Canadian Journal of Public Health

, Volume 108, Issue 5–6, pp e468–e474 | Cite as

Cyberbullying victimization and its association with health across the life course: A Canadian population study

  • Soyeon Kim
  • Michael H. Boyle
  • Katholiki Georgiades
Quantitative Research


OBJECTIVES: To examine the prevalence of cyberbullying victimization (CV), its associations with self-reported health and substance use and the extent to which age moderates these associations.

METHODS: We used the 2014 Canadian General Social Survey on Victimization (N = 31 907, mean age = 45.83, SD = 18.67) and binary logistic regression models to estimate the strength of association between CV and health-related outcomes.

RESULTS: The five-year prevalence of CV was 5.1 %. Adolescents reported the highest prevalence of CV (12.2%), compared to all other adult age groups (1.7%-10.4%). After controlling for socio-demographic covariates, individuals exposed to CV had increased odds of reporting poor mental health (OR = 4.259, 95% CI = 2.853–6.356), everyday limitations due to mental health problems (OR = 3.263, 95% CI = 2.271–4.688), binge drinking (OR = 2.897, 95% CI = 1.765–4.754), and drug use (OR = 3.348, 95% CI = 2.333–4.804), compared to those not exposed to CV. The associations between CV and self-reported mental health and substance use were strongest for adolescents and attenuated across the adult age groups.

CONCLUSION: Adolescence may represent a developmental period of heightened susceptibility to CV. Developing and evaluating targeted preventive interventions for this age group is warranted.

Key Words

Bullying mental health adolescent 


OBJECTIFS: Examiner la prévalence de la victimisation par cyberintimidation (VPC), ses associations avec la santé et la consommation de substances autodéclarées et la mesure dans laquelle l’âge modère ces associations.

MÉTHODE: Nous avons utilisé l’Enquête sociale générale canadienne sur la victimisation de 2014 (N = 31 907, âge moyen = 45,83, écart-type = 18,67) et des modèles de régression logistique binaire pour estimer la force des associations entre la VPC et les résultats de santé.

RÉSULTATS: La prévalence de la VPC sur cinq ans était de 5,1 %. Les adolescents ont déclaré le taux de prévalence le plus élevé (12,2 %) comparativement à tous les autres groupes d’âge adultes (1,7 %–10,4 %). Compte tenu des covariables sociodémographiques, les sujets exposés à la VPC présentaient une probabilité accrue de faire état d’une mauvaise santé mentale (rapport de cotes [RC] = 4,259, IC de 95 % = 2,853–6,356), de limitations quotidiennes dues à des troubles de santé mentale (RC = 3,263, IC de 95 % = 2,271–4,688), d’excès occasionnels d’alcool (RC = 2,897, IC de 95 % = 1,765–4,754) et de consommation de drogue (RC = 3,348, IC de 95 % = 2,333–4,804) comparativement aux sujets non exposés à la VPC. Les associations entre la VPC, d’une part, et la santé mentale et la consommation de substances autodéclarées, d’autre part, étaient les plus fortes chez les adolescents et s’atténuaient dans les groupes d’âge adultes.

CONCLUSION: L’adolescence pourrait représenter une période de développement où la susceptibilité à la VPC est accrue. Il est justifié d’élaborer et d’évaluer des interventions préventives ciblant ce groupe d’âge.

Mots Clés

brimades santé mentale adolescent 


  1. 1.
    Olweus D. Cyberbullying: An overrated phenomenon? Eur J Dev Psychol 2012; 9:520–38. doi: 10.1080/17405629.2012.682358.CrossRefGoogle Scholar
  2. 2.
    The National Academies of Sciences, Engineering, and Medicine. Preventing Bullying Through Science, Policy, and Practice. Washington, DC: National Academies Press, 2016.Google Scholar
  3. 3.
    Kowalski RM, Giumetti GW, Schroeder AN, Lattanner MR. Bullying in the digital age: A critical review and meta-analysis of cyberbullying research among youth. Psychol Bull 2014;140:1073–137. PMID: 24512111. doi: 10.1037/a0035618.CrossRefGoogle Scholar
  4. 4.
    Dilmac B. Psychological needs as a predictor of cyber bullying: A preliminary report on college students. Educ Sci Theory Pract 2009;9:1307–25.Google Scholar
  5. 5.
    Lindsay M, Krysik J. Online harassment among college students. Inf Commun Soc 2012;15:703–19. doi: 10.1080/1369118X.2012.674959.CrossRefGoogle Scholar
  6. 6.
    Schenk AM, Fremouw WJ. Prevalence, psychological impact, and coping of cyberbully victims among college students. J Sch Violence 2012;11:21–37. doi: 10.1080/15388220.2011.630310.CrossRefGoogle Scholar
  7. 7.
    Selkie E, Kota R, Moreno M. Relationship between cyberbullying experiences and depressive symptoms in female college students. J Adolesc Health 2014;54:S28. doi: 10.1016/j.jadohealth.2013.10.070.CrossRefGoogle Scholar
  8. 8.
    Walker CM, Sockman BR, Koehn S. An exploratory study of cyberbullying with undergraduate university students. TechTrends 2011;55:31–38. doi: 10.1007/S11528-011-0481-0.Google Scholar
  9. 9.
    Zalaquett CP, Chatters SJ. Cyberbullying in college: Frequency, characteristics, and practical implications. SAGE Open 2014;4:1–8. doi: 10. 1177/2158244014526721.CrossRefGoogle Scholar
  10. 10.
    Faucher C, Jackson M, Cassidy W. Cyberbullying among university students: Gendered experiences, impacts, and perspectives. Educ Res Int 2014;2014: 1–10. doi: 10.1155/2014/698545.CrossRefGoogle Scholar
  11. 11.
    Hango D. Cyberbullying and Cyberstalking Among Internet Users Aged 15 to 29 in Canada. Ottawa, ON: Statistics Canada, 2016.Google Scholar
  12. 12.
    Einarsen S, Skogstad A. Bullying at work: Epidemiological findings in public and private organizations. Eur J Work Organ Psychol 1996;5:185–201. doi: 10.1080/13594329608414854.CrossRefGoogle Scholar
  13. 13.
    Baruch Y. Bullying on the net: Adverse behavior on e-mail and its impact. Inf Manage 2005;42:361–71. doi: 10.1016/ Scholar
  14. 14.
    Privitera C, Campbell MA. Cyberbullying: The new face of workplace bullying? Cyberpsychol Behav 2009;12:395–400. PMID: 19594381. doi: 10. 1089/cpb.2009.0025.CrossRefGoogle Scholar
  15. 15.
    Boak A, Hamilton HA, Adlaf EM, Beitchman JH, Wolfe D, Mann RE. The Mental Health and Weil-Being of Ontario Students, 1991-2013: Detailed OSDVHS Findings. Toronto, ON: Center for Addiction and Mental Health, 2014.Google Scholar
  16. 16.
    Gamez-Guadix M, Orue I, Smith PK, Calvete E. Longitudinal and reciprocal relations of cyberbullying with depression, substance use, and problematic internet use among adolescents. J Adolesc Health 2013;53:446–52. PMID: 23721758. doi: 10.1016/j.jadohealth.2013.03.030.CrossRefGoogle Scholar
  17. 17.
    Swearer SM, Hymel S. Understanding the psychology of bullying: Moving toward a social-ecological diathesis-stress model. Am Psychol 2015;70:344–53. PMID: 25961315. doi: 10.1037/a0038929.CrossRefGoogle Scholar
  18. 18.
    Merikangas KR, He JP, Burstein M, Swanson SA, Avenevoli S, Cui L, et al. Lifetime prevalence of mental disorders in U.S. adolescents: Results from the National Comorbidity Survey Replication-Adolescent Supplement (NCS-A). J Am Acad Child Adolesc Psychiatry 2010;49:980–89. PMID: 20855043. doi: 10.1016/j.jaac.2010.05.017.CrossRefGoogle Scholar
  19. 19.
    Kessler RC, Angermeyer M, Anthony JC, de Graaf R, Demyttenaere K, Gasquet I, et al. Lifetime prevalence and age-of-onset distributions of mental disorders in the World Health Organization’s World Mental Health Survey Initiative. World Psychiatry 2007;6(3):168–76. PMID: 18188442.PubMedPubMedCentralGoogle Scholar
  20. 20.
    Ponizovsky A, Mansbach-Kleinfeld I. Prevalence of mental disorders and use of services in an immigrant adolescent population: Findings from a National Mental Health Survey. J Child Adolesc Behav 2015;3:176. doi: 10.4172/2375-4494.1000176.CrossRefGoogle Scholar
  21. 21.
    Bor W, Dean AJ, Najman J, Hayatbakhsh R. Are child and adolescent mental health problems increasing in the 21st century? A systematic review. Aust N Z J Psychiatry 2014;48(7):606–16. PMID: 24829198. doi: 10.1177/0004867414533834.CrossRefGoogle Scholar
  22. 22.
    Hammen C. Adolescent depression: Stressful interpersonal contexts and risk for recurrence. Curr Dir Psychol Sci 2009;18:200–4. PMID: 20161119. doi: 10. 1111/J.1467-8721.2009.01636.X.CrossRefGoogle Scholar
  23. 23.
    Kuehner C. Gender differences in unipolar depression: An update of epidemiological findings and possible explanations. Acta Psychiatr Scand 2003;108:163–74. PMID: 12890270. doi: 10.1034/j.1600-0447.2003.00204.X.CrossRefGoogle Scholar
  24. 24.
    Nolen-Hoeksema S, Girgus JS. The emergence of gender differences in depression during adolescence. Psychol Bull 1994;115:424–43. PMID: 8016286. doi: 10.1037/0033-2909.115.3.424.CrossRefGoogle Scholar
  25. 25.
    Hamm MP, Newton AS, Chisholm A, Shulhan J, Milne A, Sundar P, et al. Prevalence and effect of cyberbullying on children and young people: A scoping review of social media studies. JAMA Pediatr 2015;169:770–77. PMID: 26098362. doi: 10.1001/jamapediatrics.2015.0944.CrossRefGoogle Scholar
  26. 26.
    Thomas S, Wanneil B. Combining cycles of the Canadian Community Health Survey. Health Rep 2009;20:53–58. PMID: 19388369.PubMedGoogle Scholar
  27. 27.
    Holfeld B, Leadbeater BJ. The nature and frequency of cyber bullying behaviors and victimization experiences in young Canadian children. Can J Sch Psychol 2015;30:116–35. doi: 10.1177/0829573514556853.CrossRefGoogle Scholar
  28. 28.
    Eaton DK, Kann L, Kinchen S, Shanklin S, Flint KH, Hawkins J, et al. Youth risk behavior surveillance - United States, 2011. MMWR Surveill Summ 2012; 61:1–162. PMID: 22673000.PubMedGoogle Scholar
  29. 29.
    Selkie EM, Kota R, Chan YF, Moreno M. Cyberbullying, depression, and problem alcohol use in female college students: A multisite study. Cyberpsychol Behav Soc Netw 2015;18:79–86. PMID: 25684608. doi: 10.1089/cyber.2014.0371.CrossRefGoogle Scholar
  30. 30.
    Ttofi MM, Farrington DP. Effectiveness of school-based programs to reduce bullying: A systematic and meta-analytic review. J Exp Criminol 2011;7:27–56. doi: 10.1007/S11292-010-9109-1.CrossRefGoogle Scholar
  31. 31.
    Richard JF, Schneider BH, Mallet P. Revisiting the whole-school approach to bullying: Really looking at the whole school. Sch Psychol Int 2012;33:263–84. doi: 10.1177/0143034311415906.CrossRefGoogle Scholar
  32. 32.
    Merrell KW, Gueldner BA, Ross SW, Isava DM. How effective are school bullying intervention programs? A meta-analysis of intervention research. School Psychol Q 2008;23(1):26–42.CrossRefGoogle Scholar
  33. 33.
    Cross D, Brown D, Epstein M, Shaw T. Cyber Friendly Schools Project: Strengthening School and Families’ Capacity to Reduce the Academic, Social, and Emotional Harms Secondary Students Experience From Cyber Bullying (Public Education Endowment Trust, PEET). Perth, WA: Child Health Promotion Research Centre, Edith Cowan University, 2010.Google Scholar
  34. 34.
    Pearce N, Cross D, Monks H, Waters S, Falconer S. Current evidence of best practice in whole-school bullying intervention and its potential to inform cyberbullying interventions. Aust J Quid Couns 2011;21:1–21. doi: 10.1375/ajgc.21.1.1.Google Scholar
  35. 35.
    Cross D, Shaw T, Hadwen K, Cardoso P, Slee P, Roberts C, et al. Longitudinal impact of the Cyber Friendly Schools program on adolescents’ cyberbullying behavior. Aggress Behav 2016;42:166–80. PMID: 26351263. doi: 10.1002/ab. 21609.CrossRefGoogle Scholar

Copyright information

© The Canadian Public Health Association 2017

Authors and Affiliations

  • Soyeon Kim
    • 1
  • Michael H. Boyle
    • 1
  • Katholiki Georgiades
    • 1
  1. 1.Department of Psychiatry and Behavioural NeuroscienceMcMaster UniversityHamiltonCanada

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