Can alcohol use explain the increase in educational inequalities in all‐cause mortality after 2010 in the USA? A three‐way interaction analysis
Bibliographic record
Abstract
BACKGROUND AND AIMS: In the United States, the educational gap in all-cause mortality and life expectancy has dramatically increased since 2010. This study investigated whether alcohol use has contributed to the increasing educational gap in mortality by testing the three-way interaction of alcohol use, education and period on all-cause mortality. DESIGN: Cohort study with 9 years' follow-up on average. SETTING: United States. PARTICIPANTS: 207 223 males and 255 833 females aged 25 years and older from the 2000-2018 National Health Interview Survey. MEASUREMENTS: The outcome was time to all-cause death or last presumed alive by 12/31/2019 based on the National Death Index. Three-way interaction effects between educational attainment (bachelor degree or more vs. high school degree or less), alcohol use (drinking >60 g/day in males and >40 g/day in females vs. lifetime abstinence) and period (after vs. before 2010) were investigated on the multiplicative and additive scales using Cox proportional hazards and Aalen's additive hazards models, respectively, with age as the time scale. Analyses were stratified by sex and adjusted for marital status, race and ethnicity, smoking status, body mass index, physical activity and self-rated health status. FINDINGS: During the follow-up period, 30 467 and 34 618 deaths occurred in males and females, respectively, with a pronounced educational gradient. In males, the differential vulnerability to high-level alcohol use by educational attainment substantially increased after 2010 than before 2010 on both multiplicative and additive scales. Specifically, the relative all-cause mortality risk associated with drinking above 60 g per day (vs. lifetime abstinence) in males with low vs. high education increased by 89% after 2010 compared with before 2010 [three-way interaction term: hazard ratio (HR) = 1.89, 95% confidence interval (CI) = 1.03-3.47, P = 0.04 from the Cox model]. This result was further supported by the Aalen's model, indicating that the educational difference in mortality risk linked to drinking above 60 g per day increased by 8.85 additional deaths per 1000 person-years (95% CI = 0.90-16.79, P = 0.029) after 2010. No change of differential vulnerability to alcohol use was found in females. CONCLUSIONS: Alcohol use appears to be a key element in the widening educational gap in all-cause mortality risk in males after 2010 in the United States.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".