Associations between extreme precipitation and acute gastro-intestinal illness due to cryptosporidiosis and giardiasis in an urban Canadian drinking water system (1997–2009)
Bibliographic record
Abstract
Drinking water related infections are expected to increase in the future due to climate change. Understanding the current links between these infections and environmental factors is vital to understand and reduce the future burden of illness. We investigated the relationship between weekly reported cryptosporidiosis and giardiasis (n = 7,422), extreme precipitation (>90th percentile), drinking water turbidity, and preceding dry periods in a drinking water system located in greater Vancouver, British Columbia, Canada (1997-2009) using distributed lag non-linear Poisson regression models adjusted for seasonality, secular trend, and the effect of holidays on reporting. We found a significant increase in cryptosporidiosis and giardiasis 4-6 weeks after extreme precipitation. The effect was greater following a dry period. Similarly, extreme precipitation led to significantly increased turbidity only after prolonged dry periods. Our results suggest that the risk of cryptosporidiosis and giardiasis increases with extreme precipitation, and that the effects are more pronounced after a prolonged dry period. Given that extreme precipitation events are expected to increase with climate change, it is important to further understand the risks from these events, develop planning tools, and build resilience to these future risks.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".