Increased risk of respiratory illness associated with kerosene fuel use among women and children in urban Bangalore, India
Bibliographic record
Abstract
BACKGROUND: Kerosene is a widely used cooking and lighting fuel in developing countries. The potential respiratory health effects of cooking with kerosene relative to cooking with cleaner fuels such as liquefied petroleum gas (LPG) have not been well characterised. METHODS: We sampled 600 households from six urban neighbourhoods in Bangalore, India. Each household's primary cook, usually the woman of the house, was interviewed to collect information on current domestic fuel use and whether there was any presence of respiratory symptoms or illness in her or in the children in the household. Our analysis was limited to 547 adult females (ages 18-85) and 845 children (ages 0-17) in households exclusively cooking with either kerosene or LPG. We investigated the associations between kerosene use and the likelihood of having respiratory symptoms or illness using multivariate logistic regression models. RESULTS: Among adult women, cooking with kerosene was associated with cough (OR=1.88; 95% CI 1.19 to 2.99), bronchitis (OR=1.54; 95% CI 1.00 to 2.37), phlegm (OR=1.51; 95% CI 0.98 to 2.33) and chest illness (OR=1.61; 95% CI 1.02 to 2.53), relative to cooking with LPG in the multivariate models. Among children, living in a household cooking with kerosene was associated with bronchitis (OR=1.91; 95% CI 1.17 to 3.13), phlegm (OR=2.020; 95% CI 1.29 to 3.74) and chest illness (OR=1.70; 95% CI 0.99 to 2.90) after adjusting for other covariates. We also found associations between kerosene use and wheezing, difficulty breathing and asthma in adults and cough and wheezing in children, though these associations were not statistically significant. CONCLUSIONS: Women and children in households cooking with kerosene were more likely to have respiratory symptoms and illnesses compared with those in households cooking with LPG. Transitioning from kerosene to LPG for cooking may improve respiratory health among adult women and children in this population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".