Exploring the Economic and Social Aspects of Youth Smoking: A Multi-Dimensional Analysis

Vyawahare, Atharva Shyam (2024) Exploring the Economic and Social Aspects of Youth Smoking: A Multi-Dimensional Analysis. Masters thesis, Dublin, National College of Ireland.

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Abstract

Youth using tobacco product is connected to long-term health risks, like cardiovascular disease, respiratory illnesses, and various cancers. The study revolves around investigating the influence of household income, healthcare utilization, financial pressures, education, and geographic factors on youth tobacco use. The data used for this research is from the Youth Tobacco Survey (YTS), Behavioural Risk Factor Surveillance System (BRFSS), and Annual Social and Economic Supplements (ASEC), in this study various advanced machine learning models were applied like Random Forest regressor and XGBoost (a gradient boosting algorithm). The findings showed that the socioeconomic factors, particularly family income and food security, significantly impact youth smoking behaviour. whereas, XGBoost outperformed other models in predictive accuracy, while giving robust insights into these complex interactions. The Geospatial analysis was done to identify regions with higher smoking rates and with help of that those finding targeted interventions can be done on those high-risk areas. These results show valuable insights and route for policymakers aiming to reduce youth tobacco use, underscoring the need for comprehensive, data-driven public health strategies. Also, as the socioeconomic is also a factor that’s influencing the youth smoking, this study will help to target the areas with high risk and with the geospatial analysis and by this multi-dimensional approach from machine learning models and data from different sources will help to understand the factors and this study will help in public health policies while, considering all the points that could change the dynamics. This is further evidence of the significant and ongoing impact that socioeconomic elements have in influencing tobacco use among young people. The study supports this claim by using sophisticated machine learning and geospatial analysis to create one integrated model that takes into account family income, food security level, accessibility of parks for physical activity time (PIC), along with other demographic information contribute best or worse to smoking. The knowledge achievable in this study can be a key to public health interventions and policies targeting the decrease of tobacco use among youth, taking special notice of those geographic areas found at risk through geospatial analysis. Together, this versatility provides a strong foundation to tackle this substantial public health concern.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Tomer, Vikas UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > Economics Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning R Medicine > RA Public aspects of medicine > Public Health System H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Agriculture Industry > Plant products industry > Tobacco industry
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	26 Aug 2025 12:18
Last Modified:	26 Aug 2025 12:18
URI:	https://norma.ncirl.ie/id/eprint/8648

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