Prevalence of electronic nicotine delivery systems (ENDS) use among youth globally: a systematic review and meta‐analysis of country level data
Bibliographic record
Abstract
OBJECTIVE: To describe the prevalence and change in prevalence of electronic nicotine delivery systems (ENDS) use in youth by country and combustible smoking status. METHODS: Databases and the grey literature were systematically searched to December 2015. Studies describing the prevalence of ENDS use in the general population aged ≤20 years in a defined geographical region were included. Where multiple estimates were available within countries, prevalence estimates of ENDS use were pooled for each country separately. RESULTS: Data from 27 publications (36 surveys) from 13 countries were included. The prevalence of ENDS ever use in 2013-2015 among youth were highest in Poland (62.1%; 95%CI: 59.9-64.2%), and lowest in Italy (5.9%; 95%CI: 3.3-9.2%). Among non-smoking youth, the prevalence of ENDS ever use in 2013-2015 varied, ranging from 4.2% (95%CI: 3.8-4.6%) in the US to 14.0% in New Zealand (95%CI: 12.7-15.4%). The prevalence of ENDS ever use among current tobacco smoking youth was the highest in Canada (71.9%, 95%CI: 70.9-72.8%) and lowest in Italy (29.9%, 95%CI: 18.5-42.5%). Between 2008 and 2015, ENDS ever use among youth increased in Poland, Korea, New Zealand and the US; decreased in Italy and Canada; and remained stable in the UK. CONCLUSIONS: There is considerable heterogeneity in ENDS use among youth globally across countries and also between current smokers and non-smokers. Implications for public health: Population-level survey data on ENDS use is needed to inform public health policy and messaging globally.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.028 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".