A scenario-based modeling study to project the future burden of COPD in Western Europe accounting for air pollution, tobacco smoking, and e-cigarette vaping
Bibliographic record
Abstract
Rationale: COPD is imposing an immense economic and health burden. Previous modeling work quantified the burden of COPD across North America in terms of direct costs, indirect costs, and exacerbation frequency, accounting for COPD risk factors such as smoking and air pollution. We extended this modeling by also accounting for the effects of e-cigarettes (vaping). Considering for multiple risk factors, we sought to project economic and health burden of COPD across Western Europe by 2050. Methods: Using data derived from publicly available datasets, a Markov model was developed to simulate population dynamics from 2019 to 2050. Population growth and mortality were modeled across different subgroups of age, sex, tobacco smoking, and vaping. Direct costs, indirect costs, and the number of exacerbations and hospitalizations were projected in Western Europe (n_countries = 12) to 2050. Results: Incremental increases in the economic and health burden of COPD associated with vaping across Western Europe through 2050 are projected (Table). erj;64/suppl_68/OA4674/F1 F1 F1 Conclusions: Including vaping as an additional risk factor incrementally increased the economic and health burden of COPD through 2050 across Europe. Public health efforts targeting COPD risk factors may be critical to prevent this anticipated increase in the burden of COPD.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".