Water-related factors and childhood diarrhoea in African informal settlements. A cross-sectional study in Ouagadougou (Burkina Faso)
Bibliographic record
Abstract
Improved access to water is a key factor in reducing diarrhoeal diseases, a leading cause of death among children in sub-Saharan Africa. In terms of water access, sub-Saharan African cities are some of the worst off in the world, with 20% of populations supplied by an unimproved water source. This situation is even worse in informal settlement areas. Using cross-sectional data on access to water from a survey implemented in three informal neighbourhoods of the Ouagadougou Health and Demographic Surveillance System, logistic regressions are modelled to test the effect of different modalities of access to water on childhood diarrhoea. Our results show that the prevalence of diarrhoea in children is high: one-third of households with a child under 10 experienced an episode of childhood diarrhoea during the 2 weeks preceding the survey, even though 91% of the households surveyed have access to an improved water source. The results show that efforts to reduce childhood morbidity would be greatly enhanced by strengthening piped water access in informal settlement areas in Africa. In addition, this study confirms that, beyond the single measure of the main access to water, accurate variables that assess the accessibility to water are needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".