Bayesian modeling of <i>Escherichia coli</i> contamination in household drinking water in Bangladesh: evidence from the Multiple Indicator Cluster Survey 2019
Bibliographic record
Abstract
BACKGROUND: From a public health standpoint, there is merit in determining the levels of Escherichia coli in drinking water, but surveillance datasets often report censored values that may hinder traditional statistical analysis. This study aims to identify sociodemographic factors associated with the presence of E. coli in household drinking water in Bangladesh using Bayesian models for censored data, utilizing data from 6069 households in the Multiple Indicator Cluster Survey 2019. METHODS: In terms of censoring, we considered two different Bayesian regression strategies: Bayesian Tobit Poisson regression and Bayesian Censored Generalized Poisson regression. RESULTS: The Bayesian Censored Generalized Poisson regression model was identified as the optimal model for analyzing household fecal contamination. Regression analysis revealed significant associations between household E. coli levels and various factors including division, livestock ownership, location of water sources, treatment of drinking water, household head education, wealth index, source of drinking water, place of handwashing and toilet facility. Households using tube wells had lower E. coli levels than those using other sources. Furthermore, households using pit latrines had 1.03 times higher contamination levels than those using flush latrines. CONCLUSIONS: Levels of fecal contamination in household water in Bangladesh were alarming. Our findings underscore the need for targeted policy interventions in specific population segments to address household fecal contamination, highlighting the link between sociodemographic and environmental factors with E. coli levels in 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 | 0.017 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".