Social support during pregnancy and the risk of postpartum depression in Polish women: A prospective study.
Joanna ŻyrekMagdalena KlimekAnna ApanasewiczAleksandra CiochońDariusz P DanelUrszula M MarcinkowskaMagdalena MijasAnna ZiomkiewiczAndrzej GalbarczykPublished in: Scientific reports (2024)
Social support has been proposed as an important determinant of women's physical and emotional well-being during pregnancy and after childbirth. Our study aimed to examine the association between the risk of postpartum depression (PPD) and perceived social support during pregnancy. A web-based prospective study survey was conducted among Polish women. The level of social support was measured with the Berlin Social Support Scales during pregnancy. Four weeks after the birth the risk of PPD was assessed using the Edinburgh Postpartum Depression Scale. Data from 932 mothers aged 19-43 (mean 30.95; SD 3.83) were analyzed using multinomial logistic regression. Higher perceived available support (emotional and instrumental), currently received support (emotional, instrumental and informational), satisfaction with the support, and sum of score were all associated with lower risk of PPD, after controlling for selected covariates (woman's age, socioeconomic status, parity status, place of residency, education, child's Apgar score, type of delivery, complications during birth, kin assisting the labor, breastfeeding). Our results suggest that the more social support the pregnant woman receives, the lower is her risk of PPD. Since humans evolved as cooperative breeders, they are inherently reliant on social support to raise children and such allomaternal help could improve maternal well-being.
Keyphrases
- social support
- depressive symptoms
- pregnancy outcomes
- polycystic ovary syndrome
- sleep quality
- mental health
- young adults
- gestational age
- type diabetes
- machine learning
- risk factors
- physical activity
- case report
- breast cancer risk
- electronic health record
- body mass index
- cervical cancer screening
- quality improvement
- patient satisfaction
- artificial intelligence
- weight gain