The link between medical conditions and fatal drownings in Canada: a 10-year cross-sectional analysis
Bibliographic record
Abstract
BACKGROUND: Drowning accounts for hundreds of preventable deaths in Canada every year, but the impact of preexisting medical conditions on the likelihood of death from drowning is not known. We aimed to describe the prevalence of pre-existing medical conditions among people who fatally drowned in Canada and evaluate the risk of fatal drowning among people with common pre-existing medical conditions. METHODS: We reviewed all Canadian unintentional fatal drownings (2007-2016) in the Drowning Prevention Research Centre Canada's database. For each fatal drowning we established whether the person had pre-existing medical conditions and whether those conditions contributed to the drowning. We calculated relative risk (RR) of fatal drowning stratified by age and sex for each pre-existing medical condition using data from the Canadian Chronic Disease Surveillance System. RESULTS: = 616) of cases. Fatal drowning occurred more frequently in people with ischemic heart disease (RR 2.7, 95% confidence interval [CI] 2.5-3.0) and seizure disorders (RR 6.3, 95% CI 5.4-7.3) but less frequently in people with respiratory disease (RR 0.12, 95% CI 0.10-0.15). Females aged 20-34 years with a seizure disorder had a 23 times greater risk than their age- and sex-matched cohort (RR 23, 95% CI 14-39). In general, fatal drowning occurred more often while people were bathing (RR 5.9, 95% CI 4.8-7.0) or alone (RR 1.99, 95% CI 1.32-2.97) and less often in males (RR 0.92, 95% CI 0.88-0.95) or in those who had used alcohol (RR 0.72, 95% CI 0.65-0.80), among those with pre-existing medical conditions. INTERPRETATION: The risk of fatal drowning is increased in the presence of some preexisting medical conditions. Tailored interventions aimed at preventing drowning based on pre-existing medical conditions and age are needed. Initial prevention strategies should focus on seizure disorders and bathtub drownings.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".