What can lifespan variation tell us about trends in life expectancy in high income countries?
Bibliographic record
Abstract
Abstract Building on the findings of presentations 1 and 2, we turn to two further measures of population health: life expectancy at birth and lifespan variation. Life expectancy at birth provides a single figure that captures the overall mortality experience of a nation, and, in the absence of data artefact, a wide-scale environmental event such as war or natural disaster, a disease epidemic or mass migration, life expectancy can be expected to continue to improve in HICs. Concurrently lifespan variation, which measures the average gap between the age at death of an individual and the remaining life expectancy at that age, should decrease as life expectancy increases. Recent analysis of life expectancy improvements in HICs by the Office for National Statistics, using Human Mortality Database data, found that while Japan continues to see improvements, the UK and the USA fell to the bottom of the rankings. Economically, both the UK and Japan have experienced 'lost decades' of poor economic growth, in 1990s and 2010s respectively. Yet, while Japan continued to see life expectancy improvements, in the UK life expectancy stalled, and both countries saw an increase in lifespan variation. In this presentation, we will present the analysis of lifespan variation of 5 HICs: the USA, where life expectancy has declined, the UK, where gains in life expectancy have trailed behind those in other industrialised countries, Japan, which has seen sustained progress, and France and Canada, neighbours of the UK and USA respectively, which lie in the middle. We will examine what can be determined from these measures over periods of poor economic growth, and the implications for achieving 'sustainable growth'.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".