Risk Factors Cigarette Smoking Research Articles

Prevalence and Risk Factors for Chronic Obstructive Pulmonary Disease Among Adults Aged 50 and Above - 10 CKB Study Areas, China, 2020-2021.

The prevalence of COPD in Chinese individuals aged 50 years and above was obviously higher than that in younger adults, but the risk factors for this age group were unclear. The prevalence was estimated at 12.8% and 5.7% for males and females over 50 in 2020-2021, with confirmed risk factors of cigarette smoking, a family history of respiratory diseases, respiratory symptoms, and a history of cough or respiratory diseases during childhood. The findings may help clinicians and the public identify individuals at high risk of COPD and take targeted measures in a timely manner.

People living with HIV/AIDS in the Kibera informal Settlement in Nairobi City, Kenya: Is there any gender difference in Factors related to antiretroviral Therapy?

Objective: To identify gender differences in behavioural factors and treatment related factors of ARV therapy as a predictor to treatment adherence. Design: Cross-sectional study. Methods: A quantitative study using a standardized PLWHA questionnaire was conducted in the Kibera informal settlement in the capital, Nairobi, Kenya. Data were collected by community health volunteers during home visits in cooperation with ART health facilities. Results: The observed sample consisted of a total of 272 persons living with HIV/AIDS in the Kibera informal settlements, of which 64.3% were women and 35.7% were men. We found a significantly higher rate of the behavioural risk factors cigarette smoking and alcohol drinking in men compared to women. There was no significant difference in the factors of patients’ medical history between men and women. A significant difference was found in the source of ARV treatment therapy, travelling costs to ARV treatment facilities, loss of income due to visiting an ARV treatment facility and extra costs related to ARV treatment between the men and women in our study group. Another significant difference was observed in disclosure related factors - the HIV status of the current sexual partner, and the current sexual partner being on ARV therapy and disclosing this information to the current sexual partner. Conclusion: Alcohol drinking and smoking cigarettes were the most common behavioural risk factors and were more prevalent in men. Women significantly dominated in treatment related factors of ARV therapy.

Behavior change, health, and health disparities 2024: Smoking and other tobacco use among women and girls

This Special Issue (SI) of Preventive Medicine is the 11th in an annual series on behavior change, health, and health disparities. The theme of this 2024 issue is Smoking and Other Tobacco Use among Women and Girls. Cigarette smoking remains the single most preventable cause of death in the U.S., causing the premature death of more than 200,000 U.S. women annually, a mortality rate that far exceeds levels from conditions more commonly associated with premature death in women (e.g., breast cancer). Of course, cigarette smoking among women and girls is also a well-known cause of intergenerational adverse health effects. Women and girls are also using e-cigarettes and many other tobacco products that are flooding the U.S. tobacco marketplace. This SI includes eleven peer-reviewed articles that advance knowledge across a wide range of topics on disproportionate adverse effects, prevalence, and risk factors for cigarette smoking and other tobacco use in women and girls.

Frequency and risk factors of cigarette smoking among male medical students of Bacha Khan Medical College, Mardan

Tobacco consumption is a major global health issue that contributes significantly to morbidity and mortality rates. The World Health Organization (WHO) estimates that over five million deaths occur each year due to tobacco use, projected to rise to 8 million by 2030. Furthermore, current research reveals that 47% of males and 12% of females worldwide smoke. Of particular concern is cigarette and tobacco use among medical students, given their role in crafting public health policy and guiding society's tobacco prevention measures. Studies show that smoking rates among medical students in developing countries are between 16-21%, with multi-country surveys in countries like Malaysia, India, Pakistan, Nepal and Bangladesh indicating rates between 6.7% and 17.8%. For example, a study conducted on medical students in Damascus University Faculty of Medicine revealed a 10.9% prevalence of cigarette smoking, 23.5% for water pipe, and 7.3% for both, with higher rates reported among final-year students. Likewise, a study of Rawalpindi Medical College students found that 24% had smoked at least once, with greater prevalence among hostellites rather than day scholar students.

Overnight Abstinence, Ventrostriatal-Insular Connectivity, and Tridimensional Personality Traits in Cigarette Smokers.

Personality traits contribute to the risks of smoking. The striatum has been implicated in nicotine addiction and nicotine deprivation is associated with alterations in resting state functional connectivity (rsFC) of the ventral (VS) and dorsal (DS) striatum. However, it remains unclear how striatal rsFC may change following overnight abstinence or how these shorter-term changes in inter-regional connectivity relate to personality traits. In the current study, 28 smokers completed assessments with Fagerström Test of Nicotine Dependence, Tridimensional Personality Questionnaire (TPQ), as well as resting state functional magnetic resonance imaging (fMRI) scans during satiety and after overnight abstinence. We processed imaging data with published routines and evaluated the results with a corrected threshold. Smokers showed increases in the VS-insula rsFC but no significant changes in the DS rsFC after overnight abstinence as compared to satiety. The difference in the VS-insula rsFC (abstinence minus satiety) was negatively correlated with harm avoidance. These findings highlighted striatal connectivity correlates of very short-term abstinence from smoking and how the VS-insula rsFC may vary with individual personality traits, interlinking neural markers and personality risk factors of cigarette smoking at the earliest stage of abstinence.

Loss Aversion and Current, Former, and Never-Smoking Status.

Loss aversion (LA) is a bias in decision-making wherein potential losses have a greater influence on choices than equivalent gains. Such a bias may protect individuals from harm. Consistent with this idea, lower LA has been measured in individuals who endorse current cigarette smoking as well as other substance use compared to controls matched on important sociodemographic variables (age, gender, and educational attainment). The goal of the present study was to systematically replicate and extend this association between LA and smoking status by comparing those meeting criteria for current-, former-, and never-smoking status. In total, 984 individuals (N = 984) that endorsed current cigarette smoking (past 30-day use; n = 361), former-smoking (no past 30-day use, >100 cigarettes lifetime; n = 317), and never-smoking (no past 30-day use, <100 cigarettes lifetime; n = 306) were recruited using standard crowdsourcing methods and completed measures of LA (50-50 gambles) and delay discounting (DD) (monetary choice questionnaire), an important decision-making bias with an established relationship to cigarette-smoking status. Lower LA was observed in those endorsing current smoking compared to former smoking (t[952] = -9.57, Bonferroni corrected p < .0001), and never-smoking (t[952] = -3.99, Bonferroni corrected p = .0002). LA was also greater in former- compared to the never-smoking (t[952] = -5.26, Bonferroni corrected p < .0001). This pattern did not change when accounting for DD and sociodemographics. DD results replicated prior findings. The results support LA as a decision-making bias related to the risk of cigarette smoking and other substance use. Further research is needed to understand the causal contributions of LA and DD and their potential intersections. Low LA is a risk factor for cigarette smoking. This study reports higher LA among individuals that endorsed never-smoking and former-smoking status in comparison to those endorsing current cigarette smoking. LA may influence or be influenced by a change in smoking status.

Intersections between environmental reward availability, loss aversion, and delay discounting as potential risk factors for cigarette smoking and other substance use

Behavioral theory suggests that density of environmental rewarding activities and biases in decision making influence risk for substance use disorder (SUD). To better understand intersections of these potential risk factors, this study examined whether environmental reward predicted smoking status or other drug use and whether such associations were independent of two decision-making biases known to predict SUD risk, namely loss aversion and delay discounting. Individuals that reported current daily cigarette smoking (n = 186; >10 cigarettes/day) and never-smoking (n = 241; <100 cigarettes lifetime) were recruited with standard crowdsourcing methods. Participants answered questions on alcohol and other drug use. Environmental reward was assessed using the Reward Probability Index (RPI), and loss aversion (LA) and delay discounting (DD) using a gamble-acceptance task and monetary choice questionnaire, respectively. Associations of RPI, LA, and DD with cigarette smoking, alcohol use, other drug use, and combinations of co-use were examined with logistic regression controlling for sociodemographic variables (educational attainment, gender, age). Low RPI (odds ratio[OR] = 0.97, p = .006), low LA (OR = 1.22, p < .001), and high DD (OR = 1.12, p = .03), were each independently associated with increased risk for cigarette smoking, as well as other substance use, and use combinations. We saw no evidence that RPI was significantly influencing associations between LA and DD with smoking status or other substance use. Finally, RPI, but not LA or DD, was significantly associated with depressed mood and sleep disturbance. These results provide new evidence on associations of RPI with smoking status and other substance use while further documenting independent associations between LA and DD and those outcomes.

A population-based survey on sociodemographic risk factors of cigarette smoking among Iranian women of reproductive age

&#x0D; The trend of smoking among women is rising in some developing countries. The objective of this population-based cross-sectional study was to determine the sociodemographic risk factors of cigarette smoking among Iranian women of reproductive age. A total of 1359 women were randomly selected for the study. Multivariable logistic regression analysis was used to estimate the adjusted association between sociodemographic variables and cigarette smoking. The odds of cigarette smoking were higher among divorced women (OR [odds ratio]: 14.0; 95% CI [confidence interval]: 4.1–47.3) and women with a university education (OR: 2.6; 95% CI: 1.3–5.0). This study highlights an association between marital status and education with cigarette smoking among Iranian women.&#x0D;

Correlates of tobacco product initiation among youth and young adults between waves 1–4 of the population assessment of tobacco and Health (PATH) study (2013–2018)

IntroductionWhile risk factors for cigarette smoking among youth and young adults are well-documented, less is known about the correlates of initiation of other tobacco products. This study aims to provide estimates and correlates of initiation among U.S. youth and young adults. MethodsData on youth aged 12–17 (n = 10,072) and young adults aged 18–24 (N = 5,727) who provided information on cigarettes, electronic nicotine delivery systems (ENDS), cigars, pipe, hookah and smokeless tobacco use in Wave 1 (W1: 2013–2014)-Wave 4 (W4: 2016–2018) of the nationally-representative PATH Study were used to calculate ever use initiation and correlates of initiation by W4. ResultsNearly 6 million youth and 2.5 million young adults used tobacco for the first time between W1-W4. Approximately one quarter of youth and young adult ENDS never users initiated ENDS between W1-W4 of the PATH Study. Among youth, use of other tobacco products, ever substance use, and high externalizing problems were associated with initiation of most products. Among young adults, use of other tobacco products and ever substance use were associated with initiation of most products. In both youth and young adults, Hispanics were more likely to initiate hookah use than their non-Hispanic White counterparts. While male sex was a risk factor for most tobacco product initiation across both age groups, it was not associated with hookah initiation. ConclusionsCigarette and non-cigarette products shared many correlates of initiation, although there are noteworthy demographic differences. Findings can help tailor product specific interventions to reach populations at risk during preliminary stages of use.

Cigarette Smoking among Medical Students from the Western Balkan.

University student’s smoking is a significant public health problem. It is estimated that, globally, every fifth medical student is a smoker. So far, no research dealing with cigarette smoking among medical students has been conducted in the countries of the Western Balkans. The aim of this study was to examine the prevalence and risk factors of cigarette smoking among Western Balkans medical students. A cross-sectional study was conducted among 2452 students from 14 medical faculties in the Western Balkans (Republic of Slovenia, Republic of Croatia, Bosnia and Herzegovina, Republic of North Macedonia and Republic of Serbia). The data were gathered through an online survey. There were significantly more non-smokers than smokers among medical students. Only gender and parents smoking status were statistically significantly associated with students smoking status. The smokers were more often male students, who lived in urban areas prior their studies, and whose parents were both smokers. With the aim of monitoring and enhancing student population health, it is necessary for public health activists and health officials to continually survey the students’ smoking status in order to recognize the smoking influencing factors, and form and take on appropriate activities to improve the prevention of cigarette smoking among students, as well as motivate those who smoke to give up smoking, which would contribute to improving the health of the student population.

Prevalence of Cigarette Smoking and Associated Factors among Residents of Hossana Town, Southern Ethiopia.

Background Tobacco is the only legal product that kills a large number of its consumers when used as intended by producers. Information on cigarette smoking and associated factors among adults at the household level is very limited. Objective To assess prevalence of cigarette smoking and associated factors among residents of Hossana town, Hadiya zone, Southern Ethiopia, 2020. Methods and Materials A cross-sectional study design was performed. A structured questionnaire was used to collect data. Bivariate and multivariable binary logistic regression was used to identify risk factors of cigarette smoking. Variables significant at a p value of less than 0.05 were considered as independent predictors. Hosmer and Lemeshow test statistics were done to test the model fitness for the final model. Similarly, multicollinearity was checked by using collinearity statistics (tolerance and VIf). Result In total, 591 people responded to the survey, resulting in a 98.2% response rate. Among the study participants, cigarette smokers were 183 (31.0%). Educational status, alcohol use, and parental smoking were all found to have a significant relationship with cigarette smoking among research participants in Hosanna town. When compared to people with a college education or above, illiterates are approximately nine times more likely to consume cigarettes (95% CI = 9.058 (3.52, 22.469)). Alcoholics are about twice as likely as nondrinkers to smoke cigarettes (95% CI = 2.288 (1.548, 3.383)). Those who have cigarette-smoking parents are approximately twice as likely as their counterparts to smoke cigarettes (95% CI = 2.288 (1.548, 3.383)). Conclusion According to this survey, the prevalence of cigarette smoking was high. Furthermore, cigarette smoking was linked to illiteracy, alcohol consumption, and parental smoking in this study.

Social Isolation as a Risk Factor for Cigarette Smoking in Older Adults

Objective: This study examined the prevalence of social isolation and cigarette smoking in a national sample of community-dwelling older adults, and assessed the role of social isolation on the risk of cigarette smoking. Methods: Using data from 8,044 participants (age 65+ years) across two waves of the National Health and Aging Trends Study (NHATS), we analyzed the prevalence of social isolation in older adults and as a risk factor for cigarette smoking. Social isolation was measured across 4 relationship domains (Cudjoe, 2018) on a scale of 0 to 4, using objective measures of social interactions. Descriptive and logistic regression analyses were conducted to assess how social isolation is associated with smoking. Results: Preliminary results showed that 18.2% of older adults were socially isolated (3.5% severely isolated) and 7.1% of participants reported current smoking. We found that both social isolation (OR = 2.5, p<.001) and severe isolation (OR = 5.9, p<.001) increased the odds of smoking. Also, older adults with depression (OR = 1.6, p<.01) and dual-eligible beneficiaries (Medicare and Medicaid) with TRICARE coverage (OR = 4.6, p<.05) had greater odds of smoking. However, we did not find evidence that the odds of smoking varied significantly by the number of chronic conditions. Conclusion: Social isolation is associated with an increased risk of cigarette smoking among older adults. Smoking may be an important behavior in the pathway between social isolation and its association with morbidity and mortality.

Exposure to Violence Variety as a Risk Factor for Cigarette Smoking: Relevance of Sensation-Seeking and Impulsivity as Mediators

Background Prior research has indicated that exposure to violence is a risk factor associated with cigarette smoking. However, the relevance of variety of exposure for predicting this outcome has remained underexplored. This is problematic, as increased variety of exposure may represent a measure of severity of trauma exposure with less recall bias than other measures. Further, related constructs of sensation-seeking and impulsivity have yet to be investigated as mediators of this relationship. It is predicted that increased variety of exposure to violence results in dysfunctional variation in these constructs, leading to increased daily cigarette use. Methods The present study utilizes data from the first three waves of the Pathways to Desistance study to examine these relationships. Generalized structural equation modeling is used to identify direct and indirect effects of interest. A bootstrap resampling process was used to compute normalized standard errors so that indirect effects were not biased. Results Results indicated that lifetime exposure to violence variety prior to baseline predicted increased daily cigarette use at follow-up. Neither sensation-seeking nor impulsivity were significant mediators when both constructs were included in the model, but impulsivity emerged as a significant mediator when separate mediation models were estimated for each construct. Conclusions The findings indicate the potential utility of screening for variety of exposure to violence to determine adolescents who may be at-risk for high frequency cigarette smoking. Programming focused on impulsivity may play a role in addressing cigarette smoking issues stemming from exposure to violence.

Khat Chewing as a Risk Factor of Cigarette Smoking in Ethiopia Systemic Review and Meta-Analysis

Background: Tobacco use is a major public health issue and strongest lifestyle related with increase probability of Cardiovascular Disease (CVDs), the objective of this systemic review and meta-analysis was to determine whether khat chewing is risk factor of cigarette smoking In Ethiopia. Methods: Eligible studies were identified by searching different databases like PubMed, Google Scholar and Web of science for published and unpublished articles; Data were extracted from the eligible studies using data abstraction form by two independent authors. Publication bias was assessed using the visual funnel plot and Egger’s test. Meta-analysis was performed by using random-effects models with the Der Simonian and Laird method. Results: Eight studies were found to be eligible and included in the meta–analysis. Out of 3839 respondents 751(19.56%) were cigarette smokers. The proportion of cigarettes smoking among khat chewers and non khat chewers were 40.43% and 11.54% respectively. The final pooled effect size after trim and fill was found to be 1.93 (95%CI: 1.71, 2.14). This showed the presence of a significant association between khat chewing and cigarettes smoking. Conclusions: Khat chewing were found to be significantly associated with cigarette smoking therefore, effective khat chewing prevention and intervention programs are required to reduce smoking among People in Ethiopia.

Effects of e-cigarette use on cigarette smoking among U.S. youth, 2004–2018

ObjectiveTo determine if the declining trend in U.S. youth cigarette smoking changed after e-cigarettes were introduced, and if youth e-cigarette users would have been likely to smoke cigarettes based on psychosocial and demographic predictors of smoking. MethodsAn interrupted time series analysis was used for cross-sectional data from the 2004 to 2018 National Youth Tobacco Surveys (NYTS) to assess changes in cigarette and e-cigarette use over time. A multivariable logistic regression model used 2004–2009 NYTS data on psychosocial risk factors to predict individual-level cigarette smoking risk from 2011 to 2018. Model-predicted and actual cigarette smoking behavior were compared. ResultsThe decline in current cigarette smoking slowed in 2014 (−0.75 [95% CI: −0.81, −0.68] to −0.26 [95% CI: −0.40, −0.12] percentage points per year). The decline in ever cigarette smoking accelerated after 2012 (−1.45 [95% CI: −1.59, −1.31] to −1.71 [95% CI: −1.75, −1.66]). Ever and current combined cigarette and/or e-cigarette use declined during 2011–2013 and increased during 2013–2014 with no significant change during 2014–2018 for either variable. The psychosocial model estimated that 69.0% of current cigarette smokers and 9.3% of current e-cigarette users (who did not smoke cigarettes) would smoke cigarettes in 2018. ConclusionsThe introduction of e-cigarettes was followed by a slowing decline in current cigarette smoking, a stall in combined cigarette and e-cigarette use, and an accelerated decline in ever cigarette smoking. Traditional psychosocial risk factors for cigarette smoking suggest that e-cigarette users do not fit the traditional risk profile of cigarette smokers.

Changes in urban and rural cigarette smoking and cannabis use from 2007 to 2017 in adults in the United States

IntroductionRural-urban differences in cigarette and cannabis use have traditionally shown higher levels of cigarette smoking in rural areas and of cannabis use in urban areas. To assess for changes in this pattern of use, we examined trends and prevalence of cigarette, cannabis, and co-use across urban-rural localities. MethodsUrban-rural trends in current cigarette and/or cannabis use was evaluated using 11 cohorts (2007–2017) of the National Survey on Drug Use and Health (NSDUH; N = 397,542). We used logistic regressions to model cigarette and cannabis use over time, adjusting for demographics (age, gender, race/ethnicity, income, education), in addition to assessing patterns of cannabis use among cigarette smokers and nonsmokers. ResultsDespite decreases in cigarette smoking overall, between 2007 and 2017, the urban-rural disparity in cigarette smoking increased (AOR = 1.17), with less reduction in rural as compared to urban cigarette smokers. Cannabis use increased in general (AOR = 1.88 by 2017), with greater odds in urban than rural regions. Cannabis use increased more rapidly in non-cigarette smokers than smokers (AOR = 1.37 by 2017), with 219% greater odds of cannabis use in rural non-cigarette smokers in 2017 versus 2007. ConclusionsRurality remains an important risk factor for cigarette smoking in adults and the fastest-growing group of cannabis users is rural non-cigarette smokers; however, cannabis use is currently still more prevalent in urban areas. Improved reach and access to empirically-supported prevention and treatment, especially in rural areas, along with dissemination and enforcement of policy-level regulations, may mitigate disparities in cigarette use and slow the increase in rural cannabis use.

25 FUNCTIONAL ANNOTATION OF RARE STRUCTURAL VARIATION IN THE HUMAN BRAIN

Rural-urban differences in cigarette and cannabis use have traditionally shown higher levels of cigarette smoking in rural areas and of cannabis use in urban areas. To assess for changes in this pattern of use, we examined trends and prevalence of cigarette, cannabis, and co-use across urban-rural localities.Urban-rural trends in current cigarette and/or cannabis use was evaluated using 11 cohorts (2007–2017) of the National Survey on Drug Use and Health (NSDUH; N = 397,542). We used logistic regressions to model cigarette and cannabis use over time, adjusting for demographics (age, gender, race/ethnicity, income, education), in addition to assessing patterns of cannabis use among cigarette smokers and nonsmokers.Despite decreases in cigarette smoking overall, between 2007 and 2017, the urban-rural disparity in cigarette smoking increased (AOR = 1.17), with less reduction in rural as compared to urban cigarette smokers. Cannabis use increased in general (AOR = 1.88 by 2017), with greater odds in urban than rural regions. Cannabis use increased more rapidly in non-cigarette smokers than smokers (AOR = 1.37 by 2017), with 219% greater odds of cannabis use in rural non-cigarette smokers in 2017 versus 2007.Rurality remains an important risk factor for cigarette smoking in adults and the fastest-growing group of cannabis users is rural non-cigarette smokers; however, cannabis use is currently still more prevalent in urban areas. Improved reach and access to empirically-supported prevention and treatment, especially in rural areas, along with dissemination and enforcement of policy-level regulations, may mitigate disparities in cigarette use and slow the increase in rural cannabis use.

Evaluation of an innovative mobile health programme for the self-management of chronic obstructive pulmonary disease (MH-COPD): protocol of a randomised controlled trial

IntroductionChronic obstructive pulmonary disease (COPD) is the fourth leading cause of death globally. In outpatient care, the self-management of COPD is essential, but patient adherence to this remains suboptimal. The...

Genetic variants as risk factors for cigarette smoking at an early age and relapse to smoking cessation treatment: A pilot study

ObjectivesTobacco smoking is a complex and multifactorial disease involving both environmental and genetic factors. In the Mexican mestizo population, single-nucleotide polymorphisms (SNPs) associated with cigarette smoking and a greater degree of nicotine addiction have been identified; however, no possible roles have been explored in regard to the age of onset of smoking or in the success of quitting. MethodsIn this study, 151 Mexican mestizo, who smoke cigarettes, were included. They were grouped according to the age at which they started smoking: those who started smoking before 18 years of age (early smokers, ES) and those who started smoking ≥18 years of age (late smokers, LS). In addition, relapse in smoking was evaluated at the first month after the end of treatment. Genetic association was evaluated characterizing 10 SNPs in 4 genes (CHRNA5, CHRNA3, NRXN1, and HTR2A). ResultsAccording to the dominant model of genetic inheritance, rs6313 (CT+TT) of the HTR2A gene was associated (p = 0.0201) with cigarette consumption at early ages (OR = 2.68, CI = 1.18–6.07). When the risk of relapse was analyzed one month after the end of treatment, regardless of the age of onset, the T allele (rs6313) of HTR2A appeared to be a risk factor for relapse (OR = 2.92, 95% CI = 1.06–8.11); the T allele was found more frequently in those who relapsed (50.0%) compared with people who maintained abstinence (25.4%) (p = 0.0332). ConclusionsOur findings suggest that in Mexican mestizos who smoke cigarettes, the presence of the T allele in rs6313 of the HTR2A gene increases the risk for the early onset of cigarette smoking as well as the risk for relapsing one month after completing smoking cessation treatment.

Associations of risk factors of e-cigarette and cigarette use and susceptibility to use among baseline PATH study youth participants (2013–2014)

IntroductionImproved understanding of the distribution of traditional risk factors of cigarette smoking among youth who have ever used or are susceptible to e-cigarettes and cigarettes will inform future longitudinal studies examining transitions in use. MethodsMultiple logistic regression analysis was conducted using data from youth (ages 12–17 years) who had ever heard of e-cigarettes at baseline of the PATH Study (n = 12,460) to compare the distribution of risk factors for cigarette smoking among seven mutually exclusive groups based on ever cigarette/e-cigarette use and susceptibility status. ResultsCompared to committed never users, youth susceptible to e-cigarettes, cigarettes, or both had increasing odds of risk factors for cigarette smoking, with those susceptible to both products at highest risk, followed by cigarettes and e-cigarettes. Compared to e-cigarette only users, dual users had higher odds of nearly all risk factors (aOR range = 1.6–6.8) and cigarette only smokers had higher odds of other (non-e-cigarette) tobacco use (aOR range = 1.5–2.3), marijuana use (aOR = 1.9, 95%CI = 1.4–2.5), a high GAIN substance use score (aOR = 1.9, 95%CI = 1.1–3.4), low academic achievement (aOR range = 1.6–3.4), and exposure to smoking (aOR range = 1.8–2.1). No differences were observed for externalizing factors (depression, anxiety, etc.), sensation seeking, or household use of non-cigarette tobacco. ConclusionsAmong ever cigarette and e-cigarette users, dual users had higher odds of reporting traditional risk factors for smoking, followed by single product cigarette smokers and e-cigarette users. Understanding how e-cigarette and cigarette users differ may inform youth tobacco use prevention efforts and advise future studies assessing probability of progression of cigarette and e-cigarette use.