Prevalence of alcohol use disorder among individuals who binge eat: a systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Binge eating disorder (BED) is correlated with substance use. This study aimed to estimate the life-time prevalence of alcohol use disorder (AUD) among individuals with non-compensatory binge eating and determine whether their life-time prevalence of AUD is higher than in non-bingeing controls. DESIGN: A systematic search of databases (PubMed, Embase and Web of Science) for studies of adults diagnosed with BED or a related behavior that also reported the life-time prevalence of AUD was conducted. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol was followed. The protocol was registered on the International Prospective Register of Systematic Reviews (PROSPERO). SETTING: Studies originating in Canada, Sweden, the United Kingdom and the United States. PARTICIPANTS: Eighteen studies meeting the inclusion criteria were found, representing 69 233 individuals. MEASUREMENTS: Life-time prevalence of AUD among individuals with binge eating disorder and their life-time relative risk of AUD compared with individuals without this disorder. RESULTS: The pooled life-time prevalence of AUD in individuals with binge eating disorder was 19.9% [95% confidence interval (CI) = 13.7-27.9]. The risk of life-time AUD incidence among individuals with binge eating disorder was more than 1.5 times higher than controls (relative risk = 1.59, 95% CI = 1.41-1.79). Life-time AUD prevalence was higher in community samples than in clinical samples (27.45 versus 14.45%, P = 0.041) and in studies with a lower proportion of women (β = -2.2773, P = 0.044). CONCLUSIONS: Life-time alcohol use disorder appears to be more prevalent with binge eating disorder than among those without.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".