Daniel Owusu

Factual text and emotional pictures: overcoming a false dichotomy of cigarette warning labels

Background In reviewing the first set of pictorial warning labels in the USA, the courts equated textual labels with facts and information, and images with emotion. This study tested the differences in perceived informativeness and emotion between textual and pictorial cigarette warning labels. Methods An online study with 1838 US adults who were non-smokers (n=764), transitioning smokers (quit smoking in the past 2 years or currently trying to quit, n=505) or current smokers (n=569). Each participant evaluated 9 out of 81 text and pictorial cigarette warning labels. Participants reported to what extent they perceived the label as informative and factual and the negative emotions they felt while looking at each label. We used linear mixed models to account for the nesting of multiple observations within each participant. Results There were no significant differences in perceived informativeness between textual (mean 6.15 on a 9-point scale) and pictorial labels (6.14, p=0.80, Cohen’s d=0.003). Textual labels evoked slightly less emotion (4.21 on a 9-point scale) than pictorial labels (4.42, p<0.001, Cohen’s d=0.08). Perceived informativeness and emotion were strongly correlated (Pearson r=0.53, p<0.001). Conclusion Our findings contradict courts’ conclusions that pictorial messages are emotional and not factual. Pictorial labels are rated as informative and factual, textual labels evoke emotion, and emotionality and informativeness are strongly correlated. These findings serve as evidence for the Food and Drug Administration (FDA) to counteract the claim that pictorial warning labels, by definition, are not ‘purely factual and uncontroversial’.

Alcohol Consumption, Depression, Insomnia and Colorectal Cancer Screening: Racial Differences

Background: Mortality from colorectal cancer (CRC) can be reduced drastically by early detection and early treatment. However, uptake of CRC screening is relatively low, about 50% for those whom the test is highly recommended. Objectives: We examined the influence of and racial differences in depression, insomnia, alcohol use, and tobacco use on CRC screening uptake in the US. Patients and Methods: Analysis of the 2012 National Health Information Survey data was conducted. Both weighted univariate and multiple logistic regression analyses were performed in SAS to estimate the odds ratios (ORs) and their 95% confidence intervals (CIs). A total of 21511 participants were included in the analysis. Results: Prevalence of CRC screening in the participants was 19%. Adjusting for all factors, insomnia (OR = 1.18, 95%CI = 1.06 - 1.32), moderate alcohol drinking (OR = 1.16, 95%CI = 1.01 - 1.30), past smoking (OR = 1.17, 95%CI = 1.04 - 1.32), depression (OR = 1.37, 95%CI = 1.18 - 1.58), African American (AA) race, and cancer history were positively associated with CRC screening. Females and Single were inversely associated with CRC screening prevalence. In stratified analysis by races (White and AA), depression was associated with CRC screening in both races. Marital status, smoking, cancer history and insomnia were associated with CRC screening in Whites only; while alcohol use was associated with CRC screening in AAs only. Conclusions: We have found significant associations between lifestyle factors (alcohol consumption and smoking) and mental health problems (depression and insomnia) and CRC screening uptake. To improve overall CRC screening uptake in the US, it is important to consider racial differences in predictors and tailor appropriate interventions to each racial/ethnic group.

Never-Smoking Adolescents’ Exposure to Secondhand Smoke in Africa

INTRODUCTION Though Africa is in Stage 1 of the tobacco epidemic, lack of effective public smoking laws or political will implies that secondhand smoke (SHS) exposure may be high in youth. The study objective is to estimate prevalence and identify determinants of SHS exposure among never-smoker adolescents in Africa and make cross-country comparisons. METHODS Pooled data from the Global Youth Tobacco Surveys conducted in 25 African countries during 2006-2011 were used. Based on the venue of exposure in past 7 days, SHS was categorized into exposure inside, outside, and overall exposure (either inside or outside of the home), respectively. Data were analyzed in 2015 using logistic regression models to identify factors related to SHS exposure in three venues. RESULTS About 21% and 39% of adolescents were exposed to SHS inside or outside of the home, with overall exposure of 45%. In all 25 African countries, parental smoking was significantly associated with SHS exposure inside the home (ORs ranging from 3.02 [95% CI=2.0, 4.5] to 14.65 [95% CI=10.0, 21.5]). Peer smoking was associated with SHS exposure outside the home in 18 countries (ORs ranging from 1.45 [95% CI=1.0, 2.1] to 3.00 [95% CI=1.8, 5.1]). Parental smoking, peer smoking, and anti-smoking messages in media were identified as three major factors associated with SHS exposure. CONCLUSIONS A significant proportion of never-smoking adolescents in Africa are exposed to SHS, suggesting the need for countries to adopt policies to protect never smokers through the implementation of the WHO Framework Convention on Tobacco Control.

Intentions to quit tobacco smoking in 14 low- and middle-income countries based on the transtheoretical model*

INTRODUCTION Over 80% of the worlds one billion tobacco smokers reside in low- and middle-income countries (LMICs); therefore, it is important to understand factors that promote intention to quit smoking in these countries. This study evaluated factors associated with three stages of intention to quit tobacco smoking among adults in LMICs. METHODS Data from 43,540 participants of the Global Adult Tobacco Survey in 14 LMICs were analyzed. Intentions to quit smoking were categorized into precontemplation (referent category), contemplation, and preparation stages based on the transtheoretical model. A multinomial logit model was used to estimate odds ratios (OR) and 95% confidence intervals (CI). RESULTS Approximately 82%, 14%, and 4% of the smokers were in precontemplation, contemplation, and preparation stages, respectively. Rural residents had increased odds of being in contemplation stage (OR=1.41, 95% CI=1.09-1.83) compared to urban residents. Compared to homes where smoking was allowed, smoke-free homes were associated with increased odds of contemplation (OR=1.77, 95% CI=1.41-2.23) and preparation (OR=2.18, 95% CI=1.78-2.66). Exposure to anti-smoking messages in more than one media channel was associated with increased odds of contemplation (OR=1.60, 95% CI=1.33-1.92) and preparation (OR=1.73, 95% CI=1.28-2.33) compared to no exposure to anti-smoking messages. CONCLUSION The results suggest that anti-smoking media campaigns and smoke-free policies may promote intention to quit smoking in LMICs. While these suggest the need for implementation of comprehensive anti-smoking campaigns and smoke-free policies, longitudinal studies are required to confirm these findings and to evaluate how intention to quit translates into quit attempts in LMICs.

Genetic association analysis of polymorphisms in PSD3 gene with obesity, type 2 diabetes, and HDL cholesterol

BACKGROUND The pleckstrin and Sec7 domain-containing 3 (PSD3) gene has been linked to immune diseases. We examined whether the genetic variants within the PSD3 gene are associated with obesity, type 2 diabetes (T2D), and high-density lipoprotein (HDL) cholesterol level. METHODS Multiple logistic regression model and linear regression model were used to examine the associations of 259 single nucleotide polymorphisms (SNPs) within the PSD3 gene with obesity and T2D as binary traits, and HDL level as a continuous trait using the Marshfield data, respectively. A replication study of obesity was conducted using the Health Aging and Body Composition (Health ABC) sample. RESULTS 23SNPs were associated with obesity (p<0.05) in the Marshfield sample and rs4921966 revealed the strongest association (p=3.97×10-6). Of the 23SNPs, 20 were significantly associated with obesity in the meta-analysis of two samples (p<0.05). Furthermore, 6SNPs revealed associations with T2D in the Marshfield data (top SNP rs12156368 with p=3.05×10-3); while two SNPs (rs6983992 and rs7843239) were associated with both obesity and T2D (p=0.0188 and 0.023 for obesity and p=8.47×10-3 and 0.0128 for T2D, respectively). Furthermore, 11SNPs revealed associations with HDL level (top SNP rs13254772 with p=2.79×10-3) in the Marshfield data; meanwhile rs7009615 was associated with both T2D (p=0.038) and HDL level (p=4.44×10-3). In addition, haplotype analyses further supported the results of single SNP analysis. CONCLUSIONS Common variants in PSD3 were associated with obesity, T2D and HDL level. These findings add important new insights into the pathogenesis of obesity, T2D and HDL cholesterol.

Testing messages about comparative risk of electronic cigarettes and combusted cigarettes

Introduction Health agencies are grappling with communicating risks of electronic cigarettes (e-cigarettes) compared with combusted cigarettes. This study examined smokers’ responses to two types of comparative risk messages with one type incorporating more negative antismoking elements in the design. Methods In an online experiment, 1400 US adult (18+ years) current smokers and recent quitters were randomised to view one of three comparative risk messages about e-cigarettes (CR messages), one of three comparative risk messages that included more negative antismoking elements in the design (CR− messages) or a control message. Selection of outcomes was guided by the antismoking message impact framework. Multivariate analyses of covariance and logistic regression models analysed effects of messages on message evaluations, e-cigarette-related and cigarette-related beliefs and behavioural intentions. Results Both CR and CR− messages decreased smokers’ intentions to smoke cigarettes, increased intentions to switch to e-cigarettes completely and increased perceptions that e-cigarettes are less harmful than combusted cigarettes. Neither message type increased dual use intentions relative to exclusive e-cigarettes use or smoking cessation. CR messages decreased perceived absolute risks of e-cigarettes and self-exempting beliefs about smoking, whereas CR− versus CR messages produced higher self-efficacy to quit smoking. Conclusion Comparative risk communication might encourage smokers to switch to lower-harm tobacco products. Comparative risk messages with more negative antismoking elements in the design might be particularly effective, because they led to higher self-efficacy to quit smoking. Regulatory agencies may consider using comparative risk messages with more negative antismoking elements to educate the public about lower risk of e-cigarettes.

Bayesian logistic regression in detection of gene–steroid interaction for cancer at PDLIM5 locus

The PDZ and LIM domain 5 (PDLIM5) gene may play a role in cancer, bipolar disorder, major depression, alcohol dependence and schizophrenia; however, little is known about the interaction effect of steroid and PDLIM5 gene on cancer. This study examined 47 single-nucleotide polymorphisms (SNPs) within the PDLIM5 gene in the Marshfield sample with 716 cancer patients (any diagnosed cancer, excluding minor skin cancer) and 2848 noncancer controls. Multiple logistic regression model in PLINK software was used to examine the association of each SNP with cancer. Bayesian logistic regression in PROC GENMOD in SAS statistical software, ver. 9.4 was used to detect gene–steroid interactions influencing cancer. Single marker analysis using PLINK identified 12 SNPs associated with cancer (P < 0.05); especially, SNP rs6532496 revealed the strongest association with cancer (P = 6.84 × 10−3); while the next best signal was rs951613 (P = 7.46 × 10−3). Classic logistic regression in PROC GENMOD showed that both rs6532496 and rs951613 revealed strong gene–steroid interaction effects (OR = 2.18, 95% CI = 1.31−3.63 with P = 2.9 × 10−3 for rs6532496 and OR = 2.07, 95% CI = 1.24 −3.45 with P = 5.43 × 10−3 for rs951613, respectively). Results from Bayesian logistic regression showed stronger interaction effects (OR = 2.26, 95% CI = 1.2 −3.38 for rs6532496 and OR = 2.14, 95% CI = 1.14 −3.2 for rs951613, respectively). All the 12 SNPs associated with cancer revealed significant gene–steroid interaction effects (P < 0.05); whereas 13 SNPs showed gene–steroid interaction effects without main effect on cancer. SNP rs4634230 revealed the strongest gene–steroid interaction effect (OR = 2.49, 95% CI = 1.5 −4.13 with P = 4.0 × 10−4 based on the classic logistic regression and OR = 2.59, 95% CI = 1.4 −3.97 from Bayesian logistic regression; respectively). This study provides evidence of common genetic variants within the PDLIM5 gene and interactions between PLDIM5 gene polymorphisms and steroid use influencing cancer.

Who are the smokers who never plan to quit and what do they think about the risks of using tobacco products

INTRODUCTION Smoking cigarettes is the most harmful way to use tobacco. Smokers who do not plan to quit present a particular challenge in reducing the morbidity and mortality from tobacco use. Switching to a lower harm product might encourage them to end their use of combusted cigarettes. This study aimed to better understand smokers who do not intend to quit (including their demographic and worldviews as indicators of their social, cultural, and political dispositions) and their perceived risks of cigarettes and possible lower-risk products such as e-cigarettes. METHODS Participants were 2572 current smokers. Data were pooled from 2015 and 2016 cross-sectional surveys of national probability samples of U.S. adults and analyzed with multivariable logistic regressions. RESULTS Smokers who never plan to quit comprise 14.3% of current U.S. smokers and are more likely to be older (24.2% among 65+ years old vs. 9.8% among 18-24) and less likely to have ever used e-cigarettes. A one-unit increase in hierarchical worldview (measured on a 1-6 scale) was associated with a 20% increase in the odds of never planning to quit. Those who denied that cigarettes cause disease or death (aORs between 1.6 and 2.0) or were uncertain (aORs: 2.5-2.7) were more likely to never plan to quit compared to those who agreed. They did not view risks of e-cigarettes substantially different compared to smokers who plan to quit. CONCLUSION One in seven U.S. smokers never plans to quit and might benefit from interventions which reflect their hierarchical worldviews and increase their risk perceptions of combustible cigarettes.

The Use of E-cigarettes Among School-Going Adolescents in a Predominantly Rural Environment of Central Appalachia

E-cigarette use among youth in the United States (U.S.) continues to increase. In the rural Northeast Tennessee, where prevalence of tobacco use is higher than national and state averages, there is no literature on e-cigarette use to inform policies and programs. This study aimed to estimate the prevalence of e-cigarette use and examine association of e-cigarette use with two tobacco products among school-going adolescents. Data from 894 participants of a school-based survey conducted in 2016 in Northeast Tennessee were analyzed. Descriptive statistics and logistic regression analyses were conducted to estimate the prevalence and delineate the associations between e-cigarette use and other tobacco products. Approximately 11% of the participants currently used e-cigarettes, and 35% had ever used e-cigarettes. About 6% of the participants were current users of both e-cigarettes and cigarettes; 4% were current users of e-cigarettes and smokeless tobacco; 3% were current users of all three products, and 15% had ever tried all three products. More than one-half of current e-cigarette users (52%) also smoked cigarettes. Adjusting for covariates, current e-cigarette use was positively associated with cigarette smoking [Odds Ratio (OR) 27.32, 95% confidence interval (CI) 14.4–51.7] and smokeless tobacco use [OR 7.92, 95% CI 3.8–16.5]. E-cigarette use was more common among the high school students than cigarette and smokeless tobacco use, and a significant proportion of users either smoked cigarettes, used smokeless tobacco, or both. Thus, there is a critical need for preventive policies and programs to address dual and poly-use of these products.

Associations of Binge Drinking and Smoking with Type 2 Diabetes among Adults in California: Age and Race Differences

Objective:The incidence of type 2 diabetes (T2D) is rising rapidly in the world. Findings focusing on the impact of two main lifestyle factors, smoking and alcohol consumption, on T2D were mixed. We aimed to estimate the prevalence of T2D and to examine the association of lifestyle factors including smoking and binge drinking with T2D. Materials and Methods: 4046 adults with T2D and 38234 controls were selected from the 2011-2012 California Health Interview Survey (CHIS). Weighted univariate and multiple logistic regression analyses were used to estimate the odds ratios (ORs) with 95% confidence intervals (CIs). Results: The overall prevalence of T2D was 7.0% (7.2% for males and 6.8 % for females). The prevalence increased with age (1.7%, 10.3% and 17.2% for age group 18-44, 45-64 and 65+ years, respectively). Multiple logistic regression analysis showed that past smoking (OR = 1.28, 95% CI = 1.13-1.45), middle-aged adults (OR = 6.23, 95% CI = 5.09-7.62) and elderly adults (OR = 10.38, 95% CI = 8.28-13.0), nonwhite (Latino, Asian, and Africa American), obesity and poor poverty were positively associated with T2D. Furthermore, being female (OR = 0.76, 95% CI = 0.67-0.86), binge drinking (OR = 0.67, 95% CI = 0.57-0.80), and employment were negatively associated with T2D. Stratified by age and race, obesity was positively associated with T2D in all age and race groups. Past smoking was positively associated with T2D in middleaged Asian and Whites groups only. Binge drinking showed negative association with T2D in the middle-aged and elderly Whites and in the elderly Asians. Conclusion: Lifestyle factors (binge drinking and smoking) were associated with T2D. There were age and race differences in the associations of binge drinking and smoking with T2D.