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

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

American Journal of Health Promotion ◽

10.1177/0890117120968656 ◽

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.

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