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A national initiative in data science for health: an evaluation of the UK Farr Institute.
OBJECTIVE: To evaluate the extent to which the inter-institutional, inter-disciplinary mobilisation of data and skills in the Farr Institute contributed to establishing the emerging field of data science for health in the UK. DESIGN AND OUTCOME MEASURES: We evaluated evidence of six domains characterising a new field of science:defining central scientific challenges,demonstrating how the central challenges might be solved,creating novel interactions among groups of scientists,training new types of experts,re-organising universities,demonstrating impacts in society.We carried out citation, network and time trend analyses of publications, and a narrative review of infrastructure, methods and tools. SETTING: Four UK centres in London, North England, Scotland and Wales (23 university partners), 2013-2018. RESULTS: 1. The Farr Institute helped define a central scientific challenge publishing a research corpus, demonstrating insights from electronic health record (EHR) and administrative data at each stage of the translational cycle in 593 papers with at least one Farr Institute author affiliation on PubMed. 2. The Farr Institute offered some demonstrations of how these scientific challenges might be solved: it established the first four ISO27001 certified trusted research environments in the UK, and approved more than 1000 research users, published on 102 unique EHR and administrative data sources, although there was no clear evidence of an increase in novel, sustained record linkages. The Farr Institute established open platforms for the EHR phenotyping algorithms and validations (>70 diseases, CALIBER). Sample sizes showed some evidence of increase but remained less than 10% of the UK population in primary care-hospital care linked studies. 3.The Farr Institute created novel interactions among researchers: the co-author publication network expanded from 944 unique co-authors (based on 67 publications in the first 30 months) to 3839 unique co-authors (545 papers in the final 30 months). 4. Training expanded substantially with 3 new masters courses, training >400 people at masters, short-course and leadership level and 48 PhD students. 5. Universities reorganised with 4/5 Centres established 27 new faculty (tenured) positions, 3 new university institutes. 6. Emerging evidence of impacts included: > 3200 citations for the 10 most cited papers and Farr research informed eight practice-changing clinical guidelines and policies relevant to the health of millions of UK citizens. CONCLUSION: The Farr Institute played a major role in establishing and growing the field of data science for health in the UK, with some initial evidence of benefits for health and healthcare. The Farr Institute has now expanded into Health Data Research (HDR) UK but key challenges remain including, how to network such activities internationally.
A psychometric evaluation of the 16-item PHQ-ADS concomitant anxiety and depression scale in the UK biobank using item response theory.
BACKGROUND: The Patient Health Questionnaire Anxiety and Depression Scale (PHQ-ADS) provides a reliable and valid measure of concomitant depression and anxiety. However, research on its psychometric efficiency and optimal scale length using item-response theory (IRT) has not been reported. This study aimed to optimize the length of the PHQ-ADS scale without losing information by discarding items that were a poor fit to the IRT model. METHODS: The UK Biobank is a large cohort study designed to investigate risk factors for a broad range of disease. PHQ-ADS data were available from n = 152,826 participants (age = 55.87 years; SD = 7.73; 56.4 % female), 30.4 % of the entire UK Biobank sample. Psychometric properties of the PHQ-ADS were investigated using a 2-parameter IRT and Mokken analysis. Item statistics included discrimination, difficulty and Loevinger H coefficients of monotonicity. RESULTS: In the entire 16-item scale, item discrimination ranged from 1.40 to 4.22, with the item 'worrying' showing the highest level of discrimination and the item 'sleep disturbance' showing the lowest. Mokken analysis showed that the 16-item PHQ-ADS scale could be reduced to a 7-item scale without loss of test information. The reduced scale comprised mainly items measuring cognitive-affective symptoms of anxiety/depression, whereas items measuring somatic symptoms were discarded. The revised scale showed high discrimination and scalability. LIMITATIONS: Findings are limited by the use of cross-sectional data that only included the baseline online questionnaire, but not other waves. CONCLUSIONS: IRT is a useful technique for scale reductions which serve the clinical and epidemiological need to optimize screening questionnaires to reduce redundancy and maximize information. A reduced-item 7-item PHQ-ADS scale reduces the response burden on participants in epidemiological research settings, without loss of information.
Distress and neuroticism as mediators of the effect of childhood and adulthood adversity on cognitive performance in the UK Biobank study.
Childhood adversity and adulthood adversity affect cognition later in life. However, the mechanism through which adversity exerts these effects on cognition remains under-researched. We aimed to investigate if the effect of adversity on cognition was mediated by distress or neuroticism. The UK Biobank is a large, population-based, cohort study designed to investigate risk factors of cognitive health. Here, data were analysed using a cross-sectional design. Structural equation models were fitted to the data with childhood adversity or adulthood adversity as independent variables, distress and neuroticism as mediators and executive function and processing speed as latent dependent variables that were derived from the cognitive scores in the UK Biobank. Complete data were available for 64,051 participants in the childhood adversity model and 63,360 participants in the adulthood adversity model. Childhood adversity did not show a direct effect on processing speed. The effect of childhood adversity on executive function was partially mediated by distress and neuroticism. The effects of adulthood adversity on executive function and processing speed were both partially mediated by distress and neuroticism. In conclusion, distress and neuroticism mediated the deleterious effect of childhood and adulthood adversity on cognition and may provide a mechanism underlying the deleterious consequences of adversity.
Dementias Platform UK: Bringing genetics into life.
INTRODUCTION: The Dementias Platform UK (DPUK) Data Portal is a data repository bringing together a wide range of cohorts. Neurodegenerative dementias are a group of diseases with highly heterogeneous pathology and an overlapping genetic component that is poorly understood. The DPUK collection of independent cohorts can facilitate research in neurodegeneration by combining their genetic and phenotypic data. METHODS: For genetic data processing, pipelines were generated to perform quality control analysis, genetic imputation, and polygenic risk score (PRS) derivation with six genome-wide association studies of neurodegenerative diseases. Pipelines were applied to five cohorts. DISCUSSION: The data processing pipelines, research-ready imputed genetic data, and PRS scores are now available on the DPUK platform and can be accessed upon request though the DPUK application process. Harmonizing genome-wide data for multiple datasets increases scientific opportunity and allows the wider research community to access and process data at scale and pace.
Research-ready data for multi-cohort analyses: The Dementias Platform UK (DPUK) C-Surv data model
Abstract Research-ready data (that curated to a defined standard) increases scientific opportunity and rigour by integrating the data environment. The development of research platforms has highlighted the value of research-ready data, particularly for multi-cohort analyses. Following user consultation, a standard data model (C-Surv), optimised for data discovery, was developed using data from 12 Dementias Platform UK (DPUK) population and clinical cohort studies. The model uses a four-tier nested structure based on 18 data themes selected according to user behaviour or technology. Standard variable naming conventions are applied to uniquely identify variables within the context of longitudinal studies. The data model was used to develop a harmonised dataset for 11 cohorts. This dataset populated the Cohort Explorer data discovery tool for assessing the feasibility of an analysis prior to making a data access request. It was concluded that developing and applying a standard data model (C-Surv) for research cohort data is feasible and useful.