Assessing tobacco dependence among cannabis users smoking cigarettes.

INTRODUCTION: This study examines the relationship between nicotine exposure and tobacco addiction among young smokers consuming either only tobacco or only tobacco and cannabis. METHODS: Data on tobacco and cannabis use were collected by a questionnaire among 313 adolescents and young adults in Western Switzerland between 2009 and 2010. In addition, a urine sample was used to determine urinary cotinine level. Nicotine addiction was measured using the Fagerström Test for Nicotine Dependence (FTND). In this study, we focused on a sample of 142 participants (mean age 19.54) that reported either smoking only tobacco cigarettes (CIG group, n = 70) or smoking both tobacco cigarettes and cannabis (CCS group, n = 72). RESULTS: The FTND did not differ significantly between CIG (1.96 ± 0.26) and CCS (2.66 ± 0.26) groups (p = 0.07). However, participants in the CCS group smoked more cigarettes (8.30 ± 0.79 vs. 5.78 ± 0.8, p = 0.03) and had a higher mean cotinine value (671.18 ± 67.6 vs. 404.32 ± 68.63, p = 0.008) than the CIG group. Further, the association between cotinine and FTND was much stronger among the CIG than among the CCS group (regression coefficient of 0.0031 vs. 0.00099, p < 0.0001). CONCLUSION: Adolescents smoking tobacco and cannabis cigarettes featured higher levels of cotinine than youth smoking only tobacco; however, there was no significant difference in the addiction score. The FTND score is intended to measure nicotine dependence from smoked tobacco cigarettes. Hence, to accurately determine nicotine exposure and the associated dependence among young smokers, it seems necessary to inquire about cannabis consumption.

Interventions for smoking cessation in hospitalised patients.

BACKGROUND: Smoking contributes to reasons for hospitalisation, and the period of hospitalisation may be a good time to provide help with quitting. OBJECTIVES: To determine the effectiveness of interventions for smoking cessation that are initiated for hospitalised patients. SEARCH METHODS: We searched the Cochrane Tobacco Addiction Group register which includes papers identified from CENTRAL, MEDLINE, EMBASE and PsycINFO in December 2011 for studies of interventions for smoking cessation in hospitalised patients, using terms including (hospital and patient*) or hospitali* or inpatient* or admission* or admitted. SELECTION CRITERIA: Randomized and quasi-randomized trials of behavioural, pharmacological or multicomponent interventions to help patients stop smoking, conducted with hospitalised patients who were current smokers or recent quitters (defined as having quit more than one month before hospital admission). The intervention had to start in the hospital but could continue after hospital discharge. We excluded studies of patients admitted to facilities that primarily treat psychiatric disorders or substance abuse, studies that did not report abstinence rates and studies with follow-up of less than six months. Both acute care hospitals and rehabilitation hospitals were included in this update, with separate analyses done for each type of hospital. DATA COLLECTION AND ANALYSIS: Two authors extracted data independently for each paper, with disagreements resolved by consensus. MAIN RESULTS: Fifty trials met the inclusion criteria. Intensive counselling interventions that began during the hospital stay and continued with supportive contacts for at least one month after discharge increased smoking cessation rates after discharge (risk ratio (RR) 1.37, 95% confidence interval (CI) 1.27 to 1.48; 25 trials). A specific benefit for post-discharge contact compared with usual care was found in a subset of trials in which all participants received a counselling intervention in the hospital and were randomly assigned to post-discharge contact or usual care. No statistically significant benefit was found for less intensive counselling interventions. Adding nicotine replacement therapy (NRT) to an intensive counselling intervention increased smoking cessation rates compared with intensive counselling alone (RR 1.54, 95% CI 1.34 to 1.79, six trials). Adding varenicline to intensive counselling had a non-significant effect in two trials (RR 1.28, 95% CI 0.95 to 1.74). Adding bupropion did not produce a statistically significant increase in cessation over intensive counselling alone (RR 1.04, 95% CI 0.75 to 1.45, three trials). A similar pattern of results was observed in a subgroup of smokers admitted to hospital because of cardiovascular disease (CVD). In this subgroup, intensive intervention with follow-up support increased the rate of smoking cessation (RR 1.42, 95% CI 1.29 to 1.56), but less intensive interventions did not. One trial of intensive intervention including counselling and pharmacotherapy for smokers admitted with CVD assessed clinical and health care utilization endpoints, and found significant reductions in all-cause mortality and hospital readmission rates over a two-year follow-up period. These trials were all conducted in acute care hospitals. A comparable increase in smoking cessation rates was observed in a separate pooled analysis of intensive counselling interventions in rehabilitation hospitals (RR 1.71, 95% CI 1.37 to 2.14, three trials). AUTHORS' CONCLUSIONS: High intensity behavioural interventions that begin during a hospital stay and include at least one month of supportive contact after discharge promote smoking cessation among hospitalised patients. The effect of these interventions was independent of the patient's admitting diagnosis and was found in rehabilitation settings as well as acute care hospitals. There was no evidence of effect for interventions of lower intensity or shorter duration. This update found that adding NRT to intensive counselling significantly increases cessation rates over counselling alone. There is insufficient direct evidence to conclude that adding bupropion or varenicline to intensive counselling increases cessation rates over what is achieved by counselling alone.

Improved health of hospitality workers after a Swiss cantonal smoking ban.

QUESTIONS UNDER STUDY: Hospitality workers are a population particularly at risk from the noxious effects of environmental tobacco smoke (ETS). The Canton of Vaud, Switzerland banned smoking in public places in September 2009. This prospective study addresses the impact of the ban on the health of hospitality workers. METHODS: ETS exposure was evaluated using a passive sampling device that measures airborne nicotine; lung function was assessed by spirometry; health-related quality of life, ETS exposure symptoms and satisfaction were measured by questionnaire. RESULTS: 105 participants (smokers and non-smokers) were recruited initially and 66 were followed up after one year. ETS exposure was significantly lower after the ban. Hospitality workers had lower pre-ban forced expiratory volume in one second (FEV1) and forced vital capacity (FVC) values than expected. FEV1 remained stable after the ban, with a near-significant increase in the subgroup of asthmatics only. FVC increased at one year follow-up from 90.42% to 93.05% (p = 0.02) in the entire cohort; women, non-smokers and older participants gained the greatest benefit. The health survey showed an increase in physical wellbeing after the ban, the greatest benefit being observed in non-smokers. ETS exposure symptoms were less frequent after the ban, especially red and irritated eyes and sneezing. The new law was judged useful and satisfactory by the vast majority of employees, including smokers. CONCLUSION: The recent cantonal ban on smoking in public places brought about an improvement in lung function, physical well-being and ETS symptoms of hospitality workers, including smokers.

Nicotine vaccines for smoking cessation.

BACKGROUND: By reducing the amount of nicotine that reaches the brain when a person smokes a cigarette, nicotine vaccines may help people to stop smoking or to prevent recent quitters from relapsing. OBJECTIVES: The aims of this review are to assess the efficacy of nicotine vaccines for smoking cessation and for relapse prevention, and to assess the frequency and type of adverse events associated with the use of nicotine vaccines. SEARCH METHODS: We searched the Cochrane Tobacco Addiction Review Group specialised register for trials, using the term 'vaccine' in the title or abstract, or in a keyword (date of most recent search April 2012). To identify any other material including reviews and papers potentially relevant to the background or discussion sections, we also searched MEDLINE, EMBASE, and PsycINFO, combining terms for nicotine vaccines with terms for smoking and tobacco use, without design limits or limits for human subjects. We searched the Annual Meeting abstracts of the Society for Research on Nicotine and Tobacco up to 2012, using the search string 'vaccin'. We searched Google Scholar for 'nicotine vaccine'. We also searched company websites and Google for information related to specific vaccines. We searched clinicaltrials.gov in March 2012 for 'nicotine vaccine' and for the trade names of known vaccine candidates. SELECTION CRITERIA: We included randomized controlled trials of nicotine vaccines, at Phase II and Phase III trial stage and beyond, in adult smokers or recent ex-smokers. We included studies of nicotine vaccines used as part of smoking cessation or relapse prevention interventions. DATA COLLECTION AND ANALYSIS: We extracted data on the type of participants, the dose and duration of treatment, the outcome measures, the randomization procedure, concealment of allocation, blinding of participants and personnel, reporting of outcomes, and completeness of follow-up.Our primary outcome measure was a minimum of six months abstinence from smoking. We used the most rigorous definition of abstinence, and preferred cessation rates at 12 months and biochemically validated rates where available. We have used the risk ratio (RR) to summarize individual trial outcomes. We have not pooled the current group of included studies as they cover different vaccines and variable regimens. MAIN RESULTS: There are no nicotine vaccines currently licensed for public use, but there are a number in development. We found four trials which met our inclusion criteria, three comparing NicVAX to placebo and one comparing NIC002 (formerly NicQbeta) to placebo. All were smoking cessation trials conducted by pharmaceutical companies as part of the drug development process, and all trials were judged to be at high or unclear risk of bias in at least one domain. Overall, 2642 smokers participated in the included studies in this review. None of the four included studies detected a statistically significant difference in long-term cessation between participants receiving vaccine and those receiving placebo. The RR for 12 month cessation in active and placebo groups was 1.35 (95% Confidence Interval (CI) 0.82 to 2.22) in the trial of NIC002 and 1.74 (95% CI 0.73 to 4.18) in one NicVAX trial. Two Phase III NicVAX trials, for which full results were not available, reported similar quit rates of approximately 11% in both groups. In the two studies with full results available, post hoc analyses detected higher cessation rates in participants with higher levels of nicotine antibodies, but these findings are not readily generalisable. The two studies with full results showed nicotine vaccines to be well tolerated, with the majority of adverse events classified as mild or moderate. In the study of NIC002, participants receiving the vaccine were more likely to report mild to moderate adverse events, most commonly flu-like symptoms, whereas in the study of NicVAX there was no significant difference between the two arms. Information on adverse events was not available for the large Phase III trials of NicVAX.Vaccine candidates are likely to undergo significant changes before becoming available to the general public, and those included in this review may not be the first to reach market; this limits the external validity of the results reported in this review in terms of both effectiveness and tolerability. AUTHORS' CONCLUSIONS: There is currently no evidence that nicotine vaccines enhance long-term smoking cessation. Rates of serious adverse events recorded in the two trials with full data available were low, and the majority of adverse events reported were at mild to moderate levels. The evidence available suggests nicotine vaccines do not induce compensatory smoking or affect withdrawal symptoms. No nicotine vaccines are currently licensed for use in any country but a number are under development.Further trials of nicotine vaccines are needed, comparing vaccines with placebo for smoking cessation. Further trials are also needed to explore the potential of nicotine vaccines to prevent relapse. Results from past, current and future research should be reported in full. Adverse events and serious adverse events should continue to be carefully monitored and thoroughly reported.

Association between smoking and total energy expenditure in a multi-country study.

BACKGROUND: The association between smoking and total energy expenditure (TEE) is still controversial. We examined this association in a multi-country study where TEE was measured in a subset of participants by the doubly labeled water (DLW) method, the gold standard for this measurement. METHODS: This study includes 236 participants from five different African origin populations who underwent DLW measurements and had complete data on the main covariates of interest. Self-reported smoking status was categorized as either light (<7 cig/day) or high (≥7 cig/day). Lean body mass was assessed by deuterium dilution and physical activity (PA) by accelerometry. RESULTS: The prevalence of smoking was 55% in men and 16% in women with a median of 6.5 cigarettes/day. There was a trend toward lower BMI in smokers than non-smokers (not statistically significant). TEE was strongly correlated with fat-free mass (men: 0.70; women: 0.79) and with body weight (0.59 in both sexes). Using linear regression and adjusting for body weight, study site, age, PA, alcohol intake and occupation, TEE was larger in high smokers than in never smokers among men (difference of 298 kcal/day, p = 0.045) but not among women (162 kcal/day, p = 0.170). The association became slightly weaker in men (254 kcal/day, p = 0.058) and disappeared in women (-76 kcal/day, p = 0.380) when adjusting for fat-free mass instead of body weight. CONCLUSION: There was an association between smoking and TEE among men. However, the lack of an association among women, which may be partly related to the small number of smoking women, also suggests a role of unaccounted confounding factors.

Impact of a smoking ban in hospitality venues on second hand smoke exposure: a comparison of exposure assessment methods

BACKGROUND: In May 2010, Switzerland introduced a heterogeneous smoking ban in the hospitality sector. While the law leaves room for exceptions in some cantons, it is comprehensive in others. This longitudinal study uses different measurement methods to examine airborne nicotine levels in hospitality venues and the level of personal exposure of non-smoking hospitality workers before and after implementation of the law. METHODS: Personal exposure to second hand smoke (SHS) was measured by three different methods. We compared a passive sampler called MoNIC (Monitor of NICotine) badge, to salivary cotinine and nicotine concentration as well as questionnaire data. Badges allowed the number of passively smoked cigarettes to be estimated. They were placed at the venues as well as distributed to the participants for personal measurements. To assess personal exposure at work, a time-weighted average of the workplace badge measurements was calculated. RESULTS: Prior to the ban, smoke-exposed hospitality venues yielded a mean badge value of 4.48 (95%-CI: 3.7 to 5.25; n = 214) cigarette equivalents/day. At follow-up, measurements in venues that had implemented a smoking ban significantly declined to an average of 0.31 (0.17 to 0.45; n = 37) (p = 0.001). Personal badge measurements also significantly decreased from an average of 2.18 (1.31-3.05 n = 53) to 0.25 (0.13-0.36; n = 41) (p = 0.001). Spearman rank correlations between badge exposure measures and salivary measures were small to moderate (0.3 at maximum). CONCLUSIONS: Nicotine levels significantly decreased in all types of hospitality venues after implementation of the smoking ban. In-depth analyses demonstrated that a time-weighted average of the workplace badge measurements represented typical personal SHS exposure at work more reliably than personal exposure measures such as salivary cotinine and nicotine.

A systematic review of longitudinal population-based studies on the predictors of smoking cessation in adolescent and young adult smokers.

OBJECTIVE: To describe the determinants of self-initiated smoking cessation of duration of at least 6 months as identified in longitudinal population-based studies of adolescent and young adult smokers. METHODS: A systematic search of the PubMed and EMBASE databases using smoking, tobacco, cessation, quit and stop as keywords was performed. Limits included articles related to humans, in English, published between January 1984 and August 2010, and study population aged 10-29 years. A total of 4502 titles and 871 abstracts were reviewed independently by 2 and 3 reviewers, respectively. Nine articles were retained for data abstraction. Data on study location, timeframe, duration of follow-up, number of data collection points, sample size, age/grade of participants, number of quitters, smoking status at baseline, definition of cessation, covariates and analytic method were abstracted from each article. The number of studies that reported a statistically significant association between each determinant investigated and cessation were tabulated, from among all studies that assessed the determinant. RESULTS: Despite heterogeneity in methods across studies, five factors robustly predicted quitting across studies in which the factor was investigated: not having friends who smoke, not having intentions to smoke in the future, resisting peer pressure to smoke, being older at first use of cigarette and having negative beliefs about smoking. CONCLUSIONS: The literature on longitudinal predictors of cessation in adolescent and young adult smokers is not well developed. Cessation interventions for this population will remain less than optimally effective until there is a solid evidence base on which to develop interventions.

Official Positions for FRAX® clinical regarding smoking from Joint Official Positions Development Conference of the International Society for Clinical Densitometry and International Osteoporosis Foundation on FRAX®.

The worldwide prevalence of smoking has been estimated at about 50% in men, and 10% in women, with larger variations among different populations studied. Smoking has been shown to affect many organ systems resulting in severe morbidity and increased mortality. In addition, smoking has been identified as a predictor of ten-year fracture risk in men and women, largely independent of an individual's bone mineral density. This finding has eventually lead to incorporation of this risk factor into FRAX®, an algorithm that has been developed to calculate an individual's ten-year fracture risk. However, only little, or conflicting data is available on a possible association between smoking dose, duration, length of time after cessation, type of tobacco and fracture risk, limiting this risk factor's applicability in the context of FRAX®.

The Iowa Medical Smoking Cessation Program, January 15, 2009

This report by the Iowa medical Drug Utilization Review Commission is in response to a request by the General Assembly to monitor the smoking cessation benefit for Iowa medicaid members, This review is performed on an ongoing basis to ensure all the elements of the legislation are met.

Relationship between cigarette smoking and abdominal obesity

BACKGROUND: Although smokers tend to have a lower body-mass index (BMI) than non-smokers, smoking may affect body fat (BF) distribution. Some studies have assessed the association between smoking, BMI and waist circumference (WC), but, to our knowledge, no population-based studies assessed the relation between smoking and BF composition. We assessed the association between amount of cigarette smoking, BMI, WC and BF composition. METHODS: Data was analysed from a cross-sectional population-based study including 6187 Caucasians aged 32-76 and living in Switzerland. Height, weight and WC were measured. BF, expressed in percent of total body weight, was measured by electrical bioimpedance. Obesity was defined as a BMI>=30 kg/m2 and normal weight as a BMI<25 kg/m2. Abdominal obesity was defined as a WC>=102 cm for men and >=88 cm for women and normal WC as <94 cm for men and <80 cm for women. In men, excess BF was defined as %BF >=28.1, 28.7, 30.6 and 32.6 for age groups 32-44, 45-54, 55-64 and 65-76, respectively; the corresponding values for women were 35.9, 36.5, 40.5 and 44.4. Cigarette smoking was assessed using a self-reported questionnaire. RESULTS: 29.3% of men and 25.0% of women were smokers. Prevalence of obesity, abdominal obesity, and excess of BF was 16.9% and 26.6% and 14.2% in men and 15.0%, 33.0% and 27.5% in women, respectively. Smokers had lower age-adjusted mean BMI, WC and percent of BF compared to non-smokers. However, among smokers,mean age-adjusted BMI,WC and BF increased with the number of cigarettes smoked per day: among light (1-10 cig/day), moderate (11-20) and heavy smokers (>20), mean +/-SE %BF was 22.4 +/−0.3, 23.1+/−0.3 and 23.5+/−0.4 for men, and 31.9+/−0.3, 32.6+/−0.3 and 32.9+/−0.4 for women, respectively. Mean WC was 92.9+/−0.6, 94.0+/−0.5 and 96.0+/−0.6 cm for men, and 80.2+/−0.5, 81.3+/−0.5 and 83.3+/−0.7 for women, respectively. Mean BMI was 25.7+/−0.2, 26.0+/−0.2, and 26.1+/−0.2 kg/m2 for men; and 23.6+/−0.2, 24.0+/−0.2 and 24.1+/−0.3 for women, respectively. Compared with light smokers, the age-adjusted odds ratio (95% Confidence Interval) for excess of BF was 1.04 (0.58 to 1.85) formoderatesmokers and 1.06 (0.57 to 1.99) for heavy smokers in men (p-trend = 0.9), and 1.35 (0.92 to 1.99) and 2.26 (1.38 to 3.72), respectively, in women (p-trend = 0.04). Odds ratio for abdominal obesity vs. normal WC was 1.32 (0.81 to 2.15) for moderate smokers and 1.95 (1.16 to 3.27) for heavy smokers in men (p-trend < 0.01), and 1.15 (0.79 to 1.69) and 2.36 (1.41 to 3.93) in women (p-trend = 0.03). Odds ratio for obesity vs. normal weight was 1.35 (0.76 to 2.41) for moderate smokers and 1.33 (0.71 to 2.49) for heavy smokers in men (p-trend = 0.9) and 0.78 (0.45 to 1.35) and 1.44 (0.73 to 2.85), in women (p-trend = 0.08). CONCLUSIONS: WC and BF were positively and dose-dependently associated with the number of cigarettes smoked per day in women, whereas onlyWC was dose dependently and significantly associated with the amount of cigarettes smoked per day in men. This suggests that heavy smokers, especially women, are more likely to have an excess of BF and to accumulate BF in the abdomen compared to lighter smokers.

Cigarette smoking and endometrial cancer risk: the modifying effect of obesity.

The objective of this study was to evaluate the association between cigarette smoking and endometrial cancer risk by investigating potential modifying effects of menopausal status, obesity, and exogenous hormones. We pooled data from three case-control studies with the same study design conducted in Italy and Switzerland between 1982 and 2006. Overall, 1446 incident endometrial cancers and 4076 hospital controls were enrolled. Odds ratios (OR) and 95% confidence intervals (CI) were estimated using logistic regression models, conditioned on study and centre, and adjusted for age, period of interview, age at menarche, parity, and body mass index. In comparison with never smokers, current smokers showed reduced endometrial cancer risk (OR: 0.80; 95% CI: 0.66-0.96), with a 28% decrease in risk for smoking >/=20 cigarettes/day. The association did not vary according to menopausal status, oral contraceptive use, or hormone replacement therapy. However, heterogeneity emerged according to body mass index among postmenopausal women, with obese women showing the greatest risk reduction for current smoking (OR: 0.47; 95% CI: 0.27-0.81). In postmenopausal women, obesity turned out to be an important modifier of the association between cigarette smoking and the risk of endometrial cancer. This finding calls for caution in interpreting the favorable effects of cigarette smoking, considering the toxic and carcinogenic effects of tobacco.