ENDS, Cigarettes, and Respiratory Illness: Longitudinal Associations Among U.S. Youth
Bibliographic record
Abstract
INTRODUCTION: ENDS use is highly prevalent among U.S. youth, and there is concern about its respiratory health effects. However, evidence from nationally representative longitudinal data is limited. METHODS: Using youth (aged 12-17 years) data from Waves 1-5 (2013-2019) of the Population Assessment of Tobacco and Health Study, multilevel Poisson regression models were estimated to examine the association between ENDS use; cigarettes; and diagnosed bronchitis, pneumonia, or chronic cough. Current product use was lagged by 1 wave and categorized as (1) never/noncurrent use, (2) exclusive cigarette use, (3) exclusive ENDS use, and (4) dual ENDS/cigarette use. Multivariable models adjusted for age, sex, race and ethnicity; parental education; asthma; BMI; cannabis use; secondhand smoke exposure; and household use of combustible products. Data analysis was conducted in 2022-2023. RESULTS: A total of 7.4% of respondents were diagnosed with bronchitis, pneumonia, or chronic cough at follow-up. In the multivariable model, exclusive cigarette use (incident rate ratio=1.85, 95% CI=1.29, 2.65), exclusive ENDS use (incident rate ratio=1.49, 95% CI=1.06, 2.08), and dual use (incident rate ratio=2.70, 95% CI=1.61, 3.50) were associated with a higher risk of diagnosed bronchitis, pneumonia, or chronic cough than never/noncurrent use. CONCLUSIONS: These results suggest that ENDS and cigarettes, used exclusively or jointly, increased the risk of diagnosed bronchitis, pneumonia, or chronic cough among U.S. youth. However, dual use was associated with the highest risk. Targeted policies aimed at continuing to reduce cigarette smoking and ENDS use among youth, especially among those with dual use, are needed.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".