A systematic review of waterborne disease burden methodologies from developed countries
Bibliographic record
Abstract
The true incidence of endemic acute gastrointestinal illness (AGI) attributable to drinking water in Canada is unknown. Using a systematic review framework, the literature was evaluated to identify methods used to attribute AGI to drinking water. Several strategies have been suggested or applied to quantify AGI attributable to drinking water at a national level. These vary from simple point estimates, to quantitative microbial risk assessment, to Monte Carlo simulations, which rely on assumptions and epidemiological data from the literature. Using two methods proposed by researchers in the USA, this paper compares the current approaches and key assumptions. Knowledge gaps are identified to inform future waterborne disease attribution estimates. To improve future estimates, there is a need for robust epidemiological studies that quantify the health risks associated with small, private water systems, groundwater systems and the influence of distribution system intrusions on risk. Quantification of the occurrence of enteric pathogens in water supplies, particularly for groundwater, is needed. In addition, there are unanswered questions regarding the susceptibility of vulnerable sub-populations to these pathogens and the influence of extreme weather events (precipitation) on AGI-related health risks. National centralized data to quantify the proportions of the population served by different water sources, by treatment level, source water quality, and the condition of the distribution system infrastructure, are needed.
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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.019 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".