Twitter Sentiment About the US Federal Tobacco 21 Law: Mixed Methods Analysis.

BACKGROUND: On December 20, 2019, the US "Tobacco 21" law raised the minimum legal sales age of tobacco products to 21 years. Initial research suggests that misinformation about Tobacco 21 circulated via news sources on Twitter and that sentiment about the law was associated with particular types of tobacco products and included discussions about other age-related behaviors. However, underlying themes about this sentiment as well as temporal trends leading up to enactment of the law have not been explored. OBJECTIVE: This study sought to examine (1) sentiment (pro-, anti-, and neutral policy) about Tobacco 21 on Twitter and (2) volume patterns (number of tweets) of Twitter discussions leading up to the enactment of the federal law. METHODS: We collected tweets related to Tobacco 21 posted between September 4, 2019, and December 31, 2019. A 2% subsample of tweets (4628/231,447) was annotated by 2 experienced, trained coders for policy-related information and sentiment. To do this, a codebook was developed using an inductive procedure that outlined the operational definitions and examples for the human coders to annotate sentiment (pro-, anti-, and neutral policy). Following the annotation of the data, the researchers used a thematic analysis to determine emergent themes per sentiment category. The data were then annotated again to capture frequencies of emergent themes. Concurrently, we examined trends in the volume of Tobacco 21-related tweets (weekly rhythms and total number of tweets over the time data were collected) and analyzed the qualitative discussions occurring at those peak times. RESULTS: The most prevalent category of tweets related to Tobacco 21 was neutral policy (514/1113, 46.2%), followed by antipolicy (432/1113, 38.8%); 167 of 1113 (15%) were propolicy or supportive of the law. Key themes identified among neutral tweets were news reports and discussion of political figures, parties, or government involvement in general. Most discussions were generated from news sources and surfaced in the final days before enactment. Tweets opposing Tobacco 21 mentioned that the law was unfair to young audiences who were addicted to nicotine and were skeptical of the law's efficacy and importance. Methods used to evade the law were found to be represented in both neutral and antipolicy tweets. Propolicy tweets focused on the protection of youth and described the law as a sensible regulatory approach rather than a complete ban of all products or flavored products. Four spikes in daily volume were noted, 2 of which corresponded with political speeches and 2 with the preparation and passage of the legislation. CONCLUSIONS: Understanding themes of public sentiment-as well as when Twitter activity is most active-will help public health professionals to optimize health promotion activities to increase community readiness and respond to enforcement needs including education for retailers and the general public.

Analysis of a Statewide Early Intervention Program for Young Children with ASD.

We evaluated the effectiveness of a statewide Medicaid program providing in-home Early Intensive Behavioral Intervention services to young children with Autism Spectrum Disorder living in a rural southern state. Program effectiveness was assessed via the Assessment of Basic Language and Learning Skills-Revised (ABLLS-R). A multi-level growth model was used to show significant variation among children's initial ABLLS-R scores and their growth trajectories. Hispanic children tended to have lower initial scores but demonstrated similar growth compared to their non-Hispanic peers. Children who were older at initial start in the program appeared to have higher initial composite scores and higher growth trajectories. Children in more populous counties had higher initial scores but grew at similar rates to children in more rural counties.

Exploring Factors That Predict Marketing of e-Cigarette Products on Twitter: Infodemiology Approach Using Time Series.

Background: Electronic nicotine delivery systems (known as electronic cigarettes or e-cigarettes) increase risk for adverse health outcomes among naïve tobacco users, particularly youth and young adults. This vulnerable population is also at risk for exposed brand marketing and advertisement of e-cigarettes on social media. Understanding predictors of how e-cigarette manufacturers conduct social media advertising and marketing could benefit public health approaches to addressing e-cigarette use. Objective: This study documents factors that predict changes in daily frequency of commercial tweets about e-cigarettes using time series modeling techniques. Methods: We analyzed data on the daily frequency of commercial tweets about e-cigarettes collected between January 1, 2017, and December 31, 2020. We fit the data to an autoregressive integrated moving average (ARIMA) model and unobserved components model (UCM). Four measures assessed model prediction accuracy. Predictors in the UCM include days with events related to the US Food and Drug Administration (FDA), non-FDA-related events with significant importance such as academic or news announcements, weekday versus weekend, and the period when JUUL maintained an active Twitter account (ie, actively tweeting from their corporate Twitter account) versus when JUUL stopped tweeting. Results: When the 2 statistical models were fit to the data, the results indicate that the UCM was the best modeling technique for our data. All 4 predictors included in the UCM were significant predictors of the daily frequency of commercial tweets about e-cigarettes. On average, brand advertisement and marketing of e-cigarettes on Twitter was higher by more than 150 advertisements on days with FDA-related events compared to days without FDA events. Similarly, more than 40 commercial tweets about e-cigarettes were, on average, recorded on days with important non-FDA events compared to days without such events. We also found that there were more commercial tweets about e-cigarettes on weekdays than on weekends and more commercial tweets when JUUL maintained an active Twitter account. Conclusions: e-Cigarette companies promote their products on Twitter. Commercial tweets were significantly more likely to be posted on days with important FDA announcements, which may alter the narrative about information shared by the FDA. There remains a need for regulation of digital marketing of e-cigarette products in the United States.

Assessment of the knowledge and attitudes of intern doctors to medication prescribing errors in a Nigeria tertiary hospital.

CONTEXT: Junior doctors are reported to make most of the prescribing errors in the hospital setting. AIMS: The aim of the following study is to determine the knowledge intern doctors have about prescribing errors and circumstances contributing to making them. SETTINGS AND DESIGN: A structured questionnaire was distributed to intern doctors in National Hospital Abuja Nigeria. SUBJECTS AND METHODS: Respondents gave information about their experience with prescribing medicines, the extent to which they agreed with the definition of a clinically meaningful prescribing error and events that constituted such. Their experience with prescribing certain categories of medicines was also sought. STATISTICAL ANALYSIS USED: Data was analyzed with Statistical Package for the Social Sciences (SPSS) software version 17 (SPSS Inc Chicago, Ill, USA). Chi-squared analysis contrasted differences in proportions; P < 0.05 was considered to be statistically significant. RESULTS: The response rate was 90.9% and 27 (90%) had <1 year of prescribing experience. 17 (56.7%) respondents totally agreed with the definition of a clinically meaningful prescribing error. Most common reasons for prescribing mistakes were a failure to check prescriptions with a reference source (14, 25.5%) and failure to check for adverse drug interactions (14, 25.5%). Omitting some essential information such as duration of therapy (13, 20%), patient age (14, 21.5%) and dosage errors (14, 21.5%) were the most common types of prescribing errors made. Respondents considered workload (23, 76.7%), multitasking (19, 63.3%), rushing (18, 60.0%) and tiredness/stress (16, 53.3%) as important factors contributing to prescribing errors. Interns were least confident prescribing antibiotics (12, 25.5%), opioid analgesics (12, 25.5%) cytotoxics (10, 21.3%) and antipsychotics (9, 19.1%) unsupervised. CONCLUSIONS: Respondents seemed to have a low awareness of making prescribing errors. Principles of rational prescribing and events that constitute prescribing errors should be taught in the practice setting.