Scoring System Model for Early Detection of Maternity Blues in Bukittinggi, West Sumatera, Indonesia

Background: Maternity blues creates emotional instability in moms, causing them to become irritated, overly nervous, and feel incapable of being a good mother. Maternity blues may interfere with infant care and raise the risk of postpartum depression symptoms, disrupting mother and baby interactions. Maternity blues is often ignored so it is not diagnosed and if not treated properly it can become a problem and develop into postpartum depression or postpartum psychosis. Maternity blues is a serious condition that poses risks to both mothers and infants. If left untreated, Maternity blues can progress into postpartum depression, which has significant physical and psychological consequences. Early detection of Maternity blues is crucial for timely intervention and prevention. Objectives: This study aims to develop a Scoring System Model for the early detection of maternity blues , allowing for effective screening and timely management. Methods: A cross-sectional study was conducted in Bukittinggi City, West Sumatra, Indonesia, involving 126 postpartum mothers recruited consecutively. Data analysis included the calculation of odds ratios, logistic regression, and ROC curve analysis to determine the sensitivity and specificity of the prediction model. The scoring system's performance was assessed using calibration and discrimination values. Results: The developed scoring system demonstrated good calibration and discrimination, with an Area Under the Curve (AUC) value of 0.806 (95% CI: 0.732–0.881). The Hosmer & Leme show test showed a p-value of 0.724, indicating a good fit for the model. Conclusion: The proposed scoring system is a reliable tool for the early detection of maternity blues . By identifying at-risk mothers through prediction scores, appropriate interventions can be implemented to prevent the progression of maternity blues into more severe postpartum mental health disorders.