Incidence and risk patterns of anxiety and depressive disorders and categorization of generalized anxiety disorder.

CONTEXT Controversy surrounds the diagnostic categorization of generalized anxiety disorder (GAD). OBJECTIVES To examine the incidence, comorbidity, and risk patterns for anxiety and depressive disorders and to test whether developmental features of GAD more strongly support a view of this condition as a depressive as opposed to an anxiety disorder. DESIGN Face-to-face, 10-year prospective longitudinal and family study with as many as 4 assessment waves. The DSM-IV Munich Composite International Diagnostic Interview was administered by clinically trained interviewers. SETTING Munich, Germany. PARTICIPANTS A community sample of 3021 individuals aged 14 to 24 years at baseline and 21 to 34 years at last follow-up. MAIN OUTCOME MEASURES Cumulative incidence of GAD, other anxiety disorders (specific phobias, social phobia, agoraphobia, and panic disorder), and depressive disorders (major depressive disorder, and dysthymia). RESULTS Longitudinal associations between GAD and depressive disorders are not stronger than those between GAD and anxiety disorders or between other anxiety and depressive disorders. Survival analyses reveal that the factors associated with GAD overlap more strongly with those specific to anxiety disorders than those specific to depressive disorders. In addition, GAD differs from anxiety and depressive disorders with regard to family climate and personality profiles. CONCLUSIONS Anxiety and depressive disorders appear to differ with regard to risk constellations and temporal longitudinal patterns, and GAD is a heterogeneous disorder that is, overall, more closely related to other anxiety disorders than to depressive disorders. More work is needed to elucidate the potentially unique aspects of pathways and mechanisms involved in the etiopathogenesis of GAD. Grouping GAD with depressive disorders, as suggested by cross-sectional features and diagnostic comorbidity patterns, minimizes the importance of longitudinal data on risk factors and symptom trajectories.