An underappreciated cause of ocean-related fatalities: A systematic review on the epidemiology, risk factors, and treatment of snorkelling-related drowning
Bibliographic record
Abstract
AIM: Snorkelling is a popular aquatic activity which may result in fatal and non-fatal drowning. However, little is known about the scale of injury, factors impacting risk and strategies for prevention. This review assesses the current literature on snorkelling-related drowning with the aim of assessing available data, improving safety recommendations and reducing the global mortality burden. METHODS: A systematic review of peer-reviewed literature in English, Spanish and Portuguese language published between 1 January 1980 and 31 October 2020 was conducted using the PRISMA guidelines. CINAHL Complete, Embase, Medline (Ovid), PubMed, SafetyLit, SportDiscus and grey literature were searched to identify studies reporting the incidence of fatal and non-fatal snorkelling-related drowning, or associated risk factors, prevention strategies, treatments or casualty characteristics. Quality was assessed using the NIH Quality Assessment Tool. RESULTS: Forty-three studies were included (26 reporting population data, 17 case series), of which 27 (62.8%) studies reported data from Australia. Incidence was reported as about 8% of total ocean-related drownings. Case series documented 144 fatalities over 17 years. Frequent casualty characteristics include male (82.6%), pre-existing heart disease (59.4%), tourists (73%) who were inexperienced (71.0%), and lack of a buddy system (89.6%). Two at-risk profiles identified were older adult tourists with pre-existing medical conditions and local, experienced spearfishers. Twenty-two expert recommendations were developed to improve the safety of snorkellers related to individuals, tourism companies, government agencies and diving organisations. CONCLUSION: Snorkelling-related drownings are not infrequent, and there are many opportunities to improve the safety of this activity based on available data.
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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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".