Abstract 740: Prevalence and cancer-specific patterns of cannabis use among US cancer survivors, 2016-2021
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».