Relationship between exposure to household air pollution and asthma in children: a systematic review and meta-analysis
Bibliographic record
Abstract
Introduction: Over 40% of the world’s population are exposed to high levels of household air pollution (HAP) from solid fuel use. Although HAP is a risk factor for asthma morbidity in adults, the findings in children have not been extensively reviewed. Aims: To summarize the relationship between HAP exposure and asthma in children. Methods: We searched PubMed, Medline, Scopus, Web of Science, CINAHL, Cochrane, and LILACS databases to identify eligible observational and RCT studies of HAP and its link to asthma in children ≤18 years old. Article screening and data extraction were carried out in duplicate using standardised forms and quality assessed using the NIH Quality Assessment Tools. A random effects meta-analysis was performed using the generic inverse variance method. Complete protocol has been published in PROSPERO (CRD42018094283). Results: Out of 25,865 articles identified initially, 15,045 titles and abstracts were screened following duplicate removal. Data were extracted from 30 studies that reported on asthma or wheezing outcomes and met the inclusion criteria. Compared to non-solid fuel use, use of solid fuel for cooking or heating was associated with an increased risk of asthma (OR 1.19, 95% CI 0.99-1.42) and wheezing (1.2, 1.03-1.40) though statistically not significant marginally for asthma. Further analysis showed that use of wood for cooking increased the risk of asthma (1.65, 0.96-2.83) and wheeze (1.32, 0.57-3.04), though statistically not significant. There was moderate to high heterogeneity (49-87%) between the studies. Discussion: Overall, the results show exposure to solid fuel is associated with asthma and episodes of wheezing in 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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.033 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".