COVID-19 and tobacco products use among US adults, 2021 National Health Interview Survey
Bibliographic record
Abstract
Objective: A nationally representative sample of US adults was used to examine the prevalence of COVID-19 cases, testing, symptoms, and vaccine uptake, and associations with tobacco product use. Methods: Data came from the 2021 National Health Interview Survey. The 2021 Sample Adult component included 29,482 participants with a response rate of 50.9%. We investigated COVID-19-related outcomes by tobacco product use status and reported national estimates. Multivariable regression models were performed accounting for demographics (e.g., age, sex, poverty level), serious psychological distress, disability, and chronic health condition. Results: In our regression analyses, odds of self-reported COVID-19 infection were significantly lower for combustible tobacco product users (vs. non-users; Adjusted Odds ratio [AOR=0.73; 95% confidence interval [CI]=0.62-0.85]. Combustible tobacco users also were less likely to report ever testing for COVID-19 (AOR=0.88; 95% CI=0.79-0.98), ever testing positive for COVID-19 (AOR =0.66; 95% CI=0.56-0.77), and ever receiving COVID-19 vaccine (AOR=0.58; 95% CI=0.51-0.66) compared to their non-user peers. Compared to non-users, users of any type of tobacco who contracted COVID-19 had higher odds of losing smell (AOR=1.36; 95%CI=1.04-1.77), which was more pronounced among exclusive e-cigarette users. The odds of receiving vaccine were lower for all current exclusive tobacco product users compared to non-users (AORs= 0.40 to 0.70). Conclusions: Continued monitoring of tobacco product use amid the COVID-19 pandemic is crucial to inform public health policies and programs. In addition, efforts to promote COVID-19 vaccination, especially among tobacco product users, are warranted.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".