Alcohol Dependence, Co-occurring Conditions and Attributable Burden
Bibliographic record
Abstract
AIMS: Alcohol dependence is associated with high rates of co-occurring disorders which impact health-related quality of life (HRQoL) and add to the cost-of-illness. This study investigated the burden of alcohol dependence and associated co-occurring conditions on health and productivity. METHODS: A cross-sectional survey was conducted in eight European countries. Physicians (Psychiatrists and General Practitioners) completed patient record forms, which included assessment of co-occurring conditions, and patients completed matching self-completion forms. Drinking risk level (DRL) was calculated and the relationship between DRL, co-occurring conditions, work productivity, hospitalisations and rehabilitation stays was explored. RESULTS: Data were collected for 2979 alcohol-dependent patients (mean age 48.8 ± 13.6 years; 70% male). In total, 77% of patients suffered from moderate-to-severe co-occurring psychiatric and/or somatic conditions. High DRL was significantly associated with depression, greater work productivity losses, increased hospitalisations and rehabilitation stays. Co-occurring conditions were significantly associated with poorer HRQoL and decreased work productivity, with a statistical trend towards an increased frequency of rehabilitation stays. CONCLUSIONS: Alcohol-dependent patients manifest high rates of co-occurring psychiatric and somatic conditions, which are associated with impaired work productivity and HRQoL. The continued burden of illness observed in these already-diagnosed patients suggests an unmet need in both primary and secondary care.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".