Abstract 740: Prevalence and cancer-specific patterns of cannabis use among US cancer survivors, 2016-2021
Bibliographic record
Abstract
Abstract Introduction: Cannabis has therapeutic potentials for alleviating various cancer and treatment-related symptoms, such as refractory cancer pain, chemotherapy-induced nausea, and insomnia. However, less is known about the prevalence of cannabis use among US cancer survivors and its patterns by reason to use (any vs. medical), sociodemographic and lifestyle factors, state, and cancer type and history. Method: This study is a cross-sectional analysis of a US nationally representative sample of cancer survivors aged ≥ 20 years from the Behavioral Risk Factor Surveillance Survey 2016-2021. Data on the frequency of current cannabis use (past month: any use vs. daily use), reasons to use (medical vs. non-medical), participant characteristics and state cannabis legality were self-reported by 96,594 cancer survivors diagnosed with non-skin cancers. Information on cancer survivorship, including cancer type, age at diagnosis, treatment, and cancer-related pain, was further collected among 12,052 survivors. Weighted prevalence (95% confidence interval [CI]) of cannabis use (any, daily, and medical) was estimated overall and by participant characteristics, states, and cancer history and types. Weighted multivariable (MV) logistic regressions were used to evaluate correlates of cannabis use. Results and Conclusions: In 2016-2021, the prevalence of cannabis was 8.4% (95% CI, 7.9-9.0) for any use (daily use: 3.2% [95% CI, 2.9-3.5]) and 5.5% (95% CI, 5.0-5.9) for medical use among US cancer survivors. Compared to Non-Hispanic (NH) whites (7.9% [95% CI, 7.3-8.5]), NH blacks (10.4% [95% CI, 8.5-12.4]), Native Americans (16.2% [95% CI, 10.6-21.8]), and Hispanics (10.1% [95% CI, 7.7-12.4]) had a significantly higher prevalence of cannabis use. The prevalence of cannabis use was substantially higher among survivors living in states that legalized recreational use (11.2% [95% CI, 10.1-12.3]) than in states that only legalized medical use (6.7% [95% CI, 6.2-7.2]) and states where cannabis was illegal (4.9% [95% CI, 4.3-5.4]). Any cannabis use was most prevalent among cancer survivors in Nevada (21.6%), Maine (14.1%), and Alaska (12.5%). Survivors who were younger, male, not married, current smokers, drinkers, on low incomes, and of poor health status were more likely to report using cannabis than their counterparts. Among cannabis users, females, non-smokers, non-drinkers, and those with higher educational levels, higher BMI, and health conditions were more likely to use cannabis for medical reasons. By cancer type, survivors of testis (19.0%), brain (16.4%), and cervix (13.2%) cancers tended to have a higher prevalence of any cannabis use. Cancer survivors diagnosed at a younger age and reported cancer-related pain were more likely to use cannabis. Few survivors (0.4%) used cannabis during cancer treatment. Distinct cannabis use patterns were observed in US cancer survivors by lifestyle factors and cancer type. Citation Format: Chao Cao, Ruixuan Wang, Lin Yang, Electra D. Paskett, Ce Shang. Prevalence and cancer-specific patterns of cannabis use among US cancer survivors, 2016-2021 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 740.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".