{"title":"Application of time series analysis in predicting postpartum depression: integrating data from the hospitalization period and early postpartum weeks.","authors":"Fu-Mei Hsu, Hsiu-Chin Chen, Kuei-Ching Wang, Wan-Ling Ling, Nai-Ching Chen","doi":"10.1007/s00737-024-01521-6","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to explore the dynamic changes in postpartum depressive symptoms from the hospitalization period to 4-8 weeks postpartum using time series analysis techniques. By integrating depressive scores from the hospital stay and the early postpartum weeks, we sought to develop a predictive model to enhance early identification and intervention strategies for Postpartum Depression (PPD).</p><p><strong>Methods: </strong>A longitudinal design was employed, analyzing Edinburgh Postnatal Depression Scale (EPDS) scores from 1,287 postpartum women during hospitalization and at 4, 6, and 8 weeks postpartum. Descriptive statistics summarized demographic characteristics. Time Series Analysis using the Auto-Regressive Integrated Moving Average (ARIMA) model explored temporal trends and seasonal variations in EPDS scores. Correlation analysis examined the relationships between EPDS scores and demographic characteristics. Model validation was conducted using a separate dataset.</p><p><strong>Results: </strong>EPDS scores significantly increased from the hospitalization period to 4-8 weeks postpartum (p < .001). The ARIMA model revealed seasonal and trend variations, with higher depressive scores in the winter months. The model's fit indices (AIC = 765.47; BIC = 774.58) indicated a good fit. The Moving Average (MA) coefficient was - 0.69 (p < .001), suggesting significant negative impacts from previous periods' errors.</p><p><strong>Conclusions: </strong>Monitoring postpartum depressive symptoms dynamically was crucial, particularly during the 4-8 weeks postpartum. The seasonal trend of higher depressive scores in winter underscored the need for tailored interventions. Further research using longitudinal and multi-center designs was warranted to validate and extend these findings. Our predictive model aimed to enhance early identification and intervention strategies, contributing to better maternal and infant health outcomes.</p>","PeriodicalId":8369,"journal":{"name":"Archives of Women's Mental Health","volume":" ","pages":""},"PeriodicalIF":3.2000,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Archives of Women's Mental Health","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s00737-024-01521-6","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PSYCHIATRY","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose: This study aimed to explore the dynamic changes in postpartum depressive symptoms from the hospitalization period to 4-8 weeks postpartum using time series analysis techniques. By integrating depressive scores from the hospital stay and the early postpartum weeks, we sought to develop a predictive model to enhance early identification and intervention strategies for Postpartum Depression (PPD).
Methods: A longitudinal design was employed, analyzing Edinburgh Postnatal Depression Scale (EPDS) scores from 1,287 postpartum women during hospitalization and at 4, 6, and 8 weeks postpartum. Descriptive statistics summarized demographic characteristics. Time Series Analysis using the Auto-Regressive Integrated Moving Average (ARIMA) model explored temporal trends and seasonal variations in EPDS scores. Correlation analysis examined the relationships between EPDS scores and demographic characteristics. Model validation was conducted using a separate dataset.
Results: EPDS scores significantly increased from the hospitalization period to 4-8 weeks postpartum (p < .001). The ARIMA model revealed seasonal and trend variations, with higher depressive scores in the winter months. The model's fit indices (AIC = 765.47; BIC = 774.58) indicated a good fit. The Moving Average (MA) coefficient was - 0.69 (p < .001), suggesting significant negative impacts from previous periods' errors.
Conclusions: Monitoring postpartum depressive symptoms dynamically was crucial, particularly during the 4-8 weeks postpartum. The seasonal trend of higher depressive scores in winter underscored the need for tailored interventions. Further research using longitudinal and multi-center designs was warranted to validate and extend these findings. Our predictive model aimed to enhance early identification and intervention strategies, contributing to better maternal and infant health outcomes.
期刊介绍：
Archives of Women’s Mental Health is the official journal of the International Association for Women''s Mental Health, Marcé Society and the North American Society for Psychosocial Obstetrics and Gynecology (NASPOG). The exchange of knowledge between psychiatrists and obstetrician-gynecologists is one of the major aims of the journal. Its international scope includes psychodynamics, social and biological aspects of all psychiatric and psychosomatic disorders in women. The editors especially welcome interdisciplinary studies, focussing on the interface between psychiatry, psychosomatics, obstetrics and gynecology. Archives of Women’s Mental Health publishes rigorously reviewed research papers, short communications, case reports, review articles, invited editorials, historical perspectives, book reviews, letters to the editor, as well as conference abstracts. Only contributions written in English will be accepted. The journal assists clinicians, teachers and researchers to incorporate knowledge of all aspects of women’s mental health into current and future clinical care and research.
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