Recreational water exposures and illness outcomes at a freshwater beach in Toronto, Canada: A prospective cohort pilot study
Bibliographic record
Abstract
BACKGROUND: Swimming and other recreational water activities in surface waters are popular in Canada during the summer. However, these activities can also increase the risk of recreational water illness. While routine monitoring of beach water quality is conducted by local authorities each summer, little research is available in Canada about beach exposures and illness risks. METHODS: We conducted a pilot of a prospective cohort study at a popular beach in Toronto, Ontario, Canada, in 2022 to determine characteristics of beachgoers, common water and sand exposures, the incidence of recreational water illness, and the feasibility for a larger, national cohort study. We enrolled beachgoers on-site and surveyed about their exposures at the beach and conducted a follow-up survey 7 days following their beach visit to ascertain acute gastrointestinal, respiratory, skin, ear, and eye illness outcomes. We descriptively tabulated and summarized the collected data. RESULTS: We enrolled 649 households, consisting of 831 beachgoers. Water contact activities were reported by 56% of beachgoers, with swimming being the most common activity (44% of participants). Similarly, 56% of beachgoers reported digging in the sand or burying themselves in the sand. Children (≤14 years) and teenagers (15-19 years) were most likely to report engaging in water contact activities and swallowing water, while children were most likely to report sand contact activities and getting sand in their mouth. Boys and men were more likely than women and girls to report swallowing water (15.2% vs. 9.4%). Water and sand exposures also differed by household education level and participant ethno-racial identity. E. coli levels in beach water were consistently low (median = 20 CFU/100 mL, range = 10-58). The incidence of illness outcomes was very low (0.3-2.8%) among the 287 participants that completed the follow-up survey. CONCLUSIONS: The identified beach exposure patterns can inform future risk assessments and communication strategies. Excellent water quality was observed at the studied beach, likely contributing to the low incidence of illnesses. A larger, national cohort study is needed in Canada to examine risks of illness at beaches at higher risk of fecal contamination.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".