Educational attainment as a potential effect modifier of alcohol use and 100% alcohol‐attributable mortality in the United States—A longitudinal analysis of mortality linked survey data from 1997 to 2018
Bibliographic record
Abstract
AIMS: To measure effects between educational attainment and alcohol use as a driver of unequal alcohol-attributable mortality. DESIGN: Nation-wide cohort study using a longitudinal design, linking data from the 1997-2018 National Health Interview Survey to mortality data of the National Death Index in 2019. The study has an average follow-up time of 10.7 years (SD = 6.4). SETTING: United States. PARTICIPANTS: Nationally representative sample of adults aged 25 years and older. MEASUREMENTS: The outcome was time to 100% alcohol-attributable mortality, censored or last presumed alive by 31 December 2019. Socioeconomic status was operationalized via educational attainment; alcohol use was self-reported and operationalized using a categorical measure with lifetime abstainers as reference category. FINDINGS: Of a total of 562 632 adults, 901 (635 men and 266 women) died during follow-up from a 100% alcohol-attributable cause of death [15 per 100 000 person years (PY)]. We found a strong interaction effect between low education and Category III alcohol use (>60 g and >40 g per day for men and women, respectively), which was of additive nature as shown by the Aalen's additive hazards model, with 83.68 additional deaths per 100 000 PY (95% confidence interval = 16.48-150.87) found in individuals with low education with Category III drinking compared with a situation when there was no interaction between the two risk factors. A large and statistically significant relative excess risk due to interaction (RERI) of 32.05 from the Cox model supported the interaction. For individuals with low education, the risk associated with Category III drinking was double that for those with high education. CONCLUSIONS: In the United States, people with combined low education and high alcohol consumption (>60 g/day for men, >40 g/day for women) appear to have a higher risk of 100% alcohol-attributable mortality compared with other combinations of educational attainment and drinking.
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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 | 0.003 | 0.005 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".