Vaping and Health Service Use: A Canadian Health Survey and Health Administrative Data Study
Bibliographic record
Abstract
Abstract Rationale Emerging research suggests that e-cigarette (EC) use may have detrimental health effects, increasing the burden on healthcare systems. Objectives To determine whether young EC users had increased asthma, asthma attacks, and health services use (HSU). Methods This cohort study used the linked Canadian Community Health Survey (cycles 2015–16 and 2017–18) and health administrative data (January 2015–March 2018). A propensity score method matched self-reported EC users to up to five control subjects. Matched multivariable logistic and negative binomial regressions were used to calculate odds ratios, rate ratios (RRs), and 95% confidence intervals (CIs) with EC use as the exposure and asthma, asthma attacks, and all-cause HSU as the outcomes. Results Analyses included 2,700 matched Canadian Community Health Survey participants (15–30 yr), 505 (2.4% of 20,725 participants) EC users matched to 2,195 nonusers. Female EC users and nonusers had a significant twofold increase in odds of asthma attacks compared with male nonusers (OR, 2.30; 95% CI, 1.29–4.12; P = 0.005; OR, 2.29; 95% CI, 1.57–3.35; P = 0.0001, respectively). Dual EC and conventional tobacco users had a twofold increased all-cause HSU rate compared with nonusers who never smoked tobacco (RR, 2.13; 95% CI, 1.53–2.98). This rate was greater than that for EC users who never smoked tobacco (RR, 1.73; 95% CI, 1.00–3.00) and non-EC users who regularly smoke tobacco (RR, 1.72; 95% CI, 1.29–2.29). Compared with male nonusers, female EC users had the highest increased all-cause HSU (RR, 1.94; 95% CI, 1.39–2.69) over male EC users and female nonusers (RR, 1.13; 95% CI, 0.86–1.48; RR, 1.41; 95% CI, 1.16–1.71, respectively). Conclusions Current EC use is associated with significantly increased odds of having an asthma attack. Furthermore, concurrent EC use and conventional cigarette smoking are associated with a higher rate of all-cause HSU. The odds of asthma attack and all-cause HSU were highest among women. Thus, EC use may be an epidemiological biomarker for youth and young adults with increased health morbidity.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".