Drowning: Global Burden, Risk Factors, and Prevention Strategies
Bibliographic record
Abstract
Abstract Drowning has been described as a major global public health problem and has recently been acknowledged by a United Nations Declaration on Global Drowning Prevention. While drowning impacts countries of all income levels, the burden is overwhelmingly borne by low- and middle-income countries (LMICs) who account for 90% of the global death toll. In addition, there is scarce data collection on drowning in LMICs, so the magnitude of drowning may be far greater than is represented. A range of factors including sex, age, education, income, access to water, a lack of swimming skills, certain occupations like commercial fishing, geographically isolated and flood-prone locations, preexisting medical conditions, and unsafe water transport systems, influence the risk of drowning. Some behavioral factors, such as alcohol or drug consumption, not wearing life jackets, and engaging in risky behaviors such as swimming or boating alone, increase drowning risk. Geopolitical factors such as migration and armed conflict can also impact drowning risk. There is a growing body of evidence on drowning prevention strategies. These include pre-event interventions such as pool fencing, enhancing community education and awareness, providing swimming lessons, use of lifejackets, close supervision of children by adults, and boating regulations. Interventions to reduce harm from drowning include appropriate training for recognition of a drowning event, rescue, and resuscitation. An active and/or passive surveillance system for drowning, focusing on individual settings and targeting populations at risk, is required. Drowning requires coordinated multisectoral action to provide effective prevention, rescue, and treatment. Therefore, all countries should aim to develop a national water safety plan, as recommended in the WHO Global Report on Drowning. Further research is required on the epidemiology and treatment of drowning in LMICs as well as non-fatal and intentional drowning in both high-income countries (HICs) and LMICs. Effective and context-specific implementation of drowning prevention strategies, including pilot testing, scale up and evaluation, are likely to help reduce the burden of both fatal and non-fatal drowning in all countries.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".