Youth Addiction and Well-Being: Analysis of Social, Behavioral, and Economic Factors

Abstract

This study explores the complex relationship between addiction and well-being among youth by examining social, behavioral, and economic factors. It aims to identify the key determinants influencing addiction and their impact on young individuals’ physical, mental, and social well-being. Utilizing a dataset including variables such as social isolation, academic decline, financial issues, and mental and physical health problems, the study applies correlation analysis and hierarchical clustering techniques to uncover significant patterns. The results reveal that behaviors like experimentation (ρ = 0.34), social isolation (ρ = 0.28), and financial stress (ρ = 0.22) are strongly associated with addiction. These findings suggest that early risk-taking behaviors, particularly experimentation, play a critical role in the development of addiction and highlight the importance of early intervention. Social and economic stressors are also key contributors, emphasizing the need for targeted prevention strategies. The study concludes that addiction among youth is a multidimensional issue requiring holistic responses, including enhanced social support, economic assistance, and improved access to healthcare. These insights can inform effective policies and interventions aimed at reducing addiction rates and promoting well-being in young populations.

1. Introduction

Recent data indicate that substance use continues to pose a growing public health concern among young populations. According to the European Drug Report 2024, approximately 18.6% of individuals aged 15-24 in Europe reported using cannabis during 2023, making it the most prevalent illicit substance among youth (European Monitoring Centre for Drugs and Drug Addiction [EMCDDA], 2024). This rate highlights the persistence of substance experimentation among adolescents and young adults, a behavioral trend strongly linked to elevated risks of addiction and reduced well-being.

In the present study, the term youth refers specifically to the developmental stage of emerging adulthood, typically encompassing individuals aged 18 to 25 years. This period, as described by Arnett (2000), is characterized by identity exploration, instability, and experimentation, making it a critical life phase for understanding the onset of addictive behaviors. Within this context, university students represent a key subgroup for examining the intersection of risk-taking, social dynamics, and psychological well-being in relation to addiction.

Addiction is a complex and multifaceted issue that is influenced by a wide range of factors, including behavioral, psychological, social, and environmental variables. One of the most significant behavioral predictors of addiction is early experimentation with substances or risky behaviors. Numerous studies have shown that individuals who experiment with substances or engage in risky behavior at a young age are more likely to develop addiction later in life. Experimentation often serves as a precursor to more frequent substance use and behavioral issues, especially if individuals do not recognize the risks associated with their actions early on (Brooks et al., 2017; Kelly et al., 2017). Furthermore, individuals who engage in high-risk activities, such as reckless driving, impulsive decisions, and substance abuse, are at a higher risk of developing addictive behaviors. Risk-taking behavior, often linked to impulsivity and a desire for novelty, significantly contributes to addiction, as those inclined to seek thrills and novelty are more likely to experiment with substances and other behaviors that lead to addiction (Catalano et al., 2012; Rieser et al., 2019).

Social and environmental factors play a crucial role in addiction development. Social isolation is strongly associated with an increased risk of addiction, as individuals who lack social support are more likely to use substances as a coping mechanism for loneliness or emotional distress (Keyzers et al., 2020; Belfiore et al., 2024). The absence of social networks exacerbates feelings of depression, anxiety, and helplessness, all of which can drive individuals to engage in substance use or other risky behaviors. Additionally, strained relationships with family, friends, or significant others often contribute to addiction by heightening emotional distress. Chronic stress resulting from interpersonal conflicts can lead individuals to turn to substances or other maladaptive behaviors to cope with negative emotions (Heshmat, 2015; Kelly et al., 2017). The emotional strain caused by dysfunctional relationships significantly increases vulnerability to addiction, highlighting the need to address both social and emotional aspects in prevention and treatment strategies (Brooks et al., 2017; Kashif et al., 2025).

Psychological factors, such as academic decline and mental health issues, are also closely linked to addiction. Academic performance decline is particularly common among adolescents and young adults who are struggling with addiction. Studies show that a drop in academic performance often indicates underlying mental health issues such as depression or anxiety, which drive individuals to use substances as a form of relief (Belfiore et al., 2024; Baim, 2025). Additionally, mental health problems, including anxiety, depression, and trauma, are frequently found among individuals with addiction. People often use substances to alleviate symptoms of mental health problems or to manage chronic pain, and addiction becomes a maladaptive coping mechanism for these underlying issues (Brooks et al., 2017; Ghosh et al., 2025). Addressing both mental health and addiction simultaneously in treatment programs is crucial, as addiction often serves as a way to self-medicate and manage emotional distress.

Financial and legal stressors are significant contributors to addiction. Financial instability, such as unemployment, debt, and financial hardship, can increase the likelihood of addiction as individuals attempt to escape their financial struggles through substance use. Financial stress often leads to feelings of helplessness and despair, which are key drivers of substance use (Brooks et al., 2017; Kelly et al., 2017). Moreover, financial difficulties increase the temptation to engage in illegal activities, which in turn exacerbates addiction (Belfiore et al., 2024). Legal consequences, particularly those arising from criminal behavior related to substance abuse, often compound an individual’s emotional and social difficulties, creating a vicious cycle that reinforces addiction (Heshmat, 2015; Ghosh et al., 2025).

One of the most significant barriers to overcoming addiction is resistance to treatment. Denial and reluctance to seek treatment prevent many individuals from receiving the help they need. Research has shown that individuals who resist treatment or deny their addiction are less likely to recover, as they fail to recognize the negative impact of their behaviors on their lives (Kelly et al., 2017; Banarjee et al., 2025). Denial is a central feature of addiction, making it difficult for individuals to acknowledge the need for help, which prolongs the addiction cycle and worsens its impact on their lives.

Peer pressure and societal influences also play a crucial role in the development of addiction. Peer pressure, especially among adolescents and young adults, is a significant factor in the initiation of substance use. In environments where substance use is normalized or glamorized, individuals are more likely to engage in such behaviors (Keyzers et al., 2020; Rieb et al., 2020). Cultural norms that endorse or trivialize substance use further reinforce these behaviors, making it more difficult for individuals to break free from addiction once it has taken hold (Heshmat, 2015; Belfiore et al., 2024). Additionally, motivational interviewing techniques have been shown to support individuals in overcoming their addiction (Miller & Rollnick, 2018). The recovery movement also plays a vital role in helping individuals overcome addiction, as it emphasizes personal strength and collective support (Kelly & White, 2010; Davis et al., 2018). Furthermore, structural MRI studies have shown a relationship between brain structure and addiction (Giedd & Rapoport, 2010), while the association between mental health and addiction behaviors has been explored in numerous studies (Treur et al., 2021; Crawford et al., 2025). Studies have also shown that social media can influence addiction behaviors, particularly among adolescents (Feng et al., 2017). Moreover, the role of family dynamics in addiction has been explored, highlighting the importance of a supportive family environment (Cunningham et al., 2009).

Several studies have examined the complex relationship between addiction and well-being, highlighting the intricate interplay of biological, social, and psychological factors. A notable study by Belfiore et al. (2024) presents a multi-level analysis of the factors contributing to substance use disorder (SUD), emphasizing the significant role that both social and psychological elements play in the progression of addiction. The research discusses how co-occurring psychiatric symptoms often accompany SUD and complicate treatment approaches. Understanding these complex interactions is crucial for the development of more effective interventions aimed at improving mental health and well-being, while addressing the multifaceted nature of addiction (Belfiore et al., 2024; Verdejo-García & García-Fernández, 2025).

Furthermore, behavioral economics has been widely applied to understand addiction from a decision-making perspective. In their work, Heshmat (2015) and Baim (2025) highlight self-defeating behaviors associated with addiction, focusing on why individuals may act against their long-term well-being. By examining the underlying economic incentives that drive addictive behaviors, this research provides insights into how individuals often prioritize immediate gratification over long-term health, contributing to the persistence of addiction. Their findings highlight the need for interventions that consider both behavioral and economic factors in promoting long-term well-being.

In addition to these psychological and economic dimensions, the social and cultural factors influencing addiction have also been explored. Walker et al. (2023) examine the impact of globalization on addiction, particularly how social, economic, and political factors contribute to addiction-related disabilities in working populations. They argue that globalization and the associated cultural shifts increase the prevalence of addiction, impacting not only individual well-being but also broader social and economic systems. The research suggests that addiction disrupts social relationships and exacerbates issues like family strain, highlighting the importance of addressing both personal and societal factors when considering addiction’s broader effects (Singer, 2008).

Recent research further expands the understanding of addiction by emphasizing contemporary behavioral and social dynamics among young individuals. Lin et al. (2024) conduct a comprehensive scoping review on how social determinants of health influence substance use disorders, underscoring the significance of socioeconomic disparities and community-level factors in shaping addiction patterns. Avcı (2025) identifies the major risk and protective factors related to substance use among adolescents and highlights the role of family and school-based interventions in preventing early initiation. Similarly, Karanges et al. (2025) examine substance use patterns among young people seeking treatment and demonstrate that behavioral traits such as impulsivity and emotional dysregulation continue to play a central role in substance dependence. Collectively, these recent studies reinforce the multidimensional and evolving nature of addiction, emphasizing the continuing need for prevention frameworks that integrate behavioral, social, and psychological components.

These studies collectively demonstrate the complex and multifactorial nature of addiction, encompassing behavioral, economic, social, and psychological dimensions. A holistic approach that integrates these diverse factors is essential for developing effective interventions aimed at improving both addiction treatment and the overall well-being of affected individuals.

2. Research Design and Methodological Approach

The analysis utilizes the Students Drugs Addiction Dataset available on Kaggle under the Apache 2.0 License (Kaggle Dataset, 2024). The dataset comprises responses from 50,342 university students and includes multiple variables describing behavioral, psychological, social, and economic aspects of substance use. The target variable classifies participants as either addicted or non-addicted, enabling an examination of addiction-related behaviors within the student population. The following variables are analyzed as indicators of well-being dimensions:

• Experimentation: Refers to the initial experience of drug use, considered an early behavioral risk factor in addiction research.
• Academic performance decline: Indicates difficulties in sustaining academic achievement, often associated with reduced motivation or impaired concentration.
• Social isolation: Describes withdrawal from peer or community interactions, reflecting the social dimension of well-being.
• Financial issues: Represents economic strain or instability observed among individuals experiencing addictive behaviors.
• Physical and mental health problems: Encompasses health-related challenges frequently co-occurring with substance use.
• Legal consequences: Captures involvement with legal or disciplinary actions related to substance use.
• Relationship strain: Highlights interpersonal conflicts or reduced social support linked to problematic behavior.
• Risk-taking behavior: Denotes engagement in unsafe or impulsive actions, often examined as a behavioral correlate of addiction.
• Withdrawal symptoms: Represents physical or emotional discomfort following discontinuation of use.
• Denial and resistance to treatment: Reflects avoidance or reluctance to seek professional help despite awareness of the problem.

The dataset is processed and analyzed through several stages:

Data preprocessing: Missing or inconsistent entries are removed. Binary categorical variables (e.g., “Yes” and “No”) are encoded as 1 and 0 to facilitate numerical analysis. All statistical procedures are conducted using SPSS (Version 29).

Correlation analysis: Pearson’s correlation coefficients are computed to examine the relationships among variables and to identify key factors associated with addiction classification. The correlation matrix visualizes the strength and direction of associations between behavioral, psychological, and socioeconomic dimensions.

Hierarchical clustering analysis: To identify multidimensional co-occurrence patterns beyond pairwise associations, hierarchical clustering is conducted using z-score standardized variables. Euclidean distance and Ward’s linkage are employed to ensure within-cluster homogeneity. The clustering aims to group individuals with similar behavioral and psychosocial profiles, providing a structural overview of addiction-related factors.

Interpretation and visualization: Results are visualized through a 3D stacked bar chart (showing “Yes” and “No” distributions across dimensions), a feature correlation chart, and a hierarchical clustering dendrogram. These visualizations illustrate both individual relationships and higher-order groupings among addiction-related variables. Statistical significance and analytical implications are discussed comprehensively in the Section 3.

3. Results and Discussion

In this study, the variables of experimentation, academic performance decline, social isolation, financial issues, physical and mental health problems, legal consequences, relationship strain, risk-taking behavior, withdrawal symptoms, and denial and resistance to treatment are examined within the scope of well-being for 50,342 participants. The analysis initially presents the values obtained from participants for each of these variables.

In Figure 1, each column in the 3D stacked bar chart represents the proportion of “Yes” and “No” responses for a given variable. The green portion at the bottom of each column represents the percentage of “Yes” responses, while the yellow portion at the top represents the percentage of “No” responses. The chart reveals that the majority of the columns show a higher proportion of “Yes” responses, indicating that, for most variables, “Yes” is more dominant than “No.”

Figure 1. 3D stacked bar chart of percentage of Yes and No responses in each dimension.

The 3D view in Figure 1 enhances the clarity of the data, making it easier to highlight the differences between variables and to compare the proportions of “Yes” and “No” responses more effectively. The overall trend indicates that most features have a significantly higher “Yes” proportion, suggesting that the majority of data points correspond to affirmative responses. This indicates that several features are associated with positive behaviors or conditions. The visualization provides a clearer understanding of the data’s distribution by comparing the “Yes” and “No” percentages for each variable, offering valuable insights into which features are most strongly linked to addiction and how these features are distributed across the sample.

The results suggest that a significant portion of the data points indicate the presence of these behaviors or conditions. This may imply that such behaviors are prevalent within the sample, potentially linked to substance use or related psychological factors. For instance, risk-taking behavior may reflect a greater tendency among individuals to engage in risky actions, which could be associated with addiction-related behaviors. Similarly, experimentation often represents an early-stage behavior that may act as a precursor to more serious patterns, such as drug use or dependency.

A closer examination of the behavioral and psychological factors reveals that social isolation, academic performance decline, and financial issues all show a substantial proportion of “Yes” responses. This finding suggests that these features represent significant challenges in individuals’ lives, with many reporting experiences of isolation, academic difficulties, and financial stress. These factors are frequently correlated with mental health problems and may function either as precursors or as consequences of substance abuse and addiction. The overlap between social isolation and addiction class indicates that isolation contributes to a higher likelihood of developing addiction-related behaviors. Given that the sample includes university students, this finding also implies that academic decline is commonly observed among those who exhibit addiction-related behaviors or associated risk factors.

Legal consequences and physical and mental health problems also emerge as significant factors within the dataset. The high proportion of “Yes” responses for these variables indicates that individuals experiencing these issues tend to face multiple behavioral and psychological challenges simultaneously. Those reporting legal consequences are more likely to experience social and legal repercussions, a pattern frequently observed among individuals with addictive behaviors. The correlation between physical and mental health problems and the addiction class reinforces the notion that addiction and mental health difficulties are interconnected. Substance use frequently intensifies psychological distress, creating a cyclical pattern that perpetuates both addiction and mental health deterioration.

Relationship strain, characterized by a high percentage of “Yes” responses, demonstrates that interpersonal difficulties are widespread among individuals experiencing addiction-related behaviors. Substance abuse often leads to relationship breakdowns, as individuals may withdraw from family members, friends, or colleagues. The “Denial and Resistance to Treatment” variable, which also shows a high proportion of “Yes” responses, indicates that many individuals recognize their problems yet resist seeking help. This pattern reflects a well-established psychological barrier in addiction, wherein individuals acknowledge their struggles but remain reluctant to pursue treatment, thereby complicating the recovery process.

Figure 2 displays the correlations between variables and the addiction class, highlighting those that demonstrate statistically significant, moderate positive associations with addiction. The following observations are derived from the correlation coefficients:

Figure 2. Correlation of features with addiction class.

Experimentation: The variable experimentation exhibits the highest correlation with the addiction class, approximately 0.34. This finding indicates that engaging in experimental behaviors represents a strong behavioral predictor of addiction. Prevention strategies should therefore prioritize early-stage intervention to reduce the likelihood of addiction development.

Social isolation: Social isolation follows with a moderate correlation of around 0.28, suggesting that individuals who experience social disconnection are more vulnerable to addictive behaviors. This emphasizes the necessity of fostering supportive social environments as part of both prevention and rehabilitation frameworks.

Financial issues: The correlation between financial issues and the addiction class is approximately 0.22, highlighting the economic dimension of addiction. Financial instability may either contribute to or result from addictive behaviors, creating a cyclical relationship that requires both financial and psychological intervention.

Legal consequences: Legal consequences demonstrate a moderate correlation of about 0.18 with the addiction class, underscoring the social and legal ramifications of addiction. Legal challenges may both arise from and intensify addictive behaviors, reinforcing the social costs associated with substance use.

Risk-taking behavior: With a correlation of approximately 0.15, risk-taking behavior shows a meaningful psychological connection to addiction. Individuals prone to risk-taking are more likely to experiment with substances or engage in behaviors that elevate addiction risk.

Withdrawal symptoms: Withdrawal symptoms show a lower but still notable correlation of 0.12, reflecting the physiological component of addiction. This finding underscores the importance of addressing withdrawal management in comprehensive treatment approaches.

Denial and resistance to treatment: This factor demonstrates a relatively weak correlation of 0.08, suggesting that although denial and treatment resistance are meaningful psychological barriers, they are less directly linked to addiction class compared to behavioral and social factors.

Academic performance decline: The correlation for academic performance decline is relatively low at 0.05. Despite its weaker association, this variable remains relevant for specific subpopulations, particularly university students, where academic challenges may coexist with addiction-related behaviors.

Physical and mental health problems: Physical and mental health problems exhibit the lowest correlation at 0.03, indicating a limited direct association with the addiction class. However, these issues often act as co-occurring conditions or consequences rather than as primary predictors of addiction.

Overall, the chart demonstrates that experimentation, social isolation, and financial issues represent the most influential factors associated with the addiction class, with correlation values exceeding 0.20. These findings provide a quantitative foundation for prioritizing these dimensions in addiction prevention and intervention strategies. Lower-correlation variables, such as physical and mental health problems and academic performance decline, may still hold contextual significance within specific subgroups, meriting further empirical investigation.

In addition to the correlations obtained, while statistically significant, caution is required when making practical inferences. The relationships between variables may not always be linear, and some effects may be driven by unobserved interactions among behavioral, social, and psychological dimensions. Therefore, hierarchical clustering is employed as a complementary analytical technique to uncover potential multidimensional co-occurrence patterns beyond pairwise associations. The analysis is conducted on z-score standardized variables using Euclidean distance and Ward’s linkage to ensure within-cluster homogeneity (Ward, 1963). To facilitate interpretation, the dendrogram is generated for a randomly selected subset of 1000 observations, whereas the clustering is applied to the full dataset of 50,432 participants. This approach provides an additional structural perspective on how addiction-related variables group together, offering a more comprehensive understanding of the interconnections within the dataset. The dendrogram shown in Figure 3 demonstrates the clustering process and provides an intuitive representation of how samples are grouped hierarchically.

Figure 3. Hierarchical clustering dendrogram of 1000-sample subset.

The resulting dendrogram presented in Figure 3 illustrates these relationships, visually representing how addiction-related variables cluster together. The clear separation between branches indicates that certain behavioral and psychosocial patterns co-occur more frequently, reflecting underlying associations among risk-taking, social isolation, and financial issues. The model suggests that cluster distances begin to merge around 10–15 units, indicating meaningful differentiation between groups. This structural representation provides an intuitive understanding of how the dataset organizes itself, complementing the correlation findings by visually confirming multidimensional interconnections within the data.

The dendrogram presented in Figure 3 is generated from a randomly selected subsample of 1000 data points drawn from the full dataset of 50,432 participants. The chart demonstrates that the distances between clusters are clearly separated, and the merging begins at approximately 10–15 units, suggesting meaningful distinctions among clusters.

To evaluate the internal consistency and structural validity of these clusters, the K-means algorithm is applied as a comparative validation method. The Silhouette Score is employed to assess cohesion and separation, where higher values indicate more distinct and homogeneous clusters. The analysis yields Silhouette Scores of 0.276 for two clusters, 0.182 for ten clusters, and 0.147 for fifteen clusters. These findings indicate that the dataset achieves stronger within-cluster similarity and clearer boundaries when fewer clusters are defined. As the number of clusters increases, overlap becomes more evident and structural separation weakens.

This outcome suggests that addiction-related variables in the dataset naturally form a limited number of distinct behavioral and psychosocial profiles rather than multiple fragmented subgroups. The clustering interpretation bridges the statistical and conceptual perspectives, revealing how social isolation, financial stress, and behavioral risk-taking co-occur and collectively influence well-being among youth.

In this context, the analysis demonstrates that individuals with stronger social and economic stability tend to cluster together and exhibit higher levels of subjective well-being. Conversely, those experiencing financial hardship and social isolation appear in lower well-being clusters, reinforcing the interconnectedness between addiction-related behaviors and socioeconomic vulnerability.

The obtained results align with a growing body of literature emphasizing the multidimensional nature of addiction. Previous research in psychology, sociology, and economics consistently demonstrates that addiction is shaped by the interaction of behavioral, social, and economic mechanisms. Studies highlight that social support systems play a crucial role in mitigating addiction-related behaviors (Brooks et al., 2017), as strong interpersonal networks promote emotional stability and adaptive coping strategies that facilitate recovery (Catalano et al., 2012). Likewise, economic stressors such as unemployment and financial instability increase the likelihood of addictive behaviors, driving individuals to seek temporary relief through maladaptive coping strategies (Belfiore et al., 2024; Heshmat, 2015). These patterns confirm that addiction is not a single-dimensional pathology but an interconnected phenomenon that simultaneously affects and is affected by multiple aspects of human well-being.

Access to healthcare services remains a critical determinant of addiction recovery and long-term well-being. Despite advancements in treatment models, barriers such as limited affordability, geographic inequality, and fragmented care systems continue to hinder access for many individuals affected by addiction (Walker et al., 2023; Banarjee et al., 2025). Empirical studies indicate that untreated addiction often results in compounded physical, psychological, and social consequences (Roguski, 2025). Within clinical contexts, mindfulness-based therapies and neurobiological approaches demonstrate positive effects on relapse prevention and emotional regulation (Davis et al., 2018). In parallel, pharmacological interventions, particularly cannabidiol (CBD) therapies, show potential in reducing cravings and withdrawal symptoms (Navarrete et al., 2021; Redonnet et al., 2025). However, other psychosocial interventions, such as brief motivational or psychoeducational programs, produce mixed results in reducing substance use among adolescents (Spirito et al., 2017). Collectively, these findings reinforce that addiction treatment and prevention require integrative approaches that address behavioral, neurobiological, and systemic factors concurrently.

Recent empirical evidence further contextualizes these findings within the broader field of addiction and well-being research. Lin et al. (2024) demonstrate that socioeconomic inequality and unequal access to healthcare remain critical structural determinants sustaining substance use patterns across populations. Similarly, Karanges et al. (2025) reveal that young individuals seeking support for mental health issues often display overlapping behavioral and emotional vulnerabilities, illustrating that addiction is closely interwoven with psychological distress and social disadvantage rather than isolated behavioral choices. In alignment with these findings, Avcı (2025) highlights the importance of preventive interventions tailored to the psychosocial realities of youth, emphasizing early behavioral education and resilience-oriented programs. Collectively, these studies affirm the necessity of moving beyond individualistic explanations toward integrative frameworks that consider behavioral, psychological, and systemic determinants.

Taken together, these insights consolidate the conceptual foundation for understanding addiction as a multidimensional and context-dependent phenomenon, while also delineating the empirical boundaries and methodological considerations that warrant further investigation. This study is limited by its correlational design, which precludes causal inference regarding the relationships between addiction-related behaviors and well-being outcomes. The dataset consists of self-reported responses from university students, which may restrict the generalizability of the findings to broader youth populations. Additionally, potential confounding variables, such as family environment, peer influence, and genetic predispositions, are not included in the present dataset. Future studies are encouraged to employ longitudinal or mixed-method approaches that integrate contextual, demographic, and psychological dimensions to provide a more comprehensive understanding of addiction and its impact on well-being.

4. Conclusions

The present study underscores the complexity of addiction and its relationship with well-being among youth, emphasizing the necessity for a comprehensive approach to addressing addiction in young people. The findings highlight the significant role of behavioral, psychological, and socioeconomic factors in the development of addiction, with behaviors such as experimentation, risk-taking, and social isolation emerging as key contributors. These factors not only facilitate the onset of addiction but also accelerate its progression, underlining the need for multidimensional prevention and intervention strategies specifically targeting youth.

Hierarchical clustering analysis, supported by correlation findings, provides critical insights into the interactions among these variables and their collective impact on youth well-being. The analysis indicates that addiction is intricately linked with social isolation, mental health problems, and financial challenges. This comprehensive understanding should guide the development of targeted interventions, particularly for young individuals who face multiple overlapping stressors.

A holistic approach integrating psychological support, social programs, and economic assistance is essential to mitigate the risks and consequences of addiction. Early intervention programs focusing on experimentation and risk-taking behaviors can serve as preventive mechanisms to reduce addiction risk. Moreover, addressing underlying issues such as social isolation and financial instability can help lower addiction rates and significantly improve the well-being of youth.

Building upon this framework, the study emphasizes the importance of implementing youth-centered preventive and intervention strategies. Evidence-based approaches should include early behavioral education, peer-support networks, and accessible mental health counseling. Community-based initiatives that strengthen social connections and promote financial literacy can further reduce vulnerability to addictive behaviors. Collaboration among educational institutions, healthcare providers, and social organizations remains critical to ensure early detection, timely intervention, and sustained recovery.

While the study provides meaningful insights, it also highlights the need for continued research focusing on the unique challenges faced by young people. Future studies should examine the influence of macro-level factors such as national economic and healthcare systems, alongside micro-level determinants like individual mental health and social networks. Cross-regional analyses and the application of diverse methodological approaches could contribute to a deeper understanding of the complex relationship between addiction and well-being among youth.

Such multidimensional strategies not only support individuals affected by addiction but also foster healthier, more resilient communities. In this context, policy and practice efforts should prioritize school-based prevention programs, digital mental health counseling, and early behavioral education to reduce risk behaviors among youth. Intersectoral collaboration between educational institutions, healthcare systems, and social services can enhance early detection and intervention for addiction-related problems. Additionally, strengthening national action plans to integrate psychosocial support, financial literacy, and youth empowerment initiatives will contribute to sustainable well-being outcomes. Developing data-driven monitoring systems and investing in youth-centered public health campaigns can further ensure that prevention and treatment strategies remain adaptive, inclusive, and evidence-based. In addition, policy frameworks should integrate youth-centered perspectives into national addiction and mental health strategies. Strengthening collaboration between government agencies, educational institutions, and community-based organizations can ensure that prevention and rehabilitation programs are both evidence-based and culturally responsive.