Extreme Precipitation, Drinking Water And Acute Gastro Intestinal Illness In A Canadian Surface Drinking Water System: Putative Links And Future Impact Of Climate Change
Bibliographic record
Abstract
Introduction: Climate change is expected to increase the burden of waterborne acute gastrointestinal illness (AGI) due to the increased frequency and intensity of extreme precipitation events. Here we investigate the relationship between extreme precipitation and parasitic AGI and to project the impact of climate change on these illnesses. Methods: We included reported cryptosporidiosis and giardiasis cases served by a municipal surface drinking water system (DWS) in Canada from 2000-2009. The association between weekly cases and modeled extreme precipitation (>90th percentile) was assessed (up to 6 week lags), using distributed lag non-linear Poisson regression models adjusted for seasonality (in lieu of temperature), secular trend, preceding dry/wet period and holiday effects. Using the best fitting model, the mean annual case counts were predicted for 2010-2069 using downscaled precipitation projections from 10 global climate models under the representative concentration pathway 8.5. Results: Including 5738 cases, a significant increase in cryptosporidiosis and giardiasis 5-6 weeks after extreme precipitation was found during the study period 2000-2009. A greater effect was evident during the rainy season (RR, 95% CI: 1.17, 1.08-1.32 in lag 5; 1.34, 1.11-1.59 in lag 6) than the dry season (RR, 95% CI: 1.09, 1.02-1.26 in lag 5; 1.17, 1.01-1.39 in lag 6). By the 2060s, climate models indicate decrease in average weekly and extreme precipitation during dry seasons, and increase in rainy seasons compared to 2000-2009. This increases the annual disease burden by 10%-14% (ensemble mean 11%), mainly in the rainy season. Discussion: We present a modeling framework to study the impact of extreme weather on waterborne AGI and support the hypothesis that increases in extreme precipitation may increase the burden of these AGI in future. These results show the need for increasing the adaptive capacity of vulnerable DWS through standardized infrastructure.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".