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Biopsychosocial Predictors of Postpartum Depression: Protocol for Systematic Review and Meta-Analysis.

Marwa Alhaj AhmadShamsa Al AwarGehan Sayed SallamMeera AlkaabiDarya SmetaninaYauhen StatsenkoKornelia Teresa Zaręba
Published in: Healthcare (Basel, Switzerland) (2024)
During the postpartum period, psychological disorders may emerge. Aims and objectives: With the current study, we aim to explore the biological determinants that act on women during labor and incur the risk for postpartum depression (PPD). To reach the aim, we will perform the following tasks: (i) identify biological peripartum risk factors and calculate pooled prevalence of PPD for each of them; (ii) explore the strength of the relationship between peripartum risk factors and PPD; (iii) rank the predictors by their prevalence and magnitude of association with PPD. The knowledge obtained will support the development and implementation of early diagnostic and preventive strategies. Methods and analysis: We will systematically go through peer-reviewed publications available in the PubMed search engine and online databases: Scopus, Web of Science, EMBASE. The scope of the review will include articles published any time in English, Arabic, or Polish. We will deduplicate literature sources with the Covidence software, evaluate heterogeneity between the study results, and critically assess credibility of selected articles with the Joanna Briggs Institute's bias evaluation tool. The information to extract is the incidence rate, prevalence, and odds ratio between each risk factor and PPD. A comprehensive analysis of the extracted data will allow us to achieve the objectives. The study findings will contribute to risk stratification and more effective management of PPD in women.
Keyphrases
  • risk factors
  • healthcare
  • randomized controlled trial
  • depressive symptoms
  • public health
  • type diabetes
  • electronic health record
  • health information
  • social media
  • machine learning
  • quality improvement
  • deep learning