Why Employers Must Focus on the Social Determinants of Mental Health

Socio-demographic factors and mental health trajectories in Australian children and primary carers: Implications for policy and intervention using latent class analysis

Children's mental health status (MHS) is frequently influenced by their primary carers (PCs), underscoring the significance of monitoring disparities longitudinally. This research investigated the association between socio-demographic clusters and mental health trajectories among children and their PCs over time. Data from waves 6-9c2 of the Longitudinal Study of Australian Children (LSAC) were analyzed using Latent Class Analysis (LCA) to identify four socio-demographic classes among children aged 10-11 years at wave 6. Multinomial logistic regression and predictive marginal analysis explored associations between classes and mental health outcomes. PCs in Class 4 (disadvantaged and separated families with indigenous children) exhibited higher odds of borderline and abnormal MHS compared to Class 1 (prosperous and stable working families) across all waves. However, while MHS of PCs' impacted children consistently, the association with socio-demographic classes was significant only in wave 6. Class 4 children had elevated risks of mental illness compared to Class 1, while Class 3, characterized by educated working mothers, had lower risks. Reducing mental health risks entails addressing socio-economic disparities, supporting stable family structures, and offering tailored interventions like counseling and co-parenting support. Longitudinal monitoring and culturally sensitive approaches are crucial for promoting mental well-being across diverse groups.

Depressive symptoms and their correlates among predoctoral dental students in the United States

OBJECTIVE

To investigate the prevalence of self-reported depressive symptoms among predoctoral dental students in the United States and examine potential correlates.

METHODS

A survey was emailed to all 66 dental schools in the United States, inviting them to distribute it to their predoctoral students. Depressive symptoms were assessed using the Patient Health Questionnaire-9. Data collection occurred from February to April 2020. Multivariable ordinal logistic regression was used to assess associations between demographic variables and depressive symptom severity category, adjusting for potential confounding.

RESULTS

Of an estimated 25,000 predoctoral dental students at the 66 schools, 631 students from 21 schools completed the survey. A total of 24.1% were categorized as having minimal or no depressive symptoms, 33.6% as having mild depressive symptoms, and 42.3% as having moderate, moderately severe, or severe depressive symptoms. Gender (p = 0.015) and race/ethnicity (p = 0.002) were significant predictors of severity, adjusting for other variables. Students identifying as female had higher odds of self-reporting greater depressive severity symptoms compared with students identifying as male. Students identifying as African American/Black (non-Hispanic) and Asian/Pacific Islander had higher odds of self-reporting greater depressive severity symptoms compared with students identifying as White.

CONCLUSION

There is evidence of a high prevalence of depressive symptoms among predoctoral dental students in the United States. Demographic variables may be risk indicators within this population. Approaches to reduce depressive symptoms among US predoctoral dental students are needed.

The Intergenerational Impact of Structural Racism and Cumulative Trauma on Depression

Depression among individuals who have been racially and ethnically minoritized in the United States can be vastly different from that of non-Hispanic White Americans. For example, African American adults who have depression rate their symptoms as more severe, have a longer course of illness, and experience more depression-associated disability. The purpose of this review was to conceptualize how structural racism and cumulative trauma can be fundamental drivers of the intergenerational transmission of depression. The authors propose that understanding risk factors for depression, particularly its intergenerational reach, requires accounting for structural racism. In light of the profoundly different experiences of African Americans who experience depression (i.e., a more persistent course of illness and greater disability), it is critical to examine whether an emerging explanation for some of these differences is the intergenerational transmission of this disorder due to structural racism.

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

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.

Mental Illness Prevention and Mental Health Promotion: When, Who, and How

Classification as primary, secondary, or tertiary prevention is based on when during the course of disease the intervention is provided. Another approach to classification-as universal, selective, or indicated preventive interventions-relates to who receives the intervention. The social determinants of health framework also provides a guide to prevention, which requires changing both public policies and social norms. It also addresses the weaknesses of the first two approaches, such as persistent health inequities regarding who has access to preventive services. The social determinants framework is a guide to providing timely and targeted preventive interventions in a way that ensures equal access.