Associations between fine particulate matter and lung function during late childhood and adolescence: the Sustainable Household Energy Adoption in Rwanda (SHEAR) Study
Bibliographic record
Abstract
In low-resource areas, biomass burning for cooking leads to high levels of household air pollution. Lung function can be compromised by long-term pollution exposure, yet little is known about health risks during childhood. In the Sustainable Household Energy Adoption in Rwanda (SHEAR) study, we examined baseline lung function in 8-17 year-olds before implementing a household energy intervention. Forced vital capacity (FVC), forced expiratory volume in 1 second (FEV1), and FEV1/FVC ratio were measured in 283 boys and 295 girls in rural Rwanda using the EasyOne spirometer. Ultrasonic Personal Aerosol Samplers measured 48-hour fine particulate matter (PM2.5) exposure. Cross-sectional associations between age- and height-standardized lung function (z-scores) and PM2.5 levels were examined in separate models for boys and girls, adjusted for an asset index. For sensitivity analyses, we stratified models by pre-pubertal age estimates (11 for girls, 13 for boys). 96.4% FEV1 and 96.9% FVC were expert graded as high-quality. We did not observe associations between PM2.5 and lung function in primary analyses (e.g., FEV1 among girls: 0.02 per log-µg/m3 increase in PM2.5, 95% confidence interval [CI]: −0.10, 0.15). We observed suggestive associations between PM2.5 and the FEV1/FVC ratio among younger boys and older girls (e.g., among younger boys: −0.17 per log-µg/m3 increase in PM2.5, 95% CI: −0.35, 0.01). Despite the lack of clear associations, we collected robust measures in an understudied population. Our findings suggest that pubescence may be important in understanding how household air pollution affects lung function.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".