Abstract
Background:
The utility of electronic cigarettes (“e-cigarettes”) as a smoking cessation adjunct remains unclear. Similarly, it is unclear if formal tobacco treatment (pharmacotherapy and/or behavioral support) augments smoking cessation in individuals who use both cigarettes and e-cigarettes.
Methods:
We performed a longitudinal cohort study of adult outpatients evaluated in our tertiary care medical center (6/2018–6/2020). E-cigarette use, smoking status, and formal tobacco treatment (deterrent pharmacotherapy and/or behavioral support) were assessed in 6-month blocks (e.g., cohort 1 [C1]=6/2018–12/2018, C2=1/2019–6/2019, etc.) using our electronic health record. We assessed the relationship between e-cigarette use (either with or without formal tobacco treatment) and point prevalence of smoking cessation at 6 and 12 months.
Results:
111823 unique patients were included in the study. The prevalence of dual use of cigarettes and e-cigarettes increased significantly over the study period (C1=0.8%; C2=1.1%; C3=1.8%; C4=2.3%; P<0.001). The prevalence of smoking cessation at 12 months was higher among e-cigarette users (20.8%) compared to non-users (16.8%) (risk difference, 4.0% [95% CI, 2.5–5.5%]; adjusted relative risk [aRR] 1.354, 95% CI 1.252–1.464, p<.0001). Further, among dual users of cigarettes and e-cigarettes, the prevalence of smoking cessation at 12-months was higher among individuals who received tobacco treatment (29.1%) compared to individuals who did not receive tobacco treatment (16.6%) (risk difference, 9.5% [95% CI, 4.6–14.4%]; aRR 1.238, 95% CI 1.071–1.432, p=0.004).
Interpretation:
These results suggest that dual users of cigarettes and e-cigarettes benefit from formal tobacco treatment. Clinicians should consider offering formal tobacco treatment to such patients, though future trials are needed.
Classifications: Smoking cessation, electronic cigarettes
Introduction
Cigarette smoking remains the leading cause of preventable death in the United States1–3. While smoking trends have steadily decreased over the last several decades4, new types of inhaled nicotine-based products – namely electronic cigarettes (“e-cigarettes” or electronic nicotine delivery systems, ENDS) – have rapidly emerged and gained popularity among both adults and adolescents5–8. E-cigarettes have faced significant scrutiny from the medical and scientific community due to the high rates at which young individuals began using these products and the uncertain health effects of these products9.
While the potential negative consequences of e-cigarettes are well-publicized, a growing number of tobacco treatment specialists have emphasized the potential for e-cigarettes as a smoking cessation tool among current smokers9,10. Indeed, an expanding body of evidence – including both randomized trials and population-based studies – suggests that e-cigarettes may be an effective smoking cessation aid11. For example, a randomized controlled trial from England found e-cigarettes to be almost twice as effective as nicotine-replacement therapy (NRT) at achieving smoking abstinence at 1 year12. Further, a recent Cochrane Review (based on 29 randomized trials) concluded that there is “moderate-certainty evidence” that e-cigarettes increase quit rates compared to NRT13. Despite this, the United States Preventative Services Taskforce (USPSTF) does not recommend e-cigarettes for smoking cessation, citing “insufficient” evidence14. A critical gap in the current literature, identified by the USPSTF and other organizations14,15, is how e-cigarettes interact with the existing standard of care, like behavioral support and pharmacotherapy, for treating tobacco dependence16. For example, it is unclear if patients who “dual use” cigarettes and e-cigarettes also benefit from the use of FDA-approved smoking cessation therapies.
In this longitudinal cohort study, we used prospectively maintained data from the Barnes Jewish Hospital / Washington University School of Medicine (St. Louis, MO) electronic health record (EHR) to assess the relationship between e-cigarette use and smoking cessation. Among individuals who currently smoked, we examined the association between e-cigarette use and smoking cessation. We further examined the association between e-cigarette use with or without formal tobacco treatment methods (i.e., behavioral support and/or pharmacotherapy) and smoking cessation.
Methods
Study Design
We performed a retrospective cohort study of all adults evaluated at Barnes Jewish Hospital / Washington University School of Medicine (St. Louis, MO), one of the largest tertiary care centers in the United States. We queried all outpatient clinic encounters from June 1, 2018 until June 30, 2021 using our electronic health record (Epic, Verona, WI). The study proposal was reviewed and approved by the Washington University in St. Louis Human Research Protection Office and Institutional Review Board.
Patient population
Our institution, in conjunction with our cancer center (Siteman Cancer Center, St. Louis, MO), instituted the Electronic Health Record-Enabled Evidence-based Smoking Cessation Treatment (ELEVATE) program as part of the National Cancer Institute (NCI) Cancer Moonshot Project and the Cancer Center Cessation Initiative (C3I)17–19. The ELEVATE program is a hospital-wide, point-of-care, low-burden, EHR-based tool for addressing smoking cessation which has demonstrated sustained efficacy, broad reach, and reduced cost compared to other traditional models of tobacco treatment (i.e., specialist referral)18,20,21. As part of quality improvement initiatives for this program, we collect several longitudinal data elements on patient smoking behaviors and treatment compliance in a centralized database. This resulting dataset allows us to implement a learning health system, through which we efficiently generate and translate real-world data into actionable knowledge-based practice from our EHR22.
We queried our institutional database for all adult patients who were documented as currently smoking during outpatient clinic encounters within the pre-specified study dates (June 2018 to June 2020). We analyzed smoking outcomes using pre-defined 6-month cohorts (or time blocks) which correspond to our biannual data harvests (at the end of June or December). For example, “Cohort 1” includes all outpatients evaluated between June to December 2018 (C2 = January 2019-June 2019; C3 = July 2019-December 2019; C4 = January 2020-June 2020). A schematic of the study design is displayed in Figure 1.
Figure 1.
Study design
Exposure and other covariates
We queried the EHR for several discrete variables related to e-cigarette use, smoking status, tobacco-related treatments, and smoking cessation outcomes. Data on current e-cigarette use was obtained from the tobacco use tab within the social history section of the EHR (yes, no, blank). If e-cigarette use was undocumented23, patients were assumed to be non-users. From this same section, we also harvested current smoking status. Patients were excluded if a smoking assessment did not occur in the initial encounter’s time block (i.e., where smoking status was blank for all encounters, <5% of patients). Pack-year smoking history (a measure of the degree of nicotine dependence) was also abstracted where available.
To determine cessation treatment use, we extracted whether patients were prescribed or were documented as having a prescription for smoking cessation pharmacotherapy using medication lists at each encounter. The pharmacotherapies assessed were varenicline, bupropion (only if indicated for smoking deterrance), and nicotine-replacement therapy (NRT; gum, patch, inhaler, nasal spray, or lozenge)14. We also assessed whether patients were given behavioral support by extracting this information from the social history, tobacco use section, counseling flowsheets, and best practice alerts, as previously described20,21. Behavioral support included receipt of brief advice or referral to counseling through Quitlines, SmokefreeTXT, or various phone applications (QuitGuide, QuitSTART, etc.).
We also extracted several additional covariates from the EHR encounter detail tab including patient age, gender, race, and comorbidities. Comorbidities were assessed using International Classification of Disease, 10th edition (ICD-10) codes from outpatient claims data to generate an Elixhauser classification score24. Additionally, clinic specialty (surgical vs. medical) was defined based on whether a patient had a surgical visit within the time block.
Outcomes
Our outcomes were smoking cessation (point prevalence) at 6 months (secondary) and 12 months (primary). We determined smoking cessation by querying the subsequent time blocks for all adults who smoked at initial assessment. Cessation was determined based on the final assessment in each time block. For example, for Cohort 1 (6/2018 to 12/2018), 6-month smoking cessation was determined from the latest documented visit between 1/2019 and 6/2019; 12-month smoking cessation was determined from the latest documented visit between 7/2019 and 12/2019. If patients were not seen in the subsequent time block or if smoking status was not assessed during follow-up, these data were considered missing and assessed via penalized imputation (i.e., by assuming that these patients were still smoking), as is typical of most longitudinal smoking trials25. We further verified our findings given this assumption through sensitivity analyses where 10% of patients with missing follow-up data were randomly coded to have successfully quit smoking. Of note, the 6- and 12-month analyses were performed independently (i.e., patients who stopped smoking at 6 months could have relapsed and started smoking again at 12 months).
Statistical analysis
Cohort descriptive statistics were reported based on the initial patient assessment using means (standard deviation) for continuous variables and frequencies (proportions) for categorical variables. Cochran-Armitage tests were used to compare trends in e-cigarette use and smoking prevalence over the time blocks. Separate marginal generalized estimating equation (GEE) Poisson regression models (clustering at unique patient level) were constructed to assess factors associated with e-cigarette use (controlling for age, sex, race, comorbidities, medical service, cohort number) and factors associated with smoking cessation (controlling for same covariates and e-cigarette use). For the latter model, smoking-related comorbidities were also assessed individually. A sensitivity analysis including patients with available pack-year smoking history was used to verify that the association between e-cigarette use and smoking cessation was independent from the degree of nicotine dependence. In a separate sub-analysis including only dual users of cigarettes and e-cigarettes, the association between any tobacco treatment therapy and smoking cessation was assessed using GEE Poisson regression controlling for the same covariates. P values of less than 0.05 were considered statistically significant. All analyses were performed with SAS version 9.4 (SAS Institute, Cary, NC), Microsoft Excel (Redmond, WA), and GraphPad Prism version 9 (San Diego, CA).
Results
Study Population
Between June 2018 and June 2020, 111823 unique patients who were currently smoking were included in the study. Study demographics are shown in Table 1. The mean (SD) age was 51.2 (15.9) years old. A majority of patients were female (n=60707, 54.3%) and of white race (n=83415, 74.6%). In terms of documented comorbidities, 34679 (31.0%) patients had an Elixhauser score of at least 1. The most common comorbidities were hypertension (n=22282, 19.9%) and chronic pulmonary disease (n=9655, 8.6%). A total of 21732 (19.4%) patients were assessed by a surgical subspecialty.
Table 1.
Patient demographics
| Cohorts | Unique Patientsa (6/2018–6/2020) N=111823 |
||||
|---|---|---|---|---|---|
| Cohort 1 (6/2018–12/2018) N=51322 |
Cohort 2 (1/2019–6/2019) N=52649 |
Cohort 3 (7/2019–12/2019) N=47145 |
Cohort 4 (1/2020–6/2020) N=48557 |
||
| Age (n, %) | |||||
| 18–40 | 12822 (24.4%) | 12756 (24.2%) | 11223 (23.8%) | 12046 (24.8%) | 31481 (28.2%) |
| 41–54 | 13022 (24.7%) | 13373 (25.4%) | 11615 (24.6%) | 12108 (24.9%) | 27917 (25.0%) |
| 55–64 | 14020 (26.6%) | 14424 (27.4%) | 12891 (27.3%) | 13144 (27.1%) | 28477 (25.5%) |
| 65+ | 11458 (21.8%) | 12096 (23.0%) | 11416 (24.2%) | 11259 (23.2%) | 23948 (21.4%) |
| Sex (n, %) | |||||
| Male | 22751 (43.2%) | 23677 (45.0%) | 21322 (45.2%) | 21989 (45.3%) | 51104 (45.7%) |
| Female | 28569 (54.3%) | 28970 (55.0%) | 25820 (54.8%) | 26563 (54.7%) | 60707 (54.3%) |
| Race (n, %) | |||||
| White | 37667 (71.5%) | 38748 (73.6%) | 35275 (74.8%) | 36919 (76.0%) | 83415 (74.6%) |
| Black | 11940 (22.7%) | 12130 (23.0%) | 10680 (22.7%) | 10812 (22.3%) | 24989 (22.3%) |
| Other | 1715 (3.3%) | 1771 (3.4%) | 1190 (2.5%) | 826 (1.7%) | 3419 (3.1%) |
| Total comorbidities (n, %) | |||||
| 0 | 20710 (40.4%) | 20938 (39.8%) | 27320 (57.9%) | 39686 (81.7%) | 77144 (69.0%) |
| 1 | 15154 (29.5%) | 15500 (29.4%) | 11032 (23.4%) | 5843 (12.0%) | 21094 (18.9%) |
| 2+ | 15458 (30.1%) | 16211 (30.8%) | 8793 (18.7%) | 3028 (6.2%) | 13585 (12.1%) |
| Comorbidities (n, %) | |||||
| Hypertension | 12053 (23.5%) | 12742 (24.2%) | 6892 (14.6%) | 2313 (4.8%) | 22282 (19.9%) |
| Chronic pulmonary disease | 4765 (9.3%) | 5429 (10.3%) | 2575 (5.5%) | 717 (1.5%) | 9655 (8.6%) |
| Diabetes | 5535 (10.8%) | 5913 (11.2%) | 3526 (7.5%) | 1044 (2.2%) | 9425 (8.4%) |
| Peripheral vascular disease | 1736 (3.4%) | 1878 (3.6%) | 1180 (2.5%) | 545 (1.1%) | 3852 (3.4%) |
| Congestive heart failure | 1337 (2.6%) | 1431 (2.7%) | 805 (1.7%) | 431 (0.9%) | 2757 (2.5%) |
| Alcohol abuse | 705 (1.4%) | 753 (1.4%) | 434 (0.9%) | 162 (0.3%) | 1540 (1.4%) |
| Pulmonary circulation disorders | 155 (0.3%) | 132 (0.3%) | 89 (0.2%) | 53 (0.1%) | 353 (0.3%) |
| Specialty (n, %) | |||||
| Medicine | 42615 (80.9%) | 44108 (83.8%) | 39455 (83.7%) | 41787 (86.1%) | 90091 (80.6%) |
| Surgery | 8707 (16.5%) | 8541 (16.2%) | 7690 (16.3%) | 6770 (13.9%) | 21732 (19.4%) |
| E-cigarette use (n, %) | |||||
| No | 50926 (96.7%) | 52070 (98.9%) | 46306 (98.2%) | 47468 (97.8%) | 109853 (98.2%) |
| Yes | 396 (0.8%) | 579 (1.1%) | 839 (1.8%) | 1089 (2.2%) | 1970 (1.8%) |
| Smoking cessation (n, %) | |||||
| 6 months | 6852 (13.0%) | 6312 (12.0%) | 5906 (12.5%) | 5960 (12.3%) | 16792 (15.0%) |
| 12 months | 8841 (16.8%) | 8808 (16.7%) | 8009 (17.0%) | 7977 (16.4%) | 22009 (19.7%) |
For unique patient counts, because one patient could appear in multiple cohorts, the information on demographics and clinical factors are displayed from the most recent cohort. 12 patients were missing a documented sex.
Cigarette and e-cigarette prevalence
The rate of cigarette smoking over the study period is shown in Figure 2. The prevalence of cigarette smoking decreased significantly from C1 to C4 (C1 14.4%; C2 13.9%; C3 13.5%; C4 13.6%; P=0.003). Conversely, among individuals who smoked, the prevalence of dual e-cigarette use increased significantly from C1 to C4 (C1 0.8%; C2 1.1%; C3 1.8%; C4 2.3%; P<0.001). On multivariable analysis, factors associated with higher likelihood of e-cigarette use included younger age (for every 1-year increase, adjusted relative risk [aRR] 0.943, 95% CI 0.940–0.946, p<.0001, eTable 1), male sex (male vs. female, aRR 1.117, 95% CI 1.022–1.222, p=0.015), white race (non-white vs. white, aRR 0.374, 95% CI 0.325–0.431, p<.0001), nonsurgical evaluation (surgery vs. medicine, aRR 0.689, 95% CI 0.601–0.790, p<.0001), and more recent cohort (e.g., C4 vs. C1, aRR 2.385, 95% CI 2.196–2.591, p<.0001).
Figure 2. Trends in cigarette and e-cigarette use, 2018 to 2020.
Smoking frequency calculated out of total number of assessed patients in each given year; dual user frequency calculated out of total number of smokers in each given year.
E-cigarette use and smoking cessation
The overall point prevalence of tobacco cessation at 6 and 12 months after enrollment were 12.5% and 16.8%, respectively. The prevalence of smoking cessation at 12 months was higher among e-cigarette users (20.8%) compared to non-users (16.8%) (risk difference, 4.0% [95% CI, 2.5–5.5%]; aRR 1.354, 95% CI 1.252–1.464, p<.0001, Table 2 and Figure 3). The secondary outcome of smoking cessation at 6 months was also higher among e-cigarette users (14.3%) compared to non-users (12.5%) (risk difference, 1.8% [95%CI, 0.5–3.1%]; aRR 1.422, 95% CI 1.295–1.562, p<.0001, Table2 and Figure 3).
Table 2.
Multivariable model for factors associated with smoking cessation at 6 and 12 months
| Smoking cessation at 6 months | Smoking cessation at 12 months | |||||||
|---|---|---|---|---|---|---|---|---|
| Adjusted relative risk | 95% Confidence Interval | P-value | Adjusted relative risk | 95% Confidence Interval | P-value | |||
| E-cigarette use | ||||||||
| No | [1 Reference] | [1 Reference] | ||||||
| Yes | 1.422 | 1.295 | 1.562 | <.0001 | 1.354 | 1.252 | 1.464 | <.0001 |
| Age | 1.018 | 1.017 | 1.019 | <.0001 | 1.014 | 1.014 | 1.015 | <.0001 |
| Sex | ||||||||
| Female | [1 Reference] | [1 Reference] | ||||||
| Male | 0.835 | 0.813 | 0.858 | <.0001 | 0.843 | 0.823 | 0.863 | <.0001 |
| Race | ||||||||
| White | [1 Reference] | [1 Reference] | ||||||
| Non-White | 1.030 | 0.999 | 1.061 | 0.062 | 0.992 | 0.966 | 1.018 | 0.542 |
| Comorbidities | ||||||||
| 0 | [1 Reference] | [1 Reference] | ||||||
| 1 | 1.242 | 1.210 | 1.275 | <.0001 | 1.132 | 1.111 | 1.154 | <.0001 |
| 2+ | 1.562 | 1.521 | 1.603 | <.0001 | 1.314 | 1.289 | 1.340 | <.0001 |
| Specialty | ||||||||
| Medicine | [1 Reference] | [1 Reference] | ||||||
| Surgery | 0.736 | 0.707 | 0.767 | <.0001 | 0.652 | 0.628 | 0.676 | <.0001 |
| Cohort | ||||||||
| C1 | [1 Reference] | [1 Reference] | ||||||
| C2 | 0.938 | 0.916 | 0.959 | <.0001 | 1.024 | 1.009 | 1.039 | 0.001 |
| C3 | 1.073 | 1.047 | 1.100 | <.0001 | 1.113 | 1.093 | 1.133 | <.0001 |
| C4 | 1.211 | 1.178 | 1.244 | <.0001 | 1.180 | 1.155 | 1.205 | <.0001 |
Model controlling for age, sex, race, comorbidities, treating specialty, cohort, and e-cigarette use
Figure 3.
Smoking cessation rates at 6 and 12 months among smokers vs. e-cigarette users (“dual users”)
A sensitivity analysis was performed among patients with available pack-year smoking history (n=55572, 49.7%) to verify that the association between e-cigarette use and smoking cessation was independent from the degree of nicotine dependence. In this model, e-cigarette use remained associated with higher prevalence of smoking cessation at 12 months (aRR 1.884, 95% CI 1.602–2.215, p<.0001, eTable2).
Combined therapy and smoking cessation
Among patients who were dual users of cigarettes and e-cigarettes, we assessed the relationship between the addition of formal tobacco treatment and smoking cessation. The point prevalence of smoking cessation at 12 months after enrollment was higher among individuals who received tobacco treatment (29.1%) compared to individuals who did not receive tobacco treatment (16.6%) (risk difference, 9.5% [95% CI, 4.6–14.4%]; aRR 1.238, 95% CI 1.071–1.432, p=0.004, Table 3 and Figure 4). The secondary outcome of smoking cessation at 6 months after enrollment was also higher among individuals who received tobacco treatment (22.3%) compared to individuals who did not receive tobacco treatment (13.1%) (risk difference, 9.1% [95%CI, 4.7–13.6%]; aRR 1.369, 95% CI 1.095–1.712, p=0.006, Table 3 and Figure 4).
Table 3.
Multivariable model for factors associated with smoking cessation at 6 and 12 months among dual users of cigarettes and e-cigarettes
| Smoking cessation at 6 months | Smoking cessation at 12 months | |||||||
|---|---|---|---|---|---|---|---|---|
| Adjusted relative risk | 95% Confidence Interval | P-value | Adjusted relative risk | 95% Confidence Interval | P-value | |||
| Tobacco treatment | ||||||||
| No | [1 Reference] | [1 Reference] | ||||||
| Yes | 1.369 | 1.095 | 1.712 | 0.006 | 1.238 | 1.071 | 1.432 | 0.004 |
| Age | 1.018 | 1.012 | 1.023 | <.0001 | 1.015 | 1.010 | 1.019 | <.0001 |
| Sex | ||||||||
| Female | [1 Reference] | [1 Reference] | ||||||
| Male | 0.872 | 0.719 | 1.056 | 0.161 | 0.900 | 0.766 | 1.057 | 0.200 |
| Race | ||||||||
| White | [1 Reference] | [1 Reference] | ||||||
| Non-White | 0.816 | 0.593 | 1.123 | 0.213 | 0.823 | 0.631 | 1.075 | 0.153 |
| Comorbidities | ||||||||
| 0 | [1 Reference] | [1 Reference] | ||||||
| 1 | 1.296 | 1.044 | 1.609 | 0.019 | 1.218 | 1.054 | 1.406 | 0.007 |
| 2+ | 1.178 | 0.916 | 1.515 | 0.202 | 1.073 | 0.913 | 1.259 | 0.394 |
| Specialty | ||||||||
| Medicine | [1 Reference] | [1 Reference] | ||||||
| Surgery | 0.788 | 0.574 | 1.080 | 0.139 | 0.752 | 0.574 | 0.985 | 0.039 |
| Cohort | ||||||||
| C1 | [1 Reference] | [1 Reference] | ||||||
| C2 | 0.793 | 0.604 | 1.040 | 0.094 | 0.975 | 0.828 | 1.149 | 0.765 |
| C3 | 0.887 | 0.687 | 1.144 | 0.355 | 1.063 | 0.889 | 1.271 | 0.501 |
| C4 | 0.870 | 0.669 | 1.132 | 0.301 | 1.072 | 0.883 | 1.301 | 0.485 |
Model controlling for age, sex, race, comorbidities, treating specialty, cohort, and tobacco treatment
Figure 4.
Smoking cessation rates at 6 and 12 months among e-cigarette users (“dual users”) with vs. without tobacco treatment (TT)
Discussion
In this study, we assessed the relationship between e-cigarette use and smoking cessation among outpatients at a large tertiary care center in the Midwest. We found that while the prevalence of cigarette smoking decreased slightly over the study period, the prevalence of e-cigarette use among individuals who also smoked (i.e., dual use) increased significantly. We further found that individuals who used e-cigarettes were more likely to stop smoking compared to individuals who did not use these products, even when controlling for the level of nicotine dependence. Finally, we identified that among patients who were dual users of both cigarettes and e-cigarettes, formal tobacco treatment with either smoking deterrent pharmacotherapy and/or behavioral support resulted in higher rates of smoking cessation. These findings suggest that individuals seen in an outpatient setting who use both cigarettes and e-cigarettes still benefit from traditional forms of tobacco treatment.
Since the recent introduction of e-cigarettes, there has been substantial debate regarding whether these products – despite public health concerns – help with smoking cessation11,16. Most evidence examining the association between e-cigarette use and smoking cessation stems from small clinical trials (mostly outside of the U.S.) and population-based cohort or cross-sectional studies9,10. Our study, in contrast, presents real-world data on e-cigarette use which was pragmatically harvested from our tertiary healthcare system’s EHR, which also represents a geographic region with one of the highest smoking prevalences in the US26. Through this prospectively maintained learning health system, we have been able to further validate that e-cigarette use may be beneficial to a subset of smokers who are using these products for the purposes of smoking cessation. Of course, this finding must be carefully considered in light of the unclear short- and long-term health effects of these products.
While the prevalence of smoking has decreased over the last several decades, the proportion of U.S. adults who dual use cigarettes and e-cigarettes has increased27. Few studies have examined how traditional evidence-based forms of formal tobacco treatment (i.e., behavioral support and/or pharmacotherapy) in combination with e-cigarette use affect smoking cessation outcomes in this subset of patients16. A prior RCT examined e-cigarette use in addition to behavioral support versus behavioral support alone and found higher 7-day point prevalence abstinence at 12 weeks among e-cigarette users, although no difference was observed at 24 weeks28. Our data similarly found that dual users of cigarettes and e-cigarettes appear to benefit from formal, FDA-approved tobacco treatments to aid with smoking cessation. Future trials evaluating the efficacy of e-cigarettes for smoking cessation should assess if these products are most effective when used in combination with other FDA-approved cessation aids16.
Cigarette smoking is the leading cause of preventable death in the United States. While some studies have emphasized the harm reduction potential of e-cigarettes29, other studies have found that dual use of cigarettes and e-cigarettes is associated with worse health outcomes, including higher rates of pulmonary and cardiovascular disease30,31. Therefore, in order for e-cigarettes to have any harm reducing effects, these products must be used in place of traditional cigarettes, minimizing or eliminating concurrent use of both products29. Our data suggest that for patients who are dual-users of cigarettes and e-cigarettes, clinicians should routinely consider additional FDA-approved pharmacotherapy to effectively minimize this period of dual use.
This study has several strengths. Most notably, it harnesses real-world EHR data to assess e-cigarette use outside of a formal study setting. Additionally, longitudinal data on smoking status and other treatments were readily available given the data source. Conversely, this study has several limitations. First, smoking status was self-reported in this study and not biochemically confirmed. Second, we could not collect data as to whether patients continued to use e-cigarettes after smoking cessation. As discussed previously, it is important to consider the benefits of e-cigarettes for smoking cessation against the potential harms of (1) dual use and (2) long-term e-cigarette dependence. Third, given the nature of our EHR system, e-cigarette use was significantly under-captured. For example, while we found documented e-cigarette use in 1–2% of patients, population-based studies suggest that the prevalence of e-cigarette use in US adults is closer to 5–8%5,6,32. It may be that the assessment of dual use identified those who used e-cigarettes more frequently (i.e., daily). There is evidence that more frequent users of e-cigarettes are especially likely to stop smoking4. Thus, the results may be relevant to only a portion of the e-cigarette user population. Further, the increasing trend of dual use in our study may reflect better documentation as opposed to true prevalence23. Additional important data elements were also unavailable through the EHR, including intensity of e-cigarette use (daily versus non-daily), type of product (i.e., nicotine containing), and concentration of nicotine. Fourth, we assessed comorbidities using ICD codes from outpatient claims data, which likely under-captures comorbidities given the lack of inpatient data and limited timeframe33. Finally, we could not assess reasons for e-cigarette use. Others have postulated that e-cigarettes may have a benefit only in individuals who are pursuing tobacco cessation as opposed to individuals who are using these products for pleasure9. Thus, the results we obtained might reflect the effects of an intention to quit in addition to or instead of the effects of e-cigarettes per se.
In conclusion, this large cohort study demonstrated that e-cigarette use was associated with increased likelihood of smoking cessation and that formal smoking treatment methods (i.e., behavioral support and/or pharmacotherapy) increased the smoking cessation rates of e-cigarette users. Our findings indicate that dual users of cigarettes and e-cigarettes may benefit from formal smoking cessation treatment as part of their healthcare.
Supplementary Material
Key Messages.
What is already known on this topic?
It is unclear if formal tobacco treatment (pharmacotherapy and/or behavioral support) augments smoking cessation in individuals who dual use both cigarettes and e-cigarettes.
What this study adds?
In this cohort study including 111823 adult outpatients who smoked, smoking cessation was more likely among e-cigarette users compared to non-users.
Among dual users of cigarettes and e-cigarettes, individuals who received tobacco treatment had a higher likelihood of smoking cessation compared to individuals who did not receive tobacco treatment.
How this study might affect research, practice or policy?
These data suggest that dual users of cigarettes and e-cigarettes benefit from formal tobacco treatment which should be confirmed through future trials.
Source of Funding:
Funded in part by NIH 5T32HL007776–25 (BTH), NIH P30 CA091842–19S5 (L-SC), NIH P50 CA244431 (L-SC), NIH R01DA038076 (L-SC), NIH U19 CA203654 (LJB), Alvin J. Siteman Cancer Center Investment Program 5129 - Barnard Trust and The Foundation of Barnes Jewish Hospital Cancer Frontier Fund
Footnotes
Meeting Presentations: None
Conflict of Interest: None
References
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