Abstract WP185: Relationship Between E-cigarette Smoking And Stroke - A NHANES Study
Bibliographic record
Abstract
Introduction: Tobacco use is a known risk factor for stroke and other cardiovascular diseases. However, the relationship between e-cigarette smoking and stroke is largely unknown. Hypothesis: This study aims to assess the effect of e-cigarettes among people who have had a history of stroke. Methods: A cross-sectional survey was performed using the NHANES database from 2015 to 2018 in the US population. We identified participants 18 or older with a history of stroke and their smoking habits (e-cigarette, traditional, and dual smoking). The chi-square test, unpaired t-test, and multivariable logistic regression models were used to examine the association of e-cigarette consumption among the stroke population. Results: Out of a total of 266,058 respondents, 79,825 were smokers [E-cigarette: 7,756 (9.72%); Traditional: 48,625(60.91%), Dual smokers: 23,444 (29.37%)] Overall prevalence of stroke was 5.41% (n=4194) amongst various types of smokers. Amongst females with stroke, the prevalence of e-cigarette use was higher compared to traditional smoking. (36.36% vs 33.91%; p<0.001) Mexican Americans (21.21% vs. 6.02%) and other Hispanics (24.24% vs. 7.70%) had a higher prevalence of e-cigarette use than traditional smoking. (p<0.001) E-cigarette smokers were having an early onset of stroke in comparison with dual and traditional smokers. (Median age in years: 48 vs. 50 vs. 59, respectively; p<0.001). Stroke was more prevalent amongst traditional smokers compared to e-cigarette and dual smokers (6.75% vs. 1.09% vs. 3.72% p<0.001). In multivariable logistic regression analysis, the odds of having a history of stroke were higher amongst e-cigarettes in comparison with traditional smokers. (aOR:1.15; 95%CI: 1.15-1.16; p<0.001). Conclusion: Although stroke was more prevalent in traditional smokers, e-cigarette smokers had early-onset and higher odds of stroke compared to traditional smokers. More prospective studies are needed to evaluate the long-term effects and safety of e-cigarettes to mitigate the risk of cardio and cerebrovascular disorders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".