Valuing a reduction in the risk of chronic kidney disease
Bibliographic record
Abstract
Compromised kidney function is associated with an array of environmental contaminants and chemicals, including heavy metals, certain organic solvents, and polycyclic aromatic hydrocarbons (PAHs), as well as food and waterborne pathogens. Many of these hazards are subject to regulation, or may be considered for regulation, in order to reduce exposures and prevent human health risks. However, valuation estimates for kidney effects that can be used in cost-benefit analyses are few, particularly willingness-to-pay estimates. In particular, there appears to be no willingness-to-pay (WTP) estimate available for reduced risk of chronic kidney disease and therefore no estimate for the Value of a Statistical Case (VSC) of chronic kidney disease. This paper is part of the series of large scale willingness to pay (WTP) studies resulting from the Surveys to elicit Willingness to pay to Avoid Chemicals related negative Health Effects (SWACHE) project that intends to improve the basis for doing cost benefit analyses of chemicals management options and environmental policies in general. The paper details a stated preference survey estimating WTP to reduce the risk of symptomatic chronic kidney disease, termed serious kidney disease in the survey instrument, filling an important gap in the valuation literature and addressing a need for applied benefits analysis for chemicals regulation. The SWACHE serious kidney impairment survey was fielded in 10 countries: Canada, Chile, China, Denmark, Germany, Italy, Norway, Türkiye, the United Kingdom and the United States.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".