Abstract
Maternal depression (MD) was one of the most prevalent psychiatric problems worldwide. However, it easily remains untreated and misses the best time to prevent the emergence or worsening of major depressive symptoms due to under-observed stigma and the lack of effective screening tools. Thus, this study aims to develop and validate a machine learning–based MD symptoms prediction model integrating more observable and objective factors to early detect and monitor MD risk. A cross-sectional study was conducted in 10 community vaccination centers in Wenzhou, China, and a total of 1099 mothers were surveyed by using purposive sampling. A questionnaire containing questions regarding socio-demographic variables, psychophysiological variables, wife role–related variables, and mother role-related variables was used to collect data. A framework of data preprocessing, feature selection, and model evaluation was implemented to develop an optimal risk prediction model. Results demonstrated that the XG-Boost algorithm provided robust performance with the highest AUC and well-balanced sensitivity and specificity (AUC = 0.90, sensitivity = 0.74, specificity = 0.90). Furthermore, the causal mediation analysis indicated that wife-mother role conflict positively predicted MD symptoms, and it also exerted influence on mothers suffering through the mediation of anxiety and insomnia. Findings from the present study may help guide the development of MD screening tools to early detect and provide the modifiable risk factor information for timely tailored prevention.
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This project was funded by the Zhejiang Provincial Science and Technology Innovation Program (New Young Talent Program) for College Students, 2021R413086, Liuzhi Hong.
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This study was performed in accordance with the ethical standards established in the 1964 Declaration of Helsinki and its later amendments, and it has been approved by the Institutional Review Board (IRB) of Wenzhou Medical University (IRB approval number: 2021-K-23–02).
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Hong, L., Yang, A., Liang, Q. et al. Wife-Mother Role Conflict at the Critical Child-Rearing Stage: A Machine-Learning Approach to Identify What and How Matters in Maternal Depression Symptoms in China. Prev Sci (2023). https://doi.org/10.1007/s11121-023-01610-5
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DOI: https://doi.org/10.1007/s11121-023-01610-5