Sage Journals: Discover world-class research

Abstract

This study examines the sociodemographic and personal factors influencing e-cigarette use. In this study, the microdata set was obtained from the Türkiye Health Survey conducted by the Turkish Statistical Institute in 2019. Gompit regression analysis was employed to identify the factors associated with individual e-cigarette use. The study included data from 17,084 participants aged 15 or older. Findings indicate that 68.9% of e-cigarette users in Türkiye are male, and 60.8% are married. Among e-cigarette users, 62.8% also use tobacco, while 37.2% consume alcohol in addition to e-cigarettes. The results suggest that females are less likely to use e-cigarettes compared to males, but married individuals are more likely to use e-cigarettes than single individuals. Furthermore, the probability of e-cigarette use decreases with age but increases with higher levels of education. Individuals exposed to tobacco smoke, as well as those who consume both alcohol and tobacco, have a higher likelihood of using e-cigarettes. E-cigarette use poses significant societal challenges, including increased rates of disease, mortality, and workforce losses. Identifying the factors associated with e-cigarette consumption can highlight critical areas of concern, guiding efforts to reduce its use and supporting the development of effective control policies. This study underscores the importance of targeting specific groups for intervention, particularly young people, men, individuals exposed to tobacco smoke, alcohol users, individuals seeing a psychiatrist, those whose treatment is not covered by the Social Security Institution (SSI), and tobacco users. Addressing these groups can contribute significantly to reducing the prevalence and harmful effects of e-cigarette use.

Keywords

public health e-cigarette sociodemographic health survey Gompit regression Türkiye

Introduction

Smoking is responsible for millions of deaths worldwide and remains one of the leading causes of disability and premature mortality (Alkan et al., 2023). Despite its well-documented adverse effects, smoking is a notoriously difficult addiction to overcome (Alkan & Demir, 2019). In response to smoke-free laws and steadily increasing tobacco prices, many consumers have sought alternatives to traditional cigarettes (Schober et al., 2014). Electronic cigarettes (e-cigarettes) have emerged as popular substitutes, marketed as devices that enable users to smoke anywhere. These products are often promoted as potentially helpful tools for quitting smoking or reducing cigarette consumption. Through television, internet, and print advertisements, e-cigarettes are frequently advertised as a healthier alternative to traditional tobacco products (Felberbaum, 2013; Grana & Ling, 2014).

E-cigarettes were first introduced as “smokeless cigarettes” by Herbert A. Gilbert in 1963 (Gorukanti et al., 2017). Later, in 2003, electronic nicotine delivery systems (ENDS) were developed in China as devices designed for broad consumer use, and they have since gained global popularity, primarily through online platforms (Adkison et al., 2013). The e-cigarette market, described as fragmented and rapidly growing, now includes hundreds of brands (Dawkins et al., 2013).

E-cigarettes, which typically use a battery-powered heating element to produce an aerosol from a nicotine-containing solvent (e-liquid), are becoming increasingly popular (Vardavas et al., 2015). They are marketed as tools that can help smokers quit or reduce smoking by mimicking smoking-related rituals, such as hand-to-mouth movements and nicotine delivery (Polosa et al., 2011). However, there is limited information on their safety for dual users—those who use both traditional cigarettes and e-cigarettes—for exclusive e-cigarette users. Despite this, e-cigarettes have gained a significant market share as tobacco use has been increasingly restricted in public places worldwide (Schober et al., 2014). Many smokers turn to e-cigarettes to support their smoking cessation efforts. The rapid rise in e-cigarette sales has even led some analysts to predict that they may soon surpass traditional cigarettes sales (Purkayastha, 2013).

The literature contains various studies on e-cigarette use (Chen-Sankey et al., 2024; Swiatkowska et al., 2024; Ünver, 2024). Many of these studies have explored the relationship between e-cigarette use and perceived risks associated with their use (Manzione et al., 2020).

The three most commonly cited reasons for using e-cigarettes in prior research include aiding smoking cessation, reducing exposure to tobacco smoke, and their perceived appeal (Biener & Hargraves, 2015). One study reported that approximately 27% of e-cigarette users cited enjoying e-cigarette flavors as their primary reason for use, followed by protecting their families from secondhand smoke (23%) and reducing or quitting smoking (21%). These findings suggest that many users may view e-cigarettes as alternatives to traditional smoking rather than as tools for complete cessation (Alhajj et al., 2022).

Other studies have identified additional motivations for e-cigarette use, such as their status as a popular trend (Martínez et al., 2019; Puteh et al., 2018) and their use by friends and family members (Lotrean, 2015; Martínez et al., 2019). The growing popularity of e-cigarettes as a trend can be attributed to the influence of extensive marketing campaigns, particularly on the internet. In contrast, the role-modelling effect of friends and family highlights the social influences driving e-cigarette use (Alhajj et al., 2022).

A study conducted in the USA reported an e-cigarette usage prevalence of 11.5% among individuals aged 18 to 24 years (Nagel et al., 2022). This high prevalence aligns with earlier findings, highlighting the rapidly growing popularity of e-cigarette use among young adults (Saddleson et al., 2015; Tanriover et al., 2022). The e-cigarette industry’s marketing strategies often target adolescents and young adults (Ünver, 2024).

In contrast, a study involving university students from Belarus, Lithuania, Poland, Russia, and Slovakia found that only 1.1% of participants reported using e-cigarettes (Brozek et al., 2019). Similarly, in China, the prevalence rates among vocational high school students were reported at 2.1% and 3.6%, respectively (Wang et al., 2016). Among university students, e-cigarette usage rates were 0.6% in Hungary (Pénzes et al., 2016), 2.7% in Romania (Lotrean, 2015), and 13.3% in France (Rakotozandry et al., 2016).

In the United States, e-cigarette use among high school students rose significantly from 1.5% in 2011 to 19.6% in 2020 (Çetinkay et al., 2022). By 2016, e-cigarettes had become the most popular tobacco product among middle and high school students in the United States (Tsai et al., 2018).

Although e-cigarette use among young people has undeniably increased, an ongoing debate persists regarding the extent of harm it may cause (Kong et al., 2015). Concerns have been raised that e-cigarettes could serve as a gateway to tobacco products, potentially encouraging nicotine addiction or dual tobacco use (Riker et al., 2012). Given that most tobacco use begins during adolescence and young adulthood, it is crucial not to underestimate the potential harm of e-cigarettes to this vulnerable demographic (Cullen et al., 2019).

In addition to their negative impact on younger generations, passive remains a global health concern, despite the implementation of comprehensive smoke-free regulations in many countries (Öberg et al., 2011). Moreover, e-cigarettes are not emissions-free and may adversely affect indoor air quality (Schober et al., 2014).

E-cigarettes are marketed as offering a potentially reduced exposure to the harms of smoking. While these products resemble traditional cigarettes in design or function, they are not cigarettes and are often promoted as being “safer” alternatives (Goniewicz, Knysak, et al., 2014). However, the scientific evidence supporting claims that e-cigarettes have no adverse effects remains inconclusive and subject to debate.

E-cigarettes are believed to avoid emitting the toxins typically found in traditional cigarette smoke, as they do not contain or burn tobacco (Cobb & Abrams, 2011). Nevertheless, some public health experts express concern that the use ENDS could undermine laws promoting smoke-free indoor environments and perpetuate the use of nicotine and tobacco-containing products among smokers who might otherwise consider quitting (Regan et al., 2013).

Given the diverse range of diseases associated with e-cigarettes in the literature, it is notable that cases in Wisconsin and Illinois presented similar clinical findings, disease progression, and suggested a comparable pathophysiological mechanism of lung injury (Layden et al., 2020).

Relatively few side effects have been reported in connection with e-cigarette use. Mouth irritation was the most frequently reported, followed by throat irritation. Among those who experienced side effects, fewer than 16% reported any meaningful impact, and less than 3% reported severe side effects. Furthermore, there was no significant difference in reporting side effects between individuals who had quit smoking and those who continued to smoke (Dawkins et al., 2013).

Some studies indicate that smokers perceive e-cigarettes as tools for smoking cessation (Etter, 2010). These products have been reported to reduce the urge to smoke after quitting (Bullen et al., 2010), help prevent relapse among former smokers, and potentially lower overall cigarette consumption (Etter & Bullen, 2014; Polosa et al., 2011; Siegel et al., 2011).

While e-cigarettes may help conventional smokers reduce harm, they also pose the risk of increasing nicotine addiction among new users (Primack et al., 2015). Results indicate that nicotine intake from e-cigarettes can be 1.5 times greater than that from traditional cigarettes (Flouris et al., 2013). Empirical studies suggest that completely switching from tobacco cigarettes to e-cigarettes could lower the risk of illness and that using e-cigarettes may be less harmful than smoking (Shahab et al., 2017). However, despite the potential advantages, definitive conclusions about their overall impact on human health remain elusive due to limited data (Callahan-Lyon, 2014).

In recent years, several countries, including Türkiye, have imposed various bans and restrictions on the use and sale of e-cigarettes. In Türkiye, e-cigarettes, which were introduced for sale in 2007 to 2008, were later subjected to regulations and restrictions imposed by the Ministry of Health. Restrictions were imposed on the advertising, official sale, and marketing of e-cigarette products, with an official declaration stating that they were not classified as medical products (Börekçi et al., 2015). In 2013, e-cigarettes were brought under the scope of Law No. 4207 on Preventing the Harms of Tobacco Products and their Control. This legislation made it illegal to advertise e-cigarettes, use them indoors, or sell them to individuals under the age of 18. The ban was justified by the “high risk of nicotine intake at a high dose, addiction potential, and the hindrance to smoking cessation efforts” (ResmiGazete, 1996).

Furthermore, Presidential Decree No. 2149 prohibited the import of all products—whether they contain tobacco or not—intended for the consumption of tobacco-like products. This includes e-cigarettes, electronic hookahs, as well as electronic devices, their components, and solutions, due to their potential to imitate tobacco products (ResmiGazete, 2020).

When addressing e-cigarette use, it is crucial to understand the factors contributing to its prevalence. This study adopts the Theory of Planned Behavior (TPB) as its framework (Ajzen, 1991). According to TPB, behavior is determined by the intention to perform it, which in turn is influenced by three constructs: attitudes (perceived advantages and disadvantages of the behavior), subjective norms (social influences on the behavior), and perceived behavioral control (facilitators and barriers to the behavior) (Doherty et al., 2022).

TPB has been widely applied to identify factors influencing behavioral intentions, explain cognitive processes related to smoking, and examine cessation behaviors (Hukkelberg et al., 2014; Su et al., 2015). More recently, TPB has been used to investigate factors driving e-cigarette use among adolescents and young adults (Scheinfeld et al., 2019; Simpson et al., 2022; Trumbo, 2015). For instance, using a shortened version of the theory, Trumbo and Kim found that positive responses to advertisements and beliefs about lower addiction potential were predictors of students’ intentions to use e-cigarettes (Trumbo, 2015). Another recent study focused on undergraduate students and employed TPB to examine e-cigarette use, identifying attitudes and norms as key factors associated with usage. The findings suggested that challenging positive attitudes and social norms could reduce e-cigarette use among students (Scheinfeld et al., 2019).

This study explores e-cigarette use among individuals living in Türkiye by addressing the following research questions:

Research Question 1: Does e-cigarette use vary according to sociodemographic factors?

Research Question 2: Is there a relationship between individuals’ personal characteristics and e-cigarette use?

The remainder of this paper is organized as follows: Section “Methods” discusses the data, variables, and analytical methods used in the study. Section “Results” presents the results in detail. “Discussion” discusses the findings in the context of the existing literature. Finally, “Conclusion” offers macroeconomic policy recommendations to raise awareness of the adverse health effects of e-cigarette use.

Methods

Data

This study utilized data from the Türkiye Health Survey, which the Turkish Statistical Institute conducted in 2019. The purpose of the study is to gather data on health indicators, which are critical components of development indicators, reflecting the level of a country’s development. Within the context of the Türkiye Health Survey, this study aims to bridge existing knowledge gaps in the current health landscape.

The Türkiye Health Survey aims to assess the overall health profiles of individuals and collect data on health indicators, which are critical measures of a nation’s level of development. This study not only identifies national health needs but also facilitates cross-national comparisons. The sampling framework for this study is based on the “National Address Database (UAVT)” which underpins the “Address-Based Population Registration System (ADNKS),” established in 2007. The survey covers all settlements within the borders of the Republic of Türkiye.

The data was collected using a stratified two-stage cluster sampling method. In the first stage, blocks (clusters) with an average of 100 household addresses were randomly selected proportionally to their size. In the second stage, household addresses were randomly chosen from each selected cluster. This sampling approach was designed to provide a representative estimate for Türkiye (TurkStat, 2020).

For this investigation, data from 17,084 individuals aged 15 and older, included in the Türkiye Health Survey, was analyzed.

Measures and Variables

In the Türkiye Health Survey, participants were asked, “Do you currently use electronic cigarettes or similar electronic products? (yes, no)” to determine their current e-cigarette usage during the survey. The response to this question was used to generate the dependent variable for the study. Individuals who reported using e-cigarettes were coded as 1, while those who did not were coded as 0.

The independent variables in the study are the variables available in the Türkiye Health Survey and emerge from the literature review. Factors related to the sociodemographic and personal characteristics of the participants include gender, marital status, age, educational level, occupation, reimbursement of treatment expenses by the Social Security Institution, receiving psychological support, tobacco use status, exposure to tobacco smoke, and alcohol use status.

Many studies reported that e-cigarette use is associated with various factors (Laverty et al., 2018; Nagel et al., 2022; Tanriover et al., 2022; Ünver, 2024). Previous studies consistently identified gender differences as a significant factor influencing e-cigarette use (Alhajj et al., 2022; Laverty et al., 2018). Similarly, age was determined in earlier studies as a factor associated with individuals’ e-cigarette use (Brozek et al., 2019; Wills et al., 2015).

In previous studies, education level was a significant determinant of e-cigarette use (Cullen et al., 2019; Kong et al., 2015; Ünver, 2024). Moreover, a previous study reported that the coverage of treatment expenses by social security (SSI), receiving psychological support, and exposure to tobacco smoke are factors associated with e-cigarette use (Ünver, 2024). Given the results reported before, tobacco and alcohol use are also factors associated with e-cigarette consumption (Primack et al., 2015; Ünver, 2024). Additionally, professional occupation was shown to be an important determinant of e-cigarette use (Pénzes et al., 2016; Rakotozandry et al., 2016).

Ordinal and nominal variables were defined as dummy variables to observe the effects of the categories of all variables to be taken into the gompit regression model (Alkan & Abar, 2020).

This study’s independent and dependent variable are linked according to the research model presented in Figure 1. The variables in the research model are hypothesized as follows:

Figure 1.

Research Model.

Hypotheses Related to Sociodemographic Factors

Differences in e-cigarette use among demographic groups constitute an interesting area of research because demographic characteristics influence individuals’ actions before engaging in a specific behavior (Ünver & Alkan, 2021, 2022). Thus, the following hypotheses are offered:

H₁₁: There is a significant relationship between age and e-cigarette use.

H₂₁: There is a significant relationship between gender and e-cigarette use.

H₃₁: There is a significant relationship between education level and e-cigarette use.

H₄₁: There is a significant relationship between marital status and e-cigarette use.

H₅₁: There is a significant relationship between occupation and e-cigarette use.

Hypotheses Related to Personal Factors

According to the Theory of Planned Behavior (TPB), personal factors are among the most significant determinants influencing individuals’ behavioral intentions and actual behaviors (Ajzen, 1991; Unver, Aydemir, et al., 2023).

Thus, the following are offered:

H₁₂: There is a significant relationship between the coverage of treatment expenses by SSI and e-cigarette use.

H₂₂: There is a significant relationship between receiving psychological support and e-cigarette use.

H₃₂: There is a significant relationship between tobacco use and e-cigarette use.

H₄₂: There is a significant relationship between exposure to tobacco smoke and e-cigarette use.

H₅₂: There is a significant relationship between alcohol use and e-cigarette use.

Research Method

Firstly, frequencies and percentages were calculated to describe the participants’ e-cigarette use status. The association between independent variables and e-cigarette use was investigated using a chi-squared independence test. Subsequently, factors associated with individuals’ e-cigarette use status were determined through a Gompit regression analysis. Gompit regression studies have become more prevalent in the literature in recent years (Güney et al., 2023; Karaaslan & Karaaslan, 2022; Laila et al., 2021). Binary qualitative choice models are regression models in which the dependent variable is discrete with binary values. In other words, in these models, the dependent variable is a binary dummy variable, representing either the probability of an event occurring or the likelihood of choosing one of two options. The Logit and Probit models are the most commonly used binary qualitative choice models. These models use different functional forms to model probabilities based on independent variables. While the Logit model is based on the Logistic Distribution, the Probit model is based on the Standard Normal Distribution. Both distributions are symmetric around zero.

In some cases, one of the two binary states in the dependent variable occurs at a much higher frequency than the other. Zeros or ones in the dependent variable are disproportionately more frequent. In such cases, using an asymmetric distribution instead of symmetric ones becomes more appropriate. The Gompit model can be an alternative to the Logit and Probit models in these scenarios. The Gompit model is based on the Gompertz Distribution, an asymmetric distribution around zero that is more effective than symmetric distributions in capturing extreme values. The Gompit model is defined using the cumulative distribution function of the Gompertz Distribution as follows:

p (y = 1 | x) = C (x β) = 1 - e^{(- ex β)}

The parameters of this model are estimated using the Maximum Likelihood Estimation method (Cameron & Trivedi, 2005; Güriş et al., 2011).

Results

Descriptive Statistics

It was determined that, in Türkiye, 68.9% of e-cigarette users are male, and 60.8% are married. The results achieved here indicate that 62.8% of e-cigarette users also use tobacco (dual users), and 37.2% of e-cigarette users also consume alcohol. The results related to factors associated with e-cigarette use in Türkiye are presented in Table 1.

Table 1.

Findings on the Variables Associated with e-Cigarette Consumption.

		E-cigarette use		n (%)	χ2	P
Variables		No	Yes	n (%)	χ2	P
Gender	Male	7,682 (45.4)	102 (68.9)	7,784 (45.6)	32.834	.000
Gender	Female	9,254 (54.6)	46 (31.1)	9,300 (54.4)		.000
Marital Status	Unmarried	5,300 (31.3)	58 (39.2)	5,358 (31.4)	4.248	.039
Marital Status	Married	11,636 (68.7)	90 (60.8)	11,726(68.6)		.039
Individual’s age	Between 15 and 24	2,694 (15.9)	36 (24.3)	2,730 (16.0)	43.525	.000
	Between 25 and 34	3,032 (17.9)	38 (25.7)	3,070 (18.0)
	Between 35 and 44	3,350 (19.8)	45 (30.4)	3,395 (19.9)
	Between 45 and 54	2,907 (17.2)	11 (7.4)	2,918 (17.1)
	Between 55 and 64	2,507 (14.8)	6 (4.1)	2,513 (14.7)
	65 and over	2,446 (14.4)	12 (8.1)	2,458 (14.4)
Education level	Illiterate	2,188 (12.9)	6 (4.1)	2,194 (12.8)	41.239	.000
	Primary S.	5,583 (33.0)	29 (19.6)	5,612 (32.8)
	Secondary S.	2,941 (17.4)	24 (16.2)	2,965 (17.4)
	High S.	3,206 (18.9)	40 (27.0)	3,246 (19.0)
	University	3,018 (17.8)	49 (33.1)	3,067 (18.0)
Occupation	Managers	438 (2.6)	13 (8.8)	451 (2.6)	36.054	.000
	Professional occupation members	990 (5.8)	14 (9.5)	1,004 (5.9)
	Technicians/operators/ assistant professional occupation members	504 (3.0)	7 (4.7)	511 (3.0)
	Office service employees	438 (2.6)	5 (3.4)	443 (2.6)
	Service/sales personnel	1,255 (7.4)	15 (10.1)	1,270 (7.4)
	Qualified agriculture/ forestry/aquaculture personnel	763 (4.5)	2 (1.4)	765 (4.5)
	Artists/relevant occupation members	786 (4.6)	9 (6.1)	795 (4.7)
	Facility-machinery operators /assemblers	549 (3.2)	5 (3.4)	554 (3.2)
	Unqualified workers	728 (7.3)	6 (4.1)	734 (4.3)
	Unemployed	10,485 (61.9)	72 (48.6)	10,557(61.8)
Treatment expense coverage	No	1,326 (7.8)	23 (15.5)	1,349 (7.9)	11.995	.001
Treatment expense coverage	Yes	15,610 (92.2)	125 (84.5)	15,735(92.1)		.001
Receiving psychological support	No	15,823 (93.4)	132 (89.2)	15,955(93.4)	4.272	.039
Receiving psychological support	Yes	1,113 (6.6)	16 (10.8)	1,129 (6.6)		.039
Tobacco use	No	11,798 (69.7)	55 (37.2)	11,853(69.4)	72.949	.000
Tobacco use	Yes	5,138 (30.3)	93 (62.8)	5,231 (30.6)		.000
Exposure to tobacco smoke	No	10,859 (64.1)	53 (35.8)	10,912(63.9)	50.947	.000
Exposure to tobacco smoke	Yes	6,077 (35.9)	95 (64.2)	6,172 (36.1)		.000
Alcohol use	No	14,444 (85.3)	93 (62.8)	14,537(85.1)	58.279	.000
Alcohol use	Evet	2,492 (14.7)	55 (37.2)	2,547 (14.9)		.000

According to Table 1’s Chi-Square independence test findings, there is statistically significant evidence linking e-cigarette use to sociodemographic and personal factors.

Model Estimation

Table 2 lists the estimated outcomes of the Gompit regression. The independent variables in the Gompit regression model were examined for multicollinearity in the current study. At least five variance inflation factor (VIF) values indicate moderate multicollinearity. Values of 10 or more, on the other hand, suggest a significant degree of multicollinearity (Baskan & Alkan, 2023; Unver, Aydemir, et al., 2023). In this study, no variable causes multicollinearity among the variables.

Table 2.

Estimated Gompit Model Results for Variables Associated with e-Cigarette Use.

Variables	β	Standard error	P	VIF
Constant	−5.133	0.537	.000	-
Gender (reference: male)
Female	−.696	0.219	.001	1.38
Marital Status (reference: unmarried)
Married	.129	0.236	.584	1.48
Individual’s age (reference: between 15 and 24)
Between 25 and 34	−.344	0.289	.233	2.33
Between 35 and 44	−.153	0.303	.615	2.78
Between 45 and 54	−1.284	0.467	.006	2.66
Between 55 and 64	−1.942	0.516	.000	2.46
65 and over	−.333	0.486	.493	2.49
Education level (reference: illiterate)
Primary S.	.508	0.497	.307	2.66
Secondary S.	.264	0.531	.619	2.57
High S.	.855	0.493	.083	2.58
University	1.061	0.506	.036	2.91
Occupation (reference: unemployed)
Managers	.353	0.378	.351	1.13
Professional occupation members	−.165	0.356	.642	1.42
Technicians/operators/ assistant professional occupation members	−.067	0.478	.888	1.13
Office service employees	−.444	0.498	.374	1.11
Service/sales personnel	−.441	0.335	.188	1.17
Qualified agriculture/ forestry/aquaculture personnel	−1.317	0.739	.075	1.08
Artists/relevant occupation members	−.072	0.432	.867	1.16
Facility-machinery operators /assemblers	−.742	0.495	.134	1.13
Unqualified workers	−.374	0.467	.423	1.09
Treatment expense coverage (reference: no)
Yes	−.663	0.288	.021	1.05
Receiving psychological support (reference: no)
Yes	.642	0.303	.034	1.02
Tobacco use (reference: no)
Yes	.940	0.208	.000	1.23
Exposure to tobacco smoke (reference: no)
Yes	.720	0.196	.000	1.09
Alcohol use (reference: no)
Yes	.677	0.205	.001	1.18
N				17,084

When Table 2 is examined, it can be observed that the factors of gender, age (45-54, 55-64), educational status (high school, university), occupation (skilled agriculture/forestry/fishery workers), coverage of treatment expenses by the Social Security Institution (SSI), receiving psychological support, tobacco use, exposure to tobacco smoke, and alcohol use are statistically significant. Table 3 displays the marginal effects of the variables impacting e-cigarette use.

Table 3.

Marginal Effects for Factors Associated with e-Cigarette Use.

Variables	Elasticity (%)	P	Std. Error
Gender (reference: male)
Female	−69.3	.001	0.218
Marital Status (reference: unmarried)
Married	12.9	.584	0.235
Individual’s age (reference: between 15 and 24)
Between 25 and 34	−34.2	.233	0.287
Between 35 and 44	−15.2	.615	0.301
Between 45 and 54	−128	.006	0.465
Between 55 and 64	−193.6	.000	0.515
65 and over	−33.1	.493	0.483
Education level (reference: illiterate)
Primary S.	50.6	.308	0.496
Secondary S.	26.3	.619	0.529
High S.	85.2	.083	0.492
University	105.7	.036	0.504
Occupation (reference: unemployed)
Managers	35.1	.351	0.376
Professional occupation members	−16.5	.642	0.355
Technicians/operators/ assistant professional occupation members	−6.7	.888	0.476
Office service employees	−44.2	.374	0.497
Service/sales personnel	−44	.188	0.334
Qualified agriculture/ forestry/aquaculture personnel	−131.3	.075	0.737
Artists/relevant occupation members	−7.2	.867	0.430
Facility-machinery operators /assemblers	−73.9	.134	0.494
Unqualified workers	−37.3	.424	0.466
Treatment expense coverage (reference: no)
Yes	−66.0	.021	0.286
Receiving psychological support (reference: no)
Yes	63.8	.033	0.300
Tobacco use (reference: no)
Yes	93.6	.000	0.207
Exposure to tobacco smoke (reference: no)
Yes	71.7	.000	0.195
Alcohol use (reference: no)
Yes	67.4	.001	0.204

The likelihood of women using e-cigarettes is 69.3% lower than men’s, per the Gompit regression model shown in Table 3. These results are consistent with previously observed gender differences in smoking and e-cigarette use in Europe (Laverty et al., 2018; Vardavas et al., 2015). Compared to those aged 15 to 24, people are 128% and 193.6% less likely to use e-cigarettes in the 45 to 54 and 55 to 64 age groups. Previous studies also reported that e-cigarette use is particularly prevalent among adolescents and young adults. Moreover, the proportion of young individuals experimenting with e-cigarettes has doubled over the past 5 years (Filippidis, Laverty, Fernandez, et al., 2017; Filippidis, Laverty, Gerovasili, et al., 2017; Laverty et al., 2018).

Compared to non-users, tobacco users are 93.6% more likely to use e-cigarettes. Contrarily, those exposed to tobacco smoke are 71.7% more likely than others to use e-cigarettes. As shown, compared to non-drinkers, people who use alcohol have a 67.4% higher likelihood of using e-cigarettes. As stated in a previous study, individuals who consume alcohol are 0.9% more likely to use e-cigarettes in comparison to non-drinkers (Ünver, 2024). Furthermore, in another study, 11.10% of tobacco users reported starting to use e-cigarettes because they perceived them to have fewer side effects than traditional cigarettes (Göney et al., 2019).

Compared to the others, those who receive psychological help are 63.8% more likely to use e-cigarettes. A previous study carried out in Türkiye revealed that individuals who visited a psychiatrist in the past 12 months were 0.5% more likely to use e-cigarettes in comparison to those who did not (Ünver, 2024).

Compared to those who have never attended school, people with a high school or university degree are 85.2% and 105.7% more likely to use e-cigarettes, respectively. Another study on high school students aged 15 to 18 in three cities in the Russian Federation reported that 2.2% of students used e-cigarettes. In contrast, the present study revealed that Russia’s overall e-cigarette usage rate was 4% (Kong et al., 2017). Compared to the unemployed, qualified agriculture, forestry, and aquaculture workers are 131.3% less likely to use e-cigarettes. Compared to the general population, persons whose medical expenses are paid for by SSI are 66% less likely to use e-cigarettes. It was found in another study that individuals whose medical expenses were covered by social security were 0.4% less likely to use e-cigarettes compared to those without coverage (Ünver, 2024).

Discussion

This study explored the variables associated with individuals’ e-cigarette use in Türkiye using Gompit regression analysis. The findings indicate that gender, age, educational status, occupation, Social Security Institution (SSI) coverage of treatment expenses, psychological support, tobacco use, exposure to tobacco smoke, and alcohol consumption were all associated with e-cigarette use.

Gender

The analysis revealed that gender plays a significant role in e-cigarette use. Consistent with previous studies, this study found that women are less likely to use e-cigarettes compared to men (Alhajj et al., 2022; Ali et al., 2021; Laverty et al., 2018).

Age

Age is another critical factor. The results demonstrated that advancing age decreases the likelihood of e-cigarette use. Prior research has shown that regular cigarette and e-cigarette use is most prevalent among the youngest age groups, particularly those aged 15-19 years (Filippidis, Laverty, Gerovasili, et al., 2017; Goniewicz, Gawron, et al., 2014). Another study focusing on young people identified psychosocial risk factors, parental support, smoking expectations, peer influence, and thrill-seeking behavior as contributing factors to e-cigarette, cigarette, alcohol, and marijuana use (Wills et al., 2015).

Educational Status

Educational attainment was found associated with e-cigarette use significantly. Higher levels of education were associated with an increased likelihood of e-cigarette use. A previous study revealed that college students are more likely than high school students to use e-cigarettes (Kong et al., 2015), while another study found that high school students use e-cigarettes more frequently than middle school students (Cullen et al., 2019). Additionally, research conducted among U.S. teenagers and young adults indicated a correlation between parental education levels and e-cigarette use (Primack et al., 2015).

Social Security Institution (SSI) Coverage

SSI coverage of treatment expenses was another variable linked to e-cigarette use. Individuals whose treatment expenses were covered by the SSI were less likely to use e-cigarettes compared to those without such coverage. A Turkish study similarly reported that people whose medical expenses were paid for by the SSI were less likely to smoke (Alkan, 2017). This highlights the importance of preventive healthcare services, which are critical in combating health issues, reducing healthcare costs, and mitigating workforce losses (Alkan & Abar, 2020).

Psychiatric Support

The study found that seeking psychiatric care within the last 12 months was associated with e-cigarette use. Individuals who had consulted a psychiatrist during this period were more likely to use e-cigarettes than those who had not.

Exposure to Tobacco Smoke

Exposure to tobacco smoke emerged as a significant factor associated with e-cigarette use. As a primary public health concern, tobacco smoke exposure has been extensively studied through various methodologies (Do et al., 2021; Groner et al., 2015; Ulbricht et al., 2015). This study corroborates previous findings, showing that individuals exposed to tobacco smoke are more likely to use e-cigarettes than those who are not (Mbulo et al., 2021; Unver, Tekmanli, et al., 2023).

Alcohol and Tobacco Use

Finally, alcohol and tobacco use were strongly linked to e-cigarette use. The findings indicated that individuals who consume these substances are more likely to use e-cigarettes compared to non-users. Moreover, a study conducted among U.S. teenagers and young adults reported a connection between parental tobacco consumption and e-cigarette use (Primack et al., 2015).

Conclusion

This study aims to determine the sociodemographic and personal factors associated with e-cigarette use by analyzing data obtained from 17,084 individuals aged 15 and older who participated in the Türkiye Health Survey.

The results indicate that women are less likely to use e-cigarettes than men. Individuals aged 45 to 54 and 55 to 64 are less likely to use e-cigarettes than those aged 15 to 24. High school and university graduates are more likely to use e-cigarettes than those with no formal education. Individuals employed in skilled agriculture, forestry, or fisheries are less likely to use e-cigarettes than those unemployed. Those whose medical expenses are covered by social security are less likely to use e-cigarettes than others.

Individuals receiving psychological support, tobacco users, and those exposed to tobacco smoke are more likely to use e-cigarettes. Similarly, alcohol consumers are more likely to use e-cigarettes than non-consumers.

The usage of e-cigarettes is a significant global health issue. The development and rigorous implementation of e-cigarette control policies can reduce e-cigarette use. Identifying factors that affect e-cigarette use can help guide the implementation of e-cigarette control policies by highlighting the areas of concern for reducing e-cigarette use. This study particularly emphasizes the need to address young individuals, males, those exposed to tobacco smoke, alcohol users, individuals seeking psychiatric help, those not covered by social security, and tobacco users.

E-cigarette use poses a significant public health concern. The increasing prevalence of e-cigarette advertisements and online sales contributed to its widespread use. Consequently, public health measures must be implemented to address this issue. Due to the substances they contain, e-cigarettes threaten public health. Furthermore, identifying effective marketing techniques and channels that alter young people’s perceptions of harm and influence their progression toward e-cigarette use is critical for designing effective prevention campaigns aiming to reduce e-cigarette use among youth. It is well-known that e-cigarette use harms society, including disease, death, loss of workforce, and more. Combating e-cigarettes should become a component of the broader tobacco control efforts. Society must be educated about the harms of e-cigarettes, and political authorities should raise awareness and take necessary precautions. To increase awareness of the harmful effects of using e-cigarettes, appropriate strategies should be developed to reduce e-cigarette consumption by paying attention to the determinants that affect e-cigarette use and considering public health strategies. Moreover, the study’s findings can inform the creation of programs and regulations to reduce the adverse effects of e-cigarette use.

Limitations of the Study

This study has several limitations. First of all, secondary data were employed in this investigation. The dataset’s variables make up the variables needed for statistical analysis. Second, because the data are cross-sectional, it cannot draw firm conclusions about the causal links between socioeconomic characteristics and e-cigarette use (Erdogan et al., 2022; Tutar et al., 2024). A cross-sectional study is a study design used to collect information and analyze data at a specific point in time. Such studies gather data from various groups of individuals or subjects representing different populations, age groups, or other relevant categories. However, cross-sectional studies cannot establish causal relationships between variables. Since the data is collected at a single time point, it is difficult to determine the temporal order of events or whether one variable directly influences another. Finally, this study’s findings were gathered based on respondents’ responses. As a result, the data gathered using this form of data collection could be biased.

Footnotes

Acknowledgements

The authors would like to thank the Turkish Statistical Institute for its data. The views and opinions expressed in this manuscript are those of the authors only and do not necessarily represent the views, official policy, or position of the Turkish Statistical Institute.

Authors’ Note

Additionally, the authors had full access to all the data in the study and took responsibility for the integrity of the data and accuracy of the data analysis.

ORCID iD

Şeyda Ünver

Lokman Şahin

Fatih Uçan

Ömer Alkan

Ethical Considerations

We declare that all ethical guidelines for authors have been followed by all authors. Ethical approval is not required.

TurkStat is an institution that compiles, evaluates, and presents statistical information to decision-makers to prepare development plans and programs, make economic decisions, and address all other issues needed. TurkStat carries out internationally comparable statistical production activities according to the standards of organizations such as the European Union Statistical Office, the United Nations, OECD, ILO, etc. TurkStat collects data within the scope of the Official Statistics Program. The Official Statistics Program is prepared for 5-year periods based on the Turkish Statistics Law No. 5429 to determine the basic principles and standards regarding the production and publication of official statistics and to ensure the production of up-to-date, reliable, timely, transparent and impartial data in areas of need at national and international levels. The individuals included in the sample in the surveys conducted by the Turkish Statistical Institute are legally obliged to participate in the survey. According to the Turkish Statistics Law, those who do not provide the information requested within the scope of the research in the specified form and time without a valid excuse or who provide incomplete or incorrect information are warned once and asked to provide the information or correct the deficiencies and errors within 7 days. An administrative fine shall be imposed on natural persons or organs and representatives of private legal entities who, despite this warning, do not provide the information or correct the deficiencies and errors as requested. TurkStat also conducts the Türkiye Health Survey within the scope of the Official Statistics Program put into effect by law. Since the Türkiye Health Survey is conducted within the scope of legal responsibility by the state, ethical approval is not required.

The data were obtained through the joint teamwork of both the Turkish Statistical Institute (TurkStat) and the European Union Statistical Office (SOEU). We obtained this data from TurkStat in return for a contract without needing an ethics committee document and used it in our study.

For this study, secondary data were employed. Official approval was received from the Turkish Statistical Institute to use the microdata set from the Türkiye Health Survey. The Türkiye Health Survey provides many indicators in the field of health, including the utilization of health services by individuals aged 15 and over, the degree of difficulty they experience in performing their daily activities, and their smoking and alcohol use habits. The Turkish Statistical Institute also received a “Letter of Undertaking” authorizing it to use the study’s data.

The letter of undertaking for the use of micro data without restrictions in dissemination:

Article 1- This letter of undertaking determines the rules, principles and obligations of the use of micro data, which are safe to disclose apart from the Presidency.

Article 2-This letter of undertaking regulates the use of micro data sets of Türkiye Health Survey in 2019, within the framework of the Directive on Access and Use of Micro Data in line with the purpose specified in Article 1.

Article 3- The following provisions apply for the use of micro data:

a) Findings obtained by the researcher as a result of incorrect calculation only bind the researcher.

b) The researcher refers to the micro data of the Institution that he uses while disclosing the results obtained from the study.

c) The researcher is obliged to send a copy of the published report, article, publication etc. to the Institution Library within 3 months at the latest. Subsequent micro data usage requests of the researcher who is found not to fulfill this obligation are not covered.

d) The researcher cannot reproduce, give to third parties, sell or transfer the micro data set he obtained.

Article 4-The researcher, by taking into account the principles of confidentiality defined in 13. and 14. articles of Turkish Statistical Institution numbered 5429 and Regulation on Procedures and Principles Regarding Data Confidentiality and Confidential Data Security in Official Statistics, is deemed to guarantee hereby that he shall not disclose the information, table, etc. violating this principle and shall only use micro data for statistical purposes.

Consent to Participate

In compliance with the national legislation and the institutional standards, written informed permission from the patients/participants or patients/participants’ legal guardian/next of kin was not necessary.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data underlying this study is subject to third-party restrictions by the Turkish Statistical Institute. Data are available from the Turkish Statistical Institute (bilgi@tuik.gov.tr) for researchers who meet the criteria for access to confidential data. The authors of the study did not receive any special privileges in accessing the data.

References

Adkison

S. E.

O’Connor

R. J.

Bansal-Travers

Hyland

Borland

Yong

H. H.

Cummings

K. M.

McNiell

Thrasher

J. F.

Hammond

Fong

G. T.

(2013). Electronic nicotine delivery systems: International tobacco control four-country survey. American Journal of Preventive Medicine, 44(3), 207–215. https://doi.org/10.1016/j.amepre.2012.10.018

Ajzen

(1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.

Alhajj

M. N.

Al-Maweri

S. A.

Folayan

M. O.

Halboub

Khader

Omar

Amran

A. G.

Al-Batayneh

O. B.

Celebić

Persic

Kocaelli

Suleyman

Alkheraif

A. A.

Divakar

D. D.

Mufadhal

A. A.

Al-Wesabi

M. A.

Alhajj

W. A.

Aldumaini

M. A.

Khan

Al-Dhelai

T. A.

… Ziyad

T. A.

(2022). Knowledge, beliefs, attitude, and practices of E-cigarette use among dental students: A multinational survey. PloS One, 17(10), e0276191. https://doi.org/10.1371/journal.pone.0276191

Ali

F. R. M.

Dave

D. M.

Colman

G. J.

Wang

Saffer

Marynak

K. L.

Dench

Grossman

(2021). Association of e-cigarette advertising with e-cigarette and cigarette use among US adults. Addiction (Abingdon, England), 116(5), 1212–1223. https://doi.org/10.1111/add.15281

Alkan

Bayrakçeken

Yarali

(2023). Prevalence and predictors of tobacco use in older adults: A case in Turkey. Journal of Substance Use, 29(4), 562–567. https://doi.org/10.1080/14659891.2023.2194413

Alkan

. (2017). Türkiye’de gençlerin tütün kullanımında cinsiyet farklılıklarının araştırılması. Bağımlılık Dergisi, 18(2), 35–45.

Alkan

Ö.

Abar

. (2020). Determination of factors influencing tobacco consumption in Turkey using categorical data analyses. Archives of Environmental & Occupational Health, 75(1), 27–35. https://doi.org/10.1080/19338244.2018.1556200

Alkan

Ö.

Demir

. (2019). Investigation and detection of risk factors related to the period without tobacco consumption. Addicta-the Turkish Journal on Addictions, 6(4), 99–115. https://doi.org/10.5152/addicta.2020.19085

Baskan

Alkan

(2023). Determinants of intimate partner controlling behavior targeting women in Turkiye. Frontiers in Psychology, 14, Article 1174143. https://doi.org/10.3389/fpsyg.2023.1174143

10.

Biener

Hargraves

J. L.

(2015). A longitudinal study of electronic cigarette use among a population-based sample of adult smokers: Association with smoking cessation and motivation to quit. Nicotine & Tobacco Research, 17(2), 127–133. https://doi.org/10.1093/ntr/ntu200

11.

Börekçi

Bilir

Karlikaya

Grp

T. T. W.

(2015). A new area to fight: Electronic cigarette. Eurasian Journal of Pulmonology, 17(2), 80–84. https://doi.org/10.5152/ejp.2015.65375

12.

Brożek

G. M.

Jankowski

Lawson

J. A.

Shpakou

Poznański

Zielonka

T. M.

Klimatckaia

Loginovich

Rachel

Gereová

Golonko

Naumau

Kornicki

Pepłowska

Kovalevskiy

Raskiliene

Bielewicz

Krištúfková

Mróz

Majek

… Zejda

J. E.

(2019). The prevalence of cigarette and e-cigarette smoking among students in Central and Eastern Europe-Results of the YUPESS Study. International Journal of Environmental Research and Public Health, 16(13), 2297. https://doi.org/10.3390/ijerph16132297

13.

Bullen

McRobbie

Thornley

Glover

Lin

Laugesen

(2010). Effect of an electronic nicotine delivery device (e cigarette) on desire to smoke and withdrawal, user preferences and nicotine delivery: Randomised cross-over trial. Tobacco Control, 19(2), 98–103. https://doi.org/10.1136/tc.2009.031567

14.

Callahan-Lyon

(2014). Electronic cigarettes: Human health effects. Tobacco Control, 23, 36–40. https://doi.org/10.1136/tobaccocontrol-2013-051470

15.

Cameron

A. C.

Trivedi

(2005). Microeconometrics: Methods and applications. Cambridge University Press.

16.

Chen-Sankey

Weiger

La Capria

(2024). Using eye tracking to examine young adults’ visual attention to e-cigarette advertising features and associated positive e-cigarette perceptions. Annals of Behavioral Medicine, 58(6), 445–456. https://doi.org/10.1093/abm/kaae018

17.

Cobb

N. K.

Abrams

D. B.

(2011). E-cigarette of drug-delivery device? Regulating novel nicotine products. New England Journal of Medicine, 365(3), 193–195. https://doi.org/10.1056/NEJMp1105249

18.

Cullen

K. A.

Gentzke

A. S.

Sawdey

M. D.

Chang

J. T.

Anic

G. M.

Wang

T. W.

Creamer

M. R.

Jamal

Ambrose

B. K.

King

B. A.

(2019). e-Cigarette use among youth in the United States, JAMA, 322(21), 2095–2103. https://doi.org/10.1001/jama.2019.18387

19.

Duru Çetinkaya

Pazarlı Bostan

Salepçi

Görek Dilektaşlı

Elbek

Uyanusta Küçük

F. Ç.

Karadoğan

Arpaz

Dülger

Uysal

M. A.

Uzaslan

Özge

Kılınç

Dağlı

Itil

. (2022). Turkish Thoracic Society’s statement report on electronic cigarettes and heated tobacco products. Turkish Thoracic Journal, 23(4), 296–301. https://doi.org/10.5152/TurkThoracJ.2022.22018

20.

Dawkins

Turner

Roberts

Soar

(2013). “Vaping” profiles and preferences: An online survey of electronic cigarette users. Addiction, 108(6), 1115–1125. https://doi.org/10.1111/add.12150

21.

E. K.

Bradley

K. C.

Fugate-Laus

Kaur

Halquist

M. S.

Ray

Pope

M. A.

Hayes

R. B.

Wheeler

D. C.

Fuemmeler

B. F.

(2021). An examination of social and environmental determinants of secondhand smoke exposure among non-smoking adolescents. Tobacco Prevention & Cessation, 7, 20. https://doi.org/10.18332/tpc/131875

22.

Doherty

Davison

McLaughlin

Giles

Dunwoody

McDowell

Butter

Wilmont

Simpson

. (2022). Prevalence, knowledge and factors associated with e-cigarette use among parents of secondary school children. Public Health in Practice (Oxford, England), 4, 100334. https://doi.org/10.1016/j.puhip.2022.100334

23.

Erdogan

Bayraktar

Uçan

Atilgan

S. S.

(2022). The effect of perceived stress on organizational silence in emergency service doctors in Turkey: The mediating role of emotional intelligence. Frontiers in Public Health, 10, Article 1010827. https://doi.org/10.3389/fpubh.2022.1010827

24.

Etter

J. F.

(2010). Electronic cigarettes: A survey of users. BMC Public Health, 10, Article 231. https://doi.org/10.1186/1471-2458-10-231

25.

Etter

J. F.

Bullen

(2014). A longitudinal study of electronic cigarette users. Addictive Behaviors, 39(2), 491–494. https://doi.org/10.1016/j.addbeh.2013.10.028

26.

Felberbaum

(2013). Old tobacco playbook gets new use by e-cigarettes. Associated Press. https://medicalxpress.com/news/2013-08-tobacco-playbook-e-cigarettes.html

27.

Filippidis

F. T.

Laverty

A. A.

Fernandez

Mons

Tigova

Vardavas

C. I.

(2017). Correlates of self-reported exposure to advertising of tobacco products and electronic cigarettes across 28 European Union member states. Tobacco Control, 26(E2), e130–e133. https://doi.org/10.1136/tobaccocontrol-2016-053479

28.

Filippidis

F. T.

Laverty

A. A.

Gerovasili

Vardavas

C. I.

(2017). Two-year trends and predictors of e-cigarette use in 27 European Union member states. Tobacco Control, 26(1), 98–104. https://doi.org/10.1136/tobaccocontrol-2015-052771

29.

Flouris

A. D.

Chorti

M. S.

Poulianiti

K. P.

Jamurtas

A. Z.

Kostikas

Tzatzarakis

M. N.

Wallace Hayes

Tsatsakis

A. M.

Koutedakis

(2013). Acute impact of active and passive electronic cigarette smoking on serum cotinine and lung function. Inhalation Toxicology, 25(2), 91–101. https://doi.org/10.3109/08958378.2012.758197

30.

Goniewicz

M. L.

Gawron

Nadolska

Balwicki

Sobczak

(2014). Rise in electronic cigarette use among adolescents in Poland. Journal of Adolescent Health, 55(5), 713–715. https://doi.org/10.1016/j.jadohealth.2014.07.015

31.

Goniewicz

M. L.

Knysak

Gawron

Kosmider

Sobczak

Kurek

Prokopowicz

Jablonska-Czapla

Rosik-Dulewska

Havel

Jacob

3rd Benowitz

(2014). Levels of selected carcinogens and toxicants in vapour from electronic cigarettes. Tobacco Control, 23(2), 133–139. https://doi.org/10.1136/tobaccocontrol-2012-050859

32.

Gorukanti

Delucchi

Ling

Fisher-Travis

Halpern-Felsher

(2017). Adolescents’ attitudes towards e-cigarette ingredients, safety, addictive properties, social norms, and regulation. Preventive Medicine, 94, 65–71. https://doi.org/10.1016/j.ypmed.2016.10.019

33.

Göney

Topdemir

Nal

(2019). The profile of electronic cigarette users in Turkey. Eurasian Journal of Pulmonology, 21(2), 122–126. https://doi.org/10.4103/ejop.ejop_79_18

34.

Grana

R. A.

Ling

P. M.

(2014). “Smoking revolution”: A content analysis of electronic cigarette retail websites. American Journal of Preventive Medicine, 46(4), 395–403. https://doi.org/10.1016/j.amepre.2013.12.010

35.

Groner

J. A.

Huang

Nagaraja

Kuck

Bauer

J. A.

(2015). Secondhand smoke exposure and endothelial stress in children and adolescents. Academic Pediatrics, 15(1), 54–60.

36.

Güney

Alkan

Ö.

Genç

Kabakus

A. K.

(2023). Gambling behavior of husbands of married women living in Turkey and risk factors. Journal of Substance Use, 28(3), 466–472. https://doi.org/10.1080/14659891.2022.2084785

37.

Güriş

Çağlayan

Ün

(2011). Estimating of probability of homeownership in rural and urban areas: Logit, probit and gompit model. European Journal of Social Sciences, 21(3), 405–411.

38.

Hukkelberg

S. S.

Hagtvet

K. A.

Kovac

V. B.

(2014). Latent interaction effects in the theory of planned behaviour applied to quitting smoking. British Journal of Health Psychology, 19(1), 83–100. https://doi.org/10.1111/bjhp.12034

39.

Karaaslan

(2022). A study of factors affecting the alcohol consumption in Turkey. Journal of Substance Use, 27(4), 422–427. https://doi.org/10.1080/14659891.2021.1953168

40.

Kong

Idrisov

Galimov

Masagutov

Sussman

(2017). Electronic cigarette use among adolescents in the Russian Federation. Substance Use & Misuse, 52(3), 332–339. https://doi.org/10.1080/10826084.2016.1225766

41.

Kong

Morean

M. E.

Cavallo

D. A.

Camenga

D. R.

Krishnan-Sarin

(2015). Reasons for electronic cigarette experimentation and discontinuation among adolescents and young adults. Nicotine & Tobacco Research, 17(7), 847–854. https://doi.org/10.1093/ntr/ntu257

42.

Laila

Rusmita

S. A.

Cahyono

E. F.

Azman-Saini

W. N. W.

(2021). The role of financial factors and non-financial factors on corporate bond and sukuk rating Indonesia. Journal of Islamic Accounting and Business Research, 12(8), 1077–1104. https://doi.org/10.1108/jiabr-10-2019-0187

43.

Laverty

A. A.

Filippidis

F. T.

Vardavas

C. I.

(2018). Patterns, trends and determinants of e-cigarette use in 28 European Union Member States 2014–2017. Preventive Medicine, 116, 13–18. https://doi.org/10.1016/j.ypmed.2018.08.028

44.

Layden

J. E.

Ghinai

Pray

Kimball

Layer

Tenforde

M. W.

Navon

Hoots

Salvatore

P. P.

Elderbrook

Haupt

Kanne

Patel

M. T.

Saathoff-Huber

King

B. A.

Schier

J. G.

Mikosz

C. A.

Meiman

(2020). Pulmonary illness related to e-cigarette use in Illinois and Wisconsin—Final report. The New England Journal of Medicine, 382(10), 903–916. https://doi.org/10.1056/NEJMoa1911614

45.

Lotrean

L. M.

(2015). Use of electronic cigarettes among Romanian university students: A cross-sectional study. Bmc Public Health, 15, Article 358. https://doi.org/10.1186/s12889-015-1713-6

46.

Manzione

L. C.

Shan

L. P.

Azagba

(2020). Associations between risk perceptions and cigarette, e-cigarette, and dual-product use among Canadian adolescents. Tobacco Use Insights, 13, Article 1179173x20903784. https://doi.org/10.1177/1179173x20903784

47.

Martínez

Baena

Castellano

Margalef

Tigova

Feliu

Laroussy

Galimany

Puig

Bueno

López

Fernández

(2019). Prevalence and determinants of tobacco, e-cigarettes, and cannabis use among nursing students: A multicenter cross-sectional study. Nurse Education Today, 74, 61–68. https://doi.org/10.1016/j.nedt.2018.11.018

48.

Mbulo

Murty

K. S.

Zhao

L. H.

Smith

Palipudi

(2021). Tobacco use and secondhand smoke exposure among older adults in India. Journal of Aging and Health, 33(7–8), 565–576, Article 08982643211000489. https://doi.org/10.1177/08982643211000489

49.

Nagel

Hugueley

Cui

Nunez

D. M.

Kuo

A. A.

(2022). Predictors of dual e-cigarette and cigarette use. Journal of Public Health Management and Practice, 28(3), 243–247. https://doi.org/10.1097/phh.0000000000001491

50.

Öberg

Jaakkola

M. S.

Woodward

Peruga

Prüss-Ustün

(2011). Worldwide burden of disease from exposure to second-hand smoke: A retrospective analysis of data from 192 countries. Lancet, 377(9760), 139–146. https://doi.org/10.1016/s0140-6736(10)61388-8

51.

Pénzes

Foley

K. L.

Balázs

Urbán

(2016). Intention to experiment with e-cigarettes in a cross-sectional survey of undergraduate university students in Hungary. Substance Use & Misuse, 51(9), 1083–1092. https://doi.org/10.3109/10826084.2016.1160116

52.

Polosa

Caponnetto

Morjaria

J. B.

Papale

Campagna

Russo

(2011). Effect of an electronic nicotine delivery device (e-Cigarette) on smoking reduction and cessation: A prospective 6-month pilot study. BMC Public Health, 11, Article 786. https://doi.org/10.1186/1471-2458-11-786

53.

Primack

B. A.

Soneji

Stoolmiller

Fine

M. J.

Sargent

J. D.

(2015). Progression to traditional cigarette smoking after electronic cigarette use among US adolescents and young adults. Jama Pediatrics, 169(11), 1018–1023. https://doi.org/10.1001/jamapediatrics.2015.1742

54.

Purkayastha

(2013). BAT ramps-up e-cigarette expansion as sales go up in smoke. International Business Times. Retrieved October 7, 2023, from https://www.thefreelibrary.com/BAT+Rampsup+E-cigarette+Expansion+as+Sales+Go+Up+in+S%20moke.-a0338323170

55.

Puteh

S. E. W.

Manap

R. A.

Hassan

T. M.

Ahmad

I. S.

Idris

I. B.

Sham

F. M.

Lin

A. B. Y.

Soo

C. I.

Mohamed

R. M. P.

Mokhtar

A. I.

Zakaria

Lee

Nordin

A. S. A.

Ariaratnam

Yusoff

M. Z. M.

(2018). The use of e-cigarettes among university students in Malaysia. Tobacco Induced Diseases, 16, 57. https://doi.org/10.18332/tid/99539

56.

Rakotozandry

Stenger

Burel

Penna

Ramadour

Disdier

Charpin

(2016). E-cigarette use in university students and its relationship to cigarette smoking. Revue Des Maladies Respiratoires, 33(3), 233–238. https://doi.org/10.1016/j.rmr.2015.04.022

57.

Regan

A. K.

Promoff

Dube

S. R.

Arrazola

(2013). Electronic nicotine delivery systems: Adult use and awareness of the “e-cigarette” in the USA. Tobacco Control, 22(1), 19–23. https://doi.org/10.1136/tobaccocontrol-2011-050044

58.

Resmi Gazete. (1996). Tütün Ürünlerinin Zararlarının Önlenmesi ve Kontrolü Hakkında Kanun [Law on prevention and control of harms of tobacco products]. T.C. Resmi Gazete, 22829, 7 Kasım 1996.

59.

Resmi Gazete. (2020). E-sigara ve benzeri cihazların, bazı tütün mamullerinin ve tütün mamullerinin taklitinde kullanılan mamullerin ithaline ilişkin karar. [Decision on the import of e-cigarettes and similar devices, some tobacco products and products used in the imitation of tobacco products]. Resmi Gazete. https://www.resmigazete.gov.tr/eskiler/2020/02/20200225-23.pdf

60.

Riker

C. A.

Lee

Darville

Hahn

E. J.

(2012). E-cigarettes: Promise or peril? Nursing Clinics of North America, 47(1), 159–171. https://doi.org/10.1016/j.cnur.2011.10.002

61.

Saddleson

M. L.

Kozlowski

L. T.

Giovino

G. A.

Hawk

L. W.

Murphy

J. M.

MacLean

M. G.

Goniewicz

M. L.

Homish

G. G.

Wrotniak

B. H.

Mahoney

M. C.

(2015). Risky behaviors, e-cigarette use and susceptibility of use among college students. Drug and Alcohol Dependence, 149, 25–30. https://doi.org/10.1016/j.drugalcdep.2015.01.001

62.

Scheinfeld

Crook

Perry

C. L.

(2019). Understanding young adults’ e-cigarette use through the theory of planned behavior. Health Behavior and Policy Review, 6(2), 115–127. https://doi.org/10.14485/hbpr.6.2.1

63.

Schober

Szendrei

Matzen

Osiander-Fuchs

Heitmann

Schettgen

Jörres

R. A.

Fromme

(2014). Use of electronic cigarettes (e-cigarettes) impairs indoor air quality and increases FeNO levels of e-cigarette consumers. International Journal of Hygiene and Environmental Health, 217(6), 628–637. https://doi.org/10.1016/j.ijheh.2013.11.003

64.

Shahab

Goniewicz

M. L.

Blount

B. C.

Brown

McNeill

Alwis

K. U.

Feng

Wang

West

(2017). Nicotine, carcinogen, and toxin exposure in long-term e-cigarette and nicotine replacement therapy users: A cross-sectional study. Annals of Internal Medicine, 166(6), 390–400. https://doi.org/10.7326/M16-1107

65.

Siegel

M. B.

Tanwar

K. L.

Wood

K. S.

(2011). Electronic cigarettes as a smoking-cessation tool: Results from an online survey. American Journal of Preventive Medicine, 40(4), 472–475. https://doi.org/10.1016/j.amepre.2010.12.006

66.

Simpson

E. E. A.

Davison

Doherty

Dunwoody

McDowell

McLaughlin

Butter

Giles

(2022). Employing the theory of planned behaviour to design an e-cigarette education resource for use in secondary schools. BMC Public Health, 22(1), 276. https://doi.org/10.1186/s12889-022-12674-3

67.

X. F.

L. P.

Griffiths

S. M.

Gao

Lau

J. T. F.

P. K. H.

(2015). Smoking behaviors and intentions among adolescents in rural China: The application of the Theory of Planned Behavior and the role of social influence. Addictive Behaviors, 48, 44–51. https://doi.org/10.1016/j.addbeh.2015.04.005

68.

Swiatkowska

Zajdel

Balwicki

Kaleta

(2024). Is e-cigarette advertising associated with e-cigarette use among young people? New survey evidence from Poland. Frontiers in Public Health, 12, Article 1448011. https://doi.org/10.3389/fpubh.2024.1448011

69.

Tanriover

Hidiroglu

Cook

R. L.

(2022). Do family physicians perceive electronic cigarette use as a harm reduction strategy for smokers? A survey from Istanbul. Primary Health Care Research and Development, 23, Article e15. https://doi.org/10.1017/s1463423622000056

70.

Trumbo

C. W.

(2015). The effect of electronic cigarette advertising on intended use among college students. Addictive Behaviors, 46, 77–81. https://doi.org/10.1016/j.addbeh.2015.03.005

71.

Tsai

Walton

Coleman

B. N.

Sharapova

S. R.

Johnson

S. E.

Kennedy

S. M.

Caraballo

R. S.

(2018). Reasons for electronic cigarette use among middle and high school students—National Youth Tobacco Survey, United States, 2016. Mmwr-Morbidity and Mortality Weekly Report, 67(6), 196–200.

72.

TurkStat. (2020). Türkiye Sağlık Araştırması (Turkey Health Survey)TurkStat. https://www.tuik.gov.tr/media/microdata/pdf/turkiye-saglik-arastirmasi.pdf

73.

Tutar

Kücükoglu

Özdemir

Alkan

Ö.

Ipekten

O. B.

(2024). An investigation of gender differences in e-commerce shopping frequency during COVID-19: Evidence from Türkiye. Sage Open, 14(4), Article 21582440241287630. https://doi.org/10.1177/21582440241287630

74.

Ulbricht

Unger

Gross

Nauck

Meyer

John

(2015). Factors associated with secondhand smoke exposure prevalence and secondhand smoke level of children living with parental smokers: A cross sectional study. Journal of Community Health, 40(3), 501–507. https://doi.org/10.1007/s10900-014-9963-2

75.

Unver

Aydemir

A. F.

Alkan

(2023). Predictors of Turkish individuals’ online shopping adoption: An empirical study on regional difference. PloS One, 18(7), e0288835. https://doi.org/10.1371/journal.pone.0288835

76.

Unver

Tekmanli

H. H.

Alkan

(2023). Passive smoking as a risk factor among older adults: An ordered probability approach for Turkiye. Frontiers in Public Health, 11, Article 1142635. https://doi.org/10.3389/fpubh.2023.1142635

77.

Ünver

(2024). Investigation of factors affecting tobacco and e-cigarette use in Turkey. Journal of Substance Use, 30(1), 181–188. https://doi.org/10.1080/14659891.2024.2335484

78.

Ünver

Alkan

. (2021). Determinants of e-commerce use at different educational levels: Empirical evidence from Turkey. International Journal of Advanced Computer Science and Applications, 12(3), 40–49.

79.

Ünver

Ş.

Alkan

. (2022). Experienced problems with online shopping: The case of Turkey. Toros Üniversitesi İİSBF Sosyal Bilimler Dergisi, 9 (Special Issue 2nd International Symposium of Sustainable Logistics “Circular Economy”), 87–96.

80.

Vardavas

C. I.

Filippidis

F. T.

Agaku

I. T.

(2015). Determinants and prevalence of e-cigarette use throughout the European Union: A secondary analysis of 26 566 youth and adults from 27 Countries. Tobacco Control, 24(5), 442–448. https://doi.org/10.1136/tobaccocontrol-2013-051394

81.

Wang

M. P.

S. Y.

Leung

L. T.

Lam

T. H.

(2016). Electronic cigarette use and respiratory symptoms in Chinese adolescents in Hong Kong. Jama Pediatrics, 170(1), 89. https://doi.org/10.1001/jamapediatrics.2015.3024

82.

Wills

T. A.

Knight

Williams

R. J.

Pagano

Sargent

J. D.

(2015). Risk factors for exclusive e-cigarette use and dual e-cigarette use and tobacco use in adolescents. Pediatrics, 135(1), E43–E51. https://doi.org/10.1542/peds.2014-0760

Analyzing the Sociodemographic and Personal Factors that Influence e-Cigarette Use in Türkiye

Abstract

Keywords

Introduction

Methods

Data

Measures and Variables

Hypotheses Related to Sociodemographic Factors

Hypotheses Related to Personal Factors

Research Method

Results

Descriptive Statistics

Model Estimation

Discussion

Gender

Age

Educational Status

Social Security Institution (SSI) Coverage

Psychiatric Support

Exposure to Tobacco Smoke

Alcohol and Tobacco Use

Conclusion

Limitations of the Study

Footnotes

Acknowledgements

Authors’ Note

ORCID iD

Ethical Considerations

Consent to Participate

Funding

Declaration of Conflicting Interests

Data Availability Statement

References