Long-term exposure to air pollution and heart failure: a systematic review and meta-analyses
Bibliographic record
Abstract
Background: Long-term exposure to air pollution has been linked to coronary cardiovascular disease andcerebrovascular disease mor, yet litearture on heart failure (HF) is rather new and limited. We performed asystematic review and meta-analysis to investigate the association between long-term exposure to airpollution and HF.Methods: We performed an extensive literature search in PubMed, Embase and Web of Science untilSeptember 23, 2019. We identified 1,627 unique studies referring to air pollution and heart failure, fromwhich 10 were included in the meta-analyses. We applied random-effects models to combine risk estimatesof association between air pollutants and HF incidence, and estimated between studies heterogeneity. Weassessed publication bias through plots and the Egger's test.Results: We identified 10 studies investigating associations between long-term exposure to air pollutionand HF published between 2013 and 2019. All 10 studies were cohort studies, two from UK, one from theNetherlands, one from Denmark, one from Sweden, three from Canada, one from US, and one from SouthKorea. The pooled hazard ratio (HR) for association between long-term exposure to PM2.5 and HF incidencefor 1 μg/m3 increase was 1.04 (95% Confidence Interval (CI): 1.02-1.06), based on 9 studies; for PM10 pooledHR was 1.02 (1.00-1.04) per 1 μg/m3 increase, based on 3 studies; for NO2 pooled HR was 1.20 (1.09-1.32)per 20 µg/m3 based on six studies, and for O3 pooled HR was 0.97 (0.89-1.05) per 10 µg/m3 increase, basedon 4 studies. There are 2 studies on ultrafine particles, with pooled HR of 1.29 (0.77-2.17) per 10,000particles/m3. There was high heterogeneity between study-specific results for most of the analyses,attributed to different populations under study. There was little evidence of publication bias.Conclusions: We found evidence for an association between long-term exposure to air pollution and risk ofHF.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.039 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".