Average Volume of Alcohol Consumption and All‐Cause Mortality in African Americans: The NHEFS Cohort
Bibliographic record
Abstract
AIM: To analyze the relationship between average volume of alcohol consumption and all-cause mortality in African Americans. DESIGN: Prospective cohort study--the NHANES Epidemiologic Follow-Up Study (NHEFS)--with baseline data collected 1971 through 1975 as part of the first National Health and Nutrition Examination Survey (NHANES I) and follow-up through 1992. PARTICIPANTS: The analytic data set consisted of 2054 African American men (n = 768) and women (n = 1,286), 25 to 75 years of age, who were followed for approximately 19 years. MEASUREMENT: Alcohol was measured with a quantity-frequency measure at baseline. OUTCOME: All-cause mortality. RESULTS: No J-shaped curve was found in the relationship between average volume of alcohol consumption and mortality for male or female African Americans. Instead, no beneficial effect appeared and mortality increased with increasing average consumption for more than one drink a day. The reason for not finding the J-shape in African Americans may be the result of the more detrimental drinking patterns in this ethnicity and consequently the lack of protective effects of alcohol on coronary heart disease. Taking into account sampling design did not substantially change the results from the models, which assumed a simple random sample. CONCLUSIONS: If this result can be confirmed in other samples, alcohol policy, especially prevention, should better incorporate patterns of drinking into programs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".