Household fuel consumption, indoor air pollution, and respiratory health infections among children in Pakistan
Bibliographic record
Abstract
Household energy use for cooking constitutes a significant portion of energy consumption in Pakistan. The use of unclean fuels releases harmful pollutants, increasing the risk of respiratory infections, which are a leading cause of mortality among children under five worldwide. This study assesses the impact of household fuel use on respiratory infections in children under five in Pakistan. This cross-sectional study utilized data from the Pakistan Demographic and Health Survey. The population included children less than five years of age. Logistic regression models were applied to assess the relationship between household energy type and respiratory infections, adjusting for confounding factors such as wealth status, maternal tobacco use, place of residence, and maternal education. The findings revealed that children in households using clean energy fuels had lower odds of respiratory infections (odds ratio [OR]: 0.70; 95% CI: 0.60–0.80). Having a separate kitchen was associated with reduced odds (OR: 0.80; 95% CI: 0.68–0.94), while children from the wealthiest households were significantly less likely to develop respiratory infections (OR: 0.54; 95% CI: 0.44–0.66). Conversely, maternal tobacco use increased the odds of respiratory infections in children (OR: 1.65; 95% CI: 1.34–2.04). Regional differences, urban vs. rural residence, and maternal education also emerged as important determinants. This study highlights the critical public health importance of promoting clean energy sources for cooking, improving kitchen design, discouraging maternal tobacco use, and addressing socioeconomic disparities to reduce respiratory infections among children in Pakistan. Policymakers should prioritize accessible clean energy solutions and targeted health interventions to improve child health outcomes.
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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 | 0.002 | 0.003 |
| 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; both teacher heads agree on what is shown here.
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".