Asthma Trajectories in a Population-based Birth Cohort. Impacts of Air Pollution and Greenness
Bibliographic record
Abstract
RATIONALE: The heterogeneity of asthma phenotypes may explain inconsistencies in observed associations with environmental exposures. OBJECTIVES: To identify trajectories of childhood asthma and to characterize the potential impact of residential greenness and air pollution on asthma trajectory subgroups. METHODS: Linked administrative databases of medical visits were used to define the occurrence and recurrence of asthma over a 10-year follow-up period within a population-based birth cohort of more than 65,000 children. Group-based trajectory modeling was used to identify unique asthma trajectories. Weighted multinomial regression was used to assess the relationship between asthma trajectories and risk factors, including environmental exposures. MEASUREMENTS AND MAIN RESULTS: Group-based trajectory modeling distinguished four trajectories: one with no asthma representing 88.8% of the cohort, one with transient asthma (5.6% of the cohort), and two trajectories with chronic asthma with early (<1 yr; 1.5%) and late (<3 yr; 4.1%) onset during early childhood. These trajectories differed with respect to socioeconomic markers and modifiable risk factors, including maternal smoking and breastfeeding initiation. After accounting for sex, parity, breastfeeding, term birth weight, household income, maternal education, delivery mode, and smoking, an interquartile increase in nitrogen dioxide exposure increased the risk of membership in the early and late-onset chronic asthma trajectories, relative to subjects without asthma, by 50% and 20%, respectively (weighted risk ratio, 1.5 and 1.2; 95% confidence interval, 1.2-1.9 and 1.0-1.4). Greenness was not associated with any of the asthma trajectories. CONCLUSIONS: Traffic-related air pollution increased the probability of a chronic asthma trajectory.
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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 | 0.004 |
| 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 | 0.001 | 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".