Long-Term Exposure To Air Pollution And Traffic Noise And Global Cognitive Score – Results From The Heinz Nixdorf-Recall Study
Bibliographic record
Abstract
Introduction: Investigations of adverse effects of air pollution (AP) and ambient noise on cogni-tive function are scarce, and their results are inconsistent. The aim of this study was to analyze the associations of long-term exposure to AP and traffic noise with a global cognitive score (GCS) in single and two-exposure models. Data: Our analysis is based on cross-sectional data from the first follow-up examination (2006-2008) of the population-based Heinz Nixdorf Recall study, located in three adjacent in the highly urbanized German Ruhr Area. Methods: Cognitive performance was completed in 4086 of 4157 participants using five subtests: verbal fluency, labyrinth test, immediate and delayed verbal recall, and clock-drawing tests. The GCS was additively calculated using age- and education-specific z scores of the five subtests. We assessed long-term residential concentrations for size-specific particulate matter (PM) and nitrogen oxides with land use regression and dispersion models, and traffic noise (weighted 24-h (LDEN) and night-time (LNIGHT) means) according to the EU directive 2002/49/EC. Multiple linear regression models adjusted for individual risk factors (age, sex, socio-economic status, alcohol consumption, smoking status, self-reported passive smoking, any regular physical activity, and body mass index) were calculated for the association of environmental exposures with GCS. Results: In the fully adjusted model, AP and noise were negatively associated with GCS. For example, an interquartile range (IQR) increase in PM2.5 (1.43 µg/m3) was associated with a de-crease in GCS of β =-0.05 [95% confidence interval (CI) -0.0.8;-0.03] and for a 10 dB(A) increase in LDEN, β was -0.07 [-0.13; -0.01]). In two-exposure models, the estimates remained stable and significant for AP, but slightly attenuated for noise. Discussion: Long-term exposures to AP and road traffic noise were adversely associated with GCS in one- and two exposure models.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".