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Predicting Postpartum Depressive Symptoms from Pregnancy Biopsychosocial Factors: A Longitudinal Investigation Using Structural Equation Modeling

The prediction of postpartum depression (PPD) should be conceptualized from a biopsychosocial perspective. This study aims at exploring the longitudinal contribution of a set of biopsychosocial factors for PPD in perinatal women. A longitudinal study was conducted, assessment was made with a website...

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Autores principales: Martínez-Borba, Verónica, Suso-Ribera, Carlos, Osma, Jorge, Andreu-Pejó, Laura
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7696025/
https://www.ncbi.nlm.nih.gov/pubmed/33202688
http://dx.doi.org/10.3390/ijerph17228445
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author Martínez-Borba, Verónica
Suso-Ribera, Carlos
Osma, Jorge
Andreu-Pejó, Laura
author_facet Martínez-Borba, Verónica
Suso-Ribera, Carlos
Osma, Jorge
Andreu-Pejó, Laura
author_sort Martínez-Borba, Verónica
collection PubMed
description The prediction of postpartum depression (PPD) should be conceptualized from a biopsychosocial perspective. This study aims at exploring the longitudinal contribution of a set of biopsychosocial factors for PPD in perinatal women. A longitudinal study was conducted, assessment was made with a website and included biopsychosocial factors that were measured during pregnancy (n = 266, weeks 16–36), including age, affective ambivalence, personality characteristics, social support and depression. Depression was measured again at postpartum (n = 101, weeks 2–4). The analyses included bivariate associations and structural equation modeling (SEM). Age, affective ambivalence, neuroticism, positive, and negative affect at pregnancy were associated with concurrent depression during pregnancy (all p < 0.01). Age, affective ambivalence, positive affect, and depression at pregnancy correlated with PPD (all p < 0.05). Affective ambivalence (β = 1.97; p = 0.003) and positive (β = −0.29; p < 0.001) and negative affect (β = 0.22; p = 0.024) at pregnancy remained significant predictors of concurrent depression in the SEM, whereas only age (β = 0.27; p = 0.010) and depression (β = 0.37; p = 0.002) at pregnancy predicted PPD. Biopsychosocial factors are clearly associated with concurrent depression at pregnancy, but the stability of depression across time limits the prospective contribution of biopsychosocial factors. Depression should be screened early during pregnancy, as this is likely to persist after birth. The use of technology, as in the present investigation, might be a cost-effective option for this purpose.
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spelling pubmed-76960252020-11-29 Predicting Postpartum Depressive Symptoms from Pregnancy Biopsychosocial Factors: A Longitudinal Investigation Using Structural Equation Modeling Martínez-Borba, Verónica Suso-Ribera, Carlos Osma, Jorge Andreu-Pejó, Laura Int J Environ Res Public Health Article The prediction of postpartum depression (PPD) should be conceptualized from a biopsychosocial perspective. This study aims at exploring the longitudinal contribution of a set of biopsychosocial factors for PPD in perinatal women. A longitudinal study was conducted, assessment was made with a website and included biopsychosocial factors that were measured during pregnancy (n = 266, weeks 16–36), including age, affective ambivalence, personality characteristics, social support and depression. Depression was measured again at postpartum (n = 101, weeks 2–4). The analyses included bivariate associations and structural equation modeling (SEM). Age, affective ambivalence, neuroticism, positive, and negative affect at pregnancy were associated with concurrent depression during pregnancy (all p < 0.01). Age, affective ambivalence, positive affect, and depression at pregnancy correlated with PPD (all p < 0.05). Affective ambivalence (β = 1.97; p = 0.003) and positive (β = −0.29; p < 0.001) and negative affect (β = 0.22; p = 0.024) at pregnancy remained significant predictors of concurrent depression in the SEM, whereas only age (β = 0.27; p = 0.010) and depression (β = 0.37; p = 0.002) at pregnancy predicted PPD. Biopsychosocial factors are clearly associated with concurrent depression at pregnancy, but the stability of depression across time limits the prospective contribution of biopsychosocial factors. Depression should be screened early during pregnancy, as this is likely to persist after birth. The use of technology, as in the present investigation, might be a cost-effective option for this purpose. MDPI 2020-11-14 2020-11 /pmc/articles/PMC7696025/ /pubmed/33202688 http://dx.doi.org/10.3390/ijerph17228445 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Martínez-Borba, Verónica
Suso-Ribera, Carlos
Osma, Jorge
Andreu-Pejó, Laura
Predicting Postpartum Depressive Symptoms from Pregnancy Biopsychosocial Factors: A Longitudinal Investigation Using Structural Equation Modeling
title Predicting Postpartum Depressive Symptoms from Pregnancy Biopsychosocial Factors: A Longitudinal Investigation Using Structural Equation Modeling
title_full Predicting Postpartum Depressive Symptoms from Pregnancy Biopsychosocial Factors: A Longitudinal Investigation Using Structural Equation Modeling
title_fullStr Predicting Postpartum Depressive Symptoms from Pregnancy Biopsychosocial Factors: A Longitudinal Investigation Using Structural Equation Modeling
title_full_unstemmed Predicting Postpartum Depressive Symptoms from Pregnancy Biopsychosocial Factors: A Longitudinal Investigation Using Structural Equation Modeling
title_short Predicting Postpartum Depressive Symptoms from Pregnancy Biopsychosocial Factors: A Longitudinal Investigation Using Structural Equation Modeling
title_sort predicting postpartum depressive symptoms from pregnancy biopsychosocial factors: a longitudinal investigation using structural equation modeling
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7696025/
https://www.ncbi.nlm.nih.gov/pubmed/33202688
http://dx.doi.org/10.3390/ijerph17228445
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