171 Temporal Trends in Young Adult Cannabis and Tobacco Use in Relationship to Cannabis Policy
Bibliographic record
Abstract
OBJECTIVES/GOALS: Cannabis laws may impact cannabis and tobacco use, given high prevalence of co-use of these products among young adults (YAs). The objective of this study was to examine trends in YA any cannabis, blunt, cigarette, and cigar 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). METHODS/STUDY POPULATION: Using data from the National Survey on Drug Use and Health, we conducted a segmented regression analysis to calculate absolute percent change (APC) in past 30-day cannabis and tobacco use between time points. The National Cancer Institute’s Joinpoint software was used to also estimate points of inflection (Joinpoints) when the slope of a trend significantly (p<0.05) changes. Separate models were estimated for each state, with time as the independent variable measured in years. Up to three Joinpoints per model were allowed. The model with optimal Joinpoints was determined using a model selection criterion via a permutation test. Joinpoints and APCs were compared with key legalization dates to describe patterns within and across states with varying cannabis policies. RESULTS/ANTICIPATED RESULTS: Generally, the 16 states showed a steady decline in YA cigarette smoking over time, a slight decline in cigar smoking, and increases in cannabis and blunt use. AMU states had lower average 2018 prevalence of cigarette smoking than MUO states (18.3% vs. 21.5%) and higher cannabis use (32.3% vs. 21.3%). Cannabis use consistently increased following opening of MUO retail outlets. Generally, there appears to be a slight delay in cannabis use increases following AMU laws, and in some states temporary declines. For example, Washington experienced an initial decrease (-20.3%) following AMU passage (2012) then increase (+16.3%) after retail dispensaries opened in 2014. In AMU states, blunt use has surpassed cigar smoking, while in MUO states, the prevalence of blunt and cigar use is similar. DISCUSSION/SIGNIFICANCE: Introduction of cannabis laws are correlated with increases in YA cannabis and blunt use, with higher cannabis use in AMU states. Trends may also correlate other state political, economic, or social factors. Joinpoint regression can assess changes in a policy’s target behavior with no a priori assumptions regarding timing of policy effects.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".