The potential impact of increased treatment rates for alcohol dependence in the United Kingdom in 2004
Bibliographic record
Abstract
BACKGROUND: Alcohol consumption has been linked to a considerable burden of disease in the United Kingdom (UK), with most of this burden due to heavy drinking and Alcohol Dependence (AD). However, AD is undertreated in the UK, with only 8% of those individuals with AD being treated in England and only 6% of those individuals with AD being treated in Scotland. Thus, the objective of this paper is to quantify the deaths that would have been avoided in the UK in 2004 if the treatment rate for AD had been increased. METHODS: Data on the prevalence of AD, alcohol consumption, and mortality were obtained from the Adult Psychiatric Morbidity Survey, the Global Information System on Alcohol and Health, and the 2004 Global Burden of Disease study respectively. Data on the effectiveness of pharmacological treatment and Motivational Interviewing/Cognitive Behavioural Therapy were obtained from Cochrane reviews and meta-analyses. Simulations were used to model the number of deaths under different treatment scenarios. Sensitivity analyses were performed to model the effects of Brief Interventions and to examine the effect of using AD prevalence data obtained from the National Institute for Health and Clinical Excellence. RESULTS: In the UK, 320 female and 1,385 male deaths would have been avoided if treatment coverage of pharmacological treatment had been increased to 20%. This decrease in the number of deaths represents 7.9% of all alcohol-attributable deaths (7.0% of all alcohol-attributable deaths for women and 8.1% of all alcohol-attributable deaths for men). If we used lower AD prevalence rates obtained from the National Institute for Health and Clinical Excellence, then treatment coverage of pharmacological treatment in hospitals for 20% of the population with AD would have resulted in the avoidance of 529 deaths in 2004 (99 deaths avoided for women and 430 deaths avoided for men). CONCLUSIONS: Increasing AD treatment in the UK would have led to a large number of deaths being avoided in 2004. Increased AD treatment rates not only impact mortality but also impact upon the large burden of disability and morbidity attributable to AD, as well as the associated social and economic burdens.
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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 | 0.010 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".