Abstract
Introduction:
The aim of this study was to evaluate socioeconomic and racial/ethnic differences in e-cigarette uptake and harm perceptions about e-cigarettes among adults who smoke cigarettes in the United States (US).
Methods:
Five waves of the US Population Assessment of Tobacco and Health (PATH) Study (2013–2019) were used to assess transitions from exclusive cigarette smoking to [1] exclusive e-cigarette use [2] dual use [3] nonuse of either product (N=7,172). Analyses (conducted in 2022) estimated differences in transitions and e-cigarette harm perceptions by race/ethnicity, income, and education.
Results:
Hispanic (OR=0.32, 95%CI: 0.18–0.54) and Black (OR=0.38, 95%CI: 0.22–0.65) adults were less likely than White adults to transition from exclusive cigarette to exclusive e-cigarette use after one year. Adults with a Bachelor’s degree (versus less than high school, OR=2.57, 95%CI: 1.49–4.45) and adults making ≥$100,000/year (versus <$10,000, OR=3.61, 95%CI: 2.10–6.22) were more likely to transition from exclusive cigarette to exclusive e-cigarette use. Hispanic and Black adults and those with lower income and education were more likely to perceive e-cigarettes as equally or more harmful than cigarettes, which in turn was associated with lower odds of transitioning from exclusive cigarette smoking to exclusive e-cigarette use (OR=0.62, 95%CI: 0.47–0.81).
Conclusions:
Adults who were Hispanic, Black, and/or had lower socioeconomic status were less likely to use e-cigarettes to quit cigarettes. Findings provide preliminary evidence that differences in harm perceptions may contribute to disparities in e-cigarette transitions.
Keywords: Electronic cigarette use, e-cigarette uptake, tobacco product transition, racial/ethnic disparities, socioeconomic disparities
Introduction
Cigarette smoking is the leading preventable cause of disease and death in the US, accounting for >480,000 deaths annually1 and >225 billion US dollars of direct medical costs in 2014.2 While there was a gradual decline in smoking prevalence over the past several decades, socioeconomic inequalities in smoking prevalence have increased over time.3,4 Disparities also exist in smoking cessation. Black adults are more likely to attempt to quit smoking than White adults, yet have less success achieving cessation.5 Lower cessation rates are also observed among individuals with low educational attainment and low income.1,5
Though long-term effects of vaping are unknown, e-cigarettes are considered to be less harmful than cigarettes and are routinely used to quit smoking.6–8 Approximately 8.1 million US adults use e-cigarettes; among recent cigarette quitters, one in four currently vape.9 While quitting all nicotine is preferrable, it is important to consider whether switching to e-cigarettes may reduce cigarette smoking and improve health in populations with disproportionately high rates of smoking and related disease.10 The diffusion of innovations theory suggests that early adopters of new technologies tend to be more affluent and more educated.11 If individuals from disenfranchised populations are less likely to vape to reduce or quit cigarette smoking, e-cigarettes could contribute to disparities in smoking and related health harms.12 Understanding disparities in the use of e-cigarettes as a cessation tool can help regulatory agencies evaluate the overall public health impact of e-cigarettes—a criteria used in evaluation of premarket authorization applications by the US Food and Drug Administration.
Evidence on sociodemographic differences in e-cigarette use is mixed. A 2017 systematic review reported that vaping was more common among White individuals and those with more formal education.13 However, there was substantial heterogeneity across studies and the review was limited by a lack of stratification by smoking status and inclusion of predominantly cross-sectional studies. One US cross-sectional study found lower vaping prevalence among people who currently smoke who were Black and/or had lower education,14 while another found lower vaping rates among people who formerly smoked who were Black or Hispanic, but similar rates between those with less than a high school degree versus college degree.15 In a United Kingdom study, socioeconomic disadvantage was positively associated with vaping among people who formerly smoked.16
A longitudinal approach is critical for evaluating disparities in transitions from cigarette smoking to e-cigarette use, as cross-sectional analyses do not distinguish the temporal ordering of vaping relative to smoking. Using longitudinal data from the Population Assessment of Tobacco and Health (PATH) Study from 2013–2015, Harlow et al. previously documented higher rates of switching from exclusive cigarette smoking to exclusive vaping among US adults who had higher income, more education, and/or were White.17 However, given the rapid evolution of the e-cigarette market, including the rise and fall of novel fourth generation products (e.g., JUUL) between 2015–2019,18 examining disparities with recent national data is critical. Additionally, demographics of e-cigarette use changed since 2014, with increases among individuals who never smoked and recent cigarette quitters.19
Using five waves of PATH data from 2013–2019, this study evaluates differences in transitions from cigarette to e-cigarette use among populations disproportionately impacted by smoking-related burden of disease, including by race/ethnicity, income, and educational attainment. Analyses also examine differences in e-cigarette and cigarette harm perceptions in these subpopulations.
Methods
Study Sample
The analysis uses five waves of PATH data, an annual prospective cohort study that is nationally representative with the addition of survey sample weights.20,21 At Wave 1, 32,320 participants ≥18 years old were recruited into the adult cohort between September 2013-December 2014. Wave 5 data were collected between December 2018-November 2019.
Data were restructured into four person-intervals comprised of two consecutive waves approximately 12 months apart (i.e., Wave 1 – Wave 2, Wave 2 – Wave 3, Wave 3 – Wave 4, and Wave 4 – Wave 5). The analysis included adults who smoked at least 100 cigarettes in their lifetime and were smoking cigarettes every day or some days at the first wave of the interval. The analysis excluded participants who currently used e-cigarettes at the first wave of the interval and those with missing data (1–6%) on e-cigarette/cigarette use, covariates, or wave 5 longitudinal sample weights. Participants could contribute up to 4 person-interval observations if they met eligibility criteria at each interval. This nested trial design increases statistical efficiency by allowing for examination of disparities in cigarette to e-cigarette transitions simultaneously across four intervals.22 The analytic sample included 18,727 person-interval observations representing 7,172 unique individuals (Appendix Figure 1).
Westat’s IRB approved the PATH study protocol, and informed consent was obtained from all participants.21 Analyses were performed between October 2021-February 2022.
Measures
A four-category transition outcome was defined based on transitions from exclusive cigarette smoking at the first wave of each person-interval to 1) persistent exclusive cigarette smoking, 2) e-cigarette uptake and cigarette cessation (i.e., exclusive e-cigarette use), 3) e-cigarette uptake without cigarette cessation (i.e., dual use), and 4) no e-cigarette uptake and cigarette cessation (i.e., nonuse of either e-cigarettes or cigarettes) at the second wave of each person-interval.
Predictors measured at the first wave of each interval included race/ethnicity (non-Hispanic White, non-Hispanic Black, non-Hispanic all other races, and Hispanic), education (<high school, high school diploma/GED, some college/associates, ≥bachelor’s degree), and annual income (<$10,000, $10,000 to $24,999, $25,000 to $49,999, $50,000 to $99,999, and ≥$100,000 per year).
Covariates included age (18 to 24 years, 25 to 34, 35 to 44, 45 to 54, 55 to 64, and ≥65 years) and sex (male/female). Analyses also assessed two harm perceptions at the first wave of each person-interval that could influence a participant’s transition between products, including perceived harm of e-cigarettes compared to cigarettes (less harmful vs. about the same or more harmful) and perceived harm of cigarettes to health (not at all/slightly/somewhat harmful vs. very/extremely harmful).
Statistical Analysis
Random effects multinomial logistic regression estimated odds of each transition category relative to persistent cigarette smoking as a function of each predictor, adjusting for age, sex, and race/ethnicity (when not the main predictor). Models were weighted by PATH Wave 5 longitudinal weights. Random effects models accommodated interdependence of multiple person-interval observations nested within individuals, ensuring robust variance estimation. Analyses were conducted in Stata 17 (StataCorp).
Two series of supplemental regression models examined whether harm perceptions relate to observed differences in transitions. First, separate random effects logistic regression models were fitted with each of the main predictors as independent variables, and perceived harm of e-cigarettes compared to cigarettes and perceived harm of cigarettes as dependent variables. Second, separate random-effects multinomial logistic regression models were fitted with the perceived harm measures as independent variables, and the four-category transition outcome as the dependent variable.
Results
Among participants who exclusively smoked cigarettes, 53.0% were male, most (68.5%) were non-Hispanic White, 14.9% were non-Hispanic Black, 11.0% were Hispanic, and 5.6% were another race/ethnicity. There were fewer young adults (18–24y, 10.2%) and senior adults (65y+, 8.3%) than other age groups (range=17.3–23.8%). A majority had a high school (38.6%) or some college education (32.7%). Nearly half made either <$10,000 (21.2%) or $10,000-$24,999 (27.1%) per year; 7.2% earned $100,000 or more (Table 1). Distribution of baseline demographics was consistent across exposure waves.
Table 1:
Descriptive statistics of sociodemographic characteristics by exposure waves, PATH Waves 1–4, 2013–2018a
| Characteristics | Total | Wave 1 (2013–14) | Wave 2 (2014–15) | Wave 3 (2015–16) | Wave 4 (2016–18) | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| N=18,727 | N=4,055 | N=4,431 | N=5,009 | N=5,232 | ||||||
| Age | N | (%) | N | (%) | N | (%) | N | (%) | N | (%) |
| 18 to 24 | 2952 | (10.2) | 777 | (13.9) | 737 | (11.5) | 744 | (10.2) | 694 | (10.2) |
| 25 to 34 | 4515 | (23.8) | 953 | (26.1) | 1041 | (24.6) | 1210 | (23.8) | 1311 | (23.8) |
| 35 to 44 | 3516 | (20.4) | 783 | (20.0) | 827 | (19.7) | 939 | (20.4) | 967 | (20.4) |
| 45 to 54 | 3637 | (20.1) | 794 | (19.9) | 884 | (20.4) | 973 | (20.1) | 986 | (20.1) |
| 55 to 64 | 2957 | (17.3) | 567 | (14.7) | 688 | (16.9) | 802 | (17.3) | 900 | (17.3) |
| 65 and above | 1150 | (8.3) | 181 | (5.4) | 254 | (6.9) | 341 | (8.3) | 374 | (8.3) |
| Sex Assigned at Birth | ||||||||||
| Female | 9804 | (47.0) | 2108 | (45.2) | 2318 | (46.0) | 2640 | (47.0) | 2738 | (47.0) |
| Male | 8923 | (53.0) | 1947 | (54.8) | 2113 | (54.0) | 2369 | (53.0) | 2494 | (53.0) |
| Race/Ethnicity | ||||||||||
| NH White | 12030 | (68.5) | 2664 | (70.7) | 2838 | (68.7) | 3211 | (68.5) | 3317 | (68.5) |
| NH Black | 3153 | (14.9) | 667 | (14.2) | 760 | (14.7) | 846 | (14.9) | 880 | (14.9) |
| NH All other races/ethnicities | 1236 | (5.6) | 259 | (5.2) | 288 | (5.2) | 331 | (5.6) | 358 | (5.6) |
| Hispanic | 2308 | (11.0) | 465 | (9.9) | 545 | (11.4) | 621 | (11.0) | 677 | (11.0) |
| Education | ||||||||||
| Less than High school | 3266 | (17.0) | 677 | (15.1) | 752 | (15.3) | 911 | (17.0) | 926 | (17.0) |
| High school/GED | 6924 | (38.6) | 1461 | (38.8) | 1659 | (39.1) | 1858 | (38.6) | 1946 | (38.6) |
| Some college /associates | 6524 | (32.7) | 1476 | (34.3) | 1528 | (33.2) | 1708 | (32.7) | 1812 | (32.7) |
| Bachelor or above | 2013 | (11.6) | 441 | (11.8) | 492 | (12.3) | 532 | (11.6) | 548 | (11.6) |
| Annual Income | ||||||||||
| Less than $10,000 | 4455 | (21.2) | 957 | (20.7) | 1043 | (20.5) | 1207 | (21.2) | 1248 | (21.2) |
| $10,000 to $24,999 | 5324 | (27.1) | 1199 | (28.2) | 1285 | (27.6) | 1402 | (27.1) | 1438 | (27.1) |
| $25,000 to $49,999 | 4573 | (24.5) | 1020 | (26.0) | 1079 | (24.6) | 1206 | (24.5) | 1268 | (24.5) |
| $50,000 to $99,999 | 3231 | (20.1) | 647 | (18.2) | 747 | (19.7) | 895 | (20.1) | 942 | (20.1) |
| $100,000 or more | 1144 | (7.2) | 232 | (6.9) | 277 | (7.5) | 299 | (7.2) | 336 | (7.2) |
Abbreviations: NH=Non-Hispanic, GED=General Education Requirement
Unweighted N and weighted percentages
Overall, 1.7% switched from exclusive cigarette smoking to exclusive e-cigarette use, 9.2% transitioned to dual use, 9.9% switched to nonuse, and 79.2% continued to exclusively smoke cigarettes over Waves 2–5 (Table 2). In total, 11.6% (1.7% + 9.9%) reported any smoking cessation (exclusive e-cigarette use or nonuse), among whom 14.6% (1.7%/11.6%) quit by switching to e-cigarettes.
Table 2:
Sociodemographic characteristics and transitions from exclusive cigarette smoking among 18,727 person-intervals, PATH Waves 2–5
| Uptake of exclusive e-cigarette use vs. persistent exclusive cigarette smoking | Uptake of dual e-cigarette and cigarette use vs. persistent exclusive cigarette smoking | Uptake of non-use vs. persistent exclusive cigarette smoking | ||||
|---|---|---|---|---|---|---|
| Characteristic | N (%) Transitioneda | Adjusted OR (95%CI)b | N (%) Transitioneda | Adjusted OR (95%CI)b | N (%) Transitioneda | Adjusted OR (95%CI)b |
| Total | 333 (1.7) | - | 1,835 (9.2) | - | 1,782 (9.9) | - |
| Race/Ethnicity | ||||||
| NH White | 249 (2.0) | Reference | 1277 (9.9) | Reference | 1115 (9.8) | Reference |
| NH Black | 25 (0.9) | 0.38 (0.22,0.65) | 217 (6.6) | 0.58 (0.47,0.70) | 225 (6.5) | 0.54 (0.43,0.68) |
| NH All other races | 35 (2.7) | 1.14 (0.67,1.93) | 138 (9.3) | 0.85 (0.62,1.16) | 123 (13.1) | 1.57 (1.03,2.39) |
| Hispanic | 24 (0.8) | 0.32 (0.18,0.54) | 203 (8.0) | 0.69 (0.55,0.86) | 319 (14.1) | 1.78 (1.35,2.34) |
| Education | ||||||
| Less than High school | 40 (1.1) | Reference | 297 (8.6) | Reference | 227 (7.4) | Reference |
| High school/GED | 96 (1.3) | 1.04 (0.66,1.64) | 669 (8.7) | 0.93 (0.76,1.13) | 541 (8.0) | 1.20 (0.92,1.55) |
| Some college/associates | 152 (2.3) | 2.01 (1.30,3.11) | 684 (10.2) | 1.13 (0.93,1.39) | 671 (10.5) | 1.81 (1.40,2.35) |
| Bachelor or above | 45 (2.5) | 2.57 (1.49,4.45) | 185 (8.7) | 1.13 (0.86,1.49) | 343 (18.3) | 4.34 (3.16,5.96) |
| Income | ||||||
| Less than $10,000 | 49 (1.0) | Reference | 463 (10.2) | Reference | 332 (7.5) | Reference |
| $10,000 to $24,999 | 82 (1.4) | 1.46 (0.95,2.25) | 534 (9.4) | 0.93 (0.78,1.12) | 454 (8.4) | 1.14 (0.91,1.43) |
| $25,000 to $49,999 | 83 (1.9) | 1.98 (1.28,3.06) | 443 (8.9) | 0.89 (0.73,1.08) | 448 (10.4) | 1.57 (1.23,2.00) |
| $50,000 to $99,999 | 76 (2.2) | 2.33 (1.44,3.77) | 298 (8.7) | 0.87 (0.70,1.08) | 374 (12.1) | 2.07 (1.60,2.68) |
| $100,000 or more | 43 (3.3) | 3.61 (2.10,6.22) | 97 (7.9) | 0.80 (0.58,1.09) | 174 (15.2) | 3.04 (2.17,4.27) |
Abbreviations: NH=Non-Hispanic, GED=General Education Requirement. All analyses were weighted by PATH Wave 5 all-wave weights.
Unweighted N and weighted row percentages.
Models adjusted for age, sex, and race/ethnicity
Transitions from exclusive cigarette smoking to exclusive e-cigarette use were more common among adults who were non-Hispanic White and/or had higher SES (Table 2). Hispanic (OR=0.32; 95%CI:0.18–0.54) and non-Hispanic Black adults (OR=0.38; 95%CI:0.22–0.65) were less likely than non-Hispanic White adults to transition from exclusive cigarette smoking to exclusive e-cigarette use (relative to no transition) after one year. Adults with a Bachelor’s degree or higher (versus adults with <high school education) had greater odds of making this transition (OR=2.57, 95%CI:1.49–4.45). Adults who made ≥$100,000 per year (OR=3.61, 95%CI:2.10–6.22) and adults who made between $50,000-$99,999 per year (OR=2.33, 95% CI:1.44–3.77) were more likely than adults who made <$10,000 to transition to exclusive e-cigarette use.
Transitions from exclusive smoking to dual use of e-cigarettes and cigarettes were more common among non-Hispanic White adults (Table 2). Compared to Non-Hispanic White adults, Non-Hispanic Black (OR=0.58, 95%CI:0.47–0.70) and Hispanic adults (OR=0.69, 95%CI:0.55–0.86) were less likely to transition to dual use (versus no transition). Odds of transitioning to dual use were similar across education and income categories.
Hispanic adults were most likely to transition from exclusive cigarette smoking to nonuse, followed by individuals identifying as non-Hispanic all other races, with non-Hispanic Black adults least likely to transition to nonuse of e-cigarettes and cigarettes (Table 2). Compared to non-Hispanic White adults, adjusted odds ratios for transitioning to nonuse after one year (versus no transition) were 0.54 (95%CI:0.43–0.68) for non-Hispanic Black adults and 1.78 (95%CI:1.35–2.34) for Hispanic adults. SES was positively associated with transitioning from cigarettes to nonuse. Adults with a Bachelor’s degree or higher (versus <high school education) had greater odds of transitioning to nonuse (OR=4.34, 95%CI:3.16–5.96). Compared to adults who made <$10,000 per year, adults who made ≥$100,000 per year (OR=3.04, 95%CI:2.17–4.27) and adults who made between $50,000-$99,999 (OR=2.07, 95%CI:1.60–2.68) had greater odds of transitioning to nonuse after one year, relative to no transition.
Overall, 69% of participants viewed e-cigarettes as equally or more harmful than cigarettes (Table 3). Hispanic (OR=1.79, 95%CI:1.58–2.02) and non-Hispanic Black (OR=1.25, 95%CI:1.14–1.38) adults were more likely than non-Hispanic White adults to perceive e-cigarettes as equally or more harmful than cigarettes. Compared to adults with <high school education, adults with a greater education were less likely to have these perceptions of harm (ORs range=0.46–0.84). Similarly, greater income was associated with lower odds of perceiving e-cigarettes as equally or more harmful than cigarettes (ORs range=0.62–0.91).
Table 3:
Sociodemographic characteristics and tobacco product harm perceptions among 18,727 person-intervals, PATH Waves 2–5
| Perceived harm of e-cigarettes compared to cigarettes | Perceived harm to health from cigarettes | |||
|---|---|---|---|---|
| Characteristic | N (%) Perceived e-cigarettes to be same/more harmful than cigarettesa | Adjusted OR (95%CI)b | N (%) Perceive cigarettes to be very/extremely harmful to healtha | Adjusted OR (95%CI)b |
| Total | 13,013 (69.0) | - | 13,558 (72.5) | - |
| Race/Ethnicity | ||||
| NH White | 8153 (67.2) | Reference | 8643 (72.0) | Reference |
| NH Black | 2261 (71.5) | 1.25 (1.14,1.38) | 2267 (73.1) | 1.08 (0.98,1.19) |
| NH All other races | 847 (66.9) | 1.05 (0.89,1.24) | 879 (69.1) | 0.85 (0.72,1.01) |
| Hispanic | 1752 (77.7) | 1.79 (1.58,2.02) | 1769 (76.6) | 1.23 (1.09,1.40) |
| Education | ||||
| Less than High school | 2484 (76.0) | Reference | 2295 (70.3) | Reference |
| High school/GED | 4986 (71.6) | 0.84 (0.75,0.94) | 4875 (70.9) | 1.01 (0.91,1.13) |
| Some college/associates | 4343 (66.0) | 0.63 (0.57,0.71) | 4808 (73.2) | 1.11 (1.00,1.24) |
| Bachelor or above | 1200 (59.4) | 0.46 (0.40,0.53) | 1580 (79.0) | 1.59 (1.37,1.86) |
| Income | ||||
| Less than $10,000 | 3240 (72.9) | Reference | 3151 (71.4) | Reference |
| $10,000 to $24,999 | 3776 (70.5) | 0.91 (0.82,1.01) | 3820 (71.4) | 1.07 (0.97,1.19) |
| $ 25,000 to $49,999 | 3136 (68.2) | 0.84 (0.76,0.93) | 3317 (72.3) | 1.11 (1.00,1.24) |
| $50,000 to $99,999 | 2155 (66.7) | 0.80 (0.72,0.90) | 2383 (74.0) | 1.22 (1.08,1.38) |
| $100,000 or more | 706 (60.4) | 0.62 (0.53,0.73) | 887 (76.6) | 1.44 (1.21,1.72) |
Abbreviations: NH=Non-Hispanic, GED=General Education Requirement. All analyses were weighted by PATH Wave 5 all-wave weights.
Unweighted N and weighted row percentages.
Models adjusted for age, sex, and race/ethnicity
Overall, 72.5% of participants viewed cigarettes to be very or extremely harmful to health. Hispanic adults were more likely to perceive cigarettes as harmful than non-Hispanic White adults (OR=1.23, 95%CI:1.09–1.40). Greater education (ORs range=1.01–1.59) and greater income (ORs range=1.07–1.44) were positively associated with perceiving cigarettes to be very or extremely harmful to health.
Perceiving e-cigarettes to be equally or more harmful than cigarettes was associated with lower odds of switching to exclusive e-cigarette (OR=0.62, 95%CI:0.47–0.81) and dual use (OR=0.60, 95%CI:0.53–0.69) versus no transition (Table 4). Perceiving cigarettes to be very or extremely harmful to health was associated with greater odds of transitioning to exclusive e-cigarette use (OR=1.53, 95%CI:1.11–2.12) and non-use (OR=1.53, 95%CI:1.28–1.84).
Table 4:
Tobacco product harm perceptions and transitions from exclusive smoking among 18,727 person-intervals, PATH Waves 2–5
| Uptake of exclusive e-cigarette use vs. persistent exclusive cigarette smoking | Uptake of dual e-cigarette and cigarette use vs. persistent exclusive cigarette smoking | Uptake of non-use vs. persistent exclusive cigarette smoking | ||||
|---|---|---|---|---|---|---|
| Characteristic | N (%) Transitioneda | Adjusted OR (95%CI)b | N (%) Transitioneda | Adjusted OR (95%CI)b | N (%) Transitioneda | Adjusted OR (95%CI)b |
| Total | 333 (1.7) | - | 1,835(9.2) | - | 1782 (9.9) | - |
| Perceived harm of e-cigarettes | ||||||
| Less harmful than cigarettes | 143 (2.4) | Reference | 730 (12.2) | Reference | 561 (9.7) | Reference |
| Same/more harmful than cigarettes | 190 (1.4) | 0.62 (0.47,0.81) | 1105 (7.8) | 0.60 (0.53,0.69) | 1221(10.0) | 1.03 (0.88,1.21) |
| Perceived harm to health from cigarettes | ||||||
| Not/slightly/somewhat harmful | 69 (1.3) | Reference | 515 (8.7) | Reference | 394 (7.7) | Reference |
| Very/extremely harmful | 264 (1.9) | 1.53 (1.11,2.12) | 1320 (9.4) | 1.07 (0.92,1.24) | 1388 (10.8) | 1.53 (1.28,1.84) |
Abbreviations: NH=Non-Hispanic, GED=General Education Requirement. All analyses were weighted by PATH Wave 5 all-wave weights.
Unweighted N and weighted row percentages.
Models adjusted for age, sex, and race/ethnicity
Discussion
Among a representative sample of US adults who smoke cigarettes, this study examined socioeconomic and racial/ethnic differences in transitions to e-cigarette use and harm perceptions of e-cigarette and cigarette use. Overall, 1.7% of adults transitioned to exclusive e-cigarette use after one year. Transitions to exclusive e-cigarette use were more common among adults who were non-Hispanic White and/or had higher SES. Among all participants who smoked cigarettes, 69% viewed e-cigarettes as equally or more harmful than cigarettes. Hispanic and non-Hispanic Black adults, and adults with lower SES were more likely to view e-cigarettes as equally or more harmful than cigarettes, which in turn was associated with lower odds of transitioning from exclusive cigarette smoking to exclusive e-cigarette use.
Racial/ethnic and socioeconomic differences in e-cigarette uptake are consistent with earlier work using waves 1 and 2 of PATH (2013–2015).17 The current study extends this prior work by using all available waves of PATH, including the most recent data from 2019. The e-cigarette market has changed dramatically since 2015. Average nicotine concentration, e-cigarette flavors, and the number of available vaping products all increased significantly.23 The overall unit sales of e-cigarettes more than doubled between 2013–2018, increasing 40% during 2016–2017 alone.23 The changing e-cigarette landscape was largely attributed to the marketing and popularity of JUUL (introduced in 2015), which paved the way for modern e-cigarettes that come in numerous flavors, have discrete and sleek designs, and use highly concentrated nicotine salt formulas.24 In the context of drastic changes to the e-cigarette market and an increase in the overall availability of vaping products between the previous and current studies, lower-SES, Black, and Hispanic individuals continued to report lower rates of transitions from exclusive cigarette to exclusive e-cigarette use.
Sociodemographic differences in exclusive e-cigarette uptake might contribute to widening disparities in smoking-related disease. Smoking accounts for 62% of the socioeconomic disparity in mortality from tobacco-related cancers and respiratory diseases, and 34% of the socioeconomic disparity in all-cause mortality nationally.25 Additionally, Black adults face a disproportionate burden of tobacco-related morbidity and mortality, including from cancers and cardiopulmonary diseases.26 Factors contributing to disparities in smoking and related diseases include targeted tobacco-industry marketing, stress related to poverty and/or racism, and barriers to healthcare access and cessation services.27 Quitting smoking is associated with substantial gains in life expectancy,28 and recent studies demonstrate potential health benefits associated with completely switching to e-cigarettes, including reductions in respiratory symptoms29,30 and cardiovascular disease.31,32 Populations disproportionately burdened by smoking-related disease, including lower-SES and non-Hispanic Black adults who smoke, may be the least likely to experience the potential health benefits of e-cigarette substitution due to lowest rates of switching, on top of the lowest rates of smoking cessation without e-cigarettes. However, rates of transitions to exclusive e-cigarette use were low in this study (1.7%) and absolute differences in switching to e-cigarettes by race/ethnicity and SES were small. Therefore, the impact on population-level smoking disparities remains unclear.
Most adults in this study perceived e-cigarettes to be equally or more harmful than cigarettes. Additionally, nearly one-third did not believe cigarettes were very or extremely harmful to health. Prevalence of believing e-cigarettes are the same or more harmful than cigarettes has increased over the last decade.33 The outbreak of e-cigarette related lung injuries (EVALI) in 2019 led to increased e-cigarette harm perceptions among people who smoke cigarettes.34 Media coverage of EVALI often conflated nicotine and cannabis vaping, despite the outbreak being linked to contaminated tetrahydrocannabinol vape cartridges.34 Additionally, media coverage and regulatory responses to the youth vaping epidemic may have contributed to greater perceived risk of e-cigarettes. Youth vaping is a serious public health concern, and the majority of news coverage, e-cigarette policies, and education campaigns have focused on potential risks of e-cigarettes more than their role as a nicotine alternative.35 While research and policy that reduces vaping-related harms is critical, misperceptions that e-cigarettes are more harmful than cigarettes could deter adults who smoke from switching to less harmful modes of nicotine consumption. A recent study found that adults who viewed an FDA-distributed anti-vaping public service announcement were less likely to consider e-cigarettes for smoking cessation.36 Our study highlights that among people who smoke cigarettes, marginalized populations disproportionately impacted by smoking-related diseases are more likely than other populations to hold the misperception that e-cigarettes are more harmful than cigarettes, and are less likely to use e-cigarettes for cigarette cessation.
When considering tobacco regulatory efforts for e-cigarettes, policymakers primarily focus on weighing two factors – health harms from youth vaping, and health benefits from adults who use e-cigarettes to quit cigarettes.10,37 However, a third factor should likely be considered: the impact of policies on cigarette smoking disparities, including equitable access to e-cigarettes for cigarette cessation. For example, given evidence from randomized trials demonstrating efficacy of e-cigarettes for smoking cessation,38 and that substituting e-cigarettes for cigarettes reduces toxicant and carcinogen exposure,8 taxes on e-cigarettes should be reviewed for their potential impact on socioeconomic disparities in e-cigarette use as a cessation tool. By making e-cigarette products more expensive without altering the cost of cigarettes, policymakers may be restricting e-cigarette access to adults with high SES; this could lead to prolonged smoking among adults with lower SES (in addition to potential unintended consequences of increased youth smoking).39 Additionally, the Centers for Disease Control and Prevention and other health promotion agencies could consider harm reduction messaging around e-cigarettes – communicating both the risks of vaping, and the potential health benefits of exclusive e-cigarette use compared to cigarette smoking.40 This would ensure that the most-up-to-date information on vaping risks is coming from reputable health agencies rather than popular media. For example, the UK National Health Service provides evidence-based fact sheets that include both information on using e-cigarettes in the context of quitting smoking, and the risks of vaping to young people.41
Limitations
It was not possible to measure cigarette and e-cigarette behaviors between waves. Additionally, associations may not apply to longer-term behavior change; some participants who switched to exclusive e-cigarette use or nonuse likely relapsed back to cigarette smoking. Within each person-interval, a small proportion of participants were nonrespondents at the follow-up wave. However, sample weights account for nonresponse, preserving representativeness of the analytic sample. Given the limited sample size of e-cigarette use, the analysis did not incorporate measures of vaping frequency, or examine intersectional disparities in e-cigarette transitions (e.g., by race/ethnicity and SES). PATH public use datasets include one four-category combined race/ethnicity variable limiting the ability to examine more detailed racial/ethnic identities. A conservative adjustment strategy was used to avoid conditioning on potential mediators of sociodemographic factors and cigarette-e-cigarette transitions. However, there may be residual confounding by factors associated with cigarette and e-cigarette use (e.g., mental health, other drug use). Results may not generalize to other countries with different e-cigarette regulatory environments (e.g., United Kingdom). Finally, while findings provide compelling evidence that harm perceptions may explain disparities in e-cigarette transitions, a formal mediation analysis is required to test this hypothesis.
Conclusion
Health benefits associated with completely switching from smoking to vaping and the effectiveness of e-cigarettes as a smoking cessation aid are supported by a growing body of evidence.6–8,29–32,42,43 In this study of adults who smoked cigarettes, non-Hispanic Black adults, Hispanic adults, and adults with lower SES were less likely to switch to exclusive e-cigarette use. Findings may relate to perceptions in these populations that e-cigarettes are equally or more harmful than cigarettes. Disparities in e-cigarette use as a cessation tool could have impacts on population-level cigarette smoking disparities. Policymakers should identify regulations that provide equitable access to e-cigarettes as a smoking cessation aid and reduce disparities in adult cigarette smoking, while also considering the impact of policies on reducing youth vaping.
Supplementary Material
Acknowledgements
This research was funded through the American Lung Association Public Policy Research Award, National Heart, Lung, and Blood Institute 1K01HL154130-01, and American Heart Association Tobacco Center for Regulatory Science (grants P50HL120163, U54HL120163, 2U54HL120163, R01HL092577) and the National Cancer Institute (NCI) and the FDA Center for Tobacco Products (CTP) under Award Number U54CA180905. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication. Authors have no conflicts of interest. No financial disclosures were reported by the authors of this paper.
Footnotes
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CRediT author statement
Alyssa Harlow: Conceptualization, Methodology, Writing- Original Draft; Writing- Review & Editing Wubin Xie: Software, Formal Analysis, Data Curation, Visualization, Writing- Review & Editing; Abolie Goghari: Software, Formal Analysis, Data Curation, Visualization, Writing-Review & Editing; Dielle Lundberg: Conceptualization; Writing- Review & Editing; Rafeya Raquib: Conceptualization, Writing- Review & Editing; Jonathan Berlowitz: Conceptualization, Writing- Review & Editing; Andrew Stokes: Conceptualization, Methodology, Writing- Original Draft; Writing- Review & Editing; Supervision; Resources
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