Exploration of the Global Burden of Dementia Attributable to PM2.5: What Do We Know Based on Current Evidence?
Bibliographic record
Abstract
Abstract Exposure to ambient PM2.5 pollution has been linked to multiple adverse health effects. Additional effects have been identified in the literature and there is a need to understand its potential role in high prevalence diseases. In response to recent indications of PM2.5 as a risk factor for dementia, we examine the evidence by systematically reviewing the epidemiologic literature, in relation to exposure from ambient air pollution, household air pollution, secondhand smoke, and active smoking. We develop preliminary exposure‐response functions, estimate the uncertainty, and discuss sensitivities and model selection. We estimate the likely impact to be 2.1 M (1.4 M, 2.5 M; 5%–95% confidence) global incident dementia cases and 0.6 M (0.4 M, 0.8 M) deaths attributable to ambient PM2.5 pollution in 2015. This implies a combined toll from morbidity and mortality of dementia of 7.3 M (5.0 M, 9.1 M) lost disability‐adjusted life years. China, Japan, India, and the United States had the highest estimated total burden, and the per capita burden was highest in developed countries with large elderly populations. Compared to 2000, most countries in Europe, the Americas, and Southern Africa reduced the burden in 2015, while other regions had a net increase. Based on a recent systematic review of cost of illness studies for dementia, our estimates imply economic costs of US$ 26 billion worldwide in 2015. Based on this estimation, ambient PM2.5 pollution may be responsible for 15% of premature deaths and 7% of DALYs associated with dementia. Our estimates also indicate substantial uncertainty in this relationship, and future epidemiological studies at high exposure levels are especially needed.
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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 | 0.018 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".