Long-term exposure to moderate fine particulate matter concentrations and cause-specific mortality in an ageing society
Bibliographic record
Abstract
BACKGROUND: Long-term exposure to particulate matter <2.5 μm in size (PM2.5) is considered a risk factor for premature death. However, only a few studies have been conducted in areas with moderate PM2.5 concentrations. Moreover, an ageing society may be more susceptible to environmental exposure and future burden of mortality due to PM2.5. METHODS: This study estimates hazard ratios (HRs) for all-cause and cause-specific mortality from long-term exposure to moderate PM2.5 concentrations in the elderly populations of seven cities in South Korea. We also projected nationwide elderly mortality caused by long-term exposure to PM2.5, accounting for population ageing until 2045. Mortality in 1 720 230 elderly adults aged ≥65 years in 2008 was monitored across 2009-16 and linked to modelled PM2.5 concentrations. RESULTS: A total of 421 100 deaths occurred in 2009-16, and the mean of annual PM2.5 concentration ranged between 21.1 and 31.9 μg/m3 in most regions. The overall HR for a 10 μg/m3 increase in a 36-month PM2.5 moving average was 1.024 (95% confidence intervals: 1.009, 1.039). We estimated that 11 833 all-cause nationwide elderly deaths were attributable to PM2.5 exposure. Annual death tolls may increase to 17 948 by 2045. However, if PM2.5 is reduced to 5 μg/m3 by 2045, the tolls may show a lower increase to 3646. CONCLUSIONS: Long-term exposure to moderately high levels of PM2.5 was associated with increased mortality risk among the elderly. Thus, PM2.5 reduction in response to the projected ageing-associated mortality in South Korea is critical.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".