Development and validation of a postpartum depression risk score in delivered women, Iran

Abstract

BACKGROUND: Investigators describe a dramatic increase in the incidence of mood disorder after childbirth, with the largest risk in the 90 days after delivery. This study is designed to develop a relatively simple screening tool and validate it from the significant variables associated with postpartum depression (PPD) to detect delivered women at high risk of having PPD.

MATERIALS AND METHODS: In the cross-sectional study, 6,627 from a total of 7,300 delivered women, 2-12 months after delivery were recruited and screened for PPD. Split-half validation was used to develop the risk score. The training data set was used to develop the model, and the validation data set was used to validate the developed the risk factors of postpartum depression risk score using multiple logistic regression analysis to compute the β coefficients and odds ratio (OR) for the dependent variables associated with possible PPD in this study. Calibration was checked using the Hosmer and Lemeshow test. A score for independent variables contributing to PPD was calculated. Cutoff points using a trade-off between the sensitivity and specificity of risk scores derived from PPD model using the Receiver Operating Characteristic (ROC) curve.

RESULTS: The predicted and observed PPD were not different (P-value = 0.885). The aROC with area under the curve (S.E.) of 0.611 (0.008) for predicting PPD using the suggested cut-off point of -0.702, the proportion of participants screening positive for PPD was 70.9% (sensitivity) (CI 95%; 69.5, 72.3) while the proportion screening negative was 60.1% (specificity) (CI 95%; 58.2, 62.1).

CONCLUSION: Despite of the relatively low sensitivity and specificity in this study, it could be a simple, practical and useful screening tool to identify individual at high risk for PPD in the target population.

Keywords: Postpartum depression, risk score, validation