P334 Extreme precipitation, turbidity, drinking water and acute gastro-intestinal illness in a canadian surface drinking water system: mechanisms and opportunities to build resilience to climate change
Bibliographic record
Abstract
Introduction Climate change is expected to increase the burden of waterborne acute gastrointestinal illness (AGI) with the increased frequency and intensity of extreme precipitation events. Turbidity in source water is a risk factor. Here we investigate the relationship between extreme precipitation, turbidity and parasitic AGI. Further, we project the impact of climate change on these illnesses at a watershed level. Methods All 1997–2009 reported cases of cryptosporidiosis and giardiasis in a population served by a municipal surface drinking water system were analysed using distributed lag non-linear models. Precipitation was assessed for a lag up to six weeks, and adjusted for seasonality, secular trend, preceding dry/wet period and holiday effects. The mean annual case counts were predicted for 2060–2069 using downscaled daily precipitation projections from 10 global climate models under a moderate emissions growth scenario. Results Including 7422 cases, a significant increase in cryptosporidiosis and giardiasis 5–6 weeks after extreme precipitation (>90th percentile) was found during the study period. The highest rate ratio (1.17; 1.07–1.24) was identified for a lag of five weeks. A preceding dry period further increased the risk, which appears to be driven by increases in turbidity. Temperature did not contribute significantly to this risk. Climate models indicate decreases in average weekly and extreme precipitation during dry seasons in the 2060s, but increases in rainy seasons compared with 2000–2009. The overall annual disease burden increased by 6.3 % −14.2% (ensemble mean 12.1%). Discussion We found a significant risk of waterborne illness associated with extreme precipitation events in a large and well-protected municipal drinking water system. The effects were most pronounced following a dry period. To try and reduce these future risks additional filtration of finished water is being deployed for these sources. There is a need to increase resilience in water systems to address the impacts due to climate change.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".