Estimating the number of cases of acute gastrointestinal illness (AGI) associated with Canadian municipal drinking water systems
Bibliographic record
Abstract
The estimated burden of endemic acute gastrointestinal illness (AGI) annually in Canada is 20·5 million cases. Approximately 4 million of these cases are domestically acquired and foodborne, yet the proportion of waterborne cases is unknown. A number of randomized controlled trials have been completed to estimate the influence of tap water from municipal drinking water plants on the burden of AGI. In Canada, 83% of the population (28 521 761 people) consumes tap water from municipal drinking water plants serving >1000 people. The drinking water-related AGI burden associated with the consumption of water from these systems in Canada is unknown. The objective of this research was to estimate the number of AGI cases attributable to consumption of drinking water from large municipal water supplies in Canada, using data from four household drinking water intervention trials. Canadian municipal water treatment systems were ranked into four categories based on source water type and quality, population size served, and treatment capability and barriers. The water treatment plants studied in the four household drinking water intervention trials were also ranked according to the aforementioned criteria, and the Canadian treatment plants were then scored against these criteria to develop four AGI risk groups. The proportion of illnesses attributed to distribution system events vs. source water quality/treatment failures was also estimated, to inform the focus of future intervention efforts. It is estimated that 334 966 cases (90% probability interval 183 006-501 026) of AGI per year are associated with the consumption of tap water from municipal systems that serve >1000 people in Canada. This study provides a framework for estimating the burden of waterborne illness at a national level and identifying existing knowledge gaps for future research and surveillance efforts, in Canada and abroad.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".