Association of alcohol use disorder on alcohol‐related cancers, diabetes, ischemic heart disease and death: a population‐based, matched cohort study
Bibliographic record
Abstract
BACKGROUND AND AIMS: High-risk alcohol consumption is associated with compromised health. This study aimed to compare the incidence of alcohol-related cancers, diabetes, ischemic heart disease (IHD) and mortality between those with and without an indication of alcohol use disorder (AUD). DESIGN: Retrospective, population-based, matched cohort study using data from the Manitoba Population Research Data Repository. Rates were modeled using generalized linear models with either negative binomial distribution or Poisson distribution and a log offset of person-years to account for each person's time to follow-up. SETTING: Manitoba, Canada. PARTICIPANTS: Individuals aged ≥ 12 years with a first indication of AUD (index date) between 1 April 1990 and 31 March 2015 were matched to five controls based on age, sex and geographical region at index. This study included 53 410 individuals with AUD and 264 857 matched controls. MEASUREMENTS: Adjusted rate ratios (aRR) and 95% confidence intervals (CI) were determined for each outcome from 5 years prior to and 20 years after AUD detection. FINDINGS: Alcohol-related cancers (aRR = 4.85, 95% CI = 3.88-6.07 and aRR = 1.85, 95% CI = 1.35-2.53 for men and women, respectively), diabetes (aRR = 1.74, 95% CI = 1.50-2.02 and aRR = 2.43, 95% CI = 2.20-2.68) and IHD (aRR = 3.59, 95% CI = 3.31-3.90 and aRR = 2.92, 95% CI = 2.50-3.41) peaked in the 1 year prior to index for those with AUD compared with matched controls. All-cause mortality (aRR = 3.31, 95% CI = 3.09-3.55 and aRR =3.61, 95% CI = 3.21-4.04) was highest in the year of index and remained higher among cases compared with controls throughout the 20-year follow-up. CONCLUSION: People with alcohol use disorder appear to have higher rates of adverse health outcomes in the year before alcohol use disorder recognition, and death at the time of alcohol use disorder recognition, compared with matched controls.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".