Objectives For many research cohorts, it is not practical to provide a “gold‐standard” mental health diagnosis. It is therefore important for mental health research that potential alternative measures for ascertaining mental disorder status are understood. Methods Data from UK Biobank in those participants who had completed the online Mental Health Questionnaire (n = 157,363) were used to compare the classification of mental disorder by four methods: symptom‐based outcome (self‐complete based on diagnostic interviews), self‐reported diagnosis, hospital data linkage, and self‐report medication. Results Participants self‐reporting any psychiatric diagnosis had elevated risk of any symptom‐based outcome. Cohen's κ between self‐reported diagnosis and symptom‐based outcome was 0.46 for depression, 0.28 for bipolar affective disorder, and 0.24 for anxiety. There were small numbers of participants uniquely identified by hospital data linkage and medication. Conclusion Our results confirm that ascertainment of mental disorder diagnosis in large cohorts such as UK Biobank is complex. There may not be one method of classification that is right for all circumstances, but an informed and transparent use of outcome measure(s) to suit each research question will maximise the potential of UK Biobank and other resources for mental health research.
Background Feedback of potentially serious incidental findings (PSIFs) to imaging research participants generates clinical assessment in most cases. Understanding the factors associated with increased risks of PSIFs and of serious final diagnoses may influence individuals’ decisions to participate in imaging research and will inform the design of PSIFs protocols for future research studies. We aimed to determine whether, and to what extent, socio-demographic, lifestyle, other health-related factors and PSIFs protocol are associated with detection of both a PSIF and a final diagnosis of serious disease. Methods and findings Our cohort consisted of all UK Biobank participants who underwent imaging up to December 2015 (n = 7334, median age 63, 51.9% women). Brain, cardiac and body magnetic resonance, and dual-energy x-ray absorptiometry images from the first 1000 participants were reviewed systematically by radiologists for PSIFs. Thereafter, radiographers flagged concerning images for radiologists’ review. We classified final diagnoses as serious or not using data from participant surveys and clinical correspondence from GPs up to six months following imaging (either participant or GP correspondence, or both, were available for 93% of participants with PSIFs). We used binomial logistic regression models to investigate associations between age, sex, ethnicity, socio-economic deprivation, private healthcare use, alcohol intake, diet, physical activity, smoking, body mass index and morbidity, with both PSIFs and serious final diagnoses. Systematic radiologist review generated 13 times more PSIFs than radiographer flagging (179/1000 [17.9%] versus 104/6334 [1.6%]; age- and sex-adjusted OR 13.3 [95% confidence interval (CI) 10.3–17.1] p<0.001) and proportionally fewer serious final diagnoses (21/179 [11.7%]; 33/104 [31.7%]). Risks of both PSIFs and of serious final diagnoses increased with age (sex-adjusted ORs [95% CI] for oldest [67–79 years] versus youngest [44–58 years] participants for PSIFs and serious final diagnoses respectively: 1.59 [1.07–2.38] and 2.79 [0.86 to 9.0] for systematic radiologist review; 1.88 [1.14–3.09] and 2.99 [1.09–8.19] for radiographer flagging). No other factor was significantly associated with either PSIFs or serious final diagnoses. Our study is the largest so far to investigate the factors associated with PSIFs and serious final diagnoses, but despite this, we still may have missed some associations due to sparsity of these outcomes within our cohort and small numbers within some exposure categories. Conclusion Risks of PSIFs and serious final diagnosis are substantially influenced by PSIFs protocol and to a lesser extent by age. As only 1/5 PSIFs represent serious disease, evidence-based PSIFs protocols are paramount to minimise over-investigation of healthy research participants and diversion of limited health services away from patients in need.
BackgroundLinkage to routinely collected NHS data from primary, secondary care and death certificates enables identification of participants with Parkinson’s Disease (PD) within the UK Biobank cohort of 5 00 000 adults. Validation of the accuracy of this data is required prior to their use in research studies.MethodIn this validation study participants (n=125) with a code indicating PD were identified from a sample of 17 000 participants in the cohort. Diagnoses were validated by expert adjudicators, based on free text electronic medical records. Positive predictive values (PPV,% of cases identified that are true cases) were calculated.ResultsPrimary care diagnostic codes identified 93% of PD cases, with a PPV of 95%. Combined secondary care and death data identified 42% of PD cases with a PPV of 84%.Combining diagnostic and medication codes identified more participants, but did not increase the PPV.ConclusionsThis study suggests that linkage to routinely collected healthcare data is a reliable method for identifying participants with PD in the UK Biobank cohort.Primary care diagnostic codes identified the highest proportion of participants and had the highest PPV, demonstrating the value of using primary care data to identify cases of disease in large population based cohort studies.
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