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Different patterns of attentional bias in antenatal and postpartum depression.

ABSTRACT: Biased information processing in attention, memory, and interpretation is proposed to be central cognitive alterations in patients with major depressive disorder, but studies in women with peripartum depression are scarce. Because of the many similarities with depression in nonperipartum states as regards symptom profile and risk factors, we hypothesized that women with antenatal and postpartum depression would display attentional bias to negatively and positively valenced words.One hundred and seventy-seven pregnant and 157 postpartum women were included. Among these, 40 suffered from antenatal depressive disorder and 33 from postpartum depressive disorder. An emotional Stroop task with neutral, positive, negative, and negatively valenced obstetric words was used.No significant difference in emotional interference scores was noted between women with antenatal depression and nondepressed pregnant women. In contrast, women with postpartum depression displayed shorter reaction times to both positive (p = .028) and negative (p = .022) stimuli, compared with neutral words. Pregnant women on antidepressant treatment displayed longer reaction times to negatively valenced obstetric words in comparison with untreated depressed women (p = .012), and a trend toward greater interference in comparison with controls (p = .061).In contrast with the hypothesis, we found no evidence of attentional bias to emotionally valenced stimuli in women with untreated peripartum depression. However, the shorter reaction times to emotional stimuli in women with postpartum depression may indicate emotional numbing, which in turn, is a functional impairment that may have repercussions for child development and well-being. Our findings emphasize the need to identify and treat women with postpartum depression at the earliest possible time point to ensure swift recovery and support for the family.

SUBMITTER: Edvinsson A

PROVIDER: S-EPMC5698862 | biostudies-other | 2017 Nov

REPOSITORIES: biostudies-other

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Different patterns of attentional bias in antenatal and postpartum depression.

Edvinsson Åsa Å Skalkidou Alkistis A Hellgren Charlotte C Gingnell Malin M Ekselius Lisa L Willebrand Mimmie M Sundström Poromaa Inger I

Brain and behavior 20171018 11

<h4>Background</h4>Biased information processing in attention, memory, and interpretation is proposed to be central cognitive alterations in patients with major depressive disorder, but studies in women with peripartum depression are scarce. Because of the many similarities with depression in nonperipartum states as regards symptom profile and risk factors, we hypothesized that women with antenatal and postpartum depression would display attentional bias to negatively and positively valenced wor ...[more]

PMID: 29201545

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Project description:BackgroundWomen with antenatal depression often have a higher risk of developing postpartum depression (PPD) after delivery. A number of factors associated with the PDD in those previously reporting antenatal depression have been suggested, but further research is needed. This study aimed to investigate factors associated with developing subsequent postnatal depression in women who had screened positive for antenatal depression.MethodsThis study was carried out in Hangzhou women's Hospital. 578 women who experienced antenatal depression from this cohort were enrolled in this study. The sociodemographic and clinical characteristics of the participants were collected and tabulated against the incidence of postnatal depression. Binary logistic regression was used to estimate the effects of the principal underlying variables. The Chinese-version Edinburgh Postnatal Depression Scale (EPDS) was used to screen for PPD. Antenatal screening for depression was conducted at 28-34 weeks during pregnancy and postpartum depressive symptoms were assessed at 6 weeks after childbirth in the women. Path Analysis of Structural Equation Model (SEM) was employed to explore the direct, indirect, and total effects of risk factors of PPD.Results57.6% (n = 333) of the participants subsequently developed PPD in our study. The results of the logistic analysis indicated that ages ≤ 35 years old (OR = 1.852; 95%CI: 1.002-3.423), non-one-child families (OR = 1.518; 95%CI: 1.047-2.200), and rare care from partner during pregnancy (OR = 2.801; 95%CI: 1.038-7.562), the antenatal EPDS score (OR = 1.128; 95%CI: 1.052-1.209), pyrexia during pregnancy (OR = 2.43; 95%CI: 1.358-4.345), fairly good (OR = 1.836; 95%CI: 1.009-3.340), fairly bad (OR = 3.919; 95%CI:2.072-7.414) and very bad postpartum sleep quality (OR = 9.18; 95%CI: 2.335-36.241) were associated with increased risk of PPD (compared to very good postpartum sleep quality). In path analysis model, antenatal EPDS score (standardized total β = 0.173) and pyrexia during pregnancy (standardized total β = 0.132) had both direct and indirect effects (the impact on outcome variables needs to be determined through other variables) on PPD. Sleep quality after delivery (standardized β = 0.226) and one-child family (standardized β = 0.088) had direct effects only on PPD.ConclusionThe results from our study indicated that more than 50% of the women who experienced antepartum depression would subsequently develop PPD. Depressive symptoms and pyrexia during pregnancy increase PPD scores, and these effects were in part mediated via poor sleep quality during the postpartum period.

Project description:BackgroundThe aim of this study was to improve our understanding of the underlying mechanisms in the maintenance of depression. We examined attentional bias (AB) for negative and positive adjectives and general threat words in strictly-defined clinical groups of participants with pure Major Depressive Disorder (MDD) without a history of anxiety disorders (AD), mixed MDD and AD, and remitted participants.MethodWe investigated both stimulus specificity and time course of AB in these groups, adopting a cross-sectional design. Data were drawn from the large scale Netherlands Study of Depression and Anxiety (NESDA), from which we selected all participants with pure current MDD without a history of AD (n = 29), all participants with current MDD and co-morbid AD(s) (n = 86), all remitted MDD participants (n = 294), and a comparison group without (a history of) MDD or ADs (n = 474). AB was measured with an Exogenous Cueing Task covering short and long presentation times (500 and 1250 ms) and 4 stimulus types (negative, positive, threat, neutral).ResultsBoth traditional and trial level (dynamic) AB scores failed to show an AB for negative adjectives in participants with MDD or mixed MDD/AD. Specifically for long duration trials (1250 ms), remitted participants showed a larger AB traditional score (albeit the actual score still being negative) than the comparison group. The mixed MDD/AD group showed a higher trial-level AB score away from positive adjectives (1250 ms) than the comparisons. In addition, the mixed MDD/AD group showed higher and more variable trial-level AB scores away from short and towards longer presented general threat words together with a non-significant tendency to show less negative traditional AB scores for threat trials (500 ms) than the comparison group.ConclusionsAll in all, the findings do not corroborate the view that an AB towards negative or away from positive adjectives is critically involved in currently depressed individuals. Yet, the relatively high (less negative) AB score for negative adjectives in remitted individuals points to the possibility that an AB for negative information may be involved as a risk factor in the recurrence of MDD.

Project description:ImportanceThe lack of standards in methods to reduce bias for clinical algorithms presents various challenges in providing reliable predictions and in addressing health disparities.ObjectiveTo evaluate approaches for reducing bias in machine learning models using a real-world clinical scenario.Design, setting, and participantsHealth data for this cohort study were obtained from the IBM MarketScan Medicaid Database. Eligibility criteria were as follows: (1) Female individuals aged 12 to 55 years with a live birth record identified by delivery-related codes from January 1, 2014, through December 31, 2018; (2) greater than 80% enrollment through pregnancy to 60 days post partum; and (3) evidence of coverage for depression screening and mental health services. Statistical analysis was performed in 2020.ExposuresBinarized race (Black individuals and White individuals).Main outcomes and measuresMachine learning models (logistic regression [LR], random forest, and extreme gradient boosting) were trained for 2 binary outcomes: postpartum depression (PPD) and postpartum mental health service utilization. Risk-adjusted generalized linear models were used for each outcome to assess potential disparity in the cohort associated with binarized race (Black or White). Methods for reducing bias, including reweighing, Prejudice Remover, and removing race from the models, were examined by analyzing changes in fairness metrics compared with the base models. Baseline characteristics of female individuals at the top-predicted risk decile were compared for systematic differences. Fairness metrics of disparate impact (DI, 1 indicates fairness) and equal opportunity difference (EOD, 0 indicates fairness).ResultsAmong 573 634 female individuals initially examined for this study, 314 903 were White (54.9%), 217 899 were Black (38.0%), and the mean (SD) age was 26.1 (5.5) years. The risk-adjusted odds ratio comparing White participants with Black participants was 2.06 (95% CI, 2.02-2.10) for clinically recognized PPD and 1.37 (95% CI, 1.33-1.40) for postpartum mental health service utilization. Taking the LR model for PPD prediction as an example, reweighing reduced bias as measured by improved DI and EOD metrics from 0.31 and -0.19 to 0.79 and 0.02, respectively. Removing race from the models had inferior performance for reducing bias compared with the other methods (PPD: DI = 0.61; EOD = -0.05; mental health service utilization: DI = 0.63; EOD = -0.04).Conclusions and relevanceClinical prediction models trained on potentially biased data may produce unfair outcomes on the basis of the chosen metrics. This study's results suggest that the performance varied depending on the model, outcome label, and method for reducing bias. This approach toward evaluating algorithmic bias can be used as an example for the growing number of researchers who wish to examine and address bias in their data and models.

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Different patterns of attentional bias in antenatal and postpartum depression.

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Different patterns of attentional bias in antenatal and postpartum depression.

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