Associations Between Current Tobacco Use Status, Region, and Tobacco Messaging Perceptions: A Secondary Analysis
Author: Ashley Meadows; Academic Advisor: Dr. Ashley Felix, PhD; Committee Member: Dr. Joanne Patterson, PhD
ABSTRACT
Objective
We assessed how current tobacco use status and region lived in affect the perceptions surrounding tobacco educational messages for those currently not using tobacco. Analysis of messaging perceptions allows for a better understanding of how message type influences current non-users’ tobacco usage status. Our goal was to better understand the messaging that is currently being used to discover if current tobacco education prevention messages motivate current non-users to start smoking cigarettes or e-cigarettes, as well as to better understand the importance of residence in driving tobacco status and how this plays a role in their perspective on the effectiveness of tobacco education prevention messages.
Methods
Young adults aged 18-35 years living in the United States during the year 2022 were analyzed, with the influence of sociodemographic characteristics assessed. These individuals were divided into two analysis groups for tobacco usage status; current user (n=443) and not current user (n=2,363) as well as into four analysis groups for regional location; Northeast (n=471), West (n=606), South (n=1,129), and Midwest (n=600). For inclusion in the study, participants had to report their current tobacco status, report regional location, and participate in questions regarding whether messages they viewed would motivate non-users to initiate cigarette smoking or vaping. Participants with missing or incomplete demographic data were excluded from the model (N=43). Utilizing multivariate logistic regression, we examined interactions between current tobacco status and region, for their association with effects perceptions on vaping and cigarette smoking initiation on current non-users.
Results
Of the population within this study sample, 84.21% were not a current user and 40.24% were currently living in the southern region of the United States. We found that young adults who reported currently using cigarettes and/or vapes have a 1.29 (95% CI: 1.02 – 1.63) and 1.89 (95% CI: 1.36 – 2.63) greater odds of agreeing that messages will motivate non-users to start vaping or cigarette smoking respectively compared to those not currently using tobacco products. We found that after accounting for covariates in adjusted logistic regression models, the region lived in was again no longer significant with the dependent variable.
Conclusion
Effects perceptions surrounding tobacco educational messages regarding non-users potential smoking initiation differed by tobacco usage status. Individuals married/divorced as well as cisgender men may benefit from targeted intervention strategies to persuade non-users more effectively from tobacco initiation. While we did not observe regional differences in our study, future studies would need to evaluate if separating regions into individual states would produce similar results to previous tobacco research findings.
Keywords: Effects Perceptions; Smoking Status; Region; Young adults; Tobacco; United States.
INTRODUCTION
Tobacco use is the leading cause of preventable premature disease and death and is responsible for about 480,000 deaths in the United States each year (Prochaska & Benowitz, 2016). Although smoking prevalence has declined in the U.S., declines have been slower for young adults (Cantrell et al., 2018; Villanti et al., 2010). This is not surprising, as young adulthood, spanning from 18-35 years of age, is a crucial developmental period when individuals begin to experiment and regularly use tobacco products (Messeri et al., 2019). In a longitudinal analysis of PATH study data, 25.9% of young adults who reported never using nicotine/tobacco products, initiated use at follow-up, with 10% reporting new combustible tobacco use (Cooper et al., 2022). Rates of polytobacco use (use of multiple nicotine and tobacco products) and poly substance use (use of tobacco and other substances) have also increased (Collins et al., 2017). Critically, after the 1998 Master Settlement Agreement restricted companies from marketing tobacco to adolescents, Big Tobacco directed their marketing efforts to young adults (Hammond, 2005; Ling & Glantz, 2002; Hafez, 2005; Sepe et al., 2002). Young adulthood also coincides with the legal age of purchasing tobacco products (Ribisl & Mills, 2019); together, these factors may contribute to prosocial tobacco norms seen in this age group (Ribisl & Mills, 2019).
Electronic cigarettes, better known as nicotine vapes, have gained popularity among young adults and now surpass the use of traditional cigarettes (Sood et al., 2018). The uptick in usage is due, in part, to the public belief that, compared to cigarette smoking, vaping nicotine is less harmful to one’s own health and to the health of others (Romijnders et al., 2018). While smoking cessation is a commonly cited reason for vaping among middle-aged and older adults, young adults are more likely to start vaping due to peer influence and curiosity (Pepper et al., 2016; Zhu et al., 2014); however, product characteristics, including flavors (e.g., fruit or mint vs tobacco) (Pang et al., 2023), nicotine concentration and form (e.g., nicotine salts vs free-based nicotine (Benowitz, 2022), and device type (e.g. pod vs. cig-a-like) (Lee et al., 2022) are key factors in sustaining nicotine dependence (Sargent et al., 2022). Given the abuse liability of nicotine vaping among young people, the FDA has begun regulating the sale and distribution of e-cigarettes, including required warning statements (US Food and Drug Administration, 2016) and flavor bans (Tam et al., 2023). Studies suggest that young adults are interested in quitting vaping (Cuccia et al., 2021) because they are concerned about the negative impacts on vaping long-term on physical health and well-being (Cuccia et al., 2021). While e-cigarettes are less likely than traditional cigarettes to cause lung damage; significant harms, including elevated blood pressure, respiratory damage, and brain development damages are known health harms (Noar et al., 2022; Bhatnagar et al., 2014; Chen, Bullen, & Dirks, 2017 National Academies of Sciences Engineering and Medicine, 2018; U.S. Department of Health and Human Services, 2016).
Effective tobacco prevention media campaigns can significantly decrease tobacco initiation, cessation, and prevalence of tobacco use. Media campaign messages achieve this because they are communicated more quickly and effectively to wide audiences with a relatively low cost per person, and findings show that there is an association between high exposure to anti-tobacco campaigns and increased knowledge of smoking harms to one’s health (Bafunno et al., 2020). Growing literature suggests that analyzing how effective an anti-tobacco advertisement is would be to analyze the ad through the lens of effects perceptions. This effects perceptions analysis helps to better predict the impact the ad itself has on this change of behavior (Baig et al., 2021; Brennan et al., 2014; Rohde et al., 2021). Effects perceptions are judgements made by the participant about a message campaign ad’s protentional to change the continuation of smoking or cease to smoke due to the message. Examples include the extent to which a message would make one believe that e-cigarettes or cigarette smoking is harmful, or the extent to which a message would motivate one to not vape or cigarette smoke (e.g., “This ad would motivate me to stop smoking”) (Noar et al., 2020).
To attempt to prevent current non-users from ever picking up smoking at all, it is important to obtain perspectives on those young adults who currently do use tobacco to discover what forms of messaging they believe could have likely prevented them from picking up any form of tobacco product to begin with (Pang et al., 2023). Current tobacco using individuals provide critical insight on the motivations for continued smoking despite knowledge of health risks. This specific targeting of current tobacco users can help better formulate the necessary strategies to reduce the number of current non-users from tobacco initiation.
Research shows that certain people have a greater exposure to tobacco and tobacco culture because of their geographic location and demographics. These regional policy variations impact young adults and should be considered in addressing cessation interventions. Up to date U.S. census regional data shows that current cigarette smoking is highest in the Midwest and South and lowest in the West. In the Midwest, 14 of every 100 adults are currently cigarette smoking, while nearly 9 of every 100 adults who live in the West are currently cigarette smoking (CDC, 2023). Current research is still in progress to determine which regions are most prevalent to e-cigarette usage; it is not yet clear the true disparities regions in the U.S. have for e-cigarette use. However, what is known is that according to the CDC, many of the same states that are in the Midwest and Southern regions are reporting adult e-cigarette use to fall within the top 25% of all U.S. states to have highest prevalence rates (Truth Initiative, 2023). These regional disparities that the United States has is due to a combination of factors, including powerful tobacco industry interference in legislation, and prevention of local jurisdictions from adopting strong tobacco prevention measures (Truth Initiative, 2023). Research shows that the tobacco prevention and cessation programs provided are one of the primary ways of educating young adults about the dangers of beginning smoking (American Lung Association, 2023).
We evaluated whether current smoking status was associated with young adults’ perceptions of tobacco education prevention messages, including whether exposure to messages about the shared health risks of smoking and vaping may influence smoking/vaping initiation among non-users (i.e., effects perceptions). We also assessed whether region of residence moderated the association between smoking status and effects perceptions.
METHODS
STUDY DESIGN
Data came from a parent study designed to test the effectiveness of health messages about vaping and smoking on message and risk perceptions among young adults (age 18-35 years). Individuals were eligible to participate if they were 18-35 years old and reported living in the United States. We oversampled for tobacco use and self-reported minoritized sexual orientation and gender identity. The sample (N=2,849) was recruited into an online survey experiment via Prolific, an online platform for online subject recruitment for research (https://prolific.com), in fall 2022. Participants were paid $3.50 for participation via Prolific. The parent study was approved by the Institutional Review Board at our University. For inclusion in this secondary data analysis, participants had to report their current tobacco status, (whether they were currently cigarette smoking or vaping) and respond to questions about whether the message that they viewed would motivate a non-user to start vaping or cigarette smoking (N = 2,806) in a self-reported online questionnaire.
MEASURES
Outcome variables
Two separate items assessed effects perceptions, or the perceived impact of exposure to the messages on smoking and vaping behaviors among non-users, see supplemental figures 2-4 for examples of messaging participants were shown. Participants were asked to respond to the following items, “These messages will motivate non-users to start vaping” and “These messages will motivate non-users to start smoking cigarettes”. Both items were scored using a 5-point Likert-type scale (1= strongly disagree, 5= strongly agree). Responses for each item were recoded such that participants reporting “neutral (3)” to “strongly agree (5)” were coded as believing that exposure to the messages would motivate non-users to start vaping/smoking vs. those who reported “strongly disagree (1)” and “disagree (2)” were coded as believing that messages would not motivate non-users to start vaping/smoking (coded 0). Interview questions with response options are provided in Supplemental Figure S1.
Independent variables
Current tobacco user status was assessed via the two-question method. First, participants were asked if they had “Ever smoked a cigarette EVEN one puff?”. Participants who responded “Yes” were then presented with a follow-up item: “Do you currently smoke cigarettes every day, some days, or not at all?”. Those who reported currently smoking every day or some days were recoded as a “current cigarette smoker” while those reported never, or former use were recoded as a “not current cigarette smoker”. Similarly, we asked participants if they had “Ever vaped nicotine, EVEN once?”. Participants who responded “Yes” were then presented with a follow-up item: “Do you currently vape nicotine every day, some days, or not at all?”. Those who reported currently vaping every day or some days were recoded as a “current nicotine vaper” while those reported never, or former use were recoded as a “not current nicotine vaper”. To retain an adequate sample size of young adults currently engaged in nicotine and tobacco use, we combined items into a binary variable such that participants coded as a current cigarette smoker, or a current nicotine vaper were recoded as “current tobacco user” while those identified as not a current smoker or vaper were recoded as “not a current tobacco user”.
Region was assessed with a single question, “What U.S. state, district, or territory do you currently reside in?” Responses were combined into a four tiered variable per U.S. census division of land definitions (U.S. Department of Commerce, 2023). Responses were coded into “Northeast” consisting of 9 U.S. states, (Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont, New Jersey, New York, Pennsylvania; coded 0), “West” consisting of 13 U.S. states (Arizona, Colorado, Idaho, New Mexico, Montana, Utah, Nevada, Wyoming, Alaska, California, Hawaii, Oregon, Washington; coded 1), “South” consisting of 16 U.S. states (Delaware, Washington DC, Florida, Georgia, Maryland, North Carolina, South Carolina, Virginia, West Virginia, Alabama, Kentucky, Mississippi, Tennessee, Arkansas, Louisiana, Oklahoma, Texas ; coded 2), and “Midwest” consisting of 12 U.S. states (Indiana, Illinois, Michigan, Ohio, Wisconsin, Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, South Dakota; coded 3).
Sociodemographic Covariates
Race and ethnicity were assessed with a single question in which participants were asked to select all responses that applied to them: “What racial and/or ethnic groups do you identity with?” Responses were recoded into two variables. Ethnicity was coded such that participants responding “Hispanic/Latinx/Latino/Latina” were coded as Hispanic/Latinx. Race was coded such that participants reporting any Race (including one or more Races) besides “non-Hispanic White/Caucasian” were coded as non-Hispanic BIPOC (Black, Indigenous, and People of Color).
Age of participant was assessed with a single open-ended question, “How old are you? (years)”. Responses were recoded into two variables “18-24” (coded 0) and “25-35” (coded 1).
Gender identity was assessed using the two-step method in which participants were asked to report their sex-assigned-at-birth (male, female, intersex) and current gender identity (Female, Male, Transgender female/Transgender woman, Transgender male/Transgender man, Non-binary, Gender queer, Agender). Responses were recoded into a categorical variable such that participants were assigned as “Cisgender Male” (coded 0), “Cisgender Female” (coded 1), and “Transgender/Non-Binary+” (coded 2).
Income was assessed with a single item, “What is your total yearly income from all sources?” Responses were recoded into a categorical variable, where income categories were defined as “$50,000+” (coded 0), “$20,000-49,999” (coded 1), “<$20,000” (coded 2).
The community in which a participant lived was assessed with one item, “What type of community do you live in?” Response options included, “Large city” (coded 0), “Suburb near a large city” (coded 1), “Small city or town” (coded 2), and “Rural area” (coded 3).
Marital status was assessed with a single question, “What is your marital status?” Responses were recoded into a categorical variable such that participants were assigned as “Single/Divorced” (coded 0), “Partnered, cohabitating” (coded 1), “Partnered not cohabitating” (coded 2), and “Married” (coded 3).
Education level was assessed with a single question, “What is your highest grade completed?” Responses were recoded into a binary variable such that participants were assigned as “Less than high school/high school/certificate” (coded 0), and “Some college and above” (coded 1).
STATISTICAL ANALYSES
We first applied descriptive statistics to examine the sociodemographic characteristics of the sample by current tobacco status and region. We applied theory- and data-driven (Rothman et al., 2008) methods for identifying potential confounders. After identifying potential covariates and confounders based on significance in univariable models, we used multivariate logistic regressions to model the unadjusted association between baseline risk factors and (1) effects perceptions on vaping and (2) effects perceptions on cigarette smoking. Potential confounders and covariates were entered into multivariable logistic regressions to estimate adjusted effects.
We tested for an interaction between tobacco status and region to assess whether the association between tobacco status and effects perceptions specific to current non-users of cigarettes and vapes differed by region. We verified these interactions through sensitivity analysis (Tables 7-9), as well as constructed goodness of fit models to ensure the logistic regression models were adequately fit (Tables 10 and 11). As there were no significant interaction effects, we present main effects models only. An alpha of p<.05 was used to indicate statistical significance in final models.
RESULTS
SAMPLE CHARACTERISTICS
Tables 1 and 2 present sociodemographic characteristics of the sample based on current tobacco use and region lived. Participants overall were mostly white (63.44%), non-Hispanic (86.60%), cisgender female (48.36%), and had some college education or above (77.76%). Most of the participants within the sample were not a current tobacco user (84.21%) and were currently living in the southern region of the United States (40.24%).
PERCEIVED EFFECT OF MESSAGE EXPOSURE ON NON-USERS’ MOTIVATION TO BEGIN VAPING
Unadjusted logistic regression models indicated that our independent variable of interest, current tobacco status, was significantly associated with respondents’ perceptions that exposure to messages will motivate non-users to start vaping (Table 1). Among young adults in our sample, more current tobacco users agreed that messages will motivate non-users to begin vaping (21.58%) than current non-users (26.19%). Five sociodemographic covariates (age, gender, income, community type, marital status) were significantly associated with current tobacco usage status and, therefore, included in the final multivariable model.
However, unadjusted logistic regression models for our second independent variable of interest, region, indicated no statistically significant association between respondents’ perceptions that exposure to messages would motivate non-users to begin vaping (Table 2). Among young adults in our sample, more people living in the Midwest agreed that the messages would motivate non-users to begin vaping (25.17%) than those in the Northeast (22.72%), West (20.96%), and South (21.35%). Five sociodemographic covariates (race, ethnicity, income, community type, education) were significantly associated with region and, therefore, included in the final multivariable model.
After accounting for covariates in adjusted logistic regression models, there was no statistically significant association between region and dependent variable.
After accounting for covariates in adjusted logistic regression models, race, ethnicity, age, gender, income, community type, and education were no longer significantly associated with perceptions that messages would motivate non-users to begin vaping.
Final models indicated that young adults who reported currently using tobacco products had a 1.29 (95% CI: 1.02 – 1.63) greater relative odds of agreeing that exposure to messages will motivate non-users to start vaping than those not currently using tobacco products (Table 5).
Compared to those who reported their marital status as being single/divorced, those who were partnered and not cohabitating reported lower relative odds of agreeing that exposure to messages will motivate non-users to start vaping (OR: 0.71; 95% CI: 0.52 – 0.99).
PERCEIVED EFFECT OF MESSAGE EXPOSURE ON NON-USERS’ MOTIVATION TO BEGIN CIGARETTE SMOKING
Unadjusted logistic regression models indicated that our independent variable of interest, current tobacco status, was significantly associated with respondents’ perceptions that exposure to messages will motivate non-users to begin cigarette smoking (Table 1). Among young adults in our sample, more current users agreed that messages will motivate non-users to begin cigarette smoking (12.19%) than current non-users (6.26%). Two sociodemographic covariates (age, community type) and four potential confounders (race, gender, income, marital status) were significantly associated with perceptions around non-users’ motivation to begin cigarette smoking, and so were included in the final multivariable model.
However, unadjusted logistic regression models for our second independent variable of interest, region indicated no statistically significant association between respondents’ perceptions that exposure to messages would motivate non-users to begin cigarette smoking (Table 2). Among young adults in our sample, more people living in the Midwest agreed that messages were motivating nonsmokers to begin cigarette smoking (8.33%) than those in the northeast (8.07%), West (6.77%), and South (6.47%). Three sociodemographic covariates (ethnicity, community type, education) and four potential confounders (race, gender, income, marital status) were significantly associated with respondents’ perceptions that exposure to messages would motivate non-users’ to begin cigarette smoking, and so were included in the final multivariable model.
After accounting for covariates and potential confounders in adjusted logistic regression models, region was again no longer significantly significant with the dependent variable (Table 6).
After accounting for covariates and potential confounders in adjusted logistic regression models, race, ethnicity, age, income, community type, and education were no longer significantly associated with perceptions that messages would motivate non-user’s to begin cigarette smoking.
Final models indicated that young adults who reported currently using tobacco products have a 1.89 (95% CI: 1.36 – 2.63) greater relative odds of agreeing that messages will motivate non-users to start cigarette smoking than those not currently using tobacco products (Table 6).
Compared to those who identified as cisgender male, those who identified as cisgender female (OR: 0.66; 95% CI: 0.49-0.90) or transgender/NB+ (OR: 0.41; 95% CI: 0.20-0.83) reported lower relative odds of agreeing that messages will motivate non-users to start cigarette smoking.
Compared to those who reported their marital status as being single/divorced, those who were partnered and not cohabitating reported lower relative odds of agreeing that messages will motivate non-users to start cigarette smoking (OR: 0.52; 95% CI: 0.29 – 0.95).
DISCUSSION
In this secondary analysis of an experimental study testing the effectiveness of tobacco education prevention messages, we examined associations between participant characteristics and risk perceptions. To our knowledge, this is the first paper to examine how exposure to tobacco education messages is associated with young adults’ perceptions of the potential effect of exposure to these messages on smoking initiation among current non-users. In our multivariable models, we observed that current users perceived the messages as likely to motivate non-users to begin cigarette smoking or vaping. Conversely, those who were partnered and not cohabitating were less likely to perceive messages as likely to motivate non-users to initiate cigarette smoking or vaping; while those who identified as cisgender female or transgender/nonbinary were less likely to perceive messages as likely to motivate non-users to begin vaping. Contrary to our initial hypothesis, we found no significant regional differences in effects perceptions pertaining to vaping or smoking initiation among non-users. When controlling for potential confounders, other participant characteristics, including race, ethnicity, age, income, community type, and education were not associated with effects perceptions around vaping and smoking initiation among non-users.
We observed that current tobacco users perceived the messages as likely to motivate non-users to begin smoking or vaping. This belief may be related to how smoking initiation begins in this age group. Literature shows that the most likely time in one’s life to begin experimentation with nicotine products is young adulthood (Messeri et al., 2019); as such, this group may be more attentive to the potential for messaging to influence smoking and vaping uptake during young adulthood. Additionally, social pressure from peers and friends is an critical determinant of smoking and vaping initiation among young people (Littlecott et al., 2023). Accordingly, current users may believe that anti-tobacco messages are not strong enough to deter non-users from nicotine and tobacco use if other outside forces are more prominent (Kelsh et al., 2023; Tenny et al., 2024).
We also observed that those who were partnered and not cohabitating perceived the messages as less likely to motivate non-users to begin smoking or vaping. This is in contrary to literature surrounding current smoking status within the United States (CDC, 2023) as literature around tobacco usage and cohabitation indicates a higher prevalence of tobacco usage. Young adults who are not cohabiting with their partners are more likely to engage in dating behaviors, including participating in social settings where co-use of tobacco and other substances is present, including bar and parties (Trotter, 2002). In these spaces, the social pressure to use tobacco is high for young adults and tobacco use may be considered normed behavior (Littlecott et al., 2023). This however is in contrast of what we observed. Potentially, this could be due in part of stigma around the smell of cigarettes and vapes, if one partner is not smoking, they could motivate the smoking partner to quit to continue to be with them (Wadsworth et al., 2016). Additionally, having a partner could provide support measures in other way to deal with stressful situations that would otherwise cause them to initiate smoking. Furthermore, having a partner as support when attempting to quit smoking is a powerful quit technique that many smoking individuals express as beneficial on their quit journey (Branstetter et al., 2012). This demographic of young adults may believe that the effects of messages are enough to keep non-users away from initiation in combination with the support their partner provides to them.
We observed that those who identified as cisgender female or transgender/nonbinary had lower relative odds of agreeing that messages would motivate non-users to initiate vaping, co compared to cisgender males. Current census data supports that men are more likely to be currently vaping than females (CDC, 2023). This observation may be due to other coping mechanisms that females or transgender/nonbinary individuals have found for stress related factors in everyday life (Pericot-Valverde et al., 2021). Additionally, this demographic has higher rates of experiencing sexism and misogyny than their cisgender counterparts (Arayasirikul et al., 2022; Verbeek et al., 2020; White Hughto et al., 2015). Vaping may be seen as more stigmatizing within female or transgender/nonbinary population as social circles may be more disapproving of seeing them smoke (Wadsworth et al., 2016). This demographic may believe that their surroundings as well as messaging may be enough to keep non-users away from vaping initiation.
Initially, we hypothesized that those living in the south would be more likely to endorse that messages would motivate smoking or vaping initiation; however, we observed no statistically significant associations between outcomes and region. Our hypotheses were based on regional variations in smoking patterns and with regional variations in smoking policy. Our findings might reflect a lack of data regarding how long the participant lived in the current region, if they were born there, and where close family and friends live. Additionally, how we defined region is broad based upon CDC census data. However, dividing region this way is heterogeneous and includes states that have less restrictive tobacco laws (e.g. Alabama) and states/territories with more restrictive laws (e.g. District of Columbia). This lack of granular data may be contributing to missing important patterns by region that our study was unable to show. Future studies would need to further evaluate if dividing regional data into separate states would produce results like previous research findings in regard to various policies and restrictions within each state.
Due to larger sample size, our study was able to have a current tobacco users comparison group that included both cigarette and e-cigarette users to assess the unique perceptions held by this high-risk population. However, the parent study of this sample oversampled LGBTQ+ individuals, a group who have been historically targeted by Big Tobacco to begin tobacco smoking, which may further explain the higher rates of perceptions around effects messages in our study. Additionally, our study was a cross-sectional study, which obscures bidirectional associations and cannot assess changes in effects messaging perceptions over the life course. This is especially important as tobacco use can begin and end across several months in the year. Prospective analyses documenting changes in correlation across young adults’ lifetimes would help to better understand the effect of these factors on effects perceptions around tobacco use with current non-users, as well as identify critical timepoints for intervention.
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TABLES AND FIGURES
| Table 1. Sociodemographic characteristics according to current tobacco use status among young adults living in the US (N=2,806). |
|
Overall |
Not Current User |
Current User |
p |
| Sample size |
N = 2,806 |
n = 2,363 (84.21%) |
n = 443 (15.79%) |
|
| Race |
|
|
|
0.24 |
| White |
1780 (63.44%) |
1510 (63.90%) |
270 (60.95%) |
|
| BIPOC |
1026 (36.56%) |
853 (36.10%) |
173 (39.05%) |
|
| Hispanix/Latinx |
|
|
|
0.48 |
| Yes |
376 (13.40%) |
312 (13.20%) |
64 (14.45%) |
|
| No |
2430 (86.60%) |
2051 (86.80%) |
379 (85.55%) |
|
| Age |
|
|
|
0.03 |
| 18-24 |
920 (32.79%) |
795 (33.64%) |
125 (28.22%) |
|
| 25-35 |
1886 (67.21%) |
1568 (66.36%) |
318 (71.78%) |
|
| Gender |
|
|
|
<0.001 |
| Cisgender Male |
1191 (42.44%) |
967 (40.92%) |
224 (50.56%) |
|
| Cisgender Female |
1357 (48.36%) |
1168 (49.43%) |
189 (42.66%) |
|
| Transgender/NB+ |
258 (9.19%) |
228 (9.65%) |
30 (6.77%) |
|
| Income |
|
|
|
<0.001 |
| ≥$50,000 |
746 (26.59%) |
598 (25.31%) |
148 (33.41%) |
|
| $20,000-49,999 |
1003 (35.74%) |
827 (35.00%) |
176 (39.73%) |
|
| <$20,000 |
1057 (37.67%) |
938 (39.70%) |
119 (26.86%) |
|
| Community Type |
|
|
|
<0.001 |
| Large City |
833 (32.81%) |
672 (28.44%) |
161 (36.34%) |
|
| Suburb near large city |
1049 (41.32%) |
910 (38.51%) |
139 (31.38%) |
|
| Small city, town |
657 (25.88%) |
571 (24.16%) |
86 (19.41%) |
|
| Rural area |
267 (10.52%) |
210 (8.89%) |
57 (12.87%) |
|
| Marital Status |
|
|
|
0.002 |
| Single/Divorced |
1442 (51.39%) |
1238 (52.39%) |
204 (46.05%) |
|
| Partnered, cohabiting |
581 (20.71%) |
466 (19.72%) |
115 (25.96%) |
|
| Partnered, not cohabiting |
306 (10.91%) |
268 (11.34%) |
38 (8.58%) |
|
| Married |
477 (17.00%) |
391 (16.55%) |
86 (19.41%) |
|
| Region |
|
|
|
0.11 |
| Northeast |
471 (16.79%) |
383 (16.21%) |
88 (19.86%) |
|
| West |
606 (21.60%) |
526 (22.26%) |
80 (18.06%) |
|
| South |
1129 (40.24%) |
950 (40.20%) |
179 (40.41%) |
|
| Midwest |
600 (21.38%) |
504 (21.33%) |
96 (21.67%) |
|
| Education |
|
|
|
0.49 |
| <High school/HS/Certificate |
624 (22.24%) |
531 (22.47%) |
93 (20.99%) |
|
| Some College and above |
2182 (77.76%) |
1832 (77.53%) |
350 (79.01%) |
|
| Non-users Vaping motivation |
|
|
|
0.03 |
| Non-motivating |
2,180 (77.69%) |
1,853 (78.42%) |
327 (73.81%) |
|
| Motivating |
626 (22.31%) |
510 (21.58%) |
116 (26.19%) |
|
| Non-users Cigarette Motivation |
|
|
|
<0.001 |
| Non-motivating |
2,604 (92.80%) |
2215 (93.74%) |
389 (87.81%) |
|
| Motivating |
202 (7.20%) |
148 (6.26%) |
54 (12.19%) |
|
| Table 2. Sociodemographic characteristics according to region lived in among young adults living in the US (N=2,806). |
|
Overall |
Northeast |
West |
South |
Midwest |
p |
| Sample size |
N = 2,806 |
n = 471 (16.79%) |
n= 606 (21.60%) |
n= 1,129 (40.24%) |
n= 600 (21.38%) |
|
| Smoking status |
|
|
|
|
|
0.11 |
| Not Current User |
2,363 (84.21%) |
383 (81.32%) |
526 (86.80%) |
950 (84.15%) |
504 (84.00%) |
|
| Current User |
443 (15.79%) |
88 (18.68%) |
80 (13.20%) |
179 (15.85%) |
96 (16.00%) |
|
| Race |
|
|
|
|
|
<0.001 |
| White |
1780 (63.44%) |
303 (64.33%) |
322 (53.14%) |
709 (62.80%) |
446 (74.33%) |
|
| BIPOC |
1026 (36.56%) |
168 (35.67%) |
284 (46.86%) |
420 (37.20%) |
154 (25.67%) |
|
| Hispanix/Latinx |
|
|
|
|
|
<0.001 |
| Yes |
376 (13.40%) |
60 (12.74%) |
118 (19.47%) |
149 (13.20%) |
49 (8.17%) |
|
| No |
2430 (86.60%) |
411 (57.26%) |
488 (80.53%) |
980 (86.80%) |
551(91.83%) |
|
| Age |
|
|
|
|
|
0.70 |
| 18-24 |
920 (32.79%) |
147 (31.21%) |
199 (32.84%) |
383 (33.92%) |
191 (31.83%) |
|
| 25-35 |
1886 (67.21%) |
324 (68.79%) |
407 (67.16%) |
746 (66.08%) |
409 (68.17%) |
|
| Gender |
|
|
|
|
|
0.27 |
| Cisgender Male |
1191 (42.44%) |
194 (41.19%) |
282 (46.53%) |
474 (41.98%) |
241 (40.17%) |
|
| Cisgender Female |
1357 (48.36%) |
230 (48.83%) |
266 (43.89%) |
554 (49.07%) |
307 (51.17%) |
|
| Transgender/NB+ |
258 (9.19%) |
47 (9.98%) |
58 (9.57%) |
101 (8.95%) |
52 (8.67%) |
|
| Income |
|
|
|
|
|
0.03 |
| ≥$50,000 |
746 (26.59%) |
149 (31.63%) |
179 (29.54%) |
273 (24.18%) |
145 (24.17%) |
|
| $20,000-49,999 |
1003 (35.74%) |
157 (33.33%) |
202 (33.33%) |
419 (37.11%) |
225 (37.50%) |
|
| <$20,000 |
1057 (37.67%) |
165 (35.03%) |
225 (37.13%) |
437 (38.71%) |
230 (38.33%) |
|
| Community Type |
|
|
|
|
|
<0.001 |
| Large City |
833 (32.81%) |
154 (32.70%) |
223 (36.80%) |
292 (25.86%) |
164 (27.33%) |
|
| Suburb near large city |
1049 (41.32%) |
162 (34.39%) |
239 (39.44%) |
418 (37.02%) |
230 (38.33%) |
|
| Small city, town |
657 (25.88%) |
107 (22.72%) |
116 (19.14%) |
276 (24.45%) |
158 (26.33%) |
|
| Rural area |
267 (10.52%) |
48 (10.19%) |
28 (4.62%) |
143 (12.67%) |
48 (8.00%) |
|
| Marital Status |
|
|
|
|
|
0.24 |
| Single/Divorced |
1442 (51.39%) |
235 (49.89%) |
338 (55.78%) |
582 (51.55%) |
287 (47.83%) |
|
| Partnered, cohabit |
581 (20.71%) |
102 (21.66%) |
111 (18.32%) |
229 (20.28%) |
139 (23.17%) |
|
| Partnered, not cohabiting |
306 (10.91%) |
60 (12.74%) |
62 (10.23%) |
121 (10.72%) |
63 (10.50%) |
|
| Married |
477 (17.00%) |
74 (15.71%) |
95 (15.68%) |
197 (17.45%) |
111 (18.50%) |
|
| Education |
|
|
|
|
|
0.01 |
| <High school/HS/Certificate |
624 (22.24%) |
97 (20.59%) |
112 (18.48%) |
257 (22.76%) |
158 (26.33%) |
|
| Some College and above |
2182 (77.76%) |
374 (79.41%) |
494 (81.52%) |
872 (77.24%) |
442 (73.67%) |
|
| Non-users Vaping motivation |
|
|
|
|
|
0.25 |
| Non-motivating |
2,180 (77.69%) |
364 (77.28%) |
479 (79.04%) |
888 (78.65%) |
449 (74.83%) |
|
| Motivating |
626 (22.31%) |
107 (22.72%) |
127 (20.96%) |
241 (21.35%) |
151 (25.17%) |
|
| Non-users Cigarette Motivation |
|
|
|
|
|
0.43 |
| Non-motivating |
2,604 (92.80%) |
433 (91.93%) |
565 (93.23%) |
1056 (93.53%) |
550 (91.67%) |
|
| Motivating |
202 (7.20%) |
38 (8.07%) |
41 (6.77%) |
73 (6.47%) |
50 (8.33%) |
|
|
Table 3. Sociodemographic characteristics according to effects perceptions around non-users’ motivation to begin vaping among young adults living in the US (N=2,806). |
|
|
|
Full sample |
Believe that messages will not motivate non-users to start vaping |
Believe that messages will motivate non-users to start vaping |
p |
|
|
Sample size |
N = 2,806 |
n = 2,180 (77.69%) |
n = 626 (22.31%) |
|
|
|
Smoking status |
|
|
|
0.03 |
|
|
Not Current User |
2,363 (84.21%) |
1,853(85.00%) |
510 (81.47%) |
|
|
|
Current User |
443 (15.79%) |
327 (15.00%) |
116 (18.53%) |
|
|
|
Region |
|
|
|
0.25 |
|
|
Northeast |
471 (16.79%) |
364 (16.70%) |
107 (17.09%) |
|
|
|
West |
606 (21.60%) |
479 (21.97%) |
127 (20.29%) |
|
|
|
South |
1129 (40.24%) |
888 (40.73%) |
241 (38.50%) |
|
|
|
Midwest |
600 (21.38%) |
449 (20.60%) |
151 (24.12%) |
|
|
|
Race |
|
|
|
0.57 |
|
|
White |
1780 (63.44%) |
1389 (63.72%) |
391 (62.46%) |
|
|
|
BIPOC |
1026 (36.56%) |
791 (36.28%) |
235 (37.54%) |
|
|
|
Hispanix/Latinx |
|
|
|
0.78 |
|
|
Yes |
376 (13.40%) |
290 (13.30%) |
86 (13.74%) |
|
|
|
No |
2430 (86.60%) |
1890 (86.70%) |
540 (86.26%) |
|
|
|
Age |
|
|
|
0.51 |
|
|
18-24 |
920 (32.79%) |
708 (32.48%) |
212 (33.87%) |
|
|
|
25-35 |
1886 (67.21%) |
1472 (67.52%) |
414 (66.13%) |
|
|
|
Gender |
|
|
|
0.85 |
|
|
Cisgender Male |
1191 (42.44%) |
931 (42.71%) |
260 (41.53%) |
|
|
|
Cisgender Female |
1357 (48.36%) |
1051 (48.21%) |
306 (48.88%) |
|
|
|
Transgender/NB+ |
258 (9.19%) |
198 (9.08%) |
60 (9.58%) |
|
|
|
Income |
|
|
|
0.82 |
|
|
≥$50,000 |
746 (26.59%) |
580 (26.61%) |
166 (26.52%) |
|
|
|
$20,000-49,999 |
1003 (35.74%) |
785 (36.01%) |
218 (34.82%) |
|
|
|
<$20,000 |
|
815 (37.39%) |
242 (38.66%) |
|
|
|
Community Type |
|
|
|
0.38 |
|
|
Large City |
833 (32.81%) |
636 (32.28%) |
197 (34.62%) |
|
|
|
Suburb near large city |
1049 (41.32%) |
809 (41.07%) |
240 (42.18%) |
|
|
|
Small city, town |
657 (25.88%) |
525 (26.65%) |
132 (23.20%) |
|
|
|
Rural area |
267 (10.52%) |
210 (10.66%) |
57 (10.02%) |
|
|
|
Marital Status |
|
|
|
0.19 |
|
|
Single/Divorced |
1442 (51.39%) |
1116 (51.19%) |
326 (52.08%) |
|
|
|
Partnered, cohabiting |
581 (20.71%) |
450 (20.64%) |
131 (20.93%) |
|
|
|
Partnered, not cohabiting |
306 (10.91%) |
252 (11.56%) |
54 (8.63%) |
|
|
|
Married |
477 (17.00%) |
362 (16.61%) |
115 (18.37%) |
|
|
|
Education |
|
|
|
0.08 |
|
|
<High school/HS/Certificate |
624 (22.24%) |
501 (22.98%) |
123 (19.65%) |
|
|
|
Some College and above |
2182 (77.76%) |
1679 (77.02%) |
503 (80.35%) |
|
|
|
Table 4. Sociodemographic characteristics according to effects perceptions around non-users’ motivation to begin cigarette smoking among young adults living in the US (N=2,806). |
|
Full sample |
Messages WILL NOT motivate non-users to start cigarette smoking |
Messages WILL motivate non-users to start cigarette smoking |
P |
| Sample size |
N = 2,806 |
n = 2,604 (92.80%) |
n = 202 (7.20%) |
|
| Smoking status |
|
|
|
<0.001 |
| Not Current User |
2363 (84.21%) |
2215 (85.06%) |
148 (73.27%) |
|
| Current User |
443 (15.79%) |
389 (14.94%) |
54 (26.73%) |
|
| Region |
|
|
|
0.43 |
| Northeast |
471 (16.79%) |
433 (16.63%) |
38 (18.81%) |
|
| West |
606 (21.60%) |
565 (21.70%) |
41 (20.30%) |
|
| South |
1129 (40.24%) |
1056 (40.55%) |
73 (36.14%) |
|
| Midwest |
600 (21.38%) |
550 (21.12%) |
50 (24.75%) |
|
| Race |
|
|
|
0.01 |
| White |
1780 (63.44%) |
1670 (64.13%) |
110 (54.46%) |
|
| BIPOC |
1026 (36.56%) |
934 (35.87%) |
92 (45.54%) |
|
| Hispanix/Latinx |
|
|
|
0.99 |
| Yes |
376 (13.40%) |
349 (13.40%) |
27 (13.37%) |
|
| No |
2430 (86.60%) |
2255 (86.60%) |
175 (86.63%) |
|
| Age |
|
|
|
0.42 |
| 18-24 |
920 (32.79%) |
859 (32.99%) |
61 (30.20%) |
|
| 25-35 |
1886 (67.21%) |
1745 (67.01%) |
141 (69.80%) |
|
| Gender |
|
|
|
<0.001 |
| Cisgender Male |
1191 (42.44%) |
1076 (41.32%) |
115 (56.93%) |
|
| Cisgender Female |
1357 (48.36%) |
1279 (49.12%) |
78 (38.61%) |
|
| Transgender/NB+ |
258 (9.19%) |
249 (9.56%) |
9 (4.46%) |
|
| Income |
|
|
|
0.01 |
| ≥$50,000 |
746 (26.59%) |
675 (25.92%) |
71 (35.15%) |
|
| $20,000-49,999 |
1003 (35.74%) |
935 (35.91%) |
68 (33.66%) |
|
| <$20,000 |
1057 (37.67%) |
994 (38.17%) |
63 (31.19%) |
|
| Community Type |
|
|
|
0.15 |
| Large City |
833 (32.81%) |
759 (32.24%) |
74 (40.00%) |
|
| Suburb near large city |
1049 (41.32%) |
978 (41.55%) |
71 (38.38%) |
|
| Small city, town |
657 (25.88%) |
617 (26.21%) |
40 (21.62%) |
|
| Rural area |
267 (10.52%) |
250 (10.62%) |
17 (9.19%) |
|
| Marital Status |
|
|
|
0.04 |
| Single/Divorced |
1442 (51.39%) |
1321 (50.73%) |
121 (59.90%) |
|
| Partnered, cohabiting |
581 (20.71%) |
545 (20.93%) |
36 (17.82%) |
|
| Partnered, not cohabiting |
306 (10.91%) |
293 (11.25%) |
13 (6.44%) |
|
| Married |
477 (17.00%) |
445 (17.09%) |
32 (15.84%) |
|
| Education |
|
|
|
0.87 |
| <High school/HS/Certificate |
624 (22.24%) |
580 (22.27%) |
44 (21.78%) |
|
| Some College and above |
2182 (77.76%) |
2024 (77.73%) |
158 (78.22%) |
|
|
|
|
|
|
|
|
|
|
| Table 5. Binary Logistic regression model of the association between current smoking status/region and effects perception around non-users motivation to begin vaping among young adults living in the US (N=2,806) |
|
AOR |
95% CI |
| Smoking status |
|
|
| Not Current User |
Ref |
Ref |
| Current User |
1.29* |
(1.02 – 1.93) |
| Region |
|
|
| Northeast |
Ref |
Ref |
| West |
0.88 |
(0.66 – 1.18) |
| South |
0.92 |
(0.71 – 1.20) |
| Midwest |
1.16 |
(0.87 – 1.55) |
| Black, Indigenous, or other person of color |
1.06 |
(0.87 – 1.30) |
| Hispanic/Latinx |
1.03 |
(0.78 – 1.36) |
| Age 25-35 |
0.90 |
(0.73 – 1.10) |
| Gender |
|
|
| Cisgender Male |
Ref |
Ref |
| Cisgender Female |
1.04 |
(0.86 – 1.26) |
| Transgender/NB+ |
1.08 |
(0.78 – 1.51) |
| Income |
|
|
| ≥$50,000 |
Ref |
Ref |
| $20,000-49,999 |
1.04 |
(0.82 – 1.32) |
| <$20,000 |
1.17 |
(0.90 – 1.50) |
| Community Type |
|
|
| Large City |
Ref |
Ref |
| Suburb near large city |
0.97 |
(0.78 – 1.20) |
| Small city, town |
0.81 |
(0.63 – 1.05) |
| Rural area |
0.90 |
(0.64 – 1.27) |
| Marital Status |
|
|
| Single/Divorced |
Ref |
Ref |
| Partnered, cohabiting |
1.01 |
(0.79 – 1.28) |
| Partnered, not cohabiting |
0.71* |
(0.52 – 0.99) |
| Married |
1.15 |
(0.88 – 1.50) |
| Some College and above |
1.24 |
(0.99 – 1.56) |
(Note: * p<0.05)
| Table 6. Binary Logistic regression model of the association between current smoking status/region and effects perception around non-users motivation to begin cigarette smoking among young adults living in the US (N=2,806) |
|
AOR |
95% CI |
| Smoking status |
|
|
| Not Current User |
Ref |
Ref |
| Current User |
1.89*** |
(1.36 – 2.63) |
| Region |
|
|
| Northeast |
Ref |
Ref |
| West |
0.80 |
(0.50 – 1.27) |
| South |
0.82 |
(0.54 – 1.25) |
| Midwest |
1.13 |
(0.72 – 1.78) |
| Black, Indigenous, or other person of color |
1.33 |
(0.96 – 1.83) |
| Hispanic/Latinx |
0.86 |
(0.55 – 1.36) |
| Age 25-35 |
0.97 |
(0.68 – 1.38) |
| Gender |
|
|
| Cisgender Male |
Ref |
Ref |
| Cisgender Female |
0.66** |
(0.49 – 0.90) |
| Transgender/NB+ |
0.41** |
(0.20 – 0.83) |
| Income |
|
|
| ≥$50,000 |
Ref |
Ref |
| $20,000-49,999 |
0.72 |
(0.51 – 1.03) |
| <$20,000 |
0.70 |
(0.47 – 1.03) |
| Community Type |
|
|
| Large City |
Ref |
Ref |
| Suburb near large city |
0.79 |
(0.26 – 1.11) |
| Small city, town |
0.79 |
(0.52 – 1.19) |
| Rural area |
0.83 |
(0.47 – 1.47) |
| Marital Status |
|
|
| Single/Divorced |
Ref |
Ref |
| Partnered, cohabiting |
0.73 |
(0.49 – 1.11) |
| Partnered, not cohabiting |
0.52* |
(0.29 – 0.95) |
| Married |
0.70 |
(0.45 – 1.08) |
| Some College and above |
0.96 |
(0.67 -1.39) |
(Note: * p<0.05 **p<0.01 ***p<0.001)
Appendix A: Literature Review
U.S. CURRENT SMOKING PREVALENCE
Cigarette smoking remains within the United States as the leading cause of preventable death. Cigarette smoking accounts for more than 480,000 deaths each year, which is about 1 in 5 deaths in the U.S. (U.S. Department of Health and Human Services, 2021). Within the United States, “current” cigarette smoking status is defined as people who reported smoking at least 100 cigarettes during their lifetime and who smoke either daily or occasionally at the time of survey (WHO, 2024). In 2021, an estimated 28.3 million U.S. adults were currently smoking, which is approximately 12 of every 100 adults (Cornelius et al., 2023). As of now, current cigarette smoking is higher among men than women. Approximately 13% of adult men are cigarette smoking while approximately 10% of adult women are smoking (Cornelius et al., 2023). Current smoking status around the United States also varies by race/ethnicity, education level, income, U.S. census region data, marital status, sexual orientation, and by age (Cornelius et al., 2023). According to research, current cigarette smoking is highest among people aged 25-44 years of age and is lowest among people aged 18-24 years (CDC, 2023). However, this decrease in young adult’s cigarette smoking may be misleading or overly optimistic as young adults are shown to be transitioning to smoking other forms of tobacco products, such as e-cigarettes (Martinasek et al., 2021).
Continuous efforts within the United States to reduce the initiation of cigarette smoking, especially everyday cigarette smoking, remains a primary concern. One key target group that the U.S. is attempting to stop or never begin the use cigarettes is the young adult population (Barrington-Trimis et al., 2020). According to research, 87% of adults who had ever smoked cigarettes daily had tried their first cigarette by the age of 18 (Hrywna et al., 2023; U.S. Department of Health and Human Services, 2021). In March 2015, a report from the National Academy of Medicine showed that the idea “Tobacco 21” could prevent 223,000 deaths from smoking and provided insightful scientific information to help begin advocacy to increase the age of tobacco sales from 18 to 21 (American Lung Association, 2022). The passage of this law was a monumental moment in public health. A substantial proportion of beginning smokers are currently young adults, this is a shift from what was previously shown as adolescence. Being able to visibly see that shifting the law to 21 has made a significant impact of the initiation of cigarette smoking (Barrington-Trimis et al., 2020), giving public health educators more time to explain the risks of cigarette smoking to the population and prevent more individuals from smoking initiation. However, the extent to which Tobacco 21 will continue to reduce youth access to tobacco products depends on consistent implementation, as well as preventing older individuals from selling tobacco products to young adults below age 21 (Hrywna et al., 2023).
U.S. CURRENT E-CIGARETTE PREVALENCE
E-cigarettes (better known as vapes) are the second most commonly used tobacco product among U.S. adults (Erhabor et al., 2023). A continuous trend being observed across studies has shown an increase in the proportion of individuals indicating that they use e-cigarettes daily. Current e-cigarette use is based on a response of “every day” or “some days” to the question, “Do you now use e-cigarettes or other electronic vaping products every day, some days, or not at all?” This survey question can be asked of adults who had ever tried an e-cigarette, even one time (National Center for Health Statistics, 2023). These recent findings suggest a potential shift from the experimental usage of e-cigarettes to an established use (Boakye et al., 2022; Dai & Leventhal, 2019). Nationally, 4.5% of adults aged 18 and over are current e-cigarette users. Recent smaller surveys have indicated that the national number of individuals smoking e-cigarettes is expected to increase in upcoming years (Dai & Leventhal, 2019). Current e-cigarette usage within the United States varies by race/ethnicity, income, and age. White non-Hispanic adults are more likely to be current e-cigarette users than Asian non-Hispanic, Black, and Hispanic adults (National Center for Health Statistics, 2023). In 2021, people aged 18-24 were found to be most likely to use e-cigarettes among all adults (National Center for Health Statistics, 2023). Research shows 11 out of 100 young adults aged 18-24 are currently using e-cigarettes.
While e-cigarettes are seen in some age group populations to serve as a smoking cessation aid, for young adults who have had no prior exposure to combustible cigarettes, e-cigarette usage remains a public health concern (Kasza et al., 2021; Obisesan et al., 2020). The recent passage of the Tobacco 21 law also applies to e-cigarettes. The benefits that cigarette smoking reduction has seen in recent years provides a hopeful ideation that e-cigarette usage will follow a decrease in initiation by young adults. However, the tobacco 21 law holds the same as with cigarette smoking. The extent to which tobacco 21 can cause reduction in e-cigarette usage greatly depends on consistent implementation (Hrywna et al., 2023). Young adults aged 18-35 will still need to be provided educational materials as for the dangers e-cigarettes pose to one’s health, and prevention interventions to keep young adults away from e-cigarettes (Gulsen et al., 2019).
ANTI-TOBACCO AD CAMPAIGNS
Tobacco educational advertisements are marketing strategies created through public health officials to highlight the importance of tobacco cessation. Tobacco educational advertisements are broken up into various categories depending on what messages surrounding tobacco are trying to be displayed. One form of tobacco educational advertisements is absolute risk messaging. This strategy for messaging provokes the tobacco user to end all forms of tobacco products, for example, “end all smoking of cigarettes and e-cigarettes” (Krosnick et al., 2017). Relative risk messages, however, provoke the tobacco user to switch completely to vaping only instead of smoking cigarettes or smoking both vapes and cigarettes. An example of relative risk messaging would be “protect your health, switch completely from cigarette smoking to vaping only” (Yang et al., 2019).
There have been mixed findings throughout the literature about the overall effectiveness of absolute risk messages vs. relative risk messages (Barnes et al., 2017; Wackowski et al., 2016; Yang et al., 2019). However, there are advantages to both messaging types depending on what prevention methods are to be targeted. Absolute risk messaging in theory is the best way to minimize a current smoker’s risk of smoking related disease as they would be informed to quit tobacco and all forms of nicotine. However, absolute risk messaging relies heavily on alternative therapies that could cause the smoker to quit all forms of nicotine, which for many current smokers is not an option that they are willing to do (PMI Science, 2021). Contrarily, relative risk helps provide informative comparisons between cigarettes and other nicotine products. Relative risk messages can help a current smoker understand the reduced risk of completely switching to another less harmful nicotine product. While this type of messaging has a rate reduction or harm reduction positive message in play, there are disadvantages of utilizing relative risk messages as well (PMI Science, 2021). Relative risk messages can also give smokers a false sense that switching to another form of nicotine will then have low to no health risks involved, which is not supported by research around other nicotine products. Additionally, switching products may still find a current smoker unable to quit the new nicotine product (PMI Science, 2021). While reducing harm from the original cigarettes, it is still not in a current smoker’s best interest to then smoke the new nicotine product continuously for the lifetime duration.
In previous years, the research around how these campaigns were affecting tobacco cessation were done through message perceptions. Message perceptions are judgements about whether a message promotes further processing that leads to persuasion (Noar et al., 2022). Examples of message perceptions are, “This ad is attention-grabbing” or, “This ad is informative”. However, in recent studies there has been evidence to shift away from analyzing through message perceptions and to analyze tobacco educational messages through effects perceptions (Noar et al., 2022). Effects perceptions are judgements about a message’s potential to change antecedents of behavior. Examples of effects perceptions are, “This ad would motivate me not to vape” or, “This ad motivates me not to smoke” (Noar et al., 2022). Through literature it has been found that message perceptions are not as effective as effects perceptions on understanding the entire thought process of participants when viewing tobacco educational messages. Additionally, message perceptions were found to have greater measurement error than effects perceptions scales due to evidence about how message perceptions measure typically contain “noise” when participants make message ratings (Noar et al., 2022). This finding has also been shown to have similar findings in other studies; other studies have shown that only effects perceptions predicted actual effectiveness outcomes after adjusting for message perceptions, while message perceptions exhibited counterintuitive results after adjusting for effects perceptions (Baig et al., 2021; Brennan et al., 2014; Rohde et al., 2021).
REGIONAL VARIATIONS IN SMOKING
According to U.S. census data from the CDC, the United States is separated into four regions, the South, Midwest, West, and Northeast. Within the U.S. current smoking varies by region lived in. Current cigarette smoking in 2021 was found to be highest in the Midwest and the South and lowest in the West (CDC, 2023). For example, West Virginia falls within the Southern region of the United States and in 2022 had the highest smoking rate in the U.S. at 23.8% (Bascom, 2022) and Kentucky, in the Southern region as well is not far behind, where 23.6% of adults are currently smoking. Utah resides in the Western region of the United States and in 2022 had the lowest smoking rate, with less than 10% of the state’s population currently smoking (Bascom, 2022) and California, also in the west, has 7.6% of adults currently smoking.
There are clear smoking disparities among regions lived in for the U.S. population. Research shows that having protections in place such as smoke-free laws, higher tobacco taxes, and the tobacco 21 law all help to lower smoking initiation and smoking rates (Truth Initiative, 2023). States that have significantly fewer laws and protections in place are shown to have higher rates of smoking and smoking initiation. There are various reasons as to why regionality may be playing a role in smoking and initiation; social and cultural differences across the country may contribute to different attitudes towards tobacco use and passing laws surrounding tobacco control policies (Holford et al., 2023). For example, individuals living in a state where tobacco is more commonplace may not understand the harms around tobacco smoking or may face greater peer pressure from friends and family members to begin smoking at a younger age. Individuals living in states that have lower smoking rates may face criticism for beginning smoking, may not have access to outdoor smoking areas, or live in a place where it is not legal to smoke indoors. Additionally, states have varying agendas as to why laws are passed around tobacco control. For example, in Kentucky tobacco has historically been a very important part of the state’s agricultural economy and culture. Census of Agriculture revealed that tobacco accounted for more than 40% of the net cash return from agricultural sales in Kentucky (Snell & Goetz, n.d.). Comparatively to California, a state that has one of the lowest tobacco rates, shows that an increase on taxes for tobacco products, combined with anti-smoking media campaigns has helped the state remain low in smoking initiation and rates. From this, the state has seen lung cancer rates drop more than three times as fast than in the rest of the country, saving the state an estimated $86 billion in health care costs (Lovett, 2010). These driving differences in monetary state impact can be another reason as to why tobacco initiation and rates are varied across regions within the United States.
Appendix B: Supplemental figures
SENSITIVITY ANALYSIS
| Table 7. Binary Logistic regression model of the association between ever smoker vs. never smoker status/region and effects perception around non-users motivation to begin vaping among young adults living in the US (N=2,806) |
|
AOR |
95% CI |
| Smoking status |
|
|
| Never Smoker |
Ref |
Ref |
| Ever Smoker |
1.04 |
(0.86 – 1.25) |
| Region |
|
|
| Northeast |
Ref |
Ref |
| West |
0.87 |
(0.65 – 1.17) |
| South |
0.92 |
(0.71 – 1.20) |
| Midwest |
1.16 |
(0.87 – 1.54) |
| Black, Indigenous, or other person of color |
1.08 |
(0.89 – 1.31) |
| Hispanic/Latinx |
1.03 |
(0.78 – 1.36) |
| Age 25-35 |
0.90 |
(0.73 – 1.10) |
| Gender |
|
|
| Cisgender Male |
Ref |
Ref |
| Cisgender Female |
1.03 |
(0.89 – 1.30) |
| Transgender/NB+ |
1.07 |
(0.78 – 1.51) |
| Income |
|
|
| ≥$50,000 |
Ref |
Ref |
| $20,000-49,999 |
1.03 |
(0.82 – 1.32) |
| <$20,000 |
1.15 |
(0.90 – 1.50) |
| Community Type |
|
|
| Large City |
Ref |
Ref |
| Suburb near large city |
0.96 |
(0.78 – 1.20) |
| Small city, town |
0.81 |
(0.63 – 1.05) |
| Rural area |
0.91 |
(0.64 – 1.27) |
| Marital Status |
|
|
| Single/Divorced |
Ref |
Ref |
| Partnered, cohabiting |
1.01 |
(0.79 – 1.28) |
| Partnered, not cohabiting |
0.71* |
(0.52 – 0.99) |
| Married |
1.15 |
(0.89 – 1.51) |
| Some College and above |
1.24 |
(0.99 – 1.56) |
(Note: * p<0.05 **p<0.01 ***p<0.001)
| Table 8. Binary Logistic regression model of the association between ever smoker vs. never smoker status/region and effects perceptions strongly disagreeing vs. everyone else around non-users motivation to begin vaping among young adults living in the US (N=2,806) |
|
AOR |
95% CI |
| Smoking status |
|
|
| Never Smoker |
Ref |
Ref |
| Ever Smoker |
1.02 |
(0.86 – 1.20) |
| Region |
|
|
| Northeast |
Ref |
Ref |
| West |
0.95 |
(0.74 – 1.22) |
| South |
0.92 |
(0.74 – 1.14) |
| Midwest |
1.03 |
(0.80 – 1.32) |
| Black, Indigenous, or other person of color |
1.22* |
(1.04 – 1.44) |
| Hispanic/Latinx |
1.03 |
(0.78 – 1.36) |
| Age 25-35 |
0.91 |
(0.77 – 1.09) |
| Gender |
|
|
| Cisgender Male |
Ref |
Ref |
| Cisgender Female |
0.96 |
(0.81 – 1.13) |
| Transgender/NB+ |
1.18 |
(0.89 – 1.57) |
| Income |
|
|
| ≥$50,000 |
Ref |
Ref |
| $20,000-49,999 |
1.14 |
(0.82 – 1.32) |
| <$20,000 |
1.26* |
(1.01 – 1.57) |
| Community Type |
|
|
| Large City |
Ref |
Ref |
| Suburb near large city |
0.96 |
(0.78 – 1.20) |
| Small city, town |
0.81 |
(0.63 – 1.05) |
| Rural area |
0.95 |
(0.64 – 1.27) |
| Marital Status |
|
|
| Single/Divorced |
Ref |
Ref |
| Partnered, cohabiting |
0.98 |
(0.79 – 1.28) |
| Partnered, not cohabiting |
0.81 |
(0.62 – 1.04) |
| Married |
1.09 |
(0.89 – 1.38) |
| Some College and above |
1.37** |
(1.13 – 1.66) |
(Note: * p<0.05 **p<0.01 ***p<0.001)
| Table 9. Binary Logistic regression model of the association between ever vs. never smoking status/region and effects perceptions strongly disagreeing vs. everyone else around non-users motivation to begin cigarette smoking among young adults living in the US (N=2,806) |
|
AOR |
95% CI |
| Smoking status |
|
|
| Never Smoker |
Ref |
Ref |
| Ever Smoker |
1.14 |
(0.93 – 1.41) |
| Region |
|
|
| Northeast |
Ref |
Ref |
| West |
0.92 |
(0.74 – 1.22) |
| South |
0.93 |
(0.74 – 1.14) |
| Midwest |
0.98 |
(0.80 – 1.32) |
| Black, Indigenous, or other person of color |
1.34** |
(1.08 – 1.66) |
| Hispanic/Latinx |
1.03 |
(0.78 – 1.36) |
| Age 25-35 |
0.89 |
(0.71 – 1.12) |
| Gender |
|
|
| Cisgender Male |
Ref |
Ref |
| Cisgender Female |
0.75** |
(0.61 – 0.93) |
| Transgender/NB+ |
0.69 |
(0.47 – 1.03) |
| Income |
|
|
| ≥$50,000 |
Ref |
Ref |
| $20,000-49,999 |
0.80 |
(0.62 – 1.02) |
| <$20,000 |
0.83 |
(0.63 – 1.09) |
| Community Type |
|
|
| Large City |
Ref |
Ref |
| Suburb near large city |
0.92 |
(0.78 – 1.20) |
| Small city, town |
0.73* |
(0.55 – 0.98) |
| Rural area |
0.93 |
(0.64 – 1.37) |
| Marital Status |
|
|
| Single/Divorced |
Ref |
Ref |
| Partnered, cohabiting |
0.86 |
(0.65 – 1.14) |
| Partnered, not cohabiting |
0.67* |
(0.47 – 0.97) |
| Married |
1.03 |
(0.77 – 1.37) |
| Some College and above |
1.16 |
(0.90 – 1.49) |
(Note: * p<0.05 **p<0.01 ***p<0.001)
GOODNESS OF FIT
| Table 10. Hosmer-Lemeshow GOF test after logistic model of vaping motivation |
| Number of Observation |
Number of groups |
Hosmer-Lemeshow chi2(8) |
Prob > chi2 |
| 2,806 |
10 |
9.31 |
0.3170 |
Since the p-value is 0.3170 we can conclude that the model provides adequate fit to the data.
| Table 11. Hosmer-Lemeshow GOF test after logistic model of cigarette smoking motivation |
| Number of Observation |
Number of groups |
Hosmer-Lemeshow chi2(8) |
Prob > chi2 |
| 2,806 |
10 |
3.50 |
0.8992 |
Since the p-value is 0.8992 we can conclude that the model provides adequate fit to the data.
Figure 1. Variables for Analysis from Parent Study
| Variables for Analysis |
Question |
Response Options |
| Current User |
“Do you currently smoke cigarettes every day, some days, or not at all?”
AND
“Do you currently use e-cigarettes or electronic nicotine vapes every day, some days, or not at all? “ |
Everyday
some days
Not at all |
| Region |
“What US state, district, or territory do you currently reside in?” |
Drop down menu to select from of all U.S. States, districts, and territories.
OR selection choice:
“I do not live in the United States” |
| Non-users Vaping Motivation |
“Please rate each statement from 1(strongly disagree) to 5(strongly agree)” |
These messages will motivate NON-USERS to start vaping |
| Non-users Cigarette Motivation |
“Please rate each statement from 1(strongly disagree) to 5(strongly agree)” |
These messages will motivate NON-USERS to start smoking cigarettes. |
| Education |
“What is your highest grade completed?” |
11th grade
High school diploma or GED
Technical school (Vo Tech, Career Certificate, etc)
Some college (not graduated)
Associate’s Degree
Bachelor’s Degree
Master’s Degree
Doctoral Degree or other terminal Professional Degree (e.g. MD, JD) |
| Gender |
“What is your gender?” |
Female
Male
Transgender female/Transgender woman
Transgender male/Transgender man
Non-binary
Gender queer
Agender
I use different words to describe my gender |
| Marital Status |
“What is your marital status?” |
Single, never married
Dating or partnered, and living together
Dating or partnered, but not living together
Married, and living together
Married, but not living together
Divorced
Widowed |
| Hispanix/Latinx |
“What racial and/or ethnic groups do you identify with? (Check all that apply)” |
Asian
Black/African American
Hispanic/Latinx/Latino/Latina
Middle Eastern
Native American or Alaskan Native
Pacific Islander
White/Caucasian
I use other words to describe my race and ethnicity |
| Community Type |
“What type of Community do you live in?” |
Large City
Suburb near a large city
Small city or town
Rural area |
| Age |
“How old are you?” |
Text entry |
| Income |
“What is your total yearly income from all sources? Please report your individual income, not including the income of other people you might live with.” |
Less than $20,000
$20,000 – $35,000
$35,001 – $50,000
$50,001 – $75,000
Greater than $75,000 |
| Race |
“What racial and/or ethnic groups do you identify with? (Check all that apply)” |
Asian
Black/African American
Hispanic/Latinx/Latino/Latina
Middle Eastern
Native American or Alaskan Native
Pacific Islander
White/Caucasian
I use other words to describe my race and ethnicity |
