The global burden of child burn injuries in light of country level economic development and income inequality
Bibliographic record
Abstract
Child burn mortality differs widely between regions and is closely related to material deprivation, but reports on their global distribution are few. Investigating their country level distribution in light of economic level and income inequality will help assess the potential for macro-level improvements. We extracted data for child burn mortality from the Global Burden of Disease study 2013 and combined data into 1-14 years to calculate rates at country, region and income levels. We also compiled potential lives saved. Then we examined the relationship between country level gross domestic product per capita from the World Bank and income inequality (Gini Index) from the Standardized World Income Inequality Database and child burn mortality using Spearman coefficient correlations. Worldwide, the burden of child burn deaths is 2.5 per 100,000 across 103 countries with the largest burden in Sub-Saharan Africa (4.5 per 100,000). Thirty-four thousand lives could be saved yearly if all countries in the world had the same rates as the best performing group of high-income countries; the majority in low-income countries. There was a negative graded association between economic level and child burns for all countries aggregated and at regional level, but no consistent pattern existed for income inequality at regional level. The burden of child burn mortality varies by region and income level with prevention efforts needed most urgently in middle-income countries and Sub-Saharan Africa. Investment in safe living conditions and access to medical care are paramount to achieving further reductions in the global burden of preventable child burn deaths.
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 | 0.000 |
| 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) | 0.004 | 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".