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Record W6923662543 · doi:10.14288/1.0440716

Air pollution, green space and dementia risk in Canada

2024· article· en· W6923662543 on OpenAlexaboutno aff

Bibliographic record

VenuecIRcle (University of British Columbia) · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Methods and Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsDementiaInterquartile rangeAir pollutionPopulationPublic healthPopulation healthEpidemiologyRisk assessment

Abstract

fetched live from OpenAlex

Dementia is a major global population health challenge. It is not curable, and severity worsens over time. With seniors expected to comprise approximately 25% of the Canadian population by 2035, cases of dementia and its related health and financial burden are forecast to dramatically increase in the next decades. While some well-known risk factors for dementia are identified (e.g. age, sex), they do not fully explain dementia risk, therefore other potentially modifiable risk factors may be unidentified. Mounting evidence suggests connections between environmental factors and dementia, however associations between exposure to air pollution and dementia have not been adequately studied, nor have the potential protective effects of residing in neighbourhoods with more natural green space. To address these gaps, we investigated the links between long-term exposure to air pollution (e.g. fine particulate matter, PM₂ꓸ₅; nitrogen dioxide, NO₂), dementia, and the possible beneficial impacts from green space (Normalized Difference Vegetation Index) within three large population-based cohorts. In the Metro Vancouver cohort, air pollutants were associated with incidence of non-Alzheimer’s dementia (e.g., hazard ratios (HR) of 1.02 [0.98-1.05], 1.02 [0.99-1.06] per interquartile range increase in PM₂ꓸ₅ and NO₂). In the national 2001 Canadian Census Health and Environment Cohort, PM₂ꓸ₅ (1.09 [95% CI:1.08-1.10] per interquartile range increase) and NO₂ (1.08 [95% CI:1.07-1.09]) were associated with dementia mortality. These findings were supported by analysis of the Canadian Community Health Survey where individual behavioural risk factors (smoking, alcohol consumption, etc.) were available. Air pollutants were associated with increased dementia mortality (e.g., dementia HR of 1.25 [1.23-1.27] and 1.23 [1.21-1.25] per interquartile range increase in PM₂ꓸ₅ and NO₂, while HRs were attenuated (1.14 [1.12-1.16] and 1.17 [1.15-1.19]) in models including behavioural risk factors. Across the three cohorts, greenness was associated with 1-5% risk reduction in dementia. These results indicate that air pollution, even at relatively low concentrations, was linked with dementia, while living in greener areas was found to have some small protective effects. These findings contribute to the overall understanding of the relationships between built-in environment factors and dementia and can contribute to the development of public health approaches for dementia risk reduction.

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)0.003
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 score0.043
Threshold uncertainty score0.310

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.006
Science and technology studies0.0050.001
Scholarly communication0.0020.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.010
GPT teacher head0.216
Teacher spread0.207 · 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
Published2024
Admission routes1
Has abstractyes

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