Analysis of influencing factors and the construction of predictive models for postpartum depression in older pregnant women

Abstract

Background: Changes in China's fertility policy have led to a significant increase in older pregnant women. At present, there is a lack of analysis of influencing factors and research on predictive models for postpartum depression (PPD) in older pregnant women.

Aim: To analysis the influencing factors and the construction of predictive models for PPD in older pregnant women.

Methods: By adopting a cross-sectional survey research design, 239 older pregnant women (≥ 35 years old) who underwent obstetric examinations and gave birth at Suzhou Ninth People's Hospital from February 2022 to July 2023 were selected as the research subjects. When postpartum women of advanced maternal age came to the hospital for follow-up 42 d after birth, the Edinburgh PPD Scale (EPDS) was used to assess the presence of PPD symptoms. The women were divided into a PPD group and a no-PPD group. Two sets of data were collected for analysis, and a prediction model was constructed. The performance of the predictive model was evaluated using receiver operating characteristic (ROC) analysis and the Hosmer-Lemeshow goodness-of-fit test.

Results: On the 42^nd day after delivery, 51 of 239 older pregnant women were evaluated with the EPDS scale and found to have depressive symptoms. The incidence rate was 21.34% (51/239). There were statistically significant differences between the PPD group and the no-PPD group in terms of education level (P = 0.004), family relationships (P = 0.001), pregnancy complications (P = 0.019), and mother-infant separation after birth (P = 0.002). Multivariate logistic regression analysis showed that a high school education and below, poor family relationships, pregnancy complications, and the separation of the mother and baby after birth were influencing factors for PPD in older pregnant women (P < 0.05). Based on the influencing factors, the following model equation was developed: Logit (P) = 0.729 × education level + 0.942 × family relationship + 1.137 × pregnancy complications + 1.285 × separation of the mother and infant after birth -6.671. The area under the ROC curve of this prediction model was 0.873 (95%CI: 0.821-0.924), the sensitivity was 0.871, and the specificity was 0.815. The deviation between the value predicted by the model and the actual value through the Hosmer-Lemeshow goodness-of-fit test was not statistically significant (χ² = 2.749, P = 0.638), indicating that the model did not show an overfitting phenomenon.

Conclusion: The risk of PPD among older pregnant women is influenced by educational level, family relationships, pregnancy complications, and the separation of the mother and baby after birth. A prediction model based on these factors can effectively predict the risk of PPD in older pregnant women.

Keywords: Influencing factors; Older pregnant women; Postpartum depression; Prediction model.

Figure 1

Receiver operating characteristic curve evaluation of the postpartum depression risk prediction model for older pregnant women. AUC: Area under curve.