E-Cigarette Beliefs and Intentions Among U.S. Adults Before and After EVALI Outbreak

Abstract

Purpose:

This study examined attitudes, perceived control, perceived norms, intention, and policy support before and after the peak of E-cigarette or Vaping Product Use-Associated Lung Injury (EVALI) cases among 2 independent samples of U.S. adults.

Design:

This study used a successive independent samples design.

Setting:

Data was collected through online surveys in July 2019 (n = 521) and October 2019 (n = 536).

Subjects:

Participants were recruited through the Qualtrics Survey Panel. Eligibility criteria included: 1) 18 years or older, and 2) currently living in the U.S.

Measures:

The 2 surveys were collected from 2 separate samples examined e-cigarette attitudes, perceived control, perceived norms, intention, and policy support.

Analysis:

Linear regressions were used to examine the association between time, attitudinal, and belief factors associated with intention and policy support.

Results:

Participants in the October sample (n = 521) were significantly more likely to have negative attitudes towards e-cigarettes when compared to the July sample (n = 536), F(8,1047) = 52.671, p < .01, R² = 0.287. Lower perceived social acceptance towards e-cigarettes and negative attitudes were related to higher support for restricting where e-cigarettes could be used, F(11, 1042) = 63.218, p < .010, R² = .401. Higher perceived control over accessing e-cigarettes, but lower social acceptance of e-cigarettes and negative beliefs were associated with higher support for limiting places where e-cigarettes could be purchased, F(11,1039) = 36.200, p < .01, R² = .277.

Conclusion:

Results indicate that EVALI cases may have had an immediate negative effect on attitudes but did not appear to decrease intention to use e-cigarettes. Results could inform future public health campaigns' programming and research. More research is needed to understand the long-term impact of EVALI on e-cigarette use.

Keywords

e-cigarette use attitudes health behavior theory smoking prevention

Purpose

Electronic cigarettes (e-cigarettes) have rapidly evolved as a popular tobacco product in the U.S.^1,2 While the long-term health effects are currently unknown, these products have been found to contain potentially harmful chemicals, including carcinogenic substances.³ Additionally, e-cigarettes contain nicotine—which is addictive and can negatively affect brain development of adolescents.³ Some research suggests that e-cigarette use is associated with traditional cigarette use, which has known long-term health effects.⁴

In order to inform public health campaigns and understand public support for implementing policies that restrict e-cigarette sales and places that they can be used, it is important to understand the public’s beliefs and knowledge about these products. Previous studies have examined beliefs, attitudes, and knowledge regarding e-cigarettes among a variety of populations. For example, individuals who are not aware of the potential harm of e-cigarettes are more inclined to have a positive attitude towards e-cigarette use.⁵ Some adults have greater acceptance of e-cigarettes than traditional cigarettes, and view e-cigarettes as a substitute for and a healthier option than cigarettes.⁶ While previous studies have shown that some individuals are concerned about health-related consequences of and addiction to e-cigarettes, other studies showed mixed attitudes towards e-cigarettes and social networks’ approval (e.g., friends, family) of using e-cigarettes.⁷ One theoretical framework for understanding e-cigarette use is the Integrated Behavioral Model (IBM).⁸ According to this framework, intention to use e-cigarettes would be determined by pre-existing attitudes (e.g., beliefs about whether e-cigarettes are safe), perceived norms (e.g., beliefs about what others are doing and if they support e-cigarette use), and personal agency (e.g., beliefs about barriers and facilitators to using e-cigarettes). Previous formative research has indicated that this model could provide an effective framework for developing health communication campaigns to decrease e-cigarette use.⁷

In addition to existing concerns about e-cigarettes, vaping product use-associated lung injury (EVALI) is another issue. EVALI is a lung disease linked to e-cigarette use where patients experience respiratory illness.⁹ The symptoms include respiratory symptoms (e.g., shortness of breath, cough, chest pain), gastrointestinal symptoms (e.g., nausea, stomach pain), and other nonspecific constitutional symptoms (e.g., chills, fever).⁹ These cases were highlighted across the national media and on social media. While many of the EVALI cases were specifically linked to tetrahydrocannabinol (THC), some research suggests that the existing evidence is not sufficient to exclude non-THC-containing products as contributing to the EVALI cases.¹⁰ According to the Centers for Disease Control and Prevention, EVALI cases were witnessed in 2019 with a peak between August and September of 2019.⁹ A total of 2,668 hospitalizations or deaths were reported in the U.S as of January 2020 with males accounting for 66% and young adults (18-24 years old) accounting for 37% of cases.⁹ EVALI cases began to decline in September of 2019 after the identification of vitamin E acetate linked to THC products as a primary cause of EVALI.⁹ Given the national attention to these cases, public health professionals can use this novel health risk to emphasize the danger of e-cigarette use. Moreover, increased public awareness of negative effects could also increase support for policies aimed at decreasing access to e-cigarettes. To this end, this study examined if the IBM constructs (i.e. intention, perceived norms, perceived control, and attitudes) as well as policy support are associated with time period (i.e., before or after the peak of the EVALI cases).

Methods

Design

Two different surveys were collected using separate national samples, 1 survey in July of 2019 and 1 survey in October of 2019. The 2 surveys were collected from a separate sample of participants. Approval from the California Polytechnic State University's institutional review board was received prior to data collection.

Sample

Data were collected using an online survey from national samples recruited through the Qualtrics Survey Panel. Qualtrics Survey Panel recruits potential participants from an existing pool of panel members that meet specific eligibility criteria.¹¹ Quota sampling was used to ensure demographic characteristics similar to the U.S. population on sex, race, and ethnicity. Eligibility criteria included: 1) 18 years or older, and 2) currently living in the U.S. Using independent samples, the same 10-minute survey was completed at both time periods (i.e., July 2019 and October 2019). Informed consent was completed online by having participants select “Yes, I agree to participate.” The participant had to select this button in order to start the survey.

Measures

Demographics and tobacco use

Demographic variables were measured using items from an existing questionnaire, and included age, sex, race/ethnicity, educational level, and income level.¹² Current e-cigarette use was measured by asking participants to select if they used e-cigarettes “every day,” “some days,” “used but not in the last 30 days,” or “never used.” In addition, ever use was assessed by asking participants to describe if they had ever vaped or used e-cigarettes by selecting from the following: “I have never tried them,” “I have tried them, but not in the past 30 days,” and “I used them at least once in the past 30 days.” Traditional cigarette use (i.e., former and current smoking status) was measured by whether or not they had smoked at least 100 cigarettes in their life (1 = Yes, 2 = No).¹³

Integrated behavioral model (IBM) constructs

Attitudes were measured using 10 items related to perceptions towards e-cigarettes (e.g., harm to oneself, harm to others, harm to environment). Perceived norms were measured across 9 items that focused on e-cigarette approval from friends, family or others as well as the importance of doing what they think other people want them to do. Perceived control was measured across 4 items that examined access to e-cigarettes and control over e-cigarette use. All 3 constructs (i.e., attitudes, perceived norms, perceived control) were measured on 7-point Likert scales (1 = strongly disagree to 7 = strongly agree). Scores were averaged for each construct to create composite scores. Higher average values related to higher positive beliefs (i.e., higher positive attitudes towards e-cigarettes, higher perceived approval from others, and higher perceived control over accessing e-cigarettes). The items for these constructs are described in Table 1. Intention was measured on a 6-point Likert scale by asking all participants, “How likely is it that you will use an e-cigarette, even 1 or 2 puffs, at any time in the next 30 days?” (higher values reflected higher intention to use). All of these measures were adapted from previous studies.^5,8,14

Table 1.

Integrated Behavioral Model Items.

Construct	Item	Range of observed scores for time 1 (M, SD)	Range of observed score for time 2 (M, SD)
Perceived Control	If I really want to, I can get an e-cigarette	1-7 (5.85, 1.589)	1-7 (5.68, 1.750)
Perceived Control	It would be easy for me to get access to an e-cigarette	1-7 (5.78, 1.618)	1-7 (5.65, 1.720)
Perceived Control	Other people’s opinion does not affect my use of e-cigarettes	1-7 (5.57, 1.696)	1-7 (5.17, 1.894)
Perceived Control	It is in my control to use or not use e-cigarettes	1-7 (6.19, 1.352)	1-7 (6.10, 1.486)
Perceived Norms	My friends would approve of my use of e-cigarettes	1-7 (3.94, 1.927)	1-7 (3.67, 2.014)
Perceived Norms	My family would approve of my use of e-cigarettes	1-7 (3.53, 2.084)	1-7 (3.20, 2.045)
Perceived Norms	Most people I know would approve of my use of e-cigarettes	1-7 (3.80, 1.950)	1-7 (3.53, 1.986)
Perceived Norms	When it comes to things like e-cigarettes, it is important for me to follow the wishes of my closest friends	1-7 (3.36, 1.824)	1-7 (3.33, 1.846)
Perceived Norms	When it comes to things like e-cigarettes, it is important for me to follow the wishes of my doctor	1-7 (4.98, 1.697)	1-7 (5.04, 1.718)
Perceived Norms	I would approve of my friends using e-cigarettes	1-7 (4.00, 1.986)	1-7 (3.73, 2.052)
Perceived Norms	I would approve of 1 of my family members using e-cigarettes	1-7 (3.80, 2.036)	1-7 (3.49, 2.092)
Perceived Norms	I think that it is bad for other people to use e-cigarettes	1-7 (4.30, 1.943)	1-7 (4.54, 1.998)
Perceived Norms	I think other people look cool when they use e-cigarettes	1-7 (2.71, 1.769)	1-7 (2.57, 1.697)
Attitudes	E-cigarettes are bad^a	1-7 (4.59, 1.872)	1-7 (4.87, 1.886)
Attitudes	E-cigarettes are harmful to the environment^a	1-7 (4.15, 1.740)	1-7 (4.23, 1.774)
Attitudes	People risk harming other people if they use e-cigarettes^a	1-7 (3.99, 1.879)	1-7 (4.17, 1.895)
Attitudes	E-cigarettes are harmful for one’s short-term health^a	1-7 (4.66, 1.703)	1-7 (4.98, 1.737)
Attitudes	E-cigarettes are harmful for one’s long-term health^a	1-7 (5.04, 1.709)	1-7 (5.23, 1.752)
Attitudes	I like e-cigarettes	1-7 (3.59, 2.171)	1-7 (3.46, 2.251)
Attitudes	I like the flavors of e-cigarettes	1-7 (4.15, 2.107)	1-7 (3.99, 2.103)
Attitudes	E-cigarettes are much less harmful than traditional cigarettes	1-7 (4.02, 1.832)	1-7 (3.76, 1.953)
Attitudes	E-cigarettes do not contain any of the toxic chemicals that can be found in traditional cigarettes	1-7 (3.41, 1.871)	1-7 (3.32, 1.840)
Attitudes	E-cigarettes help people to quit smoking traditional cigarettes	1-7 (4.34, 1.876)	1-7 (4.33, 1.915)

^a Item was reverse coded for analysis.

E-cigarette restrictions

Support for e-cigarette policies examined 2 types of restrictions: 1) purchasing policy support, and 2) indoor and outdoor use policy support. Purchasing policy support was measured by averaging 4 items on a 7-point Likert scale (1= strongly disagree to 7= strongly agree). Items inquired about participants’ opinion on allowing e-cigarettes to be sold in age-restricted stores, online, convenience stores, and to everyone. Higher values equated to more support for restricting purchasing to e-cigarettes. Indoor or outdoor use policy support was measured by asking participants to respond to, “Should e-cigarettes be allowed in these places?” for 6 places inside/outside restaurants, inside/outside schools, and inside/outside workplaces. Response options included yes (scored as 1) and no (scored as 2) and were summed across the 6 items. Higher values equated to higher support for restricting use in indoor/outdoor places.

Analysis

Frequency and descriptive statistics were generated using SPSS v26 to examine the demographic characteristics of the sample and central tendencies in regard to questions from the Integrated Behavioral Model. Linear regressions were used to examine if time (July or October) was significantly associated with attitudes, perceived norms, perceived control, intentions, and policy support, while controlling for demographic variables (i.e., age, sex, race/ethnicity, income, education), current e-cigarette use, and traditional cigarette use. All participants were included in the regression analyses, including when examining IBM constructs and policy support. Inclusion of all participants in the intention analyses and selection of control variables were based on previous research.^15,16 For the regression analyses, age was treated as a continuous variable, female was used as the referent group, income was categorized into 1) higher than $25,000 or 2) $25,000 or below, education was classified as more than high school education or high school or below, and ethnicity was classified as Latino/Hispanic or “other.”

Results

Demographics, Cigarette Use, and E-Cigarette Use

Table 2 depicts the demographics of the merged, July and October samples. Similar sample sizes were collected in July of 2019 (n = 521) and October of 2019 (n = 536). The distribution of male and female was almost equally split for each dataset. The majority of participants within both samples identified as white (n = 805, 76.2%). In the July sample, 57.8% (n = 301) had used at least 100 cigarettes in their lifetime and 46% (n = 242) had never used e-cigarettes. There was no significant association between time of survey collection, current e-cigarette use, or traditional cigarette use. The majority of the July sample had an income $25,000 or more a year (n = 390, 74.9%), some college education or more (n = 333, 63.9%), and did not identify as Hispanic, Latino, or Spanish origin (n = 407, 78.1%). Similarly, the majority of the October sample had an income of $25,000 or more a year (n = 392, 73.1%) and some college education or more (n = 363, 67.7%). The percent of participants who reported smoking at least 100 cigarettes in their lifetime in both samples was higher than national statistics.¹³ The median age of the 2 samples was 38.5 years old, which was similar to the national average age of 38.3 in 2019.¹⁷ Overall, the breakdown of sex (female/male) was similar to the U.S. national statistics.¹⁷ In addition, the overall sample had 76.2% of participants identify as White, which is similar to the national statistic of 76.3%. Further, the overall sample had 13.2% of participants identify as Black or African American, which is similar to the 13.4% national statistic.¹⁸

Table 2.

Demographic Characteristics of Samples.

	July data	October data	Total	National averages
Characteristics	N = 521	N = 536	N = 1057
Age (years)
Mean ± SD	38.5 ± 14.2	41.17 ± 15.3	39.84 ± 14.8	38.3^a
Sex	N (%)	N (%)	N (%)	%
Male	251 (48.2)	263 (49.1)	514 (48.6)	49.2^a
Female	267 (51.2)	270 (50.4)	537 (50.8)	50.8^a
Other	3 (0.6)	3 (0.6)	6 (0.6)	NA
Race*	N (%)	N (%)	N (%)	%
White	399 (76.6)	406 (75.7)	805 (76.2)	76.3^b
Black or African American	63 (12.1)	76 (14.2)	139 (13.2)	13.4^b
American Indian or Alaska Native	7 (1.3)	11 (2.1)	18 (1.7)	1.3^b
Asian	27 (5.2)	31 (5.8)	58 (5.5)	5.9^b
Native Hawaiian or Pacific Islander	4 (0.8)	2 (0.4)	6 (0.6)	0.2^b
Other	28 (5.4)	25 (4.7)	53 (5.0)	NA
Ethnicity	N (%)	N (%)	N (%)
Hispanic, Latino/a/x, or Spanish origin	114 (21.9)	118 (22.0)	232 (21.9)	18.5^b
Educational level	N (%)	N (%)	N (%)
High school diploma/G.E.D. or above	495 (95.0)	515 (96.1)	1010 (95.6)	88.0^b

* Percentages reflect that participants could select multiple categories for Race/Ethnicity.

^a National average age and sex data obtained from published data from U.S. Census Bureau 2019 data.¹⁸

^b National data on education, race, and ethnicity obtained from U.S. Census Bureau 2020 data.¹⁹

Regression Results for IBM Constructs and Intention

Full regression results are available in Table 3. Perceived norms (b = .159, p < .001) and positive attitudes (b = .365, p < .001), as age (b = –.118, p < .01), Latino/Hispanic ethnicity (b = –.051, p < .05), current e-cigarette use (b = .196, p < .05), and traditional cigarette use (b = .157, p < .01), were significantly related to intention to use e-cigarettes; however, time of data collection (i.e., July vs October) was not a significant factor, F(11,1038) = 114.597, p < .001, R² = .548. This means that time of data collection was not significantly related to intention to use e-cigarettes. This suggests that higher social acceptance towards e-cigarettes and positive views on e-cigarettes was positively associated with higher intention to use e-cigarettes.

Table 3.

Regression Results for Outcomes of E-Cigarette Intention, Policy Support, Attitudes, Perceived Norms, and Perceived Control.

Independent variables	E-cigarette use intention beta	SE	Purchasing policy support beta	SE	Indoor/outdoor policy support beta	SE	Attitudes beta	SE	Perceived norms beta	SE	Perceived control beta	SE
Time	-.017	.088	.043	.070	.002	.088	-.066*	.067	-.037	.065	-.056	.080
Perceived Norms	.159**	.052	-.165**	.041	-.241**	.052	–		–		–
Attitudes	.365**	.050	-.323**	.040	-.273**	.050	–		–		–
Perceived Control	.035	.034	.095**	.027	-.025	.034	–		–		–
Age	-.118**	.003	.066*	.003	.109**	.003	-.127*	.002	-.136**	.002	.026	.003
Sex^a	.001	.002	-.135**	.068	.063**	.084	-.027	.064	-.032	.062	-.001	.076
Latino or Hispanic	-.051*	.131	-.018	.103	.029	.130	-.026	.099	-.022	.096	-.051	.118
Income^b	.029	.100	-.031	.079	.026	.100	.000	.076	.003	.074	.091**	.090
Education^c	.036	.093	-.001	.073	-.004	.092	-.005	.070	.002	.068	.038	.084
Current e-cig use^d	.196**	.045	-.039	.036	-.102**	.045	.279**	.033	.319**	.032	.146**	.039
Traditional cigarette use^e	.157**	.107	-.056	.085	-.109**	.107	.327**	.077	.303**	.074	.169**	.091
R²	.548	1.376	.227	1.09	.401	1.375	.287	1.05	0.340	1.018	.088	1.247

Note: All reported estimates are standardized regression coefficients. N = 1057.

^a Referent group is female.

^b Referent group is below $25,000.

^c Referent group is high school or below.

^d Current e-cigarette was measured as 1) never used, 2) used but not in the last 30 days, 3) used some days, and 4) used every day.

^e Traditional cigarette use (i.e., former and current smoking status) was measured by whether or not they had smoked at least 100 cigarettes in their life (1= Yes, 2= No).

*p < 0.05, **p < 0.01.

^c Referent group is high school or below.

^d Current e-cigarette was measured as 1) never used, 2) used but not in the last 30 days, 3) used some days, and 4) used every day.

^e Traditional cigarette use (i.e., former and current smoking status) was measured by whether or not they had smoked at least 100 cigarettes in their life (1= Yes, 2= No).

*p < 0.05, **p < 0.01.

Time of data collection (b = –.066, p = 0.15) was negatively associated with positive attitudes, F(8,1047) = 52.671, p < .001, R² = 0.287, but was not a significant factor when examining perceived norms or perceived control. Overall, the October sample had more negative views towards e-cigarettes compared to the July sample. Age (b = –.127, p < .05), current e-cigarette use (b = .279, p < .01), and traditional cigarette use (b = .327, p < .01) were also associated with attitudes towards e-cigarettes. Perceived norms (b = –.241) and attitudes (b = –.273) were significantly related to indoor/outdoor use policy support, F(11, 1042) = 63.218, p < .01, R² = .401.

Regression Results for Support for Policy Restrictions

Higher age (b = .109, p < .01), identifying as male (b = .063, p < .01), less e-cigarette use (b = –.102, p < .01), and traditional cigarette use (b = .327, p < .01) were also significantly related to indoor/outdoor use policy support. Regression results reveal that lower perceived social acceptance towards e-cigarettes and negative attitudes towards e-cigarettes was related to higher support for indoor and outdoor use policies. Perceived norms (b = –.164, p < .01), perceived control (b = 0.95, p < .01), and attitudes (b = –.326, p < .01) were significantly related to higher support for purchasing policy, F(11,1039) = 36.200, p < .01, R² = .277. Higher age (b = .066, p < .05) and being female (b = –.134, p < .01) were also associated with higher support for purchasing policy. The regression results suggest that higher perceived control over accessing e-cigarettes, but lower social acceptance of e-cigarettes and negative beliefs about e-cigarettes were associated with higher support for limiting places where e-cigarettes could be purchased.

Discussion

This study aimed to examine the association of time with intention, perceived norms, perceived control, attitudes, and policy support scores before and immediately after the peak of the EVALI cases. Overall, the results found that the October sample (i.e., immediately after the peak of EVALI cases) had significantly lower attitude scores compared to the July sample (i.e., immediately before the EVALI peak). Results suggest that there were more negative attitudes after the peak of the EVALI cases (i.e., more participants disliked e-cigarettes, thought they were harmful, etc.). This study found that perceived norms (or social acceptance of e-cigarettes) and positive attitudes (i.e., beliefs about if e-cigarettes are good, etc.) were significantly related to higher e-cigarette intentions among the 2 samples; however, there was no significant change in e-cigarette intentions immediately before and after the peak of EVALI cases. This may have been because of the recency of those cases. Research on traditional tobacco use has indicated that cigarette use has changed as a result of significant events over time.¹⁹ Given more time and that the results did suggest more negative attitudes towards e-cigarettes, there may be a decrease in intention over time.

Findings from this study provided support for using the IBM constructs for examining e-cigarette intention and policy support, particularly for attitudes (i.e., beliefs about the harm) and perceived norms (i.e., social acceptance of e-cigarettes). This study contributes to the existing limited research that has applied the model to this topic.¹⁴ Some research has also indicated that including health warning labels may decrease motivation to use e-cigarette products.²⁰ It is possible that including potential lung disease (i.e., EVALI) on warning labels could increase negative attitudes towards e-cigarettes. In terms of policy support, past research has shown that the increasing awareness of the harm of tobacco products in the early 2000s has caused a shift in support for public health policies in regards to smoking restrictions.²¹ Another study concluded that increasing awareness of the effects of traditional cigarettes on youth can increase support for raising the tobacco sale age to 21 years old, which then will have the ability to reduce tobacco use overall.²² Cities, counties, states, and federal agencies can introduce these policies, but public support may be critical for passing them. Our study indicates that focusing on social norms and attitudes related to e-cigarettes may promote greater public support for e-cigarette policies that protect the public.

The findings also highlight the potential importance of considering current e-cigarette use when developing future campaigns and programs. The results suggest that current e-cigarette users may have more positive beliefs and attitudes towards e-cigarettes, as well as a higher intention to use. This is similar to other studies that have found a positive association with current e-cigarette use and intention.¹⁴ In terms of support for indoor/outdoor policy restrictions, the relationship was the opposite. Participants who currently used e-cigarettes were less likely to support indoor/outdoor policy restrictions. In addition, findings suggest that age may also be an important factor to consider when developing public health campaigns or programs. Higher age was significantly related to positive support toward e-cigarette policies overall. This suggests that younger adults may be less likely to support policies and tailored efforts to increase support from young adults may be necessary. On the other hand, older age was negatively associated with attitudes and perceived norms. This suggests that older adults may be more likely to have negative existing attitudes about e-cigarettes while young adults may not. Results indicate that it may be important to tailor messaging for e-cigarette prevention or policy support campaigns by age and by e-cigarette use.

Limitations

This study applied a cross-sectional design with 2 independent samples (e.g., each data collection time included a different sample); therefore, the study does not examine a change in attitude scores among a specific group of people. Both samples were collected using quota sampling to increase the similarity between sample distributions and analogous national distributions for U.S. population demographics in terms of sex and race/ethnicity; however, this methodology does not yield strong external validity or allow for direct extrapolation to national statistics. In particular, the traditional cigarette use, which represent current and former cigarette users, was higher than reported in national surveys.¹³ However, it does provide data before and during the peak of EVALI cases reported in the media. Similarly, the use of a survey panel to recruit participants and the collection of self-report data may have impacted the generalizability of findings. This possibility exists despite the fact that the panel company used in this study has been shown to deliver better data quality than some of its competitors.^11,23 Finally, the second survey was conducted during the peak of EVALI cases; however, public perceptions may have continued to change in the months to follow.

Conclusion

Findings provide evidence that negative attitudes towards e-cigarettes were higher in the sample collected after the peak of the EVALI cases, but there was no association with time of data collection and intention. Intentions to use e-cigarettes were found to be associated with perceived social norms and positive attitudes toward e-cigarettes. Additionally, results build on previous research that supports focusing on perceived norms and attitudes in public health campaigns. However, it should be noted that this study had limitations that may affect the generalizability to national data. Results could inform future public health interventions by emphasizing the potential harms, including the EVALI cases, in product labeling and public health campaigns, as well as social disapproval of e-cigarettes among peers and important others. Public health interventions can increase both public awareness of the potential harmful effects of e-cigarettes, as well as attempt to gain support for policies that protect youth and the general public. Future research should examine if the EVALI cases have led to long-term changes in attitudes and e-cigarette use.

So What? Implications for Health Promotion Practitioners and Researchers

What is already known about e-cigarettes?

E-cigarette use has continued to increase in the U.S. particularly among youth and young adults.¹ Some research has suggested that attitudes and social norms can influence e-cigarette use.^5,6

What does this article add?

Results from this study indicated that the EVALI cases may have had an immediate effect on e-cigarette attitudes; however, it is important to note that the samples were collected separately and cannot be compared directly. Attitudes and social acceptance towards e-cigarettes were associated with support for e-cigarette policies and intention.

What are the implications for health promotion practice and research?

Health promotion practitioners and researchers may want to further explore if focusing on the negative harms of e-cigarettes and decreasing social acceptance of e-cigarettes can lead to decreasing or preventing e-cigarette use. In addition, practitioners and researchers developing strategies for increasing public support for e-cigarette policies aimed at decreasing overall use may want to focus on the potential negative effects and lack of social acceptance of e-cigarettes.

Footnotes

Authors’ Note

This study was approved by the California Polytechnic State University Institutional Review Board (IRB Approval #2018-296-CP).

Declaration of Conflicting Interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was generously supported by the William and Linda Frost Fund in the Cal Poly College of Science and Mathematics.

ORCID iD

Julia M. Alber, PhD, MPH

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