Cause-specific child and adolescent mortality in the UK and EU15+ countries
Bibliographic record
Abstract
OBJECTIVE: To compare cause-specific UK mortality in children and young people (CYP) with EU15+ countries (European Union countries pre-2004, Australia, Canada and Norway). DESIGN: Mortality estimates were coded from the WHO World Mortality Database. Causes of death were mapped using the Global Burden of Disease mortality hierarchy to 22 cause groups. We compared UK mortality by cause, age group and sex with EU15+ countries in 2015 (or latest available) using Poisson regression models. We then ranked the UK compared with the EU15+ for each cause. SETTING: The UK and EU15+ countries. PARTICIPANTS: CYP aged 1-19. MAIN OUTCOME MEASURE: Mortality rate per 100 000 and number of deaths. RESULTS: UK mortality in 2015 was significantly higher than the EU15+ for common infections (both sexes aged 1-9, boys aged 10-14 and girls aged 15-19); chronic respiratory conditions (both sexes aged 5-14); and digestive, neurological and diabetes/urological/blood/endocrine conditions (girls aged 15-19). UK mortality was significantly lower for transport injuries (boys aged 15-19). The UK had the worst to third worst mortality rank for common infections in both sexes and all age groups, and in five out of eight non-communicable disease (NCD) causes in both sexes in at least one age group. UK mortality rank for injuries in 2015 was in the top half of countries for most causes. CONCLUSIONS: UK CYP mortality is higher than a group of comparable countries for common infections and multiple NCD causes. Excess UK CYP mortality may be amenable to health system strengthening.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".