Trends in life expectancy and age-specific mortality in England and Wales, 1970–2016, in comparison with a set of 22 high-income countries: an analysis of vital statistics data
Bibliographic record
Abstract
BACKGROUND: Since 2010, the rate of improvement in life expectancy in the UK has slowed. We aimed to put this trend in the context of changes over the long term and in relation to a group of other high-income countries. METHODS: We compared sex-specific trends in life expectancy since 1970 and age-specific mortality in England and Wales with median values for 22 high-income countries (in western Europe, Australia, Canada, New Zealand, Japan, and the USA). We used annual mortality data (1970-2016) from the Human Mortality Database. FINDINGS: Until 2011-16, male life expectancy in England and Wales followed the median life expectancy of the comparator group. By contrast, female life expectancy was below the median and is among the lowest of the countries considered. In 2011-16, the rate of improvement in life expectancy slowed sharply for both sexes in England and Wales, and slowed more moderately in the comparator group because of negative trends in all adult age groups. This deceleration resulted in a widening gap between England and Wales and the comparators from 2011 onwards. Since the mid-2000s, for the first time, mortality rates in England and Wales among people aged 25-50 years were appreciably higher than in the comparator group. INTERPRETATION: Although many countries have seen slower increases in life expectancy since 2011, trends in England and Wales are among the worst. The poor performance of female life expectancy over the long-term is in part driven by the relative timing of the smoking epidemic across countries. The previously overlooked higher mortality among young working-age adults in England and Wales relative to other countries deserves urgent attention. FUNDING: None.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".