Bibliographic record
Abstract
OBJECTIVES: There is limited cross-national research on youth mortality. We examined age-and gender-variations in all-cause mortality among youth aged 15-34 years across 52 countries. METHODS: Using the 2014 WHO mortality database, mortality rates for all countries were computed for the latest available year between 2007 and 2012. Rates, rate ratios, and ordinary least squares (OLS) and Poisson regression were used to analyze international variation in mortality. RESULTS: Mortality rates among youth aged 15-34 years varied from a low of 28.4 deaths per 100,000 population for Hong Kong to a high of 250.6 for Russia and 619.1 for South Africa. For men aged 15-34, Singapore and Hong Kong had the lowest mortality rates (≈40 per 100,000), compared with South Africa and Russia with rates of 589.7 and 383.3, respectively. Global patterns in mortality among women were similar. Youth aged 15-24 in South Africa had 14 times higher mortality and those in the Philippines, Mexico, Russia, Colombia, and Brazil had 5-7 times higher mortality than those in Hong Kong. Youth aged 25-34 in Russia and South Africa had, respectively, 10 and 29 times higher mortality than their counterparts in Hong Kong. United States (US) had the 12th highest mortality rate among youth aged 15-24 and the 13th highest rate among youth aged 25-34. Overall, the US youth had 2-3 times higher rates of mortality than their counterparts in many industrialized countries including Hong Kong, Singapore, Netherlands, Switzerland, Germany, Norway, and Sweden. Income inequality, unemployment rate, and human development explained 50-66% of the global variance in youth mortality. Compared to the countries with low unemployment and income inequality and high human development levels, countries with high unemployment and income inequality and low human development had, respectively, 343%, 213%, and 205% higher risks of youth mortality. CONCLUSIONS AND GLOBAL HEALTH IMPLICATIONS: Marked international disparities in youth all-cause mortality largely reflect differences in violence and injury deaths and in such risk factors as unemployment, income inequality, human development, and alcohol consumption. The US ranks in the upper quartile of all-cause mortality, with youth in Canada and many western industrialized countries showing signifi cantly lower mortality risks than the US youth.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".