributed to excess weight (BMI ≥25 kg/m2), of which £517 million (78%) arose from hospital admissions with procedures. £258 million (39%) of the costs attributed to excess weight were due to musculoskeletal admissions, mainly for knee replacement surgeries. Interpretation: Excess body weight is associated with increased hospital costs for middle-aged and older women in England across a broad range of conditions, especially knee replacement surgery and diabetes. These results provide reliable up-to-date estimates of the health-care costs of excess weight and emphasise the need for investment to tackle this public health issue. Funding: Cancer Research UK; Medical Research Council; National Institute for Health Research.

ABSTRACT Objectives Dementia is a major public health concern worldwide and consequently there is an urgent need to expedite research into its causes so that preventative strategies can be sought. Given that dementia is likely to be the result of a complex interplay of many factors, large study populations are required in order to detect effects reliably. UK Biobank (UKB) is a large, population-based, prospective cohort study of over 503,000 participants aged 40-69 years when recruited between 2006 and 2010. Participant follow up is chiefly via linkage to routinely-collected health datasets such as hospital admissions, death registrations and increasingly, to primary care data. In this pilot study we sought to estimate the accuracy of using these routine data sources to identify dementia outcomes in UKB participants. Approach We created a list of ICD-10 and primary care (Read version 2) dementia codes, with the intention of maximising positive predictive value (PPV) over sensitivity. We identified UKB participants who were recruited in Edinburgh and had at least one dementia code in any of the three data sources. We searched the NHS Lothian electronic medical record (EMR) for each participant and extracted all relevant letters and investigation results. Participants were excluded if no EMR entry for that patient could be found. A neurologist adjudicated on whether dementia was present or not based on the extracted case record, providing the reference standard to which the coded data were compared. The PPV was then calculated for each data source individually and combined. A subgroup analysis was performed on participants who had a dementia code across more than one dataset. Results Among 17,000 Edinburgh-based participants (median age 57 years at recruitment in 2007/8), hospital and death data were available to 2012 with primary care data for 12,000 to 2013. 46 participants had a dementia code in at least one data source. 44 of these had available EMR data. PPVs for dementia were 41/44 (93%, 95% CI 81-99) overall, 13/15 (87%, 95% CI 60-98) for hospital admissions, 2/2 (100%, 95% CI 16-100) for death registrations, 33/34 (97%, 95% CI 85-100) for primary care, and 7/7 (100%, 95% CI 59-100) for participants with codes in ≥2 datasets. Conclusion Routinely-collected health data may be sufficiently accurate to identify dementia outcomes in UK population-based cohorts. We plan to extend this study to longer follow-up times and other regions to increase sample size, investigate dementia subtypes and assess generalisability.

ABSTRACT Aims We aimed (i) to create algorithms for identifying stroke and acute myocardial infarction (MI) cases in a large prospective population-based study (UK Biobank), using linked data from national hospital admissions data and death registers, combined with self-report data from the baseline assessment; and (ii) to assess validity by examining associations with risk factors and mortality. Methods UK Biobank is a prospective study of 503,000 participants, aged 40-69 years when recruited in 2006-2010 from centres across England, Scotland and Wales. Participants provided extensive questionnaire data on lifestyle, environment and medical history (with confirmation of self-reported medical conditions during a brief interview with a trained research nurse), had physical measures, and provided biological samples. Follow-up is principally through linkages to national health-related datasets, integrated from different data providers for each country and including cohort-wide data for ICD-coded hospital admissions and registered deaths, with follow-up to 2011 for this report. We used expert opinion and systematic reviews to identify baseline self-report items and ICD codes maximising positive predictive value for identification of stroke, MI and their subtypes. We classified participants with at least one relevant code as being first affected either before (prevalent cases) or after recruitment (incident cases). We compared cases with non-cases (controls) using logistic regression (prevalent cases) and survival analysis (incident cases) to examine associations with vascular risk factors, defined using data from the baseline assessment. We used survival analysis to compare vascular, non-vascular and all-cause mortality for cases versus controls post recruitment (prevalent cases) and post hospitalization (incident cases). Results We identified 8654 stroke cases and 13,479 MI cases. 90% of both were prevalent at recruitment, of which 29% (stroke) and 54% (MI) were identified through both research nurse-confirmed self-report and hospital admission records. During ≈1 million person years of follow-up in those without a prevalent record, we identified 871 incident strokes and 1387 incident MIs, of which 8% (stroke) and 2.9% (MI) were identified in both hospital and death records. Male sex, low socio-economic status, smoking and increased body mass index were all positively associated with both stroke and MI. Compared with age and sex-matched controls, mortality for stroke and MI cases was increased both after recruitment (prevalent cases) and after hospitalization (incident cases). Conclusion Information from linked coded datasets from different data providers can be combined with information collected at recruitment to identify and validate prevalent and incident acute MI and stroke.

OBJECTIVES:UK Biobank is a UK-wide cohort of 502,655 people aged 40-69, recruited from National Health Service registrants between 2006-10, with healthcare data linkage. Type 2 diabetes is a key exposure and outcome. We developed algorithms to define prevalent and incident diabetes for UK Biobank. The algorithms will be implemented by UK Biobank and their results made available to researchers on request. METHODS:We used UK Biobank self-reported medical history and medication to assign prevalent diabetes and type, and tested this against linked primary and secondary care data in Welsh UK Biobank participants. Additionally, we derived and tested algorithms for incident diabetes using linked primary and secondary care data in the English Clinical Practice Research Datalink, and ran these on secondary care data in UK Biobank. RESULTS AND SIGNIFICANCE:For prevalent diabetes, 0.001% and 0.002% of people classified as "diabetes unlikely" in UK Biobank had evidence of diabetes in their primary or secondary care record respectively. Of those classified as "probable" type 2 diabetes, 75% and 96% had specific type 2 diabetes codes in their primary and secondary care records. For incidence, 95% of people with the type 2 diabetes-specific C10F Read code in primary care had corroborative evidence of diabetes from medications, blood testing or diabetes specific process of care codes. Only 41% of people identified with type 2 diabetes in primary care had secondary care evidence of type 2 diabetes. In contrast, of incident cases using ICD-10 type 2 diabetes specific codes in secondary care, 77% had corroborative evidence of diabetes in primary care. We suggest our definition of prevalent diabetes from UK Biobank baseline data has external validity, and recommend that specific primary care Read codes should be used for incident diabetes to ensure precision. Secondary care data should be used for incident diabetes with caution, as around half of all cases are missed, and a quarter have no corroborative evidence of diabetes in primary care.

OBJECTIVES:1) To use data-driven method to examine clinical codes (risk factors) of a medical condition in primary care electronic health records (EHRs) that can accurately predict a diagnosis of the condition in secondary care EHRs. 2) To develop and validate a disease phenotyping algorithm for rheumatoid arthritis using primary care EHRs. METHODS:This study linked routine primary and secondary care EHRs in Wales, UK. A machine learning based scheme was used to identify patients with rheumatoid arthritis from primary care EHRs via the following steps: i) selection of variables by comparing relative frequencies of Read codes in the primary care dataset associated with disease case compared to non-disease control (disease/non-disease based on the secondary care diagnosis); ii) reduction of predictors/associated variables using a Random Forest method, iii) induction of decision rules from decision tree model. The proposed method was then extensively validated on an independent dataset, and compared for performance with two existing deterministic algorithms for RA which had been developed using expert clinical knowledge. RESULTS:Primary care EHRs were available for 2,238,360 patients over the age of 16 and of these 20,667 were also linked in the secondary care rheumatology clinical system. In the linked dataset, 900 predictors (out of a total of 43,100 variables) in the primary care record were discovered more frequently in those with versus those without RA. These variables were reduced to 37 groups of related clinical codes, which were used to develop a decision tree model. The final algorithm identified 8 predictors related to diagnostic codes for RA, medication codes, such as those for disease modifying anti-rheumatic drugs, and absence of alternative diagnoses such as psoriatic arthritis. The proposed data-driven method performed as well as the expert clinical knowledge based methods. CONCLUSION:Data-driven scheme, such as ensemble machine learning methods, has the potential of identifying the most informative predictors in a cost-effective and rapid way to accurately and reliably classify rheumatoid arthritis or other complex medical conditions in primary care EHRs.

Cathie Sudlow and colleagues describe the UK Biobank, a large population-based prospective study, established to allow investigation of the genetic and non-genetic determinants of the diseases of middle and old age.

To assess the suitability of retinal images held in the UK Biobank--the largest retinal data repository in a prospective population-based cohort--for computer assisted vascular morphometry, generating measures that are commonly investigated as candidate biomarkers of systemic disease.Non-mydriatic fundus images from both eyes of 2,690 participants--people with a self-reported history of myocardial infarction (n=1,345) and a matched control group (n=1,345)--were analysed using VAMPIRE software. These images were drawn from those of 68,554 UK Biobank participants who underwent retinal imaging at recruitment. Four operators were trained in the use of the software to measure retinal vascular tortuosity and bifurcation geometry.Total operator time was approximately 360 hours (4 minutes per image). 2,252 (84%) of participants had at least one image of sufficient quality for the software to process, i.e. there was sufficient detection of retinal vessels in the image by the software to attempt the measurement of the target parameters. 1,604 (60%) of participants had an image of at least one eye that was adequately analysed by the software, i.e. the measurement protocol was successfully completed. Increasing age was associated with a reduced proportion of images that could be processed (p=0.0004) and analysed (p

Genome-wide association studies (GWAS) have begun to identify the common genetic component to ischaemic stroke (IS). However, IS has considerable phenotypic heterogeneity. Where clinical covariates explain a large fraction of disease risk, covariate informed designs can increase power to detect associations. As prevalence rates in IS are markedly affected by age, and younger onset cases may have higher genetic predisposition, we investigated whether an age-at-onset informed approach could detect novel associations with IS and its subtypes; cardioembolic (CE), large artery atherosclerosis (LAA) and small vessel disease (SVD) in 6,778 cases of European ancestry and 12,095 ancestry-matched controls. Regression analysis to identify SNP associations was performed on posterior liabilities after conditioning on age-at-onset and affection status. We sought further evidence of an association with LAA in 1,881 cases and 50,817 controls, and examined mRNA expression levels of the nearby genes in atherosclerotic carotid artery plaques. Secondly, we performed permutation analyses to evaluate the extent to which age-at-onset informed analysis improves significance for novel loci. We identified a novel association with an MMP12 locus in LAA (rs660599; p = 2.5×10⁻⁷), with independent replication in a second population (p = 0.0048, OR(95% CI) = 1.18(1.05-1.32); meta-analysis p = 2.6×10⁻⁸). The nearby gene, MMP12, was significantly overexpressed in carotid plaques compared to atherosclerosis-free control arteries (p = 1.2×10⁻¹⁵; fold change = 335.6). Permutation analyses demonstrated improved significance for associations when accounting for age-at-onset in all four stroke phenotypes (p

The objective of this study was to determine whether: (a) markers of acute inflammation (white cell count, glucose, interleukin-6, C-reactive protein, and fibrinogen) are associated with poor outcome after stroke and (b) the addition of markers to previously validated prognostic models improves prediction of poor outcome.We prospectively recruited patients between 2002 and 2005. Clinicians assessed patients and drew blood for inflammatory markers. Patients were followed up by postal questionnaire for poor outcome (a score of>2 on the modified Rankin Scale) and death through the General Register Office (Scotland) at 6 mo. We performed a systematic review of the literature and meta-analysis of the association between interleukin-6 and poor outcome after stroke to place our study in the context of previous research. We recruited 844 patients; mortality data were available in 844 (100%) and functional outcome in 750 (89%). After appropriate adjustment, the odds ratios for the association of markers and poor outcome (comparing the upper and the lower third) were interleukin-6, 3.1 (95% CI: 1.9-5.0); C-reactive protein, 1.9 (95% CI: 1.2-3.1); fibrinogen, 1.5 (95% CI: 1.0-2.36); white cell count, 2.1 (95% CI: 1.3-3.4); and glucose 1.3 (95% CI: 0.8-2.1). The results for interleukin-6 were similar to other studies. However, the addition of inflammatory marker levels to validated prognostic models did not materially improve model discrimination, calibration, or reclassification for prediction of poor outcome after stroke.Raised levels of markers of the acute inflammatory response after stroke are associated with poor outcomes. However, the addition of these markers to a previously validated stroke prognostic model did not improve the prediction of poor outcome. Whether inflammatory markers are useful in prediction of recurrent stroke or other vascular events is a separate question, which requires further study. Please see later in the article for the Editors' Summary.