Adam Etheridge Barry

Sweeping policy changes during the COVID-19 pandemic increased alcohol availability through permitted to-go sales, potentially posing unique risks to college students. While to-go sales may make binge drinking more convenient, little remains known about these practices. Therefore, this study aimed to assess whether drinking establishments’ to-go sales practices are associated with their other operational practices and state policy. This cross-sectional analysis included 221 randomly selected bars, nightclubs, and restaurants within two miles of a large public university. Telephone interviews assessed establishment practices, and the Alcohol Policy Information System provided state alcohol to-go laws. Regression models tested whether establishment to-go sales practices were associated with their business practices (logistic regression) and state policy (generalized estimating equations). Nearly one-half (44.8%) of drinking establishments sold alcohol to-go. Establishments with higher vodka prices had nearly 30% higher odds of selling spirits to-go (aOR = 1.29) and establishments offering happy hours specials had more than twice the odds of selling beer (aOR = 2.22), wine (aOR = 2.53), and spirits to-go (aOR = 2.60). Additionally, establishments that implemented physical distance requirements had higher odds of selling wine to-go (aOR = 3.00). State to-go laws were associated with higher odds of selling wine (aOR = 3.99) and spirits to-go (aOR = 5.43) in the full sample and beer to-go (aOR = 4.92) in urban counties. Establishments that sell alcohol to-go tend to engage in other practices designed to drive sales. Evaluations of alcohol to-go sales laws on risky consumption among priority populations, including college students, are urgently needed to inform decisions about how to appropriately regulate sales.

This study examined opioid and analgesic misuse, and associated reasons for misuse, among participants without cancer in the United States.We extracted data from three distinct waves of the Nationa...

Background: Dry January, a temporary alcohol abstinence campaign, encourages individuals to reflect on their relationship with alcohol by temporarily abstaining from consumption during the month of January. Though Dry January has become a global phenomenon, there has been limited investigation into Dry January participants’ experiences. One means through which to gain insights into individuals’ Dry January-related experiences is by leveraging large-scale social media data (eg, Twitter chatter) to explore and characterize public discourse concerning Dry January. Objective: We sought to answer the following questions: (1) What themes are present within a corpus of tweets about Dry January, and is there consistency in the language used to discuss Dry January across multiple years of tweets (2020-2022)? (2) Do unique themes or patterns emerge in Dry January 2021 tweets after the onset of the COVID-19 pandemic? and (3) What is the association with tweet composition (ie, sentiment and human-authored vs bot-authored) and engagement with Dry January tweets? Methods: We applied natural language processing techniques to a large sample of tweets (n=222,917) containing the term “dry january” or “dryjanuary” posted from December 15 to February 15 across three separate years of participation (2020-2022). Term frequency inverse document frequency, k-means clustering, and principal component analysis were used for data visualization to identify the optimal number of clusters per year. Once data were visualized, we ran interpretation models to afford within-year (or within-cluster) comparisons. Latent Dirichlet allocation topic modeling was used to examine content within each cluster per given year. Valence Aware Dictionary and Sentiment Reasoner sentiment analysis was used to examine affect per cluster per year. The Botometer automated account check was used to determine average bot score per cluster per year. Last, to assess user engagement with Dry January content, we took the average number of likes and retweets per cluster and ran correlations with other outcome variables of interest. Results: We observed several similar topics per year (eg, Dry January resources, Dry January health benefits, updates related to Dry January progress), suggesting relative consistency in Dry January content over time. Although there was overlap in themes across multiple years of tweets, unique themes related to individuals’ experiences with alcohol during the midst of the COVID-19 global pandemic were detected in the corpus of tweets from 2021. Also, tweet composition was associated with engagement, including number of likes, retweets, and quote-tweets per post. Bot-dominant clusters had fewer likes, retweets, or quote tweets compared with human-authored clusters. Conclusions: The findings underscore the utility for using large-scale social media, such as discussions on Twitter, to study drinking reduction attempts and to monitor the ongoing dynamic needs of persons contemplating, preparing for, or actively pursuing attempts to quit or cut down on their drinking.

Objective: College campuses report alcohol and other drug policy violations as the most frequent reason students receive disciplinary referrals and, thus, are mandated to programming. This study sought to determine predictors of mandated students’ alcohol use frequency, and the likelihood of early-onset alcohol using college students enrolled in mandated programming engaging in current polysubstance use. Methods and participants: Employing a purposive sampling method, n = 822 participants were recruited from a pool of students who violated their university’s alcohol policy between October 2019 and July 2021. Results: Data analysis revealed early-onset alcohol use (p < .001), gender ID (p < .01), Greek Affiliation (p < .001), ethnicity (p < .05), and perceived norms (p < .001) significantly predicted alcohol frequency. Analysis also revealed engaging in early-onset alcohol use significantly predicted current participation in polysubstance use (p < 0.01), outside of controls. Conclusions: University programs should consider exploring polysubstance use targeted interventions to mitigate these harmful behaviors and associated negative consequences.

BACKGROUND Dry January, a temporary alcohol abstinence campaign, encourages individuals to reflect on their relationship with alcohol by temporarily abstaining from consumption during the month of January. Though Dry January has become a global phenomenon, there has been limited investigation into Dry January participants’ experiences. One means through which to gain insights into individuals’ Dry January-related experiences is by leveraging large-scale social media data (e.g., Twitter chatter) to explore and characterize public discourse concerning Dry January. OBJECTIVE We sought to answer the following questions: (1) What themes are present within a corpus of tweets about Dry January, and is there consistency in the language used to discuss Dry January across multiple years of tweets (2020-2022)? (2) Do unique themes or patterns emerge in Dry January 2021 tweets after the onset of the COVID-19 pandemic? and (3) What is the association with tweet composition (i.e., sentiment and human-authored vs. bot-authored) and engagement with Dry January tweets? METHODS We applied natural language processing techniques to a large sample of tweets (N = 222,917) containing the term “dry january” or “dryjanuary” posted from December 15 to February 15 across three separate years of participation (2020-2022). Term frequency inverse document frequency, k-means clustering, and principal component analysis were used for data visualization to identify the optimal number of clusters per year. Once data were visualized, we ran interpretation models to afford within-year (or within-cluster) comparisons. Latent Dirichlet Allocation topic modeling was used to examine content within each cluster per given year. Valence Aware Dictionary and sEntiment [sic] Reasoner sentiment analysis was used to examine affect per cluster per year. Botometer automated account check was employed to determine average bot score per cluster per year. Lastly, to assess user engagement with Dry January content, we took the average number of likes and retweets per cluster and ran correlations with other outcome variables of interest. RESULTS We observed several similar topics per year (e.g., Dry January resources, Dry January health benefits, updates related to Dry January progress), suggesting relative consistency in Dry January content over time. While there was overlap in themes across multiple years of tweets, unique themes related to individuals’ experiences with alcohol during the midst of the COVID-19 global pandemic were detected in the corpus of tweets from 2021. Also, tweet composition was associated with engagement, including number of likes, retweets, and quote-tweets per post. Bot-dominant clusters had fewer likes, retweets, or quote tweets compared with human-authored clusters. CONCLUSIONS Findings underscore the utility for using large-scale social media, such as discussions on Twitter, to study drinking reduction attempts and to monitor the ongoing dynamic needs of persons contemplating, preparing for, or actively pursuing attempts to quit or cut down on their drinking.

This review analyzed the effectiveness of mandated substance use interventions in reducing future cannabis or other illicit drug use. Findings indicated that early decreases in cannabis and other drug use are commonly reported among students participating in mandated interventions. The longer time elapses post-intervention, the more likely recidivism will occur. Universities are in a unique position to provide support for students who use cannabis by employing some of the effective methods discovered in this review.

Objective: Electronic cigarette use represents an important college health concern. This investigation assessed demographic and behavioral correlates associated with actual and perceived e-cigarette use among a national sample of American college students. Methods: Respondents (n = 19,861) comprised college students from over 40 distinct American higher education institutions. Multivariable logistic regression assessed whether (1) alcohol, tobacco or marijuana use were associated with e-cigarette use; (2) perceived peer use of alcohol, tobacco or marijuana were associated with perceived e-cigarette use. Results: Approximately 5% of survey respondents self-reported using e-cigarettes within the past month. More than 7 out of every 10 respondents, however, thought the ‘typical student’ was an e-cigarette user. As perceptions of typical student substance use increased, respondents were far more likely to contend the typical student used e-cigarettes. Discussion: In addition to adopting smoke-free campus policies, university officials should disseminate accurate information regarding e-cigarette behaviors of students.