Air Pollution and Dementia: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Both air pollution and dementia are current and growing global issues. There are plausible links between exposure to specific air pollutants and dementia. OBJECTIVE: To systematically review the evidence base with respect to the relationship between air pollution and later cognitive decline and dementia. METHODS: Medline, Embase, and PsychINFO® were searched from their inception to September 2018, for publications reporting on longitudinal studies of exposure to air pollution and incident dementia or cognitive decline in adults. Studies reporting on exposure to tobacco smoke including passive smoking or on occupational exposure to pollutants were excluded. Using standard Cochrane methodology, two readers identified relevant abstracts, read full text publications, and extracted data into structured tables from relevant papers, as defined by inclusion and exclusion criteria. Papers were also assessed for validity. CRD42018094299Results:From 3,720 records, 13 papers were found to be relevant, with studies from the USA, Canada, Taiwan, Sweden, and the UK. Study follow-up ranged from one to 15 years. Pollutants examined included particulate matter ≤2.5 μ (PM2.5), nitrogen dioxide (NO2), nitrous oxides (NOx), carbon monoxide (CO), and ozone. Studies varied in their methodology, population selection, assessment of exposure to pollution, and method of cognitive testing. Greater exposure to PM2.5, NO2/NOx, and CO were all associated with increased risk of dementia. The evidence for air pollutant exposure and cognitive decline was more equivocal. CONCLUSION: Evidence is emerging that greater exposure to airborne pollutants is associated with increased risk of dementia.
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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".