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Record W2755438666 · doi:10.1016/j.envint.2017.08.020

Exposure to ambient air pollution and the incidence of dementia: A population-based cohort study

2017· article· en· W2755438666 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueEnvironment International · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreEnvironment and Climate Change CanadaHealth CanadaToronto Western HospitalUniversity Health NetworkUniversity of TorontoDalhousie UniversityMcGill UniversityInstitute for Clinical Evaluative SciencesPublic Health Ontario
FundersHealth CanadaOntario Ministry of Health and Long-Term CareInstitute for Clinical Evaluative Sciences
KeywordsDementiaIncidence (geometry)Environmental healthCohortAir pollutionCohort studyPopulationPollutionMedicineAir pollutantsEnvironmental scienceDiseaseBiologyEcologyPathology

Abstract

fetched live from OpenAlex

Emerging studies have implicated air pollution in the neurodegenerative processes. Less is known about the influence of air pollution, especially at the relatively low levels, on developing dementia. We conducted a population-based cohort study in Ontario, Canada, where the concentrations of pollutants are among the lowest in the world, to assess whether air pollution exposure is associated with incident dementia. The study population comprised all Ontario residents who, on 1 April 2001, were 55–85 years old, Canadian-born, and free of physician-diagnosed dementia (~ 2.1 million individuals). Follow-up extended until 2013. We used population-based health administrative databases with a validated algorithm to ascertain incident diagnosis of dementia as well as prevalent cases. Using satellite observations, land-use regression model, and an optimal interpolation method, we derived long-term average exposure to fine particulate matter (≤ 2.5 μm in diameter) (PM2.5), nitrogen dioxide (NO2), and ozone (O3), respectively at the subjects' historical residences based on a population-based registry. We used multilevel spatial random-effects Cox proportional hazards models, adjusting for individual and contextual factors, such as diabetes, brain injury, and neighborhood income. We conducted various sensitivity analyses, such as lagging exposure up to 10 years and considering a negative control outcome for which no (or weaker) association with air pollution is expected. We identified 257,816 incident cases of dementia in 2001–2013. We found a positive association between PM2.5 and dementia incidence, with a hazard ratio (HR) of 1.04 (95% confidence interval (CI): 1.03–1.05) for every interquartile-range increase in exposure to PM2.5. Similarly, NO2 was associated with increased incidence of dementia (HR = 1.10; 95% CI: 1.08–1.12). No association was found for O3. These associations were robust to all sensitivity analyses examined. These estimates translate to 6.1% of dementia cases (or 15,813 cases) attributable to PM2.5 and NO2, based on the observed distribution of exposure relative to the lowest quartile in concentrations in this cohort. In this large cohort, exposure to air pollution, even at the relative low levels, was associated with higher dementia incidence.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.019
GPT teacher head0.294
Teacher spread0.274 · 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