Exposure–Response to High PM <sub>2.5</sub> Levels for Cardiovascular Events in High‐Risk Older Adults in Taiwan
Bibliographic record
Abstract
Background Limited studies using individual‐level health outcome data exist from countries with a wider range of particulate matter with a diameter of <2.5 μm (PM 2.5 ) levels to illustrate the shape of the exposure–response curve across a wide PM 2.5 range, including >15 μg/m 3 PM 2.5 concentrations. Taiwan reduced PM 2.5 over time with its policies, which provide opportunities to illustrate the dose–response curves and how reductions of PM 2.5 over time correlated with cardiovascular events incidence in a nationwide sample. Methods Using data from the 2009 to 2019 Taiwan National Health Insurance Database linked to nationwide PM 2.5 data, we examined the magnitude of the exposure–response curve between seasonal average PM 2.5 level and cardiovascular events‐related hospitalizations among older adults at high risk for cardiovascular events. We used history‐adjusted marginal structural models including potential confounding by individual demographic factors, baseline comorbidities, and health service measures. Results We included 373 402 older adults with high‐risk conditions in Taiwan. Using the PM 2.5 concentration <15 μg/m 3 (Taiwan regulatory standard) as a reference, the seasonal average PM 2.5 concentrations (15–23.5 and >23.5 μg/m 3 ) were associated with hazard ratio of 1.13 (95% CI, 1.09–1.18) and 1.19 (95% CI, 1.14–1.24), 1.07 (95% CI, 1.03–1.11) and 1.14 (95% CI, 1.10–1.18), 1.22 (95% CI, 1.08–1.38) and 1.31 (95% CI, 1.16–1.48), 1.04 (95% CI, 0.98–1.10) and 1.10 (95% CI, 1.04–1.16), respectively, for heart failure, ischemic stroke/transient ischemic attack, deep vein thrombosis, and myocardial infarction/acute coronary syndrome. Conclusions Further lowering PM 2.5 levels beyond current regulatory standards (15 μg/m 3 ) may effectively reduce the incidence of heart failure, deep vein thrombosis, and ischemic stroke/transient ischemic attack, and can lead to tangible health benefits in the high‐risk older population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".