Dried Cannabis Use, Tobacco Smoking, and COVID-19 Infection: Findings from a Longitudinal Observational Cohort Study
Bibliographic record
Abstract
Objective: The potential impact of cigarette and cannabis smoking on COVID-19 infection outcomes is not well understood. We investigated the association between combustible tobacco use and dried cannabis use with COVID-19 infection in a longitudinal cohort of community adults. Method: The sample comprised 1,343 participants, originally enrolled in 2018, who reported their cigarette and cannabis use in 11 assessments over 44 months, until 2022. COVID-19 infection history were self-reported after the onset of the pandemic. Univariate and multivariate logistic regression analyses were performed. The potentially confounding factor of vaccination status was also considered by stratifying data by booster vaccination self-reporting. Results: Among 1,343 participants, 820 (61.1%) reported any COVID-19 infection. Dried cannabis use (46.3% of participants, n = 721) was associated with higher self-reporting of 2+ COVID-19 infections (13.3% vs. 7.3% in non-users, p = .0004), while tobacco use (18.5% of participants, n = 248) had no significant effect (13.3% vs. 10.0% in no use group, p = .116). When stratified into single or dual substance use groups, dried cannabis-only use was associated with increased reporting of 1 or 2+ COVID-19 infections compared to substance non-users, while tobacco-only use and dual use groups were not significantly different from non-users. To account for differences in vaccination rates between substance use groups, we found that, among individuals with a COVID-19 booster vaccine, dried cannabis use was still associated with increased reporting of 2+ COVID-19 infections (p = .008). Conclusions: Our study suggests that dried cannabis use is associated with a higher likelihood of reporting 2+ COVID-19 infections. Although the study was observational and relied on self-report infection status, our findings support the need for further investigation into the impact of cannabis use on COVID-19 infection, particularly studies employing controlled experimental designs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".