Patterns of Adverse Childhood Experiences, Social Problem-Solving, and Mental Health Among Latina Immigrants

Cognitive factors as mediators of the relationship between childhood trauma and depression symptoms: the mediating roles of cognitive overgeneralisation, rumination, and social problem-solving

Background: Childhood trauma has negative immediate and long-term impacts on depression. Questions remain, however, regarding the cognitive factors influencing this relationship. This study aimed to investigate the role of three cognitive factors - cognitive overgeneralisation, rumination and social problem-solving - as mediating factors in the relationship between childhood trauma and symptoms of depression.Methods: We conducted a cross-sectional study in Iran from March to July 2023. Participants (N = 227; Mean age 32.44 ± 8.95 years) with depression completed measures of childhood trauma, depression, self-overgeneralisation, cognitive errors, memory specificity, rumination and social problem-solving. The conceptual model was assessed using structural equation modelling.Results: Structural equation modelling indicated that childhood trauma had a positive direct effect on depression symptoms. Childhood trauma had a positive indirect effect on depression symptoms through both self-overgeneralisation and rumination and a negative indirect effect on depression through effective social problem-solving strategies.Conclusions: The findings suggest increased exposure to childhood trauma may be associated with elevated depression and self-overgeneralisation, rumination, and effective social problem-solving strategies may play an important role in this relationship. These findings hold potential implications for those working with patients with depression and a history of childhood trauma.

Remote Delivery of the Cuidándome Telehealth Intervention for Self-Management of Depression and Anxiety Among Latina Immigrant Women: Randomized Controlled Trial

BACKGROUND

Growing evidence suggests that Latina immigrant survivors of adverse childhood experiences (ACEs) are at increased risk for developing and remaining with either depression or anxiety or both symptoms. This study examined the feasibility and acceptability of a telehealth intervention-Cuidándome (quee-DAN-doh-meh, "taking care of myself"). Cuidándome is a 10-week, patient-centered, trauma-informed intervention delivered by a trained facilitator that promotes self-management of depression and anxiety symptoms through improved problem-solving skills and strategies.

OBJECTIVE

The aim of this study was to examine the feasibility and acceptability of Cuidándome delivered remotely (via Zoom) with Latina immigrant ACE survivors with either depression or anxiety or both symptoms. We also estimated the effect sizes associated with the intervention on decreasing depression and anxiety symptoms and improving social problem-solving styles.

METHODS

We evaluated Cuidándome using a randomized controlled trial design. Latina immigrants (N=47) who had experienced at least 1 ACE and had at least mild depression or anxiety symptoms were randomized to Cuidándome or a comparison group delivered by trained facilitators. We assessed for changes in depression and anxiety symptoms as well as social problem-solving styles at baseline, post intervention, and 3- and 6-month follow-up.

RESULTS

Analyses indicated significant decreases over time within both Cuidándome and comparison groups for depression and anxiety symptoms and maladaptive problem-solving. The intervention effect was largest for anxiety; at 6-month follow-up, Cuidándome participants had significantly lower anxiety scores than the comparison group. In addition, we observed a greater average point reduction in depression symptoms at 6 months among Cuidándome participants (5.7 points) than in the comparison group (3.7 points).

CONCLUSIONS

A mental health program delivered via Zoom by a trained facilitator was feasible and acceptable to Latina immigrant women and can be beneficial for reducing anxiety and depression symptoms. More research is needed to assess the effectiveness of Cuidándome among a powered sample size of Latina immigrants.

TRIAL REGISTRATION

ISRCTN Registry ISRCTN16668518; https://www.isrctn.com/ISRCTN16668518.

Research and Diagnostic Algorithmic Rules (RADAR) for mood disorders, recurrence of illness, suicidal behaviours, and the patient's lifetime trajectory

The top-down Diagnostic and Statistical Manual/International Statistical Classification of Diseases categories of mood disorders are inaccurate, and their dogmatic nature precludes both deductive (as indisputable) and inductive (as top-down) remodelling of case definitions. In trials, psychiatric rating scale scores employed as outcome variables are invalid and rely on folk psychology-like narratives. Using machine learning techniques, we developed a new precision nomothetic model of mood disorders with a recurrence of illness (ROI) index, a new endophenotype class, namely Major Dysmood Disorder (MDMD), characterised by increased ROI, a more severe phenome, and more disabilities. Nonetheless, our previous studies did not compute Research and Diagnostic Algorithmic Rules (RADAR) to diagnose MDMD and score ROI, lifetime (LT), and current suicidal behaviours, as well as the phenome of mood disorders. Here, we provide rules to compute bottom-up RADAR scores for MDMD, ROI, LT and current suicidal ideation and attempts, the phenome of mood disorders, and the lifetime trajectory of mood disorder patients from a family history of mood disorders and substance abuse to adverse childhood experiences, ROI, and the phenome. We also demonstrate how to plot the 12 major scores in a single RADAR graph, which displays all features in a two-dimensional plot. These graphs allow the characteristics of a patient to be displayed as an idiomatic fingerprint, allowing one to estimate the key traits and severity of the illness at a glance. Consequently, biomarker research into mood disorders should use our RADAR scores to examine pan-omics data, which should be used to enlarge our precision models and RADAR graph.