Association of Drowning Mortality with Preventive Interventions: A Quarter of a Million Deaths Evaluation in Brazil
Bibliographic record
Abstract
In 2015, drowning in Brazil was responsible for 6,043 deaths and was the second leading cause of death in children. Although several prevention strategies have been promoted to reduce drowning, most are still based on low levels of evidence. This study evaluated the effectiveness of prevention and water safety interventions in reducing drowning mortality. Data obtained from the National Mortality System for 36 years were split in two time periods to allow the comparison of drowning mortality numbers before and after implementation of SOBRASA’s drowning prevention and water safety programs and to check for any positive effects attributable to such programs. To assess differences between the two periods, a “drowning water safety score” (DSS) was estimated and compared to mortality/100,000 of population. There were 258,834 drowning deaths over 36 years. A significant decrease of 27% in drowning rates (5.2 to 3.8/100,000; p<0.05) was observed when comparing the pre and post-preventive interventions time periods. Males died 5.3 times more frequently than females, and mortality was higher in the 15-19-year age group (16.4%;4.7/100,000) than in other age groups. A linear dependent association was observed between prevention and water safety interventions and years affiliated to the national lifesaving organization (SOBRASA). A strong and significant association (OR=241.7; CI95% [9.0–64.84]) between DSS and drowning reduction was observed. The DSS is a fundamental measure for institutions/municipalities/states/countries to estimate the efforts needed to achieve their drowning reduction goals. From this study, a DSS above 100 (i.e.: 10 actions implemented over 10 years) was able to reduce drowning deaths by as much as 2.3% a year.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".