Hospitalizations for Respiratory Problems and Exposure to Industrial Emissions in Children
Bibliographic record
Abstract
Industrial activities such as metal smelting, petroleum refining, and open mining emit air pollutants that can affect the health of surrounding communities. Few studies have assessed respiratory effects of acute exposure to industrial air emissions in children. In this study, we examined the association between daily exposure to air emissions from an industrial complex and hospitalizations for respiratory problems of children living nearby using a case crossover design. We used hospitalizations for respiratory problems of children under 5 years old living within 7.5 km of the industrial complex from January 1, 2001 to December 31, 2010. Pollutant exposure was estimated using daily mean and maximum concentrations of SO2 and PM2.5 at fixed monitoring stations located near the complex. We also calculated the daily percentage of hours that a child’s residence was downwind of the industrial complex as an indicator of exposure to emissions. Odds-ratios were adjusted for temperature, relative humidity and wind speed, and calculated using conditional logistic regressions, reported by increases of interquartile range. A significant positive association was found between hospitalization for asthma or bronchiolitis and the percentage of hours downwind (OR: 1.11, 95% CI=1.01–1.22) but large statistical variability was noted for associations with all three exposure metrics (OR maximum SO2 levels: 1.06, 95% CI=0.98–1.15; OR daily maximum PM2.5 levels: 0.97, 95% CI=0.86–1.09). The results suggest that exposure to the mixture of air pollutant emissions from an industrial complex may induce respiratory health problems in children residing nearby.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".