The relationship between alcohol intake and mortality in Whites, Blacks and Hispanics using the NHIS Mortality Linked Data Set
Bibliographic record
Abstract
We examined alcohol intake and all cause mortality in White, Black and Hispanic adults using the 1988 and 1990 National Health Interview Survey (NHIS). The NHIS and National Death Index are public use data sets with follow‐up for mortality through 2002. Alcohol intake was classified as: Lifetime abstainers (<12 drinks ever); Irregular abstainers (< 12 drinks in any 1 year, none in the past year); Former drinkers (12+ in 1 year but not in past year); and Current drinkers: <1 drink/week, 1–2 drinks/week, 3–6 drinks/week, 1 drink/day, and 2+ drinks/day. Proportional hazards analyses adjusted for BMI, age, smoking and education were conducted using SUDAAN (n=73,541). Compared to lifetime abstainers, an inverse association with mortality was observed in White men and women who consumed 1–2 drinks/week (p<0.01), and White men who consumed 3–6 drinks/week (p=0.05). Trends were similar, but not significant for Blacks or Hispanics. For current drinking quartiles, White men and women in the highest quartile were at greater risk for mortality than the lowest quartile, relative risk=1.18 (95% CI 1.04–1.33) and 1.24 (95% CI 1.07–1.43) respectively. No association was observed for Black men or Hispanic men and women; however, Black women in quartile 2 were at increased risk for mortality. Our findings suggest racial ethnic differences in the relationship of alcohol and mortality. Supported by NIAAA R21 AA015085‐01 (Crespo, PI)
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".