A Systematic Review of Waterborne Disease Outbreaks Associated with Small Non-Community Drinking Water Systems in Canada and the United States
Bibliographic record
Abstract
BACKGROUND: Reports of outbreaks in Canada and the United States (U.S.) indicate that approximately 50% of all waterborne diseases occur in small non-community drinking water systems (SDWSs). Summarizing these investigations to identify the factors and conditions contributing to outbreaks is needed in order to help prevent future outbreaks. OBJECTIVES: The objectives of this study were to: 1) identify published reports of waterborne disease outbreaks involving SDWSs in Canada and the U.S. since 1970; 2) summarize reported factors contributing to outbreaks, including water system characteristics and events surrounding the outbreaks; and 3) identify terminology used to describe SDWSs in outbreak reports. METHODS: Three electronic databases and grey literature sources were searched for outbreak reports involving SDWSs throughout Canada and the U.S. from 1970 to 2014. Two reviewers independently screened and extracted data related to water system characteristics and outbreak events. The data were analyzed descriptively with 'outbreak' as the unit of analysis. RESULTS: From a total of 1,995 citations, we identified 50 relevant articles reporting 293 unique outbreaks. Failure of an existing water treatment system (22.7%) and lack of water treatment (20.2%) were the leading causes of waterborne outbreaks in SDWSs. A seasonal trend was observed with 51% of outbreaks occurring in summer months (p<0.001). There was large variation in terminology used to describe SDWSs, and a large number of variables were not reported, including water source and whether water treatment was used (missing in 31% and 66% of reports, respectively). CONCLUSIONS: More consistent reporting and descriptions of SDWSs in future outbreak reports are needed to understand the epidemiology of these outbreaks and to inform the development of targeted interventions for SDWSs. Additional monitoring of water systems that are used on a seasonal or infrequent basis would be worthwhile to inform future protection efforts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".