Halving premature death and improving quality of life at all ages: cross-country analyses of past trends and future directions
Bibliographic record
Abstract
BACKGROUND: Although death in old age is unavoidable, premature death-defined here as death before age 70 years-is not. To assess whether halving premature mortality by 2050 is feasible, we examined the large variation in premature death rates before age 70 years and trends over the past 50 years (1970-2019), covering ten world regions and the 30 most-populous nations. This analysis was undertaken in conjunction with the third report of The Lancet Commission on Investing in Health: Global Health 2050: the path to halving premature death by mid-century. METHODS: In this cross-country analysis of past mortality trends and future directions, all analyses on the probability of premature death (PPD) were conducted using life tables from the UN World Population Prospects 2024. For each sex, country, and year, probability of death was calculated from these life tables with 1-year age-specific mortality rates. FINDINGS: Globally, PPD decreased from 56% in 1970 to 31% in 2019, although some countries saw reversals because of conflict, social instability, or HIV and AIDS. Child mortality has decreased faster than adult mortality. Among all countries, 34 halved their PPD over three decades between 1970 and 2019. Among the 30 most-populous countries, seven countries, with varying levels of baseline PPD and income, halved their PPD in the past half century. Seven of the most-populous countries had average annual rates of improvement in the period 2010-19 that, if sustained, could lead to a halving of PPD by 2050, including Korea (3·9%), Bangladesh (2·8%), Russia (2·7%), Ethiopia (2·4%), Iran (2·4%), South Africa (2·4%), and Türkiye (2·3%). INTERPRETATION: Halving premature death by 2050 is feasible, although substantial investments in child and adult health are needed to sustain or accelerate the rate of improvement for high-performing and medium-performing countries. Particular attention must be paid to countries with very low or a worsening rate of improvement in PPD. By reducing premature mortality, more people will live longer and more healthy lives. However, as people live longer, the absolute number of years lived with chronic disease will increase and investments in services reducing chronic disease morbidity are needed. FUNDING: The Norwegian Agency for Development Cooperation, the Bill & Melinda Gates Foundation, and a Norwegian Research Council Centre of Excellence grant.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".