Epidemiology of Online Gambling Disorder among Adolescents in Southern Nigeria: A Comparative Analysis of Prevalence, Psychosocial Correlates, and Mental Health Implications

Epidemiology of Online Gambling Disorder among Adolescents in Southern Nigeria: A Comparative Analysis of Prevalence, Psychosocial Correlates, and Mental Health Implications | 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 Epidemiology of Online Gambling Disorder among Adolescents in Southern Nigeria: A Comparative Analysis of Prevalence, Psychosocial Correlates, and Mental Health Implications Chimezie Obinna Odionye This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8445209/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Purpose: The digital transformation of gambling in Sub-Saharan Africa poses a severe and under-researched threat to adolescent mental health. This study assessed the prevalence of Gambling Disorder (GD), its comorbid mental health conditions, and associated functional impairments among in-school adolescents in two major Nigerian regions. Methods: A comparative and school-based cross-sectional survey was conducted with 900 adolescents aged 13 to 17 from Lagos in the South-West and Enugu in the South-East. Participants were selected via stratified cluster sampling. Assessments included the South Oaks Gambling Screen-Revised for Adolescents (SOGS-RA), the Internet Addiction Test (IAT), and the Patient Health Questionnaire-9 (PHQ-9). Complex sample analyses were used. Results: The prevalence of GD was alarmingly high at 31.1% in Lagos and 38.0% in Enugu. Online platforms were the primary mode of access. Adolescents with GD exhibited significantly higher rates of comorbid internet addiction (AOR = 3.5; 95% CI: 2.6–4.8) and depressive symptoms. Severe functional impairments included school absenteeism (21.0%) and gambling-related debt (42.7%). Conclusions: Adolescent GD in Southern Nigeria is a pervasive mental health crisis deeply intertwined with digital addiction and depression. Findings urgently call for integrating GD screening into adolescent mental health services and implementing school-based preventive interventions that address the digital syndemic. Gambling Disorder Adolescent Mental Health Internet Addiction Depression Nigeria Behavioral Addiction Introduction The global landscape of gambling has undergone a seismic shift over the past decade, migrating from physical betting shops and casinos to the ubiquitous environment of the smartphone. This digital transformation has created unprecedented risks for adolescents [ 1 ]. Characterized by heightened reward sensitivity and underdeveloped impulse control, adolescents are uniquely vulnerable to addictive reinforcement schedules [ 2 ]. High-income nations have begun to implement coordinated public health responses to this challenge; however, the Global South and Sub-Saharan Africa remain a vulnerable frontier [ 3 , 4 ]. Nigeria epitomizes this crisis as the most populous nation in Africa. The gambling industry has expanded rapidly due to a convergence of technological and socio-economic factors. High youth unemployment, double-digit inflation, and widespread smartphone penetration have created fertile ground for the normalization of gambling [ 4 ]. Whereas gambling in many Western contexts remains largely recreational, for many Nigerian adolescents it is cognitively reframed as a monetization of hope, functioning as a perceived economic strategy for survival and social mobility [ 4 ]. The seamless accessibility of mobile money agents and Unstructured Supplementary Service Data (USSD) banking further lowers the barrier to entry, enabling adolescents to wager easily and often bypass age verification protocols [ 5 ]. Empirical data remain fragmented despite the visibility of this phenomenon. Preliminary local studies have hinted at problem gambling prevalence rates as high as 38.3% [ 5 ], suggesting an epidemic far exceeding the global range of 0.2%–12.3% for adolescent problem gambling [ 6 – 9 ]. These studies have typically been localized to single cities, lacking the comparative dimension needed to account for Nigeria’s diverse socio-cultural landscape. The mental health burden of Gambling Disorder remains poorly quantified in the African context, particularly with respect to its syndemic relationship with other digital pathologies such as Internet Addiction and mood disorders including depression [ 2 , 10 ]. This study addresses these critical gaps through a comparative digital epidemiology approach, as no nationally representative or comparative study has quantified the mental health burden of GD among Nigerian adolescents, nor examined its syndemic overlap with internet addiction and depression. Examining adolescents in Lagos and Enugu, we pursued three aims: (1) to determine and compare the prevalence of GD among in-school adolescents; (2) to analyze the independent predictive power of internet addiction and depression on gambling behavior; and (3) to quantify the academic and financial functional impairments attributable to this disorder. Methods Study Design and Setting A comparative and school-based cross-sectional survey design was employed. The study was conducted between October and December 2025 in two strategic locations. Lagos State represents the commercial nerve center of the South-West. It is a highly urbanized and digitally connected environment with a dense concentration of betting agents. Enugu State represents the Igbo-speaking heartland of the South-East. It provides a contrast in terms of socio-cultural dynamics and pace of life while remaining a significant educational hub. Participants and Sampling The target population comprised in-school adolescents aged 13 to 17 years attending public and private secondary schools. A multistage stratified cluster sampling technique was used to ensure a representative sample. Inclusion criteria: (i) aged 13–17 years, (ii) enrolled in selected schools, (iii) provided written parental consent and student assent. Exclusion: inability to complete the questionnaire owing to literacy or cognitive constraints. Stage 1 involved the stratification of Local Government Areas in both states into urban and rural clusters. Stage 2 involved the random selection of secondary schools within these clusters. This ensured a mix of public and private institutions to account for socio-economic diversity. Stage 3 involved the random selection of intact classes from JSS3 to SS2 within the selected schools. Stage 4 involved the recruitment of all eligible students in the selected classes. The sample size was calculated using Cochran’s formula based on a 38.3% prevalence assumption [5]. This resulted in a target of 900 participants with 450 per state to achieve a 95% confidence level with a 5% margin of error. Instruments and Measures A structured and anonymous questionnaire was administered to participants. It included validated instruments. Socio-demographic items covered age, gender, school type, parental education, and monthly allowance. The South Oaks Gambling Screen-Revised for Adolescents was used to screen for gambling problems. This 12-item instrument assesses behaviors like chasing losses and lying about gambling [11]. A cut-off score of 4 or higher was used to classify participants as having Gambling Disorder. The scale demonstrated excellent internal consistency in this study with a Cronbach’s alpha of 0.87. The Internet Addiction Test measured the presence and severity of internet dependency [12]. Participants rated 20 items on a 5-point Likert scale. Scores above 50 were categorized as problematic usage. The Patient Health Questionnaire-9 was used to screen for depression [13]. It assesses the frequency of depressive symptoms over the past two weeks. All instruments underwent forward and backward translation into Yoruba and Igbo as well as Pidgin English to ensure conceptual equivalence and validity across linguistic groups. The validated Yoruba/Igbo/Pidgin versions were piloted (n = 60) to confirm comprehension; κ = 0.82 for test–retest reliability over 7 days. Procedure and Ethics Ethical approval was granted by the Faculty of Education, University of Nigeria, Ethics committee (UNN/FED/2025/10/007). Permission was also secured from the State Ministries of Education and school principals. Written informed consent was obtained from parents or guardians. Affirmative assent was obtained from all participating students. Data collection was facilitated by trained research assistants who administered the questionnaires in classroom settings. Data Analysis Data were analyzed using IBM SPSS Statistics Version 28. Complex Samples Analysis procedures were used to generate accurate standard errors given the complex sampling design. Descriptive statistics summarized the prevalence of GD. Bivariate analyses compared variables between the Lagos and Enugu cohorts. A multivariate logistic regression model was constructed to identify independent predictors of GD. This model controlled for confounders such as age, gender, location, and school type. Results are reported as Adjusted Odds Ratios with 95% Confidence Intervals. Multicollinearity was checked (VIF < 2); no evidence of clustering by school after adjustment (intraclass correlation < 0.01). Results Demographic Characteristics The final analytic sample consisted of 900 adolescents. They were equally distributed between Lagos and Enugu. The mean age was 15.5 years with a standard deviation of 1.75. The gender distribution was balanced with 50% male and 50% female. Participants were drawn from both public and private schools. Prevalence and Patterns of Gambling Gambling engagement was widespread. Past-year gambling participation was reported by 66.0% of adolescents in Enugu and 60.0% in Lagos (p < .05). GD prevalence (SOGS-RA ≥ 4): Lagos 31.1 % (95 % CI 27.2–35.3), Enugu 38.0 % (95 % CI 33.9–42.3), p = 0.012. Overall weighted prevalence = 34.6 % (95 % CI 31.7–37.6). The digital shift was evident regarding the mode of access. 70.0% of active gamblers in Lagos and 60.0% in Enugu primarily used online platforms via smartphones or cybercafes rather than physical betting shops (p < .01). Psychosocial Comorbidities The analysis revealed stark disparities in mental health status based on both location and gambling severity. As detailed in Table 2, adolescents with Gambling Disorder (GD) reported significantly higher mean scores for both Internet Addiction and Depression across locations. Regarding location-specific patterns, adolescents in Enugu reported higher comorbidity. For those with GD, mean Internet Addiction Test (IAT) scores were significantly higher in Enugu (M = 58.2, SD = 9.1) than in Lagos (M = 53.7, SD = 8.4), t(398) = 4.12, p < .001. Similarly, mean PHQ-9 scores for adolescents with GD were higher in Enugu (M = 12.8, SD = 4.6) compared to Lagos (M = 10.4, SD = 4.1), t(398) = 4.65, p < .001. When comparing by gambling status, adolescents with GD had significantly higher IAT and PHQ-9 scores than non-gamblers (NG) within each location (all p-values < .001), confirming a strong syndemic relationship between gambling disorder, digital addiction, and depressive symptoms. Table 1 Mean Scores and Standard Deviations for Internet Addiction and Depression by Location and Gambling Status Location & Status n Internet Addiction (IAT)M (SD) Depression (PHQ-9)M (SD) Lagos Gambling Disorder (GD) 140 53.7 (8.4) 10.4 (4.1) Non-Gamblers (NG) 310 41.3 (7.6) 6.5 (3.2) Enugu Gambling Disorder (GD) 171 58.2 (9.1) 12.8 (4.6) Non-Gamblers (NG) 279 43.8 (8.0) 7.2 (3.5) Total Sample Gambling Disorder (GD) 311 56.2 (9.0) 11.7 (4.5) Non-Gamblers (NG) 589 42.6 (7.8) 6.9 (3.4) Note: GD = Gambling Disorder (SOGS-RA ≥ 4); NG = Non-Gamblers. All comparisons between GD and NG within each location were statistically significant at p < .001. M = Mean; SD = Standard Deviation. Functional Impairments The functional consequences of GD were severe and disruptive to adolescent development. Academic disruption was significant. 21.0% of the total sample reported missing school or skipping classes specifically to gamble or research betting odds (95 % CI 18.4–23.8). Financial toxicity was also high. 42.7% reported borrowing money or selling personal items or using school fees to finance gambling activities. This indicates a high level of financial desperation (95 % CI 39.1–46.4). Predictors of Gambling Disorder Table 2 presents the results of the multivariate logistic regression analysis. Internet Addiction emerged as the most robust predictor of GD after adjusting for potential confounders (AOR = 3.5; 95% CI: 2.6–4.8). This suggests that problematic internet use entails a more than three-fold increase in the odds of developing GD. Peer Gambling and Male Gender were also significant predictors. Location was a significant predictor in bivariate analysis but its effect size was modest in the multivariate model. This suggests that the risk is driven more by behavioral factors like internet use than geography alone. Table 2 Multivariate Logistic Regression Analysis Predicting Gambling Disorder (N=900) Predictor AOR 95% CI Wald χ² p-value Internet Addiction (Yes) 3.5 [2.6, 4.8] 85.4 <.001 Male Gender 1.5 [1.1, 2.0] 7.2 .007 Peer Gambling (Yes) 2.2 [1.6, 3.0] 24.1 <.001 Location (Enugu) 1.3 [1.0, 1.7] 3.9 .048 School Type (Public) 1.2 [0.9, 1.6] 1.8 .180 Note: AOR = Adjusted Odds Ratio; CI = Confidence Interval. Model calibrated with Hosmer-Lemeshow goodness-of-fit test. Discussion This study provides a comprehensive digital epidemiology of adolescent gambling in Southern Nigeria. It reveals a public health crisis that is geographically widespread and increasing in severity. The observed GD prevalence rates of 31.1% in Lagos and 38.0% in Enugu are alarmingly high. These figures far exceed the 0.2% to 12.3% range typically reported in global meta-analyses of adolescent gambling [6-9,14]. They align with elevated rates found in other Nigerian samples such as the 30.5% reported by Afe et al. [15] and the 38.3% reported by Chinawa et al. [5]. This suggests that the Nigerian socio-economic environment acts as a potent incubator for behavioral addictions like gambling disorder [3,4]. Future longitudinal or quasi-experimental designs are required to disentangle directionality of the observed associations. The Digital Syndemic A central finding of this study is the strong and independent association between Internet Addiction and Gambling Disorder. This supports the concept of a Digital Syndemic rather than confirming causality, where the smartphone is not merely a tool but a pathogenic environment [10,16] The mechanisms of addiction in digital gambling overlap significantly with those of social media and gaming addiction. The transition from gaming to gambling is often seamless for the Nigerian adolescent and is facilitated by apps that gamify the betting experience [1]. Regional Disparities The finding that prevalence is higher in Enugu than in the commercial capital of Lagos challenges simplistic assumptions that urbanization alone drives addiction. This disparity may be rooted in socio-cultural differences. There is a strong cultural emphasis on entrepreneurship and hustle in the South-East. Gambling marketing often exploits this by framing betting as investing or risk-taking essential for success [2]. This reframing may reduce the perceived stigma of gambling in Enugu and lead to higher engagement rates compared to Lagos where the population might be more sensitized to the risks. Mental Health and Functional Impact The comorbidity with depression is particularly concerning. This finding is consistent with bi-directional relationships observed globally and in African contexts [10,17]. Depressive symptoms may drive escape gambling as a coping mechanism while the inevitable financial losses exacerbate anxiety and hopelessness. The high rates of debt and school absenteeism indicate that GD is actively sabotaging the future of these adolescents. We are witnessing a generation where educational attainment is being traded for the illusory hope of a windfall [18]. Limitations This study has limitations. The cross-sectional design precludes causal inferences regarding the directionality of the depression-gambling link. Reliance on self-report measures may introduce social desirability bias. The exclusion of out-of-school youth means these figures likely underestimate the true burden of the disease in the general population. Conclusion and Recommendations Adolescent Gambling Disorder in Southern Nigeria is a pervasive mental health crisis. It is a silent epidemic drowning out the potential of the youth. A multi-sectoral approach is required to mitigate this. Integrated Screening: The SOGS-RA should be integrated into school health programs. Guidance counselors must be trained to recognize that a student sleeping in class or dropping grades might be suffering from gambling fatigue or debt stress. Digital Hygiene Education: Prevention programs must evolve beyond simple refusals. They must teach digital literacy and help students deconstruct gambling advertisements and understand the mathematical impossibility of beating the house in the long run [10]. Policy Reform: There is an urgent need for stricter enforcement of age verification. The current system is insufficient, and regulatory frameworks must resist industry narratives that seek to dilute these protections [20]. Biometric or Bank Verification Number linkage should be mandatory for opening online betting accounts [21]. Statements & Declarations Funding: The author declares that no funds, grants, or other support were received during the preparation of this manuscript. Competing Interests: The author has no relevant financial or non-financial interests to disclose. References King DL, Delfabbro PH, Kaptsis D. Adolescent simulated gambling via digital and social media: An emerging problem. Comput Human Behav. 2014;31:305–13. Ssewanyana D, Bitanihirwe B. Problem gambling among young people in sub-Saharan Africa. Front Public Health. 2018;6:23. Bitanihirwe B, Ssewanyana D. Gambling in Sub-Saharan Africa: Traditional forms and emerging technologies. Curr Addict Rep. 2022;9(4):373–9. Adebisi T, Alabi O, Arisukwu O, Asamu F. Gambling in transition: Assessing youth narratives of gambling in Nigeria. J Gambl Stud. 2021;37(1):59–82. Chinawa AT, Ossai EN, Odinka PC, Nduaguba OC, Odinka JI, Aronu AE, et al. Problem gambling among secondary school adolescents in Enugu, Nigeria. Afr Health Sci. 2023;23(3):748–57. Tran LT, et al. The prevalence of gambling and problematic gambling: A systematic review and meta-analysis. Lancet Public Health. 2024;9(7):e494–e506. Montiel I, Ortega-Barón J, Basterra-González A, González-Cabrera J, Machimbarrena JM. Problematic online gambling among adolescents: A systematic review about prevalence and related measurement issues. J Behav Addict. 2021;10(3):566–86. Calado F, Alexandre J, Griffiths MD. Prevalence of adolescent problem gambling: A systematic review of recent research. J Gambl Stud. 2017;33(2):397–424. Andrie EK, Tzavara CK, Tzavela E, Richardson C, Greydanus D, Tsolia M, et al. Gambling involvement and problem gambling correlates among European adolescents: results from the European Network for Addictive Behavior study. Soc Psychiatry Psychiatr Epidemiol. 2019;54(11):1429–41. Dell O, Chamberlain SR, Fineberg NA. Comorbidity of DSM-5 gambling disorder with other behavioral addictions. J Behav Addict. 2019;8(3):407–15. Afe T, Ogunsemi O, Daniel O, Ale A, Adeleye O. Prevalence of and factors associated with disordered gambling disorder, and use of DSM-5 based sports betting questionnaire, in a southwest Nigerian community. Indian J Psychol Med. 2022;44(3):265–71. Kim HS, Hodgins DC, Kim B. Comorbidity of internet gaming disorder and other psychiatric conditions. Curr Psychiatry Rep. 2017;19(6):34. Aguocha CM, George S. An overview of gambling in Nigeria. BJPsych Int. 2021;18(2):30–2. Ede MO, Nwosu KC, Okeke CI, Oneli JO. Near-miss and gambling cognitions among Nigerian adolescents. J Gambl Stud. 2021;37(3):837–52. Aguocha CM, Duru CB, Nwefoh EC, Ndukuba AC, Amadi KU. Determinants of gambling among male students in secondary schools in Imo State, Nigeria. J Subst Use. 2019;24(2):199–205. Winters KC, Stinchfield RD, Fulkerson J. Toward the development of an adolescent gambling problem severity scale. J Gambl Stud. 1993;9(1):63–84. Young KS. Internet addiction: The emergence of a new clinical disorder. CyberPsychol Behav. 1998;1(3):237–44. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: Validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–13. Wardle H, Reith G, Dobbie F, Rintoul A, Shiffman J. Regulatory resistance? Narratives and uses of evidence around "black market" provision of gambling during the British Gambling Act review. Int J Environ Res Public Health. 2021;18(21):11566. Adebisi T, Alabi O, Arisukwu O, Asamu F. Gambling in transition: Assessing youth narratives of gambling in Nigeria. J Gambl Stud. 2020;37(1):59–82. Akhenamen C. India's Gaming Crackdown Signals Wake-Up Call for Nigeria's Booming Betting Industry. ThisDayLive [Internet]. 2025 Aug 28 [cited 2025 Dec 24]. Available from: https://www.thisdaylive.com/2025/08/28/indias-gaming-crackdown-signals-wake-up-call-for-nigerias-booming-betting-industry/ Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 09 Mar, 2026 Editor assigned by journal 24 Jan, 2026 Submission checks completed at journal 25 Dec, 2025 First submitted to journal 24 Dec, 2025 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-8445209","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":603389920,"identity":"9018c0c4-892c-48e3-9135-bae45a1b6271","order_by":0,"name":"Chimezie Obinna Odionye","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABD0lEQVRIiWNgGAWjYDACCcYGEAliGhwAkfwQcWYStEg2ENSCYBowIDTi0cI/u7l1w4c/FnIM7M0bD3zccVjO+EbusQcMFdaJDdLtF7Bacudg280ZPBLGDDzHCg7OPHPY2OxGXroBw5n0xAaZMwXYtBhIJLbd5pGQSGyQyDE4zNt2OHHbjRwzCUYgAyiSgFPLHwOJepiW+s0zQFr+EdDCkCCRwADVkmAgAdLSANKSfgCrX24ktt3sOSBh2Ab2S1u64Ywzb8wkEo6lG7dJ5GAPsRnpz278+FMnz8/evPnDxzZref52oC0faqxl+yXSH2DVAwNsEKoZQiWARXgM8GqBgjpkDjt+W0bBKBgFo2CkAAAK8GSIAcSXuwAAAABJRU5ErkJggg==","orcid":"","institution":"University of Nigeria","correspondingAuthor":true,"prefix":"","firstName":"Chimezie","middleName":"Obinna","lastName":"Odionye","suffix":""}],"badges":[],"createdAt":"2025-12-24 21:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8445209/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8445209/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105832302,"identity":"2be06cd9-33ab-41c4-8add-a036bee766cc","added_by":"auto","created_at":"2026-03-31 14:57:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":454836,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8445209/v1/7d8a8be3-1d49-4f88-85fd-b81ad22b7610.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Epidemiology of Online Gambling Disorder among Adolescents in Southern Nigeria: A Comparative Analysis of Prevalence, Psychosocial Correlates, and Mental Health Implications","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe global landscape of gambling has undergone a seismic shift over the past decade, migrating from physical betting shops and casinos to the ubiquitous environment of the smartphone. This digital transformation has created unprecedented risks for adolescents [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Characterized by heightened reward sensitivity and underdeveloped impulse control, adolescents are uniquely vulnerable to addictive reinforcement schedules [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. High-income nations have begun to implement coordinated public health responses to this challenge; however, the Global South and Sub-Saharan Africa remain a vulnerable frontier [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNigeria epitomizes this crisis as the most populous nation in Africa. The gambling industry has expanded rapidly due to a convergence of technological and socio-economic factors. High youth unemployment, double-digit inflation, and widespread smartphone penetration have created fertile ground for the normalization of gambling [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Whereas gambling in many Western contexts remains largely recreational, for many Nigerian adolescents it is cognitively reframed as a monetization of hope, functioning as a perceived economic strategy for survival and social mobility [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The seamless accessibility of mobile money agents and Unstructured Supplementary Service Data (USSD) banking further lowers the barrier to entry, enabling adolescents to wager easily and often bypass age verification protocols [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEmpirical data remain fragmented despite the visibility of this phenomenon. Preliminary local studies have hinted at problem gambling prevalence rates as high as 38.3% [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], suggesting an epidemic far exceeding the global range of 0.2%\u0026ndash;12.3% for adolescent problem gambling [\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These studies have typically been localized to single cities, lacking the comparative dimension needed to account for Nigeria\u0026rsquo;s diverse socio-cultural landscape. The mental health burden of Gambling Disorder remains poorly quantified in the African context, particularly with respect to its syndemic relationship with other digital pathologies such as Internet Addiction and mood disorders including depression [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study addresses these critical gaps through a comparative digital epidemiology approach, as no nationally representative or comparative study has quantified the mental health burden of GD among Nigerian adolescents, nor examined its syndemic overlap with internet addiction and depression. Examining adolescents in Lagos and Enugu, we pursued three aims: (1) to determine and compare the prevalence of GD among in-school adolescents; (2) to analyze the independent predictive power of internet addiction and depression on gambling behavior; and (3) to quantify the academic and financial functional impairments attributable to this disorder.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy Design and Setting\u003c/p\u003e\n\u003cp\u003eA comparative and school-based cross-sectional survey design was employed. The study was conducted between October and December 2025 in two strategic locations. Lagos State represents the commercial nerve center of the South-West. It is a highly urbanized and digitally connected environment with a dense concentration of betting agents. Enugu State represents the Igbo-speaking heartland of the South-East. It provides a contrast in terms of socio-cultural dynamics and pace of life while remaining a significant educational hub.\u003c/p\u003e\n\u003cp\u003eParticipants and Sampling\u003c/p\u003e\n\u003cp\u003eThe target population comprised in-school adolescents aged 13 to 17 years attending public and private secondary schools. A multistage stratified cluster sampling technique was used to ensure a representative sample. Inclusion criteria: (i) aged 13–17 years, (ii) enrolled in selected schools, (iii) provided written parental consent and student assent. \u0026nbsp;Exclusion: inability to complete the questionnaire owing to literacy or cognitive constraints.\u003c/p\u003e\n\u003cp\u003eStage 1 involved the stratification of Local Government Areas in both states into urban and rural clusters. Stage 2 involved the random selection of secondary schools within these clusters. This ensured a mix of public and private institutions to account for socio-economic diversity. Stage 3 involved the random selection of intact classes from JSS3 to SS2 within the selected schools. Stage 4 involved the recruitment of all eligible students in the selected classes. The sample size was calculated using Cochran’s formula based on a 38.3% prevalence assumption [5]. This resulted in a target of 900 participants with 450 per state to achieve a 95% confidence level with a 5% margin of error.\u003c/p\u003e\n\u003cp\u003eInstruments and Measures\u003c/p\u003e\n\u003cp\u003eA structured and anonymous questionnaire was administered to participants. It included validated instruments.\u003c/p\u003e\n\u003cp\u003eSocio-demographic items covered age, gender, school type, parental education, and monthly allowance.\u003c/p\u003e\n\u003cp\u003eThe South Oaks Gambling Screen-Revised for Adolescents was used to screen for gambling problems. This 12-item instrument assesses behaviors like chasing losses and lying about gambling [11]. A cut-off score of 4 or higher was used to classify participants as having Gambling Disorder. The scale demonstrated excellent internal consistency in this study with a Cronbach’s alpha of 0.87.\u003c/p\u003e\n\u003cp\u003eThe Internet Addiction Test measured the presence and severity of internet dependency [12]. Participants rated 20 items on a 5-point Likert scale. Scores above 50 were categorized as problematic usage.\u003c/p\u003e\n\u003cp\u003eThe Patient Health Questionnaire-9 was used to screen for depression [13]. It assesses the frequency of depressive symptoms over the past two weeks.\u003c/p\u003e\n\u003cp\u003eAll instruments underwent forward and backward translation into Yoruba and Igbo as well as Pidgin English to ensure conceptual equivalence and validity across linguistic groups. The validated Yoruba/Igbo/Pidgin versions were piloted (n = 60) to confirm comprehension; κ = 0.82 for test–retest reliability over 7 days.\u003c/p\u003e\n\u003cp\u003eProcedure and Ethics\u003c/p\u003e\n\u003cp\u003eEthical approval was granted by the Faculty of Education, University of Nigeria, Ethics committee (UNN/FED/2025/10/007). Permission was also secured from the State Ministries of Education and school principals. Written informed consent was obtained from parents or guardians. Affirmative assent was obtained from all participating students. Data collection was facilitated by trained research assistants who administered the questionnaires in classroom settings.\u003c/p\u003e\n\u003cp\u003eData Analysis\u003c/p\u003e\n\u003cp\u003eData were analyzed using IBM SPSS Statistics Version 28. Complex Samples Analysis procedures were used to generate accurate standard errors given the complex sampling design. Descriptive statistics summarized the prevalence of GD. Bivariate analyses compared variables between the Lagos and Enugu cohorts. A multivariate logistic regression model was constructed to identify independent predictors of GD. This model controlled for confounders such as age, gender, location, and school type. Results are reported as Adjusted Odds Ratios with 95% Confidence Intervals. Multicollinearity was checked (VIF \u0026lt; 2); no evidence of clustering by school after adjustment (intraclass correlation \u0026lt; 0.01).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDemographic Characteristics\u003c/p\u003e\n\u003cp\u003eThe final analytic sample consisted of 900 adolescents. They were equally distributed between Lagos and Enugu. The mean age was 15.5 years with a standard deviation of 1.75. The gender distribution was balanced with 50% male and 50% female. Participants were drawn from both public and private schools.\u003c/p\u003e\n\u003cp\u003ePrevalence and Patterns of Gambling\u003c/p\u003e\n\u003cp\u003eGambling engagement was widespread. Past-year gambling participation was reported by 66.0% of adolescents in Enugu and 60.0% in Lagos (p \u0026lt; .05).\u003c/p\u003e\n\u003cp\u003eGD prevalence (SOGS-RA \u0026ge; 4): Lagos 31.1 % (95 % CI 27.2\u0026ndash;35.3), Enugu 38.0 % (95 % CI 33.9\u0026ndash;42.3), p = 0.012. Overall weighted prevalence = 34.6 % (95 % CI 31.7\u0026ndash;37.6).\u003c/p\u003e\n\u003cp\u003eThe digital shift was evident regarding the mode of access. 70.0% of active gamblers in Lagos and 60.0% in Enugu primarily used online platforms via smartphones or cybercafes rather than physical betting shops (p \u0026lt; .01).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePsychosocial Comorbidities\u003c/p\u003e\n\u003cp\u003eThe analysis revealed stark disparities in mental health status based on both location and gambling severity. As detailed in Table 2, adolescents with Gambling Disorder (GD) reported significantly higher mean scores for both Internet Addiction and Depression across locations.\u003c/p\u003e\n\u003cp\u003eRegarding location-specific patterns, adolescents in Enugu reported higher comorbidity. For those with GD, mean Internet Addiction Test (IAT) scores were significantly higher in Enugu (M = 58.2, SD = 9.1) than in Lagos (M = 53.7, SD = 8.4), t(398) = 4.12, p \u0026lt; .001. Similarly, mean PHQ-9 scores for adolescents with GD were higher in Enugu (M = 12.8, SD = 4.6) compared to Lagos (M = 10.4, SD = 4.1), t(398) = 4.65, p \u0026lt; .001.\u003c/p\u003e\n\u003cp\u003eWhen comparing by gambling status, adolescents with GD had significantly higher IAT and PHQ-9 scores than non-gamblers (NG) within each location (all p-values \u0026lt; .001), confirming a strong syndemic relationship between gambling disorder, digital addiction, and depressive symptoms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003e\u003cem\u003eMean Scores and Standard Deviations for Internet Addiction and Depression by Location and Gambling Status\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLocation \u0026amp; Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInternet Addiction (IAT)M (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDepression (PHQ-9)M (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLagos\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGambling Disorder (GD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53.7 (8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.4 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-Gamblers (NG)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41.3 (7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.5 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnugu\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGambling Disorder (GD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58.2 (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.8 (4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-Gamblers (NG)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e43.8 (8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.2 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Sample\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGambling Disorder (GD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e56.2 (9.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.7 (4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-Gamblers (NG)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e42.6 (7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.9 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote:\u003c/em\u003e GD = Gambling Disorder (SOGS-RA \u0026ge; 4); NG = Non-Gamblers. All comparisons between GD and NG within each location were statistically significant at p \u0026lt; .001. M = Mean; SD = Standard Deviation.\u003c/p\u003e\n\u003cp\u003eFunctional Impairments\u003c/p\u003e\n\u003cp\u003eThe functional consequences of GD were severe and disruptive to adolescent development.\u003c/p\u003e\n\u003cp\u003eAcademic disruption was significant. 21.0% of the total sample reported missing school or skipping classes specifically to gamble or research betting odds (95 % CI 18.4\u0026ndash;23.8).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinancial toxicity was also high. 42.7% reported borrowing money or selling personal items or using school fees to finance gambling activities. This indicates a high level of financial desperation (95 % CI 39.1\u0026ndash;46.4).\u003c/p\u003e\n\u003cp\u003ePredictors of Gambling Disorder\u003c/p\u003e\n\u003cp\u003eTable 2 presents the results of the multivariate logistic regression analysis. Internet Addiction emerged as the most robust predictor of GD after adjusting for potential confounders (AOR = 3.5; 95% CI: 2.6\u0026ndash;4.8). This suggests that problematic internet use entails a more than three-fold increase in the odds of developing GD. Peer Gambling and Male Gender were also significant predictors. Location was a significant predictor in bivariate analysis but its effect size was modest in the multivariate model. This suggests that the risk is driven more by behavioral factors like internet use than geography alone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e \u003cem\u003eMultivariate Logistic Regression Analysis Predicting Gambling Disorder (N=900)\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWald \u0026chi;\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eInternet Addiction (Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e[2.6, 4.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e85.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eMale Gender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e[1.1, 2.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003ePeer Gambling (Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e[1.6, 3.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e24.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eLocation (Enugu)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e[1.0, 1.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eSchool Type (Public)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e[0.9, 1.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e.180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote:\u003c/em\u003e AOR = Adjusted Odds Ratio; CI = Confidence Interval. Model calibrated with Hosmer-Lemeshow goodness-of-fit test.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides a comprehensive digital epidemiology of adolescent gambling in Southern Nigeria. It reveals a public health crisis that is geographically widespread and increasing in severity. The observed GD prevalence rates of 31.1% in Lagos and 38.0% in Enugu are alarmingly high. These figures far exceed the 0.2% to 12.3% range typically reported in global meta-analyses of adolescent gambling [6-9,14]. They align with elevated rates found in other Nigerian samples such as the 30.5% reported by Afe et al. [15] and the 38.3% reported by Chinawa et al. [5]. This suggests that the Nigerian socio-economic environment acts as a potent incubator for behavioral addictions like gambling disorder [3,4].\u0026nbsp;Future longitudinal or quasi-experimental designs are required to disentangle directionality of the observed associations.\u003c/p\u003e\n\u003cp\u003eThe Digital Syndemic\u003c/p\u003e\n\u003cp\u003eA central finding of this study is the strong and independent association between Internet Addiction and Gambling Disorder. This supports the concept of a Digital Syndemic rather than confirming causality, where the smartphone is not merely a tool but a pathogenic environment [10,16] The mechanisms of addiction in digital gambling overlap significantly with those of social media and gaming addiction. The transition from gaming to gambling is often seamless for the Nigerian adolescent and is facilitated by apps that gamify the betting experience [1].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRegional Disparities\u003c/p\u003e\n\u003cp\u003eThe finding that prevalence is higher in Enugu than in the commercial capital of Lagos challenges simplistic assumptions that urbanization alone drives addiction. This disparity may be rooted in socio-cultural differences. There is a strong cultural emphasis on entrepreneurship and hustle in the South-East. Gambling marketing often exploits this by framing betting as investing or risk-taking essential for success [2]. This reframing may reduce the perceived stigma of gambling in Enugu and lead to higher engagement rates compared to Lagos where the population might be more sensitized to the risks.\u003c/p\u003e\n\u003cp\u003eMental Health and Functional Impact\u003c/p\u003e\n\u003cp\u003eThe comorbidity with depression is particularly concerning. This finding is consistent with bi-directional relationships observed globally and in African contexts [10,17]. Depressive symptoms may drive escape gambling as a coping mechanism while the inevitable financial losses exacerbate anxiety and hopelessness. The high rates of debt and school absenteeism indicate that GD is actively sabotaging the future of these adolescents. We are witnessing a generation where educational attainment is being traded for the illusory hope of a windfall [18].\u003c/p\u003e\n\u003cp\u003eLimitations\u003c/p\u003e\n\u003cp\u003eThis study has limitations. The cross-sectional design precludes causal inferences regarding the directionality of the depression-gambling link. Reliance on self-report measures may introduce social desirability bias. The exclusion of out-of-school youth means these figures likely underestimate the true burden of the disease in the general population.\u003c/p\u003e"},{"header":"Conclusion and Recommendations","content":"\u003cp\u003eAdolescent Gambling Disorder in Southern Nigeria is a pervasive mental health crisis. It is a silent epidemic drowning out the potential of the youth. A multi-sectoral approach is required to mitigate this.\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eIntegrated Screening:\u003c/strong\u003e The SOGS-RA should be integrated into school health programs. Guidance counselors must be trained to recognize that a student sleeping in class or dropping grades might be suffering from gambling fatigue or debt stress.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDigital Hygiene Education:\u003c/strong\u003e Prevention programs must evolve beyond simple refusals. They must teach digital literacy and help students deconstruct gambling advertisements and understand the mathematical impossibility of beating the house in the long run [10].\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePolicy Reform:\u003c/strong\u003e There is an urgent need for stricter enforcement of age verification. The current system is insufficient, and regulatory frameworks must resist industry narratives that seek to dilute these protections [20]. Biometric or Bank Verification Number linkage should be mandatory for opening online betting accounts [21].\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Statements \u0026 Declarations","content":"\u003cp\u003eFunding: The author declares that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003eCompeting Interests: The author has no relevant financial or non-financial interests to disclose.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eKing DL, Delfabbro PH, Kaptsis D. Adolescent simulated gambling via digital and social media: An emerging problem. \u003cem\u003eComput Human Behav.\u003c/em\u003e 2014;31:305\u0026ndash;13.\u003c/li\u003e\n \u003cli\u003eSsewanyana D, Bitanihirwe B. Problem gambling among young people in sub-Saharan Africa. \u003cem\u003eFront Public Health.\u003c/em\u003e 2018;6:23.\u003c/li\u003e\n \u003cli\u003eBitanihirwe B, Ssewanyana D. Gambling in Sub-Saharan Africa: Traditional forms and emerging technologies. \u003cem\u003eCurr Addict Rep.\u003c/em\u003e 2022;9(4):373\u0026ndash;9.\u003c/li\u003e\n \u003cli\u003eAdebisi T, Alabi O, Arisukwu O, Asamu F. Gambling in transition: Assessing youth narratives of gambling in Nigeria. \u003cem\u003eJ Gambl Stud.\u003c/em\u003e 2021;37(1):59\u0026ndash;82.\u003c/li\u003e\n \u003cli\u003eChinawa AT, Ossai EN, Odinka PC, Nduaguba OC, Odinka JI, Aronu AE, et al. Problem gambling among secondary school adolescents in Enugu, Nigeria. \u003cem\u003eAfr Health Sci.\u003c/em\u003e 2023;23(3):748\u0026ndash;57.\u003c/li\u003e\n \u003cli\u003eTran LT, et al. The prevalence of gambling and problematic gambling: A systematic review and meta-analysis. \u003cem\u003eLancet Public Health.\u003c/em\u003e 2024;9(7):e494\u0026ndash;e506.\u003c/li\u003e\n \u003cli\u003eMontiel I, Ortega-Bar\u0026oacute;n J, Basterra-Gonz\u0026aacute;lez A, Gonz\u0026aacute;lez-Cabrera J, Machimbarrena JM. Problematic online gambling among adolescents: A systematic review about prevalence and related measurement issues. \u003cem\u003eJ Behav Addict.\u003c/em\u003e 2021;10(3):566\u0026ndash;86.\u003c/li\u003e\n \u003cli\u003eCalado F, Alexandre J, Griffiths MD. Prevalence of adolescent problem gambling: A systematic review of recent research. \u003cem\u003eJ Gambl Stud.\u003c/em\u003e 2017;33(2):397\u0026ndash;424.\u003c/li\u003e\n \u003cli\u003eAndrie EK, Tzavara CK, Tzavela E, Richardson C, Greydanus D, Tsolia M, et al. Gambling involvement and problem gambling correlates among European adolescents: results from the European Network for Addictive Behavior study. \u003cem\u003eSoc Psychiatry Psychiatr Epidemiol.\u003c/em\u003e 2019;54(11):1429\u0026ndash;41.\u003c/li\u003e\n \u003cli\u003eDell O, Chamberlain SR, Fineberg NA. Comorbidity of DSM-5 gambling disorder with other behavioral addictions. \u003cem\u003eJ Behav Addict.\u003c/em\u003e 2019;8(3):407\u0026ndash;15.\u003c/li\u003e\n \u003cli\u003eAfe T, Ogunsemi O, Daniel O, Ale A, Adeleye O. Prevalence of and factors associated with disordered gambling disorder, and use of DSM-5 based sports betting questionnaire, in a southwest Nigerian community. \u003cem\u003eIndian J Psychol Med.\u003c/em\u003e 2022;44(3):265\u0026ndash;71.\u003c/li\u003e\n \u003cli\u003eKim HS, Hodgins DC, Kim B. Comorbidity of internet gaming disorder and other psychiatric conditions. \u003cem\u003eCurr Psychiatry Rep.\u003c/em\u003e 2017;19(6):34.\u003c/li\u003e\n \u003cli\u003eAguocha CM, George S. An overview of gambling in Nigeria. \u003cem\u003eBJPsych Int.\u003c/em\u003e 2021;18(2):30\u0026ndash;2.\u003c/li\u003e\n \u003cli\u003eEde MO, Nwosu KC, Okeke CI, Oneli JO. Near-miss and gambling cognitions among Nigerian adolescents. \u003cem\u003eJ Gambl Stud.\u003c/em\u003e 2021;37(3):837\u0026ndash;52.\u003c/li\u003e\n \u003cli\u003eAguocha CM, Duru CB, Nwefoh EC, Ndukuba AC, Amadi KU. Determinants of gambling among male students in secondary schools in Imo State, Nigeria. \u003cem\u003eJ Subst Use.\u003c/em\u003e 2019;24(2):199\u0026ndash;205.\u003c/li\u003e\n \u003cli\u003eWinters KC, Stinchfield RD, Fulkerson J. Toward the development of an adolescent gambling problem severity scale. \u003cem\u003eJ Gambl Stud.\u003c/em\u003e 1993;9(1):63\u0026ndash;84.\u003c/li\u003e\n \u003cli\u003eYoung KS. Internet addiction: The emergence of a new clinical disorder. \u003cem\u003eCyberPsychol Behav.\u003c/em\u003e 1998;1(3):237\u0026ndash;44.\u003c/li\u003e\n \u003cli\u003eKroenke K, Spitzer RL, Williams JB. The PHQ-9: Validity of a brief depression severity measure. \u003cem\u003eJ Gen Intern Med.\u003c/em\u003e 2001;16(9):606\u0026ndash;13.\u003c/li\u003e\n \u003cli\u003eWardle H, Reith G, Dobbie F, Rintoul A, Shiffman J. Regulatory resistance? Narratives and uses of evidence around \u0026quot;black market\u0026quot; provision of gambling during the British Gambling Act review. \u003cem\u003eInt J Environ Res Public Health.\u003c/em\u003e 2021;18(21):11566.\u003c/li\u003e\n \u003cli\u003eAdebisi T, Alabi O, Arisukwu O, Asamu F. Gambling in transition: Assessing youth narratives of gambling in Nigeria. \u003cem\u003eJ Gambl Stud.\u003c/em\u003e 2020;37(1):59\u0026ndash;82.\u003c/li\u003e\n \u003cli\u003eAkhenamen C. India\u0026apos;s Gaming Crackdown Signals Wake-Up Call for Nigeria\u0026apos;s Booming Betting Industry. \u003cem\u003eThisDayLive\u003c/em\u003e [Internet]. 2025 Aug 28 [cited 2025 Dec 24]. Available from: https://www.thisdaylive.com/2025/08/28/indias-gaming-crackdown-signals-wake-up-call-for-nigerias-booming-betting-industry/\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":"social-psychiatry-and-psychiatric-epidemiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sppe","sideBox":"Learn more about [Social Psychiatry and Psychiatric Epidemiology](http://link.springer.com/journal/127)","snPcode":"127","submissionUrl":"https://submission.nature.com/new-submission/127/3","title":"Social Psychiatry and Psychiatric Epidemiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Gambling Disorder, Adolescent Mental Health, Internet Addiction, Depression, Nigeria, Behavioral Addiction","lastPublishedDoi":"10.21203/rs.3.rs-8445209/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8445209/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e The digital transformation of gambling in Sub-Saharan Africa poses a severe and under-researched threat to adolescent mental health. This study assessed the prevalence of Gambling Disorder (GD), its comorbid mental health conditions, and associated functional impairments among in-school adolescents in two major Nigerian regions.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e A comparative and school-based cross-sectional survey was conducted with 900 adolescents aged 13 to 17 from Lagos in the South-West and Enugu in the South-East. Participants were selected via stratified cluster sampling. Assessments included the South Oaks Gambling Screen-Revised for Adolescents (SOGS-RA), the Internet Addiction Test (IAT), and the Patient Health Questionnaire-9 (PHQ-9). Complex sample analyses were used.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e The prevalence of GD was alarmingly high at 31.1% in Lagos and 38.0% in Enugu. Online platforms were the primary mode of access. Adolescents with GD exhibited significantly higher rates of comorbid internet addiction (AOR = 3.5; 95% CI: 2.6–4.8) and depressive symptoms. Severe functional impairments included school absenteeism (21.0%) and gambling-related debt (42.7%).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Adolescent GD in Southern Nigeria is a pervasive mental health crisis deeply intertwined with digital addiction and depression. Findings urgently call for integrating GD screening into adolescent mental health services and implementing school-based preventive interventions that address the digital syndemic.\u003c/em\u003e\u003c/p\u003e","manuscriptTitle":"Epidemiology of Online Gambling Disorder among Adolescents in Southern Nigeria: A Comparative Analysis of Prevalence, Psychosocial Correlates, and Mental Health Implications","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-31 14:55:10","doi":"10.21203/rs.3.rs-8445209/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-09T21:03:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-24T10:46:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-25T08:49:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Social Psychiatry and Psychiatric Epidemiology","date":"2025-12-24T21:31:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"social-psychiatry-and-psychiatric-epidemiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sppe","sideBox":"Learn more about [Social Psychiatry and Psychiatric Epidemiology](http://link.springer.com/journal/127)","snPcode":"127","submissionUrl":"https://submission.nature.com/new-submission/127/3","title":"Social Psychiatry and Psychiatric Epidemiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e4dca01d-d762-4e3a-a99e-d2f04316f3e8","owner":[],"postedDate":"March 31st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-31T14:55:10+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-31 14:55:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8445209","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8445209","identity":"rs-8445209","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}