Progress Stalled? The Uncertain Future of Mortality in High‐Income Countries
Bibliographic record
Abstract
Abstract Steady and significant improvements in life expectancy have been a bright spot for human progress for the last century or more. Recently, this success has shown signs of faltering in some high‐income countries, where mortality improvements have slowed or even reversed since the early 2010s. Combined with the large mortality shock of the COVID‐19 pandemic, guaranteed forward progress feels less certain. We review mortality trends in high‐income countries since 2000 through the COVID‐19 pandemic. While deteriorating mortality in the United States has received the most attention, countries including the United Kingdom, Canada, the Netherlands, Greece, and Germany are also seeing slowdowns. Before COVID‐19, these slowdowns largely reflected stalling improvements in cardiovascular disease mortality and increases in deaths from external causes in young and midlife for the worst‐performing countries. We discuss prospects for the future of mortality in high‐income countries, including lingering impacts of the COVID‐19 pandemic, challenges and opportunities related to the obesity epidemic, and emerging reasons for both optimism and pessimism. While biological limits to increased life expectancy may eventually dominate long‐term trends, human‐made social factors are currently holding many countries back from already achievable best‐practice life expectancy and will be key to near‐term improvements.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".