Evidence map›Paper›PMID 36798754›Full record

ArticleEClinicalMedicine2023

Detection of differential depressive symptom patterns in a cohort of perinatal women: an exploratory factor analysis using a robust statistics approach.

Kenneth J Nieser, Zachary N Stowe, D Jeffrey Newport, Jessica L Coker, Amy L Cochran

Abstract read
In one paragraph

Article in EClinicalMedicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

What it found

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Kenneth J NieserDepartment of Population Health Sciences, University of Wisconsin-Madison, United States.
Zachary N StoweDepartment of Psychiatry, University of Wisconsin-Madison, United States.
D Jeffrey NewportDepartment of Psychiatry & Behavioral Sciences, University of Texas at Austin Dell Medical School, United States.
Jessica L CokerDepartment of Psychiatry, University of Arkansas for Medical Sciences, United States.
Amy L CochranDepartment of Population Health Sciences, University of Wisconsin-Madison, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Postpartum depression can take many forms. Different symptom patterns could have divergent implications for how we screen, diagnose, and treat postpartum depression. We sought to utilise a recently developed robust estimation algorithm to automatically identify differential patterns in depressive symptoms and subsequently characterise the individuals who exhibit different patterns. Methods: Depressive symptom data (N = 548) were collected from women with neuropsychiatric illnesses at two U.S. urban sites participating in a longitudinal observational study of stress across the perinatal period. Data were collected from Emory University between 1994 and 2012 and from the University of Arkansas for Medical Sciences between 2012 and 2017. We conducted an exploratory factor analysis of Beck Depression Inventory (BDI) items using a robust expectation-maximization algorithm, rather than a conventional expectation-maximization algorithm. This recently developed method enabled automatic detection of differential symptom patterns. We described differences in symptom patterns and conducted unadjusted and adjusted analyses of associations of symptom patterns with demographics and psychiatric histories. Findings: 53 (9.7%) participants were identified by the algorithm as having a different pattern of reported symptoms compared to other participants. This group had more severe symptoms across all items-especially items related to thoughts of self-harm and self-judgement-and differed in how their symptoms related to underlying psychological constructs. History of social anxiety disorder (OR: 4.0; 95% CI [1.9, 8.1]) and history of childhood trauma (for each 5-point increase, OR: 1.2; 95% CI [1.1, 1.3]) were significantly associated with this differential pattern after adjustment for other covariates. Interpretation: Social anxiety disorder and childhood trauma are associated with differential patterns of severe postpartum depressive symptoms, which might warrant tailored strategies for screening, diagnosis, and treatment to address these comorbid conditions. Funding: There are no funding sources to declare.

Indexed as

Adverse childhood experiencesFactor analysisPostpartum depressionRobust statisticsSelf-injurious behaviorSocial phobiaSymptom heterogeneity

Identifiers

PMID36798754
PMCPMC9925853

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