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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.
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 population and clinical cohort studies. The model uses a four-tier nested structure based on 18 data themes and 137 domains selected according to user behaviour or technology. Standard variable naming conventions are applied to uniquely identify variables within the context of longitudinal studies. The 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.
Association between individual-level socioeconomic position and incident dementia using UK Biobank data: a prospective study
Background Under-education and living in poverty are known risk factors for dementia. However, single-variable makers of socioeconomic position (SEP) are often correlated and cannot reflect the overall SEP. We examined association between composite SEP and incident dementia using UK-wide data. Methods We leveraged data from the UK Biobank, a nation-wide cohort of half-a-million participants recruited across 22 assessment centres during 2006–10. Participants with data on SEP and without dementia at baseline were included. A composite individual-level metric of SEP (low, medium, or high) was developed through latent class analysis using Stata's gsem command and identified through item-response probabilities based on participants' single socioeconomic factors (ie, education, employment, and household income). Cox proportional hazard models were developed to examine the association between composite SEP and incident dementia after adjusting for age, ethnicity, lifestyle factors (eg, living alone), social interaction (eg, frequency of visits from acquaintances), urbanicity, clinical variables (eg, central obesity), and stratified by sex. As sensitivity test, we repeated our analysis with single socioeconomic factors. Electronic informed consent was obtained from participants at the UK Biobank assessment centres before participations and UK Biobank acquired ethical approval from the National Health Service National Research Ethics Service. Findings We included 340 366 adult participants (17 8195 [52·4%] were women and 32 6753 [96·0%] White) aged 38–73 with 3541 incident dementia cases over a mean follow-up period of 12·0 years (SD 1·7). Relative to participants in the highest SEP, those in the medium (HR 1·53 [95% CI 1·33–1·77]; p<0·0001) and low (2·38 [2·05–2·77]; p<0·0001) SEP were associated with higher risks of incident dementia. Sensitivity analyses consistently found higher risks of incident dementia in participants of low educational attainment (HR 1·14 [95% CI 1·03–1·27]; p=0·0107), low household income (HR 2·33 [95% CI 2·03–2·68]; p<0·0001) and being unemployed (HR 1·27 [95% CI 1·11–1·47]; p=0·0008), relative to those of high education, high household income, and being employed, respectively. Limitations of the study include a response of only 503 325 (5·5%) of 9·2 million participants registered with the National Health Service and residual confounding. Interpretation This study presented a parsimonious approach to construct a composite metric of SEP by employing three key indicators. We found that participants of low SEP were associated with an elevated risk of incident dementia. Socially deprived populations maybe more likely to be exposed to unfavourable psychosocial and environmental stressors that escalate risk of dementia. Our study further strengthens the evidence base for designing policy interventions for at-risk subgroups of lower SEP strata to reduce burdens of dementia.