Association of ambient air pollution exposure with thyroid cancer incidence trends: A geospatial epidemiological analysis
Bibliographic record
Abstract
Thyroid cancer incidence has risen sharply in Canada and across high-income countries over the past three decades, a trend that epidemiologists have partially attributed to intensified surveillance and improved imaging resolution. Whether ambient air pollution independently contributes to this rise remains contested, and geospatial epidemiological approaches offer a means of exploring this question across diverse environmental contexts without requiring individual-level exposure data. This research examined associations between municipality-level annual mean concentrations of PM2.5, NO2, SO2, and ozone and age-standardised thyroid cancer incidence rates across 87 Canadian health regions using data from 2007 to 2022. Pollutant concentration estimates were derived from the Canadian National Air Pollution Surveillance network and satellite-corrected land-use regression models. Cancer incidence data were obtained from the Canadian Cancer Registry. Negative binomial regression, adjusted for socioeconomic deprivation index, iodine intake proxy, and healthcare access score, was used to estimate incidence rate ratios. PM2.5 showed the strongest association (adjusted IRR: 1.071 per 1 µg/m³ increment, 95% CI: 1.038-1.105, p<0.001), followed by NO2 (aIRR: 1.039, p = 0.002) and SO2 (aIRR: 1.048, p = 0.021). Ozone showed a non-significant positive trend. A composite pollution index was most strongly associated with thyroid cancer incidence (aIRR: 1.082, 95% CI: 1.054-1.111). These ecological associations cannot confirm causation but generate hypotheses for individual-level cohort investigation and support the case for air quality regulation as a potential cancer control co-benefit.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".