Air Pollution and Incidence of Dementia
Bibliographic record
Abstract
Background and Objectives Studies of association between air pollution and incidence of dementia have shown discrepant results. The aim of this study was to evaluate the association between air pollution and dementia. Methods In this systematic review and meta-analysis, PubMed, MEDLINE, EMBASE, PsycINFO, Scopus, and Web of Science were searched and updated in August 2021. Population-based cohort studies that reported on hazard ratio (HR) of dementia in association with exposure to fine particulate matter (PM2·5), nitrogen oxides (NOX), nitrogen dioxide (NO2), or ozone (O3) in those aged >40 years were included. Data were extracted by 2 independent investigators. The main outcome was the pooled HR for dementia per increment of pollutant, calculated using a random-effects model. Results were reported in accordance with PRISMA guidelines. The protocol was registered in PROSPERO (registration number: CRD42020219036). Results A total of 20 studies were included in the systematic review, and 17 provided data for the meta-analysis. The total included population was 91,391,296, with 5,521,111 (6%) being diagnosed with dementia. A total of 12, 5, 6, and 4 studies were included in the meta-analyses of PM2·5, NOX, NO2, and O3, respectively. The risk of dementia increased by 3% per 1 μg/m3 increment in PM2·5 (HR, 1.03; 95% CI [1.02–1.05]; I2 = 100%). The association between dementia per 10 μg/m3 increment in NOX (HR, 1.05; 95% CI [0.99–1.13]; I2 = 61%), NO2 (HR, 1.03; 95% CI [1.00–1.07]; I2 = 94%), and O3 levels (HR, 1.01; 95% CI [0.91–1.11]; I2 = 82%) was less clear, although a significant association could not be ruled out, and there was high heterogeneity across studies. Discussion Existing evidence suggests a significant association between exposure to PM2·5 and incidence of dementia and nonsignificant association between dementia and NOX, NO2, and O3 exposure. However, results should be interpreted in light of the small number of studies and high heterogeneity of effects across studies.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.017 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".