Long-term exposure to ambient air pollution and cognitive function in older US adults
Bibliographic record
Abstract
Background: Air pollution effects on cognitive function have been increasingly recognized. Little is known about the impact of different sources of fine particulate (PM 2.5 ). We aim to evaluate the associations between long-term air pollution exposure, including source-specific components in PM 2.5 , and cognition in older adults. Methods: Cognitive assessment, including the Cognitive Abilities Screening Instrument (CASI), Digit Symbol Coding (DSC), and Digit Span (DS), was completed in 4392 older participants in the United States during 2010–2012. Residence-specific air pollution exposures (i.e., oxides of nitrogen [NO 2 /NO x ], PM 2.5 and its components: elemental carbon [EC], organic carbon [OC], sulfur [S], and silicon [Si]) were estimated by geo-statistical models. Linear and logistic regression models were used to estimate the associations between each air pollutants metric and cognitive function. Results: An interquartile range (IQR) increase in EC (0.8 μg/m 3 ) and Si (23.1 ng/m 3 ) was associated with −1.27 (95% confidence interval [CI]: −0.09, −2.45) and −0.88 (95% CI: −0.21, −1.54) lower CASI scores in global cognitive function. For each IQR increase in Si, the odds of low cognitive function (LCF) across domains was 1.29 times higher (95% CI: 1.04, 1.60). For other tests, NO X was associated with slower processing speed (DSC: −2.01, 95% CI: −3.50, −0.52) and worse working memory (total DS: −0.4, 95% CI: −0.78, −0.01). No associations were found for PM 2.5 and two PM 2.5 components (OC and S) with any cognitive function outcomes. Conclusion: Higher exposure to traffic-related air pollutants including both tailpipe (EC and NO x ) and non-tailpipe (Si) species were associated with lower cognitive function in older adults.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".