Chronic kidney disease and household behaviors in Sri Lanka: Historical choices of drinking water and agrochemical use
Bibliographic record
Abstract
This paper examines whether there are systematic differences in the historical behaviors of households that are affected and unaffected by chronic kidney disease (CKD) in Sri Lanka pertaining to their water source choices, water treatment practices, and agrochemical use. This analysis is motivated by the Sri Lankan government's largest policy response to this epidemic - to encourage communities to switch from untreated well water to publicly provided alternatives. We use recall methods to elicit information on the drinking water source and treatment choices of households over an 18-year period from 2000-2017. Our analysis is based on a survey of 1497 rural ground-water dependent households in the most CKD-affected areas of the 10 districts of Sri Lanka with the highest prevalence of CKD. Our main findings are that (a) households that have ever used a pump to extract (typically deep) drinking water from a household well are more likely to be affected by CKD; (b) we fail to find a relationship between disease status and households' use of buckets to extract (typically shallow) groundwater from their wells; and (c) those who have ever treated their shallow well water by boiling it are less likely to be affected by CKD. We also find that a greater share of CKD affected households historically used agrochemicals, used wells that were geographically removed from surface water sources, and displayed lower proxies of wealth. The implications of these findings are fourfold. First, since the systematic differences in the historical patterns of water sources and treatments used by CKD affected and non-affected households are modest, the sources of water and the treatment practices themselves may not be the sole risk factors in developing CKD. Second, although we find a negative association between boiling water and the probability of CKD, it is not obvious that a public policy campaign to promote boiling water is an appropriate response. Third, the hydrochemistry of deep and shallow well water needs to be better understood in order to shed light on the positive relationship between deep well water and disease status, and on why boiling shallow but not deep well water is associated with a lower probability of CKD. Fourth, there is a need for a deeper understanding of other risk factors and of the efficacy of preventative programs that provide alternative sources of household drinking 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".