Global and regional child deaths due to injuries: an assessment of the evidence
Bibliographic record
Abstract
BACKGROUND: Injuries result in substantial number of deaths among children globally. The burden across many settings is largely unknown. We estimated global and regional child deaths due to injuries from publicly available evidence. METHODS: We searched for community-based studies and nationally representative data reporting on child injury deaths published after year 1990 from CINAHL, EMBASE, IndMed, LILACS, Global Health, MEDLINE, SCOPUS, and Web of Science. Specific and all-cause mortality due to injuries were extracted for three age groups (0-11 months, 1-4 years, and 0-4 years). We conducted random-effects meta-analysis on extracted crude estimates, and developed a meta-regression model to determine the number of deaths due to injuries among children aged 0-4 years globally and across the World Health Organization (WHO) regions. RESULTS: < 0.001) reflecting variable data sources and study designs. For children aged 0-11 months, the pooled crude injury mortality rate was 29.6 (95% confidence interval (CI) = 21.1-38.1) per 100 000 child population, with asphyxiation being the leading cause of death (neonatal) at 189.1 (95% CI = 142.7-235.4) per 100 000 followed by suffocation (post-neonatal) at 18.7 (95% CI = 11.8-25.7) per 100 000. Among children aged 1-4 years, the pooled crude injury mortality rate was 32.7 (95% CI = 27.3-38.1) per 100 000, with traffic injuries and drowning the leading causes of deaths at 10.8 (95% CI = 8.9-12.8) and 8.8 (95% CI = 7.5-10.2) per 100 000, respectively. Among children under five years, the pooled injury mortality rate was 37.7 (95% CI = 32.7-42.7) per 100 000, with traffic injuries and drowning also the leading causes of deaths at 10.3 (95% CI = 8.8-11.8) and 8.9 (95% CI = 7.8-9.9) per 100 000 respectively. When crude mortality changes over age, WHO regions, and study period were accounted for in our model, we estimated that in 2015 there were 522 167 (95% CI = 395 823-648 630) deaths among children aged 0-4 years, with South East Asia (SEARO) recording the highest number of deaths at 195 084 (95% CI = 159476-230502), closely followed by the Africa region (AFRO) with 176523 (95% CI = 115 040-237 831) deaths. Globally, traffic injuries and drowning were the leading causes of under-five injury fatalities in 2015 with 142 661 (22.0/100 000) and 123 270 (19.0/100 000) child deaths, respectively. The exception being burns in AFRO with 57 784 deaths (38.6/100 000). CONCLUSIONS: Varying study designs, case definitions, and particularly limited country representation from Africa and South-East Asia (where we reported higher estimates), imply a need for more studies for better population representative estimates. This study may have however provided improved understanding on child injury death profiles needed to guide further research, policy reforms and relevant strategies globally.
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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 | 0.067 | 0.155 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.018 |
| Bibliometrics | 0.027 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".