Puff bars: a dangerous trend in adolescent disposable e-cigarette use.

PURPOSE OF REVIEW: Puff Bars, a brand of disposable e-cigarettes, have skyrocketed in popularity recently, presenting significant health and safety risks to adolescents. Puff Bars and other disposable e-cigarette products are attractive to children and teenagers because of their vibrant colors and flavors, low cost, and ease of access. This review examines the rise in Puff Bar popularity, describes notable health and safety concerns, and provides advice for parents and pediatricians in identifying and preventing adolescent Puff Bar use. RECENT FINDINGS: Puff Bars have increased in popularity as their product design evaded strict regulations imposed on alternate e-cigarette products, such as Juul. In addition, Puff Bars' sweet and fruit flavors and marketing on social media have made their use pervasive among youth. Puff Bar use can lead to increased risk of e-cigarette product use-associated lung injury (EVALI) and potential exposure to carcinogens. Furthermore, the developmental risks of nicotine use during adolescence include negative effects on cognitive function and detriments to short-term memory. SUMMARY: Puff Bars present a significant danger to adolescents, and it is imperative that parents and pediatricians are aware of the health risks involved with vaping. Pediatricians should explore e-cigarette use during patient visits, and parents must communicate these dangers to their children and be able to identify these products to regulate their use.

Using 311 data to develop an algorithm to identify urban blight for public health improvement.

The growth of administrative data made available publicly, often in near-real time, offers new opportunities for monitoring conditions that impact community health. Urban blight-manifestations of adverse social processes in the urban environment, including physical disorder, decay, and loss of anchor institutions-comprises many conditions considered to negatively affect the health of communities. However, measurement strategies for urban blight have been complicated by lack of uniform data, often requiring expensive street audits or the use of proxy measures that cannot represent the multifaceted nature of blight. This paper evaluates how publicly available data from New York City's 311-call system can be used in a natural language processing approach to represent urban blight across the city with greater geographic and temporal precision. We found that our urban blight algorithm, which includes counts of keywords ('tokens'), resulted in sensitivity ~90% and specificity between 55% and 76%, depending on other covariates in the model. The percent of 311 calls that were 'blight related' at the census tract level were correlated with the most common proxy measure for blight: short, medium, and long-term vacancy rates for commercial and residential buildings. We found the strongest association with long-term (>1 year) commercial vacancies (Pearson's correlation coefficient = 0.16, p < 0.001). Our findings indicate the need of further validation, as well as testing algorithms that disambiguate the different facets of urban blight. These facets include physical disorder (e.g., litter, overgrown lawns, or graffiti) and decay (e.g., vacant or abandoned lots or sidewalks in disrepair) that are manifestations of social processes such as (loss of) neighborhood cohesion, social control, collective efficacy, and anchor institutions. More refined measures of urban blight would allow for better targeted remediation efforts and improved community health.