Air Pollution and Pediatric Respiratory Hospitalizations: Effect Modification by Particle Constituents and Oxidative Potential
Bibliographic record
Abstract
Abstract Rationale Outdoor particulate and gaseous air pollutants impair respiratory health in children, and these associations may be influenced by particle composition. Objectives To examine whether associations between short-term variations in fine particulate air pollution, oxidant gases, and respiratory hospitalizations in children are modified by particle constituents (metals and sulfur) or oxidative potential. Methods We conducted a case-crossover study of 10,500 children (0–17 years of age) across Canada. Daily fine particle mass concentrations and oxidant gases (nitrogen dioxide and ozone) were collected from ground monitors. Monthly estimates of fine particle constituents (metals and sulfur) and oxidative potential were also measured. Conditional logistic regression models were used to estimate associations between air pollutants and respiratory hospitalizations, above and below median values for particle constituents and oxidative potential. Measurements and Main Results Lag-1 fine particulate matter mass concentrations were not associated with respiratory hospitalizations (odds ratio and 95% confidence interval per 10 μg/m3 increase in fine particulate matter: 1.004 [0.955–1.056]) in analyses ignoring particle constituents and oxidative potential. However, when models were examined above or below median metals, sulfur, and oxidative potential, positive associations were observed above the median. For example, the odds ratio and 95% confidence interval per 10 μg/m3 increase in fine particulate matter were 1.084 (1.007–1.167) when copper was above the median and 0.970 (0.929–1.014) when copper was below the median. Similar trends were observed for oxidant gases. Conclusions Stronger associations were observed between outdoor fine particles, oxidant gases, and respiratory hospitalizations in children when metals, sulfur, and particle oxidative potential were elevated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".