Assessing the risk of acute gastrointestinal illness attributable to three enteric pathogens from contaminated private water wells in Ontario
Bibliographic record
Abstract
The province of Ontario compromises the largest groundwater reliant population in Canada serving approximately 1.6 million individuals. Unlike municipal water systems, private well water is not required to meet water quality regulatory standards and thus source maintenance, treatment and testing remains the responsibility of the well owner. Infections associated with private drinking water systems are rarely documented given their typically sporadic nature, thus the human health effects (e.g., acute gastrointestinal illness (AGI)) on consumers remains relatively unknown, representing a significant gap in water safety management. The current study sought to quantify the risk of waterborne AGI attributed to Giardia, shiga-toxin producing E. coli (STEC) and norovirus from private drinking water sources in Ontario using Monte Carlo simulation-based quantitative microbial risk assessment (QMRA). Findings suggest that consumption of contaminated private well water in Ontario is responsible for approximately 4823 AGI cases annually, with 3464 (71.8%) and 1359 (28.1%) AGI cases predicted to occur in consolidated and unconsolidated aquifers, respectively. By pathogen, waterborne AGI was attributed to norovirus (62%; 2991/4823), Giardia (24.6%; 1186/4823) and STEC (13.4%; 646/4823). The developed QMRA framework was used to assess the potential health impacts of partial and total well water treatment system failure. In the unlikely event of total treatment failure, total mean annual illnesses are predicted to almost double (4217 to 7064 cases per year), highlighting the importance of effective water treatment and comprehensive testing programs in reducing infectious health risks attributable to private well water in Ontario. Study findings indicate significant underreporting of waterborne AGI rates at the provincial level likely biasing public health interventions and programs that are effective in monitoring and minimizing the health risk associated with private well water.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".