In Maternal and child health journal ; h5-index 43.0
OBJECTIVES : About 74.91% of U.S. mothers experience postpartum pain at 6 to 10 weeks postpartum, and one in seven U.S. mothers suffer from postpartum depression. We used machine learning to explore physical, psychological, and social factors during pregnancy and childbirth and identify the most important predictors of postpartum pain and depression.
METHODS : Data were from the Listening To Mothers III survey (2012), a national representative sample of postpartum mothers. We randomly split the dataset into a training set (N = 1467) and a test set (N = 723). The final models included 34 risk factors identified from previous literature. Postpartum pain was measured as "to what extent the pain interferes with mothers' daily life". PHQ2 scores measured depression. We used the random forest model, an aggregate of many regression trees, to accommodate potential nonlinear/interaction effects.
RESULTS : In the test data set, our models explained 15.8% of the variance in pain and 27.1% of the variance in depression. The model's strongest predictors for postpartum pain were Cesarean delivery, holding back while communicating with providers, non-use of pain relief medications, and perceived discrimination. For depression scores, the model's strongest predictors included needing help for depression during pregnancy, perceived discrimination, holding back, gestational diabetes, and pain.
CONCLUSIONS FOR PRACTICE : Mental and physical health are intertwined and should be considered integratively in the perinatal period. Besides, practitioners should also be aware of the importance of patient-provider-relationship, which both independently and interact with other risk factors to predict postpartum health.
Xu Wen, Sampson McClain
2022-Dec-16
Machine learning, Postpartum depression, Postpartum pain
