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Cull J, Thomson G, Downe S, Fine M, Topalidou A. Views from women and maternity care professionals on routine discussion of previous trauma in the perinatal period: A qualitative evidence synthesis. PLoS One 2023; 18:e0284119. [PMID: 37195971 DOI: 10.1371/journal.pone.0284119] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 03/23/2023] [Indexed: 05/19/2023] Open
Abstract
BACKGROUND Over a third of pregnant women (around 250,000) each year in the United Kingdom have experienced trauma such as domestic abuse, childhood trauma or sexual assault. These experiences can have a long-term impact on women's mental and physical health. This global qualitative evidence synthesis explores the views of women and maternity care professionals on routine discussion of previous trauma in the perinatal period. METHODS Systematic database searches (MEDLINE, EMBASE, CINAHL Plus, APA PsycINFO and Global Index Medicus) were conducted in July 2021 and updated in April 2022. The quality of each study was assessed using the Critical Appraisal Skills Programme. We thematically synthesised the data and assessed confidence in findings using GRADE-CERQual. RESULTS We included 25 papers, from five countries, published between 2001 and 2022. All the studies were conducted in high-income countries; therefore findings cannot be applied to low- or middle-income countries. Confidence in most of the review findings was moderate or high. The findings are presented in six themes. These themes described how women and clinicians felt trauma discussions were valuable and worthwhile, provided there was adequate time and appropriate referral pathways. However, women often found being asked about previous trauma to be unexpected and intrusive, and women with limited English faced additional challenges. Many pregnant women were unaware of the extent of the trauma they have suffered, or its impact on their lives. Before disclosing trauma, women needed to have a trusting relationship with a clinician; even so, some women chose not to share their histories. Hearing trauma disclosures could be distressing for clinicians. CONCLUSION Discussions of previous trauma should be undertaken when women want to have the discussion, when there is time to understand and respond to the needs and concerns of each individual, and when there are effective resources available for follow up if needed. Continuity of carer should be considered a key feature of routine trauma discussion, as many women will not disclose their histories to a stranger. All women should be provided with information about the impact of trauma and how to independently access support in the event of non-disclosures. Care providers need support to carry out these discussions.
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Affiliation(s)
- Joanne Cull
- School of Community Health and Midwifery, University of Central Lancashire, Preston, England
| | - Gill Thomson
- School of Community Health and Midwifery, University of Central Lancashire, Preston, England
| | - Soo Downe
- School of Community Health and Midwifery, University of Central Lancashire, Preston, England
| | - Michelle Fine
- Public Science Project, The Graduate Center, City University of New York, New York, United States of America
| | - Anastasia Topalidou
- School of Community Health and Midwifery, University of Central Lancashire, Preston, England
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Preis H, Djurić PM, Ajirak M, Chen T, Mane V, Garry DJ, Heiselman C, Chappelle J, Lobel M. Applying machine learning methods to psychosocial screening data to improve identification of prenatal depression: Implications for clinical practice and research. Arch Womens Ment Health 2022; 25:965-973. [PMID: 35986793 PMCID: PMC9709634 DOI: 10.1007/s00737-022-01259-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Accepted: 08/07/2022] [Indexed: 11/30/2022]
Abstract
We utilized machine learning (ML) methods on data from the PROMOTE, a novel psychosocial screening tool, to quantify risk for prenatal depression for individual patients and identify contributing factors that impart greater risk for depression. Random forest algorithms were used to predict likelihood for being at high risk for prenatal depression (Edinburgh Postnatal Depression Scale; EPDS ≥ 13 and/or positive self-injury item) using data from 1715 patients who completed the PROMOTE. Performance matrices were calculated to assess the ability of the PROMOTE to accurately classify patients. Probability for depression was calculated for individual patients. Finally, recursive feature elimination was used to evaluate the importance of each PROMOTE item in the classification of depression risk. PROMOTE data were successfully used to predict depression with acceptable performance matrices (accuracy = 0.80; sensitivity = 0.75; specificity = 0.81; positive predictive value = 0.79; negative predictive value = 0.97). Perceived stress, emotional problems, family support, age, major life events, partner support, unplanned pregnancy, current employment, lifetime abuse, and financial state were the most important PROMOTE items in the classification of depression risk. Results affirm the value of the PROMOTE as a psychosocial screening tool for prenatal depression and the benefit of using it in conjunction with ML methods. Using such methods can help detect underreported outcomes and identify what in patients' lives makes them more vulnerable, thus paving the way for effective individually tailored precision medicine.
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Affiliation(s)
- Heidi Preis
- Department of Psychology, Stony Brook University, Stony Brook, NY, 11794, USA.
- Department of Obstetrics, Gynecology and Reproductive Medicine, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, 11794, USA.
| | - Petar M Djurić
- Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Marzieh Ajirak
- Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Tong Chen
- Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Vibha Mane
- Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, 11794, USA
| | - David J Garry
- Department of Obstetrics, Gynecology and Reproductive Medicine, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Cassandra Heiselman
- Department of Obstetrics, Gynecology and Reproductive Medicine, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Joseph Chappelle
- Department of Obstetrics, Gynecology and Reproductive Medicine, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Marci Lobel
- Department of Psychology, Stony Brook University, Stony Brook, NY, 11794, USA
- Department of Obstetrics, Gynecology and Reproductive Medicine, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, 11794, USA
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