Air Pollution and the Brain: A Harmonized Analysis of Four Cohorts With the Harmonized Cognitive Assessment Protocol
Bibliographic record
Abstract
Abstract Growing evidence suggests that air pollution may have significant impacts on aging and cognitive health. This study investigates the cross-sectional associations of long-term exposure to total and source-specific fine particulate matter (PM2.5) with harmonized cognitive function measures across four countries. We included participants who had completed the Harmonized Cognitive Assessment Protocol battery of cognitive tests from Health and Retirement Study (HRS), a US nationally representative study, and its sister studies in England, Chile, and India. We linked modeled concentrations of total PM2.5 and PM2.5 from 19 emission sources to participants’ residential addresses over the 10 years preceding the cognitive assessment. To examine associations with cognitive function, we employed weighted generalized linear models adjusted for individual- and area-level confounders. The 10-year average total PM2.5 concentrations were ranging from 9.2±1.9 μg/m3 in the US to 56.5±25.9 μg/m3 in India. Although overall associations between total PM2.5 and cognitive function were modest across all countries, we identified universal and unique sources across countries. Specifically, higher wildfire-related PM2.5 was associated with poorer cognitive function in the US and India, while higher agriculture-related PM2.5 was linked to poor cognitive function in England and Chile, particularly among those living in the non-urban areas. Associations with residential PM2.5 were only observed for India. Our findings highlight the need for cross-nation studies to resolve air pollution issues from a global perspective.
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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 | 0.042 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".