Bibliographic record
Abstract
OBJECTIVE: To study cross-national inequalities in mortality of adults and of children aged <5 years using a novel approach, with clustering techniques to stratify countries into mortality groups (better-off, worse-off, mid-level) and to examine risk factors associated with inequality. DESIGN, SETTING AND PARTICIPANTS: Analysis of data from the World Development Indicators 2003 database, compiled by the World Bank. MAIN OUTCOME MEASURES: Adult and child mortality among countries placed into distinct mortality categories by cluster analysis. RESULTS: 29 countries had a high adult mortality (mean 584/1000; range 460/1000 to 725/1000) and 23 had a high child mortality (mean 207/1000, range 160/1000 to 316/1000). All these countries were in western and sub-Saharan Africa and Afghanistan. Bivariate analyses showed that relative to countries with low child mortality, those with high child mortality had significantly higher rates of extreme poverty (p<0.001), populations living in rural areas (p<0.001) and female illiteracy (p<0.001), significantly lower per capita expenditure on healthcare (p<0.001), outpatient visits, hospital beds and doctors, and lower rates of access to improved water (p<0.001), sanitation (p<0.001) and immunisations. In multivariate analyses, countries with high adult mortality had a higher prevalence of HIV infection (odds ratio per 1% increase 18.6; 95% CI 0.3 to 1135.5). Between 1960 and 2000, adult male mortality in countries with high mortality increased at >4 times the rate in countries with low mortality. For child mortality, the worse-off group made slower progress in reducing <5 mortality than the better-off group. CONCLUSIONS: Inequalities in child and adult mortality are large, are growing, and are related to several economic, social and health sector variables. Global efforts to deal with this problem require attention to the worse-off countries, geographic concentrations, and adopt a multidimensional approach [corrected] to development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".