<i>E. coli</i> contamination of drinking water sources in rural and urban settings: an analysis of 38 nationally representative household surveys (2014–2021)
Bibliographic record
Abstract
The world is not on track to achieve universal access to safely managed water by 2030, and access is substantially lower in rural areas. This Sustainable Development Goal target and many other global indicators rely on the classification of improved water sources for monitoring access. We aimed to investigate contamination in drinking water sources, comparing improved and unimproved sources in urban and rural settings. We used data from Multiple Indicator Cluster Surveys, which tested samples from the household water source and a glass of water for Escherichia coli contamination across 38 countries. Contamination was widespread and alarmingly high in almost all countries, settings, and water sources, with substantial inequalities between and within countries. Water contamination was found in 51.7% of households at the source and 70.8% in the glass of water. Some improved sources (e.g., protected wells and rainwater) were as likely to be contaminated as unimproved sources. Some sources, like piped water, were considerably more likely to be contaminated in rural than urban areas, while no difference was observed for others. Monitoring water contamination along with further investigation in water collection, storage, and source classification is essential and must be expanded to achieve universal access to safely managed water.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".