Investigating the Relationship between Socio-economic Status and Mental Illness: A Comprehensive Analysis of 1,389,125 Individuals through Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Background: The global prevalence of mental disorders highlights the urgency of understanding their determinants. Socio-economic status (SES), encompassing income, education and occupation, plays a crucial role in shaping mental health outcomes. Research consistently demonstrates a strong association between lower SES and an increased risk of mental disorders. Comprehensive synthesis through systematic review and meta-analysis is essential for informing targeted interventions to address socio-economic disparities and promote mental well-being. Methods: We searched PubMed, the Cochrane Library and the Web of Science. Studies examining the association between SES indicators (e.g., education, income and occupation) and mental disorders were included. Quality assessment and risk-of-bias evaluations were conducted using the Newcastle–Ottawa Scale. Statistical analyses were performed to calculate pooled hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between SES and mental disorders. Results: SES was associated with higher mental disorders. Educational level, income level and occupational level were examined for their association with mental disorders, all showing statistically significant correlations (pooled HR: 1.17 [95% CI: 1.2, 1.23]; 1.08 [95% CI: 1.05, 1.1]; 1.66 [95% CI: 1.59, 1.74], respectively). Despite high heterogeneity, the overall effects remained significant, as demonstrated in the sensitivity analysis. Conclusion: This systematic review and meta-analysis provide evidence of the association between SES and mental disorders, emphasising the importance of addressing socio-economic disparities in mental health. The findings underscore the need for targeted interventions to promote mental well-being and equitable access to mental healthcare services. By identifying actionable strategies to address social determinants of mental health, this study contributes to efforts aimed at reducing health inequities and fostering resilient communities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.036 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".