Comparison of algorithm-based versus single-item diagnostic measures of anxiety and depression disorders in the GLAD and COPING cohorts

Abstract

BackgroundResearch to understand the complex aetiology of depressive and anxiety disorders often requires large sample sizes, but this comes at a cost. Large-scale studies are typically unable to utilise “gold standard” phenotyping methods, instead relying on remote, self-report measures to ascertain phenotypes.AimsTo assess the comparability of two commonly used phenotyping methods for depression and anxiety disorders.MethodParticipants from the Genetic Links to Anxiety and Depression (GLAD) Study (N = 37,419) completed an online questionnaire including detailed symptom reports. They received a lifetime algorithm-based diagnosis based on DSM-5 criteria for major depressive disorder (MDD), generalised anxiety disorder (GAD), specific phobia, social anxiety disorder, panic disorder, and agoraphobia. Any anxiety disorder included participants with at least one anxiety disorder. Participants also responded to single-item questions asking whether they had ever been diagnosed with these disorders by health professionals.ResultsAgreement for algorithm-based and single-item diagnoses was high for MDD and any anxiety disorder but low for the individual anxiety disorders. For GAD, many participants with a single-item diagnosis did not receive an algorithm-based diagnosis. In contrast, algorithm-based diagnoses of the other anxiety disorders were more common than the single-item diagnoses.ConclusionsThe two phenotyping methods were comparable for MDD and any anxiety disorder cases. However, frequencies of specific anxiety disorders varied depending on the method. Single-item diagnoses classified most participants as having GAD whereas algorithm-based diagnoses were more evenly distributed across the anxiety disorders. Future investigations of specific anxiety disorders should use algorithm-based or other robust phenotyping methods.

Large‐scale remote fear conditioning: Demonstration of associations with anxiety using the FLARe smartphone app

“…limitations relating to concerns around accuracy and memory of reporting. However, the GAD-7 has been shown to have good face validity for identifying individuals with anxiety disorders (Spitzer et al, 2006) and self-reported anxiety diagnoses have been shown to have reasonable agreement (76.7%) with an algorithm-based measure of anxiety disorders (Davies et al, 2021). Agreement for selfreported anxiety diagnoses was lower when looking at specific anxiety disorders separately, which was avoided in the current article.…”

“…(for a full list of response options, see Table S3). Single-item measures assessing anxiety disorder diagnosis have been shown to have reasonable agreement (76.7%) with more detailed algorithm-based assessments (Davies et al, 2021).…”

“…This terminology was chosen to represent how mental health disorders are commonly referred to rather than their strict DSM classification (i.e., panic disorder) to help participants recognize and self-report their diagnosis. Single-item measures of self-reported, clinician diagnoses of panic attacks have been shown to have moderate agreement with algorithm-based measures of panic disorder (65.4%; Davies et al, 2021).…”