Evidence update on the respiratory health effects of vaping e-cigarettes: A systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: In this review, we aimed to explore whether nicotine e-cigarette or vaping product use impact respiratory health. METHODS: original studies published between July 2021 and December 2023 but excluded qualitative studies. Three types of e-cigarette exposure were examined: acute, short-to-medium term, and long-term. RESULTS: We included 119 studies in the main analysis, and 5 in meta-analysis. Over half of the studies had low risk of bias. Non-smoker current vapers had higher incident risk of respiratory symptoms (relative risk, RR=1.90; 95% CI: 1.28-2.83) but statistically non-significant risk of chronic obstructive pulmonary disease (COPD) (RR=2.53; 95% CI: 0.96-6.67) compared to never users. They also had lower incident risk of respiratory symptoms compared to non-vaper current smokers (RR=0.75; 95% CI: 0.64-0.89) and dual users (dual use vs vaping, RR=1.26; 95% CI: 1.03-1.55). Dual users had higher risk of incidence of respiratory symptoms and prevalence of COPD compared to never users (RR=2.53; 95% CI: 1.44-4.45 and RR=3.86; 95% CI: 1.49-10.02, respectively), and the risk was statistically similar to non-vaper current smokers (RR=0.97; 95% CI: 0.84-1.14 and RR=1.15; 95% CI: 1.00-1.33, respectively). All meta-analysis findings were of 'very low' to 'low' certainty evidence. Of the studies not included in meta-analysis, we found 'moderate' certainty evidence of higher risk of respiratory symptoms, COPD, asthma, lung inflammation and damage in non-smoker current vapers compared to non-users, inconsistent findings on the risk of COVID-19 and other respiratory infections, and no significant association with e-cigarette associated lung injury. CONCLUSIONS: E-cigarettes are associated with harms to the respiratory system. Further longitudinal research with special attention to measuring effects in different e-cigarette user populations are warranted.
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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 | 0.023 | 0.063 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.023 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".