Are the 1976–1985 birth cohorts heavier drinkers? Age‐period‐cohort analyses of the <scp>N</scp>ational <scp>A</scp>lcohol <scp>S</scp>urveys 1979–2010
Bibliographic record
Abstract
AIMS: To estimate age-period-cohort models predicting alcohol volume, heavy drinking and beverage-specific alcohol volume in order to evaluate whether the 1976-1985 birth cohorts drink relatively heavily. DESIGN: Data from seven cross-sectional surveys of the USA conducted between 1979 and 2010 were utilized in negative binomial generalized linear models of age, period and cohort effects predicting alcohol measures. SETTING: General population surveys of the USA. PARTICIPANTS: Thirty-six thousand four hundred and thirty-two US adults (aged 18 years or older). MEASUREMENTS: Monthly number of alcohol drinks, beer, wine and spirits drinks, and days drinking five or more drinks in the past year derived from beverage-specific graduated frequency questions. FINDINGS: Relative to the reference 1956-60 birth cohort, men in the 1976-1980 cohort for were found to consume more alcohol [incidence rate ratio (IRR) = 1.222: confidence interval (CI) 1.07-1.39) and to have more 5+ days (the number of days having five or more drinks) (IRR = 1.365: CI 1.09-1.71) as were men in the 1980-85 cohort for volume (IRR = 1.284: CI 1.10-1.50) and 5+ days (IRR = 1.437: CI 1.09-1.89). For women, those in the 1980-85 cohort were found to have higher alcohol volume (IRR = 1.299: CI 1.07-1.58) and more 5+ days (IRR = 1.547: CI 1.01-2.36). Beverage-specific models found different age patterns of volume by beverage with a flat age pattern for both genders' spirits and women's wine, an increasing age pattern for men's wine and a declining age pattern from those in their early 20s for beer. CONCLUSIONS: In the USA, men born between 1976 and 1985, and women born between 1981 and 1985 have higher alcohol consumption than in earlier or later years.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".