Derived Intoxicating Cannabis Vape Product Attributes and Marketing in an Online Retail Environment
Chen-Sankey, J.; LoParco, C. R.; La Capria, K.; Meng, S.; Mazzeo, R.; Vijayakumar, N.; Kong, A. Y.; Tillett, K. K.; Berg, C.; Rossheim, M. E.
Show abstract
IntroductionThe 2018 Farm Bill unintentionally allowed the proliferation of derived intoxicating cannabis vape products (DICVPs), raising concerns about associated health risks. To inform public health prevention efforts, this study analyzed the product attributes and marketing features of DICVPs in an online retail environment. MethodsIn 2023, we extracted information on product attributes and descriptions of 490 DICVPs from two online retail websites with high web traffic. In 2024, two trained coders thematically coded product descriptions for their product characteristics and marketing features. ResultsOverall, 95 unique brands and 26 unique intoxicating cannabinoids were identified. The most frequent marketing features were overall vape product design and use (99.0%), including vaping satisfaction, discreetness, convenience, and use instructions. Regulation and compliance messages (91.6%) were also prevalent, including lab testing for additives and/or chemicals, health warnings, hemp-derived labels, references to the 2018 Farm Bill, and FDA approval statements. Other prominent themes included: flavor and sensation claims (79.6%, i.e., flavor variety, fruit flavors); psychoactive effect claims (43.3%, e.g., potency or expected user experience); product quality claims (38.4%, e.g., "quality," "natural," "purity"); and other positive effect claims (33.9%, e.g., mood enhancement, relaxation). DiscussionThe DICVP online marketplace is highly fragmented with a variety of brands and intoxicating compounds. Common marketing strategies promoting appealing flavors and positive vaping experiences may increase product use interest among young people. Features related to product legality and quality may reduce perceived barriers and risks of using products. Continuous monitoring of the DICVP marketplace is needed to inform policymaking.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Exploring consumer preferences for cannabis edible products to support public health policy: A discrete choice experiment 98%
- Commercial Cannabis Product Testing: Fidelity to Labels and Regulations 97%
- Social media discourse and internet search queries on cannabis as a medicine: A systematic review 96%
Similar papers in this journal
- Patterns of blunt and cigar use in the United States, 2015-2019 95%
- Artificial Sweeteners in US-Marketed Oral Nicotine Pouch Products: Correlation with Nicotine Contents and Effects on Product Preference 93%
- Cost Comparison and Spending on Tobacco Products: Evidence from A Nationally Representative Sample of Adult E-Cigarette Users 92%
Similar papers in this journal
- A Mixed-Methods Comparison of Gender Differences in Alcohol Consumption and Drinking Characteristics among Patients in Moshi, Tanzania 93%
- “ A Man Never Cries ”: A Mixed-Methods Analysis of Gender Differences in Depression and Alcohol Use in Moshi, Tanzania 92%
- Epidemiology of alcohol use and alcohol use disorder among female sex workers in Mbeya City, Tanzania. 92%
Similar papers in this journal
- Sales of over-the-counter products containing codeine in 31 countries, 2013-2019: a retrospective observational study 91%
- Standardization of drug names in the FDA Adverse Event Reporting System: The DiAna dictionary 90%
- Patient-Reported Reasons for Antihypertensive Medication Change: A Quantitative Study Using Social Media 90%
Similar papers in this journal
- Using Twitter Data for Cohort Studies of Drug Safety in Pregnancy: A Proof-of-Concept with Beta-Blockers 88%
- Evaluating the Clinical Feasibility of an Artificial Intelligence-Powered Clinical Decision Support System: A Longitudinal Feasibility Study 88%
- Development and use analysis of ‘gestioemocional.cat’, a web app for promoting emotional self-care and access to professional mental health resources during the covid-19 pandemic 87%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.