Age–period–cohort modelling of alcohol volume and heavy drinking days in the US National Alcohol Surveys: divergence in younger and older adult trends
Bibliographic record
Abstract
AIMS: The decomposition of trends in alcohol volume and heavy drinking days into age, period, cohort and demographic effects offers an important perspective on the dynamics of change in alcohol use patterns in the United States. DESIGN: The present study utilizes data from six National Alcohol Surveys conducted over the 26-year period between 1979 and 2005. Setting United States. MEASUREMENTS: Alcohol volume and the number of days when five or more and eight or more drinks were consumed were derived from overall and beverage-specific graduated frequency questions. RESULTS: Trend analyses show that while mean values of drinking measures have continued to decline for those aged 26 and older, there has been a substantial increase in both alcohol volume and 5+ days among those aged 18-25 years. Age-period-cohort models indicate a potential positive cohort effect among those born after 1975. However, an alternative interpretation of an age-cohort interaction where drinking falls off more steeply in the late 20s than was the case in the oldest surveys cannot be ruled out. For women only, the 1956-60 birth cohort appears to drink more heavily than those born just before or after. Models also indicate the importance of income, ethnicity, education and marital status in determining these alcohol measures. CONCLUSIONS: Increased heavy drinking among young adults in recent surveys presents a significant challenge for alcohol policy and may indicate a sustained increase in future US alcohol consumption.
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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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".