THE LINK BETWEEN EDUCATIONAL ATTAINMENT AND MORTALITY OVER TIME IN THE UNITED STATES
Bibliographic record
Abstract
This study shows the relationship between low, medium and high educational attainment and death rates based on adjusted risk ratios in the US adult population stratified by white and black, males and females over time (1986–2014). Mortality relative risks, adjusted for marital status, smoking and alcohol were calculated using a generalized linear model from NHIS data for three time periods. Death rates based on these adjusted risks were apportioned for different educational groups based on educational prevalence data The findings show that relative risk ratios for mortality which took into account age, smoking, alcohol and marital status resulted in lower risk ratios compared to unadjusted risk rates for all demographic groups in the study. The probability of death based on adjusted mortality risk ratios for white males decreased over time for all educational levels. The difference at aged 65 between lowest and highest educated increased over the past decade to return to rates closer to that in the late 1980s. The difference between low and high educated white males in 2000–2014 translate to a difference of a decade of life-expectancy. The probability of death based on adjusted risk ratios also decreased over this time period for black men, black women and white women. Compared to white men, all these groups showed an improvement in terms of reduction in death rates differences between 1992–1999 and 2000–2014. However, for all time periods, black males continue to have the largest differential in absolute percentage terms in probability of death compared to any other groups. Conclusion Lower-educated white males in the United States lose up to a decade in life-expectancy compared to high-educated white males.
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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 | 0.003 |
| 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 | 0.001 |
| 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".