Global Disparities in Premature Mortality
Bibliographic record
Abstract
Importance: Persistent disparities in mortality across countries suggest uneven improvements in living standards and access to life-extending health technologies, as well as context-specific obstacles. Studies have analyzed cross-country inequality in mortality but have not widely contextualized those disparities in terms of developmental progress relative to a frontier representing a level of mortality achievable with broad access to the best health-enhancing technology and living standards available. Objective: To examine probability of premature death (PPD)-defined as probability of dying before 70 years of age-across countries and regions, benchmarking progress as years behind the lowest country-level PPD (the frontier). Design and Setting: This cross-sectional study used aggregate-level data from the 2024 United Nations World Population Prospects and Human Mortality Database to calculate PPD across 7 global regions and the 30 most populous countries. Data were analyzed from May to September 2025. Main Outcome and Measures: The primary outcomes were PPD and the number of years behind the lowest country-level PPD. Results: The frontier PPD fell from 57% to 12% from 1900 to 2019. Sub-Saharan Africa's PPD in 2019 was 52%, corresponding to the 1916 frontier PPD. However, sub-Saharan Africa had converged toward the frontier by over 40 years since 2000, when it had a 65% PPD. China has been converging toward the frontier since 1970, having been 93 years behind the frontier PPD in 1970 (with a 60% PPD) and 35 years behind in 2019 (21% PPD). The US has diverged away from the frontier, having been 29 years behind in 1970 (38% PPD) and 38 years in 2019 (22% PPD). Of the regions included, the North Atlantic (Western Europe and Canada) was the closest to the frontier, being 13 years behind in 2019 (15% PPD). The US, Central and Eastern Europe, and sub-Saharan Africa were the furthest above the 2019 PPD Preston curve (ie, they had a greater PPD than predicted by their per capita gross domestic product). Conclusions and Relevance: In this cross-sectional study, disparities in PPD were likely to reflect major inequality in access to health-enhancing technologies and living standards, as well as context-specific obstacles. Technological and medical advancements leading to universal health benefits need to be rapidly and fairly disseminated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".