Abstract
Background and aims
Evidence on the dual use profile of electronic cigarettes (e-cigarettes) and cigarettes remains limited, especially for the adult population. This study aimed to assess the prevalence and associated factors of lifetime dual use among adults who smoke in China.
Methods
The nationally representative survey of the China Health Literacy Survey (CHLS) was conducted in 2018. Participants were recruited from 31 provinces in the Mainland of China (n=21,582). Dual use was defined as self-reported ever use of both cigarettes and e-cigarettes. Multivariate logistic regression models with weights (accounting for study design, non-response rate and post hoc stratification) were used to identify the associated factors of dual use, shown in adjusted odds ratios (aOR) and 95% confidence intervals (CI).
Results
The prevalence of lifetime dual use of cigarettes and e-cigarettes was 0.82% in China, 2.95% among those who ever smoked, and 3.01% among those who currently smoked. The dual use was consistently more prevalent in urban areas than rural areas and decreased with age. In the adjusted logistic model, dual use was associated with younger age, living in urban areas, single status, high educational level, high income, poor self-reported health status, nicotine dependence, and other smoking-related factors. Withdrawal symptoms were more prevalent among those who used both cigarettes and e-cigarettes compared with those who exclusively smoked cigarettes, including the urge to smoke, hard to concentrate, quick temper, increased appetite, weight gain, involuntary hand tremor, and sleepiness (P<0.05).
Conclusion
Although the prevalence of lifetime dual use is low, the absolute number of affected individuals in China is substantial. Our study identified key risk factors associated with lifetime dual use, providing critical evidence to inform targeted interventions for at-risk populations and guide the development of tailored tobacco control strategies.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-025-25529-4.
Keywords: Dual use, Smoking, Prevalence, Associated factors, Health policy, Tobacco withdrawal symptom
Introduction
Tobacco use remains one of the most significant public health challenges and a leading preventable cause of morbidity and mortality worldwide [1]. Over the past three decades, smoking has contributed to more than 175 million deaths globally [2]. Additionally, between 2022 and 2050, an estimated 2,040 million years of life are projected to be lost due to smoking-related causes globally [3]. Since their invention in 2003 as a potential alternative to conventional cigarettes, electronic cigarettes (e-cigarettes) have rapidly gained popularity among both adults and adolescents [4]. E-cigarettes were considered as a harm reduction tool for smoking cessation by some people who smoke [5]. While a Cochrane systematic review provided high-certainty evidence that nicotine-containing e-cigarettes enhanced smoking cessation rates compared to nicotine replacement therapy, a contrasting meta-analysis demonstrated that e-cigarette use was significantly associated with reduced smoking abstinence in real-world and clinical settings [5, 6]. It is still controversial whether it can be recommended as the first-line treatment for smoking cessation due to inadequate evidence [7]. While some people who smoked transitioned successfully to exclusive e-cigarette use or complete cessation, some began to dual use—continuing to smoke conventional cigarettes alongside e-cigarettes [8]. Furthermore, emerging evidence, primarily from US-based studies, suggests that e-cigarette use—especially among adolescents—was associated with increased odds of subsequent cigarette smoking, thereby contributing to dual use patterns [9, 10].
Given the potential for undetermined hazards of dual use, the most cost-effective measure is to figure out its risk factors and offer smoking cessation help to the targeted population. A retrospective analysis in the US showed that though dual use seemed to reduce the use of combustible cigarettes, it was associated with increased total nicotine intake and higher odds of nicotine dependence [11]. People who dual use cigarettes and e-cigarettes can get nicotine from both types of products. Thus, they are more likely to report nicotine dependence, which may increase the difficulty of quitting smoking and maintain the smoking behavior [12]. In the long run, the increased intake of harmful substances and prolonged smoking duration can lead to more serious health consequences. Additionally, a study in China found that both lifetime e-cigarette-only use and dual use were associated with a higher risk of suicidal ideation when compared to non-users [13]. Current research has focused on the prevalence and risk factors for dual use in adolescents aged 13–18 years, mainly in the UK and Canada [9, 14–18]. The studies identified factors including greater social acceptance, male gender, lower perceived health risks, younger age, and more financial resources [19]. Similarly, studies on adults have associated younger age and higher education levels with more e-cigarette use [20]. However, limited evidence exists regarding risk factors for e-cigarette use among China’s adult population [21]. While adolescents remain a priority group for tobacco control initiatives, the growing phenomenon of dual use of cigarettes and e-cigarettes among adults who smoke also warrants attention, particularly in China, given its large smoking population and the potential long-term health effects of e-cigarettes.
As the largest producer of e-cigarettes, China has witnessed an increased prevalence of e-cigarettes over the past years [22]. The weighted prevalence of e-cigarette use was 1.6% among adults in 2018-19 [22]. However, evidence for the prevalence and risk factors remained limited in China concerning the dual use of cigarettes and e-cigarettes. This study aimed to (1) evaluate the prevalence of lifetime dual use of cigarettes and e-cigarettes among adults in China, and (2) estimate the associated factors of lifetime dual use of cigarettes and e-cigarettes.
Methods
Study sample
The China Health Literacy Surveys (CHLS) were a series of representative nationwide surveys conducted in 31 provinces, autonomous regions, or municipalities in the Mainland of China [23]. Detailed information on the series of surveys was reported in the previous study [24]. Briefly, this study was based on the 2018 CHLS, which investigated non-institutionalized men and women residents. The survey was conducted from May to July 2018. Among the 87,708 participants enrolled in the survey, 608 refused to participate (drop rate = 0.69%). We further excluded 1671 participants less than 20 years old due to the unmet representativeness criteria, 518 without survey weights due to missing street addresses for matching, and 63,257 who had never smoked cigarettes. The analysis, therefore, included 21,582 participants. (Supplementary Figure S1)
This study was approved by the Ethics Review Committee of the Chinese Center for Health Education (Beijing, China). The waiver of ethical approval was granted in accordance with Article 32 of the Measures for Ethical Review of Life Sciences and Medical Research Involving Humans issued by the State Council of China in February 2023. Further details can be found at:
https://www.gov.cn/zhengce/zhengceku/2023-02/28/content_5743658.htm (accessed on 7 September 2025). Informed consent was obtained from all the participants in the CHLS survey.
Sampling methods
The sampling was conducted with an administrative multistage, stratified, population-proportionate method to increase the representativeness of the sample. The procedure is shown in Supplementary Figure S2, including 5 stages. First, each of the 31 provinces (autonomous regions/municipalities) is stratified by urban and rural areas. A total of 336 monitoring districts (counties) were randomly selected using the population proportionate sampling (PPS) method, based on the 6th National Population Census data. Second, each district (county) was randomly selected with 3 streets (towns) using the PPS method, a total of 1008. Third, each street (town) was randomly selected with 2 neighborhood committees (villages) with the PPS method, including 2016 neighborhood committees (villages). Fourth, each committee (village) was randomly selected with 55 households based on a complete map of all the households. Fifth, the Kish table method was used to select one person per household, until 40 households were completed in the 55 selected ones, for a total of at least 80,640 participants nationwide. Sample size calculations had been described in other literature [24]. Quality control at all stages was shown in Supplementary Table S1.
Measurements
The outcome was defined by a multiple-choice question asking about the type of tobacco products among those who had ever smoked. Exclusive cigarette use referred to self-reported use of cigarettes only, while lifetime dual use referred to self-reported ever use of both cigarettes and e-cigarettes, referred later as “dual use”, which may not distinguish one‑off from sustained behaviors. Socio-demographic variables were collected, including sex (male or female), age, ethnicity (Han or others), residence (rural or urban), marital status (single, married, or separated/divorced/widowed), education level (primary school and less, middle and high school, or college and higher), self-reported annual family income (< 20,000, 20,000–49,999, or >49,999 RMB), and self-reported health status (good, average, or poor). Smoking-related factors included smoking status (current smoking, never smoked, former smoking), trying to quit or cut down on tobacco but failed (yes or no), cigarettes smoked per day (in numbers), smoking starting mean age, smoking pack-years, continue to use tobacco after it caused physical problems (yes or no), continue to use tobacco after it caused mental problems (yes or no), nicotine dependence (yes or no), and the Fagerström Test for Nicotine Dependence (FTND) score for the degree of nicotine dependence (mild, moderate, or severe) [25]. Nicotine dependence was diagnosed via the China Clinical Guideline for Tobacco Cessation (2015 version) if participants had 3 or more symptoms in the listed 6 criteria [24, 26]. The smoking questionnaire was adopted from the China Adult Tobacco Survey and the Global Adult Tobacco Survey [27].
Statistical analysis
Weighted proportions and 95% confidence intervals (CI) were used for descriptive statistics. The sample weights were pre-calculated considering study design, non-response and post-stratification, matching the 2010 Chinese population census by using the software SUDAAN 11.0. The study design weights included the number of urban and rural monitoring sites, the population of each site, and the nationwide percentage of the population. The non-response weights considered the absence of gender or age variables and calculated the weights based on the study design. Post-stratification weights were further calculated to match the census population data in China. Demographic comparisons without survey weights used Student’s t-test for normally distributed continuous variables, and the Wilcoxon test for non-normally distributed data. Binary logistic regression analyses were used to assess the associated factors for the dual use of cigarettes and e-cigarettes compared with those who exclusively smoked cigarettes. The results were displayed as adjusted odds ratios (aOR) and 95% CIs. To minimize confounding effects, we constructed two multivariable models with sequential adjustments to evaluate the association between potential risk factors and dual use. Model 1 was adjusted for age and sex. Model 2 was additionally adjusted for marital status, educational level, annual family income, and residence. The selection of these covariables was based on their statistically significant trend associations with dual use of cigarettes and e-cigarettes in preliminary analyses. Sex was also included as a priori variable due to its presumed potential influence on the association [22]. Missing values were imputed with a median for ordered categorical variables, or categorized into the largest group for unordered categorical variables (Table S2 in the Supplementary Materials). A two-tailed P < 0.05 was considered statistically significant. All statistical analyses were conducted by R 4.2.1.
Results
Baseline information
The sampled population covered all 31 provinces of the Mainland of China. Of the 21,582 smoked cigarettes, 20,457 (94.8%) were male, the mean age was 49.8 (standard deviation, SD = 11.9) years, 19,470 (90.3%) were Han, 12,134 (56.2%) lived in rural areas, 12,017 (55.8%) had the highest education level of middle and high school, 18,374 (85.4%) were married, and 12,301 (57.2%) were in self-reported good health status (details in Table 1).
Table 1.
Unweighted frequencies and demographic distribution of those exclusively used cigarettes or dual use of cigarettes and e-cigarettes
| Overall (N = 21,582) | Exclusive use of cigarettes (N = 21,084) | Dual use of cigarettes and e-cigarettes (N = 498) | P | |
|---|---|---|---|---|
| Sex | ||||
| Female | 1125 (5.2) | 1103 (5.2) | 22 (4.4) | 0.480 |
| Male | 20,457 (94.8) | 19,981 (94.8) | 476 (95.6) | |
| Age (mean, SD) | 49.80 (11.90) | 50.03 (11.79) | 39.99 (12.56) | < 0.001 |
| Age (year), % | < 0.001 | |||
| 20–29 | 1501 (7.0) | 1377 (6.5) | 124 (24.9) | |
| 30–39 | 3031 (14.0) | 2889 (13.7) | 142 (28.5) | |
| 40–49 | 5305 (24.6) | 5190 (24.6) | 115 (23.1) | |
| 50–59 | 6164 (28.6) | 6096 (28.9) | 68 (13.7) | |
| 60–69 | 5581 (25.9) | 5532 (26.2) | 49 (9.8) | |
| Ethnicity, % | ||||
| Han | 19,470 (90.3) | 19,012 (90.3) | 458 (92.0) | 0.235 |
| Others | 2089 (9.7) | 2049 (9.7) | 40 (8.0) | |
| Residence, % | ||||
| Rural | 12,134 (56.2) | 11,950 (56.7) | 184 (36.9) | < 0.001 |
| Urban | 9448 (43.8) | 9134 (43.3) | 314 (63.1) | |
| Marital status, % | < 0.001 | |||
| Single | 1531 (7.1) | 1440 (6.8) | 91 (18.3) | |
| Married | 18,374 (85.2) | 17,998 (85.4) | 376 (75.5) | |
| Separated/Divorced/Widowed | 1661 (7.7) | 1630 (7.7) | 31 (6.2) | |
| Education level, % | < 0.001 | |||
| Primary school or less | 7048 (32.7) | 6995 (33.2) | 53 (10.6) | |
| Middle and high school | 12,017 (55.8) | 11,751 (55.8) | 266 (53.4) | |
| College and higher | 2481 (11.5) | 2302 (10.9) | 179 (35.9) | |
| Annual family income (RMB/year), % | < 0.001 | |||
| < 20,000 | 6463 (30.0) | 6375 (30.3) | 88 (17.8) | |
| 20,000–49,999 | 7800 (36.2) | 7508 (35.7) | 292 (59.2) | |
| > 49,999 | 7272 (33.8) | 7159 (34.0) | 113 (22.9) | |
| Self-reported health status, % | 0.550 | |||
| Good | 12,301 (57.2) | 12,026 (57.2) | 275 (55.3) | |
| Average | 7648 (35.5) | 7460 (35.5) | 188 (37.8) | |
| Poor | 1571 (7.3) | 1537 (7.3) | 34 (6.8) | |
| Smoking status, % | ||||
| Current smoking | 19,146 (88.7) | 18,689 (88.6) | 457 (91.8) | 0.035 |
| Former smoking | 2436 (11.3) | 2395 (11.4) | 41 (8.2) | |
| Nicotine Dependence, % | ||||
| No | 9081 (42.1) | 8902 (42.2) | 179 (35.9) | 0.006 |
| Yes | 12,501 (57.9) | 12,182 (57.8) | 319 (64.1) | |
| FTND, % | 0.165 | |||
| 0–3 | 11,302 (52.4) | 11,021 (52.3) | 281 (56.4) | |
| 4–6 | 8746 (40.5) | 8564 (40.6) | 182 (36.5) | |
| > 6 | 1534 (7.1) | 1499 (7.1) | 35 (7.0) | |
| Cigarettes smoked per day (mean, SD) | 15.58 (9.90) | 15.60 (9.90) | 14.62 (9.75) | 0.030 |
| Cigarettes smoked per day | 0.004 | |||
| <10 | 4867 (22.7) | 4731 (22.6) | 136 (27.3) | |
| 10–19 | 9940 (46.3) | 9744 (46.5) | 196 (39.4) | |
| >20 | 6651 (31.0) | 6485 (30.9) | 166 (33.3) | |
| Smoking starting age (mean, SD) | 20.48 (5.91) | 20.51 (5.93) | 19.15 (4.78) | < 0.001 |
| Smoking starting age, % | < 0.001 | |||
| < 20 | 9797 (45.6) | 9532 (45.4) | 265 (53.2) | |
| 20–24 | 4105 (19.1) | 4047 (19.3) | 58 (11.6) | |
| > 24 | 7569 (35.3) | 7394 (35.3) | 175 (35.1) | |
| Smoking pack-years (mean, SD) | 22.89 (20.18) | 23.05 (20.19) | 16.30 (18.29) | < 0.001 |
| Smoking pack-years, % | < 0.001 | |||
| < 10 | 5100 (29.6) | 4909 (29.2) | 191 (47.4) | |
| 10–19 | 8440 (49.0) | 8317 (49.4) | 123 (30.5) | |
| > 19 | 3699 (21.5) | 3610 (21.4) | 89 (22.1) | |
| Continue to use tobacco after it caused health problems, % | ||||
| No | 15,642 (72.5) | 15,279 (72.5) | 363 (72.9) | 0.881 |
| Yes | 5935 (27.5) | 5800 (27.5) | 135 (27.1) | |
| Continue to use tobacco after it caused mental problems, % | ||||
| No | 15,857 (73.5) | 15,483 (73.5) | 374 (75.1) | 0.447 |
| Yes | 5713 (26.5) | 5589 (26.5) | 124 (24.9) | |
| Trying to quit or cut down on tobacco but failed, % | ||||
| No | 8721 (40.4) | 8566 (40.6) | 155 (31.1) | < 0.001 |
| Yes | 12,852 (59.6) | 12,509 (59.4) | 343 (68.9) | |
Student’s t-test was used for normally distributed continuous variables, and the Wilcoxon test for not normally distributed continuous variables
SD Standard deviation, FTND Fagerström Test of Nicotine Dependence
There were differences between exclusive cigarette use or dual use regarding age (mean [SD]: 39.99 [12.56] vs. 50.03 [11.79], P < 0.001), residence (urban: 63.1% vs. 43.3%, P < 0.001), marital status (single: 18.3% vs. 6.8%, P < 0.001), the education level (college and higher: 35.9% vs. 10.9%, P < 0.001), and annual family income (> 49,999 RMB/year: 22.9% vs. 34.0%, P < 0.001). For smoking-related factors, differences were shown in nicotine dependence (64.1% vs. 57.8%, P = 0.006), smoking starting age (mean [SD]: 19.15 [4.78] vs. 20.51 [5.93], P < 0.001), and smoking pack-years (mean [SD]: 16.30 [18.29] vs. 23.05 [20.19], P < 0.001). Figure 1 illustrates the age-specific prevalence of dual use by sex and region. (Fig. 1)
Fig. 1.
Prevalence of dual use of cigarettes and e-cigarettes among those who smoked by age groups. Notes: Figure (A) and (C) show the prevalence of dual use of cigarette and e-cigarette among those who ever smoked cigarettes by age groups; Figure (B) and (D) show the prevalence of dual use of cigarette and e-cigarette among those who currently smoked cigarettes by age groups
Prevalence of lifetime dual use
Among the Chinese population aged 20–69 years in mainland of China, the weighted prevalence of dual use of cigarettes and e-cigarettes was estimated as 0.82% (0.65%, 1.03%) in China, 2.95% (2.34%, 3.70%) among those who ever smoked cigarettes, and 3.01% (2.37%, 3.80%) among those currently smoked cigarettes. The range of dual use prevalence was from 0.14% in Shandong to 17.65% in Beijing among those who ever smoked cigarettes and 0.16% in Shandong to 19.47% in Beijing among those who currently smoked cigarettes (Fig. 2 and Supplementary Table S3). An estimated 8.11 million people aged 20–69 in China dual used cigarettes and e-cigarettes, including predominantly males and among younger populations (Table 2).
Fig. 2.
The geographical distribution of dual use of cigarettes and e-cigarettes in China Notes: Hong Kong, Macao, and Taiwan were not included in the survey
Table 2.
Estimates of dual cigarette and e-cigarette use prevalence and population size among Chinese adults aged 20–69 years
| Estimated total number (million, 95% CI) | Prevalence among general population (%, 95% CI) | Prevalence among those who ever smoked (%, 95% CI) | Prevalence among those currently smoked (%, 95% CI) | |
|---|---|---|---|---|
| Age (year) | ||||
| 20–29 | 2.68 (1.97–3.63) | 1.36 (1.01–1.85) | 5.83 (4.18–8.07) | 5.76 (4.13–7.99) |
| 30–39 | 2.56 (1.92–3.42) | 1.20 (0.90–1.60) | 4.41 (3.30–5.88) | 4.46 (3.29–6.02) |
| 40–49 | 1.23 (0.93–1.64) | 0.54 (0.41–0.72) | 1.89 (1.45–2.48) | 1.93 (1.48–2.52) |
| 50–59 | 0.60 (0.44–0.82) | 0.29 (0.21–0.40) | 0.95 (0.70–1.28) | 0.96 (0.68–1.35) |
| 60–69 | 0.31 (0.19–0.51) | 0.21 (0.13–0.34) | 0.67 (0.40–1.13) | 0.67 (0.38–1.20) |
| Sex | ||||
| Female | 0.24 (0.15–0.38) | 0.05 (0.03–0.08) | 2.18 (1.28–3.68) | 1.59 (0.90–2.80) |
| Male | 7.85 (6.17–9.97) | 1.56 (1.23–1.99) | 2.98 (2.38–3.73) | 3.06 (2.42–3.87) |
CI Confidence interval
Associated factors of lifetime dual use
In the weighted adjusted logistic analyses, the results were consistent in the two models. Among those who ever smoked cigarettes, older age (P trend < 0.001) and being married (aOR 0.73, 95%CI 0.55 to 0.97, P = 0.026) were associated with lower odds of dual use, compared with younger or single individuals. Urban residence (aOR 1.48, 95%CI 1.22 to 1.81, P < 0.001) or an annual family income > 49,999 was associated with higher odds of dual use (aOR 1.36, 95%CI 1.04 to 1.78, P = 0.025) compared to rural residence or an income < 20,000. Additionally, higher educational levels (middle and high school: aOR 1.91, 95%CI 1.40, 2.63, P < 0.001; college and higher: aOR 3.78, 95%CI 2.66, 5.44, P < 0.001) were associated with higher odds of dual use than those with a lower educational level. Regarding smoking-related behaviors, with nicotine dependence (aOR 1.56, 95%CI 1.29 to 1.88, P < 0.001), more cigarettes smoked per day (aOR 1.31, 95%CI 1.03 to 1.66, P = 0.028), earlier smoking starting age (aOR 1.47, 95%CI 1.11 to 2.00, P = 0.009), continue to use tobacco after it caused health problem (aOR 1.27 95%CI 1.03 to 1.56, P = 0.023), failed tobacco quit or cut-down experience (aOR 1.75, 95%CI 1.44 to 2.13, P < 0.001) were associated with increased odds of dual use. Plus, those with poor self-reported health status were more likely to report dual use (aOR 1.97, 95%CI 1.33 to 2.83, P < 0.001). In addition, nicotine dependence (aOR 1.50, 95%CI 1.23 to 1.83, P < 0.001), more cigarettes smoked per day (aOR 1.37, 95%CI 1.06 to 1.76, P = 0.016), earlier smoking starting age (aOR 1.49, 95%CI 1.10 to 2.04, P = 0.011), or failed tobacco quit or cut-down experience (aOR 1.72, 95%CI 1.40 to 2.13, P < 0.001) were associated with increased odds of dual use. Among those currently smoked cigarettes, consistent results were found that younger participants (P < 0.001), those living in urban areas (aOR 1.58, 95%CI 1.29 to 1.95, P < 0.001), single participants (aOR 1.35, 95%CI 1.01 to 1.82, P = 0.042), those with higher education level (college and higher: aOR 4.00, 95%CI 2.77, 5.86, P < 0.001), or those with poor self-reported health status (aOR 1.87, 95%CI 1.22 to 2.78, P = 0.003) were associated with increased odds of dual use (Table 3).
Table 3.
Factors associated with dual use of cigarettes and e-cigarettes versus exclusive cigarette use
| Among those who ever smoked | Among those who currently smoked | |||||||
|---|---|---|---|---|---|---|---|---|
| Model 1 | Model 2 | Model 1 | Model 2 | |||||
| aOR [95%CI] | P | aOR [95%CI] | P | aOR [95%CI] | P | aOR [95%CI] | P | |
| Sex | ||||||||
| Female | ref | ref | ref | ref | ||||
| Male | 1.09 [0.72, 1.73] | 0.698 | 1.04 [0.68, 1.66] | 0.873 | 1.25 [0.79, 2.12] | 0.378 | 1.18 [0.74, 2.01] | 0.525 |
| Age (year) | ||||||||
| 20–29 | ref | ref | ref | ref | ||||
| 30–39 | 0.55 [0.43, 0.70] | < 0.001 | 0.64 [0.48, 0.84] | 0.001 | 0.55 [0.43, 0.72] | < 0.001 | 0.64 [0.48, 0.86] | 0.003 |
| 40–49 | 0.25 [0.19, 0.32] | < 0.001 | 0.38 [0.29, 0.52] | < 0.001 | 0.25 [0.19, 0.33] | < 0.001 | 0.39 [0.29, 0.54] | < 0.001 |
| 50–59 | 0.12 [0.09, 0.17] | < 0.001 | 0.21 [0.15, 0.30] | < 0.001 | 0.13 [0.09, 0.17] | < 0.001 | 0.22 [0.15, 0.31] | < 0.001 |
| 60–69 | 0.10 [0.07, 0.14] | < 0.001 | 0.19 [0.13, 0.28] | < 0.001 | 0.11 [0.08, 0.15] | < 0.001 | 0.22 [0.14, 0.32] | < 0.001 |
| Residence | ||||||||
| Rural | ref | ref | ref | ref | ||||
| Urban | 2.01 [1.67, 2.42] | < 0.001 | 1.48 [1.22, 1.81] | < 0.001 | 2.15 [1.77, 2.62] | < 0.001 | 1.58 [1.29, 1.95] | < 0.001 |
| Marital status | ||||||||
| Single | ref | ref | ref | ref | ||||
| Married | 0.74 [0.57, 0.97] | 0.029 | 0.73 [0.55, 0.97] | 0.026 | 0.74 [0.56, 0.99] | 0.037 | 0.74 [0.55, 0.99] | 0.042 |
| Separated/Divorced/Widowed | 0.80 [0.51, 1.23] | 0.325 | 0.90 [0.57, 1.40] | 0.655 | 0.82 [0.51, 1.28] | 0.386 | 0.92 [0.57, 1.46] | 0.744 |
| Education level | ||||||||
| Primary school or less | ref | ref | ref | ref | ||||
| Middle and high school | 2.16 [1.61, 2.96] | < 0.001 | 1.91 [1.40, 2.63] | < 0.001 | 2.22 [1.63, 3.10] | < 0.001 | 1.96 [1.42, 2.75] | < 0.001 |
| College and higher | 5.33 [3.85, 7.48] | < 0.001 | 3.78 [2.66, 5.44] | < 0.001 | 5.72 [4.07, 8.18] | < 0.001 | 4.00 [2.77, 5.86] | < 0.001 |
| Annual family income (RMB/year) | ||||||||
| < 20,000 | ref | ref | ref | ref | ||||
| 20,000–49,999 | 1.03 [0.78, 1.37] | 0.817 | 0.94 [0.70, 1.25] | 0.645 | 0.99 [0.74, 1.33] | 0.950 | 0.89 [0.66, 1.19] | 0.422 |
| > 49,999 | 2.04 [1.60, 2.62] | < 0.001 | 1.36 [1.04, 1.78] | 0.025 | 2.03 [1.58, 2.63] | < 0.001 | 1.31 [1.00, 1.73] | 0.056 |
| Self-reported health status | ||||||||
| Good | ref | ref | ref | ref | ||||
| Average | 1.37 [1.13, 1.66] | 0.001 | 1.41 [1.16, 1.71] | < 0.001 | 1.36 [1.11, 1.66] | 0.002 | 1.40 [1.14, 1.71] | 0.001 |
| Poor | 1.64 [1.11, 2.33] | 0.009 | 1.97 [1.33, 2.83] | < 0.001 | 1.57 [1.03, 2.32] | 0.028 | 1.87 [1.22, 2.78] | 0.003 |
| Nicotine Dependence | ||||||||
| No | ref | ref | ref | ref | ||||
| Yes | 1.43 [1.19, 1.73] | < 0.001 | 1.56 [1.29, 1.88] | < 0.001 | 1.39 [1.15, 1.70] | 0.001 | 1.50 [1.23, 1.83] | < 0.001 |
| FTND, % | ||||||||
| 0–3 | ref | ref | ref | ref | ||||
| 4–6 | 1.00 [0.83, 1.21] | 0.975 | 1.10 [0.91, 1.34] | 0.323 | 0.99 [0.81, 1.21] | 0.940 | 1.09 [0.89, 1.33] | 0.401 |
| > 6 | 1.44 [0.98, 2.04] | 0.051 | 1.74 [1.18, 2.48] | 0.003 | 1.44 [0.98, 2.07] | 0.055 | 1.74 [1.17, 2.51] | 0.004 |
| Cigarettes smoked per day | ||||||||
| < 10 | ref | ref | ref | ref | ||||
| 10–19 | 0.99 [0.79, 1.25] | 0.949 | 1.11 [0.88, 1.41] | 0.380 | 1.06 [0.83, 1.36] | 0.624 | 1.20 [0.93, 1.54] | 0.156 |
| > 19 | 1.04 [0.83, 1.32] | 0.712 | 1.31 [1.03, 1.66] | 0.028 | 1.08 [0.84, 1.38] | 0.552 | 1.37 [1.06, 1.76] | 0.016 |
| Smoking starting age | ||||||||
| < 20 | ref | ref | ref | ref | ||||
| 20–24 | 0.90 [0.74, 1.10] | 0.313 | 0.84 [0.69, 1.03] | 0.093 | 0.91 [0.74, 1.11] | 0.354 | 0.84 [0.68, 1.03] | 0.097 |
| > 24 | 0.74 [0.54, 0.98] | 0.041 | 0.68 [0.50, 0.90] | 0.009 | 0.74 [0.54, 1.00] | 0.058 | 0.67 [0.49, 0.91] | 0.011 |
| Smoking pack-years | ||||||||
| < 10 | ref | ref | ref | ref | ||||
| 10–19 | 1.00 [0.81, 1.25] | 0.967 | 1.07 [0.86, 1.34] | 0.528 | 1.01 [0.80, 1.27] | 0.938 | 1.10 [0.87, 1.39] | 0.449 |
| > 19 | 1.05 [0.80, 1.38] | 0.731 | 1.27 [0.96, 1.68] | 0.099 | 1.10 [0.82, 1.47] | 0.535 | 1.34 [1.00, 1.81] | 0.052 |
| Continue to use tobacco after it caused health problem | ||||||||
| No | ref | ref | ref | ref | ||||
| Yes | 1.15 [0.93, 1.40] | 0.184 | 1.27 [1.03, 1.56] | 0.023 | 1.09 [0.88, 1.34] | 0.442 | 1.19 [0.96, 1.48] | 0.104 |
| Continue to use tobacco after it caused mental problems | ||||||||
| No | ref | ref | ref | ref | ||||
| Yes | 1.10 [0.89, 1.35] | 0.370 | 1.19 [0.96, 1.47] | 0.102 | 1.01 [0.81, 1.26] | 0.910 | 1.09 [0.87, 1.35] | 0.454 |
| Trying to quit or cut down on tobacco but failed | ||||||||
| No | ref | ref | ref | ref | ||||
| Yes | 1.66 [1.37, 2.02] | < 0.001 | 1.75 [1.44, 2.13] | < 0.001 | 1.65 [1.34, 2.03] | < 0.001 | 1.72 [1.40, 2.13] | < 0.001 |
Model 1 adjusted for sex and age; Model 2 adjusted for sex, age, marriage status, education level, annual family income, and residence
aOR adjusted odds ratio, CI Confidence interval, FTND Fagerström Test of Nicotine Dependence
As for withdrawal symptoms when stopping smoking or reducing the amount, dual use was associated with more withdrawal symptoms including the urge to smoke (39.7% vs. 32.3%, P = 0.022), hard to concentrate (23.1% vs. 16.3%, P = 0.006), quick-tempered (12.2% vs. 8.8%, P = 0.031), increased appetite (8.8% vs. 3.7%, P < 0.001), weight gain (7.5% vs. 4.3%, P = 0.001), involuntary hand tremor (1.8% vs. 0.9%, P = 0.032), and sleepy (11.4% vs. 6.1%, P = 0.001) (Fig. 3).
Fig. 3.
Withdrawal symptoms among those dual used of cigarettes and e-cigarettes or those exclusively smoked cigarettes Notes: No specific sub-options were provided for the category “Other group” during data collection
Discussion
To our knowledge, this nationwide representative study was the first to provide evidence for the adult lifetime dual use prevalence of cigarettes and e-cigarettes and associated factors in the Mainland of China. The overall dual use prevalence was 0.82% among the population aged 20 to 69 years old in China and 2.95% among those who had ever smoked cigarettes. Previous research on dual use has focused on adolescents [9, 14–18], while the evidence for adults was inadequate. Though the prevalence estimated seemed relatively low in China, the absolute number was huge given the population size. China has more than 300 million people who smoke and the prevalence of smoking among people aged 15 and above was 26.6% in 2020 [28]. Based on the study’s prevalence, the dual use population was estimated to be 8.11 million among those aged 20–69 years using the 2018 Chinese population data [29]. Given the large number and the dangers of dual use in increasing nicotine dependence [30], it was important to identify the associated factors.
The prevalence of dual use of cigarettes and e-cigarettes demonstrated heterogeneity across cities in China. The prevalence reached 17.65% in Beijing, followed by Liaoning with 5.91%, while Shandong had only 0.14%. One potential explanation for this difference may lie in population distribution disparities. For example, population census data show that 85.96% of Beijing’s residents live in urban areas, compared to only 49.71% in Shandong [31]. Given that e-cigarette use is more prevalent in urban settings than in rural areas, a finding consistent with both our research and previous studies [22], Beijing’s higher urban population ratio may contribute to its elevated rate of dual cigarette and e-cigarette use. Another possible explanation was the establishment of an e-cigarette brand company with a market share of more than 60% in retail sales in 2018, whose headquarters were set up in Beijing. Until 2018, adult e-cigarette use had not been effectively regulated, as China only enacted regulations focused on sales to adolescents [32]. Regulation and inspection of the overall e-cigarette market were not documented until 2020 [33]. Since 2020, China’s regulatory policy on e-cigarettes has been constantly updated and standardized, which may affect tobacco use patterns and behaviors. Thus, repeated surveillance is necessary for dual use in the future, especially for the effectiveness of policy implementation and tobacco control.
Young, single individuals with higher education levels, higher incomes, and previous unsuccessful smoking cessation attempts increased odds of dual use of cigarettes and e-cigarettes in our study. First, our study indicated that high education levels or high family income were associated with increased odds of dual use compared with exclusive cigarette use. Adolescents in Ireland with self-reported academic achievement below average were more likely to report dual use than those who do not smoke (OR 2.43, 95%CI 1.62 to 3.63), while the high family socioeconomic status may reduce dual use (OR 0.67, 95%CI 0.51 to 0.90) [18]. Cambron et al. also found that neighborhood poverty was associated with increased odds of dual use (β = 0.15, SE = 0.04, P < 0.001) [9]. This may be due to the different e-cigarette use patterns in different countries, as e-cigarettes were started as a luxury product aimed at the high-income population [22]. In China, the price of a pack of cigarettes varies significantly, ranging from a few yuan to over 100 yuan. In comparison, a standard e-cigarette cartridge, which typically lasts 2–3 days, costs approximately 30–50 yuan. For individuals who primarily consume low-cost cigarettes, e-cigarettes may represent a relatively expensive alternative. Furthermore, prior studies have not adequately addressed the price disparities between dual users of conventional cigarettes and e-cigarettes in China. Our study may provide evidence for the socio-economic disparities in this aspect. Second, among those who smoked combustible cigarettes, our study found that young single participants were more likely to report dual use than older or married individuals, which was consistent with other countries. Previous studies in France and the US showed that younger people who smoked were more likely to report dual use than exclusively use cigarettes [21, 34]. On the contrary, when compared with those exclusively used e-cigarettes, a study in New Zealand covering the nationwide population showed that people more than 45 years old were more likely to report dual use than those 15–34 years old [35]. It indicated that the e-cigarette had a strong appeal to adolescents. For the healthy development of the next generations, the results indicated a need for further research on effective interventions to reduce smoking initiation among youth, particularly regarding dual use of conventional cigarettes and e-cigarettes. Third, we found that more failed attempts to quit or reduce tobacco use, as well as continued tobacco use despite experiencing health problems, were associated with higher odds of dual use. The study in the US echoed the results [21], and another online survey in France discovered that dual use was associated with more previous quit attempts (OR 0.63, 95%CI 0.50 to 0.79) than exclusive use of e-cigarettes [34]. These results implied that although dual use related to stronger cessation intentions than exclusive use of cigarettes, those who dual use appeared to encounter more substantial barriers to successful quitting [36]. Thus, effective clinical smoking cessation interventions for those who smoked heavily should be implemented as early as possible, to prevent dual use and consequently serious adverse health consequences. As we also found that dual use was associated with poor health status, the health effects of dual use needed further study, and the dual use population needed more policy attention. Fourth, smoking a larger number of cigarettes and initiating smoking at an earlier age were associated with higher odds of dual use, which indicated that dual use may coexist with larger lifelong nicotine intake amounts. Plus, we found that urban residency was associated with increased odds of dual use, which may be related to the accessibility of e-cigarettes. The results of the risk factors for dual use suggested the high-risk group to be concerned about in China and emphasized the need to offer prompt and effective smoking cessation help.
Our study also showed that certain withdrawal symptoms were more prevalent among those who engaged in dual use, compared with those who exclusively used cigarettes. Specifically, dual use was related to more mental and physical withdrawal symptoms, including the urge to smoke, hard to concentrate, quick temper, increased appetite, weight gain, involuntary hand tremors, and sleepiness. A previous study indicated that withdrawal symptoms like smoking urge could mediate and reduce the latency from tobacco abstinence to reinstating smoking [37]. A cohort study found corroborated results that dual use did not help with quitting cigarettes or e-cigarettes, and those exclusively used cigarettes were more likely to report switching to no tobacco product use compared with individuals with dual use (17.5% vs. 14.3%) [38]. Our study also indicated that dual use was associated with more attempts to quit or cut down on smoking but failed (OR = 1.40, 95%CI 1.04 to 1.89, P = 0.027), which showed that those who dual use had a desire to quit smoking. Nevertheless, some individuals regarded e-cigarettes as a means of quitting smoking [39], which may prevent them from achieving successful smoking cessation. Thus, proper management of these withdrawal symptoms and health education on smoking cessation were important for individuals with dual use. More rigorously designed studies are needed to further explore the effects of dual use on smoking cessation and its possible long-term harms. The potential mechanisms that link dual use to withdrawal symptoms also need to be investigated to better help individuals with dual use quit smoking.
The external validity of our findings is a key strength, supported by a rigorous sampling design covering 31 provinces in mainland China using a multistage, stratified PPS approach. This method was designed to achieve representativeness across key demographic and socioeconomic dimensions. However, the study has several limitations. Firstly, this was an observational cross-sectional study without long-term follow-up to infer causality. However, with a representative large sample size, this study provided potential associated factors of dual use among Chinese aged 20 to 69 years old, providing the basis for future studies. Further investigation with a follow-up design would be valuable to understand the association between these factors and subsequent dual use. Secondly, our data were obtained via a self-reported questionnaire, which may be influenced by recall bias and social desirability bias. Nevertheless, the survey followed a rigorous survey process and information collection criteria with no identifiable information collected. Thirdly, we did not measure other potential risk factors like the nicotine intake amount, or depression, which may also affect dual use. Further analyses, including these additional factors, would provide more evidence for the risk factors of dual use. Our study did not assess potential confounders like mental health status which might influence the results. Previous studies have indicated a higher prevalence of mental health conditions like depression and anxiety among e-cigarette users [40, 41]. Thus, future studies should consider accounting for mental health factors and other potential confounders [41]. Fourthly, due to the design of the questionnaire, our study was unable to determine whether participants used both cigarettes and e-cigarettes concurrently within the past 30 days, and we could not distinguish whether dual use was out of one-time curiosity or a habitual behavior. Attention should be paid when interpreting our results, such as withdrawal symptom comparison between dual users and exclusive cigarette users. Additional studies would benefit from separating dual users into current and former categories, and understanding the differences between curiosity-driven attempts and habitual use. Nevertheless, given the limited evidence on dual use of cigarettes and e-cigarettes in China, this study can provide much-needed data to address a critical evidence gap, as conventional tobacco control surveys may underestimate the risks associated with dual use. Moving forward, future research should adopt standardized questionnaires to more accurately assess the patterns of tobacco product use, including both cigarettes and e-cigarettes.
Conclusions
This study was the first nationwide investigation into the prevalence of lifetime dual use of cigarettes and e-cigarettes among adults aged 20–69 years old in China, revealing an overall prevalence of 0.82%. Although the absolute proportion remained relatively low, the large population base translated into a substantial number of individuals. Furthermore, this study delineated the demographic and risk profile of individuals with lifetime dual use, identifying key risk factors such as poorer health status, higher nicotine dependence, increased cigarette consumption, unsuccessful smoking cessation attempts, and more severe withdrawal symptoms compared to those who exclusively used cigarettes. These findings provided critical evidence for identifying at-risk populations who may transition to dual use, enabling targeted prevention interventions.
Supplementary Information
Acknowledgements
We would like to express genuine gratitude to all the participants of this study.
Authors’ contributions
D.X. designed the study; Y.X. performed the statistical analyses and drafted the manuscript; Y.L., Z.S., Z.L., and Z.C. further revised the manuscript; Z.H., A.C., X.Z., J.L., R.Q., X.W., Y.L., X.X., Q.S., L.Z. contributed to the data collection; D.X. and C.W. supervised the analyses and interpretation. All the authors reviewed and approved the manuscript.
Funding
This work was supported by National High Level Hospital Clinical Research Funding of China (2022-NHLHCRF-LX-01), Science and Technology Project of Heilongjiang Province of China (2022ZXJ03C02), and and Noncommunicable Chronic Diseases-National Science and Technology Major Project (2023ZD0506400).
Data availability
The datasets used are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
This study was approved by the Ethics Review Committee of the Chinese Center for Health Education, and a waiver of ethical approval was granted. Informed consent was obtained from all the participants in the survey.This study was approved by the Ethics Review Committee of the Chinese Center for Health Education, and a waiver of ethical approval was granted. Informed consent was obtained from all the participants in the survey.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
The original online version of this article was revised: Author Dan Xiao was designated as one of the corresponding authors.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Change history
4/14/2026
The original online version of this article was revised: an error was identified in the authors' affiliations. These have been corrected.
Change history
4/16/2026
A Correction to this paper has been published: 10.1186/s12889-026-27346-9
Contributor Information
Dan Xiao, Email: danxiao@263.net.
Chen Wang, Email: wangchen@pumc.edu.cn.
References
- 1.World Health Organization. Tobacco. 2023; Cited 3 Apr 2025. Available from: https://www.who.int/en/news-room/fact-sheets/detail/tobacco.
- 2.GBD 2021 Risk Factors Collaborators. Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the global burden of disease study 2021. Lancet. 2024;403(10440):2162–203. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.GBD 2021 Tobacco Forecasting Collaborators. Forecasting the effects of smoking prevalence scenarios on years of life lost and life expectancy from 2022 to 2050: a systematic analysis for the global burden of disease study 2021. Lancet Public Health. 2024;9(10):e729-44. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Park SH, Duncan DT, Shahawy OE, Lee L, Shearston JA, Tamura K, et al. Characteristics of adults who switched from cigarette smoking to e-cigarettes. Am J Prev Med. 2017;53(5):652–60. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Kalkhoran S, Glantz SA. E-cigarettes and smoking cessation in real-world and clinical settings: a systematic review and meta-analysis. Lancet Respir Med. 2016;4(2):116–28. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Hartmann-Boyce J, Lindson N, Butler AR, McRobbie H, Bullen C, Begh R, et al. Electronic cigarettes for smoking cessation. Cochrane Database Syst Rev. 2022;11(11):CD010216. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Borrelli B, O’Connor GT. E-cigarettes to assist with smoking cessation. N Engl J Med. 2019;380(7):678–9. [DOI] [PubMed] [Google Scholar]
- 8.QuickStats. Cigarette smoking Status* among current adult E-cigarette Users,† by age Group - National health interview Survey,§ united States, 2015. MMWR Morb Mortal Wkly Rep. 2016;65(42):1177. [DOI] [PubMed] [Google Scholar]
- 9.Cambron C, Thackeray KJ. Socioeconomic Differences in Lifetime and Past 30-Day E-Cigarette, Cigarette, and Dual Use: A State-Level Analysis of Utah Youth. Int J Environ Res Public Health. 2022 June 21;19(13):7557.
- 10.Leventhal AM, Strong DR, Kirkpatrick MG, Unger JB, Sussman S, Riggs NR, et al. Association of electronic cigarette use with initiation of combustible tobacco product smoking in early adolescence. JAMA. 2015;314(7):700–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Martínez Ú, Martínez-Loredo V, Simmons VN, Meltzer LR, Drobes DJ, Brandon KO, et al. How does smoking and nicotine dependence change after onset of vaping? A retrospective analysis of dual users. Nicotine Tob Res. 2020;22(5):764–70. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Rostron BL, Schroeder MJ, Ambrose BK. Dependence symptoms and cessation intentions among US adult daily cigarette, cigar, and e-cigarette users, 2012–2013. BMC Public Health. 2016;16(1):814. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.X H. W L, Y X, Y Z, W W, H W, Association of conventional and electronic cigarette use with suicidality in Chinese adolescents: The moderating effect of sex and school type. Journal of affective disorders. 2024 Nov 15; Cited 21 Jul 2025;365. Available from: https://pubmed.ncbi.nlm.nih.gov/39187181/.
- 14.Aleyan S, Hitchman SC, Ferro MA, Leatherdale ST. Trends and predictors of exclusive e-cigarette use, exclusive smoking and dual use among youth in Canada. Addict Behav. 2020;109:106481. [DOI] [PubMed] [Google Scholar]
- 15.Abadi MH, Shamblen SR, Thompson K, Lipperman-Kreda S, Grube J, Richard BO, et al. Socio-temporal contextual and community factors associated with daily exclusive ENDS use and dual use with tobacco cigarettes among adolescent vapers: an ecological momentary assessment study. BMC Public Health. 2022;22(1):2289. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Wills TA, Knight R, Williams RJ, Pagano I, Sargent JD. Risk factors for exclusive e-cigarette use and dual e-cigarette use and tobacco use in adolescents. Pediatrics. 2015;135(1):e43-51. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Conner M, Grogan S, Simms-Ellis R, Scholtens K, Sykes-Muskett B, Cowap L, et al. Patterns and predictors of e-cigarette, cigarette and dual use uptake in UK adolescents: evidence from a 24-month prospective study. Addiction. 2019;114(11):2048–55. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Bowe AK, Doyle F, Stanistreet D, O’Connell E, Durcan M, Major E, et al. E-cigarette-only and dual use among adolescents in Ireland: emerging behaviours with different risk profiles. Int J Environ Res Public Health. 2021;18(1):332. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Villanueva-Blasco VJ, Belda-Ferri L, Vázquez-Martínez A. A systematic review on risk factors and reasons for e-cigarette use in adolescents. Tob Induc Dis. 2025;23. 10.18332/tid/196679.
- 20.Gallus S, Lugo A, Stival C, Cerrai S, Clancy L, Filippidis FT, et al. Electronic cigarette use in 12 European countries: results from the TackSHS survey. J Epidemiol. 2023 June;5(6):276–84.
- 21.Rhoades DA, Comiford AL, Dvorak JD, Ding K, Driskill LM, Hopkins AM, et al. Dual versus never use of E-Cigarettes among American Indians who smoke. Am J Prev Med. 2019 Sept;57(3):e59–68.
- 22.Zhao Z, Zhang M, Wu J, Xu X, Yin P, Huang Z, et al. E-cigarette use among adults in china: findings from repeated cross-sectional surveys in 2015-16 and 2018-19. Lancet Public Health. 2020;5(12):e639–49. [DOI] [PubMed] [Google Scholar]
- 23.Li Y. Introduction of 2012 Chinese residents health literacy monitoring program [in Chinese]. Chin J Heath Educ. 2014;30(6):563–5. [Google Scholar]
- 24.Liu Z, Li YH, Cui ZY, Li L, Nie XQ, Yu CD, et al. Prevalence of tobacco dependence and associated factors in china: findings from nationwide China health literacy survey during 2018-19. Lancet Reg Health West Pac. 2022 July;24:100464.
- 25.Heatherton TF, Kozlowski LT, Frecker RC, Fagerström KO. The Fagerström test for nicotine dependence: a revision of the Fagerström tolerance questionnaire. Br J Addict. 1991 Sept;86(9):1119–27.
- 26.China National Health and Family Planning Commission. China Clinical Guidelines for Tobacco Cessation (2015 version). Beijing: People’s Medical Publishing House. 2015. Available from: https://www.cfchina.org.cn/uploadfile/2016/0622/%E4%B8%AD%E5%9B%BD%E4%B8%B4%E5%BA%8A%E6%88%92%E7%83%9F%E6%8C%87%E5%8D%9720150515-final.pdf.
- 27.Li Q, Hsia J, Yang G. Prevalence of smoking in China in 2010. N Engl J Med. 2011 June;23(25):2469–70.
- 28.Chinese Report on the Health Risks of Smoking. 2020. Beijing: National Health Care Commission of China; 2020.
- 29.National Bureau of Statistics. The China Statistical Yearbook 2019. National Bureau of Statistics. 2019. Available from: https://www.stats.gov.cn/sj/ndsj/2019/indexch.htm.
- 30.Martinez U, Simmons VN, Sutton SK, Drobes DJ, Meltzer LR, Brandon KO, et al. Targeted smoking cessation for dual users of combustible and electronic cigarettes: a randomised controlled trial. Lancet Public Health. 2021 July;6(7):e500–9.
- 31.National Bureau of Statistics. Tabulation on the 2010 Population Census of the Peoples Republic of China. National Bureau of Statistics. 2012 Dec. Available from: https://www.stats.gov.cn/sj/pcsj/rkpc/6rp/indexch.htm.
- 32.State Administration of Market Supervision State Tobacco Monopoly Administration Circular on the Prohibition of the Sale of Electronic Cigarettes to Teenagers. State Administration of Market Supervision and Regulation & State Tobacco Monopoly Administration. 2018 Aug. Available from: https://www.gov.cn/zhengce/zhengceku/2018-12/31/content_5438414.htm.
- 33.Special inspection action programme for the e-cigarette market. State Administration of Market Supervision and Regulation & State Tobacco Monopoly Administration. 2020 July. Available from: https://www.samr.gov.cn/zw/zfxxgk/fdzdgknr/xyjgs/art/2023/art_d1d16d46b2754dc2a6661c9d69c65326.html.
- 34.Berlin I, Nalpas B, Targhetta R, Perney P. Comparison of e-cigarette use characteristics between exclusive e-cigarette users and dual e-cigarette and conventional cigarette users: an on-line survey in France. Addiction. 2019;114(12):2247–51. [DOI] [PubMed] [Google Scholar]
- 35.Oakly A, Martin G. Dual use of electronic cigarettes and tobacco in New Zealand from a nationally representative sample. Aust N Z J Public Health. 2019;43(2):103–7. [DOI] [PubMed] [Google Scholar]
- 36.Sweet L, Brasky TM, Cooper S, Doogan N, Hinton A, Klein EG, et al. Quitting behaviors among dual cigarette and E-cigarette users and cigarette smokers enrolled in the tobacco user adult cohort. Nicotine Tob Res. 2019;21(3):278–84. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Aguirre CG, Madrid J, Leventhal AM. Tobacco withdrawal symptoms mediate motivation to reinstate smoking during abstinence. J Abnorm Psychol. 2015;124(3):623–34. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Manzoli L, Flacco ME, Ferrante M, La Vecchia C, Siliquini R, Ricciardi W, et al. Cohort study of electronic cigarette use: effectiveness and safety at 24 months. Tob Control. 2017;26(3):284–92. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.McRobbie H, Bullen C, Hartmann-Boyce J, Hajek P. Electronic cigarettes for smoking cessation and reduction. Cochrane Database Syst Rev. 2014;(12):CD010216.
- 40.Erhabor J, Boakye E, Obisesan O, Osei AD, Tasdighi E, Mirbolouk H, et al. E-cigarette use among US adults in the 2021 behavioral risk factor surveillance system survey. JAMA Netw Open. 2023;6(11):e2340859. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Cai J, Bidulescu A. E-cigarette use or dual use of E-cigarette and combustible cigarette and mental health and cognitive impairment: findings from the National health interview Survey, 2020–2021. J Affect Disord. 2024;351:878–87. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets used are available from the corresponding author on reasonable request.