Long-Term Residential Ambient Air Pollution and Rheumatoid Arthritis: A Systematic Review
Bibliographic record
Abstract
Background: There has been increasing interest regarding the effects of air pollution on the risk of rheumatoid arthritis. Unfortunately, epidemiologic research on air pollution effects remains scant and offers conflicting results. Objectives: This study aimed to systematically review the epidemiologic literature on RA morbidity due to long-term residential air pollution. Materials and Methods: The authors independently carried out searches in MEDLINE and EMBASE through June 2015 (1974 - 2015). The searches were limited to English, Spanish and Russian. To complement the search strategy, authors and experts in the field were contacted, and hand-searches were carried out for articles included in reference lists. Peer-reviewed epidemiologic studies were eligible only if they explored the risk for RA in adults associated with air pollution exposure. Studies were omitted if they relied on self-report alone, experimental studies, short-term effects of air pollution, juvenile arthritis, or other autoimmune rheumatic diseases. Two authors independently extracted information about study characteristics. The study’s quality was assessed with the Newcastle-Ottawa scale. Results: Four relevant papers were included in a qualitative synthesis. Two studies showed significantly higher risks for RA in people living within 50 m of a heavy traffic road. No firm conclusions could be made for particulate matter. In one study, NO2 was associated with seronegative RA among smokers. The risk for SO2 was significant in one study. In the only relevant study, O3 was linked to RA. Conclusions: Proximity to road traffic might be a risk factor for RA as there are suspected effects associated with NO2 and SO2. Overall, the available evidence is too preliminary and scarce to draw firm conclusions. However, the results indicate the feasibility of further studies elucidating on the relationship between air pollution and RA.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".