Addiction-Related Outcomes of Nicotine and Alcohol Co-use: New Insights Following the Rise in Vaping
Bibliographic record
Abstract
PURPOSE: Nicotine and alcohol-containing products are some of the most commonly used substances of abuse and are both leading causes of preventable death. These substances also have significant interactions that have additive and, in some cases, multiplicative effects on the health consequences of their use. Thus, to reduce these negative consequences, it is important to understand the abuse liability of nicotine and alcohol in combination, especially in the most relevant use cases among those who are most vulnerable. Specifically, as tobacco cigarette use is continually decreasing, vaping is quickly replacing cigarettes as the primary mode of nicotine use. This pattern is especially true in adolescent populations in which vaping has grown considerably. Particularly concerning is that adolescents are more vulnerable than adults to the negative consequences of substance use. It is therefore imperative to revisit the literature as it relates to the rising state of co-use of vaping products with alcohol. Here, we review the clinical outcomes of nicotine and alcohol co-use as they relate to the abuse liability of each individually. Special attention is paid to adolescent findings, where available, as well as investigations that use nontobacco nicotine products as these may more accurately reflect the more recent trends of co-use. IMPLICATIONS: Though nicotine alone has previously been considered a proxy for tobacco and tobacco cigarette use, combustible routes of administration have been decreasing. They are, instead, being replaced by e-cigarettes that do not involve other tobacco constituents and contain additional nonnicotine constituents of their own. Unfortunately, the literature remains limited with regard to e-cigarettes and their interactions with other substances, especially their prevalent co-use with alcohol. This review attempts to discuss the current literature on nicotine and alcohol co-use in the context of the vaping epidemic, predominantly focusing on addiction-related outcomes and why e-cigarette use may be unique.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".