Electronic cigarettes (e-cigarettes) are devices that deliver a heated aerosol of nicotine in a fashion that mimics conventional cigarettes while delivering lower levels of toxins than a conventional combusted cigarette.^1-4 They are being aggressively marketed using the same messages and media channels (plus the Internet) that cigarette companies used to market conventional cigarettes in the 1950s and 1960s,⁵ including on television and radio where cigarette advertising has been prohibited for more than 40 years.

Psychosocial stress is a significant risk factor for smoking, and Blacks experience higher levels of psychosocial stress relative to other racial/ethnic groups. Limited research has comprehensively examined psychosocial stressors in relation to smoking among Blacks. We examined psychosocial stressors in relation to smoking status (current, previous, and never) in middle-aged Blacks (34–85 years, n = 592) from Milwaukee, Wisconsin, a subset of the Midlife in the United States Study II (2004–2006). Eleven stressor domains were assessed, including psychological and physical work stress, work–family conflict, perceived inequality, relationship stress, neighborhood stress, discrimination, financial stress, recent problems, stressful events, and childhood adversity. We also calculated a cumulative score. Multinomial models were adjusted for age, gender, education, and income. Seven of the 11 stressors and the cumulative score were associated with higher odds of being a current smoker compared with a never-smoker: neighborhood, financial, relationship, and psychological work stress, perceived inequality, stressful events, childhood adversity (p values <.05; ORs ranged from 1.28 to 1.77). Three stressors and the cumulative score were associated with higher odds of being a previous smoker versus a never-smoker (p < .05). Individuals who scored in the top quartile on 5 or more stressors were 3.74 (95% CI = 2.09–6.71) times as likely to be current smokers, and more than twice as likely to be previous smokers, compared with individuals with no high stressors. These results demonstrate a strong relationship between stress and smoking among urban middle-aged Blacks and suggest that cessation programs should address modifiable individual and community-level stressors.

BACKGROUND: E-cigarette use is rapidly increasing among adolescents in the United States, with some suggesting that e-cigarettes are the cause of declining youth cigarette smoking. We hypothesized that the decline in youth smoking changed after e-cigarettes arrived on the US market in 2007. METHODS: Data were collected by using cross-sectional, nationally representative school-based samples of sixth- through 12th-graders from 2004–2014 National Youth Tobacco Surveys (samples ranged from 16 614 in 2013 to 25 324 in 2004). Analyses were conducted by using interrupted time series of ever (≥1 puff) and current (last 30 days) cigarette smoking. Logistic regression was used to identify psychosocial risk factors associated with cigarette smoking in the 2004–2009 samples; this model was then applied to estimate the probability of cigarette smoking among cigarette smokers and e-cigarette users in the 2011–2014 samples. RESULTS: Youth cigarette smoking decreased linearly between 2004 and 2014 (P = .009 for ever smoking and P = .05 for current smoking), with no significant change in this trend after 2009 (P = .57 and .23). Based on the psychosocial model of smoking, including demographic characteristics, willingness to wear clothing with a tobacco logo, living with a smoker, likelihood of smoking in the next year, likelihood of smoking cigarettes from a friend, and use of tobacco products other than cigarettes or e-cigarettes, the model categorized <25% of current e-cigarette–only users (between 11.0% in 2012 and 23.1% in 2013) as current smokers. CONCLUSIONS: The introduction of e-cigarettes was not associated with a change in the linear decline in cigarette smoking among youth. E-cigarette–only users would be unlikely to have initiated tobacco product use with cigarettes.

Strong smoke-free laws are an effective public health intervention^1,2 that not only reduce secondhand smoke exposure but are followed by substantial drops in hospitalizations for cardiac and respiratory diseases and lower rates of preterm birth.^3-8 Smoke-free workplace, restaurant, and bar laws are associated with increased voluntary smoke-free policies in the home⁹ and less secondhand smoke exposure for children.^10,11 One quasiexperimental study¹² reported 4- to 28-fold reductions in the prevalence of smoking among workers in smoke-free workplaces, restaurants, and bars. Among smokers, 46% reported an increase in motivation to quit, whereas 80% of former smokers said that the laws helped them to quit. A systematic review¹³ of the effect of smoke-free laws found that smoke-free workplaces were associated with a 3.8% reduction in the prevalence of smoking and approximately 3 less cigarettes smoked per day per smoker.

The first goal of this study was to identify the most appropriate measure of cigarette smoking for identifying unique smoking trajectories among adolescents; the second goal was to describe the resulting trajectories and their characteristics. Using 15 annual waves of smoking data in the National Longitudinal Survey of Youth 1997 (NLSY97), we conducted an exploratory latent class growth analysis to determine the best of four outcome variables for yearly smoking (cigarettes per day on days smoked, days smoked per month, mean cigarettes per day, and total cigarettes per month) among individuals aged 12 to 30 (n = 8,791). Days smoked per month was the best outcome variable for identifying unique longitudinal trajectories of smoking and characteristics of these trajectories that could be used to target different types of smokers for prevention and cessation. Objective statistics were used to identify four trajectories in addition to never smokers (34.1%): experimenters (13.6%), quitters (8.1%), early established smokers (39.0%), and late escalators (5.2%). We identified a quitter and late escalator class not identified in the only other comparable latent class growth analysis. Logistic regressions were used to identify the characteristics of individuals in each trajectory. Compared with never smokers, all trajectories except late escalators were less likely to be black; experimenters were more likely to be out of school and unemployed and drink alcohol in adolescence; quitters were more likely to have a mother with a high school degree/GED or higher (versus none) and to use substances in adolescence and less likely to have ever married as a young adult; early established smokers were more likely to have a mother with a high school diploma or GED, be out of school and unemployed, not live with both parents, have used substances, be depressed, and have peers who smoked in adolescence and to have children as young adults and less likely to be Hispanic and to have ever married as young adults; and late escalators were more likely to be Hispanic, drink alcohol, and break rules in adolescence and less likely to have ever married as young adults. Because of the number of waves of data analyzed, this analysis provided a clearer temporal depiction of smoking behavior and more easily distinguishable smoking trajectories than previous analyses. Tobacco control interventions need to move beyond youth-focused approaches to reach all smokers.