Twelve worlds: a geo-demographic comparison of global inequalities in mortality
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to identify clusters of nations grouped by health outcomes in order to provide sensible groupings for international comparisons. The utility of this approach is demonstrated by comparing life expectancy and a range of health system indicators within and between each cluster. METHODS: Age- and sex-specific mortality data for 190 member states were extracted from the Burden of Disease Estimates statistics produced by the World Health Organization. A hierarchical cluster method was used to identify groupings of countries that are homogeneous in terms of mortality rates. RESULTS: 12 clusters of countries were identified. The average life expectancy of each cluster ranged from 81.5 years (cluster 1) to 37.7 years (cluster 12). The two highest ranked clusters were dominated by Western European countries, Australia, Japan and Canada. Cluster 3 included the UK and USA. The four clusters with the lowest life expectancies were characterised by different configurations of African countries. Health system indicators for workforce, hospital beds, access to medicines and measles vaccination corresponded well with a clear association with cluster life expectancy. On a per capita basis, worldwide health spending was concentrated within the three highest life expectancy clusters, especially cluster 3 containing the USA. CONCLUSIONS: Considerable inequalities in life expectancy and healthcare are made clearer when viewed across clusters of countries grouped by health outcomes. This geo-demographic taxonomy of global mortality has advantages over traditional more ad hoc systems for comparing global health inequalities and for deciding which countries appear to have the most comparable health outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".