Assessing the validity of a self-reported clinical diagnosis of schizophrenia

Abstract

BackgroundDiagnoses in psychiatric research can be derived from various sources. This study assesses the validity of a self-reported clinical diagnosis of schizophrenia.MethodsThe study included 3,029 clinically ascertained participants with schizophrenia or psychotic disorders diagnosed by self-report and/or research interview and 1,453 UK Biobank participants with self-report and/or medical record diagnosis of schizophrenia or schizoaffective disorder depressed-type (SA-D). We assessed positive predictive values (PPV) of self-reported clinical diagnoses against research interview and medical record diagnoses. We compared polygenic risk scores (PRS) and phenotypes across diagnostic groups, and compared the variance explained by schizophrenia PRS to samples in the Psychiatric Genomics Consortium (PGC).ResultsIn the clinically ascertained sample, the PPV of self-reported schizophrenia to a research diagnosis of schizophrenia was 0.70, which increased to 0.81 when benchmarked against schizophrenia or SA-D. In UK Biobank, the PPV of self-reported schizophrenia to a medical record diagnosis was 0.74. Compared to self-report participants, those with a research diagnosis were younger and more likely to have a high school qualification (clinically ascertained sample) and those with a medical record diagnosis were less likely to be employed or have a high school qualification (UK Biobank). Schizophrenia PRS did not differ between participants that had a diagnosis from self-report, research diagnosis or medical record diagnosis. Polygenic liability r2, for all diagnosis definitions, fell within the distribution of PGC schizophrenia cohorts.ConclusionsSelf-report measures of schizophrenia are justified in research to maximise sample size and representativeness, although within sample validation of diagnoses is recommended.

Artificial Intelligence for Analyzing Psychiatric Disorders in Social Media: A Quarter-Century Narrative Review of Progress and Challenges (Preprint)

“…A solution may involve obtaining consented access to EHRs to accompany the users' social media postings, as piloted by Eichstaedt et al [144]. Indeed, this means of verification is in fact crucial in studies that consider schizophrenia because diagnosis self-disclosure statements, although having high sensitivity [145], may lack specificity [114,115]. In any case, social media data obtained also needs to be broadened to better support NLP methods.…”