Age, period and cohort influences on beer, wine and spirits consumption trends in the US National Alcohol Surveys
Bibliographic record
Abstract
AIMS: To estimate the separate influences of age, period and cohort on the consumption of beer wine and spirits in the United States. DESIGN: Linear age-period-cohort models controlling for demographic change with extensive specification testing. Setting US general population 1979-2000. MEASUREMENTS: Monthly average of past-year consumption of beer, wine and spirits in five National Alcohol Surveys. Findings The strongest cohort effects are found for spirits; cohorts born before 1940 are found to have significantly higher consumption than those born after 1946, with especially high spirits consumption for men in the pre-1930s cohorts. Significant cohort effects are also found for beer with elevated consumption in the 1946-65 cohorts for men but in the pre-1940 cohorts for women. Significant negative effects of age are found for beer and spirits consumption, although not for wine. Significant period effects are found for men's beer and wine consumption and for women's spirits consumption. Increased educational attainment in the population over time is associated with reduced beer consumption and increased wine consumption. CONCLUSIONS: Changing cohort demographics are found to have significant effects on beverage-specific consumption, indicating the importance of controlling for these effects in the evaluation of alcohol policy effectiveness and the potential for substantial improvement in the forecasting of future beverage-specific consumption trends, alcohol dependence treatment demand and morbidity and mortality outcomes.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".