The Social Cost of Major Depression. A Systematic Review
Bibliographic record
Abstract
Major depression (MD) is a major cause of disability and a significant public health problem due to strong physical and mental impairment, possible complications for patients (including suicides), serious social and working problems to the patient and his/her family. We provide an overview of the social cost of Major depression worldwide. We conducted a systematic literature review. Two search engines were queried. Screening of records and summary of evidence was performed by two researchers blindly. The review was conducted in accordance with the standards of the PRISMA guidelines. Twenty studies met the inclusion criteria. Despite the heterogeneity in terms of population, setting and estimation techniques, the studies showed that the largest share of the burden of disease is represented by indirect costs. Among direct healthcare costs, inpatient care represents the most significant item, followed by outpatient care. The average total direct cost of depression ranges between €508 and €24 069, depending on the jurisdiction where the analysis was run and the range of cost items included. Indirect costs range between €1963 and €27 364. Evidence on the cost of MD in some countries is currently lacking. A deeper understanding of the drivers of the economic burden of disease is a crucial starting point for studies concerned with the cost-effectiveness of new treatment strategies.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".