Association of outdoor air and noise pollution with unprovoked seizures and <scp>new onset</scp> epilepsy: A systematic review and <scp>meta‐analysis</scp>
Bibliographic record
Abstract
Abstract Research has indicated that certain environmental exposures may increase the risk of unprovoked seizures and new onset epilepsy. This study aimed to synthesize the literature that has estimated the associations between short‐ and long‐term exposure to outdoor air and noise pollution and the risk of unprovoked seizures and new onset epilepsy. We searched Embase, MEDLINE, Scopus, Web of Science, BIOSIS Previews, Latin American and Caribbean Health Sciences Literature, Proquest Dissertations and Theses, conference abstracts, and the gray literature and conducted citation tracing in June 2023. Observational and ecological studies assessing the associations of air and noise pollution with unprovoked seizures or new onset epilepsy were eligible. One reviewer extracted summary data. Using fixed and random effects models, we calculated the pooled risk ratios (RRs) for the studies assessing the associations between short‐term exposure to air pollution and unprovoked seizures. Seventeen studies were included, 16 assessing the association of air pollution with seizures and one with epilepsy. Eight studies were pooled quantitatively. Ozone (O 3 ; RR = .99, 95% confidence interval [CI] = .99–.99) and nitrogen dioxide (NO 2 ) exposure adjusted for particulate matter (RR = 1.02, 95% CI = 1.01–1.02) on the same day, and carbon monoxide (CO) exposure 2 days prior (RR = 1.12, 95% CI = 1.02–1.22), were associated with seizure risk. A single study of air pollution and epilepsy did not report a significant association. The risk of bias and heterogeneity across studies was moderate or high. Short‐term exposure to O 3 , NO 2 , and CO may affect the risk of seizures; however, the effect estimates for O 3 and NO 2 were minimal. Additional research should continue to explore these and the associations between outdoor air pollution and epilepsy and between noise pollution and seizures and epilepsy.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.025 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 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".