What can lifespan variation reveal that life expectancy hides? Comparison of five high-income countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: In most countries, life expectancy at birth (e0) has improved for many decades. Recently, however, progress has stalled in the UK and Canada, and reversed in the USA. Lifespan variation, a complementary measure of mortality, increased a few years before the reversal in the USA. To assess whether this measure offers additional meaningful insights, we examine what happened in four other high-income countries with differing life expectancy trends. DESIGN: We calculated life disparity (a specific measure of lifespan variation) in five countries -- USA, UK, France, Japan and Canada -- using sex- and age specific mortality rates from the Human Mortality Database from 1975 to 2017 for ages 0--100 years. We then examined trends in age-specific mortality to identify the age groups contributing to these changes. SETTING: USA, UK, France, Japan and Canada. PARTICIPANTS: aggregate population data of the above nations. MAIN OUTCOME MEASURES: Life expectancy at birth, life disparity and age-specific mortality. RESULTS: The stalls and falls in life expectancy, for both males and females, seen in the UK, USA and Canada coincided with rising life disparity. These changes may be driven by worsening mortality in middle-age (such as at age 40). France and Japan, in contrast, continue on previous trajectories. CONCLUSIONS: Life disparity is an additional summary measure of population health providing information beyond that signalled by life expectancy at birth alone.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it