Measuring Recent Apparent Declines In Longevity: The Role Of Increasing Educational Attainment
Bibliographic record
Abstract
Independent researchers have reported an alarming decline in life expectancy after 1990 among US non-Hispanic whites with less than a high school education. However, US educational attainment rose dramatically during the twentieth century; thus, focusing on changes in mortality rates of those not completing high school means looking at a different, shrinking, and increasingly vulnerable segment of the population in each year. We analyzed US data to examine the robustness of earlier findings categorizing education in terms of relative rank in the overall distribution of each birth cohort, instead of by credentials such as high school graduation. Estimating trends in mortality for the bottom quartile, we found little evidence that survival probabilities declined dramatically. We conclude that widely publicized estimates of worsening mortality rates among non-Hispanic whites with low socioeconomic position are highly sensitive to how educational attainment is classified. However, non-Hispanic whites with low socioeconomic position, especially women, are not sharing in improving life expectancy, and disparities between US blacks and whites are entrenched. Findings underscore the urgency of an agenda to equitably disseminate new medical technologies and to deepen knowledge of social determinants of health and how that knowledge can be applied, to promote the objective of achieving population health equity.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".