Impact of contaminated household environment on stunting in children aged 12–59 months in Burkina Faso
Bibliographic record
Abstract
BACKGROUND: Stunting affects 165 million children worldwide, with repercussions on their survival and development. A contaminated environment is likely to contribute to stunting: frequent faecal-oral transmission possibly causes environmental enteropathy, a chronic inflammatory disorder that may contribute to faltering growth in children. This study's objective was to assess the effect of contaminated environment on stunting in Burkina Faso, where stunting prevalence is persistently high. METHODS: Panel study of children aged 1-5 years in Kaya. Household socioeconomic characteristics, food needs and sanitary conditions were measured once, and child growth every year (2011-2014). Using multiple correspondence analysis and 12 questions and observations on water, sanitation, hygiene behaviours, yard cleanliness and animal proximity, we constructed a 'contaminated environment' index as a proxy of faecal-oral transmission exposure. Analysis was performed using a generalised structural equation model (SEM), adjusting for repeat observations and hierarchical data. RESULTS: Stunting (<2 SD height-for-age) prevalence was 29% among 3121 children (median (IQR) age 36 (25-48) months). Environment contamination was widespread, particularly in rural and peri-urban areas, and was associated with stunting (prevalence ratio 1.30; p=0.008), controlling for sex, age, survey year, setting, mother's education, father's occupation, household food security and wealth. This association was significant for children of all ages (1-5 years) and settings. Lower contamination and higher food security had effects of comparable magnitude. CONCLUSIONS: Environment contamination can be at least as influential as nutritional components in the pathway to stunting. There is a rationale for including interventions to reduce environment contamination in stunting prevention programmes.
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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 | 0.002 |
| 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 | 0.000 |
| 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".