Systemic autoimmune rheumatic diseases and multiple industrial air pollutant emissions: A large general population Canadian cohort analysis
Bibliographic record
Abstract
Past investigations of air pollution and systemic autoimmune rheumatic diseases (SARDs) typically focused on individual (not mixed) and overall environmental emissions. We assessed mixtures of industrial emissions of fine particulate matter (PM2.5), nitrogen dioxide (NO2), and sulfur dioxide (SO2) and SARDs onset in Ontario, Canada. We assembled an open cohort of over 12 million adults (without SARD diagnoses at cohort entry) based on provincial health data for 2007–2020 and followed them until SARD onset, death, emigration, or end of study (December 2020). SARDs were identified using physician billing and hospitalization diagnostic codes for systemic lupus, scleroderma, myositis, undifferentiated connective tissue disease, and Sjogren’s. Rheumatoid arthritis and vasculitis were not included. Average PM2.5, NO2, and SO2 industrial emissions from 2002 to one year before SARDs onset or end of study were assigned using residential postal codes. A quantile g-computation model for time to SARD onset was developed for the industrial emission mixture, adjusting for sex, age, income, rurality index, chronic obstructive pulmonary disease (as a proxy for smoking), background (environmental overall) PM2.5, and calendar year. We conducted stratified analyses across age, sex, and rurality. We identified 43,931 new SARD diagnoses across 143,799,564 person-years. The adjusted hazard ratio for SARD onset for an increase in all emissions by one decile was 1.018 (95% confidence interval 1.013–1.022). Similar positive associations between SARDs and the mixed emissions were observed in most stratified analyses. Industrial PM2.5 contributed most to SARD risk. Industrial air pollution emissions were associated with SARDs risk.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".