Investigating Syndemic Effects of Air Pollution and Physical Inactivity on Cognitive Decline in Older Adults
Bibliographic record
Abstract
BACKGROUND: Previous research has independently associated air pollution and physical inactivity with increased mortality and morbidity. There is an ongoing debate about whether those factors interact to cause an even higher burden, suggesting potential syndemics. This study aimed to estimate the interaction between air pollution and physical inactivity on cognitive decline in older adults. METHODS: The study utilized the Northern Ireland Cohort for the Longitudinal Study of Ageing. The outcome was a ≥3 points decline in the Mini-Mental State Examination score between 2 cohort waves. Exposures were annual mean particulate matter smaller than 2.5 μm (PM2.5) in a 1-km buffer around participants' residences estimated based on national monitoring and self-reported recreational moderate to vigorous physical activity (MVPA) minutes per week. Logistic regression models were used to estimate additive and multiplicative interactions between exposures adjusting for confounders. RESULTS: Among 2836 participants, 137 (4.8%) had cognitive decline between waves. The median PM2.5 was 6.6 μg/m3 (interquartile range: 5.6-7.6), and 50% reported no MVPA in a week (interquartile range: 0-251.2). Models indicated additive (relative excess risk due to interaction = 0.63; 95% CI, -0.98 to 2.24) and multiplicative (synergy factor = 1.76; 95% CI, 0.84 to 3.72) interactions between high PM2.5 and low MVPA on the risk of cognitive decline; however, estimates were not precise. CONCLUSIONS: This study presents a novel quantitative investigation of a potential syndemic focusing on a less-explored outcome of cognitive decline. However, outcome and exposure measurements limited the certainty of our findings. Future studies should include areas with higher variation in air pollution and use more granular exposure and sensitive outcome measures.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".