Understanding the Dynamics between Vaping and Cannabis Legalization Using Twitter Opinions

Cannabis legalization has been welcomed by many U.S. states but its role in escalation from tobacco e-cigarette use to cannabis vaping is unclear. Meanwhile, cannabis vaping has been associated with new lung diseases and rising adolescent use. To understand the impact of cannabis legalization on escalation, we design an observational study to estimate the causal effect of recreational cannabis legalization on the development of pro-cannabis attitude for e-cigarette users. We collect and analyze Twitter data which contains opinions about cannabis and JUUL, a very popular e-cigarette brand. We use weakly supervised learning for personal tweet filtering and classification for stance detection. We discover that recreational cannabis legalization policy has an effect on increased development of pro-cannabis attitudes for users already in favor of e-cigarettes.Comment: Published at ICWSM 202

A Practical Guide to Calculating Cohen’s f2, a Measure of Local Effect Size, from PROC MIXED

Reporting effect sizes in scientific articles is increasingly widespread and encouraged by journals; however, choosing an effect size for analyses such as mixed-effects regression modeling and hierarchical linear modeling can be difficult. One relatively uncommon, but very informative, standardized measure of effect size is Cohen’s f2, which allows an evaluation of local effect size, i.e., one variable’s effect size within the context of a multivariate regression model. Unfortunately, this measure is often not readily accessible from commonly used software for repeated-measures or hierarchical data analysis. In this guide, we illustrate how to extract Cohen’s f2 for two variables within a mixed-effects regression model using PROC MIXED in SAS® software. Two examples of calculating Cohen’s f2 for different research questions are shown, using data from a longitudinal cohort study of smoking development in adolescents. This tutorial is designed to facilitate the calculation and reporting of effect sizes for single variables within mixed-effects multiple regression models, and is relevant for analyses of repeated-measures or hierarchical/multilevel data that are common in experimental psychology, observational research, and clinical or intervention studies

Internally-Developed Teen Smoking Cessation Programs: Characterizing the Unique Features of Programs Developed by Community-Based Organizations

We have compared the unique features of teen tobacco cessation programs developed internally by community-based organizations (N=75) to prepackaged programs disseminated nationally (N=234) to expand our knowledge of treatment options for teen smokers. Internally-developed programs were more likely offered in response to the sponsoring organization’s initiative (OR=2.16, p<0.05); had fewer trained cessation counselors (OR=0.31, p<0.01); and were more likely found in urban areas (OR=2.89, p=0.01). Internally-developed programs more often provided other substance-abuse treatment services than prepackaged programs and addressed other youth-specific problem behaviors (p≤0.05). Studies that examine the effectiveness of internally-developed programs in reducing smoking and maintaining cessation for teen smokers are warranted

A National Evaluation of Community-Based Youth Cessation Programs: End of Program and Twelve-month Outcomes

Most youth cessation treatment research consists of efficacy studies in which treatments are evaluated under optimal conditions of delivery. Less is known about the effectiveness of youth cessation treatments delivered in real-world, community based settings. A national sample of 41 community-based youth cessation programs participated in a longitudinal evaluation to identify site, program, and participant characteristics associated with successful cessation. Validated quit rates were comparable to those in randomized controlled trials; 7-day abstinence at the end of program averaged 14% and 30-day abstinence at 12 months averaged 12%. Multivariate GEE models explored predictors of smoking cessation at the end of the programs and at 12 months. Results showed correlates of both short- and long-term cessation. Findings point to the importance of both individual and community-level variables, including motivation, opportunities for and encouragement to engage in activities outside of academics, having youth participate in treatment before they become highly dependent smokers, and community norms and ordinances that discourage youth purchase, use and possession of tobacco. Providing evidence-based treatment to youth in community-based settings results in successful cessation.Keywords: Youth smoking cessation, Program evaluation, Community health promotion program