Swimmer’s itch in Canada: a look at the past and a survey of the present to plan for the future
Bibliographic record
Abstract
Abstract Background Cercarial dermatitis, colloquially “swimmer’s itch”, is a rash contracted in natural bodies of water, when people are exposed to skin-penetrating, larval flatworm parasites of the family Schistosomatidae, that emerge from aquatic snails. Swimmer’s itch is a globally-distributed, allergic condition, of which we know very little regarding local dynamics of transmission. This study aims to gather relevant information on swimmer’s itch in Canada, from multiple perspectives, including the human experience, parasite and host presence and distributions, and insight from historical perspectives. Methods Herein we utilize a mixed-methods approach towards examining the environmental health issue of swimmer’s itch in Canadian lakes from a nation-wide viewpoint, with an example from Alberta. We examine the human perspective of having contracted swimmer’s itch through a self-reporting surveillance system implemented over a 5-year period. We also conducted a 3-year species survey of parasites and intermediate snail hosts within lakes in central Alberta and compiled this data with snail and vertebrate (definitive) host survey data from across Alberta to examine potential for future spread. We compare the results from our surveys to a historical review of the literature to examine the extent of swimmer’s itch across Canada and identify where future efforts should be focused. Results Over 3800 cases of swimmer’s itch were captured across Canada by the self-reporting surveillance system. Swimmer’s itch cases were reported from every province except Prince Edward Island. Species surveys in Alberta revealed 7 new parasite and host records, with potential for swimmer’s itch to occur throughout most of the province based on host distributions. A review and comparison to the literature has highlighted several knowledge gaps surrounding schistosome species, host species and their distributions and contributions towards swimmer’s itch. Conclusions Swimmer’s itch is a greater environmental health hazard across Canada than previous literature would have alluded. This study provides proof-of-concept for the utility of a self-reporting surveillance system for swimmer’s itch in Canada. Recommendations are made towards implementing a systems-thinking approach that incorporates citizen-scientists for future research, management, and policy surrounding swimmer’s itch.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".