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Record W4406996591 · doi:10.1161/str.56.suppl_1.105

Abstract 105: Long-term exposure to ambient air pollution, including ultrafine particles, increases ischemic and hemorrhagic stroke risk among women in the California Teachers Study cohort

2025· article· en· W4406996591 on OpenAlexaff
Emily Cauble, Mike Kleeman, Sophia Wang, Meredith Franklin, Marta Epeldegui, Emma S. Spielfogel, Tarik Benmarhnia, Mandy Yao, Jingyuan Wu, Juan Zhao, Cheryl A.M. Anderson, Mitchell S.V. Elkind

Bibliographic record

VenueStroke · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineStroke (engine)CohortIschemic strokeCohort studyUltrafine particleAir pollutionEnvironmental healthEmergency medicineInternal medicineIschemia

Abstract

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Introduction: Ambient air pollution is linked to increased stroke risk, but it is unclear if associations apply to long-term exposures, equally to different stroke subtypes (ischemic versus hemorrhagic) and differ by pollutants. Of particular interest is exposure to ultrafine particles (PM0.1) for which there is sparse literature on its association with stroke. Hypothesis: Long-term exposures to particulate matter (PM0 2.5, PM10, PM0.1), ozone (O 3 ), and nitrogen dioxide (NO 2 ) are associated with stroke risk. Methods: In a prospective cohort study of 133,477 women enrolled in the California Teachers Study in 1995-96, we assessed 110,120 participants who resided in California from 2000-2018 for residential exposure to 5 air pollutants (O 3 , NO 2 , PM2.5, PM10, and PM0.1) based on a chemical transport model (4-km grid) applied to geocoded residential histories across follow-up (median follow-up=19 years). Strokes were identified with ICD-9 and ICD-10 codes via linkage of cohort participants to California state hospitalization records (Department of Health Care Access and Information). Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CI) for associations between the air pollutants (average exposure across follow-up) with total stroke (n=4,348) and stroke subtypes (ischemic stroke n=3,596; hemorrhagic stroke n=752), adjusted for stroke risk factors. Results: Consistent with state-wide trends, median levels of NO 2 and PM masses assessed among participants declined and plateaued in recent years (2000-2018), whereas median annual O 3 levels increased. The exposures observed among study participants also reflected that of the State; annual median PM2.5=8.23 ug/m 3 (range: 0.84-85.6). We observed increased risks for total, ischemic, and hemorrhagic strokes per ug/m3 increase of PM2.5, PM10.0, and PM0.1 (overall stroke: HR PM0.1 =1.43, 95% CI=1.29-1.58; HR PM2.5 =1.45, 95% CI=1.30-1.63; HR PM10 =1.36, 95% CI=1.23-1.51); the risk magnitudes were higher for hemorrhagic stroke (HR PM0.1 =1.57, 95% CI=1.22-2.02; HR PM2.5 =1.84, 95% CI=1.40-2.42; HR PM10 =1.62, 95% CI=1.27-2.08). Associations observed between NO 2 and stroke were also elevated (HR=1.12 per ppm, 95% CI=1.07-1.18). Conclusion: All PM masses, including ultrafine particles, were associated with overall stroke risk and for both ischemic and hemorrhagic stroke. Further delineation of independent versus combined effect of pollutants is warranted and will be presented.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.211

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.017
GPT teacher head0.285
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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