Validation of UK Biobank data for mental health outcomes: A pilot study using secondary care electronic health records. (April 2022) - Li, Zhenpeng; Kormilitzin, Andrey; Fernandes, Marco; Vaci, Nemanja; Liu, Qiang; Newby, Danielle; Goodday, Sarah; Smith, Tanya; Nevado-Holgado, Alejo J; Winchester, Laura
Graphical abstract: Highlights: Linking UK Clinical Record Interactive Search (UK-CRIS) and UK Biobank. Comparison of diagnosis for 854 individuals present in both datasets. Electronic health records from UK-CRIS used to understand self-reported data in UK Biobank. Abstract: UK Biobank (UKB) is widely employed to investigate mental health disorders and related exposures; however, its applicability and relevance in a clinical setting and the assumptions required have not been sufficiently and systematically investigated. Here, we present the first validation study using secondary care mental health data with linkage to UKB from Oxford - Clinical Record Interactive Search (CRIS) focusing on comparison of demographic information, diagnostic outcome, medication record and cognitive test results, with missing data and the implied bias from both resources depicted. We applied a natural language processing model to extract information embedded in unstructured text from clinical notes and attachments. Using a contingency table we compared the demographic information recorded in UKB and CRIS. We calculated the positive predictive value (PPV, proportion of true positives cases detected) for mental health diagnosis and relevant medication. Amongst the cohort of 854 subjects, PPVs for any mental health diagnosis for dementia, depression, bipolar disorder and schizophrenia were 41.6%, and were 59.5%, 12.5%, 50.0% and 52.6%, respectively. Self-reported medication records in UKB had general PPV of 47.0%, with the prevalence of frequently prescribed medicines to each typical mental health disorder considerably different from the information provided by CRIS. UKB is highly multimodal, but with limited follow-up records, whereas CRIS offers a longitudinal high-resolution clinical picture with more than ten years of observations. The linkage of both datasets will reduce the self-report bias and synergistically augment diverse modalities into a unified resource to facilitate more robust research in mental health.