Ambient air pollution and the prevalence of rhinoconjunctivitis in adolescents: a worldwide ecological analysis
Bibliographic record
Abstract
Whether exposure to outdoor air pollution increases the prevalence of rhinoconjunctivitis in children is unclear. Using data from Phase Three of the International Study of Asthma and Allergies in childhood (ISAAC), we investigated associations of rhinoconjunctivitis prevalence in adolescents with model-based estimates of ozone, and satellite-based estimates of fine (diameter < 2.5 μm) particulate matter (PM 2.5 ) and nitrogen dioxide (NO 2 ). Information on rhinoconjunctivitis (defined as self-reported nose symptoms without a cold or flu accompanied by itchy watery eyes in the past 12 months) was available on 505,400 children aged 13–14 years, in 183 centres in 83 countries. Centre-level prevalence estimates were calculated and linked geographically with estimates of long-term average concentrations of NO 2 , ozone and PM 2.5 . Multi-level models were fitted adjusting for population density, climate, sex and gross national income. Information on parental smoking, truck traffic and cooking fuel was available for a restricted set of centres (77 in 36 countries). Between centres within countries, the estimated change in rhinoconjunctivitis prevalence per 100 children was 0.171 (95% confidence interval: − 0.013, 0.354) per 10% increase in PM 2.5 , 0.096 (− 0.003, 0.195) per 10% increase in NO 2 and − 0.186 (− 0.390, 0.018) per 1 ppbV increase in ozone. Between countries, rhinoconjunctivitis prevalence was significantly negatively associated with both ozone and PM 2.5 . In the restricted dataset, the latter association became less negative following adjustment for parental smoking and open fires for cooking. In conclusion, there were no significant within-country associations of rhinoconjunctivitis prevalence with study pollutants. Negative between-country associations with PM 2.5 and ozone require further investigation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".