Burden of Disease Associated with Alcohol Use Disorders in the United States
Bibliographic record
Abstract
BACKGROUND: Alcohol use disorders (AUD) have long been considered to be some of the most disabling mental disorders; however, empirical data on the burden of disease associated with AUD have been sparse. The objective of this article is to quantify the burden of disease (in disability-adjusted life years [DALYs] lost), deaths, years of life lost due to premature mortality (YLL), and years of life lost due to disability (YLD) associated with AUD for the United States in 2005. METHODS: Statistical modeling was based on epidemiological indicators derived from the National Epidemiologic Survey on Alcohol and Related Conditions. Formal consistency analyses were applied. Risk relations were taken from recent meta-analyses and the disability weights from the burden of disease study of the National Institutes of Health. Monte Carlo simulations were used to derive confidence intervals. All analyses were performed by sex and age. Sensitivity analyses were undertaken on key indicators. RESULTS: In the United States in 2005, 65,000 deaths, 1,152,000 YLL, 2,443,000 YLD, and 3,595,000 DALYs were associated with AUD. For individuals 18 years of age and older, AUD were associated with 3% of all deaths (5% for men and 1% for women), and 5% of all YLL (7% for men and 2% for women). The majority of the burden of disease associated with AUD stemmed from YLD, which accounted for 68% of DALYs associated with AUD (66% for men and 74% for women). The youngest age group had the largest proportion of DALYs associated with AUD stemming from YLD. CONCLUSIONS: Using data from a large representative survey (checked for consistency) and by combining these data with the best available evidence, we found that AUD were associated with a larger burden of disease than previously estimated. To reduce this disease burden, implementation of prevention interventions and expansion of treatment are necessary.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".