Alcoholic beverage types consumed by population subgroups in the United States: Implications for alcohol policy to address health disparities
Bibliographic record
Abstract
INTRODUCTION: We aimed to identify alcoholic beverage types more likely to be consumed by demographic subgroups with greater alcohol-related health risk than others, mainly individuals with low socio-economic status, racial/ethnic minority status and high drinking levels. METHODS: Fractional logit modelling was performed using a nationally representative sample of US adult drinkers (analytic N = 37,657) from the National Epidemiologic Survey on Alcohol and Related Conditions Waves 2 (2004-2005) and 3 (2012-2013). The outcomes were the proportions of pure alcohol consumed as beer, wine, liquor and coolers (defined as wine-/malt-/liquor-based coolers, hard lemonade, hard cider and any prepackaged cocktails of alcohol and mixer). RESULTS: Adults with lower education and low or medium income were more likely to drink beer, liquor and coolers, while those with a 4-year college/advanced degree and those with high income preferred wine. Excepting Asian adults, racial/ethnic minority adults were more likely to drink beer (Hispanics) and liquor (Blacks), compared with White adults. High- or very-high-level drinkers were more likely to consume liquor and beer and less likely to consume wine (and coolers), compared with low-level drinkers. High-level and very-high-level drinkers, who were less than 10% of all drinkers, consumed over half of the total volume of beer, liquor and coolers consumed by all adults. DISCUSSION AND CONCLUSIONS: Individuals with low socio-economic status, racial/ethnic minority status or high drinking level prefer liquor and beer. As alcohol taxes, sales and marketing practices all are beverage-specific, targeted approaches to reduce consumption of these beverages, particularly among individuals with these profiles, are warranted.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".