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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".