Improving Access to Hygiene, Sanitation, and Drinking Water in King County and Beyond: Success Factors and Costs
Bibliographic record
Abstract
CONTEXT: Most major urban areas in the US, including Seattle and King County, have a long-standing lack of public restrooms, handwashing stations, and drinking water, presenting public health risks. OBJECTIVE: To aid decision-makers in expanding access, we review available information regarding successful hygiene programs in urban settings to identify shared characteristics and costs. DESIGN: We reviewed 10 journal articles, 49 news articles, and 54 pieces of gray literature including reports, white papers, and online resources describing real-world hygiene, sanitation, and drinking water programs in US and global urban settings. We selected programs in 8 cities and applied a thematic analysis to identify shared success factors. We also summarized costs where available. SETTINGS: Calgary (Canada), Denver (Colorado), London (United Kingdom), Los Angeles (California), Portland (Oregon), San Francisco (California), Seattle (Washington), and Vancouver (Canada). RESULTS: Successful programs usually provide frequent cleaning and maintenance, are designed and operated to discourage crime and misuse, leverage existing infrastructure, and include mobile solutions. Cities can expect an initial cost of at least $133 000 per toilet and annual operating costs of at least $100 000 per toilet. CONCLUSIONS: By employing proven solutions and bringing them to scale over time, cities can promote health while improving quality of life and facilitating movement through public spaces for all. Costs should be understood in the context of expenses such as sidewalk cleaning and human waste removal that are necessitated by a lack of restrooms.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".