No change in life expectancy: the devil is in the detail
Bibliographic record
Abstract
Abstract Background The rate of improvement in life expectancy in high income countries has slowed down over the past few years, and instances where life expectancy is lower than a year before are increasingly common. This paper aims to analyse changes in life expectancy over the last decade to better understand what causes and age groups contribute to the slowdown. Methods We use WHO mortality data by age and cause to construct life tables, and we use Arriaga decomposition method to analyse the contribution of specific causes and age groups to changes in life expectancy in Australia, Canada, France, Germany, Netherlands, United Kingdom and the United States of America. We look at the change between 2007-2012 and 2012-2017 (or latest available). Results All countries experienced a slowdown in life expectancy in the past 5 years (2012-2017), in comparison to the preceding period. Slowdown in under 65s was particularly pronounced, with younger age groups only contributing minimally (between 0.4 years for males in Germany and -0.4 years for males in the United States) to changes in life expectancy. Among people aged 65 and over, gains ranged between 0.05 years for females in France and 0.6 years for males in the Netherlands. Certain causes of death contributed negatively to change in life expectancy between 2012 and 2017, with notable increases in deaths from accidental poisonings in males (up to -0.09 year in the UK and Canada, and -0.34 in the US) and suicides (up to -0.08 year in Australia and -0.07 in the US). Conclusions While recent slowdown in life expectancy gains in high income countries is often attributed to lack of improvement in people of older ages, we show that, beyond this, there are increases in mortality in younger age groups from external causes, that contribute negatively to change in life expectancy in some countries. This pattern is of a particular concern, as deterioration in preventable mortality points to broader worsening of socio-economic climate. Key messages Improvements in life expectancy in high income countries slowed down markedly over the past few years, but contributing mortality patterns differ for age groups and causes of death across countries. Persistent increases in preventable mortality from certain external causes in younger age groups in Australia, Canada, US and UK point to broader deterioration of socio-economic climate.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".