Recurrence of major depressive disorder and its predictors in the general population: results from The Netherlands Mental Health Survey and Incidence Study (NEMESIS)
SourcePsychological Medicine, 43, 1, (2013), pp. 39-48
Article / Letter to editor
Display more detailsDisplay less details
SW OZ BSI KLP
SubjectExperimental Psychopathology and Treatment
Background. Knowledge of the risk of recurrence after recovery from major depressive disorder (MDD) in the general population is scarce. Method. Data were derived from 687 subjects in the general population with a lifetime DSM-III-R diagnosis of MDD but without a current major depressive episode (MDE) or dysthymia. Participants had to be at least 6 months in remission, and were recruited from The Netherlands Mental Health Survey and Incidence Study (NEMESIS), using the Composite International Diagnostic Interview (CIDI). Recency and severity of the last MDE were assessed retrospectively at baseline. Recurrence of MDD was measured prospectively during the 3-year follow-up. Kaplan-Meier survival curves were used to measure time to recurrence. Determinants of time to recurrence were analyzed using proportional hazard models. Results. The estimated cumulative recurrence of MDD was 13.2% at 5 years, 23.2% at 10 years and 42.0% at 20 years. In bivariate analysis, the following variables predicted a shorter time to recurrence : younger age, younger age of onset, higher number of previous episodes, a severe last depressive episode, negative youth experiences, ongoing difficulties before recurrence and high neuroticism. Multivariably, younger age, a higher number of previous episodes, a severe last depressive episode, negative youth experiences and ongoing difficulties remained significant. Conclusions. In this community sample, the long-term risk of recurrence was high, but lower than that found in clinical samples. Subjects who had had an MDE had a long-term vulnerability for recurrence. Factors predicting recurrence included illness- and stress-related factors.
Upload full text
Use your RU credentials (u/z-number and password) to log in with SURFconext to upload a file for processing by the repository team.