Impact of Air Pollution on Cognitive Impairment in Older People: A Cohort Study in Rural and Suburban China
Bibliographic record
Abstract
Background: The impact of air pollution on cognitive impairment in older people has not been fully understood. It is unclear which air pollutants are the culprit. Objective: We assessed the associations of six air pollutants and air quality index (AQI) with cognitive impairment. Methods: We examined 7,311 participants aged ≥60 years from the ZJMPHS cohort in China. They were interviewed for baseline socio-demographic and disease risk factors in 2014, and re-interviewed in 2015 and 2016, respectively. The presence of cognitive impairment was determined by the Chinese version of the Mini-Mental State Examination. Daily area-level data monitored for air pollution during 2013–2015 was then examined for associations with cognitive impairment in logistic regression models. Results: Over the two years follow-up, 1,652 participants developed cognitive impairment, of which 917 were severe cases. Continuous air pollution data showed the risk of cognitive impairment increased with exposure to PM2.5 (fully adjusted odds ratio [aOR] 1.04, 95%CI 1.01–1.08), PM10 (1.03, 1.001–1.06), and SO2 (1.04, 1.01–1.08), but not with NO2, CO, O3, and AQI. Categorized data analysis for low, middle, and high level exposure demonstrated that the aOR increased with PM2.5 and AQI, somehow with PM10 and CO, but not significantly with SO2 and NO2, and decreased with O3. The patterns for these associations with severe cognitive impairment were stronger. Conclusion: Lowering PM2.5, PM10, SO2, and CO level could reduce the risk of cognitive impairment in older Chinese. Strategies to target most important air pollutants should be an integral component of cognitive interventions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".