Maternal smoking, biofuel smoke exposure and child height-for-age in seven developing countries
Bibliographic record
Abstract
BACKGROUND: Children are at high risk of exposure to environmental tobacco smoke and biofuel smoke at home in developing countries. This study examines whether exposure to cigarette and biofuel smoke is associated with height-for-age of children (0-59 months) in seven developing countries. METHODS: The data are from Demographic and Health Surveys conducted in Cambodia, Dominican Republic, Haiti, Jordan, Moldova, Namibia and Nepal between 2005 and 2007. The respondents were women (15-49 years) and their children in seven countries (n = 28 439), and men (15-59 years) from four countries. The outcome is a physical measurement of child height-for-age in standard deviation units. RESULTS: Multilevel regression analysis showed that the country of residence modified the association between maternal smoking and child height-for-age. Exposure to maternal smoking was associated negatively with child height-for-age in Cambodia, Namibia and Nepal, whereas it was not in other countries. Multilevel regression analysis revealed that biofuel smoke exposure was associated with a decrease in child height-for-age [b = -0.13, 95% confidence interval (CI) = -0.19 to -0.07, P < 0.001]. No interaction was found between country of residence and biofuel smoke exposure. Multinomial logistic regression results showed that biofuel smoke exposure was associated with both stunting [odds ratio (OR) = 1.25, 95% CI = 1.08-1.44, P = 0.002) and severe stunting (OR = 1.27, 95% CI = 1.02-1.59, P = 0.04), after controlling for covariates. CONCLUSION: The findings suggest that exposure to maternal smoking and biofuel smoke may contribute to growth deficiencies in young children. Programmes are needed to ensure smoke-free home environments for children.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".