The prevalence of e-cigarettes has rapidly increased among adolescents, while conventional cigarette use has decreased in this age demographic. However, evidence suggests that e-cigarette use may, in fact, induce conventional cigarette smoking over time. This study explored the social influence garnered from the prevalence of e-cigarette use in peer networks, as well as, from the general population as a mechanism contributing to overall smoking behaviors. Therefore, an agent-based model was used, in which young agents repeatedly choose to either smoke conventional cigarettes and/or e-cigarettes or remain non-smokers. This decision was based on the agent’s assessment of the utility derived from smoking and their attitude towards smoking. These utility equations were impacted by the agent’s reaction to various deceptive marketing tactics including advertisements and enticing flavors. Also, a ‘crossover’ effect between use of the different types of smoking was assumed. The model was calibrated with the 2020 U.S National Youth Tobacco Survey data to reflect the real world. The use was modeled under three policy proposals, to predict future use if interventions and restrictions were enforced. The results indicate that the social influence of e-cigarette use prevalence affects one’s choice to initiate or continue conventional cigarette smoking. Results suggest that the implementation of cessation programs and early childhood interventions can decrease e-cigarette prevalence rates by 12% while a ban on all e-cigarette flavors can decrease rates by 10%.
Although conventional cigarette smoking has strikingly declined in the last several decades among youth and young adults in the United States1, we have witnessed a significant rise in the use of newly emerged tobacco products among these age demographics in recent years2. Among these increases, none has been as substantial as the rise in e-cigarette use among youth and young adults, which have reached and remained at epidemic levels3. The sudden rise within this age group is alarming given that e-cigarettes mimic the sensations provided by conventional cigarette smoking but in a manner that masks the harshness of the chemicals while promoting the product as a safer alternative using appealing flavors4’5. The use of e-cigarettes in the youth population presents more of a risk given that tobacco product use varies among different population groups, including within Black Americans, LGBTQ individuals, women, those suffering from mental health conditions, and those with low socioeconomic status6. We have also witnessed Big Tobacco Companies use deceptive marketing tactics by conveying themes of sexual content, independence, rebellion, and celebrity figures to target and appeal to youth and young adults, particularly focusing on targeting the vulnerable groups at risk mentioned previously.
On top of that, more studies have concluded that young people who use e-cigarettes are more likely to start smoking conventional cigarettes and other tobacco products than their peers who do not vape. Regardless that the rapid rise of e-cigarette use in teenagers has been followed by a decrease in use of conventional cigarettes, escalating evidence suggests that vaping may prompt the initiation of cigarette smoking among young people, determining another long-term public health crisis7’8. Since only a few studies have investigated the association between e-cigarette use and cigarette use in adolescents, given the recent popularity of these products, a possible mechanism for this association concerns the social influence, norms, and attitudes with regard to one’s general population and peer network9’10.
Social influence from an adolescent’s peers and other groups of people they are connected with through social networking is another main factor that affects the initiation and continued use of e-cigarettes and conventional cigarettes11. The risk of smoking and vaping among youth aged 10 to 19 years old is doubled if they have friends who use tobacco products12. This phenomenon of peer influence and pressure from friends is particularly dangerous for current e-cigarette users. According to a study by JAMA Pediatrics, half of vape users would like to quit; however, cessation programs are not widely available and lack evidence-based approaches. Researchers add that it is even harder for teen users to quit compared to adults because of the difficulty of giving up the social status that is associated with vaping13.
This paper variably evaluates the effect of social influences, peer pressure, and deceptive marketing tactics such as advertisements and flavorings, have on the use of e-cigarette, cigarettes, and dual use within adolescents. I used an agent-based microsimulation model that enables the exploration of an individual’s decision making with exposure to advertisements that target youth, enticements offered by flavors such as candy and fruit, and social influences. Agent-based modeling is ‘a computational approach in which agents with a specified set of characteristics interact with each other and with their environment according to predefined rules’14. This simulation has been used in applications of public health issues like disease transmission, health behaviors, and epidemiology, and provides insight into the mechanisms of patterns between populations so policy interventions and proposals can be implemented into the real-world14’15’16. This is a particularly effective tool for focusing on the effect marketing tools and social influence from an adolescent’s peer network has on their perspective and attitude towards smoking and vaping. Through this study, I aimed to simulate the acceptance of the use of e-cigarettes or conventional cigarettes among youth in high school, as well as factors in their surrounding society that can impact the prevalence of smoking and/or vaping through hypothetical experiments. My model can be adjusted to predict the effects policy proposals or interventions such as banning flavors have on the number of youth and young adults who are users of these dangerous products.
In this study, a decision model for each agent, a high school student, in the simulation was designed using the figure below.
Throughout the simulation, each agent is presented with a decision that evaluates their smoking/non-smoking behavior based on the utility calculation derived. This is calculated by summing and multiplying the agent’s marginal utility obtained from their smoking or non-smoking status and the utility under the social influence from close peers, effect of advertisements, and overall satisfaction. Additionally, I found it critical to include that youth populations classified as “at risk’ are more likely than their peers to become vulnerable throughout this decision cycle. These young agents’ decisions are uniquely affected, depending on their openness towards using different types of smoking and smoking products. Social influence that comes from one’s peers is established by evaluating the number of smokers or vapers in their close network, while societal influence is constructed in regards to the number of advertisements the agent is witnessing and how accessible flavored products are. Once this behavioral decision is made, the agent’s behavioral status as well as the status of their associated network is revised, if necessary, in order to prepare for the model’s next cycle of decision making. This process represents the constant changing and evolving social influence from one’s surrounding society. Then after comparing the model to the real-world trend data among youth smoking/e-smoking prevalence, I made some modifications so each agent’s vulnerability is accurately depicted through their decision-making. More specifically, young agents make decisions to start, continue or quit the use of conventional cigarettes and/or e-cigarettes according to their utility calculation, based on the assumption that agents would make these decisions based on their personal perceptions, while also susceptible to social influence from their overall peer networks and from society in general.
These relationships were modeled in this simulation by utilizing the 2020 National Youth Tobacco Survey (NYTS) and the software NetLogo, a multi-agent programmable modeling environment17’18. The NYTS is available from the Centers for Disease Control and Prevention Office on Smoking and Health and provides nationally representative data about middle and high school youth’s tobacco-related beliefs, attitudes, behaviors, and exposure to pro- and anti-tobacco influences. The distribution of smoking and e-smoking statuses of the agents in the model were determined using the percentage of current users in the last 30 days among U.S high school students, based on the 2020 NYTS. Therefore, the agents were in a network in which agents preferred to associate themselves with other agents that already had more connections within the network15. These network connections were generated based on the response to the question ‘How many of your four closest friends smoke cigarettes?’ in the survey.
Then to measure the quantity of the impact social influence has on the agents is through their perceptions of the use of conventional cigarettes and e-cigarettes. I modeled that the perceived social influence impacts an agents’ “openness” to smoking or vaping, which is simply their tendency to accept or reject use. This tendency was measured using the responses provided the the questions: “If one of your friends offered you a cigarette, would you smoke it?” and “Do you think you will smoke a cigarette anytime during the next year?”
As for representing the factors that deceptive marketing tactics play in the initiation and continuation of product use, I assumed that the number of advertisements seen may alter one’s perception towards smoking/e-smoking. The model demonstrates this through a function that for every additional ad seen “openness” is not only impacted, but it contributes to the overall smoking and e-smoking utility calculation. I also found it critical to model how the enticing flavors offered contribute to the rising use of e-cigarettes among youth. The availability of the flavors throughout the decision-cycle is assumed to determine the agents’ satisfaction with use of the product, as well as act as an initiator for new teen users.
Furthermore, a ‘crossover’ was incorporated into the simulation to represent the possible interconnection between the prevalence of e-cigarette use and that of conventional cigarette use. In the baseline cycle, it was assumed the ‘crossover’ 0, which indicated that the influences of use of both products are independent at first. After completely developing the baseline model, I began exploring the numerous scenarios to examine how 3 policy proposals around the issue and other interventions regarding the use of conventional cigarette use and e-cigarette use interactively affect the prevalence rates. To get the most accurate results possible, the simulation was run 100 times for each scenario so the average of the results could be recorded. The execution time of NetLogo was relatively slow, so the computer used could only support the 100 runs.
Fig 2 depicts the results from the baseline simulation regarding the prevalence rates of smoking and e-smoking, in comparison with the actual trends recorded from 2020. It should be noted that the prevalence rates of conventional cigarettes, as well as e-cigarette use included dual smokers. The model continued for 12 ticks, each tick representing 1 year, starting at 2020 and ending in the year 2032. This suggests that this agent-based model predicts the prevalence trends of conventional cigarette, e-cigarette and dual smoking use among high school teenagers in the United States for future years.
Figures 3, 4, and 5 shows the prevalence rates of different smoking types after the simulation was put under specific counterfactual scenarios and changes to society were added. The results suggest implementing a variety of policy proposals that aim to address the three main factors of e-cigarette and cigarette use in teens, flavors, peer pressure, and deceptive marketing tactics, is effective in decreasing the prevalence rates of high school users.
After the baseline model was adjusted in Fig 3, e-cigarette prevalence rates decreased by 10% and smoking rates saw a slight decrease of 2%. For Fig 4, e-cigarette prevalence rates decreased by 8% and smoking rates decreased by 2%. Lastly, the 3rd policy proposal model, represented by Fig 5, saw e-cigarette prevalence rates decrease by 12% and smoking rates decrease by 1%. If none of these policy proposals were implemented into society, we would witness e-cigarette use within the baseline model to increase drastically over time. Dual smoking prevalence rates, as well as conventional smoking rates would also rise within high school students in the U.S.
Over the last several years, there has been continued debate over the safety of e-cigarettes, however, recently the consensus reached is that e-cigarette use poses a lower risk than the use of conventional cigarettes19’20. Even if e-cigarette use may be a lower-risk alternative to conventional smoking, it remains contentious whether these new products can actually replace conventional cigarette smoking. Furthermore, it is important for independent research to focus on the influence e-cigarette use has on young people at risk for tobacco initiation21.
It is noteworthy to discuss that despite a rapid increase in e-cigarette users among high school teenagers, the baseline simulation revealed that the number of conventional cigarette smokers rained almost unchanged due to the increase in the number of dual users. This occurred because young conventional cigarette smokers are more likely to become dual smokers compared to transitioning to e-cigarette smoking only. Also a portion of e-cigarette initiators also become dual smokers eventually. Previous studies may explain this phenomenon because their findings indicate that youth are motivated to start using e-cigarettes by curiosity and wanting to experiment, rather than with the intention to quit smoking21’22.
The findings presented in this study further indicate that the influence of e-cigarette use on the initiation of conventional cigarettes increases, a rise in prevalence of e-cigarette smoking in society will be followed by a rise in dual smoking, and consequently a rise in conventional cigarette use among youth. This suggests that advertisements and other policies that aim to encourage e-cigarettes as a healthy alternative to conventional cigarettes may need to be re-examined to prevent a promotion of tobacco use in youth.
This research also indicates the importance of implementing restrictions and interventions such as a flavor ban, online sales, funding cessation and quitting programs, early childhood education, etc. Although these models show the predicted effect of each policy proposal, more research should be conducted focusing on the outcomes when multiple policies and restrictions are enforced.
Also, it is important to address the several limitations confronted during this study. First, the social networks constructed within young people were conceptual and fixed throughout the simulations due to a lack of real-world data on youth network dynamics at the population level. Therefore, there was an emphasis on influences at the level of the whole network rather than the influences at the local level. Future studies should examine the phenomenon of peer pressure, more specifically, the effects of these networks in detail. Second, this model was developed based on the National Youth Tobacco Survey data in the United States, and, consequently, any limitations in the collection of that data may also apply to this model. In addition, this model specifically focused on teenagers in high school, and cannot be applied beyond that age demographic in the United States; independent model development is required for other populations. Third, although this simulation relied on a utility-based model of smoking choice behavior, there has been recent neurological research presented on the reward reinforcement learning system that provides an alternative for modeling behavioral choices of nicotine substance users23’24’25. However, less research has mentioned how nicotine affects smoking initiation among adolescents rather than how nicotine hinders smoking cessation by creating nicotine-induced rewards. Lastly, the limited data availability prevented this study from holding the capability to integrate behaviors other than smoking. With improved data availability, future studies should incorporate not only smoking, but other risky behaviors among adolescents, in an effort to better apprehend the impact social influence plays in one’ s quality of life.
This agent-based model simulation issues insight into the mechanism behind the decisions young people make regarding their open attitudes to e-cigarette use and how the prevailing popularity of e-cigarettes in one’s close networks and society at large can work as strong drivers for rapid and massive prevalence increases. Previous models have focused specifically on how one of many factors play a role in e-cigarette initiation and use among youth, however, this model aims to depict the numerous factors and influences involved within a teenager’s decision to e-smoke, smoke, or do both. Using e-cigarettes is dangerous, and the alarming rise in teenage users should be taken seriously among health professionals, as well as lawmakers.
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