Long-Term Exposure To Air Pollution And Traffic Noise And Global Cognitive Score – Results From The Heinz Nixdorf-Recall Study
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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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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 | 0.001 |
| 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) | 0.000 | 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 it