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<p>To identify profiles of psychosocial characteristics among pregnant women with antepartum depression, and to investigate the association between these profiles and the severity of antepartum depression. A total of 448 women with antepartum depression were recruited using convenience sampling from one hospital in China. Data were collected from June to October 2023 using a self-report questionnaire. Firstly, latent profile analysis was used to identify psychosocial profiles across a wide range of negative psychosocial characteristics (anxiety, stress, susceptibility personality traits and rumination) and positive psychosocial characteristics (psychological resilience, social support, self-efficacy and family function). We then explored differences in demographic and obstetric characteristics between different profiles and the relationship between profiles and the severity of antepartum depression by conducting univariate analysis and multinomial logistic regression analysis. This study identified three distinct psychosocial profiles among women with antepartum depression: “Well-adjusted”&#xa0;(54.7%), “Vulnerable” (40.2%), and “Adverse” (5.1%). In addition, education, smoking history and perinatal period were influencing factors of the stratification of psychosocial characteristics pregnant women with antepartum depression. Furthermore, the “Vulnerable” and “Adverse” group has higher risk of developing severe antepartum depression (OR = 3.555, <i>p</i> &lt; 0.001, 95% CI = 1.824–6.927; OR = 56.201, <i>p</i> &lt; 0.001, 95% CI = 16.989–189.917). This study, which took a person-centered approach, validated that women with antepartum depression had a heterogeneity of psychosocial characteristics. The findings give evidence for medical professionals to construct complex interventions based on the psychosocial characteristics of many critical components.</p>

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Identifying profiles of psychosocial characteristic“s among pregnant women with antepartum depression: a latent profile analysis

  • 期刊论文

  • 发表日期：

    2025年7月12日

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  • Hongyan Xie,
  • Shengnan Cong,
  • Jingjing Han,
  • Xiaoqing Sun,
  • Shiqian Ni,
  • Rong Zhu,
  • Yun Gao,
  • Shijuan Mei,
  • Lijuan Jiang,
  • Meng yao Wang,
  • Yaxuan Wu,
  • Aixia Zhang

摘要

To identify profiles of psychosocial characteristics among pregnant women with antepartum depression, and to investigate the association between these profiles and the severity of antepartum depression. A total of 448 women with antepartum depression were recruited using convenience sampling from one hospital in China. Data were collected from June to October 2023 using a self-report questionnaire. Firstly, latent profile analysis was used to identify psychosocial profiles across a wide range of negative psychosocial characteristics (anxiety, stress, susceptibility personality traits and rumination) and positive psychosocial characteristics (psychological resilience, social support, self-efficacy and family function). We then explored differences in demographic and obstetric characteristics between different profiles and the relationship between profiles and the severity of antepartum depression by conducting univariate analysis and multinomial logistic regression analysis. This study identified three distinct psychosocial profiles among women with antepartum depression: “Well-adjusted” (54.7%), “Vulnerable” (40.2%), and “Adverse” (5.1%). In addition, education, smoking history and perinatal period were influencing factors of the stratification of psychosocial characteristics pregnant women with antepartum depression. Furthermore, the “Vulnerable” and “Adverse” group has higher risk of developing severe antepartum depression (OR = 3.555, p < 0.001, 95% CI = 1.824–6.927; OR = 56.201, p < 0.001, 95% CI = 16.989–189.917). This study, which took a person-centered approach, validated that women with antepartum depression had a heterogeneity of psychosocial characteristics. The findings give evidence for medical professionals to construct complex interventions based on the psychosocial characteristics of many critical components.