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

Author

Listed:
  • Verónica Martínez-Borba

    (Department of Basic and Clinical Psychology and Psychobiology, Jaume I University, Avda, Vicent Sos Baynat s/n, 12071 Castellon de la Plana, Spain
    Instituto de Investigación Sanitaria de Aragón, C/ San Juan Bosco, 13, 50009 Zaragoza, Spain)

  • Carlos Suso-Ribera

    (Department of Basic and Clinical Psychology and Psychobiology, Jaume I University, Avda, Vicent Sos Baynat s/n, 12071 Castellon de la Plana, Spain)

  • Jorge Osma

    (Instituto de Investigación Sanitaria de Aragón, C/ San Juan Bosco, 13, 50009 Zaragoza, Spain
    Department of Psychology and Sociology, Universidad de Zaragoza, C/ Atarazanas, 4, 44003 Teruel, Spain)

  • Laura Andreu-Pejó

    (Instituto de Investigación Sanitaria de Aragón, C/ San Juan Bosco, 13, 50009 Zaragoza, Spain
    Nursing Department, Jaume I University, Avda, Vicent Sos Baynat s/n, 12071 Castellon de la Plana, Spain)

Abstract

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.

Suggested Citation

  • Verónica Martínez-Borba & Carlos Suso-Ribera & Jorge Osma & Laura Andreu-Pejó, 2020. "Predicting Postpartum Depressive Symptoms from Pregnancy Biopsychosocial Factors: A Longitudinal Investigation Using Structural Equation Modeling," IJERPH, MDPI, vol. 17(22), pages 1-15, November.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:22:p:8445-:d:445184
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    References listed on IDEAS

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    1. Sathyanarayanan Doraiswamy & Anupama Jithesh & Sonia Chaabane & Amit Abraham & Karima Chaabna & Sohaila Cheema, 2020. "Perinatal Mental Illness in the Middle East and North Africa Region—A Systematic Overview," IJERPH, MDPI, vol. 17(15), pages 1-20, July.
    2. Jeannette Milgrom & Yafit Hirshler & John Reece & Charlene Holt & Alan W. Gemmill, 2019. "Social Support—A Protective Factor for Depressed Perinatal Women?," IJERPH, MDPI, vol. 16(8), pages 1-17, April.
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    Cited by:

    1. Dahlia Tharwat & Marion Trousselard & Dominique Fromage & Célia Belrose & Mélanie Balès & Anne-Laure Sutter-Dallay & Marie-Laure Ezto & Françoise Hurstel & Thierry Harvey & Solenne Martin & Cécile Vig, 2022. "Acceptance Mindfulness-Trait as a Protective Factor for Post-Natal Depression: A Preliminary Research," IJERPH, MDPI, vol. 19(3), pages 1-18, January.
    2. Juan Xiong & Qiyu Fang & Jialing Chen & Yingxin Li & Huiyi Li & Wenjie Li & Xujuan Zheng, 2021. "States Transitions Inference of Postpartum Depression Based on Multi-State Markov Model," IJERPH, MDPI, vol. 18(14), pages 1-11, July.

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