Vaping-associated lung illness (VALI) in Canada: a descriptive analysis of VALI cases reported from September 2019 to December 2020
Bibliographic record
Abstract
INTRODUCTION: The aim of this study was to explore demographic and clinical characteristics of vaping-associated lung illness (VALI) cases reported in Canada from September 2019 to December 2020; compare the epidemiology of VALI cases in Canada to e-cigarette or vaping product use-associated lung injury (EVALI) cases in the US; and examine possible explanations for differences between the two countries. METHODS: A federal/provincial/territorial task group developed a national outbreak definition, minimum dataset and case report form for identification and surveillance of VALI cases in Canada. Descriptive analysis explored the characteristics and epidemiology of reported VALI cases. RESULTS: Of the 20 VALI cases reported, none resulted in a death. Of all cases, 5 (25%) involved youth aged 15 to 19 years, 10 (50%) adults aged 20 to 49 years and 5 (25%) aged 50 years and older. Sixty percent of patients were men. Half (50%) required breathing assistance. Three-quarters (75%) reported using nicotine-containing vaping products, and 40% reported use of cannabis-containing vaping products; of those who reported frequency of vaping, most (71%) reported vaping daily. VALI cases were reported at a lower prevalence (0.9 per million) than EVALI (8.5 per million). Demographics and vaping behaviour also differed. CONCLUSION: VALI cases were reported in Canada between September 2019 and December 2020; however, there was a much lower prevalence and they may have been caused by different factors from the EVALI outbreak in the US. The factors influencing VALI in Canada are complex and multifactorial. Research is needed to understand the short- and long-term health effects of nicotine and cannabis vaping.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".