Childhood Exposure to Air Pollution as a Potential Contributor of Chronic Non-Respiratory Inflammatory Disorders: A Longitudinal Prospective Cohort Study in Hamilton, Canada
Bibliographic record
Abstract
This study examines the relationship between childhood exposure to air pollution and diagnosis with chronic non-respiratory health outcomes in adulthood. This prospective cohort study uses data collected in the 1970/1980s from 395 children, including exposure to air pollution. Over thirty years later, a survey collected data on various health outcomes, including diagnosis with arthritis, high blood pressure, long-term skin conditions, and hay fever allergies. Logistic regression modeling was performed to examine the relative contribution of childhood exposure to air pollution on chronic non-respiratory health outcomes in adulthood. Childhood exposure to SO2 emerged as a significant predictor of arthritis (OR = 2.73, 95% CI 1.20 - 6.18) and high blood pressure (OR = 2.82, 95% CI 1.23 - 6.47). Other significant predictors include respiratory symptoms during childhood, family income during childhood and adulthood, property tenure, employment status, residential exposures, life events, physical activity, and body mass index. Childhood exposure to air pollution did not emerge as a significant predictor of long-term skin conditions or hay fever allergies. Findings contribute to the debate on the health effects of air pollution, indicating that the health impacts of childhood exposure to air pollution may include chronic inflammatory disorders in adulthood.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".