Long-term exposures to ambient PM<sub>1</sub> and NO<sub>2</sub> pollution in relation to mild cognitive impairment of male veterans in China
Bibliographic record
Abstract
Abstract Mild cognitive impairment (MCI) is an intermediate stage of cognitive decline between normal ageing and dementia or Alzheimer’s disease in the elderly. However, evidence is very limited in China for the association between air pollution and MCI. This study aims to examine the associations of long-term exposure to air pollution and MCI, using data from the Chinese Veteran Clinical Research Platform. A national investigation on mental health of veterans was conducted in 277 veteran communities in 18 cities across China. In total, 1,861 MCI cases and 3,188 controls were randomly selected using a stratified cluster sampling strategy from December 2009 to December 2011. Participants’ cognitive function was first assessed using the Mini Mental State Examination and the Montreal Cognitive Assessment in the Chinese version, and then further confirmed by clinical examination. Participants’ mean exposures to PM 1 (particulate matter with aerodynamic diameter ⩽1 μ m) and NO 2 (nitrogen dioxide) during the 3 years before the investigation were estimated using satellite remote sensing data, meteorological variables and land use information. The association between historical exposure to air pollution and MCI was examined using Logistic regression. After controlling for individual-level and regional-level confounders, we found historical exposures to PM 1 and NO 2 significantly increased the risk of MCI. The odds ratios (ORs associated with per 10 µ g m −3 increase in air pollution) and 95% confidence intervals for PM 1 and NO 2 were 1.08 (1.04, 1.13) and 1.07 (1.02, 1.13), respectively. In the multi-pollutant models, higher OR for PM 1 while lower OR for NO 2 were observed compared to single-pollutant models. High levels of PM 1 and NO 2 pollution significantly increased the risk of cognitive decline among male veterans in China. Given the causal air pollution-MCI relationship, good air quality may help to reduce the burden of mental disorders among elderly veterans in China.
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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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".