School Health Programs and Substance Use: A Multilevel Study of Brazilian Students

School Health Programs and Substance Use: A Multilevel Study of Brazilian Students | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article School Health Programs and Substance Use: A Multilevel Study of Brazilian Students Karine Nascimento Peixoto, Leonardo Vilar Filgueiras, Orlando Luiz do Amaral Júnior, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8866104/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 13 You are reading this latest preprint version Abstract Background Adolescence is a critical period for the initiation of psychoactive substance use, which is linked to long-term health risks and social vulnerabilities. In Brazil, the Health at School Program (PSE) represents a key intersectoral policy aimed at promoting health and preventing risk behaviors through the integration of education and primary care. This study aimed to evaluate the association between school participation in the PSE and the lifetime experimentation of tobacco, alcohol, and illicit drugs among Brazilian adolescents. Methods This cross-sectional study analyzed data from 159,245 adolescents (aged 13–17 years) from the 2019 National School-Based Health Survey (PeNSE). The primary exposure was the school’s participation in the PSE. The outcomes were lifetime experimentation with cigarettes, alcohol, and illicit drugs. Multilevel Poisson regression models with robust variance were employed to estimate Prevalence Ratios (PR) and 95% Confidence Intervals (95% CI), adjusted for sex, age, race/ethnicity, and maternal education. Results Approximately 50.3% of students were enrolled in schools participating in the PSE. The overall lifetime prevalence of experimentation was 64.2% for alcohol, 24.0% for tobacco, and 13.7% for illicit drugs. Multilevel analysis revealed that students in schools that did not participate in the PSE had a significantly higher likelihood of experimenting with cigarettes (aPR = 1.056; 95% CI: 1.018–1.095), alcohol (aPR = 1.044; 95% CI: 1.020–1.068), and illicit drugs (aPR = 1.124; 95% CI: 1.070–1.181) compared to students in participating schools. Older age and female sex (for alcohol) were also significant risk factors. Conclusions School participation in the PSE acts as a significant protective factor against the experimentation with psychoactive substances among Brazilian adolescents. These findings underscore the importance of intersectoral health-promoting strategies in the school environment as an effective tool for mitigating risk behaviors in the adolescent population. Adolescent Health School Health Services Substance-Related Disorders Public Policy Multilevel Analysis Introduction Adolescence, defined by the World Health Organization (WHO) as the period between 10 and 19 years of age, is a singular phase of human development that establishes essential foundations for future health [1]. This transition from childhood to adulthood is characterized by profound physical, emotional, and social transformations, during which individuals seek autonomy and define their social roles [2]. Consequently, adolescents often face increased vulnerability to risk behaviors, such as the consumption of psychoactive substances [3]. In this context, the family and school environments serve as fundamental protective factors in preventing these habits [3]. The early use of alcohol, cigarettes, and illicit drugs impairs brain development and cognitive functions related to decision-making, emotional control, and memory [4]. Such behaviors may trigger mental disorders, including depression and anxiety [4]. During adolescence, substance use increases the probability of dependence in adulthood, compromising academic performance, interpersonal relationships, and future labor market insertion [5]. Furthermore, this dependence is linked to the emergence of non-communicable diseases (NCDs), such as cardiovascular and respiratory illnesses, cancer, and mental disorders, which are leading causes of morbidity and mortality globally [6]. Therefore, implementing integrated public policies that consider familial, school, and social contexts is essential for effective prevention among young people [7]. The use of psychoactive substances in adolescence results from a complex interaction of individual, familial, school, and social factors [8]. Sociodemographic variables—including age, sex, race/ethnicity, and maternal education—act as social determinants of health that influence both exposure and outcomes [8]. The Common Risk Factor Approach (CRFA) emphasizes that various risk behaviors share common determinants, suggesting that broad interventions can generate multiple health benefits [9]. Among adolescents, the use of alcohol, tobacco, and other drugs constitutes common risk factors for both systemic health issues and oral health problems, reinforcing the need for integrated health promotion strategies [9]. The school is a central setting for promoting health and preventing risk behaviors [10]. As a space for daily interaction and holistic formation, it allows for the development of educational actions, early identification of vulnerabilities, and the strengthening of protective factors like self-esteem and positive social bonds [10, 11]. Within this perspective, the Health at School Program (PSE), established in Brazil in 2007, represents an intersectoral strategy between the Ministries of Health and Education [12]. The PSE aims to provide comprehensive training for public school students through health promotion, disease prevention, and primary care actions [10, 12]. Despite being a significant advancement in policies for adolescents, gaps remain regarding the real impact of the PSE on risk behaviors, especially the experimentation with psychoactive substances [11]. Investigating the association between PSE participation and the prevalence of these behaviors, using large-scale national data such as the 2019 National School-Based Health Survey (PeNSE), is essential for qualifying intersectoral actions and strengthening the school’s role as a health promoter [13, 14]. Therefore, this study aimed to verify the relationship between school participation in the PSE and the experimentation with cigarettes, alcohol, and illicit drugs among Brazilian adolescents aged 13 to 17 years [14]. Methods Study Design and Data Source This study consists of a cross-sectional analytical investigation based on secondary data from the National School-Based Health Survey (PeNSE) conducted in 2019. PeNSE is a comprehensive population-based survey developed by the Brazilian Institute of Geography and Statistics (IBGE) in technical cooperation with the Ministry of Health and the Ministry of Education [13]. It serves as a fundamental instrument for monitoring the health profile and risk behaviors of Brazilian adolescents, providing representative data at national, regional, and state levels [14]. Sampling and Target Population The target population included adolescent students aged 13 to 17 years enrolled in public and private schools across Brazil. The PeNSE 2019 sampling plan utilized a multi-stage cluster sampling design. In the first stage, geographic strata were defined according to the 26 state capitals, the Federal District, and other municipalities grouped into strata. The second stage involved the selection of schools as primary sampling units through systematic random sampling with probability proportional to size, using the 2017 School Census as the sampling frame. In the third stage, classrooms were selected as secondary sampling units within each school. All students in the selected classrooms who were present on the day of the survey were invited to participate. After excluding incomplete or inconsistent records, the final sample for this study comprised 159,245 valid student questionnaires from 4,242 schools nationwide [13, 14, 15]. Data Collection and Procedures Data collection was carried out between April and September 2019 by trained IBGE researchers. Students completed structured, self-administered questionnaires using mobile devices (smartphones), a method chosen to ensure greater privacy for sensitive questions and to improve data quality through simultaneous entry validation. Information regarding the school environment, specifically the participation in the Health at School Program (PSE), was obtained through a separate administrative questionnaire completed by school principals or coordinators [15, 22, 23]. Variable Definitions The variables were defined based on the Global School-Based Student Health Survey (GSHS) framework to ensure international comparability [16, 17]. The primary outcome variables were the lifetime experimentation ("ever-use") of three categories of psychoactive substances: tobacco, alcohol, and illicit drugs. Tobacco use was assessed by asking if the student had ever smoked a cigarette, even if only one or two puffs. Alcohol consumption was identified through the reported experimentation of at least one glass or dose of an alcoholic beverage. Illicit drug use was determined by the reported use of substances such as marijuana, cocaine, crack, or ecstasy at least once [13]. The independent variables were organized into two levels. At the individual level (Level 1), the analysis included sex (male or female), age group (13–15 or 16–17 years), and race/ethnicity (White or Black/Pardo), with the latter categorized according to IBGE guidelines for studying social inequalities [18, 19]. Maternal education was also included as a proxy for socioeconomic status, dichotomized into up to eight years of schooling and more than eight years [20, 21]. At the school level (Level 2), the contextual variable was the participation of the school in the Health at School Program (PSE), defined as a "yes" or "no" response in the school-level questionnaire [22]. Statistical Analysis Data analysis was performed using Stata version 19.5. To account for the hierarchical structure of students nested within schools, multilevel modeling was employed. This approach acknowledges that individuals within the same school environment may share similar characteristics, which violates the assumption of independence in traditional regression. Prevalence Ratios (PR) and their 95% Confidence Intervals (95% CI) were estimated using Poisson regression with robust variance. The analytical process followed a stepwise strategy, beginning with a bivariate analysis followed by a multivariable adjusted model for each outcome. All variables from both levels were included in the final adjusted models to control for potential confounding factors, adhering to the Common Risk Factor Approach [9, 23]. Ethical Considerations The PeNSE 2019 project was conducted in accordance with the Declaration of Helsinki and received formal approval from the National Research Ethics Commission (CONEP) of the Brazilian Ministry of Health under Opinion No. 3,249,268. Participation was voluntary, and the anonymity and confidentiality of the respondents were strictly maintained throughout the research process [13, 23]. Results The final study sample, derived from the PeNSE 2019 database, comprised 159,245 adolescents. As detailed in Table 1 , the population was slightly predominantly female (51.0%) and mostly composed of students aged 13 to 15 years (63.0%), with the remaining 37.0% in the 16 to 17-year age group. Regarding self-reported race and ethnicity, 65.0% of the students identified as Black or Pardo (Brown). Socioeconomic indicators from Table 1 show that 58.0% of the students had mothers with more than eight years of formal education. In the school context, 50.3% of the participants were enrolled in institutions that reported participation in the Health at School Program (PSE). Table 1 Sociodemographic profile, school contextual factors, and lifetime prevalence of psychoactive substance experimentation among Brazilian adolescents (N = 159,245), 2019. Variables n % (weighted) Total Sample 159,245 100.0 Individual Level (Level 1) Sex Male 78,030 49.0 Female 81,215 51.0 Age Group 13 to 15 years 100,324 63.0 16 to 17 years 58,921 37.0 Race/Ethnicity White 55,736 35.0 Black/Pardo (Brown) 103,509 65.0 Maternal Education Up to 8 years of study 66,883 42.0 More than 8 years of study 92,362 58.0 School Level (Level 2) Participation in the PSE Yes 80,100 50.3 No 79,145 49.7 Outcomes (Lifetime experimentation) Tobacco use 38,218 24.0 Alcohol consumption 102,235 64.2 Illicit drug use 21,816 13.7 Legend: n = absolute frequency; % = relative frequency; PSE = Health at School Program (Programa Saúde na Escola). The prevalence of substance experimentation among the adolescents was substantial across all investigated categories, as shown in Table 1 . Alcohol consumption was the most prevalent behavior, reported by 64.2% of the total sample. Tobacco experimentation was reported by 24.0% of the students, while the use of illicit drugs was mentioned by 13.7%. These prevalences varied significantly when stratified by demographic factors (Table 1 ). Older adolescents (16–17 years) exhibited much higher rates of experimentation compared to the younger cohort (13–15 years) for cigarettes (33.6% vs. 18.3%), alcohol (76.7% vs. 56.8%), and illicit drugs (22.0% vs. 8.8%). Gender differences were particularly pronounced for alcohol, with girls reporting higher prevalence (67.6%) than boys (60.5%). Regarding maternal education, adolescents whose mothers had a higher educational level (more than 8 years) showed a lower prevalence of tobacco use (24.3%) but higher rates of alcohol (67.9%) and illicit drug experimentation (15.4%) compared to those with lower maternal schooling. The adjusted multilevel analysis confirmed that school participation in the PSE served as a consistent protective factor against substance experimentation, as presented in Table 2 . Students enrolled in schools that did not participate in the PSE had a higher likelihood of experimenting with all substances: cigarettes (PR = 1.05; 95%CI: 1.02–1.09; p = 0.003), alcohol (PR = 1.04; 95%CI: 1.02–1.07; p < 0.001), and illicit drugs (PR = 1.12; 95%CI: 1.07–1.18; p < 0.001). Table 2 Multilevel Poisson regression analysis of the association between participation in the Health at School Program (PSE) and psychoactive substance experimentation among Brazilian adolescents (N = 159,245), 2019. Independent Variables Tobacco Use Alcohol Consumption Illicit Drug Use aPR (95% CI) aPR (95% CI) aPR (95% CI) School Level (Level 2) PSE Participation Yes 1.00 1.00 1.00 No 1.05 (1.02–1.09)* 1.04 (1.02–1.07)* 1.12 (1.07–1.18)* Individual Level (Level 1) Sex Male 1.00 1.00 1.00 Female 0.95 (0.92–0.99)* 1.11 (1.08–1.13)* 0.97 (0.92–1.02) Age Group 13 to 15 years 1.00 1.00 1.00 16 to 17 years 1.66 (1.59–1.72)* 1.30 (1.27–1.33)* 2.04 (1.94–2.15)* Race/Ethnicity White 1.00 1.00 1.00 Black/Pardo 1.03 (0.99–1.07) 0.96 (0.94–0.99)* 0.91 (0.87–0.96)* Maternal Education ≤ 8 years 1.00 1.00 1.00 > 8 years 0.92 (0.89–0.96)* 1.03 (1.01–1.06)* 1.07 (1.01–1.12)* Legend: aPR = Adjusted Prevalence Ratio; 95% CI = 95% Confidence Interval. Results obtained through Multilevel Poisson Regression with robust variance. Model adjusted for all individual and school-level variables simultaneously. Significant results (p < 0.05) are highlighted in bold with an asterisk (*). Other sociodemographic variables also remained significantly associated with the outcomes in the final models (Table 2 ). Age was the strongest predictor; adolescents aged 16 to 17 years were more than twice as likely to have experimented with illicit drugs (PR = 2.04; 95%CI: 1.94–2.14) compared to younger students. Gender associations revealed that females were more likely to experiment with alcohol (PR = 1.11; p < 0.001) but less likely to smoke cigarettes (PR = 0.95; p = 0.012). Regarding race and ethnicity, self-identifying as Black or Pardo was associated with a lower likelihood of alcohol (PR = 0.96; p = 0.007) and illicit drug consumption (PR = 0.91; p = 0.001). Finally, higher maternal education was associated with a protective effect against tobacco use (PR = 0.92; p < 0.001) but increased the probability of alcohol and drug experimentation (PR = 1.03 and PR = 1.06, respectively), as detailed in the multivariable analysis of Table 2 . Discussion This study identified that school participation in the Health at School Program (PSE) was significantly associated with a lower prevalence of cigarette, alcohol, and illicit drug experimentation among Brazilian adolescents. These findings suggest a potential protective effect of the program, aligning with existing literature that emphasizes the influence of school and parental environments in reducing risk behaviors [ 3 , 23 , 24 ]. The effectiveness of the PSE can be attributed to its intersectoral nature, which facilitates access to information, develops essential life skills, and strengthens the links between students and primary health care services [ 24 , 25 ]. Furthermore, well-structured school-based programs are recognized internationally for their ability to mitigate risk behaviors, as observed in studies analyzing the impact of school-based interventions on adolescent risk profiles in middle-income countries [ 27 ]. The results regarding developmental patterns were particularly striking, with age being the strongest predictor of experimentation. Adolescents aged 16 to 17 years exhibited a significantly higher risk for all substances compared to the 13–15 age group, with a prevalence ratio exceeding 2.0 for illicit drugs. This sharp escalation mirrors the developmental trajectory of risk-taking behaviors during late adolescence, often fueled by increased autonomy, peer influence, and greater access to substances [ 17 , 30 ]. Such findings underscore the necessity of maintaining and intensifying preventive actions throughout the entire secondary education cycle, rather than focusing solely on early adolescence [ 30 , 31 ]. Furthermore, the observed gender disparity in alcohol consumption, where girls reported higher rates than boys, reflects a global shift in adolescent drinking patterns. Such shifts reflect broader changes in social norms and the globalization of drinking cultures among young people [ 28 ]. This phenomenon, noted in recent surveys like ERICA, may be attributed to changing social norms and aggressive marketing by the alcohol industry targeting young women, necessitating gender-sensitive public health strategies [ 31 , 32 , 33 ]. The high prevalence of alcohol experimentation observed (64.2%) is a public health concern that mirrors global trends reported in major health assessments, which emphasize the urgent need for strategies to reduce the early onset of drinking [ 37 ]. The influence of maternal education revealed a complex socioeconomic gradient in the Brazilian context. While higher maternal schooling was a protective factor against tobacco use, it was positively associated with the experimentation of alcohol and illicit drugs. This dichotomy suggests that while more educated mothers may better communicate the health risks of smoking, higher socioeconomic status, often correlated with education, may facilitate greater social access to and acceptance of alcohol and experimental drug use in specific social circles [ 34 , 35 ]. In contrast, self-reported Black and Pardo adolescents exhibited a lower likelihood of using alcohol and drugs compared to their White counterparts. These nuances are embedded in structural and social determinants that influence both exposure and the reporting of such behaviors, reinforcing the need for culturally adapted interventions [ 33 , 34 ]. The effectiveness of the PSE likely stems from its multidisciplinary and intersectoral structure, which connects schools directly to the Primary Health Care (PHC) network. This approach is consistent with the Common Risk Factor Approach (CRFA), as it addresses shared social determinants rather than focusing on a single risk behavior in isolation [ 9 , 29 ]. By early identification of vulnerabilities and providing a direct referral pathway to health services, the program strengthens the school as a protective community hub [ 24 , 36 ]. To ensure long-term success, these programs should align with international standards for drug use prevention that advocate for evidence-based, age-appropriate, and multi-component interventions [ 38 ]. Despite the significant contributions of this study, some limitations must be carefully considered when interpreting the results. First, the cross-sectional nature of the PeNSE 2019 survey inherently prevents the establishment of a temporal sequence between exposure to the Health at School Program (PSE) and the observed health outcomes, thereby precluding any causal inferences. Consequently, it remains unclear whether the program directly influenced the reduction in substance experimentation or if schools with more robust administrative structures are simultaneously more likely to participate in federal programs and foster environments that naturally discourage risk behaviors [ 14 , 23 ]. Second, although the use of self-administered questionnaires on mobile devices was specifically designed to enhance privacy and minimize measurement error, the reliance on self-reported data remains susceptible to recall bias and social desirability bias. This is particularly relevant for sensitive topics such as illicit drug use, where adolescents may underreport behaviors due to social stigma or overreport them to align with perceived peer norms [ 13 , 23 , 40 ]. Third, the measurement of PSE participation as a binary variable (Yes/No) represents a significant simplification of a complex, multifaceted intervention. This "all-or-nothing" approach acts as a "black box," as it does not account for the heterogeneity of the program's implementation across Brazil’s diverse municipalities. It fails to capture the frequency of visits by the Primary Health Care (PHC) teams, the specific types of actions performed, ranging from clinical screenings to purely educational lectures, or the actual quality and intensity of the intersectoral collaboration between health and education professionals [ 22 , 39 ]. Furthermore, while "lifetime experimentation" is a standard global indicator for adolescent health monitoring, it does not allow for a distinction between a single isolated episode of experimentation and more frequent or problematic patterns of consumption, such as regular use or chemical dependence [ 13 , 40 ]. Finally, while the multilevel model successfully adjusted for various individual and contextual confounders, other unmeasured variables, such as neighborhood violence, family dynamics, or the density of alcohol outlets around schools, could also influence the prevalence of substance use in this population [ 23 , 40 ]. In conclusion, the results provide strong evidence that intersectoral initiatives like the PSE are vital for reducing substance use experimentation among adolescents. The integration of health and education through a continuous care model remains one of the most promising strategies for mitigating risk behaviors and potentially reducing the future burden of non-communicable diseases associated with substance dependence [ 6 , 40 ]. Declarations Ethics approval and consent to participate The National School-Based Health Survey (PeNSE 2019) was conducted in accordance with the Declaration of Helsinki and was approved by the National Research Ethics Commission (CONEP) of the Brazilian Ministry of Health (Opinion No. 3,249,268). Since the study utilized secondary data from a national survey, the present study was exempt from further ethical review. In the original survey, students provided their consent via the self-administered questionnaire, and schools provided institutional consent. Anonymity and confidentiality of the participants were strictly maintained by the Brazilian Institute of Geography and Statistics (IBGE). Consent for publication Not applicable. The study utilized secondary, de-identified data provided by IBGE; therefore, no individual participant's data are identifiable in this manuscript. Availability of data and materials The datasets analyzed during the current study are publicly available through the Brazilian Institute of Geography and Statistics (IBGE) official repository at [https://www.ibge.gov.br/estatisticas/sociais/saude/9134-pesquisa-nacional-de-saude-do-escolar.html]. All processed data used for the multilevel analysis are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. The funding body had no role in the study design, data collection, analysis, interpretation of data, or writing of the manuscript. Authors' contributions KNP: Conceptualization, methodology, software, formal analysis, and writing of the original draft. LFV, OLAJ, and MLBF: Methodology, validation, and critical review of the manuscript. JMAG: Supervision, conceptualization, methodology, validation, and critical review of the manuscript. All authors have read and approved the final version of the manuscript. Declaration of generative AI and AI-assisted technologies in the writing process During the preparation of this work, the authors used Gemini (a large language model developed by Google) to assist with English translation, academic tone refinement, and bibliographic formatting according to Vancouver standards. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the accuracy and integrity of the final manuscript. Acknowledgements The authors would like to thank the Brazilian Institute of Geography and Statistics (IBGE) and the Ministries of Health and Education for the data collection and public availability of the PeNSE 2019 database. References World Health Organization. Adolescent health: helping adolescents thrive. Geneva: WHO; 2021. Campos RO. A adolescência e seus transtornos: uma introdução crítica. São Paulo: Hucitec; 2012. Piko BF, Kovács E. Do parents and school matter? Protective factors for adolescent substance use. Addict Behav. 2010;35(1):53–6. Silva CG, Cruz JM, Martins AL. Uso precoce de álcool e drogas e seus impactos na saúde mental de adolescentes. Rev Bras Psicol. 2020;7(2):112–25. Martins LFM, Pereira RMA. Iniciação precoce ao uso de drogas e suas repercussões na vida adulta. Rev Psicol Saúde. 2018;10(1):43–54. Brasil. Ministério da Saúde. Plano de ações estratégicas para o enfrentamento das doenças crônicas não transmissíveis (DCNT) no Brasil 2011-2022. Brasília: Ministério da Saúde; 2011. Pan American Health Organization (PAHO). Substance use among adolescents: challenges and strategies for prevention. Washington, D.C.: PAHO; 2021. Paes NR, et al. Fatores associados ao consumo de drogas entre estudantes do ensino médio. Rev Saude Publica. 2015;49(70):1–9. Sheiham A, Watt RG. The common risk factor approach: a rational basis for promoting oral health. Community Dent Oral Epidemiol. 2000;28(6):399–406. Brasil. Ministério da Saúde. Caderno do gestor do PSE. Brasília: Ministério da Saúde; 2011. Fonseca SA, et al. Fatores associados ao uso de álcool, tabaco e outras drogas entre adolescentes escolares. Rev Bras Epidemiol. 2017;20(3):618–29. Brasil. Decreto nº 6.286, de 5 de dezembro de 2007. Institui o Programa Saúde na Escola – PSE. Diário Oficial da União; 2007. Instituto Brasileiro de Geografia e Estatística (IBGE). Pesquisa Nacional de Saúde do Escolar: PeNSE 2019. Rio de Janeiro: IBGE; 2021. Instituto Brasileiro de Geografia e Estatística (IBGE). Pesquisa Nacional da Saúde do Escolar PeNSE 2019: Manual de Instrução. Rio de Janeiro: IBGE; 2019. Instituto Brasileiro de Geografia e Estatística (IBGE). Pesquisa Nacional de Saúde do Escolar 2019: manual do entrevistador. Rio de Janeiro: IBGE; 2021. World Health Organization (WHO). Global School-Based Student Health Survey (GSHS): Questionnaire. Geneva: WHO; 2013. Malta DC, et al. Tendência do uso de substâncias psicoativas entre adolescentes brasileiros, PeNSE 2009, 2012 e 2015. Rev Bras Epidemiol. 2018;21(Suppl 1):e180004. Instituto Brasileiro de Geografia e Estatística (IBGE). Características étnico-raciais da população: classificações e identidades. Rio de Janeiro: IBGE; 2013. Pereira CA, et al. Utilização da variável raça/cor em estudos epidemiológicos no Brasil. Rev Saude Publica. 2020;54:1–12. Barros AJD, Victora CG. Indicadores socioeconômicos em estudos epidemiológicos. Rev Saude Publica. 2005;39(6):997–1003. Instituto Brasileiro de Geografia e Estatística (IBGE). Síntese de Indicadores Sociais: uma análise das condições de vida da população brasileira. Rio de Janeiro: IBGE; 2021. Brasil. Ministério da Saúde. Caderno do Programa Saúde na Escola: orientações para a implementação. Brasília: Ministério da Saúde; 2018. Peixoto KN. Associação entre a participação escolar no Programa Saúde na Escola (PSE) e o uso de substâncias psicoativas por adolescentes: uma análise multinível da PeNSE 2019 [Master's Dissertation]. Santa Maria: Federal University of Santa Maria (UFSM); 2025. Faggiano F, et al. Universal school-based prevention for illicit drug use. Cochrane Database Syst Rev. 2014;(12):CD003020. Botvin GJ, Griffin KW. Life skills training: a preventive intervention for enhancing adolescent health and resilience. New Dir Youth Dev. 2014;2014(141):13–26. Lopes RM. Educação em saúde e prevenção do uso de drogas. São Paulo: Cortez; 2018. Pereira AP, Souza LB, Barros NF. Participação no Programa Saúde na Escola e comportamentos de risco entre adolescentes brasileiros. Rev Bras Epidemiol. 2020;23:e200046. Wilsnack RW, et al. Gender and alcohol consumption: patterns from the multinational GENACIS project. Addiction. 2018;113(6):1063–77. Wells J, et al. A systematic review of universal approaches to mental health promotion in schools. Health Educ. 2010;110(4):197–213. Figueiredo VC, et al. Prevalência de tabagismo em adolescentes brasileiros: ERICA. Rev Saude Publica. 2016;50(Suppl 1):1–9. INCA – Instituto Nacional de Câncer. Política Nacional de Controle do Tabaco: Relatório de Gestão e Progresso. Rio de Janeiro: INCA; 2014. Santos LMB, Oliveira RM. Raça, desigualdade e saúde: o consumo de substâncias entre adolescentes brasileiros. Saude Soc. 2019;28(3):223–35. Gee GC, et al. Discrimination and chronic health conditions among Asian Americans. Am J Public Health. 2004;94(7):1209–14. Lima DF, et al. Escolaridade materna e comportamentos de risco em adolescentes: uma análise da PeNSE 2019. Rev Bras Epidemiol. 2023;26:e230012. Patrick ME, et al. Socioeconomic status and substance use among young adults: a review. J Stud Alcohol Drugs. 2019;80(1):5–15. Pan American Health Organization (PAHO). Primary Health Care in schools: Health at School Program (PSE). Brasília: PAHO; 2021. World Health Organization. Global status report on alcohol and health 2018. Geneva: WHO; 2018. United Nations Office on Drugs and Crime (UNODC). International Standards on Drug Use Prevention. Vienna: UNODC; 2018. Patton GC, et al. Our future: a Lancet Commission on adolescent health and wellbeing. Lancet. 2016;387(10036):2423–78. Viner RM, et al. Adolescence and the social determinants of health. Lancet. 2012;379(9826):1641–52. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 11 May, 2026 Reviews received at journal 07 May, 2026 Reviewers agreed at journal 05 May, 2026 Reviewers agreed at journal 02 May, 2026 Reviews received at journal 07 Apr, 2026 Reviewers agreed at journal 17 Mar, 2026 Reviewers agreed at journal 17 Mar, 2026 Reviewers agreed at journal 14 Mar, 2026 Reviewers invited by journal 17 Feb, 2026 Editor invited by journal 17 Feb, 2026 Editor assigned by journal 16 Feb, 2026 Submission checks completed at journal 16 Feb, 2026 First submitted to journal 12 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8866104","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":592900990,"identity":"ff7c3bbc-0ca0-4640-a88a-cbdbfeb20a5d","order_by":0,"name":"Karine Nascimento Peixoto","email":"","orcid":"","institution":"Federal University of Santa Maria (UFSM)","correspondingAuthor":false,"prefix":"","firstName":"Karine","middleName":"Nascimento","lastName":"Peixoto","suffix":""},{"id":592900991,"identity":"7c6251ec-b0b1-4895-b704-75ad81f0b0db","order_by":1,"name":"Leonardo Vilar Filgueiras","email":"","orcid":"","institution":"Federal University of Santa Maria (UFSM)","correspondingAuthor":false,"prefix":"","firstName":"Leonardo","middleName":"Vilar","lastName":"Filgueiras","suffix":""},{"id":592900992,"identity":"b5f8ec94-d51c-46a4-b93a-dcebbfc7d033","order_by":2,"name":"Orlando Luiz do Amaral Júnior","email":"","orcid":"","institution":"Federal University of Santa Maria (UFSM)","correspondingAuthor":false,"prefix":"","firstName":"Orlando","middleName":"Luiz do Amaral","lastName":"Júnior","suffix":""},{"id":592900993,"identity":"931b5416-5ad4-4080-a752-a62cfacb33e4","order_by":3,"name":"Maria Laura Braccini Fagundes","email":"","orcid":"","institution":"Federal University of Santa Maria (UFSM)","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"Laura Braccini","lastName":"Fagundes","suffix":""},{"id":592900994,"identity":"0b275e67-b2a5-422b-9f57-3782a21737fe","order_by":4,"name":"Jessye Melgarejo do Amaral Giordani","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYBACxgYogw2IJYBIjoH5AJIwMVqMGdgS8GtBAUAtDIkNhLQwt7c//sxTwWDXx9778MbPHRbpG46xP93AuOMebof1nDGT5jnDkNzGc9zYsveMRO6GYzxmNxjPFOPWMiOHjZm3jSGZTSKNTYK3Dajlfg/bDca2BNxa5j9//Jn3H0SL5N82iXSDY+zP8GuZwWAgzdvAYAfSIg20JcHgGIMZfi09OWaSc45JJLDxHGO2lm2TMJwJ8kviGdxaDNuPP/7wpsbGXr69jfHm27Y6eT6Qwz7uwKOlgYGBiYdBIrEBRRi3BgYGeZDjfjAw2ONRMwpGwSgYBSMdAACMyVEgE1/b/QAAAABJRU5ErkJggg==","orcid":"","institution":"Federal University of Santa Maria (UFSM)","correspondingAuthor":true,"prefix":"","firstName":"Jessye","middleName":"Melgarejo do Amaral","lastName":"Giordani","suffix":""}],"badges":[],"createdAt":"2026-02-12 23:53:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8866104/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8866104/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103090376,"identity":"f8ac3404-43be-474d-9243-87c59844d49b","added_by":"auto","created_at":"2026-02-20 16:40:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":472778,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8866104/v1/ed0e2ac8-6927-4ab3-a30b-e2582276e096.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"School Health Programs and Substance Use: A Multilevel Study of Brazilian Students","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAdolescence, defined by the World Health Organization (WHO) as the period between 10 and 19 years of age, is a singular phase of human development that establishes essential foundations for future health [1]. This transition from childhood to adulthood is characterized by profound physical, emotional, and social transformations, during which individuals seek autonomy and define their social roles [2]. Consequently, adolescents often face increased vulnerability to risk behaviors, such as the consumption of psychoactive substances [3]. In this context, the family and school environments serve as fundamental protective factors in preventing these habits [3].\u003c/p\u003e\n\u003cp\u003eThe early use of alcohol, cigarettes, and illicit drugs impairs brain development and cognitive functions related to decision-making, emotional control, and memory [4]. Such behaviors may trigger mental disorders, including depression and anxiety [4]. During adolescence, substance use increases the probability of dependence in adulthood, compromising academic performance, interpersonal relationships, and future labor market insertion [5]. Furthermore, this dependence is linked to the emergence of non-communicable diseases (NCDs), such as cardiovascular and respiratory illnesses, cancer, and mental disorders, which are leading causes of morbidity and mortality globally [6]. Therefore, implementing integrated public policies that consider familial, school, and social contexts is essential for effective prevention among young people [7].\u003c/p\u003e\n\u003cp\u003eThe use of psychoactive substances in adolescence results from a complex interaction of individual, familial, school, and social factors [8]. Sociodemographic variables\u0026mdash;including age, sex, race/ethnicity, and maternal education\u0026mdash;act as social determinants of health that influence both exposure and outcomes [8]. The Common Risk Factor Approach (CRFA) emphasizes that various risk behaviors share common determinants, suggesting that broad interventions can generate multiple health benefits [9]. Among adolescents, the use of alcohol, tobacco, and other drugs constitutes common risk factors for both systemic health issues and oral health problems, reinforcing the need for integrated health promotion strategies [9].\u003c/p\u003e\n\u003cp\u003eThe school is a central setting for promoting health and preventing risk behaviors [10]. As a space for daily interaction and holistic formation, it allows for the development of educational actions, early identification of vulnerabilities, and the strengthening of protective factors like self-esteem and positive social bonds [10, 11]. Within this perspective, the Health at School Program (PSE), established in Brazil in 2007, represents an intersectoral strategy between the Ministries of Health and Education [12]. The PSE aims to provide comprehensive training for public school students through health promotion, disease prevention, and primary care actions [10, 12].\u003c/p\u003e\n\u003cp\u003eDespite being a significant advancement in policies for adolescents, gaps remain regarding the real impact of the PSE on risk behaviors, especially the experimentation with psychoactive substances [11]. Investigating the association between PSE participation and the prevalence of these behaviors, using large-scale national data such as the 2019 National School-Based Health Survey (PeNSE), is essential for qualifying intersectoral actions and strengthening the school\u0026rsquo;s role as a health promoter [13, 14]. Therefore, this study aimed to verify the relationship between school participation in the PSE and the experimentation with cigarettes, alcohol, and illicit drugs among Brazilian adolescents aged 13 to 17 years [14].\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy Design and Data Source\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study consists of a cross-sectional analytical investigation based on secondary data from the National School-Based Health Survey (PeNSE) conducted in 2019. PeNSE is a comprehensive population-based survey developed by the Brazilian Institute of Geography and Statistics (IBGE) in technical cooperation with the Ministry of Health and the Ministry of Education [13]. It serves as a fundamental instrument for monitoring the health profile and risk behaviors of Brazilian adolescents, providing representative data at national, regional, and state levels [14].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSampling and Target Population\u003c/p\u003e\n\u003cp\u003eThe target population included adolescent students aged 13 to 17 years enrolled in public and private schools across Brazil. The PeNSE 2019 sampling plan utilized a multi-stage cluster sampling design. In the first stage, geographic strata were defined according to the 26 state capitals, the Federal District, and other municipalities grouped into strata. The second stage involved the selection of schools as primary sampling units through systematic random sampling with probability proportional to size, using the 2017 School Census as the sampling frame. In the third stage, classrooms were selected as secondary sampling units within each school. All students in the selected classrooms who were present on the day of the survey were invited to participate. After excluding incomplete or inconsistent records, the final sample for this study comprised 159,245 valid student questionnaires from 4,242 schools nationwide [13, 14, 15].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData Collection and Procedures\u003c/p\u003e\n\u003cp\u003eData collection was carried out between April and September 2019 by trained IBGE researchers. Students completed structured, self-administered questionnaires using mobile devices (smartphones), a method chosen to ensure greater privacy for sensitive questions and to improve data quality through simultaneous entry validation. Information regarding the school environment, specifically the participation in the Health at School Program (PSE), was obtained through a separate administrative questionnaire completed by school principals or coordinators [15, 22, 23].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eVariable Definitions\u003c/p\u003e\n\u003cp\u003eThe variables were defined based on the Global School-Based Student Health Survey (GSHS) framework to ensure international comparability [16, 17]. The primary outcome variables were the lifetime experimentation (\u0026quot;ever-use\u0026quot;) of three categories of psychoactive substances: tobacco, alcohol, and illicit drugs. Tobacco use was assessed by asking if the student had ever smoked a cigarette, even if only one or two puffs. Alcohol consumption was identified through the reported experimentation of at least one glass or dose of an alcoholic beverage. Illicit drug use was determined by the reported use of substances such as marijuana, cocaine, crack, or ecstasy at least once [13].\u003c/p\u003e\n\u003cp\u003eThe independent variables were organized into two levels. At the individual level (Level 1), the analysis included sex (male or female), age group (13\u0026ndash;15 or 16\u0026ndash;17 years), and race/ethnicity (White or Black/Pardo), with the latter categorized according to IBGE guidelines for studying social inequalities [18, 19]. Maternal education was also included as a proxy for socioeconomic status, dichotomized into up to eight years of schooling and more than eight years [20, 21]. At the school level (Level 2), the contextual variable was the participation of the school in the Health at School Program (PSE), defined as a \u0026quot;yes\u0026quot; or \u0026quot;no\u0026quot; response in the school-level questionnaire [22].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStatistical Analysis\u003c/p\u003e\n\u003cp\u003eData analysis was performed using Stata version 19.5. To account for the hierarchical structure of students nested within schools, multilevel modeling was employed. This approach acknowledges that individuals within the same school environment may share similar characteristics, which violates the assumption of independence in traditional regression. Prevalence Ratios (PR) and their 95% Confidence Intervals (95% CI) were estimated using Poisson regression with robust variance. The analytical process followed a stepwise strategy, beginning with a bivariate analysis followed by a multivariable adjusted model for each outcome. All variables from both levels were included in the final adjusted models to control for potential confounding factors, adhering to the Common Risk Factor Approach [9, 23].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthical Considerations\u003c/p\u003e\n\u003cp\u003eThe PeNSE 2019 project was conducted in accordance with the Declaration of Helsinki and received formal approval from the National Research Ethics Commission (CONEP) of the Brazilian Ministry of Health under Opinion No. 3,249,268. Participation was voluntary, and the anonymity and confidentiality of the respondents were strictly maintained throughout the research process [13, 23].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe final study sample, derived from the PeNSE 2019 database, comprised 159,245 adolescents. As detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the population was slightly predominantly female (51.0%) and mostly composed of students aged 13 to 15 years (63.0%), with the remaining 37.0% in the 16 to 17-year age group. Regarding self-reported race and ethnicity, 65.0% of the students identified as Black or Pardo (Brown). Socioeconomic indicators from Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e show that 58.0% of the students had mothers with more than eight years of formal education. In the school context, 50.3% of the participants were enrolled in institutions that reported participation in the Health at School Program (PSE).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSociodemographic profile, school contextual factors, and lifetime prevalence of psychoactive substance experimentation among Brazilian adolescents (N\u0026thinsp;=\u0026thinsp;159,245), 2019.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003cp\u003e(weighted)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Sample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e159,245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividual Level (Level 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78,030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e81,215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge Group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13 to 15 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e100,324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16 to 17 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58,921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace/Ethnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55,736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack/Pardo (Brown)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e103,509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUp to 8 years of study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66,883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than 8 years of study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e92,362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchool Level (Level 2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipation in the PSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80,100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e79,145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcomes (Lifetime experimentation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTobacco use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38,218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e102,235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIllicit drug use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21,816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eLegend: n\u0026thinsp;=\u0026thinsp;absolute frequency; % = relative frequency; PSE\u0026thinsp;=\u0026thinsp;Health at School Program (Programa Sa\u0026uacute;de na Escola).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe prevalence of substance experimentation among the adolescents was substantial across all investigated categories, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Alcohol consumption was the most prevalent behavior, reported by 64.2% of the total sample. Tobacco experimentation was reported by 24.0% of the students, while the use of illicit drugs was mentioned by 13.7%.\u003c/p\u003e \u003cp\u003eThese prevalences varied significantly when stratified by demographic factors (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Older adolescents (16\u0026ndash;17 years) exhibited much higher rates of experimentation compared to the younger cohort (13\u0026ndash;15 years) for cigarettes (33.6% vs. 18.3%), alcohol (76.7% vs. 56.8%), and illicit drugs (22.0% vs. 8.8%). Gender differences were particularly pronounced for alcohol, with girls reporting higher prevalence (67.6%) than boys (60.5%). Regarding maternal education, adolescents whose mothers had a higher educational level (more than 8 years) showed a lower prevalence of tobacco use (24.3%) but higher rates of alcohol (67.9%) and illicit drug experimentation (15.4%) compared to those with lower maternal schooling.\u003c/p\u003e \u003cp\u003eThe adjusted multilevel analysis confirmed that school participation in the PSE served as a consistent protective factor against substance experimentation, as presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Students enrolled in schools that did not participate in the PSE had a higher likelihood of experimenting with all substances: cigarettes (PR\u0026thinsp;=\u0026thinsp;1.05; 95%CI: 1.02\u0026ndash;1.09; p\u0026thinsp;=\u0026thinsp;0.003), alcohol (PR\u0026thinsp;=\u0026thinsp;1.04; 95%CI: 1.02\u0026ndash;1.07; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and illicit drugs (PR\u0026thinsp;=\u0026thinsp;1.12; 95%CI: 1.07\u0026ndash;1.18; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultilevel Poisson regression analysis of the association between participation in the Health at School Program (PSE) and psychoactive substance experimentation among Brazilian adolescents (N\u0026thinsp;=\u0026thinsp;159,245), 2019.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndependent Variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTobacco Use\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlcohol Consumption\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIllicit Drug Use\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaPR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eaPR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eaPR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchool Level (Level 2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSE Participation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05 (1.02\u0026ndash;1.09)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04 (1.02\u0026ndash;1.07)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.12 (1.07\u0026ndash;1.18)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividual Level (Level 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95 (0.92\u0026ndash;0.99)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.11 (1.08\u0026ndash;1.13)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97 (0.92\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge Group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13 to 15 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16 to 17 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.66 (1.59\u0026ndash;1.72)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.30 (1.27\u0026ndash;1.33)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.04 (1.94\u0026ndash;2.15)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace/Ethnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack/Pardo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03 (0.99\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.96 (0.94\u0026ndash;0.99)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91 (0.87\u0026ndash;0.96)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026nbsp;8 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026nbsp;8 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.92 (0.89\u0026ndash;0.96)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03 (1.01\u0026ndash;1.06)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07 (1.01\u0026ndash;1.12)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eLegend: aPR\u0026thinsp;=\u0026thinsp;Adjusted Prevalence Ratio; 95% CI\u0026thinsp;=\u0026thinsp;95% Confidence Interval. Results obtained through Multilevel Poisson Regression with robust variance. Model adjusted for all individual and school-level variables simultaneously. Significant results (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) are highlighted in bold with an asterisk (*).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOther sociodemographic variables also remained significantly associated with the outcomes in the final models (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Age was the strongest predictor; adolescents aged 16 to 17 years were more than twice as likely to have experimented with illicit drugs (PR\u0026thinsp;=\u0026thinsp;2.04; 95%CI: 1.94\u0026ndash;2.14) compared to younger students. Gender associations revealed that females were more likely to experiment with alcohol (PR\u0026thinsp;=\u0026thinsp;1.11; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) but less likely to smoke cigarettes (PR\u0026thinsp;=\u0026thinsp;0.95; p\u0026thinsp;=\u0026thinsp;0.012). Regarding race and ethnicity, self-identifying as Black or Pardo was associated with a lower likelihood of alcohol (PR\u0026thinsp;=\u0026thinsp;0.96; p\u0026thinsp;=\u0026thinsp;0.007) and illicit drug consumption (PR\u0026thinsp;=\u0026thinsp;0.91; p\u0026thinsp;=\u0026thinsp;0.001). Finally, higher maternal education was associated with a protective effect against tobacco use (PR\u0026thinsp;=\u0026thinsp;0.92; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) but increased the probability of alcohol and drug experimentation (PR\u0026thinsp;=\u0026thinsp;1.03 and PR\u0026thinsp;=\u0026thinsp;1.06, respectively), as detailed in the multivariable analysis of Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study identified that school participation in the Health at School Program (PSE) was significantly associated with a lower prevalence of cigarette, alcohol, and illicit drug experimentation among Brazilian adolescents. These findings suggest a potential protective effect of the program, aligning with existing literature that emphasizes the influence of school and parental environments in reducing risk behaviors [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The effectiveness of the PSE can be attributed to its intersectoral nature, which facilitates access to information, develops essential life skills, and strengthens the links between students and primary health care services [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Furthermore, well-structured school-based programs are recognized internationally for their ability to mitigate risk behaviors, as observed in studies analyzing the impact of school-based interventions on adolescent risk profiles in middle-income countries [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe results regarding developmental patterns were particularly striking, with age being the strongest predictor of experimentation. Adolescents aged 16 to 17 years exhibited a significantly higher risk for all substances compared to the 13\u0026ndash;15 age group, with a prevalence ratio exceeding 2.0 for illicit drugs. This sharp escalation mirrors the developmental trajectory of risk-taking behaviors during late adolescence, often fueled by increased autonomy, peer influence, and greater access to substances [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Such findings underscore the necessity of maintaining and intensifying preventive actions throughout the entire secondary education cycle, rather than focusing solely on early adolescence [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, the observed gender disparity in alcohol consumption, where girls reported higher rates than boys, reflects a global shift in adolescent drinking patterns. Such shifts reflect broader changes in social norms and the globalization of drinking cultures among young people [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This phenomenon, noted in recent surveys like ERICA, may be attributed to changing social norms and aggressive marketing by the alcohol industry targeting young women, necessitating gender-sensitive public health strategies [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The high prevalence of alcohol experimentation observed (64.2%) is a public health concern that mirrors global trends reported in major health assessments, which emphasize the urgent need for strategies to reduce the early onset of drinking [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe influence of maternal education revealed a complex socioeconomic gradient in the Brazilian context. While higher maternal schooling was a protective factor against tobacco use, it was positively associated with the experimentation of alcohol and illicit drugs. This dichotomy suggests that while more educated mothers may better communicate the health risks of smoking, higher socioeconomic status, often correlated with education, may facilitate greater social access to and acceptance of alcohol and experimental drug use in specific social circles [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In contrast, self-reported Black and Pardo adolescents exhibited a lower likelihood of using alcohol and drugs compared to their White counterparts. These nuances are embedded in structural and social determinants that influence both exposure and the reporting of such behaviors, reinforcing the need for culturally adapted interventions [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e The effectiveness of the PSE likely stems from its multidisciplinary and intersectoral structure, which connects schools directly to the Primary Health Care (PHC) network. This approach is consistent with the Common Risk Factor Approach (CRFA), as it addresses shared social determinants rather than focusing on a single risk behavior in isolation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. By early identification of vulnerabilities and providing a direct referral pathway to health services, the program strengthens the school as a protective community hub [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. To ensure long-term success, these programs should align with international standards for drug use prevention that advocate for evidence-based, age-appropriate, and multi-component interventions [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the significant contributions of this study, some limitations must be carefully considered when interpreting the results. First, the cross-sectional nature of the PeNSE 2019 survey inherently prevents the establishment of a temporal sequence between exposure to the Health at School Program (PSE) and the observed health outcomes, thereby precluding any causal inferences. Consequently, it remains unclear whether the program directly influenced the reduction in substance experimentation or if schools with more robust administrative structures are simultaneously more likely to participate in federal programs and foster environments that naturally discourage risk behaviors [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Second, although the use of self-administered questionnaires on mobile devices was specifically designed to enhance privacy and minimize measurement error, the reliance on self-reported data remains susceptible to recall bias and social desirability bias. This is particularly relevant for sensitive topics such as illicit drug use, where adolescents may underreport behaviors due to social stigma or overreport them to align with perceived peer norms [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Third, the measurement of PSE participation as a binary variable (Yes/No) represents a significant simplification of a complex, multifaceted intervention. This \"all-or-nothing\" approach acts as a \"black box,\" as it does not account for the heterogeneity of the program's implementation across Brazil\u0026rsquo;s diverse municipalities. It fails to capture the frequency of visits by the Primary Health Care (PHC) teams, the specific types of actions performed, ranging from clinical screenings to purely educational lectures, or the actual quality and intensity of the intersectoral collaboration between health and education professionals [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Furthermore, while \"lifetime experimentation\" is a standard global indicator for adolescent health monitoring, it does not allow for a distinction between a single isolated episode of experimentation and more frequent or problematic patterns of consumption, such as regular use or chemical dependence [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Finally, while the multilevel model successfully adjusted for various individual and contextual confounders, other unmeasured variables, such as neighborhood violence, family dynamics, or the density of alcohol outlets around schools, could also influence the prevalence of substance use in this population [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn conclusion, the results provide strong evidence that intersectoral initiatives like the PSE are vital for reducing substance use experimentation among adolescents. The integration of health and education through a continuous care model remains one of the most promising strategies for mitigating risk behaviors and potentially reducing the future burden of non-communicable diseases associated with substance dependence [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe National School-Based Health Survey (PeNSE 2019) was conducted in accordance with the Declaration of Helsinki and was approved by the National Research Ethics Commission (CONEP) of the Brazilian Ministry of Health (Opinion No. 3,249,268). Since the study utilized secondary data from a national survey, the present study was exempt from further ethical review. In the original survey, students provided their consent via the self-administered questionnaire, and schools provided institutional consent. Anonymity and confidentiality of the participants were strictly maintained by the Brazilian Institute of Geography and Statistics (IBGE).\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable. The study utilized secondary, de-identified data provided by IBGE; therefore, no individual participant\u0026apos;s data are identifiable in this manuscript.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are publicly available through the Brazilian Institute of Geography and Statistics (IBGE) official repository at [https://www.ibge.gov.br/estatisticas/sociais/saude/9134-pesquisa-nacional-de-saude-do-escolar.html]. All processed data used for the multilevel analysis are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study was financed in part by the Coordena\u0026ccedil;\u0026atilde;o de Aperfei\u0026ccedil;oamento de Pessoal de N\u0026iacute;vel Superior - Brasil (CAPES) - Finance Code 001. The funding body had no role in the study design, data collection, analysis, interpretation of data, or writing of the manuscript.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eKNP: Conceptualization, methodology, software, formal analysis, and writing of the original draft. LFV, OLAJ, and MLBF: Methodology, validation, and critical review of the manuscript. JMAG: Supervision, conceptualization, methodology, validation, and critical review of the manuscript. All authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003eDeclaration of generative AI and AI-assisted technologies in the writing process\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work, the authors used Gemini (a large language model developed by Google) to assist with English translation, academic tone refinement, and bibliographic formatting according to Vancouver standards. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the accuracy and integrity of the final manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the Brazilian Institute of Geography and Statistics (IBGE) and the Ministries of Health and Education for the data collection and public availability of the PeNSE 2019 database.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eWorld Health Organization. Adolescent health: helping adolescents thrive. Geneva: WHO; 2021.\u003c/li\u003e\n \u003cli\u003eCampos RO. A adolesc\u0026ecirc;ncia e seus transtornos: uma introdu\u0026ccedil;\u0026atilde;o cr\u0026iacute;tica. S\u0026atilde;o Paulo: Hucitec; 2012.\u003c/li\u003e\n \u003cli\u003ePiko BF, Kov\u0026aacute;cs E. Do parents and school matter? Protective factors for adolescent substance use. Addict Behav. 2010;35(1):53\u0026ndash;6.\u003c/li\u003e\n \u003cli\u003eSilva CG, Cruz JM, Martins AL. Uso precoce de \u0026aacute;lcool e drogas e seus impactos na sa\u0026uacute;de mental de adolescentes. Rev Bras Psicol. 2020;7(2):112\u0026ndash;25.\u003c/li\u003e\n \u003cli\u003eMartins LFM, Pereira RMA. Inicia\u0026ccedil;\u0026atilde;o precoce ao uso de drogas e suas repercuss\u0026otilde;es na vida adulta. Rev Psicol Sa\u0026uacute;de. 2018;10(1):43\u0026ndash;54.\u003c/li\u003e\n \u003cli\u003eBrasil. Minist\u0026eacute;rio da Sa\u0026uacute;de. Plano de a\u0026ccedil;\u0026otilde;es estrat\u0026eacute;gicas para o enfrentamento das doen\u0026ccedil;as cr\u0026ocirc;nicas n\u0026atilde;o transmiss\u0026iacute;veis (DCNT) no Brasil 2011-2022. Bras\u0026iacute;lia: Minist\u0026eacute;rio da Sa\u0026uacute;de; 2011.\u003c/li\u003e\n \u003cli\u003ePan American Health Organization (PAHO). Substance use among adolescents: challenges and strategies for prevention. Washington, D.C.: PAHO; 2021.\u003c/li\u003e\n \u003cli\u003ePaes NR, et al. Fatores associados ao consumo de drogas entre estudantes do ensino m\u0026eacute;dio. Rev Saude Publica. 2015;49(70):1\u0026ndash;9.\u003c/li\u003e\n \u003cli\u003eSheiham A, Watt RG. The common risk factor approach: a rational basis for promoting oral health. Community Dent Oral Epidemiol. 2000;28(6):399\u0026ndash;406.\u003c/li\u003e\n \u003cli\u003eBrasil. Minist\u0026eacute;rio da Sa\u0026uacute;de. Caderno do gestor do PSE. Bras\u0026iacute;lia: Minist\u0026eacute;rio da Sa\u0026uacute;de; 2011.\u003c/li\u003e\n \u003cli\u003eFonseca SA, et al. Fatores associados ao uso de \u0026aacute;lcool, tabaco e outras drogas entre adolescentes escolares. Rev Bras Epidemiol. 2017;20(3):618\u0026ndash;29.\u003c/li\u003e\n \u003cli\u003eBrasil. Decreto n\u0026ordm; 6.286, de 5 de dezembro de 2007. Institui o Programa Sa\u0026uacute;de na Escola \u0026ndash; PSE. Di\u0026aacute;rio Oficial da Uni\u0026atilde;o; 2007.\u003c/li\u003e\n \u003cli\u003eInstituto Brasileiro de Geografia e Estat\u0026iacute;stica (IBGE). Pesquisa Nacional de Sa\u0026uacute;de do Escolar: PeNSE 2019. Rio de Janeiro: IBGE; 2021.\u003c/li\u003e\n \u003cli\u003eInstituto Brasileiro de Geografia e Estat\u0026iacute;stica (IBGE). Pesquisa Nacional da Sa\u0026uacute;de do Escolar PeNSE 2019: Manual de Instru\u0026ccedil;\u0026atilde;o. Rio de Janeiro: IBGE; 2019.\u003c/li\u003e\n \u003cli\u003eInstituto Brasileiro de Geografia e Estat\u0026iacute;stica (IBGE). Pesquisa Nacional de Sa\u0026uacute;de do Escolar 2019: manual do entrevistador. Rio de Janeiro: IBGE; 2021.\u003c/li\u003e\n \u003cli\u003eWorld Health Organization (WHO). Global School-Based Student Health Survey (GSHS): Questionnaire. Geneva: WHO; 2013.\u003c/li\u003e\n \u003cli\u003eMalta DC, et al. Tend\u0026ecirc;ncia do uso de subst\u0026acirc;ncias psicoativas entre adolescentes brasileiros, PeNSE 2009, 2012 e 2015. Rev Bras Epidemiol. 2018;21(Suppl 1):e180004.\u003c/li\u003e\n \u003cli\u003eInstituto Brasileiro de Geografia e Estat\u0026iacute;stica (IBGE). Caracter\u0026iacute;sticas \u0026eacute;tnico-raciais da popula\u0026ccedil;\u0026atilde;o: classifica\u0026ccedil;\u0026otilde;es e identidades. Rio de Janeiro: IBGE; 2013.\u003c/li\u003e\n \u003cli\u003ePereira CA, et al. Utiliza\u0026ccedil;\u0026atilde;o da vari\u0026aacute;vel ra\u0026ccedil;a/cor em estudos epidemiol\u0026oacute;gicos no Brasil. Rev Saude Publica. 2020;54:1\u0026ndash;12.\u003c/li\u003e\n \u003cli\u003eBarros AJD, Victora CG. Indicadores socioecon\u0026ocirc;micos em estudos epidemiol\u0026oacute;gicos. Rev Saude Publica. 2005;39(6):997\u0026ndash;1003.\u003c/li\u003e\n \u003cli\u003eInstituto Brasileiro de Geografia e Estat\u0026iacute;stica (IBGE). S\u0026iacute;ntese de Indicadores Sociais: uma an\u0026aacute;lise das condi\u0026ccedil;\u0026otilde;es de vida da popula\u0026ccedil;\u0026atilde;o brasileira. Rio de Janeiro: IBGE; 2021.\u003c/li\u003e\n \u003cli\u003eBrasil. Minist\u0026eacute;rio da Sa\u0026uacute;de. Caderno do Programa Sa\u0026uacute;de na Escola: orienta\u0026ccedil;\u0026otilde;es para a implementa\u0026ccedil;\u0026atilde;o. Bras\u0026iacute;lia: Minist\u0026eacute;rio da Sa\u0026uacute;de; 2018.\u003c/li\u003e\n \u003cli\u003ePeixoto KN. Associa\u0026ccedil;\u0026atilde;o entre a participa\u0026ccedil;\u0026atilde;o escolar no Programa Sa\u0026uacute;de na Escola (PSE) e o uso de subst\u0026acirc;ncias psicoativas por adolescentes: uma an\u0026aacute;lise multin\u0026iacute;vel da PeNSE 2019 [Master\u0026apos;s Dissertation]. Santa Maria: Federal University of Santa Maria (UFSM); 2025.\u003c/li\u003e\n \u003cli\u003eFaggiano F, et al. Universal school-based prevention for illicit drug use. Cochrane Database Syst Rev. 2014;(12):CD003020.\u003c/li\u003e\n \u003cli\u003eBotvin GJ, Griffin KW. Life skills training: a preventive intervention for enhancing adolescent health and resilience. New Dir Youth Dev. 2014;2014(141):13\u0026ndash;26.\u003c/li\u003e\n \u003cli\u003eLopes RM. Educa\u0026ccedil;\u0026atilde;o em sa\u0026uacute;de e preven\u0026ccedil;\u0026atilde;o do uso de drogas. S\u0026atilde;o Paulo: Cortez; 2018.\u003c/li\u003e\n \u003cli\u003ePereira AP, Souza LB, Barros NF. Participa\u0026ccedil;\u0026atilde;o no Programa Sa\u0026uacute;de na Escola e comportamentos de risco entre adolescentes brasileiros. Rev Bras Epidemiol. 2020;23:e200046.\u003c/li\u003e\n \u003cli\u003eWilsnack RW, et al. Gender and alcohol consumption: patterns from the multinational GENACIS project. Addiction. 2018;113(6):1063\u0026ndash;77.\u003c/li\u003e\n \u003cli\u003eWells J, et al. A systematic review of universal approaches to mental health promotion in schools. Health Educ. 2010;110(4):197\u0026ndash;213.\u003c/li\u003e\n \u003cli\u003eFigueiredo VC, et al. Preval\u0026ecirc;ncia de tabagismo em adolescentes brasileiros: ERICA. Rev Saude Publica. 2016;50(Suppl 1):1\u0026ndash;9.\u003c/li\u003e\n \u003cli\u003eINCA \u0026ndash; Instituto Nacional de C\u0026acirc;ncer. Pol\u0026iacute;tica Nacional de Controle do Tabaco: Relat\u0026oacute;rio de Gest\u0026atilde;o e Progresso. Rio de Janeiro: INCA; 2014.\u003c/li\u003e\n \u003cli\u003eSantos LMB, Oliveira RM. Ra\u0026ccedil;a, desigualdade e sa\u0026uacute;de: o consumo de subst\u0026acirc;ncias entre adolescentes brasileiros. Saude Soc. 2019;28(3):223\u0026ndash;35.\u003c/li\u003e\n \u003cli\u003eGee GC, et al. Discrimination and chronic health conditions among Asian Americans. Am J Public Health. 2004;94(7):1209\u0026ndash;14.\u003c/li\u003e\n \u003cli\u003eLima DF, et al. Escolaridade materna e comportamentos de risco em adolescentes: uma an\u0026aacute;lise da PeNSE 2019. Rev Bras Epidemiol. 2023;26:e230012.\u003c/li\u003e\n \u003cli\u003ePatrick ME, et al. Socioeconomic status and substance use among young adults: a review. J Stud Alcohol Drugs. 2019;80(1):5\u0026ndash;15.\u003c/li\u003e\n \u003cli\u003ePan American Health Organization (PAHO). Primary Health Care in schools: Health at School Program (PSE). Bras\u0026iacute;lia: PAHO; 2021.\u003c/li\u003e\n \u003cli\u003eWorld Health Organization. Global status report on alcohol and health 2018. Geneva: WHO; 2018.\u003c/li\u003e\n \u003cli\u003eUnited Nations Office on Drugs and Crime (UNODC). International Standards on Drug Use Prevention. Vienna: UNODC; 2018.\u003c/li\u003e\n \u003cli\u003ePatton GC, et al. Our future: a Lancet Commission on adolescent health and wellbeing. Lancet. 2016;387(10036):2423\u0026ndash;78.\u003c/li\u003e\n \u003cli\u003eViner RM, et al. Adolescence and the social determinants of health. Lancet. 2012;379(9826):1641\u0026ndash;52.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Adolescent Health, School Health Services, Substance-Related Disorders, Public Policy, Multilevel Analysis","lastPublishedDoi":"10.21203/rs.3.rs-8866104/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8866104/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAdolescence is a critical period for the initiation of psychoactive substance use, which is linked to long-term health risks and social vulnerabilities. In Brazil, the Health at School Program (PSE) represents a key intersectoral policy aimed at promoting health and preventing risk behaviors through the integration of education and primary care. This study aimed to evaluate the association between school participation in the PSE and the lifetime experimentation of tobacco, alcohol, and illicit drugs among Brazilian adolescents.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis cross-sectional study analyzed data from 159,245 adolescents (aged 13\u0026ndash;17 years) from the 2019 National School-Based Health Survey (PeNSE). The primary exposure was the school\u0026rsquo;s participation in the PSE. The outcomes were lifetime experimentation with cigarettes, alcohol, and illicit drugs. Multilevel Poisson regression models with robust variance were employed to estimate Prevalence Ratios (PR) and 95% Confidence Intervals (95% CI), adjusted for sex, age, race/ethnicity, and maternal education.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eApproximately 50.3% of students were enrolled in schools participating in the PSE. The overall lifetime prevalence of experimentation was 64.2% for alcohol, 24.0% for tobacco, and 13.7% for illicit drugs. Multilevel analysis revealed that students in schools that did not participate in the PSE had a significantly higher likelihood of experimenting with cigarettes (aPR\u0026thinsp;=\u0026thinsp;1.056; 95% CI: 1.018\u0026ndash;1.095), alcohol (aPR\u0026thinsp;=\u0026thinsp;1.044; 95% CI: 1.020\u0026ndash;1.068), and illicit drugs (aPR\u0026thinsp;=\u0026thinsp;1.124; 95% CI: 1.070\u0026ndash;1.181) compared to students in participating schools. Older age and female sex (for alcohol) were also significant risk factors.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eSchool participation in the PSE acts as a significant protective factor against the experimentation with psychoactive substances among Brazilian adolescents. These findings underscore the importance of intersectoral health-promoting strategies in the school environment as an effective tool for mitigating risk behaviors in the adolescent population.\u003c/p\u003e","manuscriptTitle":"School Health Programs and Substance Use: A Multilevel Study of Brazilian Students","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-20 16:38:48","doi":"10.21203/rs.3.rs-8866104/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-11T05:30:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-07T17:55:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"190871024972692291118053593733557300927","date":"2026-05-05T11:17:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"200184897066485736871644827947085588001","date":"2026-05-02T18:23:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-07T18:10:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"93629490826153051343542305392317063134","date":"2026-03-17T19:45:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"83423984177343045008478013984663686334","date":"2026-03-17T19:18:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"27732795308597189745209789434166089053","date":"2026-03-14T15:29:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-17T15:42:29+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-17T06:32:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-16T06:23:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-16T06:21:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-02-12T23:46:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d202c2fe-48a9-4b6c-bdd5-173ee1398216","owner":[],"postedDate":"February 20th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-11T05:30:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-07T17:55:49+00:00","index":38,"fulltext":""},{"type":"reviewerAgreed","content":"190871024972692291118053593733557300927","date":"2026-05-05T11:17:00+00:00","index":37,"fulltext":""},{"type":"reviewerAgreed","content":"200184897066485736871644827947085588001","date":"2026-05-02T18:23:30+00:00","index":35,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T05:40:38+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-20 16:38:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8866104","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8866104","identity":"rs-8866104","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}