Does Vaping Increase the Odds of Asthma?: A Canadian Community Health Survey Study
Bibliographic record
Abstract
Rationale: While vaping is thought to be a safer alternative to smoking, emerging research suggests that e-cigarette (EC) use may have long-term health effects and may worsen pre-existing health conditions such as asthma. The objective of this study is to determine whether youth and young adults who are current EC users have increased odds of self-reported asthma and an asthma attack in the last 12 months. Methods: A cross-sectional study was conducted using the 2015-16 and 2017-18 cycles of the Canadian Community Health Survey (CCHS). The CCHS is a cross-sectional survey that collects self-reported information about health status, health determinants, and behaviours. The study cohort included all Canadians aged 12 years and over who participated in the CCHS. A propensity score method was used to identify five matched controls to individuals with self-reported EC use. Individuals were matched by CCHS cycle, age, sex, province of residence, local health unit, body mass index (BMI), household income, education, mental health, smoking history, and life stress. Odds ratios and 95% confidence intervals (CI) with asthma as the outcome and EC use as the exposure were determined using matched logistic regression, adjusting for potential confounding variables including those used for the propensity score matching. Modelling by smoking subgroup was also conducted. Results: The study cohort included 17,190 matched individuals from 222,949 CCHS respondents. 16.7% reported EC use in the past 30 days. Univariate analysis found an odds ratio of 1.19 (95% CI: 1.05-1.34) between EC use and self-reported asthma. Adjusting for potential confounders, individuals with EC use had 19% higher odds of having asthma (95% CI: 1.05-1.35). Current and former smokers had 20% (95% CI: 1.02-1.41) and 33% (95% CI: 1.05-1.69) higher odds of having asthma, respectively, while never smokers did not have significant associations, adjusted for potential confounders. Among those with asthma, EC users had 29% (95% CI: 1.02-1.63) higher odds of having an asthma attack in the last 12 months, adjusted for potential confounders. Conclusions: Current EC use is associated with significantly increased odds of having asthma, that is comparable to current smokers. These findings suggest that EC use is a modifiable risk factor for asthma to be considered in the primary care of youth and young adults. Figure Adjusted odds ratios of self-reported asthma from matched multivariable logistic regression for selected variables. Odds ratios adjusted for age, sex, province, BMI, household income, education, mental health, smoking history, and life stress.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".