Background. Policymakers need estimates of the impact of tobacco control (TC) policies to set priorities and targets for reducing tobacco use. We systematically reviewed the independent effects of TC policies on smoking behavior.Methods. We searched MEDLINE (through January 2012) and EMBASE and other databases through February 2009, looking for studies published after 1989 in any language that assessed the effects of each TC intervention on smoking prevalence, initiation, cessation, or price participation elasticity. Paired reviewers extracted data from studies that isolated the impact of a single TC intervention.Findings. We included 84 studies. The strength of evidence quantifying the independent effect on smoking prevalence was high for increasing tobacco prices and moderate for smoking bans in public places and antitobacco mass media campaigns. Limited direct evidence was available to quantify the effects of health warning labels and bans on advertising and sponsorship. Studies were too heterogeneous to pool effect estimates.Interpretations. We found evidence of an independent effect for several TC policies on smoking prevalence. However, we could not derive precise estimates of the effects across different settings because of variability in the characteristics of the intervention, level of policy enforcement, and underlying tobacco control environment.

Non-smokers are exposed to tobacco smoke from the burning cigarette and the exhaled smoke from smokers. In spite of decades of development of approaches to assess secondhand smoke exposure (SHSe), there are still unresolved methodological issues. This manuscript summarises the scientific evidence on the use of SHSe reported measures and their methods, objectives, strengths and limitations; and discusses best practices for assessing behaviour leading to SHSe for lifetime and immediate or current SHSe. Recommendations for advancing measurement science of SHSe are provided. Behavioural measures of SHSe commonly rely on self-reports from children and adults. Most commonly, the methodology includes self, proxy and interview-based reporting styles using retrospective recall or diary-style reporting formats. The reporting method used will vary based upon the subject of interest, assessment objectives and cultural context. Appropriately implemented, reported measures of SHSe provide an accurate, timely and cost-effective method for assessing exposure time, location and quantity in a wide variety of populations.

Background In the USA, tobacco marketing expenditure is increasingly concentrated at the point of sale (POS). Previous studies have demonstrated an association between exposure to tobacco POS advertising and increased smoking initiation, but limited evidence is available on adult smokers’ decisions and behaviours. Methods An immediate post-cigarette purchase survey was administered to 301 cigarette purchasers outside of two grocery stores in Vermont to assess the prevalence of unplanned purchases and opinions about POS tobacco advertising and displays. Results In total, 11.3% of purchases were reported as unplanned. Certain groups were more likely to make unplanned purchases including: 18–24-year-olds (OR: 2.1, 95% CI 1.0 to 4.4), less than daily smokers (OR: 5.6, 95% CI 1.9 to 16.9), smokers who made 3+ quit attempts in the previous year (OR: 2.4, 95% CI 0.9 to 6.0), those who plan to quit in the next month (OR: 3.7, 95% CI 1.6 to 9.0), and those who agreed that tobacco POS advertising makes quitting smoking harder (OR: 2.3, 95% CI 1.1 to 4.8). Overall, 31.2% of participants agreed that tobacco POS advertising makes quitting smoking harder. Individuals who intended to quit within the next month, made 3+ quit attempts in the last year, or made an unplanned cigarette purchase were the most likely to agree. Conclusions Young adults and individuals making multiple quit attempts or planning to quit in the next month are more likely to make unplanned cigarette purchases. Reducing unplanned purchases prompted by tobacco POS advertising could improve the likelihood of successful cessation among smokers.

Smoking likely exacerbates comorbidities which people living with HIV (PLWH) are predisposed. We assessed prevalence and correlates of smoking among PLWH in South Africa, which has 7 million PLWH but inadequate reporting of smoking. A cross-sectional survey was conducted among randomly selected adults with HIV infection in Klerksdorp, South Africa. Current smoking was assessed by questionnaire, exhaled carbon monoxide (eCO), and urine cotinine. Of 1210 enrolled adults, 753 (62%) were women. In total, 409 (34%) self-reported ever smoking: 301 (74%) were current and 108 (26%) were former smokers. Using eCO and urine cotinine tests, 239 (52%) men and 100 (13%) women were defined as current smokers. Nearly all smokers (99%) were receiving ART, and had a median (IQR) CD4 count of 333 cells/μL (181–534), viral load of 31 IU/mL (25–4750), and BMI of 21 kg/m² (19–24). Adjusted analysis among men showed higher odds of smoking with marijuana use (OR = 7.5, 95% CI = 4.1 to 14.6). Among women, 304 (43%) reported using snuff, compared to only 11 (3%) of men, and snuff use was inversely associated with smoking (OR = 0.1; 95% CI = 0.05 to 0.2). A subset of participants (n = 336) was asked about alcohol use, which was positively associated with smoking for men (OR = 8.1, 95% CI = 2.8 to 25.9) and women (OR = 8.5, 95% CI = 2.9 to 26.8). Smoking prevalence among PLWH in South Africa is alarmingly high. Prevention and cessation strategies that consider marijuana and alcohol use are needed. As long-term HIV care continues to improve, more people living with HIV (PLWH) will die of diseases, including tuberculosis, for which smoking plays an important causal role. The prevalence of smoking is markedly higher among PLWH in high-resource settings, but data for Africa and other low-resource settings that shoulder the brunt of the HIV epidemic has previously not been well documented. We report an alarmingly high prevalence of smoking among PLWH in South Africa, particularly among men, and a strong association between current smoking and use of other substances.

Mobile phone–based messaging support and biomarker feedback independently show evidence of increasing an individual’s likelihood of quitting smoking. However, the combination of these two strategies to facilitate smoking cessation has not been adequately explored. We conducted a randomized controlled trial in Baltimore, Maryland, to assess the efficacy of COach2Quit, a smartphone application that provides exhaled carbon monoxide readings with message support. The primary outcome was self-reported and biochemically verified smoking cessation at 30-day follow-up. Secondary outcomes were reduction in smoking, motivation to quit, and engagement and satisfaction with COach2Quit. An intention-to-treat analysis was conducted. Adult smokers were randomized 1:1 to receive brief advice and COach2Quit (intervention, n = 50) or brief advice only (control, n = 52). Thirteen participants were lost to follow-up. At 30-day follow-up, one participant in each arm quit smoking. Median change in carbon monoxide levels (in parts per million (ppm)) (intervention: −3.0 [interquartile range (IQR) −12.0, 2.0]; control: −2.5 [IQR −9.0, 2.0]) and median change in number of cigarettes smoked per day (intervention: −5.5 [IQR −14.0, −1.0]; control: −6.0 [IQR −10.0, −2.0]) was similar between study arms. There was no significant difference in mean percent change in the Reasons for Quitting scale score (intervention: 6.3 [95% confidence interval = −2.2% to 14.8%]; control: −3.6 [95% confidence interval = −9.2% to 2.1%]). A majority (n = 32, 91%) of participants liked having COach2Quit to help them quit smoking. There were no significant differences in smoking cessation, smoking reduction, and motivation to quit between study arms. However, high satisfaction with the COach2Quit application indicates its feasibility and acceptability as a smoking cessation tool. Smoking is the leading preventable cause of morbidity and mortality in the United States. Although counseling and pharmacotherapy are efficacious for smoking cessation, they are not easily accessible or desirable to all smokers, highlighting the need for identifying other interventions. There is evidence for the efficacy of mobile phone–based messaging support for smoking cessation. However, there is limited research on the efficacy of biomarker feedback, much less interventions that combine these two approaches. This research contributes to filling this gap and identifying novel interventions to facilitate smoking cessation.