Cigarette smoking and Ecuadorian adolescents: A cross-sectional survey and policy review

This study investigates the relationship between cigarette smoking and adolescents in Ecuador, South America. Using the Social Learning Theory as a basis, the cross-sectional survey focuses attention on such social influences as the smoking habits of family members and peers as well as, the role of cigarette advertisements. Actual use prevalence, access to cigarettes and knowledge and attitudes about smoking are also obtained.^ The survey was conducted in both urban and rural areas, with 50 schools in 40 different communities participating. Two thousand four hundred and fifty-seven adolescents aged 9 to 15 years completed a self-administered questionnaire. This part of the study was conducted in collaboration with the international health organization Amigos de las Americas (AMIGOS). Staff assigned to the AMIGOS Ecuador projects worked with local health and education officials to implement the cross-sectional survey in the field.^ The key informant survey and subsequent policy review were designed to illuminate the social, cultural and institutional environment for anti-smoking activities and interventions in Ecuador. Key individuals involved with this issue on both national and local levels were interviewed. A review of past legislative efforts and present anti-smoking laws was also conducted.^ The current smoking prevalence among the study population was 8.6 percent. Findings from the cross-sectional survey revealed significant positive associations between the smoking habits of siblings and peers and the smoking behavior of the adolescents surveyed. Significant age and gender differences were also found in association with several different variables.^ The policy review found an unfavorable environment for anti-smoking efforts. Several factors contribute to this including, most importantly, lack of funding and lack of public support. The present anti-smoking law is often vague and lacks important provisions, such as a prohibition on selling tobacco products to minors.^ Together, the two surveys provide comprehensive information for the purpose of designing smoking prevention interventions. Using the results from the two surveys, recommendations for intervention are proposed. ^

A study of the impact of cigarette advertising on adolescent susceptibility to smoking among 5th-, 8th-, and 12th-grade students in Hempstead and Hitchcock Independent School Districts

This study assessed the impact of cigarette advertising on adolescent susceptibility to smoking in the Hempstead and Hitchcock Independent School Districts. A convenience sample of 217 youths, 10-19 years of age was recruited in the study. Students completed both a paper-and-pencil and a computer-aided questionnaire in April 1996. Adolescents were defined as susceptible to smoking if they could not definitely rule out the possibility of future smoking. For the analysis, an index was devised: a 5-point index of an individual's receptivity to cigarette advertising. The index is determined by the number of positive responses to five survey items (recognizing cigarette brand logos, recognizing cigarette advertisement's pictures, recognizing cigarette brand slogans, evaluating adolescent attitudes toward cigarette advertising, and the degree to which adolescents were exposed to cigarette advertisements). Using logistic regression, we assessed the independent importance of the index in predicting susceptibility to smoking and ever smoking after adjusting for sociodemographic variables, perceived school performance and family composition. Of students surveyed, 54.4% of students appeared to have started the smoking uptake process as measured by susceptibility to smoking. Camel was recognized by the majority of students (88%), followed by Marlboro (41.5%) and Newport (40.1%). The pattern for recognition of the cigarette advertisements was the same as the pattern of market for cigarette. Advertisement featuring the cartoon character Joe Camel was significantly more appealing to adolescents than were advertisements with human models, with animal models, and with text only (p $<$ 0.001). Text only advertisement was significantly less appealing than other types of advertisements. The cigarette advertisement with White models (Marlboro) had significantly higher appeal to White students than to African-American students (p $<$ 0.001). The cigarette advertisement featuring African-American models (Virginia Slims) was significantly more appealing to African-American students than other ethnic groups (p $<$ 0.001). Receptivity to cigarette advertising was to be an important concurrent predictor of past smoking experience and intention to smoke in the future. Adolescents who scored in the fourth quartile of the Index of Receptivity to Cigarette Advertising were 7.54 (95% confidence interval (CI) = 1.92-29.56) times as likely to be susceptible to smoking, and were 4.56 (95% CI = 1.55-13.38) times as likely to have tried smoking, as those who scored in the first quartile of the Index. The findings confirmed the hypothesis that cigarette advertising may be a strong current influence in encouraging adolescents to initiate the smoking uptake process than sociodemographic variables, perceived school performance and family composition. ^

Smoking, apolipoprotein E genotypes, and mortality (the Ludwigshafen RIsk and Cardiovascular Health study).

AIMS The genetic polymorphism of apolipoprotein E (APOE) has been suggested to modify the effect of smoking on the development of coronary artery disease (CAD) in apparently healthy persons. The interaction of these factors in persons undergoing coronary angiography is not known. METHODS AND RESULTS We analysed the association between the APOE-genotype, smoking, angiographic CAD, and mortality in 3263 participants of the LUdwigshafen RIsk and Cardiovascular Health study. APOE-genotypes were associated with CAD [ε22 or ε23: odds ratio (OR) 0.56, 95% confidence interval (CI) 0.43-0.71; ε24 or ε34 or ε44: OR 1.10, 95% CI 0.89-1.37 compared with ε33] and moderately with cardiovascular mortality [ε22 or ε23: hazard ratio (HR) 0.71, 95% CI 0.51-0.99; ε33: HR 0.92, 95% CI 0.75-1.14 compared with ε24 or ε34 or ε44]. HRs for total mortality were 1.39 (95% CI 0.39-0.1.67), 2.29 (95% CI 1.85-2.83), 2.07 (95% CI 1.64-2.62), and 2.95 (95% CI 2.10-4.17) in ex-smokers, current smokers, current smokers without, or current smokers with one ε4 allele, respectively, compared with never-smokers. Carrying ε4 increased mortality in current, but not in ex-smokers (HR 1.66, 95% CI 1.04-2.64 for interaction). These findings applied to cardiovascular mortality, were robust against adjustment for cardiovascular risk factors, and consistent across subgroups. No interaction of smoking and ε4 was seen regarding non-cardiovascular mortality. Smokers with ε4 had reduced average low-density lipoprotein (LDL) diameters, elevated oxidized LDL, and lipoprotein-associated phospholipase A2. CONCLUSION In persons undergoing coronary angiography, there is a significant interaction between APOE-genotype and smoking. The presence of the ε4 allele in current smokers increases cardiovascular and all-cause mortality.

Predicting smoking cessation and its relapse in HIV-infected patients: the Swiss HIV Cohort Study

OBJECTIVES: The aim of the study was to assess whether prospective follow-up data within the Swiss HIV Cohort Study can be used to predict patients who stop smoking; or among smokers who stop, those who start smoking again. METHODS: We built prediction models first using clinical reasoning ('clinical models') and then by selecting from numerous candidate predictors using advanced statistical methods ('statistical models'). Our clinical models were based on literature that suggests that motivation drives smoking cessation, while dependence drives relapse in those attempting to stop. Our statistical models were based on automatic variable selection using additive logistic regression with component-wise gradient boosting. RESULTS: Of 4833 smokers, 26% stopped smoking, at least temporarily; because among those who stopped, 48% started smoking again. The predictive performance of our clinical and statistical models was modest. A basic clinical model for cessation, with patients classified into three motivational groups, was nearly as discriminatory as a constrained statistical model with just the most important predictors (the ratio of nonsmoking visits to total visits, alcohol or drug dependence, psychiatric comorbidities, recent hospitalization and age). A basic clinical model for relapse, based on the maximum number of cigarettes per day prior to stopping, was not as discriminatory as a constrained statistical model with just the ratio of nonsmoking visits to total visits. CONCLUSIONS: Predicting smoking cessation and relapse is difficult, so that simple models are nearly as discriminatory as complex ones. Patients with a history of attempting to stop and those known to have stopped recently are the best candidates for an intervention.

Assessment of smoking behaviour in a dental setting: a 1-year follow-up study using self-reported questionnaire data and exhaled carbon monoxide levels.

OBJECTIVES This study analyses the changes in smoking habits over the course of 1 year in a group of patients referred to an oral medicine unit. MATERIALS AND METHODS Smoking history and behaviour were analysed at baseline and after 1 year based on a self-reported questionnaire and on exhaled carbon monoxide levels [in parts per million (ppm)]. During the initial examination, all smokers underwent tobacco use prevention and cessation counselling. RESULTS Of the initial group of 121 patients, 98 were examined at the follow-up visit. At the baseline examination, 33 patients (33.67 %) indicated that they were current smokers. One year later, 14 patients (42.24 % out of the 33 smokers of the initial examination) indicated that they had attempted to stop smoking at least once over the follow-up period and 15.15 % (5 patients) had quit smoking. The mean number of cigarettes smoked per day by current smokers decreased from 13.10 to 12.18 (p = 0.04). The exhaled CO level measurements showed very good correlation with a Spearman's coefficient 0.9880 for the initial visit, and 0.9909 for the follow-up examination. For current smokers, the consumption of one additional cigarette per day elevated the CO measurements by 0.77 ppm (p < 0.0001) at the baseline examination and by 0.84 ppm (p < 0.0001) at the 1-year follow-up. CONCLUSIONS In oral health care, where smoking cessation is an important aspect of the treatment strategy, the measurement of exhaled carbon monoxide shows a very good correlation with a self-reported smoking habit. CLINICAL RELEVANCE Measurement of exhaled carbon monoxide is a non-invasive, simple and objective measurement technique for documenting and monitoring smoking cessation and reduction.

Volitional processes and daily smoking: Examining inter- and intraindividual associations around a quit attempt

The Health Action Process Approach (HAPA) assumes that volitional processes are important for effective behavioral change. However, intraindividual associations have not yet been tested in the context of smoking cessation. This study examined the inter- and intraindividual associations between volitional HAPA variables and daily smoking before and after a quit attempt. Overall, 100 smokers completed daily surveys on mobile phones from 10 days before until 21 days after a self-set quit date, including self-efficacy, action planning, action control, and numbers of cigarettes smoked. Negative associations between volitional variables and daily numbers of cigarettes smoked emerged at the inter- and intraindividual level. Except for interindividual action planning, associations were stronger after the quit date than before the quit date. Self-efficacy, planning and action control were identified as critical inter- and intraindividual processes in smoking cessation, particularly after a self-set quit attempt when actual behavior change is performed.

Invisible social control as predictor of daily negative affect and smoking after a self-set quit date

The dual-effects model of social control proposes that social control leads to increased psychological distress but also to better health practices. However, findings are inconsistent, and recent research suggests that the most effective control is unnoticed by the receiver (i. e., invisible). Yet, investigations of the influence of invisible control on daily negative affect and smoking have been limited. Using daily diaries, we investigated how invisible social control was associated with negative affect and smoking. Overall, 100 smokers (72.0 % men, age M = 40.48, SD = 9.82) and their nonsmoking partners completed electronic diaries from a self-set quit date for 22 consecutive days, reporting received and provided social control, negative affect, and daily smoking. We found in multilevel analyses of the within-person process that on days with higher-than-average invisible control, smokers reported more negative affect and fewer cigarettes smoked. Findings are in line with the assumptions of the dual-effects model of social control: Invisible social control increased daily negative affect and simultaneously reduced smoking at the within-person level.

Change in physical activity after smoking cessation: the Coronary Artery Risk Development in Young Adults (CARDIA) study

AIMS To estimate physical activity trajectories for people who quit smoking, and compare them to what would have been expected had smoking continued. DESIGN, SETTING AND PARTICIPANTS A total of 5115 participants in the Coronary Artery Risk Development in Young Adults Study (CARDIA) study, a population-based study of African American and European American people recruited at age 18-30 years in 1985/6 and followed over 25 years. MEASUREMENTS Physical activity was self-reported during clinical examinations at baseline (1985/6) and at years 2, 5, 7, 10, 15, 20 and 25 (2010/11); smoking status was reported each year (at examinations or by telephone, and imputed where missing). We used mixed linear models to estimate trajectories of physical activity under varying smoking conditions, with adjustment for participant characteristics and secular trends. FINDINGS We found significant interactions by race/sex (P = 0.02 for the interaction with cumulative years of smoking), hence we investigated the subgroups separately. Increasing years of smoking were associated with a decline in physical activity in black and white women and black men [e.g. coefficient for 10 years of smoking: -0.14; 95% confidence interval (CI) = -0.20 to -0.07, P < 0.001 for white women]. An increase in physical activity was associated with years since smoking cessation in white men (coefficient 0.06; 95% CI = 0 to 0.13, P = 0.05). The physical activity trajectory for people who quit diverged progressively towards higher physical activity from the expected trajectory had smoking continued. For example, physical activity was 34% higher (95% CI = 18 to 52%; P < 0.001) for white women 10 years after stopping compared with continuing smoking for those 10 years (P = 0.21 for race/sex differences). CONCLUSIONS Smokers who quit have progressively higher levels of physical activity in the years after quitting compared with continuing smokers.

The joint influence of gender and amount of smoking on weight gain one year after smoking cessation

Weight gain is often associated with smoking cessation and may discourage smokers from quitting. This study estimated the weight gained one year after smoking cessation and examined the risk factors associated with weight gain in order to identify socio-demographic groups at higher risk of increased weight after quitting. We analyzed data from 750 adults in two randomized controlled studies that included smokers motivated to quit and found a gradient in weight gain according to the actual duration of abstinence during follow-up. Subjects who were abstinent for at least 40 weeks gained 4.6 kg (SD = 3.8) on average, compared to 1.2 kg (SD = 2.6) for those who were abstinent less than 20 weeks during the 1-year follow-up. Considering the duration of abstinence as an exposure variable, we found an age effect and a significant interaction between sex and the amount of smoking before quitting: younger subjects gained more weight than older subjects; among light smokers, men gained more weight on average than women one year after quitting, while the opposite was observed among heavy smokers. Young women smoking heavily at baseline had the highest risk of weight gain after quitting.