US Exceptionalism? International Trends in Midlife Mortality
Bibliographic record
Abstract
Abstract Background Rising midlife mortality in the United States (US) has raised concerns, particularly the increase in “deaths of despair” (due to drugs, alcohol, and suicide). While life expectancy is also stalling in other countries such as the UK, whether midlife mortality is rising outside the US is not known. Methods We document trends in midlife mortality in the US, UK and a group of 16 high-income countries in Western Europe, Australia, Canada, New Zealand, and Japan, as well as 7 Central and Eastern European (CEE) countries from 1990-2019. We use annual mortality data from the World Health Organization Mortality Database to analyze sex and age-specific (25-44, 45-54, and 55-64) age-standardized death rates across 13 major cause-of-death categories. Findings US midlife mortality rates worsened since 1990 for several causes of death including drug- related, alcohol-related, suicide, metabolic disease, nervous system disease, respiratory disease, and infectious/parasitic diseases. Deaths due to homicide, transport accidents, and cardiovascular disease declined overall since 1990 but saw recent increases or stalling of improvements. Midlife mortality has also recently increased in the UK for 45-54-year-olds, and in Canada, Poland, and Sweden among 25-44-year-olds. Conclusion The US is increasingly falling behind not only high-income but also CEE countries heavily impacted by the post-Soviet mortality crisis of the 1990s. While levels of midlife mortality in the UK are substantially lower than in the US overall, there are signs that UK midlife mortality is worsening relative to the rest of Europe.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".