Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 7, 394-406 2025 Publisher: Learning Gate DOI: 10.55214/25768484.v9i7.8588 © 2025 by the authors; licensee Learning Gate © 2025 by the authors; licensee Learning Gate History: Received: 7 May 2025; Revised: 6 June 2025; Accepted: 10 June 2025; Published: 4 July 2025 * Correspondence: widiawandini@gmail.com Who keeps smoking? A repeated cross-sectional analysis of adolescent behaviour in Indonesia Widiawan Sukarno Ardhianto1*, Hartono2, Vitri Widyaningsih3, Tri Mulyaningsih4 1Department of Public Health, Faculty of Medicine, Universitas Sebelas Maret, Surakarta, Indonesia, Department of Nursing, STIKes Budi Luhur, Cimahi, Indonesia; widiawandini@gmail.com (W.S.A.). 2Faculty of Medicine, Universitas Sebelas Maret, Surakarta, Indonesia; hartono65@staff.uns.ac.id (H.). 3Department of Public Health, Faculty of Medicine, Universitas Sebelas Maret, Surakarta, Indonesia; vitri_w@staff.uns.ac.id (V.W.). 4Department of Economics, Faculty of Economics and Business, Universitas Sebelas Maret, Surakarta, Indonesia; trimulyaningsih@staff.uns.ac.id (T.M.). Abstract: Adolescent smoking remains a significant public health issue, particularly in low- and middle- income countries such as Indonesia. This study examined how socio-demographic, economic, environmental, and psychological factors influence smoking initiation and persistence among adolescents. A repeated cross-sectional analysis was conducted using data from the Indonesian Family Life Survey (IFLS) waves 3 (2000), 4 (2007), and 5 (2014), focusing on adolescents aged 15–19 years. Smoking initiation was defined as having ever smoked, and persistence as continued smoking after initiation. Logistic regression was used to assess associations with age, gender, education, academic performance, parental smoking, income, school characteristics, area of residence, and depressive symptoms. Results showed that while overall initiation declined, older adolescents (17–19 years), males, and those from low-income households remained at higher risk. Being employed increased the odds of initiation, while higher education was protective. Persistence was more likely among males, unemployed adolescents, and those with smoking parents. Depression was associated with increased initiation but lower persistence, suggesting experimentation rather than long-term use. These findings highlight the roles of socioeconomic and mental health factors in shaping smoking behavior. Targeted tobacco control interventions and integrated mental health support are urgently needed, particularly for male adolescents and those exposed to parental smoking. Keywords: Adolescent smoking, Indonesia, Smoking initiation, Smoking persistence, Tobacco control. 1. Introduction Smoking is a major risk factor for non-communicable diseases (NCDs) and remains a global public health concern. Smoking increases the risk of developing chronic conditions such as bronchitis, lung cancer, coronary heart disease, and stroke, all of which have been increasing in prevalence worldwide [1, 2]. According to the latest estimates, tobacco use was responsible for 8.7 million deaths globally [3]. In 2022, there were approximately 1.25 billion smokers worldwide [4] with over 80% of them having started smoking between the ages of 14 and 25 years, and 18.5% becoming regular smokers by the age of 15 [5]. The rising number of adolescent smokers across various countries underscores the importance of understanding the factors contributing to smoking initiation and persistence [6, 7]. Studies indicate that one in five high school students has experimented with smoking, and one in eleven becomes a habitual smoker [8-10]. In Asia, adolescents typically initiate smoking between the ages of 10 and 14 [11] whereas in Africa, smoking initiation has been reported as early as 7 years old or younger [12]. https://orcid.org/0009-0006-5075-4662 https://orcid.org/0000-0001-6112-497X https://orcid.org/0000-0003-0116-7120 https://orcid.org/0000-0002-1679-4349 395 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 7: 394-406, 2025 DOI: 10.55214/25768484.v9i7.8588 © 2025 by the authors; licensee Learning Gate Adolescence is a critical period of neurocognitive and hormonal development [13] during which individuals are shaped by personality traits, behavioral tendencies, and socio-environmental influences [14]. During this stage, adolescents are particularly vulnerable to nicotine addiction and smoking initiation [15] increasing the likelihood of transitioning to regular smoking [16, 17]. Without effective interventions to prevent early smoking initiation, tobacco use is likely to persist across generations [18]. Despite global efforts to curb adolescent smoking through tobacco control measures, the continued high prevalence of youth smoking in many countries highlights the urgent need for enhanced intervention strategies [19]. To develop effective adolescent tobacco control programs, a comprehensive understanding of the determinants of smoking initiation and persistence is essential [20]. Although various global tobacco control policies, including the WHO Framework Convention on Tobacco Control (FCTC), have been implemented to reduce smoking initiation among young people, the effectiveness of these measures varies across different populations [21]. Strategies such as higher tobacco taxation ), advertising bans , and school-based interventions have had mixed success, particularly in low- and middle-income countries (LMICs) [22-24] where cigarettes remain affordable, and tobacco marketing remains pervasive. Indonesia, in particular, has one of the highest smoking prevalence rates among adolescents in Southeast Asia. Despite existing regulations, such as pictorial health warnings and partial advertising bans, aggressive tobacco industry marketing and the availability of low-cost cigarettes continue to contribute to high smoking initiation rates among young people [25]. A growing body of literature has identified multiple factors influencing adolescent smoking behaviors, which can be grouped into several determinant categories. Sociodemographic factors such as age, sex, education level, and school performance play a significant role, with studies showing that male adolescents and those with lower academic achievement are more likely to initiate smoking [12, 26, 27]. Socioeconomic determinants, including parental occupation, parental education, parental smoking status, household income, and individual purchasing power, have also been linked to smoking initiation and persistence. Adolescents from families with smokers or low-income backgrounds are often at higher risk [28, 29]. Environmental influences further contribute to smoking behavior. Place of residence (urban or rural), school type (public or private), and school location may shape accessibility to cigarettes and exposure to tobacco marketing [30, 31]. For instance, schools in urban settings or near retail cigarette outlets may increase students' risk of smoking [32]. In addition, psychological factors particularly symptoms of depression, anxiety, or emotional distress have been shown to correlate strongly with both smoking initiation and continued use [33] 6 Adolescents with poor mental health may use tobacco as a coping mechanism [34, 35]. Analyzing the determinants of smoking initiation and persistence among adolescents is crucial, as adolescence is a formative period in which lifelong behaviors are often established [36]. Initiating smoking at a young age is associated with a higher risk of long-term nicotine dependence and the development of non-communicable diseases [37] which pose a significant burden to national health systems. Identifying the underlying factors that lead adolescents to start and continue smoking is essential for developing targeted and effective prevention strategies. In the Indonesian context, exposure to cigarette advertising, weak regulatory enforcement, and peer or family influence are key contributors to youth smoking behavior [38]. A long-term analysis of adolescent smoking patterns provides an opportunity to assess how social dynamics and public health policies have evolved over time. The findings of this study are expected to inform evidence-based interventions and support policymakers in designing youth-centered tobacco control initiatives. 2. Methods This study employs a repeated cross-sectional design using secondary data from the Indonesian Family Life Surveys (IFLS) across three waves: IFLS 3 (2000), IFLS 4 (2007), and IFLS 5 (2014). The 396 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 7: 394-406, 2025 DOI: 10.55214/25768484.v9i7.8588 © 2025 by the authors; licensee Learning Gate IFLS is a nationally representative survey that collects comprehensive health, socioeconomic, and demographic information from individuals and households in Indonesia. 2.1. Study Population and Data Collection This study utilized data from 13 out of Indonesia’s 27 provinces, selected through a stratified random sampling design to ensure representativeness, including North Sumatra, West Sumatra, South Sumatra, Lampung, DKI Jakarta, Central Java, DI Yogyakarta, East Java, West Java, Bali, West Nusa Tenggara, South Kalimantan, and South Sulawesi [39]. These provinces were selected to represent approximately 83% of the Indonesian population from the initiation of IFLS in 1993, capturing regional diversity in smoking behaviors and associated determinants. The study population consists of adolescents aged 15–19 years at the time of each survey wave. Smoking behavior was assessed through self-reported responses regarding smoking initiation and persistence. 2.2. Definition of Smoking Initiation and Persistence Smoking initiation was identified based on responses to the survey question: "Have you ever had a habit of chewing tobacco, smoking tobacco with a pipe, smoking self-rolled tobacco, or smoking cigarettes/cigars?". Adolescents who answered “yes” were classified as having initiated smoking. Smoking persistence was determined using the survey question "Is your smoking habit still ongoing?’. Respondents who reported continued smoking were classified as persistent smokers. 2.3. Determinants of Smoking Behavior Several factors were analyzed as potential determinants of smoking initiation and persistence, categorized as follows: 1. Sociodemographic Determinants: age, sex, education level, and school performance. 2. Socioeconomic Determinants: parental occupation, parental education, parental smoking status, parental income, personal income, cigarette price, cigarette type, and cigarette brand. 3. Environmental Determinants: place of residence (urban/rural), school management type (public/private), and school location. 4. Psychological Determinants: depression, assessed using standardized mental health indicators in the IFLS dataset. 2.4. Statistical Analysis Descriptive analyses were conducted to examine trends in smoking initiation and persistence across survey waves. Bivariate analyses were performed to assess associations between smoking behavior and each determinant. Multivariable logistic regression models were then used to identify factors associated with smoking initiation and persistence. The models were adjusted for potential confounders, and results were reported as odds ratios (ORs) with 95% confidence intervals (CIs). 2.5. Ethical Considerations Ethical approval for this study was obtained from the Medical and Health Research Ethics Committee, Faculty of Medicine, Universitas Sebelas Maret, with approval number: 01/02/01/2022/04. In addition, permission to use the IFLS 3–5 data was granted by RAND Corporation, the organization responsible for conducting the IFLS surveys. 3. Results 9,399 adolescents aged 15–19 years were included in the analysis across three survey waves (2000, 2007, and 2014). The proportion of male respondents was 49.01%, and most were enrolled in public school. The prevalence of smoking initiation was 20.94% in 2000, 18.48% in 2007, and 18.97% in 2014, while smoking persistence rates were 96.84%, 97.45%, and 91.62%, respectively. 397 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 7: 394-406, 2025 DOI: 10.55214/25768484.v9i7.8588 © 2025 by the authors; licensee Learning Gate Figure 1 presents the trends in adolescent smoking behavior across the three survey waves. The prevalence of smoking initiation showed a trend, with the highest rates observed in wave 4 (2007), while smoking persistence was highest in wave 3 (2000). A slight decline in smoking initiation was observed in wave 5 (2014), suggesting potential effects of tobacco control measures implemented during this period. However, despite this decline, smoking persistence remained relatively stable, indicating challenges in cessation efforts among adolescents. Figure 1. The trends in adolescent smoking behavior across the three survey waves. Table 1 presents the distribution of adolescent smoking initiation and persistence across different survey waves (2000, 2007, and 2014) based on key demographic, socioeconomic, environmental, and psychological determinants. For smoking initiation, the proportion of new smokers varied across waves, with a general decline over time. A higher prevalence of initiation was observed among late adolescents, males, those with elementary-junior high school education, those unemployed, and those with good academic performance. Socioeconomic factors such as parental education and income also showed variations, with higher initiation rates among adolescents from lower-middle-income households. Additionally, adolescents residing in urban areas and attending public schools were more likely to start smoking compared to their rural and private school counterparts. For smoking persistence, males had significantly higher persistence rates compared to females. Unemployed adolescents who were unemployed, had smoking parents, or had not experienced depressive symptoms also demonstrated a higher likelihood of continuing smoking. Table 2 summarizes the association between determinants and smoking behavior. The sociodemographic factors influencing smoking behavior among adolescents reveal significant trends across various aspects. Age plays a crucial role, as late adolescents (aged 17–19) demonstrated a significantly higher likelihood of initiating smoking compared to their younger counterparts in early adolescence (OR: 3.211, 95% CI: 2.344 - 4.398). Gender differences were also evident, with males consistently being more likely to start smoking across all survey waves. Moreover, male adolescents exhibited a higher rate of smoking persistence in the fourth wave compared to females (OR: 0.006, 95% CI:0.003 - 0.012). Education and school performance also emerged as critical determinants. Adolescents with lower academic achievement were found to have a greater likelihood of initiating smoking, particularly in the fifth survey wave (OR: 1.478, 95% CI:1.074 - 2.034). Additionally, employment status appeared to influence smoking behavior, as adolescents who were employed showed a consistently higher likelihood of initiating smoking compared to their unemployed peers (OR: 2.417, 95% CI: 1.510 - 3.870). 398 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 7: 394-406, 2025 DOI: 10.55214/25768484.v9i7.8588 © 2025 by the authors; licensee Learning Gate Table 1. Characteristics of the selected respondents according to IFLS wave. Characteristic Initiation Persistence Wave 3 (n=3,476) Wave 4 (n=2,965) Wave 5 (n=2,958) All waves (n=9.399) Wave 3 (n=728) Wave 4 (n=548) Wave 5 (n=561) All waves (n=1,837) Sociodemographic Age Early Adolescents 178 (24,5) 121 (22,0) 179 (31,9) 478 (26,0) 169 (22,97) 117 (21,91) 152 (29,57) 438 (24,99) Late Adolescents 550 (75,5) 427 (77,9) 382 (68,0) 1,359 (73,9) 536 (76,03) 417 (78,09) 362 (70,43) 1,315 (75,01) Gender Female 7 (0,96) 2 (0,36) 8 (1,43) 17 (0,93) 4 (0,57) 0 (0,00) 3 (0,58) 7 (0,40) Male 721 (99,0) 546 (99,64) 553 (98,57) 1,820 (99,07) 701 (99,43) 534 (100,0) 511 (99,42) 1,746 (99,60) Adolescent Education Not in School / Yet to Attend School 165 (22,6) 132 (24,09) 97 (17,29) 394 (21,45) 164 23,26) 130 (24,34) 91 (17,70) 385 (21,96) Elementary - Junior High School 352 (48,3) 237 (43,25) 223 (39,75) 812 (44,20) 343 (48,65) 232 (43,45) 201 (39,11) 776 (44,27) Senior High School 203 (27,8) 165 (30,11) 227 (40,46) 595 (32,39) 192 (27,23) 159 (29,78) 209 (40,66) 560 (31,95) Diploma - Bachelor’s Degree 8 (1,10) 14 (2,55) 14 (2,50) 36 (1,96) 60 (0,85) 13 (2,43) 13 (2,53) 32 (1,83) School Performance Elementary School Good (≥ average) 531 (72,94) 496 (90,51) 212 (37,79) 1,239 (67,45) 513 (72,77) 482 (90,26) 199 (38,72) 1,194 (68,11) Poor (< average) 197 (27,06) 52 (9,49) 349 (62,21) 598 (32,55) 192 (27,23) 52 (9,74) 315 (61,28) 559 (31,89) Junior High School Good (≥ average) 543 (74,59) 347 (63,32) 401 (71,48) 1,291 (70,28) 524 (74,33) 340 (63,67) 369 (71,79) 1233 (70,34) Poor (< average) 185 (24,41) 201 (36,68) 160 (28,52) 546 (29,72) 181 (25,67) 194 (36,33) 145 (28,21) 520 (29,66) Senior High School Good (≥ average) 365 (50,14) 337 (61,50) 285 (50,80) 987 (53,73) 353 (50,07) 330 (61,80) 262 (50,97) 945 (53,91) Poor (< average) 363 (49,86) 211 (38,50) 276 (49,20) 850 (46,27) 352 (49,93) 204 (38,20) 252 (49,03) 808 (46,09) Employment Status Unemployed 357 (49,04) 275 (50,18) 316 (56,33) 948 (51,61) 338 (47,94) 266 (49,81) 280 (54,47) 884 (50,43) Employed 371 (50,96) 273 (49,82) 245 (43,67) 889 (48,39) 367 (52,06) 268 (50,19) 234 (45,53) 869 (49,57) Socioeconomic Household Head's Employment Status Unemployed 86 (11,81) 73 (13,32) 81 (14,44) 240 (13,06) 80 (11,35) 73 (13,67) 76 (14,79) 229 (13,06) Employed 642 (88,19 475 (86,68) 480 (85,56) 1,597 (86,94) 625 (88,65) 461 (86,33) 438 (85,21) 1,524 (86,94) Household Head's Education Not in School / Yet to Attend School 364 (50,00) 260 (47,45) 205 (36,54) 829 (45,13) 357 (50,64) 257 (48,13) 191 (37,16) 805 (54,92) Elementary - Junior High School 251 (34,48) 187 (34,12) 198 (35,29) 636 (34,62) 242 (34,33) 183 (34,27) 183 (35,60) 608 (34,68) 399 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 7: 394-406, 2025 DOI: 10.55214/25768484.v9i7.8588 © 2025 by the authors; licensee Learning Gate Senior High School 78 (10,71) 72 (13,14) 125 (22,28) 275 (14,97) 74 (10,50) 66 (12,36) 113 (21,98) 253 (14,43) Diploma - Bachelor’s Degree 35 (4,81) 29 (5,29) 33 (5,88) 97 (5,28) 32 (4,54) 28 (5,24) 27 (5,25) 87 (4,96) Household Head's Smoking Status No 197 (27,06) 139 (25,36) 160 (28,52) 496 (27,00) 187 (26,52) 138 (25,84) 149 (28,99) 474 (27,04) Yes 531 (72,94) 409 (74,64) 401 (71,48) 1,341 (73,00) 518 (73,48) 396 (74,16) 365 (71,01) 1,279 (72,96) Household Income Lowest 164 (22,53) 102 (18,61) 108 (19,25) 374 (20,36) 154 (21,84) 101 (18,91) 101 (19,65) 356 (21,31) Lower-middle 200 (27,47) 135 (24,64) 86 (15,33) 421 (22,92) 197 (27,94) 134 (25,09) 80 (15,56) 411 (23,45) Middle 188 (25,82) 124 (22,63) 90 (16,04) 402 (21,88) 183 (25,96) 121 (22,66) 83 (16,15) 387 (22,08) Upper-middle 107 (14,70) 109 (19,89) 149 (26,56) 365 (19,87) 105 (14,89) 106 (19,85) 135 (26,26) 346 (19,74) Highest 69 (9,48) 78 (14,23) 128 (22,82) 275 (14,97) 66 (9,36) 72 (13,48) 115 (22,37) 253 (14,43) Personal Income Lowest 177 (24,31) 102 (18,61) 92 (16,40) 371 (20,20) 167 (23,69) 101 (18,91) 89 (17,32) 357 (20,37) Lower-middle 228 (31,32) 134 (24,45) 72 (12,83) 434 (23,63) 224 (31,77) 131 (24,53) 66 (12,84) 421 (24,02) Middle 163 (22,39) 116 (21,17) 104 (18,54) 383 (20,85) 160 (22,70) 114 (21,35) 95 (18,48) 369 (21,05) Upper-middle 107 (14,70) 124 (22,63) 161 (28,70) 392 (21,34) 103 (14,61) 119 (22,28) 145 (28,21) 367 (20,94) Highest 53 (7,28) 72 (13,14) 132 (15,85) 257 (13,99) 51 (7,23) 69 (12,92) 119 (23,15) 239 (13,63) Pocket Money Up to Rp, 30,000 615 (84,48) 1.802 (60,92) 1.230 (50,89) 1.388 (57,91) 601 (85,25) 397 (74,34) 387 (75,29) 1.385 (79,01) Rp, 30,000 - Rp, 50,000 38 (5,22) 176 (5,95) 405 (16,76) 158 (6,59) 36 (5,11) 37 (6,93) 15 (2,92) 88 (5,02) Rp, 50,000 - Rp, 84,207 40 (5,49) 321 (10,85) 364 (15,06) 290 (12,10) 37 (5,25) 35 (6,55) 27 (5,25) 99 (5,65) Rp, 84,207 - Rp, 155,460 20 (2,75) 392 (13,25) 203 (8,40) 330 (13,77) 17 (2,41) 31 (5,81) 53 (10,31) 101 (5,76) More than Rp, 155,460 15 (2,06) 267 (9,03) 215 (8,90) 231 (9,64) 14 (1,99) 34 (6,37) 32 (6,23) 80 (4,56) Environment Residence Rural 357 (49,04) 283 (51,64) 217 (38,68) 857 (46,65) 350 (49,65) 280 (52,43) 199 (38,72) 829 (47,29) Urban 371 (50,96) 265 (48,36) 344 (61,32) 980 (53,35) 355 (50,35) 254 (47,57) 315 (61,28) 924 (52,71) School Type Private 231 (31,73) 41 (7,48) 47 (8,38) 319 (17,37) 226 (32,06) 41 (7,68) 46 (8,95) 313 (17,86) Public 497 (68,27) 507 (92,52) 514 (91,62) 1,518 (82,63) 479 (67,94) 493 (92,32) 468 (91,05) 1,440 (82,14) Psychology Not Depressed 643 (88,32) 513 (93,61) 388 (69,16) 1,544 (84,05) 629 (89,22) 502 (94,01) 360 (70,04) 1,491 (85,05) Depressed 85 (11,68) 35 (6,39) 173 (30,84) 293 (15,95) 76 (10,78) 32 (5,99) 154 (29,96) 262 (14,95) Total 728 (100) 548 (100,0) 561 (100,0) 1,837 (19,54) 705 (100,0) 534 (100,0) 514 (100,0) 1.753 (95,4) 400 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 7: 394-406, 2025 DOI: 10.55214/25768484.v9i7.8588 © 2025 by the authors; licensee Learning Gate Table 2. Adjusted regression analyses of determinants for smoking initiation and persistense across IFLS waves. Variable Initiation Persistence Wave 3 OR Wave 4 OR Wave 5 OR Wave 3 OR Wave 4 OR Wave 5 OR (95% CI) (95% CI) (95% CI) (95% CI) (95% CI) (95% CI) Sociodemographic Age Early Adolescents ref. ref. ref. ref. ref. ref. Late Adolescents 2.112 (1.617 - 2.758)*** 3.211 (2.344 - 4.398)*** 2.215 (1.650 - 2.972)*** 2.381 (0.790 - 7.178) 1.734 (0.513 - 5.854) 2.495 (1.066 - 5.840)* Gender Male ref. ref. ref. ref. ref. ref. Female 0.004 (0.002 - 0.008)*** 0.001 (0.000 - 0.006)*** 0.006 (0.003 - 0.012)*** 0.151 0.024 - 0.962)* 0.039 (0.006 - 0.249)*** Adolescent Education Not in School / Yet to Attend School ref. ref. ref. ref. ref. ref. Elementary - Junior High School 0.712 (0.529 - 0.959)* 1.006 (0.720 - 1.405) 1.098 (0.751 - 1.604) 0.286 (0.034 - 2.398) 1.729 (0.217 - 13.749) 0.839 (0.274 - 2.566) Senior High School 0.671 (0.459 - 0.980)* 0.812 (0.542 - 1.217) 0.867 (0.580 - 1.296) 0.173 (0.020 - 1.518) 1.152 (0.149 - 8.887) 0.721 (0.200 - 2.594) Diploma - Bachelor’s Degree 0.614 (0.236 - 1.599) 0.751 (0.305 - 1.847) 0.951 (0.419 - 2.156) 0.030 (0.001 - 1.009) 0.334 (0.019 - 5.803) 1.497 (0.156 - 14.355) School Performance Elementary School Good (≥ average) ref. ref. ref. ref. ref. ref. Poor (< average) 1.146 (0.832 - 1.579) 0.824 (0.558 - 1.218) 0.927 (0.700 - 1.228) Junior High School Good (≥ average) ref. ref. ref. ref. ref. ref. Poor (< average) 1.192 (0.818 - 1.737) 0.472 (0.146 - 1.524) 1.478 (1.074 - 2.034)* Senior High School Good (≥ average) ref. ref. ref. ref. ref. ref. Poor (< average) 1.244 (0.955 - 1.621) 1.972 (0.610 - 6.379) 0.584 (0.425 - 0.803)*** Employment Status Unemployed ref. ref. ref. ref. ref. ref. Employed 1.553 (1.088 - 2.219)* 1.859 (1.263 - 2.735)** 2.417 (1.510 - 3.870)*** 1.282 (0.269 - 6.122) 0.192(0.032 - 1.154) 0.885 (0.241 - 3.249) Socioeconomic Household Head's Employment Status Unemployed ref. ref. ref. ref. ref. ref. Employed 0.746 (0.288 - 1.927) 0.398 (0.122 - 1.292) 1.152 (0.417 - 3.183) 1.731(0.371 - 8.073) Household Head's Education Not in School / Yet to Attend School ref. ref. ref. ref. ref. ref. 401 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 7: 394-406, 2025 DOI: 10.55214/25768484.v9i7.8588 © 2025 by the authors; licensee Learning Gate Elementary - Junior High School 0.848 (0.654 - 1.098) 0.786 (0.585 - 1.057) 0.858 (0.634 - 1.160) 0.652 (0.214 - 1.981) 1.454 (0.237 - 8.900) 0.972 (0.402 - 2.349) Senior High School 0.696 (0.473 - 1.022) 0.611 (0.417 - 0.897)* 0.750 (0.538 - 1.045) 0.870 (0.174 - 4.347) 0.230 (0.039 - 1.367) 0.765 (0.285 - 2.055) Diploma - Bachelor’s Degree 0.895 (0.526 - 1.524) 0.897 (0.488 - 1.650) 0.519 (0.302 - 0.893)* 1.255 (0.081 - 19.554) 1.001 (0.045 - 22.280) 0.303 (0.080 - 1.147) Household Head's Smoking Status No ref. ref. ref. ref. ref. ref. Yes 1.562 (1.217 - 2.007)*** 2.462 (1.850 - 3.277)*** 1.866 (1.424 - 2.445)*** Household Income Lowest ref. ref. ref. ref. ref. ref. Lower-middle 1.426 (1.049 - 1.938)* 1.020 (0.677 - 1.537) 0.736 (0.456 - 1.187) 3.113 (0.779 - 12.445) 0.346 (0.062 - 1.913) 0.118 (0.031 - 0.452)** Middle 1.229 (0.863 - 1.750) 1.267 (0.807 - 1.988) 0.801 (0.500 - 1.284) 2.473 (0.310 - 19.737) 0.785 I0.128 - 4.829) 0.101 (0.029 - 0.348)*** Upper-middle 1.761 (1.176 - 2.637)** 1.342 (0.850 - 2.119) 0.848 (0.534 - 1.348) 1.303 (0.249 - 6.816) 0.242 (0.051 - 1.148) 0.105 (0.026 - 0.415)** Highest 2.013 (1.177 - 3.443)* 0.888 (0.523 - 1.506) 0.740 (0.440 - 1.245) 1.289 (0.122 - 13.654) 0.125 (0.017 - 0.913)* 0.098 (0.023 - 0.416)** Personal Income Lowest ref. ref. ref. ref. ref. ref. Lower-middle Middle Upper-middle Highest Pocket Money Up to Rp. 30,000 ref. ref. ref. ref. ref. ref. Rp. 30,000 - Rp. 50,000 1.963 (1.136 - 3.394)* 1.299 (0.678 - 2.487) 1.145 (0.485 - 2.705) 0.712 (0.050 - 10.203) 5.577 (0.389 - 79.925) 7.361 (0.688 - 78.766) Rp. 50,000 - Rp. 84,207 3.299 (1.893 - 5.750)*** 1.591 (0.799 - 3.170) 1.017 (0.466 - 2.221) 0.394 (0.026 - 5.892) 4.908 (0.442 - 54.547) 7.343 (1.131 - 47.670)* Rp. 84,207 - Rp. 155,460 2.373 (1.173 - 4.801)* 2.444 (1.178 - 5.071)* 1.866 (0.852 - 4.090) 0.451 (0.025 - 8.188) 7.271 (0.683 - 77.365) 10.695 (1.710 - 66.872)* More than Rp. 155,460 3.144 (1.545 - 6.398)** 3.368 (1.509 - 7.516)** 1.943 (0.805 - 4.690) 0.502 (0.021 - 11.957) 18.499 (1.420 - 240.995)* 9.872 (1.090 - 89.391)* Environment Residence Rural ref. ref. ref. ref. ref. ref. Urban 1.153 (0.906 - 1.468) 0.999 (0.758 - 1.317) 1.082 (0.837 - 1.399) School Type Private ref. ref. ref. ref. ref. ref. Public 0.793 (0.589 - 1.069) 1.013 (0.627 - 1.636) 0.637 (0.401 - 1.013) 1.214 (0.394 - 3.739) 0.229 (0.046 - 1.131) Psychology Not Depressed ref. ref. ref. ref. ref. ref. Depressed 1.598 (1.092 - 2.338)* 1.189 (0.671 - 2.105) 1.482 (1.139 - 1.930)** 0.202 (0.069 - 0.588)** 0.264 (0.060 - 1.165) 0.462 (0.225 - 0.946)* Note: Robust see form in parentheses *** p<0.001, ** p<0.01, * p<0.05. 402 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 7: 394-406, 2025 DOI: 10.55214/25768484.v9i7.8588 © 2025 by the authors; licensee Learning Gate Beyond socio demographic influences, socioeconomic factors also played a significant role. Parental smoking was a strong predictor of adolescent smoking initiation, with adolescents from smoking households having significantly higher odds of beginning to smoke in every survey wave (OR: 1.562 , 95% CI: 1.217 - 2.007), 2.462, 95% CI: 1.850 - 3.277), 1.866, 95% CI: 1.424 - 2.445). Household income showed a complex relationship with smoking behavior; while a higher household income increased the likelihood of smoking initiation in the third survey wave (OR: 1.761, 95% CI: 1.176 - 2.637), it acted as a protective factor against smoking persistence in the fifth wave (OR: 0.101, 95% CI: 0.029 - 0.348)). Similarly, access to greater pocket money was associated with an increased likelihood of smoking initiation in wave three (OR: 3.299, 95% CI: 1.893 - 5.750) and a higher risk of smoking persistence in wave five (OR: 18.499, 95% CI: 1.420 - 240.995) . Environmental factors, such as place of residence and school type, were examined but did not show significant associations with smoking initiation or persistence. Psychological factors, particularly depression, also influenced smoking behavior. Adolescents experiencing symptoms of depression were more likely to initiate smoking (OR: 1.482, 95% CI: 1.139 - 1.930). However, depression appeared to act as a protective factor against smoking persistence. This suggests that while depressed adolescents were more inclined to experiment with smoking, they were less likely to continue smoking in the long term (OR: 0.202, 95% CI: 0.069 - 0.588). These findings highlight the multifaceted nature of smoking behavior among adolescents, influenced by sociodemographic, socioeconomic, environmental, and psychological determinants. Understanding these factors is crucial in developing targeted interventions to reduce smoking initiation and promote smoking cessation among young individuals. 4. Discussion This study examined the determinants of adolescent smoking initiation and persistence during 3 years survey period using data from the Indonesian Family Life Survey (IFLS) across three survey waves (2000, 2007, and 2014). The findings highlight the role of demographics, socioeconomic, environmental, and psychological factors in shaping adolescent smoking behavior. 4.1. Trends in Adolescent Smoking Initiation and Persistence Our results indicate a general decline in smoking initiation over time, which is consistent with global trends in tobacco control efforts. Despite this decrease, smoking initiation remains prevalent, particularly among late adolescents and males. Meanwhile, smoking persistence was highest in Wave 4 and Wave 3 , followed by a decrease in Wave 5. The slight decline in persistence by 2014 may reflect the impact of evolving tobacco control measures or shifting social norms around smoking. These findings underscore the importance of strengthening both prevention and cessation interventions to reduce smoking rates among adolescents effectively. 4.2. Demographic and Socioeconomic Determinants Age and gender were significant predictors of smoking initiation and persistence. Late adolescents (17–19 years old) were more lik ely to start smoking compared to younger adolescents,. possiblypossibly due to increased autonomy and social exposure [40]. Males had higher rates of both initiation and persistence. This alignsaligning with previous studies that highlight gender differences in tobacco use, potentially influenced by social norms and peer pressure [41, 42]. Employment status was also a significant factor, with employed adolescents more likely to initiate smoking compared to those who were unemployed. Possible explanations include increased financial independence, exposure to smoking peers in the workplace, or stress associated with employment [43]. Socioeconomic status further influenced smoking behaviors, as adolescents from lower- to middle-income households showed higher rates of smoking initiation. This trend may reflect parental smoking habits, easier access to inexpensive cigarettes, or lower awareness of smoking-related health risks within these communities 403 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 7: 394-406, 2025 DOI: 10.55214/25768484.v9i7.8588 © 2025 by the authors; licensee Learning Gate [28, 29, 44]. These findings underscore the multifactorial nature of adolescent smoking behavior, shaped by a complex interplay of demographic and socioeconomic variables. 4.3. Environmental and Psychological Factors Unlike demographic and socioeconomic determinants, environmental factors such as place of residence and school type did not show a strong association with smoking initiation or persistence. This suggests that smoking behaviors may be more influenced by individual and familial factors rather than broader environmental settings. Psychological determinants, particularly depression, had a complex relationship with smoking behavior. Adolescents experiencing depressive symptoms were more likely to initiate smoking, possibly as a coping mechanism for stress or emotional distress [45]. However, depression appeared to act as a protective factor against smoking persistence. This finding suggests that depression may lead adolescents to experiment with smoking, it does not necessarily result in long-term tobacco use. Future research should explore the underlying mechanisms behind this relationship to develop targeted interventions for adolescents with mental health concerns. 4.4. Implications for Tobacco Control Policies The findings from this study underscore the critical need for comprehensive and targeted tobacco control policies aimed at adolescents, particularly during late adolescence—a pivotal period for smoking initiation. To effectively address this public health concern, a multifaceted approach that integrates educational, regulatory, socioeconomic, and mental health strategies is imperative. 4.4.1. Strengthening School-Based Prevention Programs School-based smoking prevention programs have demonstrated significant efficacy in reducing smoking initiation among adolescents. Evidence suggests that school-based smoking prevention programs are most effective when they utilize interactive, student-centered methods such as peer-led discussions and experiential learning [46]. Meta-analyses confirm that high-intensity, sustained programs conducted by trained educators are particularly effective for reducing smoking initiation in young adolescents [47]. 4.4.2. Developing Workplace Interventions for Young Workers Our findings indicate that employment status is associated with a higher likelihood of smoking initiation. As adolescents enter the workforce and gain financial independence, they may also gain greater access to cigarettes. Implementing workplace-based prevention and cessation programs, particularly in industries with a high proportion of young workers, can help fill this gap. Workplace smoking cessation programs, including group therapy, individual counseling, and nicotine replacement therapy, have been effective in promoting cessation However, participation rates in such programs can be low. Strategies to improve engagement include active communication, manager training to encourage participation, and making programs accessible by offering them at the workplace or nearby and reimbursing time spent [48]. Financial incentives have also been shown to significantly increase long-term smoking abstinence when combined with group training programs [49] . 4.4.3. Addressing Socioeconomic Disparities Adolescents from lower socioeconomic backgrounds were more likely to initiate smoking, consistent with global evidence.. While increasing tobacco taxes is a widely endorsed strategy to reduce smoking prevalence, its effectiveness in narrowing socioeconomic disparities remains uncertain [50, 51]. Therefore, additional measures are necessary, such as strengthening parental education on the dangers of smoking and enforcing stronger tobacco control policies. Community-based interventions that involve parents and teachers have shown promise in reducing smoking prevalence among adolescents [52]. 404 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 7: 394-406, 2025 DOI: 10.55214/25768484.v9i7.8588 © 2025 by the authors; licensee Learning Gate 4.4.4. Integrating Mental Health Support into Tobacco Preventions The association between depressive symptoms and smoking initiation highlights the need to integrate mental health considerations into tobacco control efforts. Adolescents experiencing psychological distress may turn to smoking as a coping mechanism. Thus, preventive programs should incorporate screening and early intervention for mental health issues. School-based programs targeting students with symptoms of depression or anxiety have demonstrated promising results in reducing smoking uptake. Promoting adolescent mental well-being more broadly may serve as a long-term strategy for preventing smoking initiation and reducing dependence [53]. Together, these strategies emphasize the need for a holistic, evidence-informed approach to adolescent tobacco control—one that accounts for developmental, social, and structural factors influencing smoking behavior. Such integrated interventions will be essential for achieving sustained reductions in youth smoking and preventing the continuation of tobacco use into adulthood. 4.5. Strengths and Limitations A key strength of this study is the use of a large, nationally representative dataset spanning multiple years, allowing for the analysis of long-term trends in adolescent smoking behavior. However, some limitations should be acknowledged. First, the self-reported nature of smoking behavior may introduce reporting bias. Second, the study does not account for policy changes or external factors influencing smoking trends. Finally, the study identifies associations between various determinants and smoking behavior, and it, does not establish causal relationships. 5. Recommendations This study provides valuable insights into the determinants of adolescent smoking initiation and persistence in Indonesia. The findings emphasize the importance of demographic, socioeconomic, and psychological factors in shaping smoking behaviors. Effective tobacco control policies should focus on early prevention, addressing socioeconomic disparities, and integrating mental health support to reduce adolescent smoking rates. Future research should further explore the causal mechanisms behind smoking behaviors and evaluate the impact of existing tobacco control policies on youth smoking trends. Transparency: The authors confirm that the manuscript is an honest, accurate, and transparent account of the study; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained. This study followed all ethical practices during writing. Acknowledgement: This research received no specific grant from funding agencies in the public, commercial, or not-for- profit sectors. We would like to express our sincere gratitude to the researchers whose work has contributed to this study. Their invaluable research on adolescent smoking behaviors provided a strong foundation for our analysis. Copyright: © 2025 by the authors. This open-access article is distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). https://creativecommons.org/licenses/by/4.0/ 405 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 7: 394-406, 2025 DOI: 10.55214/25768484.v9i7.8588 © 2025 by the authors; licensee Learning Gate References [1] A. Chugh, N. Jain, and M. Arora, "Prevention and control of tobacco use as a major risk factor for non-communicable diseases (ncds): A lifecourse approach," Healthy Lifestyle: From Pediatrics to Geriatrics, pp. 173-197, 2022. https://doi.org/10.1007/978-3-030-85357-0_9 [2] R. K. Upadhyay, "Chronic non-communicable diseases: Risk factors, disease burden, mortalities and control," Acta Scientific Medical Sciences, vol. 6, no. 4, 2022. https://doi.org/10.31080/ASMS.2022.06.1227 [3] World Health Organization, "WHO report on the global tobacco epidemic, 2023: protect people from tobacco smoke," World Health Organization, 2023. https://www.who.int/publications/i/item/9789240077164 [4] World Health Organization, "WHO global report on trends in prevalence of tobacco use 2000-2025," World Health Organization, 2020. https://www.who.int/publications/i/item/9789240039322 [5] M. B. Reitsma, L. S. Flor, E. C. Mullany, V. Gupta, S. I. Hay, and E. Gakidou, "Spatial, temporal, and demographic patterns in prevalence of smoking tobacco use and initiation among young people in 204 countries and territories, 1990–2019," The Lancet Public Health, vol. 6, no. 7, pp. e472-e481, 2021. [6] I. Moor et al., "Socioeconomic inequalities in adolescent smoking across 35 countries: a multilevel analysis of the role of family, school and peers," The European Journal of Public Health, vol. 25, no. 3, pp. 457-463, 2015. [7] GBD 2019 Tobacco Collaborators, "Spatial, temporal, and demographic patterns in prevalence of smoking tobacco use and attributable disease burden in 204 countries and territories, 1990–2019: A systematic analysis from the global burden of disease study," Lancet (London, England, vol. 397, no. 10292, p. 2337, 2019. [8] M. Yu and L. B. Whitbeck, "A prospective, longitudinal study of cigarette smoking status among North American Indigenous adolescents," Addictive Behaviors, vol. 58, pp. 35-41, 2016. [9] T. W. Wang, "Tobacco product use and associated factors among middle and high school students—United States, 2019," MMWR. Surveillance Summaries, vol. 68, 2019. [10] G. M. Anic, "Frequency of use among middle and high school student tobacco product users—United States, 2015– 2017," MMWR. Morbidity and Mortality Weekly Report, vol. 67, 2018. [11] T. Talip, Z. Murang, N. Kifli, and L. Naing, "Systematic review of smoking initiation among Asian adolescents, 20052015: Utilizing the frameworks of triadic influence and planned behavior," Asian Pacific Journal of Cancer Prevention, vol. 17, no. 7, pp. 3341-3355, 2016. [12] S. P. Veeranki et al., "Age of smoking initiation among adolescents in Africa," International Journal of Public Health, vol. 62, pp. 63-72, 2017. [13] B. Luna, B. Tervo-Clemmens, and F. J. Calabro, "Considerations when characterizing adolescent neurocognitive development," Biological Psychiatry, vol. 89, no. 2, pp. 96-98, 2021. [14] V. Milenkova and A. Nakova, "Personality development and behavior in adolescence: characteristics and dimensions," Societies, vol. 13, no. 6, p. 148, 2023. [15] Z. Munn, M. D. Peters, C. Stern, C. Tufanaru, A. McArthur, and E. Aromataris, "Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach," BMC Medical Research Methodology, vol. 18, pp. 1-7, 2018. [16] M. Yuan, S. J. Cross, S. E. Loughlin, and F. M. Leslie, "Nicotine and the adolescent brain," The Journal of Physiology, vol. 593, no. 16, pp. 3397-3412, 2015. [17] J. J. Prochaska and N. L. Benowitz, "Current advances in research in treatment and recovery: Nicotine addiction," Science Advances, vol. 5, no. 10, p. eaay9763, 2019. [18] C. L. Perry et al., "Youth or young adults: which group is at highest risk for tobacco use onset?," Journal of Adolescent Health, vol. 63, no. 4, pp. 413-420, 2018. [19] S. Towns, J. R. DiFranza, G. Jayasuriya, T. Marshall, and S. Shah, "Smoking Cessation in Adolescents: targeted approaches that work," Paediatric Respiratory Reviews, vol. 22, pp. 11-22, 2017. [20] B. Dick and B. J. Ferguson, "Health for the world's adolescents: a second chance in the second decade," Journal of Adolescent Health, vol. 56, no. 1, pp. 3-6, 2015. [21] H. Hiilamo and S. Glantz, "Global implementation of tobacco demand reduction measures specified in framework convention on tobacco control," Nicotine and Tobacco Research, vol. 24, no. 4, pp. 503-510, 2022. [22] S. S. Hawkins, N. Bach, and C. F. Baum, "Impact of tobacco control policies on adolescent smoking," Journal of Adolescent Health, vol. 58, no. 6, pp. 679-685, 2016. [23] H. Megatsari, E. Astutik, K. Gandeswari, S. K. Sebayang, S. R. Nadhiroh, and S. Martini, "Tobacco advertising, promotion, sponsorship and youth smoking behavior: The Indonesian 2019 Global Youth Tobacco Survey (GYTS)," Tobacco Induced Diseases, vol. 21, p. 163, 2023. [24] C. Hoe, R. D. Kennedy, M. Spires, S. Tamplin, and J. E. Cohen, "Improving the implementation of tobacco control policies in low-and middle-income countries: a proposed framework," BMJ Global Health, vol. 4, no. 6, p. e002078, 2019. [25] T. M. Meem, F. B. Khurram, K. N. Islam, and M. S. Khan, "Prevalence and associated factors of tobacco smoking exposure among youths in southeast asia: evidence based on global youth tobacco survey," Khulna University Studies, pp. 144-157, 2022. https://doi.org/10.1007/978-3-030-85357-0_9 https://doi.org/10.31080/ASMS.2022.06.1227 https://www.who.int/publications/i/item/9789240077164 https://www.who.int/publications/i/item/9789240039322 406 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 7: 394-406, 2025 DOI: 10.55214/25768484.v9i7.8588 © 2025 by the authors; licensee Learning Gate [26] A. Vallata, J. O'Loughlin, S. Cengelli, and F. Alla, "Predictors of cigarette smoking cessation in adolescents: A systematic review," Journal of Adolescent Health, vol. 68, no. 4, pp. 649-657, 2021. [27] G. Brunello, M. Fort, N. Schneeweis, and R. Winter‐Ebmer, "The causal effect of education on health: What is the role of health behaviors?," Health Economics, vol. 25, no. 3, pp. 314-336, 2016. [28] N. Gautam, G. Dessie, M. M. Rahman, and R. Khanam, "Socioeconomic status and health behavior in children and adolescents: a systematic literature review," Frontiers in Public Health, vol. 11, p. 1228632, 2023. [29] R. J. Courtney, S. Naicker, A. Shakeshaft, P. Clare, K. A. Martire, and R. P. Mattick, "Smoking cessation among low- socioeconomic status and disadvantaged population groups: A systematic review of research output," International Journal of Environmental Research and Public Health, vol. 12, no. 6, pp. 6403-6422, 2015. [30] G. Ghozali, H. Tanjung, and R. Masnina, "Literature review the relationship of family environment with smoking behavior in adolescents," Science Midwifery, vol. 10, no. 4, pp. 2911-2920, 2022. [31] C. Lovato et al., "School and community predictors of smoking: A longitudinal study of Canadian high schools," American Journal of Public Health, vol. 103, no. 2, pp. 362-368, 2013. [32] J. Cantrell et al., "Cigarette price variation around high schools: Evidence from Washington DC," Health & Place, vol. 31, pp. 193-198, 2015. [33] Taylor Amy E. et al, "Investigating the possible causal association of smoking with depression and anxiety using Mendelian randomisation meta-analysis: the CARTA consortium," BMJ Open, vol. 4, no. 10, p. e006141, 2014. [34] S. Harding et al., "The determinants of young adult social well-being and health (DASH) study: diversity, psychosocial determinants and health," Social Psychiatry and Psychiatric Epidemiology, vol. 50, pp. 1173-1188, 2015. [35] J. O. Lee, L. J. Horwood, W. J. Lee, D. A. Hackman, G. F. McLeod, and J. M. Boden, "Social causation, social selection, or common determinants? examining competing explanations for the link between young adult unemployment and nicotine dependence," Nicotine and Tobacco Research, vol. 22, no. 11, pp. 2006-2013, 2020. [36] K. Hatano, S. Hihara, K. Sugimura, and T. Kawamoto, "Patterns of personality development and psychosocial functioning in japanese adolescents: A four-wave longitudinal study," Journal of Youth and Adolescence, vol. 52, no. 5, pp. 1074-1087, 2023. [37] S. Mittal et al., "Impact of smoking initiation age on nicotine dependency and cardiovascular risk factors: a retrospective cohort study in Japan," European Heart Journal Open, vol. 4, no. 1, p. oead135, 2024. [38] K. D. Artanti, R. D. Arista, and T. I. K. Fazmi, "The influence of social environment and facility support on smoking in adolescent males in Indonesia," Journal of Public Health Research, vol. 13, no. 1, p. 22799036241228091, 2024. [39] RAND, "Indonesian family life survey (IFLS)," 2016. https://www.rand.org/well-being/social-and-behavioral- policy/data/FLS/IFLS.html [40] Frobel Wiebke et al, "Substance use in childhood and adolescence and its associations with quality of life and behavioral strengths and difficulties," BMC Public Health, vol. 22, no. 1, p. 275, 2022. [41] B. K. Oyewole, V. J. Animasahun, and H. J. Chapman, "Tobacco use in Nigerian youth: A systematic review," PloS one, vol. 13, no. 5, p. e0196362, 2018. [42] D. Effendi, A. P. Nugroho, Z. K. Nantabah, A. D. Laksono, and L. Handayani, "Determinants of tobacco use among adolescents and young adults in Indonesia: An analysis of IFLS-5 data," Indian Journal of Forensic Medicine & Toxicology, vol. 15, no. 3, p. 2765, 2021. [43] Steen Pernille Bach et al, "Subjective social status is an important determinant of perceived stress among adolescents: A cross-sectional study," BMC Public Health, vol. 20, pp. 1-9, 2020. [44] N. Hammami, M. A. Da Silva, and F. J. Elgar, "Trends in gender and socioeconomic inequalities in adolescent health over 16 years (2002–2018): Findings from the Canadian Health Behaviour in School-aged children study," Health Promotion and Chronic Disease Prevention in Canada: Research, Policy and Practice, vol. 42, no. 2, p. 68, 2022. [45] S. M. B. Billah and F. I. Khan, "Depression among urban adolescent students of some selected schools," Faridpur Medical College Journal, vol. 9, no. 2, pp. 73-75, 2014. [46] D. P. Mpousiou et al., "Evaluation of a school-based, experiential-learning smoking prevention program in promoting attitude change in adolescents," Tobacco Induced Diseases, vol. 19, p. 53, 2021. [47] R. Song and M. Park, "Meta-analysis of the effects of smoking prevention programs for young adolescents," Child Health Nursing Research, vol. 27, no. 2, p. 95, 2021. [48] N. L. Poole et al., "A qualitative study assessing how reach and participation can be improved in workplace smoking cessation programs," Tobacco prevention & cessation, vol. 9, p. 07, 2023. [49] F. A. van den Brand, G. E. Nagelhout, B. Winkens, N. H. Chavannes, and O. C. van Schayck, "Effect of a workplace- based group training programme combined with financial incentives on smoking cessation: a cluster-randomised controlled trial," The Lancet Public Health, vol. 3, no. 11, pp. e536-e544, 2018. [50] N. L. Fleischer et al., "Taxation reduces smoking but may not reduce smoking disparities in youth," Tobacco Control, vol. 30, no. 3, pp. 264-272, 2021. [51] A. R. Riley, "State cigarette taxes, smoking cessation, and implications for the educational gradient in mortality," Social Science & Medicine, vol. 362, p. 117398, 2024. https://www.rand.org/well-being/social-and-behavioral-policy/data/FLS/IFLS.html https://www.rand.org/well-being/social-and-behavioral-policy/data/FLS/IFLS.html 407 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 7: 394-406, 2025 DOI: 10.55214/25768484.v9i7.8588 © 2025 by the authors; licensee Learning Gate [52] F. Carrión-Valero, J. A. Ribera-Osca, J. M. Martin-Moreno, and A. Martin-Gorgojo, "Prevention of tobacco use in an adolescent population through a multi-personal intervention model," Tobacco Prevention & Cessation, vol. 9, p. 37, 2023. [53] United Nation Development Program, "UNDP Issue Brief: Mental health conditions and tobacco use, addressing the interconnected health and development burden.. UNDP," United Nation Development Program, 2023.