Temporal Trends in Young Adult Cannabis and Tobacco Use in States with Different Cannabis Policies
Bibliographic record
Abstract
Objective: Cannabis legalization may impact both cannabis and tobacco use, given the high prevalence of co-use (including blunt use) among young adults (YAs) in the United States. The objective of this descriptive ecological study was to examine trends in YA cannabis and tobacco use from 2002-2018 in states that passed adult and medical use (AMU) or medical use only (MUO) cannabis laws during that time (N = 16). Method: Using data from the National Survey on Drug Use and Health, we conducted a segmented regression analysis to calculate absolute percent change in past 30-day cannabis, blunt, cigarette, and cigar use between time points. We descriptively compared points of slope inflection with key legalization dates. Results: All states showed a decline in YA cigarette smoking over time, a slight decline in cigar smoking, and increases in cannabis and blunt use. Cannabis use increased following opening of MUO retail outlets and, in several states, increased following adult use law implementation and/or opening of retail outlets. For example, in Maine, cannabis use plateaued after a MUO law was adopted (2009) until about 1-2 years after retail outlets opened (2011), when YA cannabis use increased by 22.4% (95% CI: 19.0, 29.4) and continued increasing steadily after adult use was adopted (2017). Conclusions: Cannabis and blunt use increased more in states where AMU laws were in place compared to those with MUO laws, though causality was not assessed. Varying trends may correlate with cannabis policies, tobacco policies and other political, economic, or social factors at the state level.
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 | 0.000 | 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 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".