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      Heterogeneity in perinatal depression: how far have we come? A systematic review

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      Archives of Women's Mental Health
      Springer Nature

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          Abstract

          <p class="first" id="P1">Despite perinatal depression (PND) being a common mental disorder affecting pregnant women and new mothers, limited attention has been paid to the heterogeneous nature of this disorder. We examined heterogeneity in PND symptom profiles and symptom trajectories. Literature searches revealed 247 relevant studies, 23 of which were included in the final review. The most common statistical approaches used to explore symptom and trajectory heterogeneity were latent class model and growth mixture model. All but one study examined PND symptom trajectories, and provide collective evidence of at least three heterogeneous patterns: low, medium or chronic-high symptom levels. Social and psychological risk factors were the most common group of predictors related to a higher burden (high sum of score) of depressive symptoms. These studies were consistent in reporting poorer health outcomes for children of mothers assigned to high burden symptom trajectories. Only one study explored heterogeneity in symptom profile, and was the only one to describe the specific constellations of depressive symptoms related to the PND heterogeneous patterns identified. Therefore, there is limited evidence on the specific symptoms and symptom configurations that make up PND heterogeneity. We suggest directions for future research to further clarify the PND heterogeneity and its related mechanisms. </p>

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          Most cited references30

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          Depression sum-scores don’t add up: why analyzing specific depression symptoms is essential

          Most measures of depression severity are based on the number of reported symptoms, and threshold scores are often used to classify individuals as healthy or depressed. This method – and research results based on it – are valid if depression is a single condition, and all symptoms are equally good severity indicators. Here, we review a host of studies documenting that specific depressive symptoms like sad mood, insomnia, concentration problems, and suicidal ideation are distinct phenomena that differ from each other in important dimensions such as underlying biology, impact on impairment, and risk factors. Furthermore, specific life events predict increases in particular depression symptoms, and there is evidence for direct causal links among symptoms. We suggest that the pervasive use of sum-scores to estimate depression severity has obfuscated crucial insights and contributed to the lack of progress in key research areas such as identifying biomarkers and more efficacious antidepressants. The analysis of individual symptoms and their causal associations offers a way forward. We offer specific suggestions with practical implications for future research. Electronic supplementary material The online version of this article (doi:10.1186/s12916-015-0325-4) contains supplementary material, which is available to authorized users.
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            Data-driven subtypes of major depressive disorder: a systematic review

            Background According to current classification systems, patients with major depressive disorder (MDD) may have very different combinations of symptoms. This symptomatic diversity hinders the progress of research into the causal mechanisms and treatment allocation. Theoretically founded subtypes of depression such as atypical, psychotic, and melancholic depression have limited clinical applicability. Data-driven analyses of symptom dimensions or subtypes of depression are scarce. In this systematic review, we examine the evidence for the existence of data-driven symptomatic subtypes of depression. Methods We undertook a systematic literature search of MEDLINE, PsycINFO and Embase in May 2012. We included studies analyzing the depression criteria of the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) of adults with MDD in latent variable analyses. Results In total, 1176 articles were retrieved, of which 20 satisfied the inclusion criteria. These reports described a total of 34 latent variable analyses: 6 confirmatory factor analyses, 6 exploratory factor analyses, 12 principal component analyses, and 10 latent class analyses. The latent class techniques distinguished 2 to 5 classes, which mainly reflected subgroups with different overall severity: 62 of 71 significant differences on symptom level were congruent with a latent class solution reflecting severity. The latent class techniques did not consistently identify specific symptom clusters. Latent factor techniques mostly found a factor explaining the variance in the symptoms depressed mood and interest loss (11 of 13 analyses), often complemented by psychomotor retardation or fatigue (8 of 11 analyses). However, differences in found factors and classes were substantial. Conclusions The studies performed to date do not provide conclusive evidence for the existence of depressive symptom dimensions or symptomatic subtypes. The wide diversity of identified factors and classes might result either from the absence of patterns to be found, or from the theoretical and modeling choices preceding analysis.
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              Heterogeneity of postpartum depression: a latent class analysis.

              (2015)
              Maternal depression in the postpartum period confers substantial morbidity and mortality, but the definition of postpartum depression remains controversial. We investigated the heterogeneity of symptoms with the aim of identifying clinical subtypes of postpartum depression.
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                Author and article information

                Journal
                Archives of Women's Mental Health
                Arch Womens Ment Health
                Springer Nature
                1434-1816
                1435-1102
                February 2017
                October 29 2016
                February 2017
                : 20
                : 1
                : 11-23
                Article
                10.1007/s00737-016-0691-8
                5507213
                27796597
                c96d4703-ac57-419c-8369-1e805b26c6cc
                © 2017

                http://www.springer.com/tdm

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