Air pollution and neighborhood racial composition and their association with memory and memory decline in NHATS
Bibliographic record
Abstract
Abstract Background Memory decline is a harbinger of Alzheimer’s disease (AD). Air pollution was recently added as a dementia risk factor by the Lancet Commission, and area‐level factors, such as neighborhood racial composition (a measure of structural racism) may modify the effects of air pollution. We examined the relationship between air pollution and neighborhood racial composition on memory and decline in a sample of older adults in the United States. Method NHATS is a nationally‐representative sample of 8,245 participants 65+ years at baseline (2011). Annual averages of fine particulate matter (PM2.5, IQR = 2.2 μg/m3), nitrogen dioxide (NO2, IQR = 5.5 ppb), and ozone (O3, IQR = 8.4 ppb) at baseline and Census ACS data were assigned to the Census tract of participants. The Index of Concentration at the Extremes, defined as: [(number in most disadvantaged extreme, i.e., Blacks) ‐ (number in most privileged extreme, i.e., Whites)]/(total people) was calculated for each census tract as a measure of neighborhood racial composition. A composite measure of episodic memory was constructed as the average of z‐scored delayed and immediate word list recall. We used linear mixed effects models to examine the association between each air pollutant and neighborhood racial composition with memory and memory decline over 6 years, adjusting for a number of individual‐ (age, race/ethnicity, gender, education, smoker, practice effects) and area‐ (census division, urban tract) level covariates, with random effects for census tract and participant. Result Increases in air pollution (per IQR) were weakly associated with worse memory at baseline [PM2.5 ‐0.02 (95% CI: ‐0.05, 0.006); NO2 ‐0.03 (‐0.06, 0.002); O3 ‐0.03 (‐0.06, ‐0.0004) SD‐units in memory]. PM2.5 increased the rate of memory decline by ‐0.01 (‐0.02, ‐0.0008); while NO2 increased the rate of memory decline by ‐0.008 (‐0.01, ‐0.0007). Neighborhood racial composition was significantly associated with worse memory (for more disadvantaged neighborhoods) in all air pollution models, but it did not modify the effect of air pollution on memory nor memory decline. Conclusion In this cohort of older adults, we found that PM2.5 and NO2 increase the rate of memory decline, while neighborhood racial composition does not modify this effect of air pollution.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".