Danyal Ewaz, Ali Rahimi, Sharareh Shayan, Nasar Ahmad Shayan
{"title":"Tobacco Use Patterns Among University Students in Herat, Afghanistan: A Cross-sectional Study.","authors":"Danyal Ewaz, Ali Rahimi, Sharareh Shayan, Nasar Ahmad Shayan","doi":"10.34172/ahj.1547","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Tobacco use is highly prevalent in Afghanistan, posing a significant challenge among young people, including university students. This study aims to investigate tobacco product usage patterns and associated factors among male students at Herat University, Afghanistan, addressing the critical need for understanding and addressing this public health issue.</p><p><strong>Methods: </strong>In this cross-sectional study conducted between April and May 2021, 640 male university students were surveyed using interview-based stratified random sampling to assess cigarette, smokeless tobacco (ST), hookah, and e-cigarette use alongside sociodemographic factors. Logistic regression identified significant predictors.</p><p><strong>Findings: </strong>The prevalence was 35.3% for cigarette smoking, 15% for ST use, 14.1% for e-cigarette vaping, and 35.5% for hookah smoking. In the cigarette model, predictors included age (OR=1.20), mother's education (secondary/high school OR=2.19; university OR=2.68), friends' use (OR=9.54), and employment status (OR=2.52). The hookah model highlighted friends' use (OR=31.05), marital status (OR=2.10), employment status (OR=1.76), and mother's education (secondary/high school OR=2.18; university OR=3.57) as predictors. In the ST model, predictors were friends' use (OR=20.12), employment status (OR=3.37), and mother's education (secondary/high school OR=2.91). Lastly, the e-cigarette model revealed the predictors of friends' use (OR=7.91) and employment status (OR=1.87).</p><p><strong>Conclusion: </strong>Tobacco use among Afghan male university students is significantly influenced by peer behavior, employment status, and parental education. Interventions should target accessibility and sociocultural attitudes and include educational programs and policy measures to reduce tobacco consumption in the university setting.</p>","PeriodicalId":33943,"journal":{"name":"Addiction and Health","volume":"16 4","pages":"237-247"},"PeriodicalIF":0.0000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11811540/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Addiction and Health","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.34172/ahj.1547","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/10/29 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Tobacco use is highly prevalent in Afghanistan, posing a significant challenge among young people, including university students. This study aims to investigate tobacco product usage patterns and associated factors among male students at Herat University, Afghanistan, addressing the critical need for understanding and addressing this public health issue.
Methods: In this cross-sectional study conducted between April and May 2021, 640 male university students were surveyed using interview-based stratified random sampling to assess cigarette, smokeless tobacco (ST), hookah, and e-cigarette use alongside sociodemographic factors. Logistic regression identified significant predictors.
Findings: The prevalence was 35.3% for cigarette smoking, 15% for ST use, 14.1% for e-cigarette vaping, and 35.5% for hookah smoking. In the cigarette model, predictors included age (OR=1.20), mother's education (secondary/high school OR=2.19; university OR=2.68), friends' use (OR=9.54), and employment status (OR=2.52). The hookah model highlighted friends' use (OR=31.05), marital status (OR=2.10), employment status (OR=1.76), and mother's education (secondary/high school OR=2.18; university OR=3.57) as predictors. In the ST model, predictors were friends' use (OR=20.12), employment status (OR=3.37), and mother's education (secondary/high school OR=2.91). Lastly, the e-cigarette model revealed the predictors of friends' use (OR=7.91) and employment status (OR=1.87).
Conclusion: Tobacco use among Afghan male university students is significantly influenced by peer behavior, employment status, and parental education. Interventions should target accessibility and sociocultural attitudes and include educational programs and policy measures to reduce tobacco consumption in the university setting.