Unintentional drowning mortality, by age and body of water: an analysis of 60 countries
Bibliographic record
Abstract
BACKGROUND: To examine unintentional drowning mortality by age and body of water across 60 countries, to provide a starting point for further in-depth investigations within individual countries. METHODS: The latest available three years of mortality data for each country were extracted from WHO Health Statistics and Information Services (updated at 13 November 2013). We calculated mortality rate of unintentional drowning by age group for each country. For countries using International Classification of Disease 10 (ICD-10) detailed 3 or 4 Character List, we further examined the body of water involved. RESULTS: A huge variation in age-standardised mortality rate (deaths per 100 000 population) was noted, from 0.12 in Turkey to 9.19 in Guyana. Of the ten countries with the highest age-standardised mortality rate, six (Belarus, Lithuania, Latvia, Russia, Ukraine and Moldova) were in Eastern Europe and two (Kazakhstan and Kyrgyzstan) were in Central Asia. Some countries (Japan, Finland and Greece) had a relatively low rank in mortality rate among children aged 0-4 years, but had a high rank in mortality rate among older adults. On the contrary, South Africa and Colombia had a relatively high rank among children aged 0-4 years, but had a relatively low rank in mortality rate among older adults. With regard to body of water involved, the proportion involving a bathtub was extremely high in Japan (65%) followed by Canada (11%) and the USA (11%). Of the 13 634 drowning deaths involving bathtubs in Japan between 2009 and 2011, 12 038 (88%) were older adults aged 65 years or above. The percentage involving a swimming pool was high in the USA (18%), Australia (13%), and New Zealand (7%). The proportion involving natural water was high in Finland (93%), Panama (87%), and Lithuania (85%). CONCLUSIONS: After considering the completeness of reporting and quality of classifying drowning deaths across countries, we conclude that drowning is a high-priority public health problem in Eastern Europe, Central Asia, Japan (older adults involving bathtubs), and the USA (involving swimming pools).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".