Association between chlorine-treated drinking water, the gut microbiome, and enteric pathogen burden in young children in Haiti: An observational study
Bibliographic record
Abstract
OBJECTIVE: The effects of sanitation and hygiene interventions on the gut microbiome and enteric pathogen burden are not well understood. We measured the association between free chlorine residue (FCR) levels in drinking water, microbiome composition, and stool enteric pathogens in infants and young children in Haiti. METHODS: FCR levels were measured in household drinking water and enteric pathogen burden was evaluated using multiplex RT-PCR of stool among 131 children from one month to five years of age living in Mirebalais, Haiti. Microbiome profiling was performed using metagenomic sequencing. RESULTS: Most individuals lived in households with undetectable FCR measured in the drinking water (112/131, 86%). Detection of enteric pathogen DNA in stool was common and did not correlate with household water FCR. The infant microbiome in households with detectable FCR demonstrated reduced richness (fewer total number of species, P = 0.04 Kruskall-Wallis test) and less diversity by Inverse Simpson measures (P = 0.05) than households with undetectable FCR. Infants in households with a detectable FCR were more likely to have abundant Bifidobacterium. Using in vitro susceptibility testing, we found that some Bifidobacterium species were resistant to chlorine. CONCLUSIONS: FCR in household drinking water did not correlate with enteric pathogen burden in our study.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".