Associations between particulate matter elements and early-life pneumonia in seven birth cohorts: Results from the ESCAPE and TRANSPHORM projects
Bibliographic record
Abstract
Evidence for a role of long-term particulate matter exposure on acute respiratory infections is growing. However, which components of particulate matter may be causative remains largely unknown. We assessed associations between eight particulate matter elements and early-life pneumonia in seven birth cohort studies (N total=15,980): BAMSE (Sweden), GASPII (Italy), GINIplus and LISAplus (Germany), INMA (Spain), MAAS (United Kingdom) and PIAMA (The Netherlands). Annual average exposure to copper, iron, potassium, nickel, sulfur, silicon, vanadium and zinc, each respectively derived from particles with aerodynamic diameters ≤ 10 μm (PM10) and 2.5 μm (PM2.5), were estimated using standardized land use regression models and assigned to birth addresses. Cohort-specific associations between these exposures and parental reports of physician-diagnosed pneumonia between birth and two years were assessed using logistic regression models adjusted for host and environmental covariates and total PM10 or PM2.5 mass. Combined estimates were calculated using random-effects meta-analysis. There was substantial within and between-cohort variability in element concentrations. In the adjusted meta-analysis, pneumonia was weakly associated with zinc derived from PM10 (OR: 1.47 (95% CI: 0.99, 2.18) per 20 ng/m(3) increase). No other associations with the other elements were consistently observed. The independent effect of particulate matter mass remained after adjustment for element concentrations. In conclusion, associations between particulate matter mass exposure and pneumonia were not explained by the elements we investigated. Zinc from PM10 was the only element which appeared independently associated with a higher risk of early-life pneumonia. As zinc is primarily attributable to non-tailpipe traffic emissions, these results may suggest a potential adverse effect of non-tailpipe emissions on health.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.004 |
| 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".