Frontiers in public health · 2026
Risk factors and predictive modeling of postpartum depression among postpartum women: empirical evidence from Chongqing, China.
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Jiang Jiaming, Lai Guichuan, Li Wenlong, Liu Yingcheng, Zeng Haijiao, Liu Tian, Liu Xinjing, Wang Qian, Xie Biao, Zhong Xiaoni
Аннотация
BACKGROUND: Postpartum depression (PPD) is a prevalent mental health condition that significantly impacts the wellbeing of mothers and their families. Early identification of high-risk women continues to be a challenge in public health practice. This study focuses on postpartum women in Chongqing, China, aiming to identify key psychosocial and demographic risk factors for PPD. Using logistic regression, we constructed and validated a predictive model for early screening, which could guide targeted preventive interventions.
METHODS: This cross-sectional study was conducted from January 2018 to July 2019 at four hospitals in Chongqing, China. A total of 892 valid questionnaires were collected based on predefined inclusion and exclusion criteria. Univariate and multivariable logistic regression analyses were performed to identify predictors of PPD and to construct a predictive model. The model's performance was evaluated in terms of discrimination, calibration, and clinical utility using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA), respectively. The dataset was randomly divided into a training set (70%) for model development and a validation set (30%) for internal validation.
RESULTS: Among the 892 participants, the prevalence of PPD was 10.2%. Multivariable logistic regression analysis identified four independent predictors: low delivery-related knowledge (OR = 5.47, 95% CI: 2.08-14.40), family dysfunction (moderate: 7.03, 3.79-13.02; severe: 5.14, 2.08-12.72), low social support (3.92, 1.06-14.42), and cesarean section (2.31, 1.31-4.09). The AUC of the model was 0.83 in both the training and validation sets. The calibration curve demonstrated good agreement between predicted and observed outcomes, and DCA confirmed its potential clinical utility.
CONCLUSION: Key risk factors for PPD in this study include low delivery-related knowledge, family dysfunction, low social support, and cesarean section. The developed model performs well in the early identification of high-risk women, enabling timely interventions to improve maternal mental health.
METHODS: This cross-sectional study was conducted from January 2018 to July 2019 at four hospitals in Chongqing, China. A total of 892 valid questionnaires were collected based on predefined inclusion and exclusion criteria. Univariate and multivariable logistic regression analyses were performed to identify predictors of PPD and to construct a predictive model. The model's performance was evaluated in terms of discrimination, calibration, and clinical utility using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA), respectively. The dataset was randomly divided into a training set (70%) for model development and a validation set (30%) for internal validation.
RESULTS: Among the 892 participants, the prevalence of PPD was 10.2%. Multivariable logistic regression analysis identified four independent predictors: low delivery-related knowledge (OR = 5.47, 95% CI: 2.08-14.40), family dysfunction (moderate: 7.03, 3.79-13.02; severe: 5.14, 2.08-12.72), low social support (3.92, 1.06-14.42), and cesarean section (2.31, 1.31-4.09). The AUC of the model was 0.83 in both the training and validation sets. The calibration curve demonstrated good agreement between predicted and observed outcomes, and DCA confirmed its potential clinical utility.
CONCLUSION: Key risk factors for PPD in this study include low delivery-related knowledge, family dysfunction, low social support, and cesarean section. The developed model performs well in the early identification of high-risk women, enabling timely interventions to improve maternal mental health.