Reductions in Quality of Life Associated With Common Mental Disorders
Bibliographic record
Abstract
OBJECTIVE: Traditional burden-of-disease estimates often exclude personality disorders, which are associated with significant mortality and morbidity. The aim of this study was to estimate the health-related quality of life (HRQoL) and annual population-level quality-adjusted life-year (QALY) losses associated with different mental and physical health conditions. In particular, it sought to quantify the impact of personality disorders on quality of life, at an individual and population level. METHOD: This was a secondary analysis of data from the National Epidemiologic Survey on Alcohol and Related Conditions, a nationally representative survey of the US general population collected from 2001 to 2005 (N = 34,653). Health-related quality of life (measured using the Short-Form Health Survey-6D) was the main outcome of interest. Regression analysis assessed the impact of various mental (based on DSM-IV criteria) and physical health conditions on HRQoL scores, and this impact was combined with the prevalence of disorders to estimate the population-level burden of disease. RESULTS: Mood disorders were associated with the highest decrease in HRQoL scores, followed by strokes, psychotic illness, and arthritis (P < .01). The greatest annual population QALY losses were caused by arthritis, mood disorders, and personality disorders. CONCLUSIONS: Quality-adjusted life year losses associated with personality disorders ranked behind only mood disorders and arthritis. Personality disorders were associated with significant reductions in quality of life, despite the fact that they are often excluded from traditional burden of disease estimates.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".