Abstract
Background:
E-cigarettes have been extensively marketed and popularized worldwide despite their harmful effects. To effectively plan and implement preventive measures, comprehensive analyses are needed to understand the influence of individual and contextual factors on their use.
Objective:
This study aimed to analyze the influence of poverty and demographic and socioeconomic patterns on e-cigarette use in Colombia.
Methods:
This study is based on a secondary analysis of the 2019 Colombian Survey on Psychoactive Substance Use, which included 49,756 individuals aged between 12 and 68. State-level multidimensional poverty and individual health-related, socioeconomic, and demographic characteristics were analyzed. Two-level regression models adjusted for the individual and contextual effects.
Results:
The prevalence of vaping was 4.4% (95% CI: 4.2%-4.6%), with substantial variation across departments, ranging from 0.0% to 9.6%. In the multilevel models, younger age, male sex, technical or higher education, middle-income stratum, not contributing economically to the household, affiliation with the subsidized health scheme, history of tobacco smoking, alcohol consumption, and self-reported use of other drugs were all significantly associated with vaping. The estimated median odds ratio for multidimensional poverty was 1.23 (95% CI: 1.14-1.33; p= 0.012).
Conclusions:
E-cigarette use in Colombia is a health risk and an indicator of social vulnerability that is influenced by structural determinants. Urgent action from health authorities, the education system, regulatory bodies, and civil society is needed to prevent the normalization of vaping among youth. If left unaddressed, vaping could worsen health inequalities and lead to chronic addiction-related disorders in underserved communities.
Keywords: Electronic nicotine delivery systems, vaping, cannabis, smoking, tobacco smoking, tobacco products, substance-related disorders
Graphical Abstract
Socioeconomics of e-cigarette use in Colombia: 2019 National Substance Use Survey.
Remark
| 1) Why was this study conducted? |
| In Colombia, with an estimated 4.37% prevalence of e-cigarette use, particularly in Bogotá, a high rate of vaping use among younger users highlights the need for local research to inform public health interventions. |
| 2) What were the most relevant results of the study? |
| E-cigarette use in Colombia varied across states and was associated with factors like younger age, male sex, higher education, middle income, substance use, and multidimensional poverty, highlighting disparities in a low-middle income country. |
| 3) What do these results contribute? |
| Along with individual characteristics, contextual socioeconomic factors need to be studied to understand the patterns of e-cigarette consumption better. Educational campaigns, government resources and local initiatives must be specially allocated to populations living in socioeconomically disadvantaged contexts. |
Introduction
Electronic cigarettes (e-cigs) are the most commonly used nicotine products among youth from developed countries 1, and their use has been strongly linked to respiratory illness 2. In 2019, the Center for Disease Control and Prevention (CDC) in the USA coined the term “E-cigarette or vaping product use-associated lung injury” (EVALI) for an emerging deadly respiratory illness, primarly diagnosed in youth who vape unregulated products containing vitamin E acetate, an additive product used in tetrahydrocannabinol-based products (THC) 3. Other short-term health effects have been reported, including seizures 4, increased cardiovascular risk 5, and acute gastrointestinal and systemic symptoms 6. Regarding long-term effects, evidence remains limited, and it may take many years to establish them. In the absence of conclusive evidence, e-cigs are often considered less harmful than tobacco cigarettes, facilitating their extensive marketing and widespread use as an alternative to smoking or even as cessation devices 7.
Among youth, perceived health risks associated with e-cigarette contents have been linked to various demographic factors, including gender, sexual orientation, race, and socioeconomic status (SES). Specifically, young males from suburban areas, individuals from low-income households, LGBTQ youth, racial minorities, and adolescents from families with lower levels of parental education are more likely to perceive e-cigarettes as less harmful and to initiate their use at an earlier age 8. In low- and middle-income countries, the limited available evidence suggests that the prevalence of e-cigarette use is approximately 5%. Evidence disaggregated by age groups is scarce; however, it has been reported that a non-negligible proportion of users are adolescents 9. Youth populations from impoverished areas are more frequently exposed to the sale and use of these products. They are also more vulnerable to secondhand exposure at home, school, or public spaces 10. Racial and ethnic minorities are more likely to engage in vaping behaviors as a means of relaxation or to cope with stress and anxiety 11. Additionally, factors such as exposure to social media, being between 21 and 30 years old, unemployment, perceived poor health status, and having friends or family members who use e-cigarettes have been associated with use, dependence, or difficulty quitting 12.
In Colombia, the prevalence of e-cigarette use is estimated at 4.37% (95% CI: 4.20-4.56), with the highest concentration reported in Bogotá, the capital city. Among e-cigarette users, 26.4% of individuals aged 45 years or younger have been reported to consume marijuana 13 regularly. Despite these figures, current national regulations on vaping are limited to prohibitions on advertising and promotion, sales to minors, and use in indoor public spaces. No restrictions have been established regarding the types of e-cigarettes that may be sold or their contents 14.
Considering this context, locally grounded research on e-cigarette use is imperative to advance our understanding of vaping behaviors and to inform the development of effective public health interventions. This study aimed to analyze the sociodemographic patterns of e-cigarette use in the Colombian population to provide evidence to guide the design and implementation of targeted public health actions.
Material and Methods
Data source and settings
This is a multilevel secondary analysis based on the results of the 2019 national survey on the use of psychoactive substances in Colombia. Data on sociodemographic factors, health-related information, and e-cigarette use were used. A total of 49,756 surveys were included in the analysis from individuals between 12 and 65 years old, living in urban and rural areas across 138 municipalities out of 1,122 (distributed in 32 states) in the country 15. The sampling strategy employed a multistage, probabilistic, and stratified selection scheme with national representativeness. Municipalities were selected as the primary sampling units, blocks within them as secondary units, and dwellings and households as the third and fourth stages, respectively. Further methodological details are reported elsewhere 15.
Individual-level variables
Vaping-defined as having used e-cigarettes or vaporizers containing nicotine or THC at least once in their lifetime-was the primary outcome variable (1 = yes, 0 = no). We also examined exclusive e-cigarette use (individuals who have report to vape but never have used smoked tobacco or illicit drugs), exclusive tobacco use (individuals who have smoked tobacco and never have used e-cigarettes or illegal drugs), dual use (individuals who have used e-cigarettes and tobacco at least once in their lifetime), and polysubstance use (individuals who have used e-cigarettes, tobacco, and at least one illicit drug at least once in their lifetime).
Independent variables included sociodemographic characteristics such as sex (female/male); age (analyzed both as a continuous variable and in categories: 12-14, 15-19, 20-39, 40-59, and 60-65 years) 16; educational level (primary education or less, middle or secondary education, technical education, bachelor’s degree, postgraduate degree); status as a household financial contributor (contributor/non-contributor); self-reported engagement in paid work during most of the previous week (yes/no); race or ethnicity (belonging to a minority population group or not); socioeconomic status (based on the national household classification system, with stratum one as the lowest and stratum six as the highest); and type of affiliation with the national health system (contributory or subsidized).
Tobacco use was defined as self-reported lifetime use (yes/no). Alcohol consumption was measured as use within the past month (yes/no). Lifetime use (yes/no) was also assessed for the following substances: marijuana, cocaine, heroin, methamphetamine, opioid analgesics, lysergic acid diethylamide (LSD), hallucinogenic mushrooms, ayahuasca (yage), cacao, 2C-B, and the non-medical use of tranquilizers, stimulants, and analgesics/opioids.
Regional-level variables
State-level socioeconomic status was analyzed using nationwide multidimensional poverty measures in 2018 17. Multidimensionally poor populations were defined as those deprived in 5 out of 15 indicators: education, childhood and youth conditions, employment, health, access to public utilities, and housing conditions. Linkage between individual- and regional-level data was done using the administrative codes for each department, and all records were matched and analyzed.
Statistical analysis
Sample characteristics were described using absolute and relative frequencies for categorical variables, and measures of central tendency and dispersion for continuous variables. To ensure representativeness, weighted estimates were calculated using expansion factors. Exclusive, dual, and polysubstance users were characterized according to sex, age, and socioeconomic variables. Bivariate analyses for individual-level variables were conducted using the independent χ² test, while contextual-level variables were assessed using the Wald test. Variables with p-values below 0.20 in the bivariate analysis were considered for inclusion in the multivariate models. The effect of individual-level variables for both vaping and exclusive vaping use was preliminarily assessed using a one-level stepwise logistic regression model; statistically significant variables (p <0.05) were subsequently included in the multilevel models. Individual and contextual-level variables were adjusted with a two-level multilevel logit regression model 18, in which the state variable was considered a random effect. A median OR (MOR) 19 was used to evaluate the variability of the outcome variable between regions to generate a reference value for comparison between two potential subjects in regions with opposite values of the regional aggregation variable under study, due to area-level variables. The MOR translates the area-level variance to the odds ratio scale; therefore, MOR is a measure that allows comparison with the individual OR 19. This research shows the extent to which the individual probability of having ever smoked e-cigs or vaporizers is determined by State-level socioeconomic status. All analyses were carried out using Stata version 18 software 20.
Results
Vaping prevalence among the Colombian population was 4.4% (95% CI: 4.2-4.6). The overall average age of those surveyed was 38.0 (SD: 14.8, 95% CI: 37.9-38.2) with a female proportion of 58% (95% CI: 57.6-58.4). Around 33% (95% CI: 32.8-33.7) reported having smoked tobacco; of these, 5,903 (35.8%) confirmed to have smoked tobacco in the previous 12 months. Usage of illegal drugs varied from 0.1% (0.07-0.13) of heroin consumption to 8.0% (7.7-8.2) of marijuana use. Medications without prescription use were reported in 1.8% (1.7-1.9) for tranquilizers, 0.1% (0.09-0.2) for stimulants, and 0.1% (0.09-0.2) for opioids (Table 1).
Table 1. E-cigarettes use and individual characteristics.
| Total (N=49,756) | E-cigarettes users (N=2,178) | Non-E-cigarettes users (N=47,578) | p-value | |
|---|---|---|---|---|
| Age (Mean (SD, CV) / Weighted mean (SE)) | 38.0 (14.8, 0.4) / 35.7 (0.10) | 27.8 (10.3, 0.4) | 38.5 (14.8, 0.4) | <0.001 |
| Working hours per week (Mean (SD, CV) / Weighted mean (SE)) | 48.4 (14.7, 0.3) / 48.7 (0.11) | 48.3 (15.1, 0.3) | 48.4 (14.7, 0.3) | 0.781 |
| N (% / %exp.) | N (%) | N (%) | ||
| Male sex | 20,898 (42.0 / 48.2) | 1,362 (62.5) | 19,536 (41.0) | <0.001 |
| Primary or less level education | 7,482 (15.1 / 13.2) | 68 (3.1) | 7,414 (15.6) | <0.001 |
| Secondary level education | 22,893 (46.0 / 49.5) | 950 (43.6) | 21,943 (46.1) | |
| Technical education | 8,453 (17.0 / 16.1) | 404 (18.5) | 8,049 (16.9) | |
| Bachelor’s degree | 8,754 (17.6 / 17.2) | 623 (28.6) | 8,131 (17.1) | |
| Graduate degree | 2,145 (4.3 / 3.9) | 132 (6.1) | 2.013 (4.2) | |
| SES 1 - Lowest | 14,460 (29.2 / 23.0) | 303 (13.9) | 14,157 (29.9) | <0.001 |
| SES 2 | 17,380 (35.1 / 37.8) | 736 (33.9) | 16,644 (35.2) | |
| SES 3 | 12,834 (25.9 / 29.2) | 744 (34.3) | 12,090 (25.5) | |
| SES 4 | 3,038 (6.1 / 6.3) | 238 (10.9) | 2,800 (5.9) | |
| SES 5 | 1,128 (2.3 / 2.5) | 92 (4.2) | 1,036 (2.2) | |
| SES 6 - Highest | 628 (1.3 / 1.2) | 56 (2.6) | 572 (1.2) | |
| Worked most of time last week | 28,691 (57.66) | 1,196 (54.9) | 27,495 (57.8) | 0.008 |
| Household financial contributor | 33,834 (68.0 / 63.6) | 1,318 (60,5) | 32,516 (68.3) | <0.001 |
| Subsidized health coverage | 17,209 (37.9 / 33.5) | 495 (25.9) | 16,714 (38.4) | <0.001 |
| Tobacco use | 16,557 (33.3 / 33.3) | 1,706 (78.3) | 14,851 (31.2) | <0.001 |
| Belong to a race minority | 9,143 (18.4 / 15.4) | 243 (11.1) | 8,900 (18.7) | <0.001 |
| Single/widowed/divorced | 26,922 (54.1 / 53.6) | 1,628 (74.7) | 25,294 (53.1) | <0.001 |
| Alcohol use in last month | 14,536 (55.2 / 55.2) | 1,395 (74.1) | 13,141 (53.7) | <0.001 |
| Energy drinks | 13,626 (27.4 / 31.2) | 1,281 (58.8) | 12,345 (25.9) | <0.001 |
| Other drugs | ||||
| Tranquilizers without prescription | 909 (1.8 / 1.8) | 174 (7.9) | 735 (1.5) | <0.001 |
| Stimulant without prescription | 63 (0.1 / 0.1) | 19 (0.8) | 44 (0.1) | <0.001 |
| Inhaled drugs | 181 (0.4 / 0.3) | 48 (2.2) | 133 (0.3) | <0.001 |
| Methylene chloride-based drug | 111 (0.2 / 0.3) | 50 (2.3) | 61 (0.1) | <0.001 |
| Popper | 656 (1.3 / 1.4) | 298 (13.6) | 358 (0.7) | <0.001 |
| Marijuana | 3,982 (8.0 / 8.3) | 894 (41.0) | 3,088 (6.5) | <0.001 |
| Cocaine | 973 (2.0 / 2.1) | 258 (11.8) | 715 (1.5) | <0.001 |
| Cocaine paste (basuco) | 310 (0.6 / 0.5) | 30 (1.3) | 280 (0.6) | <0.001 |
| MDMA (Ecstasy/Molly) | 294 (0.6 / 0.7) | 143 (6.5) | 151 (0.3) | <0.001 |
| Heroin | 49 (0.1 / 0.1) | 12 (0.5) | 37 (0.1) | <0.001 |
| Methamphetamine | 63 (0.1 / 0.2) | 34 (1.5) | 29 (0.1) | <0.001 |
| Opioid Analgesics | 390 (0.8 / 0.9) | 63 (2.9) | 327 (0.7) | <0.001 |
| LSD (lysergic acid diethylamide) | 285 (0.6 / 0.6) | 131 (6.0) | 154 (0.3) | <0.001 |
| Hallucinogenic mushrooms | 192 (0.4 / 0.4) | 81 (3.7) | 111 (0.2) | <0.001 |
| Ayahuasca (yage) | 585 (1.2 / 0.8) | 80 (3.6) | 505 (1.0) | <0.001 |
| Cacao | 109 (0.2 / 0.2) | 33 (1.5) | 76 (0.1) | <0.001 |
| 2C-B | 160 (0.3 / 0.3) | 84 (3.8) | 76 (0.1) | <0.001 |
Unweighted figures followed by the weighted estimates, are presented separated by a slash.
CV= Coefficient of variation; SE= linearized standard error; SES= Socioeconomic Status; %exp = expanded proportion.
The average age of e-cigs onset was 24.5 (95% CI: 24.1-24.9) years. E-cig users were younger compared with non-users, with an average age of 27.8 years (95% CI: 27.3-28.2) and 38.5 years (38.3-38.6), respectively (p <0.001). Most of the e-cig users were male (62.5%, 95% CI: 60.4-64.5), with a level of education of technical or higher (53.2%), and were single, widowed, or divorced (74.8%, 95% CI: 72.9-76.5). When compared to non-users, alcohol, energy drinks and illegal drugs usage frequencies were higher in the e-cigs user group (p <0.001). Working days per week did not show significant differences between users and non-users of e-cigs (p= 0.781). However, the proportion of individuals who financially contributed to the household was lower among e-cigarette users (p <0.001). Details are shown in Table 1. At the state level, vaping prevalence varied from 0.0% (0.00-0.49) in the Archipelago of San Andrés and Providencia to 9.57% (8.71-10.59) in Caldas, a region in the central west of Colombia, with a positive association between e-cig usage and multidimensional poverty index (p <0.001).
When analyzing age- and sex-specific patterns, the prevalence of vaping was 2.7% (95% CI: 2.0-3.5) among adolescents aged 12 to 14, 4.2% (3.6-4.9) among those aged 15 to 19, and 0.7% (0.6-0.8) among adults aged 20 to 39. Among individuals aged 40 to 59, the prevalence was 0.1% (0.04-0.13), and among those aged 60 to 65, it was 0.04% (0.01-0-2).
Among women, the prevalence of e-cigarette use was 2.8% (95% CI: 2.6-3.0), compared to 6.5% (95% CI: 6.2-6.9) among men. For both sexes, most users were between 20 and 39 years old; however, approximately 20% were aged 15 to 19. Employment status-measured by self-reported work during most of the previous week and status as a household financial contributor-was not associated with e-cigarette use among women. Among men, all socioeconomic variables and the use of other substances, except for basuco, showed significant differences between e-cigarette users and non-users (Table 2).
Table 2. Sociodemographic characteristics of vaping users by sex.
| Variables | Women | Men | ||||
|---|---|---|---|---|---|---|
| Vaping users (N=816) | Non-users (N=28028) | p-value | Vaping users (N=1361) | Non-users (N=19522) | p-value | |
| n (%) | n (%) | n (%) | n (%) | |||
| Aged 12 to 14 | 18 (2.2) | 804 (2.8) | <0.001 | 37 (2.7) | 840 (4.3) | <0.001 |
| 15 to 19 | 158 (19.3) | 1943 (6.9) | 284 (20.8) | 1626 (8.3) | ||
| 20 to 39 | 508 (62.2) | 12117 (43.2) | 884 (64.9) | 8509 (43.5) | ||
| 40 to 59 | 109 (13.3) | 10082 (35.9) | 141 (10.3) | 6746 (34.5) | ||
| 60 to 65 | 23 (2.8) | 3096 (11.0) | 16 (1.1) | 1815 (9.3) | ||
| Primary education or less | 29 (3.5) | 4487 (16.0) | <0.001 | 39 (2.8) | 2927 (14.9) | <0.001 |
| Secondary education | 311 (38.1) | 12478 (44.5) | 639 (46.9) | 9465 (48.4) | ||
| Technical education | 175 (21.4) | 5021 (17.9) | 229 (16.8) | 3028 (15.5) | ||
| Bachelor degree | 247 (30.2) | 4825 (17.2) | 376 (27.6) | 3306 (16.9) | ||
| Postgraduate degree | 54 (6.6) | 1217 (4.3) | 78 (5.7) | 796 (4.1) | ||
| SES 1 - Lowest | 92 (11.3) | 8240 (29.5) | <0.001 | 211 (15.5) | 5917 (30.5) | <0.001 |
| SES 2 | 289 (35.5) | 9920 (35.5) | 447 (33.0) | 6724 (34.6) | ||
| SES 3 | 274 (33.6) | 7080 (25.4) | 470 (34.7) | 5010 (25.8) | ||
| SES 4 | 99 (12.1) | 1683 (6.0) | 139 (10.2) | 1117 (5.7) | ||
| SES 5 | 36 (4.4) | 621 (2.2) | 56 (4.1) | 415 (2.1) | ||
| SES 6 - Highest | 24 (2.9) | 344 (1.2) | 32 (2.3) | 228 (1.1) | ||
| Worked most of time last week | 407 (49.8) | 13513 (48.2) | 0.341 | 789 (57.9) | 13982 (71.6) | <0.001 |
| Household financial contributor | 468 (57.3) | 16976 (60.5) | 0.067 | 850 (62.4) | 15540 (79.5) | <0.001 |
| Subsidized health coverage | 186 (25.6) | 10241 (39.6) | <0.001 | 309 (26.2) | 6473 (36.7) | <0.001 |
| Tobacco use | 599 (73.4) | 6594 (23.5) | <0.001 | 1107 (81.3) | 8257 (42.2) | <0.001 |
| Belong to a race minority | 66 (8.1) | 5123 (18.2) | <0.001 | 177 (13.0) | 3777 (19.3) | <0.001 |
| Single/widowed/divorced | 598 (73.3) | 14706 (52.4) | <0.001 | 1030 (75.6) | 10588 (54.2) | <0.001 |
| Alcohol use in last month | 503 (73.6) | 5868 (47.4) | <0.001 | 892 (74.4) | 7273 (60.1) | <0.001 |
| Energy drinks | 429 (52.5) | 6340 (22.6) | <0.001 | 852 (62.5) | 6005 (30.7) | <0.001 |
| Tranquilizers without prescription | 67 (8.2) | 429 (1.5) | <0.001 | 107 (7.8) | 306 (1.5) | <0.001 |
| Stimulant without prescription | 5 (0.6) | 17 (0.1) | <0.001 | 14 (1.0) | 27 (0.1) | <0.001 |
| Inhaled drugs | 15 (1.8) | 36 (0.1) | <0.001 | 33 (2.4) | 97 (0.5) | <0.001 |
| Methylene chloride-based drug | 13 (1.6) | 18 (0.1) | <0.001 | 37 (2.7) | 43 (0.2) | <0.001 |
| Popper | 86 (10.5) | 114 (0.4) | <0.001 | 212 (15.6) | 244 (1.2) | <0.001 |
| Marijuana | 254 (31.1) | 1020 (3.6) | <0.001 | 640 (47.0) | 2068 (10.6) | <0.001 |
| Cocaine | 61 (7.5) | 153 (0.5) | <0.001 | 197 (14.4) | 562 (2.9) | <0.001 |
| Cocaine paste (basuco) | 5 (0.6) | 45 (0.2) | 0.002 | 25 (1.8) | 235 (1.2) | 0.042 |
| MDMA (Ecstasy/Molly) | 43 (5.3) | 46 (0.2) | <0.001 | 100 (7.3) | 105 (0.5) | <0.001 |
| Heroin | 3 (0.3) | 5 (0.02) | <0.001 | 9 (0.6) | 32 (0.2) | <0.001 |
| Methamphetamine | 9 (1.1) | 4 (0.01) | <0.001 | 25 (1.8) | 25 (0.1) | <0.001 |
| Opioid Analgesics | 20 (2.4) | 216 (0.8) | <0.001 | 43 (3.2) | 111 (0.6) | <0.001 |
| LSD (lysergic acid diethylamide) | 30 (3.7) | 43 (0.2) | <0.001 | 101 (7.4) | 111 (0.6) | <0.001 |
| Hallucinogenic mushrooms | 22 (2.7) | 24 (0.1) | <0.001 | 59 (4.3) | 87 (0.5) | <0.001 |
| Ayahuasca (yage) | 23 (8.2) | 263 (0.9) | <0.001 | 57 (4.2) | 242 (1.2) | <0.001 |
| Cacao | 4 (0.5) | 16 (0.1) | <0.001 | 29 (2.1) | 60 (0.3) | <0.001 |
| 2C-B | 20 (2.4) | 20 (0.1) | <0.001 | 64 (4.7) | 56 (0.3) | <0.001 |
The regression model for individual-level variables and e-cigarette usage indicated an inverse association with age and belonging to a minority group. In contrast, tobacco and alcohol use, as well as being single, widowed, or divorced, were positively associated with e-cigarette usage. Regarding socioeconomic factors, financially contributing to the household was negatively associated with e-cigarette use, while having paid healthcare coverage, a high educational degree, and a higher socioeconomic status were positively associated. Additionally, consumption of energy drinks, tranquilizers, inhalers, poppers, marijuana, and ecstasy were also positively associated with e-cigarette usage. (Table 3, Model I).
Table 3. Significant effects at the individual- and regional-level variables.
| Model I* | Model II** | Model III*** | ||||
|---|---|---|---|---|---|---|
| Ad. OR (95% CI) | p-value | Ad. OR (95% CI) | p-value | Ad. OR (95% CI) | p-value | |
| Age | 0.94 (0.93-0.94) | <0.001 | 0.94 (0.93-0.94) | <0.001 | 0.93 (0.92-0.94) | <0.001 |
| Male sex | 1.24 (1.10-1.40) | <0.001 | 1.25 (1.11-1.41) | <0.001 | 1.27 (1.12-1.42) | <0.001 |
| Tobacco use | 5.24 (4.55-6.04) | <0.001 | 5.27 (4.57-6.08) | <0.001 | 5.54 (4.80-6.38) | <0.001 |
| Race minority | 0.77 (0.64-0.92) | 0.004 | ||||
| Single/widowed/divorced | 1.49 (1.30-1.70) | <0.001 | 1.46 (1.28-1.67) | <0.001 | 1.47 (1.28-1.68) | <0.001 |
| Alcohol use in last month | 1.62 (1.43-1.84) | <0.001 | 1.66 (1.46-1.89) | <0.001 | 1.68 (1.48-1.90) | <0.001 |
| Tranquilizers | 1.45 (1.12-1.89) | 0.005 | 1.52 (1.17-1.96) | 0.002 | ||
| Inhalants | 0.57 (0.35-0.94) | 0.028 | 0.49 (0.30-0.80) | 0.005 | ||
| Poppers | 2.06 (1.62-2.62) | <0.001 | 1.94 (1.52-2.48) | <0.001 | ||
| Marijuana | 2.23 (1.95-2.55) | <0.001 | 2.16 (1.88-2.48) | <0.001 | 2.65 (2.33-3.01) | <0.001 |
| MDMA (ecstasy) | 1.56 (1.11-2.19) | 0.010 | 1.60 (1.14-2.25) | 0.006 | ||
| Energy drinks | 1.49 (1.33-1.67) | <0.001 | 1.50 (1.33-1.70) | <0.001 | ||
| Household financial contributor | 0.65 (0.57-0.75) | <0.001 | 0.66 (0.58-0.76) | <0.001 | 0.67 (0.58-0.77) | <0.001 |
| Subsidized health coverage | 0.70 (0.60-0.80) | <0.001 | 0.71 (0.62-0.82) | <0.001 | 0.69 (0.60-0.79) | <0.001 |
| Secondary level education | 1.60 (1.14-2.26) | 0.007 | 1.61 (1.14-2.27) | 0.007 | 1.67 (1.18-2.34) | 0.003 |
| Technical education | 1.52 (1.07-2.17) | 0.021 | 1.57 (1.10-2.25) | 0.013 | 1.66 (1.17-2.37) | 0.005 |
| Bachelor degree | 1.72 (1.21-2.46) | 0.003 | 1.82 (1.27-2.60) | 0.001 | 1.90 (1.33-2.71) | <0.001 |
| Graduate degree | 2.04 (1.36-3.05) | 0.001 | 2.21 (1.47-3.32) | <0.001 | 2.29 (1.53-3.43) | <0.001 |
| SES 2 | 1.69 (1.41-2.03) | <0.001 | 1.65 (1.37-1.99) | <0.001 | 1.70 (1.41-2.06) | <0.001 |
| SES 3 | 2.12 (1.75-2.56) | <0.001 | 1.93 (1.58-2.37) | <0.001 | 2.04 (1.67-2.50) | <0.001 |
| SES 4 | 2.52 (1.97-3.22) | <0.001 | 2.25 (1.74-2.91) | <0.001 | 2.42 (1.87-3.13) | <0.001 |
| SES 5 | 2.92 (2.11-4.06) | <0.001 | 2.65 (1.89-3.73) | <0.001 | 2.86 (2.04-4.02) | <0.001 |
| SES 6 - High | 2.10 (1.37-3.22) | 0.001 | 1.87 (1.21-2.90) | 0.005 | 2.14 (1.40-3.26) | <0.001 |
| Random effects | MOR (95% CI) | p-value | ||||
| Multidimensional poverty | 1.23 (1.14-1.33) | 0.012 | ||||
*Model I: One-level logistic model.
**Model II: Two-level logit model with individual variables only.
***Model III: Two-level logit model with individual and regional variables.
In the multilevel models, once adjusted for individual-level variables, the effect of race, coca paste and opioids were no longer significant (Table 3, Model II), but persisted when the model included individual- and region-level variables simultaneously. A positive effect of the multidimensional poverty index was found, confirming that populations from the poorest areas have a greater frequency of e-cig use (Table 3, Model III). When adjusted for multidimensionally poverty, the individual variables and categories significantly related to vape or electronic cigarette use were age (inverse association) (OR: 0.93, CI 95%: 0.92-0.94), sex (male, positive association) (1.27, 1.12-1.42), education (technical and upper level, positive association), middle income stratum (SES 3, 4 and 5) (positive association), financially contributing to household (negative association) (0.67, 0.58-0.77), adscription to subsidized health system (negative association) (0.69, 0.60-0.79), ever tobacco smoke (5.54, 4.80-6.38) and alcohol use (during last month, positive association) (1.68, 1.48-1.90), marital status (without a partner, positive association) (1.47, 1.28-1.68) and self-report of using other drugs (positive associations) (2.65, 2.33-3.01). Adjusted analyses indicated a significant interstate variability. The MOR for MP was 1.23 (1.14-1.33; p= 0.012).
Exclusive, dual, and poly-substance use
The prevalence of exclusive e-cigarette use was 0.7% (95% CI: 0.6-0.8). Among these users, the majority were male (53.7%, 95% CI: 48.6-58.6), and nearly half were aged 15 to 19 years (44.2%, 95% CI: 39.3-49.2). Most had completed middle or secondary education (57.6%, 95% CI: 52.6-62.5), and approximately 86% belonged to low or middle-low socioeconomic strata (SES 1, 2, and 3). Only 32.1% (95% CI: 27.6-36.9) contributed financially to their household, and 34.4% (95% CI: 29.5-39.6) were affiliated with the contributory healthcare regime (Table 4).
Table 4. Characteristics of exclusive, dual, and polyusers.
| E-cigs only N=380 N (%) | p-value | Tobacco only N=12085 N (%) | p-value | Dual users N=1706 N (%) | p-value | Polyusers N=882 (1.8%) N (%) | p-value | |
|---|---|---|---|---|---|---|---|---|
| Male sex | 204 (53.7) | <0.001 | 6349 (52.5) | <0.001 | 1107 (64.9) | <0.001 | 635 (72.0) | <0.001 |
| Aged 12 to 14 | 45 (11.8) | <0.001 | 29 (0.2) | <0.001 | 6 (0.4) | <0.001 | 3 (0.3) | <0.001 |
| 15 to 19 | 168 (44.2) | 321 (2.7) | 230 (13.5) | 137 (15.5) | ||||
| 20 to 39 | 151 (39.7) | 4829 (39.9) | 1199 (70.3) | 649 (73.6) | ||||
| 40 to 59 | 14 (3.7) | 4845 (40.1) | 234 (13.7) | 82 (9.3) | ||||
| 60 to 65 | 2 (0.5) | 2061 (17.1) | 37 (2.2) | 11 (1.3) | ||||
| Primary education or less | 9 (2.4) | <0.001 | 2458 (20.4) | <0.001 | 55 (3.2) | <0.001 | 26 (2.9) | <0.001 |
| Secondary education | 219 (57.6) | 5218 (43.2) | 680 (39.9) | 356 (40.4) | ||||
| Technical education | 50 (13.2) | 1939 (16.1) | 336 (19.7) | 170 (19.3) | ||||
| Bachelor degree | 96 (25.3) | 1837 (15.2) | 510 (29.9) | 269 (30.5) | ||||
| Postgraduate degree | 6 (1.6) | 623 (5.2) | 124 (7.3) | 60 (6.8) | ||||
| SES 1 | 73 (19.2) | <0.001 | 3189 (26.6) | <0.001 | 218 (12.8) | <0.001 | 112 (12.7) | <0.001 |
| SES 2 | 137 (36.1) | 4228 (35.2) | 562 (33.1) | 274 (31.2) | ||||
| SES 3 | 118 (31.1) | 3337 (27.8) | 598 (35.22) | 322 (36.6) | ||||
| SES 4 | 32 (8.4) | 781 (6.5) | 198 (11.7) | 106 (12.1) | ||||
| SES 5 | 12 (3.2) | 313 (2.6) | 74 (4.4) | 43 (4.9) | ||||
| SES 6 | 8 (2.1) | 155 (1.3) | 48 (2.8) | 22 (2.5) | ||||
| Worked most of time last week | 106 (27.9) | <0.001 | 7931 (65.6) | <0.001 | 1062 (62.3) | <0.001 | 550 (62.4) | 0.004 |
| Household financial contributor | 122 (32.1) | <0.001 | 9523 (78.8) | <0.001 | 1166 (68.4) | 0.754 | 593 (67.2) | 0.623 |
| Subsidized health coverage | 116 (34.4) | 0.185 | 3996 (36.8) | 0.006 | 355 (24.0) | <0.001 | 182 (23.6) | <0.001 |
| Age (mean (SD, CV)) | 20.9 (8.0, 0.4) | <0.001 | 43.5 (14.1, 0.3) | <0.001 | 29.7 (10.9, 0.4) | <0.001 | 27.8 (9.4, 0.3) | <0.001 |
CV= Coefficient of variation
Exclusive tobacco users, dual users (e-cigarettes and tobacco), and poly-substance users showed similar distributions in terms of socioeconomic status, with most belonging to SES levels 1, 2, and 3 (Table 4).
The prevalence of exclusive tobacco use was 24.3% (95% CI: 23.9-24.7). Nearly 60% of users were aged 40 years or older. Most had completed secondary education (43.2%, 95% CI: 42.3-44.0), were actively employed (65.6%, 95% CI: 64.7-66.5), and contributed financially to their household (78.8%, 95% CI: 78.0-79.5). The prevalence of dual use was 3.4% (95% CI: 3.3-3.6), and that of polysubstance use was 1.8% (95% CI: 1.6-1.9). Most users in these latter groups were between 20 and 39 years old and had attained only secondary education (Table 4).
Adjusted models indicated that age, marital status, alcohol consumption, energy drink use, financial contribution to the household, and socioeconomic stratum were significantly associated with exclusive e-cigarette use. Male sex and educational level were no longer related to exclusive e-cigarette use when compared to general vaping users. Inter-departmental variability remained, with a median odds ratio (MOR) for multidimensional poverty of 1.13 (95% CI: 1.10-1.16; p= 0.005) (Table 5).
Table 5. Significant effects for Exclusive Vaping Use at individual- and regional-level variables .
| Model I* | Model II** | Model III*** | ||||
|---|---|---|---|---|---|---|
| Ad. OR (95%CI) | p-value | Ad. OR (95%CI) | p-value | Ad. OR (95%CI) | p-value | |
| Aged 15 to 19 | 0.62 (0.39-0.98) | 0.041 | 0.6 (0.38-0.95) | 0.029 | 0.6 (0.38-0.95) | 0.029 |
| 20 to 39 | 0.15 (0.09-0.25) | <0.001 | 0.15 (0.09-0.24) | <0.001 | 0.15 (0.09-0.24) | <0.001 |
| 40 to 59 | 0.02 (0.01-0.05) | <0.001 | 0.02 (0.01-0.05) | <0.001 | 0.02 (0.01-0.05) | <0.001 |
| 60 to 65 | 0.01 (0-0.1) | <0.001 | 0.01 (0-0.09) | <0.001 | 0.01 (0-0.09) | <0.001 |
| Single/widowed/divorced | 2.37 (1.63-3.44) | <0.001 | 2.32 (1.6-3.38) | <0.001 | 2.32 (1.6-3.38) | <0.001 |
| Alcohol use in last month | 1.34 (1.05-1.71) | 0.019 | 1.35 (1.06-1.73) | 0.016 | 1.35 (1.06-1.73) | 0.016 |
| Energy drinks | 1.6 (1.26-2.04) | <0.001 | 1.58 (1.24-2.02) | <0.001 | 1.58 (1.24-2.03) | <0.001 |
| Household financial contributor | 0.49 (0.37-0.66) | <0.001 | 0.50 (0.37-0.67) | <0.001 | 0.5 (0.37-0.67) | <0.001 |
| SES 2 | 1.66 (1.18-2.34) | 0.004 | 1.66 (1.16-2.36) | 0.005 | 1.66 (1.16-2.36) | 0.005 |
| SES 3 | 2.03 (1.43-2.9) | <0.001 | 1.97 (1.36-2.85) | <0.001 | 1.97 (1.36-2.85) | <0.001 |
| SES 4 | 2.56 (1.59-4.13) | <0.001 | 2.45 (1.5-4.01) | <0.001 | 2.46 (1.5-4.02) | <0.001 |
| SES 5 | 3.05 (1.55-6.03) | 0.001 | 2.86 (1.42-5.73) | 0.003 | 2.86 (1.43-5.75) | 0.003 |
| SES 6 - High | 3.39 (1.4-8.16) | 0.007 | 3.16 (1.29-7.71) | 0.012 | 3.17 (1.29-7.75) | 0.012 |
| Random effects | MOR (95%CI) | p-value | ||||
| Multidimensional poverty | 1.13 (1.10-1.16) | 0.005 | ||||
Stratified models were developed for each age category, except for individuals aged 40 to 59 and 60 to 65, due to small sample sizes of exclusive e-cigarette users in these groups (n = 14 and n = 2, respectively). Among adolescents aged 12 to 14 years, exclusive e-cigarette use was associated with energy drink consumption and middle-low socioeconomic status (SES 3). In those aged 15 to 19 years, younger age, alcohol consumption in the past month, use of energy drinks, and low to middle socioeconomic status (SES 2, 3, and 4) were associated with exclusive vaping. Among adults aged 20 to 39 years, exclusive e-cigarette use was also associated with younger age, energy drink use, middle to high socioeconomic status (SES 3, 4, 5, and 6), and being single, widowed, or divorced (Table 6).
Table 6. Significant effects for Exclusive Vaping Use by age category.
| Age 12 to 14 | Age 15 to 19 | Age 20 to 39 | ||||
|---|---|---|---|---|---|---|
| Ad. OR (95%CI) | p-value | Ad. OR (95%CI) | p-value | Ad. OR (95%CI) | p-value | |
| Age | 0.79 (0.69-0.9) | <0.001 | 0.84 (0.81-0.88) | <0.001 | ||
| Alcohol use in last month | 1.48 (1.03-2.13) | 0.036 | ||||
| Single/widowed/divorced | 1.84 (1.23-2.76) | 0.003 | ||||
| Energy drinks | 2.59 (1.41-4.75) | 0.002 | 1.85 (1.28-2.69) | 0.001 | 1.67 (1.21-2.32) | 0.002 |
| SES 2 | 0.92 (0.39-2.20) | 0.857 | 2.02 (1.23-3.33) | 0.006 | 1.54 (0.96-2.47) | 0.076 |
| SES 3 | 2.88 (1.35-6.15) | 0.006 | 1.98 (1.15-3.4) | 0.013 | 2.26 (1.41-3.62) | 0.001 |
| SES 4 | 2.59 (0.70-9.61) | 0.156 | 3.16 (1.54-6.48) | 0.002 | 2.14 (1.07-4.28) | 0.032 |
| SES 5 | 2.46 (0.3-20.30) | 0.402 | 3.02 (0.84-10.84) | 0.090 | 3.65 (1.56-8.56) | 0.003 |
| SES 6 - High | 1 (empty) | - | 1.64 (0.21-13.11) | 0.640 | 4.17 (1.42-12.21) | 0.009 |
Discussion
This study verified that the prevalence of e-cigarette use in Colombia is not negligible, reaching 4.4% among the population aged 12 to 65 years, a figure consistent with estimates reported in other low- and middle-income countries 21. The early average onset age (24.5 years), together with the significantly younger age of current users, reveals a worrying shift in the initiation of substance use. The prevalence observed in the 15-19 age group reinforces this concern and suggests that vaping may be replacing or complementing traditional tobacco as an entry point into other substance use. The prevalence of male and unmarried individuals among e-cigarette users aligns with findings from other contexts 22 and highlights how gender norms, social fragmentation, and possibly emotional vulnerability contribute to the adoption of this behavior. The inverse association between vaping and financial contribution to the household, contrasted with the positive association of vaping with higher education, contributory health insurance, and middle socioeconomic strata, suggests a complex intersection between economic dependency, perceived social mobility, and individual autonomy that could be further explored through longitudinal approaches.
The study also illustrates a clear pattern of behavioral clustering, where vaping co-occurs with the consumption of alcohol, energy drinks, marijuana, ecstasy, and other psychoactive substances. These results are consistent with previous literature linking e-cigarette use with risk-prone behaviors, peer influence, and the search for emotional regulation mechanisms in youth populations 23,24. The syndromic nature of these associations supports the hypothesis that vaping is not an isolated habit but rather part of a broader psychosocial risk profile, potentially tied to underlying mental health challenges, low access to healthy coping alternatives, and permissive social environments. The geographical variation in prevalence, reaching up to 9.57% in departments such as Caldas, and the positive association with multidimensional poverty indicate that this behavior is not merely an individual choice, but a reflection of territorial differences. The MOR value for poverty underscores how structural deprivation contributes to the propagation of behaviors with potential long-term health consequences, particularly in contexts with limited regulatory capacity and institutional oversight 25,26.
From a public health perspective, these findings call for urgent, evidence-based and equity-oriented interventions. Identifying high-prevalence territories and vulnerable subgroups encourages designing localized strategies that address individual risk factors and contextual and structural drivers. In departments with low socioeconomic development, it is likely that public awareness of the risks associated with e-cigarette use is insufficient, and that these products circulate in informal markets with little to no regulation. The vulnerability of adolescents in these settings-commonly exposed to social media marketing, weak institutional control, and environments permissive of substance use-warrants early, school-based, and community-centered interventions. These should include the regulation of advertisement content, restrictions on access and flavoring, and explicit inclusion of vaping in national prevention campaigns. Moreover, the co-occurrence of vaping with alcohol and drug use calls for integrated approaches that address multiple forms of substance use concurrently, recognizing their common socio-emotional and structural roots 27,28. Although targeted policies can be helpful in controlling the vaping epidemic, evidence suggests that policies that allow its sale, as the one established in Colombia in 2024 14, can lead to higher usage figures 26; therefore, a systemic strategy is needed that considers youth as a population in transition, exposed to globalized risk factors but still governed by local conditions of vulnerability and exclusion.
Despite its methodological robustness, including a nationally representative sample and multilevel analysis, this study is not exempt from limitations. The cross-sectional design restricts causal inferences, and using self-reported data introduces the risk of underreporting and recall bias. Likewise, while the findings are highly informative for the Colombian context, their external validity may be limited in countries with different demographic or regulatory environments. Nevertheless, the consistency of associations across age groups and the strength of the observed structural gradients justifies serious consideration from policymakers. Future research should prioritize methodological designs capable of capturing temporal transitions in vaping behavior, with special attention to gateway patterns, dependence trajectories, and quitting attempts. It is also imperative to explore the potential post-pandemic dynamics in vaping trends, considering the psychological aftermath of COVID-19, changes in youth sociability, and increased exposure to online marketing environments 29.
Conclusions
E-cigarette use in Colombia is emerging not only as an individual health risk but as an indicator of social vulnerability, psychosocial fragmentation, and territorial inequality. The associations with youth, single status, economic dependency, and co-use of other substances confirm that vaping is embedded in a broader field of structural determinants. Urgent action-not only from health authorities, but from the education system, regulatory bodies, and civil society-are needed to halt the normalization and propagation of vaping among youth. Considering our findings, vaping looks like a syndemic phenomenon that, if left unaddressed, could exacerbate health inequalities and generate a new wave of chronic, addiction-related disorders in already underserved communities. Colombia, and other countries facing similar dynamics, must seize the opportunity to implement bold, early, and context-sensitive strategies before the social and health costs become irreversible.
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