Assessing the potential human health impacts of freshwater consumption: considering inequalities in water availability to assess the consequences of domestic water deprivation
Bibliographic record
Abstract
The consumption of freshwater can increase local competition among ecosystems needs, agriculture, and domestic users. This competition can lead to reduced domestic water availability and subsequent inadequate hygiene practices leading to water-related diseases. Among the few attempts to develop endpoint-oriented characterization models and factors to assess this impact pathway, limitations still need to be addressed to improve the effect factor (EF) representing the marginal increase in health damage associated with 1 m 3 of water consumed. This research proposes a revised country-scale effect model assessing diarrheal diseases due to domestic water deprivation, considering different levels of water availability within a country. The calculation of the EF is based on the principle that the probability of negative health consequences associated with depriving a domestic user of 1 m 3 of water depends on the quantity used daily by this user. Three classes of domestic water users are defined based on their range of daily water use. EFs are calculated for each class of domestic users building on a comparative risk assessment methodology and households’ levels of access to drinking water and sanitation from the Joint Monitoring Program. Country-specific EFs are computed as the weighted sum of class-specific EF proportionally to the population within each class. Revised country-specific EFs are used to overwrite the generic constant EF used in previous characterization models and to compute new characterization factors (CFs). Class-specific effect factors equal to 1.35e − 3, 3.44e − 4, and 7.53e − 5 DALY/m 3 for the three classes of users, showing a 57, 89, and 98% reduction compared to previous characterization models. Country-specific EF values range from 7.5e − 05 to 8.7e − 04 DALY/m 3 deprived ( M : 2.5e − 04; SD: 1.8e − 04), representing a reduction of 72.2 to 97.6% compared to previous models. New 11987 CFs were compiled ranging from 0 to 7.63e − 04 DALY/m 3 consumed ( M : 1.9e − 6; SD: 2.1e − 5). The global potential impact induced by water consumption over the year 2019 computed with our model reaches 2.77e + 7 DALYs, corresponding to 50% of the water-related burden of diarrheal disease calculated by a recent epidemiology study, which confirms the plausibility of our results. Unlike previous methods, our revised EFs acknowledge inequalities in domestic water consumption within a country. Revised EFs are calculated by country, going beyond the global resolution of previous models, compared to which they show a reduction of 72 to 98%. A sanity check confirms the plausibility of our CFs but does not rule out a potential overestimation. Future research is needed to provide empirical evidence supporting a causal link between water deprivation and diarrheal diseases and to assess uncertainties of the model results.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".