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

Background Understanding and improving outcomes for people with anxiety or depression often requires large studies. To increase participation and reduce costs, such research is typically unable to utilise “gold-standard” methods to ascertain diagnoses, instead relying on remote, self-report measures. Aims To assess the comparability of remote diagnostic methods for anxiety and depression disorders commonly used in research. Method Participants from the UK-based GLAD and COPING NBR cohorts ( N = 58,400) completed an online questionnaire between 2018-2020. Responses to detailed symptom reports were compared to DSM-5 criteria to generate algorithm-based diagnoses of major depressive disorder (MDD), generalised anxiety disorder (GAD), specific phobia, social anxiety disorder, panic disorder, and agoraphobia. Participants also self-reported any prior diagnoses from health professionals, termed single-item diagnoses. “Any anxiety” included participants with at least one anxiety disorder. Agreement was assessed by calculating accuracy, Cohen’s kappa, McNemar’s chi-squared, sensitivity, and specificity. Results Agreement between diagnoses was moderate for MDD, any anxiety, and GAD, but varied by cohort. Agreement was slight to fair for the phobic disorders. Many participants with single-item GAD did not receive an algorithm-based diagnosis. In contrast, algorithm-based diagnoses of the phobic disorders were more common than single-item diagnoses. Conclusions Agreement for MDD, any anxiety, and GAD was higher for cases in the case-enriched GLAD cohort and for controls in the general population COPING NBR cohort. For anxiety disorders, single-item diagnoses classified most participants as having GAD, whereas algorithm-based diagnoses distributed participants more evenly across the anxiety disorders. Further validation against gold standard measures is required.