ArticleDepression and anxiety2026
Symptom Structure of Depression and Anxiety in Mothers Following Child Loss: A Network and Bayesian Graph Analysis.
Article in Depression and anxiety, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Symptom Structure of Depression and Anxiety in Mothers Following Child Loss: A Network and Bayesian Graph Analysis.Depression and anxiety · 2026Article
- Depression and Artificial Intelligence Anxiety Among Chinese University Students: A Bayesian Network Analysis.Depression and anxiety · 2026Article
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6 authors.
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Abstract
Background: Bereavement, especially following the loss of a child, is a profoundly distressing life event associated with heightened risks of depression and anxiety. However, limited evidence exists on the symptom-level structure and statistical interrelations of these conditions among bereaved individuals, particularly in low- and middle-income countries (LMICs). Methods: Using nationally representative data from the 2022 Bangladesh Demographic and Health Survey (BDHS), we identified 2276 bereaved mothers. We applied psychological network analysis to estimate the partial correlation network structure of nine-item Patient Health Questionnaire (PHQ-9) depressive and seven-item Generalized Anxiety Questionnaire (GAD-7) anxiety symptoms. Centrality, predictability, and bridge metrics were computed. Network comparison tests (NCTs) assessed structural invariance across bereavement subgroups. Bayesian directed acyclic graphs (DAGs) were used to explore conditional dependency patterns and probabilistic edge orientations among symptoms. Results: The prevalence of probable major depressive disorder (MDD) was 6.69% (95% CI: 5.52-7.86). Prevalence estimates were comparable by bereavement recency, with overlapping confidence intervals among mothers bereaved within the past 3 years (5.34%, 95% CI: 3.06-9.15) and those bereaved more than 3 years earlier (6.83%, 95% CI: 5.69-8.19). The symptom network revealed interconnected domains corresponding to anxiety and depression symptoms. Trouble relaxing and psychomotor disturbance showed the highest strength centrality, while suicidal ideation exhibited the highest predictability in the network. Bridge centrality analysis identified feeling afraid, sadness, irritability, and psychomotor disturbance as the strongest cross-domain connectors linking anxiety and depression symptoms. Bayesian DAG analysis indicated strong conditional dependencies among worry-related anxiety symptoms, while psychomotor disturbance showed a strong conditional association with suicidal ideation. Conclusions: This study offers novel symptom-level insights into bereavement-related anxiety and depression among mothers in Bangladesh. The observed symptom patterns are consistent with enduring depressive and anxiety symptoms rather than acute grief alone. Symptom-focused approaches targeting central and bridge symptoms may support more efficient screening and scalable intervention strategies in bereaved populations.
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