Postpartum Depression and Maternity Blues in Immigrants

Gonidakis, Fragiskos

doi:10.5772/30915

Cited by 4 publications

(5 citation statements)

References 69 publications

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“…A personal or family history of depression greatly and significantly increased the risk of postpartum depression in three studies, with odds ratios as high as 24.9 and 29.7 . Women with more anxious personality traits were also more likely to develop postpartum depression . Significantly, women with a history of psychiatric morbidity had lower levels of social support, highlighting the complex and likely bi‐directional association between these variables .…”

Section: Resultsmentioning

confidence: 99%

“…The majority of these were excluded upon retrieval of the full text as they did not meet inclusion criteria. Forty‐five articles reporting on 40 studies with a total of 19 349 (7895 migrant women and 11 454 women in comparison groups) were included in the final analysis …”

Section: Resultsmentioning

confidence: 99%

“…For our results we included each individual woman only once. Of these, 38 studies (7,766 migrant women) provided data on prevalence (Table S1), 12 studies (1977 migrant women) on associated factors (Table S2), and two studies (247 participants) on interventions (Table S3). Study outcomes were depression (40 studies), anxiety (three studies), and post‐traumatic stress disorder (PTSD; two studies) .…”

Section: Resultsmentioning

confidence: 99%

“…No studies assessed the presence of psychotic disorders. Thirteen different scales were used, with the EPDS (23 studies) and CES‐D (nine studies) being the most common. Sample sizes ranged from 31 to 909 migrant women and mean ages ranged from 24 to 33 years.…”

Section: Resultsmentioning

confidence: 99%

“…Only two studies included refugees or asylum seekers . Fourteen studies included a non‐migrant comparison group and, of these, nine (64%) reported higher rates of mental illness in the migrant groups. North America (22 studies) was the most heavily represented destination region, followed by Europe (six studies) and Australasia (six studies) .…”

Section: Resultsmentioning

confidence: 99%

See 4 more Smart Citations

Migration and perinatal mental health in women from low‐ and middle‐income countries: a systematic review and meta‐analysis

Fellmeth

Fazel

Plugge

2016

BJOG

151

150

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Background Migrant women are at risk of perinatal mental disorders due to stressors experienced before, during and after migration.Objectives This systematic review and meta-analysis summarises the prevalence, associated factors and interventions for perinatal mental disorders in migrant women from low-and middleincome countries (LMIC).Search Strategy We systematically searched nine electronic databases and the grey literature using a predefined search strategy.Selection Criteria Studies were included if they assessed pregnant or postpartum migrants from LMIC, used a structured tool and a casecontrol, cross-sectional, cohort or intervention study design.Data collection and analysis Data was double-extracted. We calculated pooled prevalence of depression and weighted mean anxiety and depression scores. We calculated crude odds ratios from risk factor studies and summarised intervention studies descriptively.Main results Forty studies were identified from 10 123 references. Pooled prevalence was 31% [95% condidence interval (CI) 23%-40%] for any depressive disorder and 17% (95% CI 12-23%) for major depressive disorder. Previous depression and lower social support were associated with perinatal depression. There were insufficient data to assess the burden of anxiety, post-traumatic stress disorder or psychosis in this population.Conclusions One in three migrant women from LMIC experiences symptoms of perinatal depression. Social support is an important protective factor. Evidence on LMIC women relocating to other LMIC is lacking. Given the adverse consequences of perinatal mental illness on women and their children, further research in low-resource settings is a priority.Keywords Low-and middle-income countries, mental health, migration, postpartum, pregnancy, refugee.Tweetable Abstract One in three migrant women from low-and middle-income countries has symptoms of perinatal depression.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Migration and perinatal mental health in women from low‐ and middle‐income countries: a systematic review and meta‐analysis

Fellmeth

Fazel

Plugge

2016

BJOG

151

150

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Machine Learning Methods for Predicting Postpartum Depression: Scoping Review

Saqib¹,

Khan²,

Butt³

2021

JMIR Ment Health

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Background Machine learning (ML) offers vigorous statistical and probabilistic techniques that can successfully predict certain clinical conditions using large volumes of data. A review of ML and big data research analytics in maternal depression is pertinent and timely, given the rapid technological developments in recent years. Objective This study aims to synthesize the literature on ML and big data analytics for maternal mental health, particularly the prediction of postpartum depression (PPD). Methods We used a scoping review methodology using the Arksey and O’Malley framework to rapidly map research activity in ML for predicting PPD. Two independent researchers searched PsycINFO, PubMed, IEEE Xplore, and the ACM Digital Library in September 2020 to identify relevant publications in the past 12 years. Data were extracted from the articles’ ML model, data type, and study results. Results A total of 14 studies were identified. All studies reported the use of supervised learning techniques to predict PPD. Support vector machine and random forest were the most commonly used algorithms in addition to Naive Bayes, regression, artificial neural network, decision trees, and XGBoost (Extreme Gradient Boosting). There was considerable heterogeneity in the best-performing ML algorithm across the selected studies. The area under the receiver operating characteristic curve values reported for different algorithms were support vector machine (range 0.78-0.86), random forest method (0.88), XGBoost (0.80), and logistic regression (0.93). Conclusions ML algorithms can analyze larger data sets and perform more advanced computations, which can significantly improve the detection of PPD at an early stage. Further clinical research collaborations are required to fine-tune ML algorithms for prediction and treatment. ML might become part of evidence-based practice in addition to clinical knowledge and existing research evidence.

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Machine learning methods for predicting postpartum depression: A scoping review (Preprint)

Saqib¹,

Khan²,

Butt³

2021

Preprint

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BACKGROUND Machine learning (ML) offers vigorous statistical and probabilistic techniques that can successfully predict certain clinical conditions using large volumes of data. A review of ML and big data research analytics in maternal depression is pertinent and timely given the rapid technological developments in recent years. OBJECTIVE This paper aims to synthesize the literature on machine learning and big data analytics for maternal mental health, particularly the prediction of postpartum depression (PPD). METHODS A scoping review methodology using the Arksey and O’Malley framework was employed to rapidly map the research activity in the field of ML for predicting PPD. A literature search was conducted through health and IT research databases, including PsycInfo, PubMed, IEEE Xplore and the ACM Digital Library from Sep 2020 till Jan 2021. Data were extracted on the article’s ML model, data type, and study results. RESULTS A total of fourteen (14) studies were identified. All studies reported the use of supervised learning techniques to predict PPD. Support vector machine (SVM) and random forests (RF) were the most commonly employed algorithms in addition to naïve Bayes, regression, artificial neural network, decision trees and extreme gradient boosting. There was considerable heterogeneity in the best performing ML algorithm across selected studies. The area under the receiver-operating-characteristic curve (AUC) values reported for different algorithms were SVM (Range: 0.78-0.86); RF method (0.88); extreme gradient boosting (0.80); logistic regression (0.93); and extreme gradient boosting (0.71) respectively. CONCLUSIONS ML algorithms are capable of analyzing larger datasets and performing more advanced computations, that can significantly improve the detection of PPD at an early stage. Further clinical-research collaborations are required to fine-tune ML algorithms for prediction and treatments. ML might become part of evidence-based practice, in addition to clinical knowledge and existing research evidence.

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Postpartum Depression and Maternity Blues in Immigrants

Cited by 4 publications

References 69 publications

Migration and perinatal mental health in women from low‐ and middle‐income countries: a systematic review and meta‐analysis

Migration and perinatal mental health in women from low‐ and middle‐income countries: a systematic review and meta‐analysis

Machine Learning Methods for Predicting Postpartum Depression: Scoping Review

Machine learning methods for predicting postpartum depression: A scoping review (Preprint)

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