The dynamic course of peripartum depression across pregnancy and childbirth
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CitationDekel, S. Ein-Dor, T. Ruohomäki, A. Lampi, J. Voutilainen, S. Tuomainen, TP. Heinonen, S. Kumpulainen, K. Pekkanen, J. Keski-Nisula, L. Pasanen, M. Lehto, SM. (2019). The dynamic course of peripartum depression across pregnancy and childbirth. Journal of psychiatric research, 113, 72-78. 10.1016/j.jpsychires.2019.03.016.
Peripartum depression (PPD) pertaining to depression in pregnancy and postpartum is one of the most common complications around childbirth with enduring adverse effects on mother and child health. Although psychiatric symptoms may improve or worsen over time, relatively little is known about the course of PPD symptoms and possible fluctuations.
We applied a person-centered approach to examine PPD symptom patterns across pregnancy and childbirth. 824 women were assessed at three time points: first trimester (T1), third trimester (T2), and again at eight weeks (T3) postpartum. We assessed PPD symptoms, maternal mental health history, and childbirth variables.
Growth mixture modeling (GMM) analysis revealed four discrete PPD symptom trajectory classes including chronic PPD (1.1%), delayed (10.2%), recovered (7.2%), and resilient (81.5%). Delivery complications were associated with chronic PPD but also with the recovered PPD trajectory class. History of mental health disorders was associated with chronic PPD and the delayed PPD class.
The findings underscore that significant changes in a woman's depression level can occur across pregnancy and childbirth. While a minority of women experience chronic PDD, for others depression symptoms appear to significantly alleviate over time, suggesting a form of recovery. Our findings support a personalized medicine approach based on the woman's symptom trajectory. Future research is warranted to identify the mechanisms underlying modifications in PPD symptoms severity and those implicated in recovery.
Subjectsperipartum depression childbirth prospective sample symptom trajectory growth modeling
Link to the original itemhttp://dx.doi.org/10.1016/j.jpsychires.2019.03.016
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