Co-use of cannabis and prescription opioids in adults in the USA: a population-based, cross-sectional analysis of the NHANES from 2009 to 2018
Bibliographic record
Abstract
INTRODUCTION: Cannabis and cannabinoids continue to gain popularity as adjuncts or alternatives to opioids in pain management, with evolving evidence of effectiveness. The relationship between cannabis and opioid use has previously been investigated in smaller cohorts or ecological samples, but not yet in a nationally representative sample. METHODS: A cross-sectional analysis of adults in the USA was undertaken using National Health and Nutrition Examination Survey (NHANES) data from 2009 to 2018. The primary exposure was self-reported use of at least one opioid-containing prescription medication in the 30 days prior to survey administration. The outcome of interest was self-reported cannabis use in the same period. Multivariable logistic regression was used to adjust for sociodemographic and health-related covariates, and NHANES survey sample weights were included in modeling. Prescription opioid users were then subclassified as short-term users (<90 days) or chronic users (≥90 days) in secondary analysis. RESULTS: A total 10,928 survey respondents were included in analyses, representing 110 million adults in the USA aged 18-59. In this weighted cohort, 5.6%±0.4% reported a recent opioid prescription. Among prescription opioid users, 18.4%±3.1% reported recent cannabis use, not significantly different from 17.7%±0.7% among non-users (OR 1.05, 95% CI 0.81 to 1.36, p=0.714). After adjustment for covariates, opioid users were significantly less likely to have recently used cannabis (adjusted OR, aOR 0.70, 95% CI 0.51 to 0.97, p=0.032). When opioid users were subclassified by duration of prescription, there was no detectable difference in recent cannabis use between chronic opioid users and short-term opioid users (aOR 1.11, 95% CI 0.70 to 1.78, p=0.649). CONCLUSION: Recent prescription opioid use was associated with decreased odds of cannabis use in this cross-sectional analysis of a nationally representative cohort. These findings suggest that use of cannabis or prescription opioids may not independently promote use of the other.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".