A systematic review and meta-analysis of air pollution and increased risk of frailty
Bibliographic record
Abstract
BACKGROUND: Environmental air pollution is increasingly recognised as a potential contributor to frailty. This systematic review and meta-analysis aimed to synthesise existing evidence on the associations between environmental air pollution and frailty in middle-aged and older adults, providing insights into the impact of air pollution on public health. METHODS: The systematic review and meta-analysis were conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Statement 2020. Four electronic databases were searched without restrictions on language, publication status, or year of publication. RESULTS: Of the 145 publications identified through the systematic search, 18 were included. Meta-analyses indicated a 19% increased risk of frailty due to air pollution (fine particulate matter ≤2.5 microns) [n = 9 studies; pooled odds ratio (OR) 1.19; 95% confidence interval (CI) 1.10-1.27], a 28% increase with exposure to household solid fuels (n = 4 studies; OR 1.28; 95% CI 1.16-1.40) and a 59% increase due to exposure to secondhand smoke (n = 3 studies; OR 1.59; 95% CI 0.46-2.72). Except for the meta-analysis on air pollution, no heterogeneity or risk of publication bias was observed amongst the included studies. The Joanna Briggs Institute checklist confirmed high methodological quality across all included studies. CONCLUSIONS: Environmental exposures, including air pollution, the use of unclean household fuels and exposure to secondhand smoke, significantly increase the risk of frailty. These findings underscore the urgent need to raise awareness and establish effective public health strategies to reduce these environmental risks and associated frailty, particularly in light of population ageing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.075 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.041 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".