Impact of California’s tobacco and cannabis policies on the retail availability of little cigars/cigarillos and blunt wraps

Timberlake, David S.; Rhee, Joshua Unbin; Silver, Lynn; Padon, Alisa A.; Vos, Robert O.; Unger, Jennifer B.; Andersen-Rodgers, Elizabeth

doi:10.1016/j.drugalcdep.2021.109064

Cited by 7 publications

(2 citation statements)

References 28 publications

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“…Previous analyses of these data found less co-use among people who live in an area with a ban on flavored cigarillos [86], which could suggest removing flavored cigar products could reduce cannabis use. As the vast majority of cigarillos are flavored, a flavor ban may result in reduced availability of cigarillos and blunt wraps, consistent with what was found in an analysis of flavor bans in California [87]. However, living in an area with a flavor ban was not related to motivation to quit cigarillo or cannabis use in this study.…”

Section: Discussionsupporting

confidence: 88%

Cigarillo Flavor and Motivation to Quit among Co-Users of Cigarillos and Cannabis: A Structural Equation Modeling Approach

Glasser

Nemeth

Quisenberry

et al. 2022

IJERPH

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Flavored cigar restrictions have the potential to benefit public health. Flavor availability facilitates cigarillo use, but it is unknown if flavor impacts patterns of co-use of cigarillos and cannabis, an increasingly prevalent behavior among young adults. Data were collected (2020–2021) in a cross-sectional online survey administered to a convenience sample of young adults who smoked cigarillos from 15 areas with high cigar use prevalence. We assessed the relationship between flavored cigarillo use and motivation to quit cannabis and cigarillo use among past 30-day co-users (N = 218), as well as several covariates (e.g., cigarillo price and flavor/cannabis policy). Flavored cigarillo perceived appeal and harm were hypothesized parallel mediators. Most co-users reported usually using flavored cigarillos (79.5%), which was not significantly associated with motivation to quit cigarillos or cannabis. Perceived cigarillo harm (β = 0.17, 95% CI = 0.00, 0.33), advertising exposure (β = 0.12, 95% CI = 0.00, 0.24), and income (among racial/ethnic minorities; β = −0.13, 95% CI = −0.25, −0.02) were significant predictors of motivation to quit cigarillos. There were no significant predictors of motivation to quit cannabis. Cigarillo flavor was not associated with motivation to quit, so findings could suggest that banning flavors in cigars may have a neutral impact on co-use with cannabis among young adults.

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Section: Discussionsupporting

confidence: 88%

Cigarillo Flavor and Motivation to Quit among Co-Users of Cigarillos and Cannabis: A Structural Equation Modeling Approach

Glasser

Nemeth

Quisenberry

et al. 2022

IJERPH

View full text Add to dashboard Cite

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“…The distance metric is used to calculate the accuracy, with Euclidean distance being the most popular method(Timberlake et al 2021). Predictions are formed by searching the whole dataset for the N most comparable cases, i.e., the testing point's neighbors.…”

mentioning

confidence: 99%

Development of Novel Convolutional Neural Network-Based Model for Sales Forecast in an Electronic Retail Store during Festive Seasons and Comparison of Prediction Accuracy with Deep Belief Network

2022

pnr

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The aim of this paper is to implement a Novel Convolutional Neural Network based has model for Sales forecast in an Electronic Retail Store during Festive seasons and a comparison of prediction accuracy with a Deep Belief Network. Materials and Methods: Deep Belief Network (N=10) and Novel Convolutional Neural Network algorithm (N=10), n is iterated at different times for predicting the accuracy percentage of accidents that happened. Two sample groups are taken into consideration and tested, G-power is a calculation that contains two different groups, alpha (0.05), and power (80%). Results: It was observed that the Deep Belief Network algorithm obtains an accuracy of 77.14% and the Novel Convolutional Neural Network has 84.86%. This Deep Belief Network appears to have a significance of p=0.019 than the Novel Convolutional Neural Network, that is p<0.05 using an independent sample forT-test analysis. Conclusion: The Deep Belief Network technique appears to have more significance than the Novel Convolutional Neural Network algorithm. The analysis generally works in a variety of end-use industries, and the results demonstrate that this strategy is important. The result proves that the Novel Convolutional Neural Network approaches to predict the retail sales store prediction.

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Flavored Cannabis Use and Cannabis-Tobacco Co-use: Patterns In U.S. States With Legalized Nonmedical Adult Use

Watkins

Thompson

Feld

et al. 2023

American Journal of Preventive Medicine

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Impact of California’s tobacco and cannabis policies on the retail availability of little cigars/cigarillos and blunt wraps

Cited by 7 publications

References 28 publications

Cigarillo Flavor and Motivation to Quit among Co-Users of Cigarillos and Cannabis: A Structural Equation Modeling Approach

Cigarillo Flavor and Motivation to Quit among Co-Users of Cigarillos and Cannabis: A Structural Equation Modeling Approach

Development of Novel Convolutional Neural Network-Based Model for Sales Forecast in an Electronic Retail Store during Festive Seasons and Comparison of Prediction Accuracy with Deep Belief Network

Flavored Cannabis Use and Cannabis-Tobacco Co-use: Patterns In U.S. States With Legalized Nonmedical Adult Use

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