scholarly journals The affective neuroscience of socioeconomic status: implications for mental health

2020 ◽  
Vol 44 (5) ◽  
pp. 202-207 ◽  
Author(s):  
Yu Hao ◽  
Martha J. Farah
Keyword(s):  
Mental Health ◽  
Socioeconomic Status ◽  
Emotion Regulation ◽  
Mental Disorders ◽  
Emotional Processing ◽  
Science Research ◽  
Neural Mechanisms ◽  
Affective Neuroscience ◽  
Neuroscience Research ◽  
Cortical Regions

SummaryWe review basic science research on neural mechanisms underlying emotional processing in individuals of differing socioeconomic status (SES). We summarise SES differences in response to positive and negative stimuli in limbic and cortical regions associated with emotion and emotion regulation. We discuss the possible relevance of neuroscience to understanding the link between mental health and SES. We hope to provide insights into future neuroscience research on the etiology and pathophysiology of mental disorders relating to SES.

Prevalence of Mental Disorders by Socioeconomic Status in Australia: A Cross-Sectional Epidemiological Study

2020 ◽  
pp. 089011712096865
Author(s):  
Rubayyat Hashmi ◽  
Khorshed Alam ◽  
Jeff Gow ◽  
Sonja March
Keyword(s):  
Mental Health ◽  
Risk Factors ◽  
Socioeconomic Status ◽  
Mental Disorders ◽  
National Health Survey ◽  
Cross Sectional ◽  
Lower Odds ◽  
Socioeconomic Groups ◽  
Income Groups ◽  
Weighted Prevalence

Purpose: To present the prevalence of 3 broad categories of mental disorder (anxiety-related, affective and other disorders) by socioeconomic status and examine the associated socioeconomic risk factors of mental disorders in Australia. Design: A population-based, cross-sectional national health survey on mental health and its risk factors across Australia. Setting: National Health Survey (NHS), 2017-2018 conducted by the Australian Bureau of Statistics (ABS) Participants: Under aged: 4,945 persons, Adult: 16,370 persons and total: 21,315 persons Measures: Patient-reported mental disorder outcomes Analysis: Weighted prevalence rates by socioeconomic status (equivalised household income, education qualifications, Socio-Economic Index for Areas (SEIFA) scores, labor force status and industry sector where the adult respondent had their main job) were estimated using cross-tabulation. Logistic regression utilizing subsamples of underage and adult age groups were analyzed to test the association between socioeconomic status and mental disorders. Results: Anxiety-related disorders were the most common type of disorders with a weighted prevalence rate of 20.04% (95% CI: 18.49-21.69) for the poorest, 13.85% (95% CI: 12.48-15.35) for the richest and 16.34% (95% CI: 15.7-17) overall. The weighted prevalence rate for mood/affective disorders were 20.19% (95% CI: 18.63-21.84) for the poorest, 9.96% (95% CI: 8.79-11.27) for the richest, and 13.57% (95% CI: 12.99-14.17) overall. Other mental disorders prevalence were for the poorest: 9.07% (95% CI: 7.91-10.39), the richest: 3.83% (95% CI: 3.14-4.66), and overall: 5.93% (95% CI: 5.53-6.36). These patterns are also reflected if all mental disorders were aggregated with the poorest: 30.97% (95% CI: 29.15-32.86), the richest: 19.59% (95% CI: 18.02-21.26), and overall: 23.93% (95% CI: 23.19-24.69). The underage logistic regression model showed significant lower odds for the middle (AOR: 0.75, 95% CI: 0.53 -1.04, p < 0.1), rich (AOR: 0.71, 95% CI: 0.5-0.99, p < 0.05) and richest (AOR: 0.6, 95% CI: 0.41-0.89, p < 0.01) income groups. Similarly, in the adult logistic model, there were significant lower odds for middle (AOR: 0.84, 95% CI: 0.72-0.98, p < 0.05), rich (AOR: 0.73, 95% CI: 0.62-0.86, p < 0.01) and richest (AOR: 0.76, 95% CI: 0.63-0.91, p < 0.01) income groups. Conclusion: The prevalence of mental disorders in Australia varied significantly across socioeconomic groups. Knowledge of different mental health needs in different socioeconomic groups can assist in framing evidence-based health promotion and improve the targeting of health resource allocation strategies.

scholarly journals Effectiveness, emotion regulation training on mental health of mothers with mental disorders

2017 ◽  
Vol 2 (1) ◽  
pp. 17-23 ◽  
Author(s):  
Zolfaghar Abyar ◽  
Elham Rahimi ◽  
Alireza Mardmor ◽  
Saeid Ahmadi Nasrabad Sofla ◽  
Narges Zamani ◽  
...  
Keyword(s):  
Mental Health ◽  
Emotion Regulation ◽  
Mental Disorders

Social Networks and Internet Emotional Relationships on Mental Health and Quality of Life in Students: Structural Equation Modelling

2021 ◽  
Author(s):  
Fatemeh Aliverdi ◽  
zohreh mahmoodi ◽  
Zahra Mehdizadeh Tourzani ◽  
Leili Salehi ◽  
Mostafa Qorbani ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Mental Health ◽  
Social Networks ◽  
Socioeconomic Status ◽  
Mental Disorders ◽  
Causal Relationship ◽  
Negative Relationship ◽  
Direct Path ◽  
Emotional Relationships

Abstract Background: Social networks and relationships create a sense of belonging and social identity and therefore have a major effect on mental health and quality of life, especially in young people. The present study was conducted to determine the predictor role of social networks and Internet emotional relationships on mental health and quality of life in students. Methods: The present cross-sectional study was conducted in 2021 on 350 students at Alborz University of Medical Sciences selected by convenience sampling. Data were collected using five questionnaires: Socioeconomic Status, Social Networks, Internet Emotional Relationships Mental Health, Quality of Life and a checklist of demographic details. Data were analyzed in SPSS-25, PLS-3, and Lisrel-8.8.Results: According to the path analysis results, mental health had the most significant positive causal relationship with Internet emotional relationships in the direct path (B=0.22) and the most negative relationship with socioeconomic status (B=-0.09). Mental health was assessed using DASS-21, in which higher scores mean higher mental disorders. Quality of life had the highest negative causal relationship with the DASS-21 score in the direct path (B=-0.26) and the highest positive relationship with socioeconomic status in the indirect path (B=0.023). The mean duration of using social networks (B=-0.067) and Internet emotional relationships (B=-0.089) had the highest negative relationship with quality of life.Conclusion: The use of the Internet and virtual networks, Internet emotional relationships and unfavourable socioeconomic status were associated with mental disorders and reduced quality of life in the students. Since students are the future of any country, it is necessary for policymakers to further address this group and their concerns.

scholarly journals Adult attachment and mental health: The role of emotion regulation

2018 ◽  
Vol 2 (9) ◽  
Author(s):  
Zeinab Mortazavizadeh ◽  
Simon Forstmeier
Keyword(s):  
Mental Health ◽  
Emotion Regulation ◽  
Mental Disorders ◽  
Attachment Style ◽  
Adult Attachment ◽  
Insecure Attachment ◽  
Clinical Samples ◽  
Published Data ◽  
Increased Risk

Attachment style is one of the most significant driving forces across the lifespan as identified by a variety of studies. Many studies have suggested that there might be an association between attachment and mental health. In a different thread of research, studies provided evidence that dysfunctional emotion regulation has a potential impact on an individual’s mental health. The present review has the purpose to examine available papers on the association of adult attachment with mental health with particular emphasis on the role of emotion regulation in this association. A database search was conducted for published data in Psych INFO, Elsevier and Google Scholar by combining search terms: ‘adult attachment’, ‘mental health, diseases or illnesses’ and ‘emotion regulation’. A total of 182 studies were identified and screened, and finally 19 studies met inclusion criteria. Results of this review revealed a moderate association between insecure attachment and several mental disorders. Also, individuals with insecure attachment showed more difficulty in emotion regulation. However, some studies found no positive association between avoidant attachment and mental disease. This review suggests that individual with insecure attachment showed an increased risk for mental disorders and that and emotion dysregulation might mediate this relationship. However, most of studies had non-clinical samples which could affect result. Therefore, further research is needed to examine clinical samples regarding the attachment in adulthood and mental health and the role of emotion regulation to come up with more accurate results in the future.

Trait Worry and Neural Correlates of Emotion Regulation

2017 ◽  
Vol 225 (3) ◽  
pp. 214-222 ◽  
Author(s):  
Marie Kristin Neudert ◽  
Rudolf Stark ◽  
Laura Kress ◽  
Andrea Hermann
Keyword(s):  
Emotion Regulation ◽  
Emotional Processing ◽  
Cognitive Reappraisal ◽  
Neural Correlates ◽  
Neural Mechanisms ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Fmri Study ◽  
Negative Feelings ◽  
Healthy Females

Abstract. Pathological worrying is of high transdiagnostic relevance and is related to maladaptive emotion regulation processes. Dysfunctional emotion regulation and its underlying neural mechanisms might contribute to the maintenance of fear over time. Therefore, this functional magnetic resonance imaging (fMRI) study aims at investigating the association of trait worry with neural correlates of emotion regulation. Twenty-six healthy females were instructed to passively look at aversive pictures, to distract themselves with a neutral thought, or to down- and up-regulate negative feelings via cognitive reappraisal in response to repeatedly presented aversive pictures. Trait worry was not related to cognitive reappraisal but to distraction, which leads to a greater reduction of self-reported negative feelings and insula activation in individuals with higher trait worry. The current study indicates that the neural mechanisms underlying distraction seem to be altered in pathological worrying and may prevent adaptive emotional processing of aversive stimuli leading to the maintenance of fear.

Mental disorders and distress: Associations with demographics, remoteness and socioeconomic deprivation of area of residence across Australia

2015 ◽  
Vol 50 (12) ◽  
pp. 1169-1179 ◽  
Author(s):  
Joanne C Enticott ◽  
Graham N Meadows ◽  
Frances Shawyer ◽  
Brett Inder ◽  
Scott Patten
Keyword(s):  
Mental Health ◽  
Socioeconomic Status ◽  
Psychological Distress ◽  
Mental Disorders ◽  
National Health ◽  
Health Survey ◽  
National Survey ◽  
National Health Survey ◽  
Socioeconomic Disadvantage ◽  
Health And Wellbeing

Objectives: Australian policy-making needs better information on socio-geographical associations with needs for mental health care. We explored two national surveys for information on disparities in rates of mental disorders and psychological distress. Methods: Secondary data analysis using the 2011/2012 National Health Survey and 2007 National Survey of Mental Health and Wellbeing. Key data were the Kessler 10 scores in adults in the National Health Survey ( n = 12,332) and the National Survey of Mental Health and Wellbeing ( n = 6558) and interview-assessed disorder rates in the National Survey of Mental Health and Wellbeing. Estimation of prevalence of distress and disorders for sub-populations defined by geographic and socioeconomic status of area was followed by investigation of area effects adjusting for age and gender. Results: Overall, approximately one person in 10 reported recent psychological distress at high/very-high level, this finding varying more than twofold depending on socioeconomic status of area with 16.1%, 13.3%, 12.0%, 8.4% and 6.9% affected in the most to least disadvantaged quintiles, respectively, across Australia in 2011/2012. In the most disadvantaged quintile, the percentage (24.4%) with mental disorders was 50% higher than that in the least disadvantaged quintile (16.9%) in 2007, so this trend was less strong than for Kessler10 distress. Conclusion: These results suggest that disparities in mental health status in Australia based on socioeconomic characteristics of area are substantial and persisting. Whether considering 1-year mental disorders or 30-day psychological distress, these occur more commonly in areas with socioeconomic disadvantage. The association is stronger for Kessler10 scores suggesting that Kessler10 scores behaved more like a complex composite indicator of the presence of mental and subthreshold disorders, inadequate treatment and other responses to stressors linked to socioeconomic disadvantage. To reduce the observed disparities, what might be characterised as a ‘Whole of Government’ approach is needed, addressing elements of socioeconomic disadvantage and the demonstrable and significant inequities in treatment provision.

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