Modeling the Prediction of Baby Blues Risk Against the Variables of Maternal Age, Parity, and Delivery Method

Elisa Danik Kurniawati(1*), Dewi Ratna Sulistina(2), Tanti Tri Lestary(3)


(1) Universitas Negeri Malang
(2) Universitas Negeri Malang
(3) Universitas Borneo Tarakan
(*) Corresponding Author

Abstract


Baby Blues Syndrome (BBS) is a common, yet critical, transient mood disorder during the postpartum period, serving as a significant risk factor for severe postpartum depression. Identifying high-risk mothers based on common obstetric factors, such as maternal age, parity, and delivery method, is essential for timely intervention in clinical practice. This study aimed to develop an integrated prediction model for the risk of BBS based on the simultaneous contribution of maternal age, parity, and delivery method. This quantitative study used a cross-sectional design involving a sample of 46 postpartum mothers. Data analysis utilized the Chi-Square test for bivariate analysis and logistic regression for multivariate modelling. Multivariate logistic regression analysis demonstrated that the final model was statistically significant and explained 51.1% of the variation in BBS incidence. The strongest predictor of BBS was the C-Section delivery method, increasing the risk by 13.5 times compared to normal delivery. Additionally, Primipara was a significant predictor, increasing the risk by 8.4 times. Maternal Age was not found to be a significant predictor in the final model. C-Section and Primipara are the most significant obstetric risk predictors for BBS.


Keywords


risk prediction; baby blues; age; parity; delivery method

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References


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DOI: https://doi.org/10.26714/jk.15.1.2026.22-25

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