ObjectivesA previous individual participant data meta‐analysis (IPDMA) identified differences in major depression classification rates between different diagnostic interviews, controlling for depressive symptoms on the basis of the Patient Health Questionnaire‐9. We aimed to determine whether similar results would be seen in a different population, using studies that administered the Edinburgh Postnatal Depression Scale (EPDS) in pregnancy or postpartum.MethodsData accrued for an EPDS diagnostic accuracy IPDMA were analysed. Binomial generalised linear mixed models were fit to compare depression classification odds for the Mini International Neuropsychiatric Interview (MINI), Composite International Diagnostic Interview (CIDI), and Structured Clinical Interview for DSM (SCID), controlling for EPDS scores and participant characteristics.ResultsAmong fully structured interviews, the MINI (15 studies, 2,532 participants, 342 major depression cases) classified depression more often than the CIDI (3 studies, 2,948 participants, 194 major depression cases; adjusted odds ratio [aOR] = 3.72, 95% confidence interval [CI] [1.21, 11.43]). Compared with the semistructured SCID (28 studies, 7,403 participants, 1,027 major depression cases), odds with the CIDI (interaction aOR = 0.88, 95% CI [0.85, 0.92]) and MINI (interaction aOR = 0.95, 95% CI [0.92, 0.99]) increased less as EPDS scores increased.ConclusionDifferent interviews may not classify major depression equivalently.

Postpartum depression is a frequent and disabling condition whose pathophysiology is still unclear. In recent years, the study of the neural correlates of mental disorders has been increasingly approached using magnetic resonance techniques. In this review we synthesize the results from studies on postpartum depression in the context of structural, functional, and spectroscopic magnetic resonance studies of major depression as a whole. Compared to the relative wealth of data available for major depression, magnetic resonance studies of postpartum depression are limited in number and design. A systematic literature search yielded only eleven studies conducted on about one hundred mothers with postpartum depression overall. Brain magnetic resonance findings in postpartum depression appear to replicate those obtained in major depression, with minor deviations that are not sufficient to delineate a distinct neurobiological profile for this condition, due to the small samples used and the lack of direct comparisons with subjects with major depression. However, it seems reasonable to expect that studies conducted in larger populations, and using a larger variety of brain magnetic resonance techniques than has been done so far, might allow for the identification of neuroimaging signatures for postpartum depression.

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Background. To assess personality characteristics of women who develop perinatal depression. Methods. The study started with a screening of a sample of 453 women in their third trimester of pregnancy, to which was administered a survey data form, the Edinburgh Postnatal Depression Scale (EPDS) and the Minnesota Multiphasic Personality Inventory 2 (MMPI-2). A clinical group of subjects with perinatal depression (PND, 55 subjects) was selected; clinical and validity scales of MMPI-2 were used as predictors in hierarchical cluster analysis carried out. Results. The analysis identified three clusters of personality profile: two “clinical” clusters (1 and 3) and an “apparently common” one (cluster 2). The first cluster (39.5%) collects structures of personality with prevalent obsessive or dependent functioning tending to develop a “psychasthenic” depression; the third cluster (13.95%) includes women with prevalent borderline functioning tending to develop “dysphoric” depression; the second cluster (46.5%) shows a normal profile with a “defensive” attitude, probably due to the presence of defense mechanisms or to the fear of stigma. Conclusion. Characteristics of personality have a key role in clinical manifestations of perinatal depression; it is important to detect them to identify mothers at risk and to plan targeted therapeutic interventions.