Dataset Information

Risk factors for anxiety and depression among pregnant women during the COVID-19 pandemic: A web-based cross-sectional survey.

ABSTRACT: INTRODUCTION:The article presents a protocol of a cross-sectional study of mental health of pregnant women in relation to the coronavirus disease 19 (COVID-19) pandemic. The primary aim is to compare differences in anxiety and depression scores of pregnant women between countries affected by the COVID-19 pandemic. The secondary aim is to assess demographic, economic, and social aspects affecting maternal anxiety and depression scores among pregnant women worldwide in the time of the COVID-19 pandemic. Finally, we will be able to compare differences in perception of the different aspects of the COVID-19 pandemic (social distancing, restrictions related to delivery) between countries and according to the epidemic status (number of infected patients, number of reported deaths). The comparisons will also be done according to the COVID-19 status of the participants. METHODS AND ANALYSIS:It is a web-based anonymous survey of pregnant women living in countries affected by the COVID-19 pandemic. The survey is comprised of 3 sections:Web-based recruitment for health research has proven to be cost-effective and efficient. At current times with the COVID-19 pandemic, limited resources and social distancing restrictions, performing a mental health study involving pregnant women on a large international scale cannot be safely conducted without involving social-media.The fears of pregnant women fall into 3 categories: the medical condition, the economic status and the organization of daily activity.The study has received approval of the medical ethics committee and has been registered on Clinicaltrials.gov. Results will be published in peer-reviewed journals and made public through all available media.

SUBMITTER: Kajdy A

PROVIDER: S-EPMC7387043 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Risk factors for anxiety and depression among pregnant women during the COVID-19 pandemic: A web-based cross-sectional survey.

Kajdy Anna A Feduniw Stepan S Ajdacka Urszula U Modzelewski Jan J Baranowska Barbara B Sys Dorota D Pokropek Artur A Pawlicka Paulina P Kaźmierczak Maria M Rabijewski Michał M Jasiak Hanna H Lewandowska Roksana R Borowski Dariusz D Kwiatkowski Sebastian S Poon Liona C LC

Medicine 20200701 30

<h4>Introduction</h4>The article presents a protocol of a cross-sectional study of mental health of pregnant women in relation to the coronavirus disease 19 (COVID-19) pandemic. The primary aim is to compare differences in anxiety and depression scores of pregnant women between countries affected by the COVID-19 pandemic. The secondary aim is to assess demographic, economic, and social aspects affecting maternal anxiety and depression scores among pregnant women worldwide in the time of the COVI ...[more]

PMID: 32791708

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Project description:Background: Maternal depression and anxiety are significant public health concerns that play an important role in the health and well-being of mothers and children. The COVID-19 pandemic, the consequential lockdowns and related safety restrictions worldwide negatively affected the mental health of pregnant and postpartum women. Methods: This regional study aimed to develop a machine learning (ML) model for the prediction of maternal depression and anxiety. The study used a dataset collected from five Arab countries during the COVID-19 pandemic between July to December 2020. The population sample included 3569 women (1939 pregnant and 1630 postpartum) from five countries (Jordan, Palestine, Lebanon, Saudi Arabia, and Bahrain). The performance of seven machine learning algorithms was assessed for the prediction of depression and anxiety symptoms. Results: The Gradient Boosting (GB) and Random Forest (RF) models outperformed other studied ML algorithms with accuracy values of 83.3% and 83.2% for depression, respectively, and values of 82.9% and 81.3% for anxiety, respectively. The Mathew's Correlation Coefficient was evaluated for the ML models; the Naïve Bayes (NB) and GB models presented the highest performance measures (0.63 and 0.59) for depression and (0.74 and 0.73) for anxiety, respectively. The features' importance ranking was evaluated, the results showed that stress during pregnancy, family support, financial issues, income, and social support were the most significant values in predicting anxiety and depression. Conclusion: Overall, the study evidenced the power of ML models in predicting maternal depression and anxiety and proved to be an efficient tool for identifying and predicting the associated risk factors that influence maternal mental health. The deployment of machine learning models for screening and early detection of depression and anxiety among pregnant and postpartum women might facilitate the development of health prevention and intervention programs that will enhance maternal and child health in low- and middle-income countries.

Project description:Objectives: Good maternal health and fetal development require sufficient and good quality of sleep during pregnancy. This study investigated sleep duration and quality in pregnant women, assessing factors with possibly influence on sleep. Method: A cross-sectional survey was conducted on pregnant women between June and August in 2015 in 16 hospitals in five provinces in China. A total of 2345 pregnant women aged 18 years and older were surveyed. Insufficient sleeping duration was defined as sleeping of less than 7 h per day. Excessive sleep duration was defined as sleeping of more than 9 h per day. Results: A total of 561 (23.9%) participants reported insufficient sleeping duration, whereas 485 (20.9%) claimed excessive sleep duration. A total of 358 (15.2%) of pregnant women reported problems regarding sleep quality. Compared to pregnant women with sufficient sleeping duration, those with insufficient sleeping duration were prone to have poor sleep quality, whereas those with excessive sleeping duration featured low possibility of poor sleep quality. High-risk groups of insufficient sleep duration include women of Han nationality, with siblings, in their first trimester of pregnancy, receiving care in low-capacity/quality hospital settings, and with daily or 1-3 days of secondhand smoke exposure. High-risk groups of excessive sleep duration include women living in rural areas, unemployed, in their third trimester of pregnancy, and receiving care in medium-capacity/quality hospital settings. High-risk groups of poor sleep quality include women of non-Han nationality, low income level, in their third trimester of pregnancy, and with insufficient sleep duration. Conclusions: Insufficient/excessive sleep duration and poor sleep quality commonly occur during pregnancy in China. Findings provide a better understanding of the influencing factors of insufficient/excessive sleep duration and poor quality of sleep. These findings have some implications for future interventions on sleep among pregnant women.

Dataset Information

Risk factors for anxiety and depression among pregnant women during the COVID-19 pandemic: A web-based cross-sectional survey.

Publications

Risk factors for anxiety and depression among pregnant women during the COVID-19 pandemic: A web-based cross-sectional survey.

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