Educational Disparities in Adult Mortality Across U.S. States: How Do They Differ, and Have They Changed Since the Mid-1980s?
Bibliographic record
Abstract
Adult mortality varies greatly by educational attainment. Explanations have focused on actions and choices made by individuals, neglecting contextual factors such as economic and policy environments. This study takes an important step toward explaining educational disparities in U.S. adult mortality and their growth since the mid-1980s by examining them across U.S. states. We analyzed data on adults aged 45-89 in the 1985-2011 National Health Interview Survey Linked Mortality File (721,448 adults; 225,592 deaths). We compared educational disparities in mortality in the early twenty-first century (1999-2011) with those of the late twentieth century (1985-1998) for 36 large-sample states, accounting for demographic covariates and birth state. We found that disparities vary considerably by state: in the early twenty-first century, the greater risk of death associated with lacking a high school credential, compared with having completed at least one year of college, ranged from 40 % in Arizona to 104 % in Maryland. The size of the disparities varies across states primarily because mortality associated with low education varies. Between the two periods, higher-educated adult mortality declined to similar levels across most states, but lower-educated adult mortality decreased, increased, or changed little, depending on the state. Consequently, educational disparities in mortality grew over time in many, but not all, states, with growth most common in the South and Midwest. The findings provide new insights into the troubling trends and disparities in U.S. adult mortality.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".