Abstract
Objectives
Tobacco use remains a significant public health concern, particularly among youth, who are at a higher risk of developing long-term addiction and related health complications. The early initiation of tobacco use, often during adolescence and young adulthood, contributes to the prevalence of smoking-related diseases later in life. Despite various anti-tobacco initiatives, the consumption of tobacco products continues to be widespread among young people. Understanding the patterns, and predictors, of tobacco use in this demographic is crucial for developing targeted interventions. This study aimed to assess the prevalence of tobacco use among youth, and identify key risk factors, in this vulnerable population.
Methods
This cross-sectional study was conducted among 675 undergraduate students at five selected colleges in Delhi using a two-stage stratified random sampling method. Data were collected through a pre-designed, pre-tested, self-administered questionnaire, incorporating validated tobacco-related questions from the Global Adult Tobacco Survey (GATS). Data analysis was performed using SPSS version 21.0. Chi-square tests were used to compare sociodemographic variables between tobacco users and non-users, while bivariate and multivariable logistic regression identified factors influencing tobacco use.
Results
Of the 675 college students studied the mean age of participants was 19.62 years (SD ± 1.33), 52.6 % were females and 47.4 % males. The overall prevalence of ever and current tobacco use was 38.9 % and 23.7 %, respectively. Cigarettes were reported as the most commonly used tobacco product, with 33 % of participants reporting ever smoking and 20.3 % being current smokers. Hookah was the second most popular, with 27.9 % and 12 % students reporting ever and current use. Lifetime/Ever use of e-cigarettes and smokeless tobacco was reported by 14.7 % and 5.3 %, respectively, while current use was low for both (2.4 % and 2.1 %). Multivariate analysis revealed that current tobacco use was significantly associated with academic performance, living away from parents, substance use, high psychological distress, and the father’s educational status.
Conclusion
The findings highlight a relatively high prevalence of tobacco use among college students, particularly in urban areas, emphasizing the need for targeted interventions. Cigarettes ranked as the top choice of tobacco product among the study population, underlining the urgency for health professionals, educators, and policymakers to implement specific strategies aimed at preventing tobacco use among youth.
Introduction
Tobacco use, with a primary emphasis on cigarette smoking, stands as a significant and pervasive public health challenge with dire global implications. The toll exacted by tobacco is staggering, with over eight million lives claimed annually worldwide [1]. Of this grim statistic, seven million fatalities are directly attributed to tobacco use, while an additional 1.2 million result from exposure to second-hand smoke [2]. The ubiquity of cigarette smoking makes it the most prevalent form of tobacco consumption on a global scale, although other products such as water pipe tobacco, various smokeless tobacco items, cigars, cigarillos, rolling tobacco, and bidis also play roles in the wider landscape of tobacco usage [3]. Alarmingly, data gleaned from previous studies underscores that nearly 90 % of adult smokers embark on their tobacco journey before the age of 18 [4]. In 2015, the prevalence of current tobacco users, encompassing all forms of tobacco, constituted 24.9 % of the global population. Within this context, the prevalence of tobacco use among youth aged 1524 in 2015 was estimated at 17 %, with notable disparities between genders, where 27.6 % of males and 5.6 % of females were identified as users [5].
Zooming in on India, insights from the GATS-2 survey reveal that the overall prevalence of any form of tobacco usage among youth stands at 11.9 % [6]. More granularly, the prevalence of current smoking and smokeless tobacco use is identified at 5 % and 10.9 %, respectively [6]. Delving deeper into the behavioral aspects, the mean age of initiation for tobacco use in the Indian context is found to be 20.9 years, with the average age of commencing daily smoking at 17 years and the initiation of smokeless tobacco usage at 22.3 years [7]. The repercussions of tobacco use are far-reaching and manifold, encompassing a heightened risk of various ailments, including cancers, heart disease, stroke, lung diseases, diabetes, and chronic obstructive pulmonary disease (COPD), which comprises conditions such as emphysema and chronic bronchitis. Furthermore, tobacco usage elevates susceptibility to tuberculosis, certain ocular diseases, and immune system disorders, such as rheumatoid arthritis [8].
Tobacco use among adolescents and youth in India has emerged as a critical and multifaceted public health concern. The juxtaposition of the world’s largest youth population with a persistent prevalence of tobacco consumption presents a complex challenge with significant implications for both current and future generations. This issue not only threatens the health and well-being of Indian adolescents and youth but also poses long-term socio-economic consequences. India’s demographic landscape is characterized by a burgeoning youth segment, with a substantial percentage falling within the age bracket of 15–24 years. This age group is particularly vulnerable to the allure of tobacco, with the potential for lifelong addiction and the associated health risks. Understanding the dynamics of tobacco use among Indian youth is crucial, as it sets the stage for effective interventions, policy development, and public health initiatives.
Hence the current study was undertaken to delve into the prevailing patterns, prevalence rates, and socio-cultural factors illuminating the complex tapestry of tobacco use among college students and youth in Delhi, India. By shedding light on the nuanced interplay between tobacco and youth in India, we aim to provide a foundation for informed decision-making, preventive measures, and targeted interventions that can safeguard the health and future of this vital demographic.
Materials and methods
Study area
Delhi university is a centrally located university in Delhi which was founded in 1922 by an Act of the Central Legislative Assembly. Delhi is divided into 11 administrative zones. For the purpose of this study, we categorized these 11 zones into the following five zones:
Central Zone: New Delhi and Central Delhi,
East Zone: East Delhi and Shahdara,
South Zone: South and South-east Delhi,
West Zone: West & South-west Delhi,
North Zone: North, North-east and North-west Delhi.
Study setting and population
The present study was carried out across five colleges affiliated to Delhi University (DU), chosen randomly from the above-mentioned North, South, East, West and Central zones of Delhi. One college from each zone was chosen to get a representative sample of the study participants. This cross-sectional study was conducted among undergraduate students at colleges affiliated to DU. Inclusion criteria were all students currently enrolled as full-time students in undergraduate programs in the selected Co-educational Colleges affiliated to the university. Students who were enrolled as part-time students or enrolled in correspondence or distance-learning programs and foreign students enrolled in exchange programs were excluded from the study. Colleges providing education for students with special needs, professional educational programs e.g. College of Nursing, Medicine, and Dental Colleges etc. were also excluded.
Sample size
This study forms part of a larger research project, and the sample size calculation was determined based on parameters established in a previously published study [9]. The final sample size calculated was 675.

Flowchart for selection of study participants.
Sampling Technique: Two-stage Stratified Random Sampling was performed
First Stage: Random selection of one College from each zone using random number tables.
Second Stage: Random selection of 135 Students from each college; 45 each from 1st, 2nd and 3rd years of graduation (Figure 1).
Study methodology
A list of eligible colleges affiliated with Delhi University (DU) was organized by zone (North, South, East, West, Central). One college per zone was randomly selected using random number tables, and the principals of the selected institutions were approached for written permission to conduct the survey among undergraduate students. If a college declined participation, the subsequent institution on the list was contacted, following the same procedure until one college per zone consented. Upon obtaining permissions, a list of enrolled students was compiled, categorized by academic year and subject stream. From each academic year (first, second, and third), 45 students meeting the inclusion criteria were randomly selected to ensure adequate representation. The investigator briefed teachers at the selected colleges about the study, and each college assigned a professor as a liaison between the investigator and participants. Data collection days were coordinated with college authorities, and participants were invited through the liaison on predetermined dates. Absent students received up to three reminders; if still unavailable, a new participant was randomly selected. Surveys were conducted in designated classrooms without college staff present, ensuring privacy. Participation was voluntary and anonymous, with students instructed not to include their names. Questionnaires, distributed by the investigator, were self-administered in English or Hindi, though all chose English. The investigator remained available to clarify any questions during the process.
Study tools
A pre-designed, pre-tested, self-administered questionnaire was used, translated into Hindi, and pre-tested at another university in the Delhi NCR region to ensure reliability. Based on the pilot study results, modifications were made. The questionnaire included sections on socio demographic details (age, gender, religion, parental education, residence, family income, academic status, and self-reported height and weight) and health risk behaviors. The behaviors were assessed using questions adapted from the CDC’s YRBS-2019, covering tobacco use, alcohol, dietary habits, and physical activity. The validated questionnaire, available in the public domain, is widely approved for use internationally [10]. The current study specifically analysed tobacco related behaviors.
Operational definitions used in the study
Tobacco Use: was assessed based on a dichotomous variable on the percentage of students who smoked cigarettes, cigars, beedi, hookah, e-cigarette or used smokeless tobacco.
Ever/LifetimeTobacco User: – Those who used any tobacco products mentioned above in their life at least once.
Current Tobacco User: – Those who used any tobacco products (cigarettes, cigars, beedi, hookah, e-cigarette or used smokeless tobacco) any time (one or more days) in the last 30 days.
Never Tobacco User: – Those who had never used any form of tobacco in their lifetime.
Data analysis
The collected data were entered into a Microsoft Excel spreadsheet in a coded format. Relevant variables were generated, and appropriate coding was applied to each. The data analysis was conducted using a licensed version of IBM SPSS Statistics software (version 21, IBM Corp., Armonk, NY). Descriptive statistics were used to summarize all variables: categorical data were presented as frequencies or proportions, while continuous data were reported as means with standard deviations (SD). Bivariate analysis was performed using the Chi-square test or Fisher’s exact test, as appropriate, and binary logistic regression to assess the association between current tobacco use and potential risk factors. Following the descriptive analysis, bivariate logistic regression analysis was conducted using the chi-square test to identify factors that independently correlated with tobacco use. Variables that showed significant associations (p<0.2) in the bivariate analysis were included in the multivariate regression analysis. Multiple logistic regression was then applied to examine the relationship, if any, and obtain adjusted odds ratios with corresponding 95 % confidence intervals for each characteristic. A p-value <0.05 was deemed statistically significant in determining the significance of the findings.
Ethical considerations
Clearance for the study was taken from the Institutional Ethics Committee. Institutional Review Board, Vardhman Mahavir Medical College & Safdarjung Hospital issued approval IEC/VMMC/SJH/THESIS/2019-10/04. Written permission was taken from the principals of each selected college. Written informed consent was taken from the participants, and personal information was kept strictly confidential. The survey was completely anonymous, and there is no way of linking the responses back to the students or the college.
Results
The study was conducted among 675 undergraduate students from Delhi University, with a mean age of 19.62 years (SD= ± 1.328). Participants were aged between 17 and 23 years, with 48.9 % in the 17–19 age group and 51.1 % in the 20–23 group. Of the participants, 52.6 % were female and 47.4 % male. Most identified as Hindu (84.4 %), followed by Muslim (8.7 %) and Christian (2.7 %). A majority (74.8 %) were from urban areas, and 63.3 % of mothers had at least a graduate degree. Regarding fathers, 65.5 % had a graduate or postgraduate education. Socio-economic status showed 71 % of participants from the upper class, 17.4 % from the upper middle class, and 7.1 % from the middle class. Most students were pursuing Arts (34.5 %), followed by science (33.8 %) and Commerce (31.7 %). Academically, 75.1 % had grades above 70 %. In terms of future plans, 45.9 % intended to continue their studies, 22.8 % aimed to work, and 22.4 % planned to both work and study.
Regarding parental employment, 72.4 % of participants had fathers working, 24.8 % had both parents working, and 1.6 % reported that only their mothers were employed. 75.7 % of participants lived with their parents, while 24.3 % lived away, including 12.5 % in hostels. BMI analysis showed 14.3 % underweight, 49.6 % normal weight, and 36 % overweight or obese. Among underweight participants, 76 % were female, while 55.2 % of overweight participants were male. 56.9 % of obese participants were male (Table 1).
Distribution of study participants according to socio-demographic characteristics (n=675).
| Characteristics | Number (%) | |
|---|---|---|
| 1. Age groupa (years) | ||
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| 17–19 | 330 (48.9) | |
| 20–23 | 345 (51.1) | |
| aMean age=19.62 years; SD=± 1.328; Max=23 Min=17; Range=6 | ||
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| 2. Gender | ||
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| Female | 355 (52.6) | |
| Male | 320 (47.4) | |
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| 3. Place of birth | ||
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| Rural | 170 (25.2) | |
| Urban | 505 (74.8) | |
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| 4. Religion | ||
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| Hinduism | 570 (84.4) | |
| Islam | 59 (8.7) | |
| Christianity | 28 (4.1) | |
| Others | 18 (2.7) | |
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| 5. Mothers educational status | ||
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| Graduate and above | 427 (63.3) | |
| Not a graduate | 248 (36.7) | |
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| 6. Fathers educational status | ||
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| Graduate and above | 442 (65.5) | |
| Not a graduate | 233 (34.5) | |
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| 7. Socio-economic class b | ||
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| Upper (I)>Rs. 7,770 | 477 (71) | |
| Upper middle (II) Rs. 3,808–7,769 | 117 (17.4) | |
| Middle (III) Rs. 2,253–3,808 | 48 (7.1) | |
| Lower middle (IV) Rs. 1,166–2,253 | 25 (3.7) | |
| Lower V<Rs. 1,166 | 5 (0.8) | |
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| 8. Year of college | ||
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| 1st year | 225 (33.3) | |
| 2nd year | 225 (33.3) | |
| 3rd year | 225 (33.3) | |
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| 9. Educational stream | ||
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| Science | 228 (33.8) | |
| Commerce | 214 (31.7) | |
| Arts | 233 (34.5) | |
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| 10. Academic performance | ||
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| Grades≥70 % | 507 (75.1) | |
| Grades<70 % | 168 (24.9) | |
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| 11. Body mass index | ||
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| Underweight<18.5 kg/m2 | 96 (14.3) | |
| Normal (18.5–22.9 kg/m2) | 333 (49.6) | |
| Overweight (23–24.9 kg/m2 | 105 (15.6) | |
| Obese >25 kg/m2) | 137 (20.4) | |
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aAccording to modified B.G. Prasad scale classification (2021), bthree students did not report monthly family income.
Prevalence of tobacco use
Out of 675 participants, 262 (38.9 %) participants responded that they had used or tried one or more forms of tobacco products at least once in their lifetime. Of those 262 participants; 160 (61.1 %) participants used some form of tobacco products in the past 30 days. Almost, a quarter of the total study participants had used some form of tobacco in the past 30 days (Table 2).
Distribution of study participants according to tobacco use (n=675).
| Tobacco usera | Number (%) |
|---|---|
| 1. Ever tobacco user | |
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| Yes | 262 (38.9) |
| No | 413 (61.1) |
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| 2. Current tobacco user | |
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| Yes | 160 (23.7) |
| No | 515 (76.3) |
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aIncludes smoking, smokeless tobacco, hookah and e-cigarettes.
Cigarettes were found to be the most popular form of tobacco product used amongst the study participants. Almost one-third of the study participants had ever tried smoking in their lifetime, a fifth of the study participants reported to have smoked in the past one month. Hookah was the second most popular method, with 188 (27.9 %) participants responding to have ever smoked a hookah at least once in their life and 12 % (81/675) reporting usage within the past 30 days.
Lifetime usage of e-cigarette and smokeless tobacco was prevalent amongst 14.7 % (99/675) and 5.3 % (36/675) of respondents, respectively. The current usage of both the products was only 2.4 % (16/675) and 2.1 % (14/675) amongst the study participants, indicating less inclination towards these. To compute the overall prevalence of current tobacco use, individual prevalence of four of the above-mentioned tobacco products was assessed. Among the 675 study respondents one-fifth were current smokers. After combing the individual prevalence, the overall prevalence of current tobacco use was reported as 23.7 % (160/675) in our study (Table 3).
Patterns of current tobacco use amongst the study participantsa (n=675).
| Pattern of current tobacco use | |
|---|---|
| Type of tobacco useda | Number (%) |
| 1. Current cigarette smokers | |
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| Yes | 137 (20.3) |
| No | 538 (79.7) |
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| 2. Current smokeless tobacco users | |
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| Yes | 14 (2.1) |
| No | 661 (97.9) |
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| 3. Current hookah users | |
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| Yes | 81 (12) |
| No | 594 (88) |
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| 4. Current E-cigarette users | |
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| Yes | 16 (2.4) |
| No | 659 (97.6) |
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aNot mutually exclusive.
Factors associated with current tobacco use
Among the 675 participants, a higher proportion of students aged 20–23 years (31.3 %) were current tobacco users compared to those aged 17–19 years (15.8 %), with a significant difference (p<0.05). Tobacco use was also more prevalent among males (29.1 %) than females (18.9 %) (p<0.05). The educational status of parents was significantly associated with tobacco use, with higher rates among students whose mothers or fathers did not graduate (44 % vs. 11.8 % for mothers, and higher for fathers as well) (p<0.05).
No significant differences were found in tobacco use based on rural or urban residency or socio-economic status (p>0.05). The chi-square analysis showed that third-year students had the highest rate of tobacco use (33.8 %), followed by second year (20.9 %) and first-year students (16.4 %) (p<0.05). Current tobacco use was also linked to lower academic grades, living away from parents, and higher rates of alcohol use, substance abuse, and psychological stress (p<0.05). No significant association was found between tobacco use and the students’ field of study (science, arts, or commerce) (p>0.05) (Table 4).
Factors associated with current tobacco use amongst study participants (n=675).
| Characteristic | Current tobacco use | Total n (%) | p-Value | ||
|---|---|---|---|---|---|
| No n (%) | Yes n (%) | ||||
| I. Age group | <0.01a,b | ||||
| 17–19 | 278(84.2) | 52(15.8) | 330(100) | ||
| 20–23 | 237(68.7) | 108(31.3) | 345(100) | ||
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| II. Gender | <0.01 a,b | ||||
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| Female | 288(81.1) | 67(18.9) | 355(100) | ||
| Male | 227(70.9) | 93(29.1) | 320(100) | ||
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| III. Place of origin | 0.835b | ||||
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| Rural | 131(77.1) | 39(22.9) | 170(100) | ||
| Urban | 384(76) | 121(24) | 505(100) | ||
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| IV. Educational status of parents | |||||
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| A. Mother graduate or above | <0.01 a,b | ||||
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| No | 139(56) | 109(44) | 248(100) | ||
| Yes | 376(88.1) | 51(11.9) | 427(100) | ||
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| B. Father graduate or above | <0.01 a,b | ||||
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| No | 121(51.9) | 112(48.1) | 233(100) | ||
| Yes | 394(89.1) | 48(10.9) | 442(100) | ||
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| V. Socio-economic status | 0.053c | ||||
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| Upper or upper middle class | 445(74.9) | 149(25.1) | 594(100) | ||
| Middle class | 42(87.5) | 6(12.5) | 48(100) | ||
| Lower middle or lower | 26(86.7) | 4(13.3) | 30(100) | ||
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| VI. Year of graduation | <0.01 a | ||||
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| 1st year | 188(83.6) | 37(16.4) | 225(100) | ||
| 2nd Year | 178(79.1) | 47(20.9) | 225(100) | ||
| 3rd year | 149(66.2) | 76(33.8) | 225(100) | ||
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| VII. Stream | 0.177 | ||||
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| Science | 179(78.5) | 49(21.5) | 228(100) | ||
| Commerce | 168(78.5) | 46(21.5) | 214(100) | ||
| Arts | 168(72.1) | 65(27.9) | 233(100) | ||
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| VIII. Grades | <0.01 a | ||||
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| ≥70 % | 366(87.2) | 141(12.8) | 507(100) | ||
| <70 % | 47(36.3) | 121(63.7) | 168(100) | ||
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| IX. Living with parents | <0.01 a | ||||
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| Yes | 445(87.1) | 66(12.9) | 511(100) | ||
| No | 70(42.7) | 94(57.3) | 164(100) | ||
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| X. Ever substance use | <0.01 a | ||||
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| No | 471(86.6) | 73(13.4) | 544(100) | ||
| Yes | 44(33.6) | 87(66.4) | 131(100) | ||
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| XI. Psychological distress | <0.01 a | ||||
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| Low | 255(87) | 38 (13) | 293(100) | ||
| Medium | 136(72.7) | 51(27.3) | 187(100) | ||
| High | 124 (63.6) | 71(36.4) | 195(100) | ||
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aSignificant association at p-Value level (<0.05); bchi-square test; cfischer exact test.
After controlling for factors which were independently associated with current tobacco use (p≤0.2) Table 4; the multivariate analysis results revealed odds of current tobacco use to be significantly higher among students with grades ≤70 [aOR=6.7, CI=3.8–11]; living away from parents [aOR=3, CI=1.8–5.2]; using substances [aOR=5.6, CI=3.2–8.6]; and high psychological distress [aOR=1.9, CI=1.1–3.7]. Those students whose fathers were graduates or above had significantly lower odds of currently using tobacco [aOR=0.4, CI=0.2–0.7]. However, mother’s educational status did not increase the odds of using tobacco after controlling for all factors. Moreover, age, gender, socio-economic status, year of study, and study subject, did not significantly increase the odds of current tobacco use amongst the study participants (Table 5).
Bivariate and multivariate logistic regression analysis for factors associated with current tobacco use (n=675).
| Variable | Total n (%) | Unadjusted OR; (95 %CI) | p-Value | Adjusted OR; (95 %CI) | p-Value | |
|---|---|---|---|---|---|---|
| I. Age group | ||||||
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| 17–19 | 330(48.9) | Reference | Reference | |||
| 20–23 | 345(51.1) | 2.4(1.7–3.5) | <0.01 a | 0.9(0.4–2.2) | 0.9 | |
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| II. Gender | ||||||
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| Female | 355(52.6) | Reference | Reference | |||
| Male | 320(47.4) | 1.8(1.2–2.5) | <0.01 a | 1.5(0.8–2.6) | 0.1 | |
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| III. Socio-economic status | ||||||
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| Upper | 594(88.4) | 2.1(0.7–6.3) | 0.15 | 2.5(0.7–4.0) | 0.2 | |
| Middle | 48(7.2) | 0.9(0.3–3) | 0.9 | 1.2(0.3–3) | 0.2 | |
| Lower | 30(4.4) | Reference | Reference | |||
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| III. Educational status of mother | ||||||
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| Graduate or above | 427(63.3) | Reference | Reference | |||
| Not a graduate | 248(36.7) | 0.2(0.1–0.3) | <0.01 a | 0.6(0.3–1.3) | 0.1 | |
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| IV. Educational status of father | ||||||
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| Graduate or above | 442(65.5) | 0.2(0.1–0.3) | <0.01 a | 0.4(0.2–0.7) | <0.01 a | |
| Not a graduate | 233(34.5) | Reference | Reference | |||
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| V. Year of study | ||||||
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| 1st year | 225(33.3) | Reference | Reference | |||
| 2nd year | 225(33.3) | 1.3(0.8–2.1) | 0.2 | 0.8(0.4–1.9) | 0.7 | |
| 3rd year | 225(33.3) | 2.5(1.6–4.1) | <0.01 a | 1.9(0.7–4.9) | 0.2 | |
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| VI. Stream | ||||||
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| Science | 228(33.7) | Reference | Reference | |||
| Commerce | 214(31.8) | 1(0.6–1.5) | 0.9 | 1.6(0.8–3.2) | 0.2 | |
| Arts | 233(35.5) | 1.4(0.9–2.1) | 0.1 | 1.4(0.7–2.6) | 0.3 | |
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| VII. Grades | ||||||
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| ≥70 % | 507(75.1) | Reference | Reference | |||
| ≤70 % | 168(24.9) | 9(6.0–13.1) | <0.01 a | 6.7(3.8–11) | <0.01 a | |
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| VIII. Living with parents | ||||||
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| Yes | 511(75.7) | Reference | Reference | |||
| No | 164(24.3) | 9.1(6.2–13.5) | <0.01 a | 3(1.8–5.2) | <0.01 a | |
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| IX. Ever substance use | ||||||
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| No | 544(80.6) | Reference | Reference | |||
| Yes | 131(19.4) | 12(8.2–19.7) | <0.01 a | 5.6(3.2–8.6) | < 0.01 a | |
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| X. Psychological distress | ||||||
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| Low | 293(43.4) | Reference | Reference | |||
| Medium | 187(27.7) | 2.5(1.6–4.0) | <0.01 a | 1.6(0.8–3.2) | 0.1 | |
| High | 195(28.9) | 3.8(2.4–6.0) | <0.01 a | 1.9(1.1–3.7) | 0.04 a | |
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aSignificant association at p-Value level (<0.05).
Discussion
The present study was conducted among 675 undergraduate students from five selected colleges to assess the prevalence of tobacco use and the factors associated with it. The study found that 38.9 % of participants had ever used any form of tobacco, with 23.7 % participants reporting current use. These findings are similar to those of Malhotra et al. [11], who reported 34 % for ever tobacco use and 25 % for current use. Among the specific types of tobacco use, 31.3 % of students had ever smoked, and 20.3 % were current smokers. The prevalence of ever hookah use was 27 %, while 14.7 % of students had ever used e-cigarettes. Smokeless tobacco use was relatively low, with 5.3 % reporting ever use and 2.2 % using it currently. Comparisons with other studies revealed both higher and lower prevalence rates. For example, Anand et al. [12] reported a lower rate of 22.9 % for ever tobacco use among female students at Delhi University, while studies by Nagendra et al., Kumar et al., and Mohanan et al. reported much lower prevalence rates (7.4 %, 16.4 %, and 7.2 %, respectively) [13], [14], [15]. In contrast, smokeless tobacco use was higher in studies by Sharma et al. and Malhotra et al., which reported 11 % and 8.2 %, respectively [11], 16]. However, prevalence rates similar to the current study were found in a study by Jodalli et al., which reported 4.8 % for smokeless tobacco use [17]. The variation in prevalence across these studies can be attributed to differences in study settings, populations, and time periods.
In the present study, no significant difference was observed between current tobacco use and gender (p>0.05). This finding contrasts with previous studies by Jodalli et al. (Mangalore, Karnataka, 2020), Janeswar et al. (Bhubaneswar, Odisha, 2019), Kumar et al. (Delhi, 2014), Mohanan et al. (Udupi, Karnataka, 2014), Mukhopadhyay et al. (West Bengal, 2012), and Sharma et al. (Delhi, 2010), all of which reported gender-based differences in tobacco use [14], 15], [17], [18], [19], [20]. In the current study, a higher percentage of males (58.1 %) were current tobacco users compared to females (41.9 %). However, after controlling for other factors, the association between gender and tobacco use was found to be non-significant. This discrepancy could be attributed to differences in the operational definition of tobacco use. In the present study, tobacco use was defined as the consumption of any form of tobacco, including cigarettes, smokeless tobacco, hookah, or e-cigarettes, whereas many previous studies have focused solely on smoking or smokeless tobacco.
Interestingly, the current study also suggests a shift in trends, with an increasing number of females using tobacco compared to earlier years. In addition, the study found a significant association between current tobacco use and the father’s educational status. Students whose fathers had attained at least a graduate-level education were 0.4 times less likely to be current tobacco users compared to their peers (p<0.05). This aligns with the notion that higher parental education may play a protective role in preventing tobacco use among youth.
The study further revealed a significant association between academic performance and current tobacco use, with students performing poorly academically being six times more likely to use tobacco compared to their high-performing counterparts. This finding is consistent with previous research by Mukhopadhyay et al. (West Bengal, 2012) and Jones et al. (USA, 2020), who also found low academic achievement to be associated with higher tobacco use [19], 21].
Additionally, students’ living arrangements were significantly related to tobacco use. Those living away from their parents (in hostels, with friends, or alone) were three times more likely to use tobacco compared to those living with their parents. This is in line with findings from Peltzer et al., who also observed higher tobacco use among students living independently [22].
Psychological distress was another factor significantly associated with current tobacco use. Students experiencing high levels of psychological distress were 1.9 times more likely to use tobacco than those with low or no psychological distress (p<0.05). This finding is supported by previous studies, such as those by Jones et al., Peltzer et al., and Grant et al., who also noted a positive correlation between psychological distress and tobacco use [21], [22], [23].
Substance abuse was similarly found to be a strong predictor of tobacco use, with individuals who had ever used any substance being 5.6 times more likely to use tobacco currently compared to those who had never used substances (p<0.05). This is consistent with studies by Grant et al. and Peltzer et al., which also highlighted the link between substance use and tobacco consumption [22], 23].
However, no significant associations were found between age, socioeconomic status (SES), study subject, year of study, or place of origin (rural/urban) and current tobacco use. These results align with studies by Sharma et al., who found no relationship between SES and tobacco use, and Jodalli et al., who reported no significant associations between age or rural-urban location and tobacco use [16], 17]. In contrast, Kumar et al. observed a significant rural-urban difference in tobacco use, indicating that regional factors may also play a role in tobacco consumption patterns [24].
In summary, the current study provides insights into the complex factors influencing tobacco use among students, highlighting the significance of family education, academic performance, living arrangements, psychological distress, and substance abuse as key predictors, while gender, age, and SES appeared to have a lesser impact.
The study’s strengths include the use of a pre-designed, pre-tested, and validated tool for data collection, enhancing the reliability of the results. It was conducted across five different administrative zones in Delhi, ensuring a representative sample of participants. Additionally, a scientific methodology was employed for sample size calculation and participant selection, and identifiers were removed to maintain anonymity and reduce social-desirability bias. However, the study also has limitations. Its cross-sectional design prevents establishing causality. Conducted among students from a single university in Delhi, the findings may not be generalizable to other universities or the broader population in India. Additionally, as the study was based in an urban setting, the results may not reflect the experiences of students in non-urban areas. The self-report nature of the survey may also introduce under-reporting or over-reporting biases.
Despite its limitations, the findings from this study are promising and contribute valuable insights to the existing body of evidence on tobacco use patterns among this vulnerable population. These findings can help inform policymakers in addressing this growing issue in society.
Conclusions
In conclusion, this study highlights a relatively high prevalence of tobacco use among college students, particularly in urban areas, emphasizing the need for targeted interventions. Cigarettes ranked as the top choice of tobacco product among the study population, underlining the urgency for health professionals, educators, and policymakers to implement specific strategies aimed at preventing tobacco use among youth. Despite existing regulations governing the sale of tobacco and alcohol, underage individuals continue to access these substances, calling for a re-evaluation and stricter enforcement of laws. Additionally, monitoring systems should be established to prevent violations, with harsher penalties for those selling to minors. The current health system, which focuses predominantly on curative care, must shift towards a greater emphasis on health promotion and disease prevention. This shift is especially crucial for youth, where preventive measures can be more effective than treatment. Comprehensive, evidence-based educational and promotional programs targeting multiple health risk behaviors are essential for deterring young people from engaging in risky behaviors. These programs should be developed based on scientific evidence and tailored to the specific needs of college students, incorporating their input for greater relevance and success. Furthermore, involvement of teachers and parents, alongside the sensitization of the wider community, is vital in addressing tobacco use in this population. Parents significantly influence adolescents’ choices and decisions, serving as vital role models. Understanding their impact is essential and future studies can study this aspect. Longitudinal studies across universities and colleges are needed to better understand trends and refine intervention strategies.
Acknowledgments
The authors extend their appreciation to all study participants for their valuable contribution.
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Research ethics: Clearance for the study was obtained from the Institutional Ethics Committee of VMMC and Safdarjung Hospital (IEC/VMMC/SJH/THESIS/2019-10/04).
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Informed consent: Informed consent was obtained from all individuals included in this study, or their legal guardians or wards.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None.
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Data availability: The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.
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© 2025 the author(s), published by De Gruyter, Berlin/Boston
This work is licensed under the Creative Commons Attribution 4.0 International License.
Artikel in diesem Heft
- Frontmatter
- Mental Health and Well-being
- Investigating the determinants of mental health literacy in school students: a school-based study
- Examination of quality of life and expressed emotion in adolescents with attention deficit hyperactivity disorder with and without specific learning disorder
- A systematic review and meta-analysis to determine the effect of pranayama in reducing anxiety and stress in adolescents
- Depression and anxiety among transgender-identifying adolescents in psychiatric outpatient care
- Substance Use and Risk Behaviours
- Adolescents’ knowledge, attitude and perceived risks towards e-cigarette usage in Johor Bahru, Malaysia
- Beyond the puff: unravelling patterns and predictors of tobacco usage among adolescents and youth in Delhi, India
- Violence, Trauma, and Safety
- Development and psychometric properties of the adolescent risk behavior questionnaire
- “Tracing the impact of childhood adversity on social anxiety in late adolescence: the moderating role of social support and coping strategies”
Artikel in diesem Heft
- Frontmatter
- Mental Health and Well-being
- Investigating the determinants of mental health literacy in school students: a school-based study
- Examination of quality of life and expressed emotion in adolescents with attention deficit hyperactivity disorder with and without specific learning disorder
- A systematic review and meta-analysis to determine the effect of pranayama in reducing anxiety and stress in adolescents
- Depression and anxiety among transgender-identifying adolescents in psychiatric outpatient care
- Substance Use and Risk Behaviours
- Adolescents’ knowledge, attitude and perceived risks towards e-cigarette usage in Johor Bahru, Malaysia
- Beyond the puff: unravelling patterns and predictors of tobacco usage among adolescents and youth in Delhi, India
- Violence, Trauma, and Safety
- Development and psychometric properties of the adolescent risk behavior questionnaire
- “Tracing the impact of childhood adversity on social anxiety in late adolescence: the moderating role of social support and coping strategies”